LAND TENURE IN THE SUGAR CREEK WATERSHED: A CONTEXTUAL ANALYSIS OF LAND TENURE AND SOCIAL NETWORKS, INTERGENERATIONAL FARM SUCCESSION, AND CONSERVATION USE AMONG FARMERS OF WAYNE COUNTY, OHIO
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree
Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Jason Shaw Parker, M.A.
* * * * *
The Ohio State University 2006
Dissertation Committee: Approved By Professor Richard H. Moore, Adviser
Professor Richard W. Yerkes ______Adviser Professor Deborah H. Stinner Anthropology Graduate Program
Copyrighted by Jason Shaw Parker 2006 ABSTRACT
Settlers of the Midwestern United States brought with them perceptions and attitudes towards natural resources, farming, and land tenure that influenced
settlement patterns and the development of rural communities. Additionally, land
is a necessity in farming and access to land allows for the reproduction of the
social unit, the agrifamily household system, and the spatial and temporal
continuity of ethnic communities built on aggregates of these smaller systems
(Bennett 1982, Salamon 1992:96). These cultural forms persist in the Sugar
Creek Watershed in the forms of community involvement and organization, land
tenure and farm enterprise type and succession, and management styles. As
such, local social organization and land tenure play important roles in farm
management strategies that affect land tenure and adoption of conservation measures.
Conservation adoption research and community watershed initiatives are
difficult endeavors for which anthropologists have called for the inclusion of
ethnographic methods, local indicators, and perspectives from ecological
anthropology in the development and implementation of such projects in
developing and post-industrial capitalist states (Bennett 1993, Moran 1990,
Salamon 1992, Moore 1996, Kottak 1997, Nazarea 1997, Rhoades 1997, Thu
ii and Durrenberger 1998). Rural Sociologists recently expressed the need for local behavior and social network investigations in answering questions related to rural community relationships (Barham et al 2003, McIntosh and Lee 2003).
In this dissertation research, three research objectives were tested to assess magnitude and intensity of the relationships between these variables in the Sugar Creek Watershed. This research location is ideal for this research because of its combination of social and environmental attributes that make it ideal for answering questions of land tenure in relation to larger environmental problems because of differences in cultural heritage, social organization, and farm types among the residents. The first question asks how ethnicity, social relationships, and attitudes toward farming condition contemporary land tenure arrangements. The second question was posed in order to ascertain if ethnicity and level of socio-cultural integration of the farm household can be used as independent variables in understanding relationships among farm size, land use and tenure, and use and preference for conservation. The third and final research question was posited in order to understand the degree to which the variables of farm size, farm succession and inheritance, and enterprise type correlate with land tenure and preferences for conservation.
An exploratory analysis was conducted to investigate local conservation preferences and behaviors as measures of “quality of life” as an expansion of
iii Goldschmidt’s findings (1978) that relate to farm size and land tenure with quality of life experienced by members of a rural community.
iv DEDICN
For Ben In all of his many roles: mentor, colleague, friend
v ACKNOWLEDGMENTS
This research was supported by grants from the United States Department of Agriculture’s Sustainable Agriculture Research and Education (SARE, #NCR019-01), Program and the Ohio Environmental Protection Agency’s 319 Non Point Source Remediation Program (#02(h)EPA11). With admiration and respect, I express gratitude to my advisor, Richard Moore, for his incisive and erudite guidance in all the stages of my graduate training. I thank my other dissertation committee members, Richard Yerkes, Deborah Stinner, and the late Ben Stinner. They each shared their great knowledge of the research topics in helping shape and expand my own. The following assisted me in data collection at various stages of this research: Richard Moore (HCRD, AMP-OSU), Mark Weaver (Political Science, College of Wooster); Scot Long (OSU-AMP); the College of Wooster Sophomore Intern Program students (alphabetically by surname: Christopher Beck, Margaux Day, Charles Fischer, Justin Hart, Erin Kernen, Genevieve Knabel); and other members of the Agroecosystems Management Program including Kathy Bielek, Charles Goebel, Lois Grant, Fred Hitzhusen, Casey Hoy, Deana Hudgins, Dave McCartney, V. Krishna Prasad, and Robin Taylor, whose multi-disciplinary perspectives contributed to my own. A great many people assisted in my research by kindly sharing their experiences and knowledge. Among others, I thank the farm households of Jerry Berg, the Besancons, Sherri and Jeff Gochnauer, Joe and Jean Hartzler, Arlen and Jean Hostetler, Rex Miller, Don Ramseyer, Brian and Heidi Rennecker, Paul Rohrer, Ed Shoup and family, John Steffen, the Studers, Ervin Weaver, Harry Weaver, Henry Wengerd, Jr., and Wayne H. Wengerd. I also want to thank the members of the collaborating institutions for their assistance and expert knowledge including Wayne County Soil and Water Conservation
vi District, Wayne County OSU Extension, Wayne County Health Department, Wayne County Auditor, and the National Resource Conservation Service Wayne County Office. I wish to express gratitude to several members of the faculty of Youngstown State University who supported me in various capacities. They are: John R. White and the late Mark T. Shutes for their guidance and instilling me with their enthusiasm for understanding the human condition and encouragement to continue in graduate school, Gary Fry for emphasizing the importance of balance, Keith Lepak for teaching me to express myself precisely and succinctly, and Rosemary D’Apolito and Jiang Qi for their encouragement and assistance in broadening my research interests to include quantitative analyses and encouraging me to continue my graduate education. My sincere appreciation goes to my fiancé, Shoshanah Inwood, for her professional and personal assistance and support. I am deeply grateful for the love, support and encouragement of my family and the values of fairness and parity and the spirit of logical inquiry they instilled in me: my parents James B. (IV) and Barbara Parker, my brother and sister-in-law James B. (V) and Jill Parker; and, the memories of my grandparents James B. (Jr.) and Sophia Parker, and Howard and Gertrude Toulmin, and uncle William Howard Toulmin.
vii VITA
March 2, 1973 ...... ………… Born – Youngstown, Ohio, United States of America
1995 ………………………… B.A. Anthropology, Youngstown State University
1999 – 1999 ………………… Graduate Research Associate Department of Anthropology The Ohio State University
1999 ………………………… M.A. Anthropology, The Ohio State University
1999 – 2001 ………………… Graduate Research Associate Agroecosystems Management Program Ohio Agricultural Research and Development Center The Ohio State University 2001 – present ………………. Research Associate Agroecosystems Management Program Ohio Agricultural Research and Development Center The Ohio State University
PUBLICATIONS
Research Publications
1. 2006. J. Parker, R. Moore, M. Weaver. Land Tenure as a Variable in Community Based Watershed Projects: Some Lessons from the Sugar Creek Watershed, Wayne & Holmes County, Ohio. Resubmitted to Society and Natural Resources.
2. Prasad, V. K., A. Ortiz, B. Stinner, D. McCartney, J. Parker, D. Hudgins, C. Hoy and R. Moore. 2005. Exploring the Relationship between Hydrologic Parameters and Nutrient Loads Using Digital Elevation Model and GIS – A Case Study from Sugarcreek Headwaters, Ohio, U.S.A. Environmental Monitoring and Assessment (2005) 110: 141– 169.
3. 2005. Moore, R. H., J. Parker, and M. Weaver. 2003. Lessons from the Sugar Creek Farmers in Ohio. Resubmitted to Culture and Agriculture.
viii 4. 2005. Prasad, V. K, J. Cardina, F. Hitzhusen, I. Ibayoh, R. Moore, J. Parker, B. Stinner, Deb Stinner and Casey Hoy 2004. Quantifying agroecosystem health at the landscape scale with combined biophysical and socioeconomic data. In revision.
5. 2005. K. P. Vadrevu, J. Cardina, F. Hitzhusen, I. Ibayoh, R. Moore, J. Parker, B. Stinner, D. Stinner and C. Hoy. Correlations among spatially referenced socioeconomic and biophysical variables related to agroecosystem health. In revision.
Technical Reports and Proceedings
1. 2005. Moore, Richard, D. McCartney, L. Williams, M. Wood, J. Christner, P. Anderson, J. Kwolek, R. Ramseyer, S. Ankrom, D. Miklos, L. Grant, D. Hudgins, S. Long, J. Parker, K. Prasad, B. Stinner. A Plan to Reduce Phosphorus Loading and Improve Stream Ecological Function in the Middle Fork and Adjoining Watersheds of the Sugar Creek Watershed: Joint Recommendations for the Alpine Cheese Phosphorus Nutrient Trading Plan. Alpine Cheese Company NDPES Permit Application to Ohio EPA. Ohio EPA. 49 pages. December.
2. 2002. Moore, R., B. Stinner, P. C. Goebel, J. Parker, D. Hudgins, D. McCartney, M. Weaver. Social Creek Social Indicators: Tapping Subheadwater TMDL Potential in the Headwaters of the Ohio River. Proceedings of EMAP (Environmental Measurement and Assessment Program—USEPA) Symposium 2002: The Condition of Our Nation’s Streams and Rivers from the Mountains to the Coast. Kansas City, MO, May 8th.
ix
FIELD OF STUDY
Major Field: Anthropology
Areas of Study: Applied Anthropology, Conservation Adoption, Cultural Anthropology, Cultural Ecology, Historical Immigration, Innovation Diffusion, Land Tenure, Local Knowledge, Midwestern United States Society, Ohio Agriculture, Intergenerational Farm Transfer, Social Organization, Sustainable Agriculture.
x
TABLE OF CONTENTS
ABSTRACT...... ii
DEDICATION ...... v
ACKNOWLEDGMENTS ...... vi
VITA ...... viii
TABLE OF CONTENTS...... xi
LIST OF TABLES ...... xvi
LIST OF FIGURES...... xviii
CHAPTERS:
CHAPTER 1 INTRODUCTION...... 1
1.1 Statement of Problem...... 1 1.2 Objectives of this Study...... 5 1.3 Significance of this Study...... 6 1.4 Introduction to the Research Setting...... 9 1.5 Limitation of this Study...... 15 1.6 Structure of this Dissertation ...... 15
CHAPTER 2 ECOLOGICAL CONTEXT: GEOGRAPHY, HISTORY AND ETHNICITY...... 18
2.1 Introduction...... 18 2.2 Biophysical Landscape Description ...... 18 2.2.1 Geology and Glacial History 19 2.2.2 Geography 22 2.2.3 Climate 24 2.2.4 Soils 24 2.2.5 Flora and Fauna 25
xi 2.3 Prehistory and History of Ohio and the Sugar Creek Watershed...... 27 2.3.1 Early Settlers 28 2.3.2 19th and 20th Century History and Land Use 35 2.4 Ethnicity of the Watershed and Surrounding Areas...... 42 2.4.1 Settlement and Immigration History 43 2.4.2 German and British Ethnic Groups 45 2.4.3 Anabaptist Ethnic Groups 47 2.4.4 Ethnicity, Landscape and Farming 49
CHAPTER 3 LITERATURE REVIEW ...... 65
3.1 Introduction...... 65 3.1.1 Two Ecologies? 66 3.2 Cultural Ecology and Social Change ...... 70 3.2.1 Smallholder Type 85 3.2.2 Social Capital 85 3.2.3 Land Use 90 3.2.4 Farm Succession 101 3.2.5 Time and Liturgical Order 105 3.2.6 Settlement Pattern 106 3.2.7 Conservation and Land Tenure 107 3.3 Understanding Conservation Adoption...... 109 3.3.1 Participatory Process 114 3.3.2 Effects of Policy on Conservation 115 3.3.3 Local Knowledge and Stewardship 121 3.3.4 Morality and Conservation 122
CHAPTER 4 METHODOLOGY...... 126
4.1 Institutional Arrangements...... 126 4.2 Community Selection...... 127 4.3 Data Collection Techniques...... 128 4.3.1 Qualitative and Quantitative Methods 128 4.3.2 Units of Analysis 131 4.3.3 Drop-off and Pick-up Surveys 134 4.3.4 Geographic Information Systems 136 4.4 Data Collection Problems ...... 138 4.4.1 Under Representation of New Order Amish 138 4.4.2 Lack of Adequate Measures of Farm Income 139 4.4.3 Measuring the Incidence of Amish Leasing 140 4.4.4 Reconciling Survey and Census of Agriculture Data 140 4.5 Data Analysis and Hypothesis Testing...... 141 4.6 Fieldwork Timetable ...... 144 4.7 Limitations of Methods ...... 145 4.8 Length of Study ...... 146
xii CHAPTER 5 SOCIAL AND POPULATION CHARACTERISTICS OF THE SUBWATERSHEDS ...... 147
5.1 Introduction...... 147 5.2 Political Unit Descriptors...... 147 5.3 Watersheds and Hydrologic Unit Codes (HUCs) ...... 150 5.4 Land Cover and Land Use ...... 155 5.4.1 Land Use Description 155 5.4.2 Sugar Creek Land Use and Land Cover 156 5.4.3 Upper Sugar Creek Subwatershed 160 5.4.4 North Fork Subwatershed 164 5.4.5 Little Sugar Creek Subwatershed 169 5.5 Population Characteristics ...... 172 5.5.1 Census Data 172 5.5.2 Upper Sugar Creek 176 5.5.3 North Fork 177 5.5.4 Little Sugar Creek 179 5.6 Amish Church Districts...... 181 5.6.1 North Fork Amish Church Districts 182 5.6.2 Little Sugar Creek Amish Church Districts 184
CHAPTERS 6–8 DATA ANALYSES AND INTERPRETATION ...... 188
CHAPTER 6 ETHNICITY, SOCIAL RELATIONSHIPS, FARMING ATTITUDES, AND LAND TENURE BY SUBWATERSHED...... 191
6.1 Introduction...... 191 6.2 Analytical Perspective ...... 194 6.3 Land Tenure, Social Relationships and Ethnicity...... 197 6.3.1 General Correlations 203 6.3.2 ANOVA of Ethnicity and Land Tenure Variables by Subwatersheds 206 6.3.3 Mean Difference Between Groups 210 6.4 Upper Sugar Creek ...... 219 6.4.1 Social Networks 219 6.4.2 Strategies for Intergenerational Farm 226 6.5 North Fork and Little Sugar Creek ...... 265 6.5.1 Social Networks 265 6.5.2 Strategies for Intergenerational Farm Succession 269 6.6 Commons...... 284 6.7 Conclusion...... 287
CHAPTER 7 ETHNICITY AND LEVEL OF SOCIOCULTURAL INTEGRATION OF FARM HOUSEHOLDS BY SUBWATERSHED ...... 294
7.1 Introduction...... 294
xiii 7.2 Analytical Perspective ...... 295 7.3 Levels of Sociocultural Integration and Social Networks...... 296 7.3.1 Context: Family Farms as Firms 297 7.3.2 Social Networks and Social Capital 299 7.3.3 Place, Interaction and Embeddedness 302 7.3.4 LSCI and Liturgical Order among the Sugar Creek Farming Communities 304 7.3.5 LSCI and Conservation 306 7.4 Data Construction and Analysis ...... 311 7.5 Results: Correlations and ANOVA ...... 314 7.5.1 Correlations 315 7.5.2 ANOVA of LSCI 318 7.5.3 Mean Differences Between Groups 324 7.5.4 Discussion LSCI ANOVA 365 7.6 ANOVA of Farm Type ...... 369 7.6.1 Mean Differences Between Groups 373 7.7 Conclusion...... 387
CHAPTER 8 THE EFFECTS OF FARM SIZE, SUCCESSION AND ENTERPRISE TYPE ON LAND TENURE AND CONSERVATION PREFERENCES...... 390
8.1 Introduction...... 390 8.1.1 Goldschmidt’s Findings 392 8.1.2 Variable Descriptions and Explanation of Indexes 395 8.1.3 Descriptive Statistics 399 8.1.4 ANOVA of Dependent Variables 402 8.1.5 Mean Differences Between Groups 406 8.1.6 Discriminant Analysis 431 8.2 Land Tenure Discriminant Analysis Model 1 ...... 433 8.2.1 Model 433 8.2.2 Results 434 8.2.3 Discussion 440 8.3 Land Tenure Discriminant Analysis Model 2 ...... 440 8.3.1 Model 441 8.3.2 Results 442 8.3.3 Discussion 448 8.4 Conservation Use Discriminant Analysis Model ...... 449 8.4.1 Model 449 8.4.2 Results 450 8.4.3 Discussion 453 8.5 Conclusion...... 454
xiv CHAPTER 9 CONCLUSION ...... 460
9.1 Introduction...... 460 9.2 Chapters 6 – 8: A Synthesis ...... 462 9.3 Gender, Power and Structure: three unexplored factors...... 468 9.3.1 Power and Structure in Decision-Making in Sugar Creek 469 9.3.2 American Agrarianism and the Myth of a Classless Society 471 9.3.3 Removing Barriers to Alternative Action 475 9.4 Conclusion...... 478
APPENDIX A: UPPER SUGAR CREEK SOCIAL SURVEY...... 481
APPENDIX B: NORTH FORK, LITTLE SUGAR CREEK AND MAINSTEM SOCIAL SURVEY ...... 491
APPENDIX C: GEOPHYSICAL AND CLIMATE MAPS ...... 500
APPENDIX D: CLIMATE DATA...... 506
APPENDIX E: SURVEY DATA DICTIONARY, SYNTAX AND MODELS ...... 508
APPENDIX F: BASE GIS METADATA...... 524
BIBLIOGRAPHY ...... 525
xv
LIST OF TABLES
Table 2.1 Spatial Extent and Founding Date of the Four Counties of the Sugar Creek ... 24 Table 4.1 Subwatershed Response Rates...... 136 Table 5.1 Population and Spatial Extent of the Four County Regions and the Sugar Creek Watershed ...... 148 Table 5.2 Sugar Creek and Subwatershed Population and Population Density ...... 174 Table 5.3 Upper Sugar Creek Subwatershed Population Profile by Census Block...... 177 Table 5.4 North Fork Subwatershed Population Profile by Census Block...... 179 Table 5.5 Little Sugar Creek Subwatershed Population Profile By Census Block ...... 181 Table 6.1 Chapter 6 Correlations Matrix ...... 204 Table 6.2 Descriptive Statistics for Subwatershed ANOVA Variables...... 208 Table 6.3 ANOVA of Statistically Significant Variables...... 209 Table 6.4 ANOVA Subwatersheds Tukey HSD Multiple Comparisons...... 210 Table 7.1 Correlations Matrix...... 316 Table 7.2 Heritage Group ANOVA Descriptive Statistics ...... 320 Table 7.3 ANOVA of Heritage Groups ...... 323 Table 7.4 Tukey HSD Multiple Comparisons ...... 324 Table 7.5 Heritage Group ANOVA Descriptive Statistics for Water Quality Decisions and Agency Trust Scores ...... 341 Table 7.6 Heritage Group ANOVA for Decision Makers and Agency Trust ...... 343 Table 7.7 Tukey HSD Multiple Comparisons ...... 343 Table 7.8 Farm Type Descriptive Statistics...... 371 Table 7.9 ANOVA of Farm Type...... 371 Table 7.10 Tukey HSD Multiple Comparison...... 373 Table 8.1 Descriptions and coding of concepts ...... 395 Table 8.2 Land Tenure Categories and Frequencies...... 396 Table 8.3 Farm Types Categories and Frequencies...... 397 Table 8.4 Intergenerational Farm Success Categories and Frequencies...... 398 Table 8.5 Variables for Discriminant Analysis of Land Tenure (TEN_TYPX)...... 399 Table 8.6 Variables for Discriminant Analysis of Conservation Use (CON_USE2)..... 399 Table 8.7 Conservation Practice by Land Tenure Category (N, %) ...... 400 Table 8.8 Conservation Preferences by Land Tenure Category (N, %) ...... 400 Table 8.9 Descriptive Statistics of Owned Agricultural Land in Wayne County, the Upper Sugar Creek Watershed, and of the Upper Sugar Creek Partners...... 401 Table 8.10 Land Tenure Categories ANOVA Descriptive Statistics ...... 403 Table 8.11 ANOVA Land Tenure Categories ...... 405 Table 8.12 ANOVA Land Tenure Categories Tukey HSD Multiple Comparisons ...... 406 Table 8.13 Conservation Use ANOVA Descriptive Statistics...... 421 xvi Table 8.14 ANOVA Conservation Use ...... 422 Table 8.15 ANOVA Conservation Use Categories Tukey HSD Multiple Comparisons423 Table 8.16 Discriminant Analysis of Land Tenure Model 1Group Statistics...... 434 Table 8.17 Discriminant Analysis of Land Tenure Model 1Tests of Equality of Group Means...... 435 Table 8.18 Discriminant Analysis of Land Tenure Model 1 Pooled Within-Group Matrices (a) ...... 435 Table 8.19 Discriminant Analysis of Land Tenure Model 1 Eigenvalues...... 436 Table 8.20 Discriminant Analysis of Land Tenure Model 1 Wilks’ Lambda ...... 436 Table 8.21 Discriminant Analysis of Land Tenure Model 1 Functions at Group Centroids ...... 437 Table 8.22 Discriminant Analysis of Land Tenure Model 1 Standardized Canonical Discriminant Function Coefficients...... 439 Table 8.23 Discriminant Analysis of Land Tenure Model 1 Structure Matrix...... 439 Table 8.24 Discriminant Analysis of Land Tenure Model 1 Classification Results (a). 440 Table 8.25 Discriminant Analysis of Land Tenure Model 2 Group Statistics...... 442 Table 8.26 Discriminant Analysis of Land Tenure Model 2 Tests of Equality of Group Means...... 443 Table 8.27 Discriminant Analysis of Land Tenure Model 2 Pooled-Within-Group Matrices (a) ...... 443 Table 8.28 Discriminant Analysis of Land Tenure Model 2 Eigenvalues...... 444 Table 8.29 Discriminant Analysis of Land Tenure Model 2 Wilks’ Lambda ...... 445 Table 8.30 Discriminant Analysis of Land Tenure Model 2 Functions at Group Centroids ...... 446 Table 8.31 Discriminant Analysis of Land Tenure Model 2 Standardized Canonical Discriminant Function Coefficients...... 447 Table 8.32 Discriminant Analysis of Land Tenure Model 2 Structure Matrix...... 448 Table 8.33 Discriminant Analysis of Land Tenure Model 2 Classification Results (a). 448 Table 8.34 Discriminant Analysis of Conservation Use Group Statistics...... 450 Table 8.35 Discriminant Analysis of Conservation Use Tests of Equality of Group Means ...... 451 Table 8.36 Discriminant Analysis of Conservation Use Pooled Within-Group Matrices (a) ...... 451 Table 8.37 Discriminant Analysis of Conservation Use Eigenvalues ...... 451 Table 8.38 Discriminant Analysis of Conservation Use Wilks’ Lambda...... 451 Table 8.39 Discriminant Analysis of Conservation Use Functions at Group Centroids 452 Table 8.40 Discriminant Analysis of Conservation Use Standardized Discriminant Function Coefficients...... 452 Table 8.41 Discriminant Analysis of Conservation Use Structure Matrix ...... 453 Table 8.42 Discriminant Analysis of Conservation Use Classification Results (a) ...... 453
xvii
LIST OF FIGURES
Figure 1.1 Sugar Creek Subwatersheds ...... 14 Figure 2.1 Ohio Counties and Townships of Sugar Creek Watershed...... 23 Figure 2.2 The Greenville Treaty Line, established 1795 ...... 34 Figure 2.3 Sugar Creek Subwatersheds Based on the Ohio EPA’s 11-Digit HUCs...... 37 Figure 2.4 Dialect Regions of American English...... 43 Figure 2.5 Wayne County Population Growth ...... 44 Figure 2.6 Wayne County Residence Patterns from 1995 to 2000...... 45 Figure 2.7 Ancestry of Wayne County ...... 51 Figure 2.8 Acres of Land in Farms per County ...... 56 Figure 2.9 Wheat and Oats Grown in Ohio from 1850 to 2002 ...... 57 Figure 2.10 Barley and Hay Grown in Ohio from 1850 to 2002...... 58 Figure 2.11 Corn and Soybeans for All Years, Grown in Ohio from 1850 to 2002...... 59 Figure 2.12 Animals Raised in Ohio from Select Years...... 60 Figure 2.13 Crops raised in Wayne County...... 63 Figure 2.14 Animals raised in Wayne County...... 64 Figure 5.1 United States Geological Survey (USGS) Hydrologic Unit Codes (HUCs) for the Mississippi Drainage System...... 152 Figure 5.2 United States Geological Survey (USGS) HUCs of the Muskingum Sub-Basin. Sugar Creek Watershed (HUC 0504001100) in dark gray...... 153 Figure 5.3 U.S. Environmental Protection Agency (USEPA) 14-digit HUCs of the Sugar Creek Watershed. Upper Sugar Creek (2308), North Fork (2313), and Little Sugar Creek (2310) are in dark gray...... 154 Figure 5.4 Survey of Sugar Creek Watershed Land Cover ...... 158 Figure 5.5 Sugar Creek Percent Land Cover ...... 159 Figure 5.6 Land Cover within 30-Feet of the Stream ...... 160 Figure 5.7 Survey of Upper Sugar Creek Land Use ...... 162 Figure 5.8 Upper Sugar Creek Land Use...... 163 Figure 5.9 Land Cover within 30-Feet of the Stream ...... 164 Figure 5.10 Survey of North Fork Land Use ...... 166 Figure 5.11 North Fork Subwatershed Land Use ...... 167 Figure 5.12 Land Cover within 30-Feet of the Stream ...... 168 Figure 5.13 Survey of Little Sugar Creek Land Cover...... 170 Figure 5.14 Little Sugar Creek Subwatershed Land Cover ...... 171 Figure 5.15 Land Cover within 30-Feet of the Stream ...... 172 Figure 5.16 Age Distributions by Subwatershed ...... 175 Figure 5.17 North Fork Dispersed Amish Church District Patterns*...... 183 Figure 5.18 Little Sugar Creek Clustered Amish Church District Patterns...... 185 xviii Figure 5.19 Farmstead and Residential Settlement Patterns in the Upper Sugar Creek and Little Sugar Creek Subwatersheds...... 187 Figure 6.1 Static Nature of Farm Household and ...... 202 Figure 6.2 Tenure - Acres Owned...... 212 Figure 6.3 Heritage Index ...... 213 Figure 6.4 Education Level...... 214 Figure 6.5 Implemented No-till ...... 215 Figure 6.6 Installed Grass Waterways ...... 216 Figure 6.7 Trust EPA ...... 217 Figure 6.8 Farm Size...... 218 Figure 6.9 Church Locations and Proximities to Local Towns ...... 221 Figure 6.10 Case Study Farm Household Participants* ...... 228 Figure 6.11 HH1a Tenure History...... 231 Figure 6.12 HH2 Tenure History...... 236 Figure 6.13 HH3 Tenure History...... 239 Figure 6.14 HH4 Tenure History...... 241 Figure 6.15 HH5 Tenure History...... 243 Figure 6.16 HH6a Tenure History...... 246 Figure 6.17 HH7 Tenure History...... 250 Figure 6.18 HH8 Tenure History...... 252 Figure 6.19 HH9 Tenure History...... 255 Figure 6.20 HH1b Tenure History...... 257 Figure 6.21 HH11 Tenure History...... 258 Figure 6.22 HH7a Tenure History...... 261 Figure 6.23 HH13 Tenure History...... 262 Figure 6.24 HH14 Tenure History...... 264 Figure 6.25 HH15 Tenure History...... 270 Figure 6.26 HH16 Family Farm...... 273 Figure 6.27 HH17 Tenure History...... 274 Figure 6.28 HH18 Tenure History...... 276 Figure 6.29 HH19 Tenure History...... 277 Figure 6.30 HH20 Tenure History...... 281 Figure 6.31 HH21 Tenure History...... 282 Figure 7.1 Farm Type ...... 327 Figure 7.2 Subwatershed...... 328 Figure 7.3 Number of Years Farmed ...... 329 Figure 7.4 Conservation Use...... 330 Figure 7.5 Desired Recreational Use ...... 331 Figure 7.6 Education Level...... 333 Figure 7.7 Implemented No-Till...... 334 Figure 7.8 Fishing Use...... 335 Figure 7.9 Perception of Pollution in the Sugar Creek ...... 336 Figure 7.10 Aesthetics More Important...... 337 Figure 7.11 Off-farm Income Index...... 338 Figure 7.12 Conservation Index...... 340
xix Figure 7.13 Federal Government Decision Makers...... 349 Figure 7.14 State Government Decision Makers...... 350 Figure 7.15 Local Government Decision Makers...... 351 Figure 7.16 Coalition of Decision Makers...... 352 Figure 7.17 Individual Owners as Decision Makers...... 353 Figure 7.18 Trust in EPA...... 354 Figure 7.19 Trust in USDA...... 355 Figure 7.20 Trust in ODA...... 356 Figure 7.21 Trust in SWCD...... 357 Figure 7.22 Trust in MWCD...... 358 Figure 7.23 Trust in OSU Extension...... 359 Figure 7.24 Trust in Farm Bureau...... 360 Figure 7.25 Trust in ODNR ...... 361 Figure 7.26 Trust in WHD...... 362 Figure 7.27 Trust in County Commissioners...... 363 Figure 7.28 Trust in North Fork Task Force...... 364 Figure 7.29 Trust in Township Trustees...... 365 Figure 7.30 Heritage Index ...... 377 Figure 7.31 Tenure Category ...... 378 Figure 7.32 Educational Level...... 379 Figure 7.33 Intergenerational Farm Succession Index ...... 380 Figure 7.34 No-Till Conservation Tillage ...... 381 Figure 7.35 Manure Management...... 383 Figure 7.36 Economic Viability of Sugar Creek ...... 384 Figure 7.37 Acres Leased Out for Farming ...... 385 Figure 7.38 Off-farm Income...... 386 Figure 8.1 Conservation Index...... 413 Figure 8.2 Conservation Use...... 414 Figure 8.3 Recreational Use Score...... 415 Figure 8.4 Farm Succession Index...... 416 Figure 8.5 No-Till Conservation Tillage Use ...... 417 Figure 8.6 Manure Management Use...... 418 Figure 8.7 Farm Size...... 419 Figure 8.8 Farm Type ...... 420 Figure 8.9 Farm Succession Index...... 424 Figure 8.10 Grain Farm...... 425 Figure 8.11 Farm Size...... 426 Figure 8.12 Percent of Off-Farm Income ...... 427 Figure 8.13 Number of Years Farmed ...... 428 Figure 8.14 Education Level...... 429 Figure 8.15 Age ...... 430
xx
CHAPTER 1
INTRODUCTION
1.1 Statement of Problem
Anthropologists have called for the inclusion of ethnographic methods,
local indicators, and perspectives from ecological anthropology in developing
watershed and local development initiatives in post-industrial capitalist states as
well as developing states (Bennett 1993, Moran 1990, Salamon 1992, Moore
1996, Kottak 1997, Nazarea 1997, Rhoades 1997, Thu and Durrenberger 1998).
Rural Sociologists recently expressed the need for local behavior and social
network investigations in answering questions related to rural community
relationships (Barham et al 2003, McIntosh and Lee 2003). Land is a necessity in
farming and access to land allows for the reproduction of the social unit, the
agrifamily household system, and the spatial and temporal continuity of ethnic
communities built on aggregates of these smaller systems (Bennett 1982,
Salamon 1992:96).
The purpose of this dissertation research is to investigate potential relationships among key land tenure variables using ethnographic interviews, survey, and historical document research methods to test for relationships
among key land tenure variables in the Sugar Creek Watershed, Wayne County, 1 Ohio. This is done with the proposed goal of demonstrating the importance of
land tenure considerations in conservation-based watershed initiatives and an
exploration of the use of conservation preferences and behavior as a measure of
“quality of life” in expanding Goldschmidt’s findings (1978) that relate quality of
life to farm size and land tenure. In addition, it is important to understand how the practice of local farm succession and continuity of the farm family expressed through ethnic and enterprise differences affect community organization and efficacy in responding to water quality problems. The Sugar Creek Watershed, in
Holmes, Stark, Tuscarawas and Wayne Counties, Ohio, is uniquely suited for this research because of its combination of social and environmental attributes that make it ideal for answering questions of land tenure in relation to larger environmental problems because of differences in cultural heritage, social organization, and farm types.
Sugar Creek Land Tenure
In the Sugar Creek Watershed, like most of rural Holmes, Stark,
Tuscarawas and Wayne Counties, land rights take the form of formal and informal tenure arrangements as well as contemporary and traditional commons.
Each of these arrangements are affected by the complex historical and contemporary social, economic and political levels of interaction as well as physical and biological characteristics of the private property or commons in question. Formal tenure arrangements in the Sugar Creek, in some form of private property agreement, arise from one of the following: inheritance through intergenerational succession by way of a gift, landlord/tenant relationships
2 through rental or lease agreements; land ownership that may or may not include
other social relationships with a local loan agent, parent or community member
as a condition of sale and/or transfer through purchase or inheritance;
partnerships with kin or non-kin, or, some form of sharecropping arrangements wherein the farmer may be the owner who works with another farmer in production. Social relations that relate to the land in these many forms are also passed with these tenure rights. Commons formally consist of areas that are held publicly by residents, depending on the level of political organization, such as parks, public squares, firehouses, public lands, and administrative districts among others. Yet, there are many other informal land tenures that exist extending from a long history of land uses and responsibilities by various families that continue to the present such as established lane access, family cemeteries, and stream corridor use for activities such as hunting and fishing.
Beyond these formal arrangements, there are many current and emerging
forms of land rights and commons that are found in the Sugar Creek. Starting
with a large-scale look and then moving into more detail, the Sugar Creek
Watershed is located in Ohio, a state that is moving along the continuum of
ecological transition from a rural agrarian condition to what Goldschmidt calls
“urbanized” rural areas (1978). As described in results of a recent survey of Ohio
agriculture, environment and ecological issues (Sharp 2001), there is a trend in
Ohio for migration away from urban areas and towards suburban and exurban
communities. This is not unique to Ohio as Mills and Hazarika (2001) note that
rural areas are growing in America not because people are choosing to stay, but
3 rather urban and suburban residents are moving to rural areas. As population
growth creates increased demand for alternative land uses, farm families find it
difficult to expand their enterprises to accommodate the next generation’s desire
to farm. In this environment of structural and demographic rural change, farmers at rural-urban interfaces find it difficult to compete with urban land uses (housing,
industrial, and commercial) that gain increasingly higher prices per acre than agricultural uses. In the Sugar Creek, the farmers who cannot afford the price of new land purchases are gaining access to land through innovative uses of kin and other social networks.
Among the different cultural configurations and social organizations of the
Sugar Creek you find different expectations with respect to tenure and commons.
In these traditional Ohio farm communities with family histories “on the land” with a potential of more than 190 years of continual contribution to the local agroecosystem, certain informal arrangements arise. Below is a non-exhaustive list of some of these arrangements:
1. Access to farming another person’s property because that person’s family had rights to it in the past. Such rights can include access to deer hunting, hiking, water for animals, berry picking, animal and mushroom hunting, fishing, family cemeteries etc. 2. Rental or lease agreements wherein the renter or leaser pays “below-the- local “going rate for agricultural land because of social relationships that exists between the parties. These can include a reduced rate to: a. a relative or family member, b. landowner who perceives that the person s/he is letting cultivate his/her land “needs the help”,
4 c. Reduced rent or lease rate in exchange for a custom service, use or loan of equipment, or other agricultural exchange, d. a person based on the future or past duration of the agreement, e. compensate for a surrounding land use that makes agriculture more difficult. As the needs and wants of the people living in the watershed change
through time, so does the relationship they have with their environment. This
modification of the environment provides feedback that produces a concomitant change in society through evolving patterns of valuation, perception and use of the water and land through drainage development and agriculture, that impact and is impacted by the environment, settlement patterns, climate and surrounding agroecosystems. In this ecological transition, as described by
Bennett (1976), aspects of “nature” in the Sugar Creek enter the socionatural system thereby becoming a resource for human use and potential commodity, such as land for agricultural use, housing, streamside trails or hunting grounds.
Understanding the relationships behind land tenure is a necessary step in understanding this phenomenon of transition.
1.2 Objectives of this Study
Specifically, the proposed goals of this research are to investigate the
following three hypotheses:
1. Ethnicity, social relationships, and attitudes toward farming will condition contemporary land tenure arrangements. 2. Ethnicity and level of socio-cultural integration of the farm household, as independent variables, will affect farm size, land use and tenure, and use and preferences for conservation in which more traditional and less 5 socioculturally integrated groups will have smaller farms, diversified land uses, more secure land tenure and greater preferences and use of Best Management Practices. 3. Farm size, farm succession and inheritance, and enterprise type will correlate with land tenure and preferences for conservation where positive relationships will be found between medium-sized farms, higher levels of farm succession, and non-grain farm types with more secure land tenure and positive preferences for conservation practices and use.
The units of analysis in this research are threefold. The first is the geographical range of the subwatershed. The second is the population of agriculturalist households who own land along the Sugar Creek Watershed in three subwatersheds of the Upper Sugar Creek, North Fork, and Little Sugar
Creek. And, the final unit is the land parcel that comprises, when aggregated with all holdings of the individual’s tenure rights, the land resources from which agrifamily system derives their living.
1.3 Significance of this Study
When I initiated my research among the Anabaptist farmers of Wayne and
Holmes Counties, I had my own doubts regarding the authenticity of researching agricultural communities in my home state of Ohio, at least insofar as traditional anthropological fieldwork was conducted. In making this decision, I was influenced by the work of two anthropologists that helped focus my work “at home”. The first was Roy Rappaport’s (1993) call for anthropologists to turn their attention to problems within the U.S. grounded in the reality that addressing the
6 root causes of these problems are central to finding solutions for many of the
critical issues of the world community. This is to say that the world’s socio-
ecological problems that dominate scientific discourse are causally related to
policy and decisions made at the center of the political economy of the United
States and other Western European states. Another key idea is one of the central
components of Julian Steward’s cultural ecology, the culture core (Steward
1955). Many cultural and social features of a society fit into its culture core, and
they vary depending on the culture and the level of sociocultural integration of
that society. One part of this core is food production, which is the focus of this
work by emphasizing that if you understand how a society feeds itself, you are
closer to an understanding of what the workings of that society’s social and
cultural systems.
The results of this research are critical to an understanding of social, conservation, and economic policy concerns of rural communities. Understanding the relationships that exist in the Sugar Creek Watershed among social organization, cultural continuity, and intergenerational farm succession can provide a basis of rural development projects and community watershed initiatives. This basis can be achieved by framing the goals and scale of the project in the context of local community social structure and residents’ vision of their community, and accounting for the wants and needs of the residents. If the hypotheses are tenable, the results implicate a need for more ethnographic research and local collaboration by government agencies at all levels in conceptualizing and planning such projects.
7 Anthropological contributions to land tenure, land use and social perceptions give valuable insight to owner and user arrangements as well as consequences of changing land rights. Jeffersonian agrarian values favoring owner-cultivators who will participate in local government, are elevated in academic and popular discourse as the foundations of rural farming in America.
In the context of these values, expressed at different levels of social integration, the problems of land tenure and land use are optimally analyzed. Jefferson, and other authors to the present (e.g. Aldo Leopold, Wendell Berry, and Wes
Jackson) have equated involvement in farming and ownership of land with good morality and citizenship by making the connections among being a land owner, an interested and engaged citizen and having vested interests in ensuring a just and moral society based on common social values and standards. Salamon
(1998), citing Marty Strange (1988), states that Jeffersonian values establish land tenure in the form of private property. It is through this land tenure system that many American farmers and rural residents believe the agrarian values, emphasizing local community and good stewardship, are protected and common rights to property are responsibly managed.
Anthropologist Walter Goldschmidt (1978) demonstrated relationships between land tenure, farm size and quality of life of local communities in rural
California during the first wave of industrialization of agriculture. As a final measure of significance for this study, it is my intent to explore the idea that conservation behavior and attitudes can act as measures of “quality of life” in which the positive use of conservation and preferences are correlated with a
8 healthy environment and healthy people. The relationship between farm size, land tenure and conservation adoption, including the observations of social networks and land tenure among the Sugar Creek Partners, follow Goldschmidt’s
(1978) findings relating farm size and land tenure with community “quality of life”.
If Goldschmidt’s findings do apply to conservation adoption as a measure of community well-being, then Buttel’s (1983) findings of a bi-modal farm distribution of small and large-scale farms complicates the long-term outlook for increased adoption rates of sustainable farming systems and conservation practices, as already witnessed in the increase of large-scale, unsustainable factory farms.
1.4 Introduction to the Research Setting
The context of this research is that of a watershed in the process of Total
Maximum Daily Loading (TMDL) planning from the Ohio Environmental
Protection Agency (OEPA). Their evaluation states that ecological imbalances in agriculture cause nutrient loading of streams and rivers and is a major environmental issue in Ohio, and throughout the Midwest. State and Federal
Government concern focuses on hypoxia in the Gulf of Mexico where aquatic life is deprived of the necessary oxygen to survive as a result of excessive nutrient and sediment loading from the Mississippi Basin (USEPA 2000). The OEPA focuses on attainment of aquatic use designations for water bodies as indicators
9 in evaluating the condition of a stream or river.1 Subsequently, local farmers in
the Sugar Creek organized to develop their own solutions to the problem of
surface water quality, beginning by working with members of the
Agroecosystems Management Program (AMP) at the Ohio Agricultural Research and Development Center (OARDC, the agricultural experiment station of The
Ohio State University), in Wooster, Ohio. One outcome of this process was
receiving a 319(h) Non Point Source Grant from OEPA to provide resources for
assisting the community in water quality remediation (Moore et al 2002). It is in
this setting that the researcher, employed as the Project Coordinator of the
OEPA 319(h) Grant and Research Associate as an agent of the OARDC, conducted this dissertation work.
The Sugar Creek Watershed study sites, located predominantly in Wayne
County, Ohio, are part of the sub-basin drainage system, the Muskingum Basin,
which because of its combination of social and physical environmental attributes
make it ideal for answering questions of land tenure in relation to larger
environmental problems. Wayne and Holmes Counties produce diverse
agricultural products and represent two of the largest dairy counties in Ohio
(2002 Census of Agriculture). The historic settlement patterns of the counties
overlap with the watershed to produce a gradient in social organization and land
use and offer a microcosm of Midwestern American agricultural communities.
There exists an approximately twenty-year difference in settling the
northern and southern parts of the Sugar Creek because of the Greenville Treaty
1 For more details on aquatic life use attainment, see Ohio EPA’s Division of Surface Water website: http://www.epa.state.oh.us/dsw/bioassess/AquaticLifeGoal.html Last visited 03/10/06. 10 Line that separated the early Ohio colonists and the Native Americans until the
early 19th Century. After 1795, and prior to Native American’s being forced to
cede all land tenure rights to the new colonists, all Ohio lands north of the treaty
line were owned by these indigenous people. This is a significant development in
early Ohio land tenure because there is an approximate one-generation
difference in settlement that may yield differences in pattern historically and today. The different patterns of land tenure and social organization for the three
subwatersheds that are presented are as follows:
Moving from northwest to southeast, there is a gradual transition in
cultural configuration, social organization and economic patterns that are
associated (not determined or cause/effect) with changes in biological and physical attributes. The Upper Sugar Creek, or headwaters of the watershed, is
predominantly farmed by residents of Apostolic and Mennonite faith who are of
German and Swiss decent with a minority of French Catholics, Methodists and
Lutherans, and no Amish.
Labor is more difficult to recruit in the Upper Sugar Creek because of
demographic (i.e. family size) and economic (i.e. labor costs) reasons. As a
result, and, in some part due to, tractors and combines are used. Grain farming is
the dominant agricultural type using a two-year corn-soybean rotation, followed
by dairy and other animal husbandry that often includes a multiple-year rotation of crops. The average total farm size, including leased land, is approximately
2872 acres. Almost two-thirds of the farmers lease some land for production.
2 Farm sizes are based on survey response averages for each subwatershed. 11 The North Fork, southeast of the Upper Sugar Creek, is close to the
largest non-English speaking and “English as a Second Language” section of
Wayne County (1990 Census) and encompasses a local center of commerce, an agricultural wholesale auction house in the town of Kidron. This subwatershed is a mixture of Amish, Old Order and the more conservative Swartzentruber Amish, and non-Amish farms managing a mixture of grain and dairy farming. Survey results show the average total farm size is around 228 acres with average leasing being less than in the Upper Sugar Creek.
Towards the west and north of the North Fork, in the Little Sugar Creek,
the cultural configuration and social organization changes to almost all Old Order
and Swartzentruber Amish. Drainage patterns in this long and narrow valley
move north then east before joining the mainstem of the Sugar Creek. Animal
husbandry of many kinds (dairy, beef, hogs, and both layer and broiler poultry)
mixed with a four-year/five-crop rotation of corn; various small grains such as
barley, spelt, or wheat; and oats under-seeded with clover and alfalfa-hay for two
years. Pasture is also present (some fixed, some rotationally grazed). The
average farm size in the Little Sugar Creek is approximately 96 acres and – formal leasing is minimal. There is an abundance of local labor in the Little Sugar
Creek that provides positive feedback for supporting labor-intensive small dairy operations using draft horses. As change comes to this part of the Sugar Creek, there has been a recent trend for Amish to specialize in dairy production. Land pressures have led to declining numbers of Amish farmers where fewer than
20% of Amish in the Wayne-Holmes Settlement, and nearly 10% outside of the
12 settlement, work on a farm (Donnermeyer and Cooksey 2004). This has resulted
in a growing number of non-farm Amish households living on small parcels; some
with various woodworking, blacksmithing and engine workshops.
All three subwatersheds (Figure 1.1) have social systems that are
intensifying production and decreasing fallow cycles, but accomplishing this in different ways. The general trend in Upper Sugar Creek has been for farmers to increase their scale of operations so average acreages have almost doubled in the last twenty years. In the North Fork and Little Sugar Creek, the recent use of milking machines has encouraged farmers to increase herd size and this in turn has resulted in changing the rotation pattern. It is suspected that these changes have led to increased pressure on the natural resources and ecological functions of the local ecosystem.
13 1000 0 1000 2000 Miles
United States of America
Upper Sugar Creek
Orrville
Wooster Little Sugar Creek Dalton Massillon
Wayne Summit County County North Fork
Mount Eaton
Line le Treaty Greenvil
Millersburg Holmes County New Philadelphia
# 2 Tuscarawas County Coshocton
County N
W E 2024Miles S State of Ohio Sugar Creek Subwatershed Data Source: 1990 U.S. Census TIGER/Line Files, USGS 8 and 14-digit HUC Coverages
Figure 1.1 Sugar Creek Subwatersheds 14 1.5 Limitation of this Study
As stated in the introduction, survey data and other methods were used in this analysis. The survey was collected as one approach to gain public input into a developing water quality remediation project. As such, the conservation practice data was elicited based on a “presence” or “absence” and does not examine the extent of use. Additionally, conservation is measured using nine
BMPs (4 practices and 5 preferences) because an exhaustive list of BMPs was not provided. Similarly to Burton and Walford (2005:335), farm size is based solely on the total acreage farmed and does not include measures of farm income as others have suggested are important (Gasson 1969, Lund and Price
1998). This limits the direct generalization of findings because farm income is not explicitly included, which may be a determinant factor in farm size for those that are designated Confined Animal Feeding Operations (CAFOs), requiring a
National Pollutant Discharge Elimination System (NPDES) permit. However, this watershed has few NPDES permit holding farms.
1.6 Structure of this Dissertation
This dissertation consists of nine chapters that provide information
regarding the nature of the research problem and subsequent analysis and
conclusions. Chapter 2 – Ecological Context: Geography, History and
Ethnicity spatially and temporally contextualizes the research and findings. The analytical perspective and relevant literature regarding cultural ecology, land tenure, and farm succession are presented in Chapter 3 – Literature Review.
15 The reader should refer to this section for explanation of concepts and ideas
encountered in the analyses chapters that are not explicitly presented (Chapter
6, 7, and 8). Information outlining setting and reasons for the particular
approaches utilized, the methods employed and their justifications are found in
the Methodology chapter, Chapter 4 – Methodology. Land use and land cover of the subwatersheds as well as demographics are presented in Chapter 5 –
Social and Population Characteristics of the Subwatersheds.
Data and analysis are presented in Chapters 6, 7 and 8. Chapter 6 –
Ethnicity, Social Relationships, Farming Attitudes and Land Tenure by
Subwatershed presents the results of interviews and historical document
research in answering hypothesis one, in which the relationships between land tenure with ethnicity, social relationships and attitudes towards farming are examined. Hypothesis two, as presented in Chapter 7 – Ethnicity and Level of
Sociocultural Integration by Subwatershed, describes the relationships between land tenure, on-farm conservation behavior and sociocultural integration of farm households. The results of the investigation of hypothesis three are presented in Chapter 8 – The Effects of Farm Size, Succession and
Enterprise Type on Land Tenure and Conservation Practices. The data and results of discriminant analysis and other statistical procedures used to investigate relationships of land tenure and conservation behavior with farm size and type, succession and inheritance are analyzed and presented. Conclusions and ideas for future research of the land tenure and watershed based conservation topic are presented in Chapter 9 – Conclusions.
16 The interconnectedness of the concepts examined in each chapter
revealed several emergent properties of this dissertation in its evolution as a
document. On the surface, the concepts of land tenure, farm type, social
organization, farm succession and conservation adoption behavior appeared, in
the initial proposal stages of this work, to be discrete and easily operationalized
as measurable phenomena that were to be extracted from survey data. However,
with the addition of qualitative details from observation, document research, and interviews, the notion of measurement discreteness became much more obscured and it became clear to me that these concepts were in fact intricately related at multiple levels. I attempt to make clear in each chapter how I perceived
these multiple interactions and the level at which they occurred.
17
CHAPTER 2
ECOLOGICAL CONTEXT: GEOGRAPHY, HISTORY AND ETHNICITY
2.1 Introduction
This chapter outlines the context and historical background for the
research. I begin with a look at the geological history and its affects on
geography and later human habitation. This includes flora, fauna, climate and soils. Periods of human habitation are briefly presented with major events germane to land tenure and human land use from the prehistoric period to the
present. Finally, an exploration of ethnic and heritage diversity in the Sugar
Creek is presented with emphasis on contemporary farming populations of the
watershed.
2.2 Biophysical Landscape Description This is a community study with an emphasis on watersheds and as such,
the population of people living within the Sugar Creek Watershed and the area drained by the Sugar Creek, the watershed, are the units of analysis for this research. The watershed is predominantly in the Ohio counties of Wayne,
Holmes, Stark, and Tuscarawas with a small fraction of the southwestern end of the watershed flowing from Coshocton County near Baltic, with the entire
18 watershed flowing to New Philadelphia where it empties into the Tuscarawas
River. The overall direction of the watershed is southeast from Smithville, in
central Wayne County, to New Philadelphia in Tuscarawas County. These five
county political boundaries are found within the United States Geological Survey
(USGS) and the Ohio Department of Natural Resources (ODNR) Major Land
Resource Areas (MLRAs) of the Eastern Ohio Till Plain/Northeastern Forage and
Forest Region in the north of Sugar Creek and the Western Allegheny
Plateau/East and Central Farming and Forest Region in the south of the
watershed. These MLRAs transition from the former resource area to the latter in
the watershed just 0.4 miles south of the Village of Wilmot in northern Holmes
and Tuscarawas Counties. Although the three subwatersheds, the Upper Sugar
creek, North Fork and Little Sugar Creek are spatially contiguous, each has a
unique drainage pattern that empties to the Mainstem of the Sugar Creek without passing through the others.
2.2.1 Geology and Glacial History
Ohio’s surface geology consists of exposed soils and rocks originating in the Ordovician (505-438mya) through the more recent early Permian (286-245 mya) Epochs. Geologic systems in between include, from early to late, Silurian,
Devonian, Mississippian, and Pennsylvanian. The latest geologic depositions are from the Permian epoch and are found in the far southeastern part of the state, near Marietta; the earliest are in the southwestern corner, around present day
Cincinnati (Appendix C: Map 4). Pre-glacial drainage patterns of the Teays River
19 system created deep cuts in the bedrock draining north and west from Ohio and through Indiana and Illinois to the Mississippi River. The glacial and interstitial periods of the Pleistocene Epoch, during which early humans first colonized
North America, changed the soils and drainage of the northern part of North
America.
The glaciations of Ohio, as part of a continuum of glacial activity across
North America, brought about distinct geologic systems that make the Northern and Western parts of the state geologically unique from the Southeastern, filling in many of the ancient riverbed valleys and covering the glacially scraped and smoothed bedrock with deposits from successive glacial periods. At the fullest extent of the latest glacial ice sheet, the Wisconsinan covered over two-thirds of
Ohio stopping when it encountered the foothills of the Appalachian Mountains that are a major part of Southeastern Ohio. This period ended approximately
14,000 BPE bringing with it warming trends, which persist through the current day, with human assistance, resulting in the melting of the glacial ice sheets.
Prior to the last series of glaciers that ended the Pleistocene and ushered in the
Holocene, there were periods of erosion and non-deposition that occurred during the late Permian, Mesozoic and Tertiary Periods. This means that the latest sedimentary depositions occurred during the early Permian, about 280mya
(Hansen 1997).
Today, Ohio’s Physiographic Regions and the MLRAs (Appendix C: Map
2) as designated by the USGS and ODNR, follow the glaciated and unglaciated geologic boundary that expands across North America forming the Continental
20 Divide, forming much of the modern landscape. As a result, the variation created
in minerals and regional landscapes create an array of ecosystems for various
flora and fauna. Sugar Creek Watershed is situated along this divide in the
geologic formation. The northern part of the watershed is located in central
Wayne, northern Holmes and southwestern Stark counties. This region is in the
Glaciated Allegheny Plateau physiographic region. The southern part of the
watershed extends southeast through central and southern Holmes and
northwestern Tuscarawas counties, which are in the (unglaciated) Western
Allegheny Plateau.
The Killbuck-Glaciated Pittsburgh Plateau district, the predominant
physiographic district of the Glaciated Allegheny Plateau region, and the area in which the northern Sugar Creek drains, consists of flat uplands and ridges with elevations between 600 and 1500 feet. It has alternating steep valleys and end and ground moraines consisting of thin to thick Wisconsinan clays and loamy soils over Pennsylvanian shale, sandstones, coals and conglomerates. This glaciated region is bounded in the north and west by the sandstones of the glaciated Allegheny and Portage escarpment and the Wisconsinan glacial margin in the south and east that is adjacent to the Appalachian foothills.
The Muskingum-Pittsburgh Plateau is the physiographic district of the
Allegheny Plateau region making the southern portion of the watershed moderate
to high relief valleys with glacial outwash lake deposits resulting from the melting
of the glacial ice sheets with elevations ranging from 650 to 1400 feet. The
subsoil bedrock consists of Pennsylvanian siltstone, coal, claystone, and
21 sandstone. Soils are a combination of silt-loamy colluviums and Wisconsinan gravel, sand and lacustrine silt (Brockman no date given).
Along with changing drainage patterns of the land that would become
Ohio, the geology of the area provides a landscape in which the Sugar Creek
Watershed drains land from valleys formed by glacial end moraines through which water flows over ground moraines, in the north, and colluvium that water flows to ancient lake basins, in the south. Today, these water flows provide surface and groundwater for millions of Ohioans and the sands and gravels make
Ohio the fifth-largest extractor of these materials in the nation. These mineral rich outwashes also provide for present day economic activity of the brick and ceramics industries that still thrive throughout the southern part of the Sugar
Creek, and southeastern Ohio in general (Hansen 1997).
2.2.2 Geography
The Sugar Creek is encompasses parts of the four counties, including numerous townships, shown in Figure 2.1, Holmes, Stark, Tuscarawas, and
Wayne. Table 2.1 shows the spatial extent of each county.
There is a small, but negligible area of the Sugar Creek that extends into
Crawford Township in Coshocton County at the village of Baltic, Ohio. This small area will not be covered in this research.
As discussed in the previous section, the topography of these counties was created mainly through glacial activity of the last ice age that left its mark through the creation of gently sloping moraines and areas of poorly drained soils.
22 The soils of the watershed range from loamy to silty and are capable of producing high crop yields when artificially drained.
Data Source: U.S. Census TIGER/Line Files, U.S.G.S.
Figure 2.1 Ohio Counties and Townships of Sugar Creek Watershed.
23
County Area (mi2) Date Established Holmes 423.0 1/20/1824 Stark 576.2 2/13/1808 Tuscarawas 576.6 3/15/1808 Wayne 555.4 8/15/1796 Source: U.S. Census TIGER/Line Files
Table 2.1 Spatial Extent and Founding Date of the Four Counties of the Sugar Creek
2.2.3 Climate
The four-county region varies slightly in annual average temperature and
precipitation. The northern extent and focal part of the watershed, in Wayne
County, has a yearly average temperature of 48.5 °F with a daily average low in
January of 16.9 °F and a daily average high in July of 81.9 °F. In 2000, the
average daily high was 60°F, with the average daily low of 40°F. Normal
precipitation is 38.92 inches, while in 2000 it was slightly higher at 49.9 inches
(Appendix D: Climate Data).
2.2.4 Soils
General soil orders and suborders for this part of the state are two-fold:
udalfs alfisols, in the northern part of the study area, and ochrepts inceptisols, in
the south. These soil suborder boundaries follow the MLRAs and Physiographic
Region boundaries outlined above. Characteristics of the udalfs alfisols include
slight to moderate sloping with corn and soybeans, small grains and pasture
agriculture. Defining features of ochrepts inceptisols are gently sloping to steep
inclines and small grain and pasture agriculture (Brady 1974).
24 2.2.5 Flora and Fauna
Prior to European settlement, this region was forested with a variety of woody tree and other plant species. Wayne County has been attributed various descriptions by the original settlers. One pioneer of Green Township noted it as a place with forests consisting of “vines of venom, thorns and under-brush” (Kieffer
1876). Despite this nefarious description, a contradictory tale from Swiss
Mennonites portrays Wayne County positively stating that is has much good water and the land is “fertile and level, but not too level” (Lehman 1969). These descriptions offer interpretations of the diverse landscapes the early settlers encountered in Wayne County: the environment of Green Township that was ripe with swampland and dense forest, and the area that became the Swiss immigrant settlement of Sonnenberg in the present townships of Sugar Creek, eastern (east of the swamp) East Union and northern Paint was higher, hilly ground that served as a familiar landscape comparable to Bern, Switzerland, and easily settled.
The “Gordon Vegetation Zones” map of Ohio displays several main plant zones for the counties comprising the Sugar Creek Watershed that would have covered the land up to this period. These include beech forest, elm-ash swamp forest, mixed oak, and oak sugar maple forests in Northern Wayne County and the Upper Sugar Creek, and mixed oak and oak sugar maple in southern Wayne
(where the North Fork and Little Sugar Creek are located) as well as Holmes,
Stark and Tuscarawas Counties.
25 According to the 1876 state of agriculture report for Ohio, the ten most
abundant tree species found in Wayne County, are, in order, white oak (Quercus
alba), sugar maple (Acer saccharum), American beech (Fagus grandifolia),
shagbark hickory (Carya ovata), poplar (Populus alba), black oak (Quercus velutina), chestnut (Acer rubrum), red oak (Quercus rubra), American elm
(Ulmus Americana) and black cherry (Prunus serotina) (Ohio State Board of
Agriculture 1877).
Johnson’s Woods, northeast of Orrville in Wayne County, a state nature
preserve, is an example of pre-European forest coverage of this part of Ohio
(Goebel et al 2003). Today, much of Ohio’s virgin forests, including the Sugar
Creek area, was harvested for fuel and lumber during settlement and can be found in the century barns and homes that dot the landscape.
As a result of the wide scale transformation of the landscape to
agricultural uses, Ohio was largely deforested by 1900, leaving only 12% of its
forests untouched (this has since changed with a total cover of 30% 1991). From forest surveys, Goebel et al (2003) documents common contemporary tree species for these eco-regions.
As described in more detail later in Chapter 5, agricultural land use
consists predominantly of grain, beef cattle, hogs and dairy agriculture with smaller contributions from vegetable, poultry and egg farms.
26 2.3 Prehistory and History of Ohio and the Sugar Creek
Watershed
Native populations at the time of Columbus are widely estimated from varying sources as being between 60-thousand and 100-million people (Lovell
1992). Current research estimates native populations at 40-80 million (Denevan
1992) suggesting that the disorganized social systems and sparsely populated areas first encountered by Europeans explorers in the interior were the result of diseases that had reached and decimated local populations throughout the
Midwest and other regions. Diseases such as small pox and strains of influenza for which native populations would have had no resistance, would have reached societies in the interior of the continent by way of contact with other mobile natives who interacted with Europeans either through trade or as a result of displacement from their traditional lands.
Much of the literature on Pre-Columbian Native American agriculture
refers to it as slash and burn or swidden systems (Brasser 1971, Watson 1988 –
both cited in Doolittle 2004). However, Doolittle (2004) finds that local settlement
of Eastern Woodland Native Americans were more permanent than expected
because the ethnohistorical evidence does not provide instances where the
terms slashing, shifting and burning are used in the same accounts. Instead,
what his research reveals is a pattern of clearing and burning that provides fields
for permanent cultivation. Some of the historical bias towards slash and burn
cultivation is thought to come from a general bias towards people thought to be
primitive and backwards, a bias against a conquered people. Patterson and 27 Sassaman (1988) believe that the early European chroniclers encountered abandoned agricultural fields that they assumed to be the result of shifting cultivation, when more likely they were the product of disease and decimated native populations.
2.3.1 Early Settlers The history of early Ohio settlement by Europeans is a tumultuous period
of violence and domination among Native American groups and later European
Colonial power over Native Americans for possession and control of trade and land. First, the Iroquois competed for control of the fur trade for economic gain
and later in cooperation with previous enemies in the hopes of curbing the
colonial juggernaut.
Early European explorers encountered indigenous populations throughout
North America having varied cultural forms of adjusting to their environments as
they looked for new lands to claim. The early establishment of British and French
colonies along the eastern portion of what is present day Ontario, in Canada, and
New England, in the United States of America, created cultural change among
the native populations that were encountered directly and indirectly as well as
change in social structure. Many native populations were displaced and it is
asserted by Lovell (1992) that several million decimated by disease prior to
formal contact.
In what was referred to as “Ohio Country”, the French and British trade
companies worked in collaboration with Iroquois to establish trade for furs in
exchange for steel tools, muskets, and other European technology. These trade
28 routes lead to competition and later domination of the Iroquois over the other
Native Americans of the region. The Iroquois Confederation of Five Nations,
formed by Hiawatha in 1575, is the result of major shifts in social structure on the
part of the native peoples to deal with the new colonial realities.
One major area of change was the growth in popularity of animal skins
and furs in Europe. European fashion had not had access to so many
inexpensive furs as a result of over-harvesting in Europe. With this new market came new possibilities for commerce and the Iroquois moved into this role. As the European colonial administrations vied for control of this part of North
America, the Iroquois filled the role of “Indian administrator” and liaison of the fur trade. They become the brokers of the fur trade for both French and English and in return received steel tools, clothing and modern European weaponry.
Between Montreal and New York City, the Iroquois had a monopoly on the
beaver skin market as it grew into a cash crop until about 1640 when they had all
but exterminated them in the lands east of the Great Lakes. It was at this time that the Iroquois began their conquest of what became known as the Ohio Lands.
For clarification, the Ohio lands are those that are bordered by
Pennsylvania and New York to the east and were drained by the Ohio River, and
the Ontario Peninsula in Canada. The Beaver Wars, or the Wars of the Iroquois,
were fought between the Iroquois and the native populations of these lands that
included the Tobacco and Neutral Huron Nations in the Ohio Lands, and the
Attiwandaron of the Ontario Peninsula. These nations were crushed leaving Ohio as a “no man’s land” by 1854 (Hurt 1996:6). Later that year, the Erie Nation was
29 also vanquished, leaving the lands south of Lake Erie open for Iroquois exploitation. Thus, according to most sources, Hurt (1996) states that, prior to direct colonial control, the Ohio Lands were virtually uninhabited and owned by right of conquest of the Iroquois. These lands were used as “hunting preserves” for meat and the continued commerce of the fur trade. Iroquois dominion of the region remained until the early-to-mid-eighteenth century when American Indian nations from the east began to migrate there to avoid poor conditions they experienced in the east from colonial policy and Iroquois administration that grew after the death of William Penn in 1718.
Shawnee, Delaware and Wyandot groups began claiming Ohio Lands as
their home, establishing towns and strongholds throughout the region. Favorable
trading conditions with the French also acted as an economic draw. By the
1730’s, Ohio became a refuge from the Iroquois and the colonies to the east.
This began a long series of battles for the control of the region by the French and
British using the Native American nations as proxies and agents that culminated
in the Seven Years War, or what Americans refer to as the “French and Indian
War”. In response to the political economy of the region, the Native Americans at
one time or another held loyalties to both the French and British during this
period of warfare. With the English as victors, the resolution of the war with the
Treaty of Paris in 1763, gave the British de facto dominion of the lands because
they exercised hegemony over the Iroquois who claimed ownership by conquest
a century earlier. The Quebec Act of 1774 placed the land ceded by the French,
30 from the Hudson Bay to the Ohio River, within the Province of Quebec. This
would bring peace to Ohio until the Revolutionary War.
During the Revolutionary War, or War of Independence, the English used
their relationships with native groups on the Ohio frontier to engender their loyalty
and disrupt supplies and create further tensions within the colonies and
territories. But, once the Revolutionary War ended and the English government
ceded the colonial lands to the Continental Congress, and transferring dominion
over the Iroquois and therefore control of the Ohio Lands. In 1787, the Northwest
Ordinance was passed creating a territory that included the lands west of the
Pennsylvanian border and northwest of the Ohio River and east of the
Mississippi in what are now Wisconsin, Michigan, Illinois, Indiana, Ohio, and part of Minnesota. In 1803, the settler population of the Ohio lands was enough to allow it to become a state and as such, it became the seventeenth state to enter
the Union of the United States of America, and one of a maximum of five states
mandated to be created from the Northwest Territory (Hurt 1996).
With the gradual removal of native groups through conflict and
appeasement, the southeast part of the future state began to open to settlement
by European colonists. By act of the Continental Congress, the Land Ordinance
of 1785 initiated the first survey of the future state. Lead by Thomas Hutchins,
the Seven Ranges of southeast Ohio was the first land surveyed. This survey would lay the foundation upon which the settlement of the land would follow. By
using a grid system creating townships, instead of the metes and bounds of the
eastern states, a systematic division of land into arbitrarily square land parcels
31 was achieved. This system was the beginnings of the Public Lands Survey
System (PLSS) that is used today. East-west “baselines” and north-south
“principal meridians” were established as reference points for dividing lands into
six-square-mile “hundreds” (the lands of the Connecticut Western Reserve are
divided into five-square-mile “hundreds”), or townships that were further
subdivided into one square mile “lots”, or sections; Thus townships were
composed of thirty-six sections of 640 acres. Sections are further subdivided into
“quarter sections” of 160 acres and are referred to as “northeast quarter section”
and so forth based on geographic orientation; in turn, these are divisible into
“half-sections” of 80 acres.
With the exception of the Virginia Military District lands, all lands in Ohio
are divided using this rationalized system of parceling land. The “township and
range” parceling became the system for labeling land. For example, the site of the largest white oak (Quercus alba) encountered by the first settlers was on the present day Rohrer Farm in Green Township, Wayne County, which is Township
17 North, Range 12 West, Section 22, southeast subsection, or T17N R12W S22
SE.
The westward expansion of European settlers created a great deal of
tension among settlers and native groups that included the Shawnee, Wyandot
(a.k.a. Huron), Delawares (a.k.a. Lenape) and Seneca. Many disputes rose from
these growing tensions that erupted in violence causing the newly formed US
Government to intervene, often times brutally. The ensuing Indian Wars continued until the savage pacification of the Ohio Native Americans by U.S.
32 Army General “Mad” Anthony Wayne, in 1794, that lead to their endorsement of
the 1795 Treaty of Greenville. In the course of bringing peace to the frontier,
Wayne was responsible for burning and razing many Native villages, including
those in the Wayne County vicinity. For Native American people, the treaty
provisioned a reservation west of the Cuyahoga and Tuscarawas Rivers and
north of a boundary drawn between Fort Laurens, near the Tuscarawas River,
and Fort Laramie on the Greater Miami River. The reservation continued across
northwest Ohio to Indiana at Fort Recovery, then south to the confluence of the
Kentucky and Ohio Rivers (Figure 2.2); and bringing a temporary end to the sporadic and violent conflicts between white settlers and Native Americans by restricting their access to Ohio land in the northwest quarter of the state. This successfully pushed the native populations out of the main area of conflict, leaving that land to the settlers. By default, this artificial separation of native and
settler populations has created a potential time capsule in which to test land
tenure and settlement theories. Although the results of the treaty were overtly negative towards the Native Americans, the treaty document was the first to recognize Native sovereignty within the boundaries of the United States, and would set a precedent for future treaties and legal actions. In the end, Wayne died of gout, a death of a sickly man, not a warrior, and was interred in Radnor,
Pennsylvania.
33 Indian Reservation
Data Source: U.S. Census TIGER/Line Files, U.S.G.S.
Figure 2.2 The Greenville Treaty Line, established 1795
34 2.3.2 19th and 20th Century History and Land Use
Over time and as the land claimed by settlers failed to satiate their needs,
a series of successive treaties were drafted and ratified with the Indians until
there was just one reservation of twelve square miles in Northwest Ohio, then
nothing. The last Indian lands in Ohio were ceded with the Wyandot Treaty of
1842, the main provision of which was for the final removal of all Indians from
Ohio. The Wyandot were the sole remaining native group and they exchanged
their small 7880-acre Ohio reservation in Ohio for 148,000 acres west of the
Mississippi.
Wayne County was established in 1796 by Proclamation of the
Continental Congress of the United States, seven years prior to Ohio becoming a
state. The other three counties, Holmes, Stark, and Tuscarawas were
established by Acts of Congress of the State of Ohio, in 1824, 1808, and 1808,
respectively. Originally, Wayne County consisted of all the land north and west of
the Greenville Treaty Line.
Settlement meant granting land to recent European migrants. Through a
rational system of successive subdivision of the Ohio Territory into government lands and townships that are aggregated into counties, Ohio was parceled and sold (Ohio Auditor of State 2002). One of the conditions of this early settlement was the planting of apple orchards for production of cider and a source of
sweetness by way of apple sauce (Pollan 2001), thus explaining the bevy of early orchards that appeared after settlement.
35 The Sugar Creek Watershed
Sugar Creek is located in north central Ohio, USA, predominantly in
Holmes, Stark, Tuscarawas and Wayne Counties (four of the five leading dairy
producing counties in Ohio). The Sugar Creek watershed is in the headwaters of
the Muskingum Watershed, Ohio’s largest hydrologic basin draining 8,051
square miles of North Central and Southeastern Ohio’s twenty-four county region, or approximately one-fifth of Ohio. Because it is located just south of the
Continental Divide, it is a headwater to the Mississippi system. Within the Sugar
Creek are six subwatersheds at the 11-digit Hydrologic Unit Code (Figure 2.3):
Upper Sugar Creek (79.4 mi2), Lower Sugar Creek (74.2 mi2), North Fork (18.0
mi2), Middle Fork (47.3 mi2), South Fork (61.6 mi2), Indian Trail/Walnut Creek
(48.1 mi2), and East Branch (28.2 mi2). The historic settlement patterns of these
local counties overlap with the watershed to produce a gradient in social
organization and land use and offer a microcosm of Midwestern American
agricultural communities. Farming practices in these six agricultural
subwatersheds range from the traditional Amish farming practices (Stinner et al
1989, Moore et al 1999, Bender 2003) in many of the southern subwatersheds,
to the conventional row crop production that is found in the northern
subwatersheds that dominates the Upper Sugar Creek.
36 Upper Sugar Creek
STARK WAYNE
North Fork
Middle Fork
HOLMES Lower Sugar Creek
Walnut & Indian Trail Creek
South Fork TUSCARAWAS N
W E East Branch
S
Data Source: USGS 11-Digit HUC, as used in the 2002 Sugar Creek Watershed TMDL Final Report.
Figure 2.3 Sugar Creek Subwatersheds Based on the Ohio EPA’s 11-Digit HUCs
37 Because of the diverse landscape found in the four counties of the Sugar
Creek Watershed, early settlers’ accounts of the area are themselves diverse
and are relative to the geography they encountered and social systems they
established. Some describe a paradise and others a fearful, loathsome place. It
is the author’s opinion that the descriptions varied by the attitudes of the settler’s
and their associated communities (e.g. settlers in search of “freedom” and a new
life, spoke more fondly of the areas than those in search of land and riches).
North-central Wayne County, the location of the Upper Sugar Creek, was surrounded on three sides with swamp and marshland that acted as barriers to the early settlers – for travel, trade and agriculture. One account of the land of
Green Township comes from D. L. Kieffer who wrote in 1876, while reflecting on his settlement:
Dark and Fearful in aspect; deep beyond measures in magnitude; dense and unbroken in itself; and interwoven thickly with vines of venom, thorns and under-brush, and high rankling weeds of every description, was the veil of the forest which once covered the face of this country. Devoid in habitations of man, unsoiled in sheet, it lay over the entire field of Green Township until 1811. Indians and wild beasts sporting through swamps and dusky avenues; serpents and reptiles hissing from their lurking places, abounded in swarming populaces all over the land; while over the feathery tribe, which seemed to join in emission of uncouth notes from every tree- top. Amid this wild state of things, the first settlement was made upon the soils of Green. (D.L. Kieffer 1876:1)
Looking back on the settlement of the state from a pioneering perspective
of “land to be tamed”, according to the Dr. Charles E. Thorne, “the settlement of
Ohio was no weakling’s task.” Unlike other western territories, Ohio was a part of
the eastern woodlands and not what became a prairie state. Early settlers, not
38 known for their conservation mindset, brought with them various attitudes
regarding the land and traditions with regard to making a living from it; traditions
and attitudes that were not adapted to this type of environment. Instead of
learning how native populations adapted to the land, they entered into a process of shaping the land to their culture. As such, “wild animals were to be killed; wild men were to be driven back, and a forest was to be felled before the pioneer could begin his little frontier farm” (Lloyd et al 1918:13).
Other accounts focus on the swamp lands in and around Green, (including
the Tamarack swamp of Baughman, the Hackett swamp in East Union, the
Bauffman swamp in Chippewa, and the River Styx swamp of Milton Townships
(Caldwell 1873) referring to them as the “Dismal Swamp” and “Shades of Death”.
Douglass (1878:83-84) states that “It was a wilderness and dismal swamp then,
the scream of the panther, the howl of the wolf, and the barking of the fox
echoing within its borders”. These labels are due to the experiences of many
people who were attacked by animals and/or contracted malaria while passing
through or lived nearby. However, this area was a renowned hunting ground for
Native Americans and the early settlers. Bear, deer, and other prey lived within this land and fish were abundant in supply and variety – making it a “fishing and hunting resort” of the Native Americans who visited here. Wild potatoes and cranberries were readily available in such quantities that people from miles around were able to survive from them alone (Douglass 1878). It seems that the only part of eastern Wayne County which did not have a swamp nearby was
Sugar Creek Township and the adjacent parts of Paint and East Union that, as
39 described later, would be the heart of the Swiss Mennonite community of
Sonnenberg. These swamp areas as well as other water resources surely were a
source of conflict as colonial expansion encroached on native resources. The
difference between usufruct and fee simple ownership must have caused similar
conflicts, as settlers, prepared to defend their land, would take up arms against
perceived trespassers whose intent, following past practice, was to hunt or fish.
Land use conflicts between white settlers and Native Americans were
common in the early frontier of Ohio. Many of them resulted from cultural
differences that involved conceptual models of the disposition and access to land
as well as traditional gender roles. Although the settlers had diverse cultural
backgrounds, as did the Native Americans, Ohio settlers all understood the
concepts of “absolute ownership” of land and legal provisions for permanent
transfer of property rights for the accumulation of wealth. Although the native
groups in Ohio had been residents for just a century before being removed by the
new government of the Untied States, and many had adopted tools and methods of hunting from white settlers through interaction and trade, but usufruct rights
were still the dominant land use concept.
Additionally, and notwithstanding the presence of native villages
throughout the Ohio Valley, most native groups practiced a “seasonal round” in
which they cultivated corn, beans and squash (mainly pumpkins) in the spring,
summer, and fall, hunted throughout the year, and wintered in camps near
riparian zones, thus abandoning their fields during those months. For the white
settlers, an abandoned field represented an opportunity for a land claim. Usufruct
40 rights in which land tenure was granted while land was in use, but rescinded once land went fallow and entered disuse, was a foreign concept to white settlers.
This same conceptual disconnect between groups resulted in conflicts over water use, access to streams and stream corridors for fishing and hunting purposes, as well as winter camps. Another source of contention was in the gendered source of tenure rights, that is, native women controlled access to land since agriculture was generally their domain, while hunting and war was the domain of men. As the abundance of animals for the fur trade rapidly diminished, the colonial governments attempted to impose agriculture and permanence of settlement on the native people, yet this too was resisted because it was viewed as emasculating for men to undertake the work of women (Hurt 1996).
Kieffer’s description provides a vivid account of the forest cover that was found nearly covering the entire area of Green Township. He states that all but the southwest quarter of Section 3 was covered with this dense forest and the entire township was “well watered” with each section having ample supply of natural springs. The settlers of Ohio, and their descendants contributed to the deforestation of Ohio that was nearly complete by 1900. Family life cycles were a factor in this, as they currently are in Amazonia (as reported by Moran et al
2000). The expansion of new enterprises is often associated with less capital intensive and more resource dependent forms of production. As Ohioans began to recognize the size of their ecological footprint and new technologies were developed, such as rural electrification and the petroleum dependent internal
41 combustion engine, it became possible to relax the pressure on forests and allow
re-growth.
2.4 Ethnicity of the Watershed and Surrounding Areas
Multiple socioeconomic and political crises of the Eighteenth and
Nineteenth Centuries in Europe acted as a push factor in immigration to North
America, and the United States in particular. Early migration resulted from the depopulation of the countryside, and later urban areas, because of war and economic class-struggle. The patterns of immigration are evident in social and language patterns throughout the United States. Figure 2.4 shows linguistic patterns of regional dialectical differences. These differences correspond to patterns of immigration in which the peopling of Ohio followed an observable pattern in which most northeastern Ohioans migrated from the New England region (hence the name being the Connecticut Western Reserve), and much of central Ohio was populated by migrants from Western Pennsylvania, Virginia and
Kentucky.
42
Data Source: U.S. Census TIGER/Line Files; Based on Labov, Ash & Boberg http://www.ling.upenn.edu/phono_atlas/NationalMap/NationalMap.html Last visited 3/10/06.
Figure 2.4 Dialect Regions of American English
2.4.1 Settlement and Immigration History
Early settlers to the Ohio valley came from the eastern colonies that later
became states. They came predominantly, but not exclusively, from Connecticut,
Pennsylvania, New Jersey, Kentucky and Virginia. Some settlers where veterans
of the Revolutionary War who were rewarded for their state militia service with land grants in the new Northwest Territory. This is the case for people who entered the Virginia Military District and the Connecticut Western Reserve land 43 apportionments and others. While migration west continued from the eastern
states, after the close of the “Ohio Frontier” in the late 1840s, many people came to Ohio as immigrants rather than settlers3 by moving directly to specific parts of the state from their country of origin. Figure 2.5 shows the growth of population of
Wayne County. Residents of Wayne County tend to be long-term residents as seen in Figure 2.6.
Wayne County Population 1860-2000 4 6 1 6 ,5 8 1 ,4 3 0 1 4 1 1 120,000 2 , 0 7 ,1 7 1 9 9 4 7 100,000 6 , 8 5 0 1 4 7 2 ,7 80,000 6 5 6 2 0 8 ,5 8 3 6 7 0 4 0 7 5 , 0 5 8 1 0 0 0 ,3 60,000 1 , , ,8 , 7 5 ,4 , 0 9 1 4 5 7 8 4 2 4 3 3 3 40,000 3 3 20,000 Population Count 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 8 8 8 8 9 9 9 9 9 9 9 9 9 9 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 Year of Census
Data Source: U.S. Census TIGER/Line Files, U.S. Census of Population 2000
Figure 2.5 Wayne County Population Growth
The construction of the canal systems and the development of small towns as market centers greatly increased the prosperity of farmers on the Ohio
3 Although the distinction is narrow, for my purposes, a settler is one who first moved to an area uninhabited by other Europeans and bringing their way of life; an immigrant is one who moves to a “settled” area and adapts to the larger established society. In reality, both groups are immigrants. 44 frontier. This prosperity became a magnet for immigrants who were assisted in their journey to Ohio by the canals.
Population Residency over 5 Years 3 70,000 62,30 60,000 41 50,000 ,5 40 40,000 6 7 ,1 5 30,000 2 69 20,000 15,365 ,7 10 96 Population County ,5 10,000 4 1 89 0 5 5 9 y 5 9 9 ty t e te 9 9 n t a 1 n u ta t 9 1 n u o s 1 n i o c s t i . c e n in S t e . e n m re re s U m re a e e u a S ff h o e fe i h h S if D w t e e n D s m i l a e E S s u o h t n re fe if D Where did residents live in 1995?
Data Source: U.S. Census TIGER/Line Files, U.S. Census of Population 2000
Figure 2.6 Wayne County Residence Patterns from 1995 to 2000
2.4.2 German and British Ethnic Groups
Settlers of Dutch and British (Scotch, Scotch-Irish, English) descent were among the first to move to Ohio as pioneers and later speculators and entrepreneurs looking to capitalize on the expansion of available lands. These early people brought with them material and ideational culture from the east as
45 well as social structures, including networks, as they established settlements and
began a new life with expanded social and political ties to the eastern states. As a result of the English manor system, early settlers and land speculators were accustomed to ideas of ownership and sale of land for profit. Consequently, as land became available in Indiana and westward, many of these British settlers and farmers sold their depleted lands and moved to larger, cheaper land. In the
Sugar Creek and vicinity, these lands were later purchased by Swiss and
German migrants.
Germans migrated to Ohio beginning in the 17th Century. Between 1850
and 1900, the Germans were always greater than 25% of the total immigration
population, in 1860 they comprised over 30% of foreign-born citizens, and in the
early 20th Century, they were the largest. The first Germans were urban artisans and shop owners, but later immigrants would seek to replicate the agrarian
societies they had left. The period from 1816-1917 saw greatly increased
migration rates due to “disastrous harvests and economic dislocation after the
Napoleonic wars in rural Germany”. Most of these early German and Swiss
immigrants hail from agrarian backgrounds. (Thernstrom 1980:406-410).
Irish and Scotch-Irish, differentiated mainly by the politics and religious
disputes between the Irish-Catholics and Scotch and Irish Protestants,
respectively, immigrated to the United States over several successive decades.
The Irish Rebellion of 1798 caused many to leave “the land of harp and
shamrock” to the New World – many came to Ohio, some, including a man
named Robert Barr, settling in the Sugar Creek Valley – in Sugar Creek
46 Township, Stark County. One of the attractive qualities of Ohio to early settlers was the abundance of raw materials with which a pioneer could make a living.
Some, like Massum Metcalf (settling in 1810), came to eastern Stark County and later moved to Sugar Creek because the eastern “county was too thickly settled; he could hear his neighbor’s dog bark, and it was [already] so cleared up that he could not fell a tree at his door for firewood” (Perrin 1881:512). Between the cessation of the Napoleonic Wars and 1900 saw a number of dislocations of these people as famine caused many to leave Ireland (Thernstrom 1980:904).
2.4.3 Anabaptist Ethnic Groups
Anabaptist is an umbrella of Christian faiths that draw their inspiration and lessons from three reformation students, in Zurich, Switzerland, 1525, who would later be persecuted by their former teacher, Ulrich Zwingli. Acting against a tradition of centuries of dominance from state sponsored church, they refused acceptance of state sponsored baptism and to defer to the scriptural authority and interpretation of the Catholic Church. Anabaptists insisted on baptism as adults. Frustrated by years of oppression, war and poverty, Anabaptists throughout the many political units that comprised Germany, Switzerland and eastern France looked to the literal teachings of Jesus Christ, to the exclusion of any state interpretations, for ways to live lives of love and service to one another
(Hostetler 1993, Kraybill and Hostetler 2001).
Today, descendants of this Christian philosophy are found around the world, from Europe and Asia, and North America and Africa, and in many
47 degrees of traditionalism (e.g. keeping with the “original” doctrine and actions of
Anabaptists). Conduct, including dress, speech patterns, and appropriate technology and work define the boundaries among the many groups who call them descendants of the Anabaptist movement. Included in this list are several groups found in the Sugar Creek Watershed (but not necessarily in the study areas) and its surrounding counties. They are, in order of traditionalism from most to least: various Old Order Amish congregations and New Order Amish,
New Order Amish Fellowship, Beachy Amish and Conservative Mennonites,
Apostolic Christian Church, Church of the Brethren, and General Conference
Mennonite Churches (Kraybill & Hostetler 2001). The differences among
adjacent groups on this continuum of traditionalism are small in comparison with
non-Anabaptist groups, but between traditional and worldly groups, the
differences are vast. Among the Old Order Amish, the emic description of this
concept of traditionalism is comparable to their classification of worldliness and,
according to John Hostetler, there are degrees of worldliness whereby the Old
Order rank churches from low to high. “A low order (the most simple and humble)
is one that observes strict discipline, teaches separation from the world, and
practices social avoidance” (Hostetler 1993:93), the latter being one of the
largest issues of debate and subsequent schism among Church District
affiliations. Beyond the Anabaptist affiliations of the Amish, the Amish refer to the
rest of the world as the “English”, a term originally a distinction based on language use, but now, since most Amish learn English as children, it is a term that simply denotes anyone who is not Amish.
48 Anabaptist settlements were established during the middle to late 19th
Century throughout most of Wayne and Holmes Counties. Most notably are the two Apostolic congregations of northern Wayne County (Rittman and Smithville), the historical Mennonite community of Sonnenberg, and the Amish Holmes
County Settlement, which currently extend to the southeast of Wayne and parts of Stark, Tuscarawas and Coshocton Counties.
2.4.4 Ethnicity, Landscape and Farming
The character of contemporary Midwestern towns is in part a product of
the rich and diverse history of settlement and immigration by the ancestors of
today’s ethnic and religious groups. The social and physical environments they
encountered provided challenges and opportunities to adjust to these new
surroundings and offered freedom to develop institutions within the framework of
the larger political-economy of the United States. Settlement of the state,
importation of social structures, and development of communities created an
economy of resource extraction for urban commercial centers in the eastern
states. In the early part of the 20th Century, researchers at the Ohio Agricultural
Experiment Station (OAES) explain in their history of Ohio agriculture that
“experience has shown that the human history of a soil may have a more
important bearing on its present condition than the source or manner of its
formation” (Lloyd et al 1918:5). They further explain that the influence of a
population on agriculture greatly depends on the traditions they brought with
them to this new land.
49 The various surveys of Ohio and district tracts each have a distinctive
history to them that stem from the geography as well as the first surveys and
sales that attracted specific segments of European and Colonial American
settlers. “Yankees” from Connecticut, Massachusetts, and New York were the
predominant groups that came to the Western Reserve, above the 41st Parallel to
Lake Erie. Historically, their impact on Ohio’s agriculture was seen in the prevalence of New England cheese-makers and acting as the largest milk market of the state. By 1918, many of the dairy farms had transitioned into
“unsustainable” corn and hog farms. Pennsylvania Dutch, from the
Pennsylvanian Counties of York, Bucks, Lancaster and Washington, came to the
Congress Lands counties of Columbiana, Stark, Richland and Wayne bringing an agriculture that relied heavily on wheat production. These counties became the leading counties in wheat production. Ohio Military District counties of Holmes,
Tuscarawas and Monroe attracted Swiss immigrants who brought with them dairy and the “small cooperative, Switzer cheese factory” model of cheese production that catered to New York City and Philadelphia markets (Lloyd et al
1918). The Virginia Military District surveyed and parceled to reward Virginian soldiers for participation in the Revolutionary War as well as appeasement of that state’s legislature for ceding their claim to the Ohio lands, transferred a large farm model to the district. Large farms ranged in size from 1,000 to 10,000 acres.
Tobacco farms of this district were imported from Kentucky to Adams, Brown and
Clermont. One last example is the development of a once thriving wine industry along the Lake Erie shore that began on Kelly’s Island. Dantas Kelly sold the
50 island to a German from the Rhine Valley who brought to America varieties of grapes to establish a vineyard. Hops were first grown in Ashtabula County for local brew-masters. Figure 2.7 show the ancestry of Wayne County.
Ancestry of Wayne County in 2000 Ancestries with populations less than 1% are aggregated in "Other"
Dutch, 2,683, 2% Other, 17,126, English, 10,676, 16% 10% French, 3,474, 3%
U.S.A, 11,052, 10%
Swiss, 6,191, 6% German, 33,367, Scottish, 2,187, 30% 2% Scotch-Irish, 1,703, 2% Polish, 2,083, 2% Irish, 12,760, Hungarian, 1,246, 1% Italian, 4,224, 4% 12%
Data Source: U.S. Census TIGER/Line Files
Figure 2.7 Ancestry of Wayne County
51 The Sugar Creek and its tributaries were the center of early pioneer life,
serving as a lifeline to the outside world in the sense that the creek provided
water as well as a source of transportation through the dense terrain by way of
navigable waterways. Though agriculture allowed settlers to grow food for their
own consumption, marketing was difficult because overland travel to market was
cumbersome and unreliable, leaving the stream, rivers and, later, canals as the
preferred mode of transportation. Another practical issue was the conversion of
grains to food – in the first decade of the 19th Century, the nearest grist mills
were often a county or two away – Kieffer (1876), Douglass (1878) and Perrin
(1881) each refer to the arduous journeys transporting grain to the grist mill.
Aside from transportation, the first use of the stream was for milling grain.
Newman’s Creek and the Sugar Creek each had several such mills built, which
replaced the “turn about” mills that served as a communal grist mill (One such
mill was in Green Township, on the farm of D. L. Kieffer).4 Access to water from
the creeks was essential to daily living, physically, socially, economically and
spiritually, because creeks were sources of drinking, recreation, watering
animals, bathing, washing clothes and spiritual cleansing during Baptism.
Another important stream use was hunting. This activity crossed private
property boundaries that incorporated the stream. According to Jonathan Wood
(in Perrin 1881:512), “wild game was plenty, wolves, deer, bears, wild cats and
turkeys were plenty… [Hunters] never traveled anywhere without a rifle, powder horn, shot pouch, and a sheath-knife suspended to a belt”. As such, a hunter’s
4 These human powered mills were alternatively referred to as “sweat mills, because by sweat it was run” (Kieffer 1876:5). 52 chase might traverse several property lines before success. Regardless of whose
property on which the animal was killed, common law held that the kill belonged
to the hunter and “he who would disturb it, did so at the peril of his own life”.
A common theme cited across historical references (Kieffer 1876,
Douglass 1878, and Perrin 1881) is the loneliness of life on the early frontier.
With low population density landscapes and long hour days devoted to subsistence activities, and later, for some, marketing, there was little time for nurturing social relationships with neighboring families or creating and belonging to social societies. This sentiment is still expressed by rural residents, but to a lesser extent due to rural electrification, telephones and internet capacities.
The opening of Ohio for widespread immigration began with the Canal
construction in 1825 (ending in 1832) with two independent systems running
south to north to connect the Ohio River to Lake Erie. The development of the
Ohio-Erie and the Erie-Miami Canal systems fueled a rapid expansion in Ohio’s
population as the state and local governments looked for ways to partner with
business to rapidly settle the newly conquered lands. These two canals
connected settlers to the outside world by opening markets for agricultural
production. The Ohio-Erie Canal served the eastern portion of the state including
the Sugar Creek vicinity where Massillon and Canal Fulton (Fulton at the time)
became market centers (Perrin 1881). Canals operated for two decades before
the railroads were built to further connect and provide infrastructure for
agricultural and industrial extraction. The rails were initially laid to compliment the
canal systems, with little overlap in service areas. But, by the 1860s, the railroad
53 came to dominate transportation (Lloyd et al 1918) and the canals began a slow
decline that culminated in the 1913 floods, during which time many miles of canal
were damaged and, due to the greater efficiency of the rail system, they were
never repaired.
Agriculture in Ohio remained relatively small in scale and unchanged until
about 1865, with the aftermath of the American Civil War. Military conscription
and a high number of rural conscripts left many farms with a labor deficit as
young men went off to fight. Ohio was not immune to this draw of rural youth.
Some went voluntarily and others were compelled by the draft. Labor saving devices became a necessity as “mature farmers” were left to fill their labor needs
with new inventions like McCormicks Reaper and other machinery. Lloyd et al
(1918:14) infer that very few of the seasoned farmers went to war because
agricultural production data from this period does not indicate a decline, but there
is evidence of an increase in farm mechanization.
Occupation structures also began to change after 1865 as the state’s
population began to increase. Unlike previous pioneering settlers, most of these
new immigrants came to Ohio’s cities and provided labor as the economy began
transitioning from predominantly agricultural to an incipient industrial mode of
production with urban industry fueling the demand for coal and coke production
in the hinterlands. This growth of urban, non-agricultural population, in
conjunction with young farmers wanting to push further west to abundant cheap
lands, further enhanced the need for intensive agricultural production methods.
By the 1870’s, chemical fertilizers became available and state drainage laws
54 changed thereby allowing farmers to “reclaim” the fertile lands of swamps and
marshes for row crops and other farm uses while extending the productivity of
depleted land. These factors allowed farmers to continue to work the land with
fewer person-hours. However, in the Wayne-Holmes County area, many
Anabaptist groups would continue to farm with the use of horses and labor of
large families into the 21st Century.
Ohio agriculture suffered a severe decline in the 1890s as less populated
western states were better able to incorporate new farm technologies developed
for larger scale agriculture. At the same time, much of the State’s development
energy was focused in urban areas as it fostered the development of steel
producing and manufacturing industries fueled by lavish coal deposits of the
southeastern part of Ohio. Subsequently farming was in decline and many
farmers sold their land and moved to the cities in search of wage labor. Near the
end of the 19th Century, the western frontier appeared to “close” as the jump in
competition leveled out across the agricultural sectors with the assistance of the
Great Plains drought (Pfeffer 1983) and Ohio once again saw a resurgence in it’s
agricultural production as land values began rising. It was said by many farmers
that farming was again seen as something worth preserving and a heritage that
could be passed to their children (Lloyd et al 1918). Figure 2.8 shows the changes in the total number of acres of farmland per county of the watershed.
55 Number of Acres of Ohio Land in Agriculture From 1850-1997
350,000
300,000
250,000
Acres (N) 200,000
56 150,000
100,000 1850 1900 1920 1930 1950 1987 1992 1997 Holmes 161,029 253,357 258,743 242,519 242,192 186,018 177,194 171,732 Stark 245,365 342,290 328,911 260,726 256,882 153,302 136,612 136,737 Tuscarawas 254,832 339,786 312,604 275,710 262,090 153,277 148,479 141,358 Wayne 241,936 338,149 333,949 322,128 318,373 263,457 246,938 240,905 OHIO 14,469,133 24,501,985 23,515,888 21,514,059 20,969,411 14,103,085 14,247,969 14,997,381 Agriculture Census Date
Data Source: U.S.D.A. Census of Agriculture 1850, 1900, 1920, 1930, 1950, 1987, 1992, 1997
Figure 2.8 Acres of Land in Farms per County of which the Sugar Creek Watershed is a Part
56 A comparison of agricultural censuses from 1850 to the current 2002
Census of Agriculture shows a substantial shift in Ohio agricultural production.
Such a shift moved from wheat and oats as staple crops, to corn and soybeans as commodity crops. Figure 2.9 and Figure 2.10 detail changing production rates of four comparable crops: wheat, oats, hay, and barley.
60,000,000
50,000,000
40,000,000
30,000,000
20,000,000
10,000,000
0 1850 1870 1950 2002 wheat (bu) 14,487,351 27,882,159 53,039,880 46,929,358 oats (bu) 13,472,742 25,347,549 42,281,470 3,254,377
Data Source: U.S.D.A. Economic Research Service 1850, 1870, 1950, and 2002.
Figure 2.9 Wheat and Oats Grown in Ohio from 1850 to 2002
57
3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 1850 1870 1950 2002 barley (bu) 354,358 1,715,221 464,208 312,127 hay (tons, dry) 1,443,142 2,289,565 2,978,813 2,728,144
Data Source: U.S.D.A. Economic Research Service 1850, 1870, 1950, and 2002.
Figure 2.10 Barley and Hay Grown in Ohio from 1850 to 2002
58 Soybeans do not enter the census until the twentieth century when they
begin to dominate production volumes relative to other crops, except corn (Figure
5 2.11). Corn and soybeans are charted separately because of different scales of
production of each and have come to dominate Midwestern agriculture, replacing wheat and oats as high volume products.
300,000,000
250,000,000
200,000,000
150,000,000
100,000,000
50,000,000
0 1850 1870 1950 2002 corn (bu) 65,172,011 69,405,773 168,048,185 254,817,899 soybeans (bu) 0 0 18,794,324 149,809,069
Data Source: U.S.D.A. Economic Research Service 1950 and 2002; Lloyd et al 1918
Figure 2.11 Corn and Soybeans for All Years, Grown in Ohio from 1850 to 2002
5 Corn and soy bean bushels per acre are calculated from average acres and bushels per acre given in Lloyd et al (1918) for time periods 1850-1859 and 1860-1869, respectively. Corn in the early Census was referred to as “Indian Corn”. 59 Animal agriculture transitioned away from sheep and focused more on chicken poultry, with extensive specialization in beef and dairy. Figure 2.12 details changing production rates of hogs, sheep, dairy cows, beef cattle and poultry. It is important to note that a zero (0) in a column does not mean there was no production but that early censuses did no accounting of those crops or animals (i.e. corn, soybeans and chickens were not included in the censuses taken in the 1800s) likely due to their ubiquitous presence on every farm.
6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 0 1850 1870 1950 2002 hogs 1,964,770 1,723,968 3,156,027 1,422,966 beef 749,067 758,221 278,713 260,702 sheep 3,942,920 4,928,635 1,142,955 149,936 dairy 544,499 654,390 873,702 261,759
Data Source: U.S.D.A. Economic Research Service 1850, 1870, 1950, and 2002.
Figure 2.12 Animals Raised in Ohio from Select Years from 1850 to 2002 Census of Agriculture
The enormous number of sheep raised in Ohio is a result of the high demand of wool as an export product that would be made into textile in the
60 Northeast. In 1850, Ohio had nearly five-hundred-thousand more sheep than
New York (3,453,241), and twice as many as the next largest producer,
Pennsylvania (1,822,357). By 1870, Ohio was producing nearly twice as much wool (#20,539,643) as the second and third largest wool producers, California
(#11,391,743) and New York (#10,599,225). The largest thirteen Ohio counties
(east from Columbiana, Jefferson and Belmont along the Ohio River, then west to Delaware and Morrow including the eight counties in between) produced over half of the sheep accounted for in 1870 placing the majority of sheep and wool production in the mid-central east and southeast (USDA 1850, 1870). Wool production decreased by the late 19th Century due to falling cotton prices in the south (Pfeffer 1983) and improved cotton-milling technologies.
The general trend is a decrease in animal agricultural production. This chart does not account for productivity gains through bioengineered animals.
These gains mean, for example, that fewer cows are needed to produce the same quantity of milk. Regional production and market centers have developed as a result of increased capacity of cost-efficient transportation, which led farmers away from diversified operations to specialization of a few farm products.
Dairy products were commonly produced on every farm in the early decades of Ohio. In 1849, nearly 21 million pounds of cheese was produced on
Ohio farms with the vast majority concentrated in Northeast Ohio. In successive decades, farm production gave way to factory production and by 1909 production was just over 613,000 pounds per year. Lorain, Cuyahoga (Cleveland vicinity),
Wayne, Mahoning (Youngstown vicinity) and Hamilton (Cincinnati vicinity) were
61 the remaining counties with large on-farm cheese production volumes in 1910.
Farm-made butter followed a different trend, one of increased production between 1849 and 1909 where it increased from almost 35 million pounds, to almost 64 million pounds per year (Lloyd et al 1918).
Average farm size has increased in Ohio. In 2002, the average was 188 acres. That is up from just over a hundred years ago when the average size was
88.5 acres. In 1860, however, the average size was 118.0 acres showing a decrease, then increase in average farm sizes around the same periods of contraction and expansion in Ohio agriculture. Tenancy was 19.3 percent in 1880 and gradually increased to 28.4 percent by 1910 (Lloyd et al 1918). The total percentage of farms operated by tenants in Ohio, in 2002 (USDA 2002), is 44 percent. This is above the U.S. national average of 41 percent (ibid.).
Farm productivity in the Sugar Creek Watershed counties follow the general pattern of the state. And, although sheep are not as numerous in the
Census data for Holmes, Stark and Wayne as they are for Tuscarawas, there are an abundance of sheep in all counties. The following figures (2.13 and 2.14) display crop and animal production data for Wayne County from the 1870, 1950, and 2002 Censuses of Agriculture. Although corn and soybeans come to dominate crop production, there still exists a diversity of crops still raised in the county. And, hay and barley have a slight increase from the 1950 level of production.
62 3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 1870 1950 2002 corn (bu) 920,537 2,759,984 3,216,467 soybeans (bu) 0 49,125 1,473,822 wheat (bu) 709,119 1,421,074 741,168 oats (bu) 897,965 1,161,178 473,714 barley (bu) 43,537 4,807 45,917 hay (tons, dry) 55,881 66,746 108,495
Data Source: U.S.D.A. Economic Research Service 1870, 1950, and 2002.
Figure 2.13 Crops raised in Wayne County as reported in the 1870, 1950 and 2002 Census of Agriculture
63
While most animal production in Ohio is in decline, Wayne, like Holmes
County, has increased dairy production. And, Wayne alone has increased hog production. Beef and sheep production has decreased in both. Tuscarawas and
Stark counties have both declined in all areas of production over the long-term as shown in these three temporal “snapshots” of production.
80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 1870 1950 2002 hogs 35,746 41,318 48,411 dairy 12,218 24,060 33,420 beef 13,581 5,639 4,009 sheep 69,227 10,771 6,381
Data Source: U.S.D.A. Economic Research Service 1870, 1950, and 2002. *Data for chickens has been intentionally omitted from this chart because of the large scale of production (371,079 in 1950, and 799,272 in 2002) that would dramatically overshadow the other animal categories.
Figure 2.14 Animals raised in Wayne County as reported in the 1870, 1950 and 2002 Census of Agriculture
64
CHAPTER 3
LITERATURE REVIEW
3.1 Introduction
Consistent with Conrad Kottak’s definition of the “New Ecological
Anthropology” (1999), the theoretical orientation of this research is cultural ecology in a broad sense with the goal of solving an applied environmental problem using ethnographic and local ecological approaches. In accomplishing this, several approaches and concepts will be employed to understand and explain issues surrounding land tenure and perceptions of environmental degradation as outlined below.
The impact that national and international levels of socio-cultural integration have on local cultures are incorporated into an understanding of local social patterns and their relation to land tenure (Steward 1955, Geertz 1963,
Rappaport 1984, Moran 1996) by using an approach that focuses on households and emphasizes individual perceptions (Salamon 2003). Little attention has been paid to aspects of land tenure in watershed studies outside of factors such as using acreage owned or farmed for descriptive purposes. A study of rural community organization and farm management styles and how these styles are
65 influenced by state and federal government policy is the best way to understand
the root causes of environmental problems (Bennett 1976, Goldschmidt 1998).
Baudry (1993) (in Kleiman and Erickson 1996) suggests that most of the
land use decisions in rural areas are made by individual farmers of the land albeit
within the constraints of larger socioeconomic systems and under the regulations
of state institutions. Likewise, Stinner and Blair (1990:138) and others (Bennett
1993, Ryszkowski 2002:3) state that human social systems may be the greatest
factor in remediation of environmental problems and land tenure is at the nexus
of interaction between human societies and their environment (McCracken et al
1999, Moran 2000). According to Shipton (1994:347), land is at the center of the
mental and material wherein “religion, ritual, and cognition, on the one hand, and
adaptation, sustenance, and production, on the other, cannot be kept pure of
each other”. It is in this context that the importance of local perceptions and
actions become critical to the success of watershed and development initiatives.
3.1.1 Two Ecologies?
Habermas (1971), as described in Lansing (1993), proposes two views of nature that are separated for evaluation: “external nature” that is nature beyond
the confines of human alteration and includes such places as remote regions of the earth (if any now exist) and distant galaxies, and “humanized nature” that are
those areas of nature that are “shaped by human intention”. Edmund Leach
(1965:25) suggests that what is natural in the environment is actually a product of human perception, that “[t]he environment is not a natural thing; it is a set of
66 interrelated precepts, a product of culture…” He continues by stating that the
environment is something that is not “discoverable objectively” but rather it “is a
matter of perception”. John Bennett (1976) suggests these “humanized natures”,
or as he refers to them, socionatural systems, are those that have undergone an
ecological transition in which formerly unused aspects of the physical
environment are incorporated into human social systems through the extension
of cultural meaning to attributes of the phenomena, such as value and
ownership. “Humans acting on a humanized environment” is a circular pattern in
which we interact with the environment, create meanings and perceptions of it
which further reinforces and adjusts our representations of the environment for
future action. Moreover, Maurice Godelier (1986) asserts that our representations
of the environment are the basis of our interactions with it, whereas most other
species interact based on instinct that frames behavior making behavioral
change amenable only to the degree that instinct will permit feedback. Changes
in patterns of interaction between humans and the environment occur through
modification of human understandings and meanings of the environment and not
just alterations of the physical landscape or human institutions.
Roy Rappaport has much to say on this topic (1979, 1984, 1999) and
builds on these ideas with the assertion, stated in the opening paragraph of his
Introduction to “Ritual and Religion in the Making of Humanity” (1999). In this he states that humans are
a species that lives, and can only live, in terms of meanings it must construct in a world devoid in intrinsic meaning but subject to physical laws. (1999:1)
67 His words bridge the divide between the ideal perspectives of those who believe that there is no “objective reality” and those who do. Those that see science and human understanding subjectively believe that humans live in terms of constructed meanings created through successive generations of perception and interaction with their environment. The others are those who would have you believe that language and thought are less important in examining human societies, that, as Harris states (1999), culture is the sum of behaviors acted by humans, not their thought, perceptions or intention. Human thought and perception allow for foresight and visioning “the possible, the plausible, and the valuable” through the use of language, which, as a product of evolution, “does not merely permit such thought but also requires it and makes it inevitable”
(Rappaport 1999:8, original emphasis). This is to say that humans live through meanings regarding the natural world that only they can create. Within these constraints of perception, the human species “[is] constructed out of symbolically conceived and performatively established cosmologies, institutions, rules, and values” (ibid.). However, there is a caveat in evolution’s gift of language and meaning: our perceptions and constructions of the physical world are never completely accurate and always partially incomplete (Rappaport 1979) and that the “social construction of nature does not necessarily imply its social control”
(Sheridan 1995:43). As a result, it is possible for meanings for the world to contradict physical laws of the world. Such a disconnect between human understandings and physical laws lead to maladaptative behaviors in which human societies live outside of or in conflict with the physical world, buffering the
68 resulting perturbations through social institutions and systems that result in stratification and inequality at higher levels of sociocultural integration.
An example, from the Sugar Creek, is the nascent nutrient trading market that is developing. Prior to OARDC and other government intervention, nutrient-
rich effluent from local cheese houses and agricultural field run-off and tile
discharge were viewed as economic “externalities”. These nutrients are being
incorporated in a nutrient trading plan that will allow milk purchasing and value
adding businesses to pay their suppliers to modify farming practices. In this
system, the nutrient phosphorus (PO4), which is plentiful in cheese effluent, is
costly to remove below 10ppm (parts per million), thus making more economic
sense to value the phosphorus coming from farmer’s fields that may be less
costly to reduce. From a purely natural science, and even economic, perspective,
it is clear that the most efficient approach to water quality is for people to modify their behavior in a manner consistent with reduced daily loadings of contaminants. However, although human societies are subject to principles of
nature, they do not act in direct response to them; their actions are mediated by
perception that forms cognitive models for the world (see Rappaport below) that
are operationalized through social organization, such as institutions, and it is
within this framework that it is necessary for problems to be addressed.
Rappaport (1971a) and Flannery (1972, building on Rappaport) discuss
the concept of cybernetics as a model for human social systems. It is my belief
that change in human behavior is most effective when caused to act from within
the social system. Radical change is most often resisted through passive
69 aggression, over-compliance (Moore 1993) or to the point of resistance taking
the form of violent conflict. Rappaport (1971a, 1984) and Kottak (1998, 1999)
refer to Romer’s Rule of adaptation. The “new ecological anthropology” call for
anthropologists to work within human social systems making change that
accounts for reference values and patterns of established behavior within a
society, and building from them as an approach to social change (Kottak 1999).
3.2 Cultural Ecology and Social Change
The theoretical contributions of Julian Steward emphasize the idea of the
"unity and creative capacity of the human mind" (Manners 1964:18). He also saw
humans as a “domesticated animal” who is physically guided by culture, which
provided the basis for much of early hominid evolution. To extend this
assumption further, Homo sapiens evolution, he believed, was the result of
cultural development more so than physical conditions. These ideas are at the
heart of the concept of cultural ecology.
Human beings and their culture are part of the “web of life” and their
experience of the “web of life” is in turn affected by culture (Steward 1955:31).
Steward treats culture separately because, in the prevailing ecologies of the day,
competition in the physical environment was the key to survival of the species
and forms of cooperation were viewed as “auxillary” or secondary functions to
survival. Based on this premise, he sees humans as unique in their forms of cooperation arising from culture and not merely from their physiology. Human
individuals’ and society’s success and survival are interdependent with the
70 integration of multiple levels of organization and not just their immediate physical
environment.
Steward believed that there were two dimensions to studying human behavior, which consisted of distinguishing between behavior exhibited by all humans and behavior that varies among humans. In the first instance, he attributes universal behavior to organic, biological and psychological factors.
Behavior patterns that vary among humans, he believed, are the domain of culture, the superorganic. The desire to eat and rest are organic patterns of behavior yet how they manifest themselves in the form of dietary habits of what is considered food, its source and preparation, as well as where one sleeps, when and for how long, are the products of culture (Steward 1955:8). Steward was not a biological reductionist, but rather sought answers to cultural change in the interactions between humans and their physical environment.
I believe the methodology of cultural ecology, as outlined by Steward, is
intentionally vague, giving the user latitude to modify them depending on the
uniqueness of the culture and the environment in which the people live. As
described, Steward said that we must analyze the environmental effects on culture at the local level before we attempt nomethetic laws of cultural evolution
(In this cautionary advice it is possible to see his connections with the early
historical particularists such as Franz Boas and Alfred Kroeber). There are three
methodological components to cultural ecology.
71 They are as follows:
1. Ascertain the culture core comprised of environment and technology and
other cultural components that relate to subsistence and economic
activities,
2. Exploration of the social organization of labor to accomplish subsistence
and economic activities,
3. And, decide how the culture core affects other aspects of the culture not
related to subsistence and technology. If the researcher determines that
there are various behaviors permissible by the environment, then it is
necessary to refer to the specific histories of the people for explanation.
Steward’s theory focuses on particular cultures rather than the “concept of culture”. As such, Steward’s work moves anthropology away from tautological approaches of “culture begetting culture” and focuses on the effects of the environment on culture (Bohannon 1988:332). This change in focus signaled a new direction in anthropology in which culture, as the superorganic, was seen as an aspect of human behavior interacting with the environment in a non- deterministic way instead of reacting to the environment. The interaction of cultural traits in different environments and how those differences of interaction affected social organization were of main concern (Steward 1955:38). Or, to state it another way, he asks if there are particular behaviors and modes of organization required for an environment? And, what is the range of possible behaviors from which “to choose”? His research findings show that there are no
72 deterministic, singular responses to the physical environment for Homo sapiens
groups but instead there is a variety of cultural adjustments that can be made
providing a range of efficacy in meeting its demands. Years later, Roy Rappaport
(1979), in his discussion of cognitive models, states that the development of
Homo sapiens has not relied on Western scientific models for responses and
adjustment to the environment but rather such responses have developed over time and many are successful. He further states that it is necessary to understand that the cognitive models that individuals have for the world do not
necessarily have to match the operational models of Western science perfectly,
and in fact, they never do; they need only be close enough to produce a
sustainable and desired result of sustaining the overall system. This same
approach is where Kottak believe the “new ecological anthropology” should be
concerned, in “starting where people are” to understand and be active in applied
research and outreach.
There are three primary contributions that Steward has made to
anthropological theory with the cultural ecology methodology. They are:
multilineal evolution, culture types, and levels of sociocultural integration (Moore
1997:181). The understanding of each component of his theory is only possible
in the context of the others. At the center of his theory was the idea of the culture
core that he adapted from Kroeber’s conception of reality culture.
73 As defined by Steward, the “culture core” is:
The constellation of features which are most closely related to subsistence activities and economic arrangements. The core includes such social, political, and religious patterns as are empirically determined to be closely connected with these arrangements. (emphasis added) (Bohannon 1988:327)
Where Steward differed from Kroeber in his assessment of the culture core was
in the value he placed on it. Kroeber put his emphasis on the secondary cultural
traits whereas Steward puts it on the culture core. Mostly historical developments, invention and diffusion that are important, but not empirically
reliable for comparison, determine secondary features (McGee 1996:224).
Changes that occur in the culture core will be reflected in subsequent changes in
the secondary features. However, the secondary features do not have the ability
to create change in the culture core (Manners 1964:19-20). Steward’s work broke
from others because he did not pursue research that aspired to find the
“essences” of cultures. Understanding the essence of a culture was seen by
others as the key to understanding what it meant to be a member of that culture.
Much of Steward’s work lacked a reporting of the norms and styles and focused on the adaptive relationships instead to avoid such broad generalizations and target the specifics that may lead to change and similarities among groups.
Steward suggested that patterns of similarities existed among such similar groups like the Great Basin societies and the San of southern Africa. This kind of cross-cultural comparison based on environmental variables was new because of the emphasis on human choice interacting with the environment (Moore
74 1997:183). Previous assumptions relied on the environment determining cultural adaptations or creating possibilities that were the result of historical processes.
According to Ellen (1982), cultural ecology was critiqued for the vague theoretical concepts, intuitive methods that are not rigorously defined, a functionalist approach, and a narrow focus on subsistence economy that overlooked other contributing factors such as political, social, and population attributes. Ellen (1982:61) also notes that the culture core is too vague in what traits it consists of to be a useful conceptual tool. In short, Steward focused on how the environment interacts with culture in a linear approach of the environment acting on culture, and left it to later cultural ecologists to formulate the relationships as a system with feedback loops (Ellen 1982:63-63) such as that of Rappaport’s analysis of cybernetics. Correspondingly, Kottak focuses on criticisms of Steward’s approach that include an emphasis on stability and maintenance of relationships rather than diachronic change, the relationships tend to be circular, and the system is simple and bounded within a culture and an environment (Kottak 1999).
Steward’s concept of multilineal evolution states that parallels of cultural development occur independently in history and that cultural parallels may develop with similar causes. Steward said we need to get at the “parallels and similarities which occur cross-culturally” and then conceive “law-like” statements about them (Bohannon 1988:321; Steward 1972:14). Steward focused in
“specificity and reliability” of the information instead of finding universality’s which generally lack scientific validation. He compared five civilizations and found this
75 theoretical approach to be verifiable. Egypt, China, Meso-American,
Mesopotamia and the Andes civilizations were compared for which Steward
decided they developed independently (Cultural Causality and Law, 1949). Each civilization developed in similar ecological regions (arid and semi-arid) and in similar ways, and each had a comparable economy of irrigation and agriculture producing a surplus of food that allowed for non-subsistence activities (Steward
1972:206-208). Though these civilizations share similar lines of development, not every civilization (or society) follows the same lines of development; thus cultural development being multilineal (Moore 1988:188).
Perhaps one of the most important components of Stewards cultural
ecology is his concept of culture type because it explains how combinations of
traits in the culture core interact with the environment in explanations of
adaptation and change. The culture type also underscores the relationships
among the culture core, environment, and levels of sociocultural integration
(discussed below) and provides a framework for understanding the concept of
multilineal evolution. The culture type differed from previous emphasis on culture
areas of Kroeber and Wissler and evolutionary stages of Leslie White, the former
being a spatial designation for diffused cultural traits and the latter a unilineal
attempt at categorizing cultures by complexity of technology and energy use.
Cultural type is used heuristically because it is comprised of common social
structures that are found among societies operating in similar environmental
conditions, not necessarily similar environments. So, for example, the patrilineal
band of the !Kung of South Africa and that of the Austrailian Aborigines share
76 similar traits because their common marginal environments provide a range of possible social structures that are appropriate to those environments (Steward
1955:122-124). Another example of culture types in the context of multilinear evolution is the cultural ecology exhibited by the Carrier Indians (Steward
1955:173-177) who lived close to the Sekani but with different environmental conditions. The Carrier’s territory had more resources available for their subsistence that enabled them to develop a potlatch form of redistribution; the
Sekani were only marginally able to perform the same.
According to Moore (1988), “the Shoshone and Northern Paiute were to
Julian Steward what the Trobriand Islanders were to Malinowski: a pivotal
ethnographic case which exemplified broad cultural patterns”. He then quotes
Steward as saying that the societies of the Great Basin had “pursuits concerned with the problems of daily existence [that] dominated their activities to an extraordinary degree and limited and conditioned their institutions” (Moore
1988:182). He found that other similar societies (to the Great Basin groups) scattered throughout the world in similarly marginal environments had much in common with one another (this principal was first elucidated in The Economic and Social Basis of Primitive Bands, 1936). These commonalities included low population densities, hunting of non-migratory prey and walking as their principal mode of transportation. These aspects of a society he lumped together into what he called a "band" which was described in “The Patrilineal Band” (1955). In this manuscript, Steward discusses the basis of culture types explaining that certain types are more likely to exist under similar technological and environmental
77 circumstances. Other predictable traits of the patrilineal band include exogamy,
patrilocality, patrilineality, and land ownership and lineage composition. Steward
referred to these cultural types as “collections of elements of the culture core that
regularly occur cross-culturally, such as the patrilineal band”.
Types are not fixed or linear in development. They are dependent on the
nature of the surrounding environment but not determined by it. Each
environment, as alluded to earlier, presents a range of possibilities for a culture
to use. However, cultures do not utilize all of the possibilities. Even in the Great
Basin cultures, there exist a range of possibilities, however, that range is much
narrower because of the extreme environment (McGee 1996:224-226).
Level of sociocultural integration is the third concept of the cultural ecology methodology, which operationalizes Steward’s ideas regarding hierarchical levels of organization above the family that extend to the community and state (i.e. economic and political systems that act beyond the individual and family). As mentioned above, Steward saw human survival as not only dependent on the adaptation to the immediate physical environment, but survival was interconnected with other levels of sociocultural integration wherein environmental factors in far off places (i.e. oil production in Saudi Arabia and the
1972 energy crisis) may have an immediate impact on the lives of those physically removed, and in which subsistence does not necessarily extend from such places. Steward’s statements regarding level of sociocultural integration distinguishes between two collections of cultural features: features that function and should be analyzed at the national level, and features analyzed at the levels
78 of groups or subcomponents of society. For example, in order to understand the
impact of mass media on American social organization, the researcher must
attend to different levels of society to collect data and draw conclusions by focusing on “sources of” and “influences on” information that supplies media outlets, and local influences and perceptions of media sources. Ethnographic methods work at local levels, making anthropologists uniquely qualified to conduct research and analysis, yet such methods do not work at a “national” level because not all aspects of this cultural level represent shared behaviors that are observable (i.e. Scientific or artistic phenomena embraced by the national culture may not be absent among some subgroups of local people) (Steward
1955:47-50). The Western Shoshoni and most Great Basin Shoshoni cultural features were practically all organized at the family or band level. In most of
Europe at the time these ethnographic observations were made, cultural features operated at multiple levels of integration: societies cooperated in work parties, organized public works, served in the national army etc (Steward 1955:54).
The differences that exist between Julian Steward’s cultural ecology and
the subsequent works of anthropologists who continued his approach are in traits
emphasized in the culture core by the researcher as being important for
adaptation of the society studied. The differences are in areas that Steward did
not explore, but as a result of the inclusive nature of cultural ecology, many of the
differences are accounted for in the methodology.
The cultural ecology of Steward shares much in common with the
ecological anthropology Geertz and Rappaport (Kottak 1999). However, there
79 are also differences in the fine points of theory, yet in comparison to previous
theories in anthropology (i.e. unilineal evolutionists, historical particularists,
environmental determinism and possibilism, functionalism, and structuralism)
there is much continuity. I do not want to minimize the importance of these
differences because they have much to do with the philosophy that guides the
theories, but in the big picture they are fine points of distinction. These
subsequent anthropologists expanded Steward’s ideas to investigate other
dimensions of culture including the religious and political.
According to John Bennett (in Moran 2000:56), the distinctions between
Steward’s cultural ecology and the ecological anthropology of Geertz (1963),
Rappaport (1984), Vayda (1974), and others is “artificial” and depends heavily on
the scale of the society being studied. Most of the difference is not new. Rather
Geertz and Rappaport explore new areas of cultural ecology by incorporating ritual in the culture core (Rappaport) or exploring complex societies and using
social and political traits as explanatory forces (Geertz).
Instead of culture and environment interacting as two separate
phenomena, as Steward conceptualized, Geertz and Rappaport make arguments
for treating them as different aspects of the same phenomena using the
ecosystem approach as the encompassing framework within which populations,
in the biological understanding of the term, interact. Ellen refers to this as
“monism” in the ecological approach used by Geertz and Rappaport in which
environment and culture are viewed as parts of the same system and culture no
longer maintains a “superorganic” status, instead there is “mutual causality”
80 (Ellen 1982:75-76). Geertz and Rappaport also deal less with culture types and
emphasize the importance of the interaction between ecological variables of
population and ecosystem, which is a departure from Steward’s calculated
comparative categories (i.e. patrilineal band).
Clifford Geertz, in his early work, applied a cultural ecology approach in
his problem oriented study of the processes of ecological change in Indonesia
conducted during the 1950s. He takes a historic and ecological approach in
examining the relationships among agricultural productivity, modes of production,
and population density as three structures that exhibit remarkable differences as a result of the different development of Indonesia’s two major ecosystems, that of the swidden, or shifting cultivation of the Outer Islands and the wet-rice agriculture (sawah) in the mainland or Inner Indonesia. It is these differences that
Geertz sought to explain through cultural ecology in an analysis of the social,
economic and political climate to try to understand what needs to be done to
improve the current “malaise” of Indonesian agriculture (Geertz 1963:154).
Geertz found differences between two ecosystems that developed through
different ecological forces. The swidden system was not as strongly influenced
by Dutch Colonialism, had a dramatically lower population, required more land,
and was more like the surrounding forest in it’s diversity of species and canopy
structure and nutrient cycling; The sawah found in Inner Indonesia was more
intensive in it’s management, contained nearly 66% of the population and much
less land, had fewer species and developed through a historic process of Dutch
incentives, quotas, and tariffs promoting cash crops on otherwise limited
81 amounts of agricultural land. The sawah’s demonstrated to be more stable over
longer periods of time so long as the human component of intensive nutrient
management continued. In the face of external pressure, the Inner Indonesian
people maintained their social structures by adjusting their production and social
organization and internally differentiating their subsistence activities. It is the
consistent intensification and internal differentiation of the subsistence activities, acting with increased population and land scarcity that is the basis of Indonesia’s
agricultural involution.
In investigating the ecological relationships with culture, Geertz gives
more explanatory power to the secondary cultural traits than Steward does in
creating the prevailing sociocultural conditions that mark the state of Indonesia’s agricultural involution. Overlooking the flexibility of the culture core, Geertz accuses Steward’s cultural ecology of being privileged in its assertion that culture core traits are primary in relating to the environment with everything else secondary. And, that an assumption that those core traits are more important should not be a priori but should come after investigating the facts (Geertz
1963:10-11). In rectifying this assumption, Geertz included the impact of the
Dutch political economy and the interactions among other parts of society to
“form an interconnected whole” (Moran 2000:59). The involution of Indonesian agriculture is the result of government policy and external inputs of economic and political hegemony of the Dutch into a rural agricultural economy. All of which fits with Steward’s initial awareness of these issues as described in his discussions of the levels of sociocultural integration. The Indonesian case was far too
82 complicated for a simple ethnographic study that included individual and local
village behavior. Rather, an examination of the implicit and explicit relationships
among the local people, the colonial government, and Dutch East India Company
was a necessary step in understanding the interactions and effects that the
environment had on local culture. The “environment” includes more than the
physical and biological non-human phenomena and extends to the regional,
national and global levels of sociocultural integration within which the national and local Indonesian social organization and structure developed.
Geertz states that we should be “concerned… with determining the
relationships that exist between the processes of external physiology in which
man is, in the nature of things inextricably embedded, and the social and cultural
processes in which he is, with equal inextricability, also embedded” (Geertz
1963:6). The more we investigate these relationships, the more we find that they
are alternative forms “of the same systemic process”. He makes the distinction
with the analogy of the artist using a chisel to make a statue and asks if it is the
artist using the chisel or is the nervous system manipulating materials in the
environment? In asking this rhetorical question he wants cultural ecology to move
from questions of how the environment shapes culture, or how culture interact
with the environment, to “how is the ecosystem organized” (Geertz 1963:8-9).
Pigs for the Ancestors (Rapapport 1984) provides an ecosystem and
population study of the Tsembaga Maring of Papua New Guinea, focusing on
Maring ritual regulation of warfare and pig production to maintain ecological
homeostasis (Note: Rappaport emphasizes in his 1984 Prologue (1984:414) that
83 his use of this term does not mean static). Pigs for the Ancestors is an account of
the regulation of the relationships of the Tsembaga to their neighbors by
mediating conflict, and the ratio of swidden production to available land and their
main protein source, the pigs.
In using ecological population and ecosystem as units of analysis, there is
a shift from “culture” and “environment” to an emphasis on an ecosystems
approach and populations as units of analysis. Rappaport states that he is not
sacrificing the primary goal of studying culture and, on the contrary, by treating
the population instead of “the culture” in his analysis he is arguing that culture is
a means of survival and that populations act in the environment using that
means. This follows approaches of evolutionary biologists, like E.O. Wilson, that
view culture as an adaptive strategy upon which natural selection acts
(Rappaport 1979). Rappaport defines the ecological population as
An aggregate of organisms having in common a set of distinctive means by which they maintain a common set of material relations within the ecosystem in which they participate. (Rappaport 1979:238)
Change in the system, for Steward, arose from environmental changes in
subsistence base along with innovation and diffusion in a one-way, but non-
deterministic, relationship in which culture adapts to the environment with a
range of options.
Rappaport focuses on systems approaches incorporating cybernetics, or
“on-off” switches with feedback loops, in a linear system where the ecosystem, working as single unit, or system, responds to human activities through energy
84 flows. The complexity of the ecosystem functions by providing negative feedback
to those species that “sustain the species supporting them” and contributing to
the efficiency and stability of the ecosystem instead of functioning to maintain
efficiency in energy capture (H.T. Odum cited in Rappaport 1971:351,354).
Similar approaches to understanding coupled human-biophysical ecosystems are taken in agroecosystems analysis (Conway 1987, Prasad et al 2005a, 2005b).
3.2.1 Smallholder Type
Robert Netting describes a culture type called the smallholder, which he
advocates as having an adaptation well suited to our current environmental
problems of resource degradation and scarcity of land. Smallholders adapt to
local conditions of increasing population densities, scarcity of land, financial
resources, and market forces using locally adapted agricultural skill and the
intimate knowledge of their holdings (i.e. geography and geology, soil fertility,
intercropping sequences, drainage, nutrient cycling etc). This is done to get the
most from the land in a long-term sustainable relationship instead of increasing
the scale of the operation. Netting sees smallholding as a viable alternative
cultural type in high population areas with intense market pressures. Less intensive technology and labor that is more intensive are aspects of smallholder production.
Tool usage that requires more human labor and less dependence on
mechanical means often allows people closer contact with their environment. As
such, technology is a factor in a group’s direct interaction and degree of
85 familiarity with the local environment (Steward 1972:40). Tenure and settlement
patterns are related to farm scale. Smallholding farms often have a scattered
pattern of tenure (Conklin 1954, Netting 1993) or a diversity of crops and crop
varieties (Moore et al 1999) that take advantage of differences in plant species
lifecycles to spread the labor demand over the growing season. Large-scale
agricultural societies usually have large tracts of land close to each other and use a mono-cropping strategy that is tied to commodity markets (Netting 1981).
Between these two agricultural strategies are many of the farms in the
Sugar Creek. In the Upper Sugar Creek are more technologically intensive and larger-scale farming systems compared to the North Fork and Little Sugar Creek; and technology becomes increasingly labor intensive from the North Fork to the
Little Sugar Creek as Amish Orders change from a mixture of Swartzentruber and Old Order to predominantly Old Order with dispersed Swartzentruber Church
Districts.
The Amish offer an American model of a smallholder group (Salamon
1985) in the sustainable management of their farms through intercropping,
Integrated Pest Management (IPM), and collective management of labor. As pointed out by Salamon and Netting, this cultural type is useful for explicating the success the Amish have had in maintaining their unique way of life, separate but still connected. Like the smallholders described by Netting, the Amish have connections to the larger market economy and work within those constraints but also abstain from the material excess that comes from an individual focused society (Netting 1993). In general, Amish farmers have highly integrated
86 worldviews that incorporate a reverence for the land with sustainable intensive
farming practices, which include small fields and four-year, five-crop rotations
(Moore et al 1999). Kraybill and Hostetler (2001) encapsulate the Amish
worldview, from the researcher’s perspective, as degrees of traditionalism in
which various orders of Amish and other Anabaptist faiths are ranked by the
degree to which they adhere to older, traditional modes of behavior, livelihoods
and dress. John Hostetler (1993) used a similar categorization of worldliness, which differed from the previous in that it ranked from the Amish perspective to
more worldly affiliations.
3.2.2 Social Capital
Social relationships and networks are found in all communities, the intensity of which depends on multiple factors (Flora 1995) including degree of social embeddedness of local residents in the community6. The degree to which
people in the community are hierarchically differentiated from each other through
prestige, financial disparities and power, and are involved in social networks that
span multiple levels of society (i.e. state agricultural commodity organizations,
exporting agribusiness) can be a mediating effect on local social networks and
lead to stratification7. Community participation acts as another measure of social
networks in which members of the community are more engaged and thus more
embedded locally when, as examples, businesses are locally owned and
supported, trades are diverse and offer opportunity, neighbors attend the same
6 Flora (1995) outlines several metrics used to calculate the forms of capital of a community that include social, human, cultural, financial, built, natural, and political. 7 See Chapter 7 for analysis of levels of sociocultural integration in the Sugar Creek Watershed. 87 religious services, and raise their children together (Granovetter 1985). I believe
that Flora’s concepts of capital are merely a redefinition of Steward’s culture core
for a more inclusive economic oriented audience. As Flora parses various
aspects of society into the seven modes of capital, one or more modes of capital becomes the dominant form for social organization that makes a community
“tick”. This is the same as Steward’s formulation of the culture core as being that part of the culture with which most other aspects interact and reference for operation.
Anthropologists have investigated and written about social networks for
decades, emphasizing the strength and functions of a variety of types of
networks (Durrenberger 2002) and the areas of overlap and interconnection. As
a discipline, however, we have not endeavored to construct a lexicon that lends
to cross-disciplinary use. On the other hand, sociologists and other disciplinary
practitioners have striven to construct an all-encompassing theoretical and
methodological system that will provide a common “currency” for quantifying
social networks and the strengths and weaknesses in interpersonal relationships
for comparison with economic metrics (i.e. financial capital). Social capital is such
a construct. The goal of social capital is to make comparable such phenomena
as human relationships and their effect in decisions with economic criteria. The
end result is a series of capitals that include natural capital, social capital,
economic capital among others, and the list continues to grow. Putnam (1993,
1995) writes of social capital as a property of organized groups. Bourdieu (1991)
states that social capital is a quality of individuals who work collectively through
88 social networks to achieve goals in their interests, which are in conflict with other
members of society. Durrenberger (August 2002 SfAA Newsletter) warns that
this attempt to make everything social into a form of capital is a mistake. He
states that this conceptualization of capital distorts social realities by remaking
them into classless, equal access systems in which participant’s control, equally,
this form of capital without class prejudice. Among the cited research in this rejection of the social capital concept, he cites anthropologist Katherine
Newman’s work (1988, 1993, 2000) on the middle-class ideological construct of
self-sufficiency in which individual initiative and drive lead to access and success.
“[T]hat talent and initiative determine success is a dimension of middle class
ideology, not a sociological datum” (Durrenberger 2002:4). Morton Freid (1967,
cited in Durrenberger 2002) writes that there are not system-wide points of view
in socially stratified systems, and that to understand such systems, you must look
at the level in the system in which you are investigating with the understanding
that an advantageous idea or action at one level may be harmful to others at
other levels within the system.
Stephen Lansing’s contributions to understanding communally managed
resources in Southern Bali, in the form of “water temples”, and the Skokomish of
Western Washington State incorporates use rights and commons with concepts
of natural and social capital. In Southern Bali, communal water temples
historically emerged to manage access to water through cooperative
management and maintenance of irrigation facilities. The Indonesian
government’s plan to install a formal system to control irrigation by making each
89 farm, or subak, an autonomous unit created major changes and left the management of the common water resource to the state (Lansing and Kremer
1993). The imposition of Green Revolution varieties of rice coupled with the change in commons management created a situation that replaced a once homeostatic (in the sense that Rappaport describes in the Epilogue to Pigs for the Ancestors 1984:414-415) ecosystem with one that experiences continual perturbation. In the case of the Skokomish, the government enforced its eminent domain right to change the tenure relationships in the Skokomish River to construct a dam. Not only did the dam alter tenure relationships in the form of private property, but also altered the natural capital of the region to such an extent that common resources of salmon from the river ceased to be abundant enough to sustain the people. The diminishing natural capital led to a decrease in the social capital of the Skokomish because the change in resource base altered the social organization and structure of their society (Lansing 1998).
3.2.3 Land Use
Land is a necessity in creating social and ethnic identities in rural areas
(Salamon 1992). The system of land tenure found in Ohio originated in the
Colonial system of land administration, itself an extension of the English
cadastral system originating in Feudal Europe when persons were tied to the
land, and, for the owners, “land was the primary symbol and source of wealth”
and ownership was publicly recorded to ensure access (Ting and Williamson
1999:2). This system of ownership evolved and eventually dissolved the bonds
90 that held people to the land they worked, providing ownership to them. Secure
access to land is still important for rural communities because it allows for foresight and planning.
Studies suggest the incentives to invest in conservation practices lie in
secure access to land through ownership (Soule et al 2000, Salamon et al 1997).
Results of the 2002 U.S. Census of Agriculture show that Ohio has 77,800 farms
with an average size of 188 acres, while the U.S. average is 433, which is
skewed by the large farms of the Great Plains. The national average for rented
land is 41 percent, with Midwestern states having higher averages, with Illinois
and Indiana topping the list both at 62 percent, respectively, and Ohio is 44
percent (USDA 2002).
Farm size is one of many important indicators of household decision-
making related to conservation adoption (Shucksmith 1993, Howden and
Vanclay 2000, cited in Burten and Walford 2005:336) and is a “major constraint
on land use decision-making” (Ilberly and Bowler 1998, cited in Burton and
Walford 2005:336) and a limiting factor in the adoption and implementation of
conservation measures (Morris and Potter 1995, Battershill and Gilg 1997,
Wilson 1997, Wilson and Hart 2000, McNally 2002). Likewise, the size of the
area used for conservation measures is also affected by farm size measured by
total farm area (Potter et al 1991).
Although ownership, and size of the area owned, are indicators of secure
access and a predictor of sustainable land use and conservation adoption, the
91 strength and nature of the tenant/landlord relationship is just as important for
cases where land is leased.
Three sets of rights exist with regard to land tenure that overlap public and private property distinctions. They are: use rights, transfer rights, and administrative rights. Implicit and explicit use rights generally come from the subsistence base, socially defined needs, and demographics of a society
(Barfield 1997:273). Large-scale agricultural societies usually have large tracts of land close to each other; whereas other smallholder societies around the world often have smaller, scattered holdings (Netting 1981). Both patterns are found in the Sugar Creek but tenants generally are the ones with scattered land tenure.
These de jure distinctions of legal rights are but one set of rules operating in a tenure system. There are a whole host of other de facto realities operating within a land tenure system (Adler 1996).
Early anthropological definitions of land tenure were simple and functional
stating that land tenure was a system of “rights and privileges” that humans use
to protect their resources and resource sources from “others” (Bohannon 1960,
Gluckman 1963). It is only in the last half of the Twentieth Century that
anthropologists moved to studies of land tenure (Bohannon 1960, Crocombe
1974, Lundsgarde 1974, Shipton, 1984, Wilmsen 1989, Netting 1987, Sorensen
1988, Moore 1990, Moran 1993). From these studies, the definition of land tenure has evolved becoming more than a distinction between public and private ownership. Such a system incorporates relationships among people and the land, and rights and responsibilities among those people to the land. It
92 incorporates a social history of use and access by individuals and groups joined through kinship ties and social relations (Ingold 1986:138). Or, As Moore describes from his ethnographic work in Japan, land tenure in the form
of use rights and private property rights reflect past, present, and future social relationships regarding land and labor. A full explanation of these use rights entails more than just rental rates and marginal utility of action undertaken by landlords and tenants. It must include the totality of responsibilities and obligations of each party towards each other and the land. (Moore 1990:51)
For example, rental rates may be affected by past use rights of tenants and often
do not keep up with current market values because of a moral component
existing in community relationship between landlord and tenant (Moore 1990:53).
Archeologist Michael Adler states that:
Land tenure systems comprise sets of social strategies that human groups develop to alleviate environmental uncertainty by socially circumscribing human use of productive resources. (1996:338)
An open tenure system in which all participants have equal and, more
importantly, unlimited access creates a great deal of risk and uncertainty for all
individuals and/or groups involved. A system of land tenure creates social
boundaries that always carry risk since they limit options of use to those who live
within these boundaries. Some limitations may include access to resources,
freedom of movement across landscapes, and “equitability”, as defined by
Gordon Conway (1987), of livelihood options from resource access. From public
to private use, there is a continuum of access and resource use for all groups
that interact with the land that may vary across ethnic groups and, in complex
hierarchically stratified societies, across socio-economic class through disparate
93 power relationships. For example, Frederick Barth’s (1956) analysis of Swat ethnic groups revealed that three not necessarily peaceful ethnic groups were
able to co-exist in the same region, using the same land, through a complicated
land tenure system that recognized and adjusted to seasonality of resources.
Adler (ibid.) lists four characteristics of land tenure systems, which are listed
below:
1. Access to land and resources on the land is acquired through
social mediation in which the resources do not determine
individual or group access in and of themselves. Rather control
of resources comes from social mediation in which
acknowledgement is made of who has the most direct “rights of
access” to these resources and land use and mediation-based
systems” allow flexibility in access rights where adjustments and
responses to social and landscape changes in local or
international ecology become possible through social feedback
loops.
2. Natural systems are ecologically complex and as a result, so
are the social systems of land tenure that grant access.
3. This access is based on particular resources used and the
spatial and temporal extent of use.
4. Land tenure systems are not the same as a system of land use.
Flexibility of land tenure takes its extreme form in a process of land reform
during which the holdings are redistributed among the people. Another process
94 of modification to existing tenure relationships is through changing the rules by
which people enter into a tenure relationship such as zoning. The first is farther
reaching in its potential to change the social structure of a society, whereas the
latter is a more ongoing process that may only alter social organization (Barfield
1997:275). Bauer (1987) describes the tenure system of the Tigray, of Ethiopia,
who maintain flexibility between the de jure and de facto realities of land use and
responsibilities that meets the changing needs of societal members or segments
of the nearby community. Community size conveys prestige across the Tigray
society so the tenure system vacillates between open and closed to
accommodate, within prescribed limits, new immigrant families. However, in an
effort to protect the resource base, immigrant tenure is generally restricted to
short-term to accommodate ecological perturbations and avoid testing the limits
of its resilience.
The Kenyan Wataiti observe differential levels of access by society
members that determines who can use the land and determine use, who might
have access and who can “dispose of use rights”. The Wataiti have a concept of bundled use rights that are determined by relationships among the people of the
Wataiti community including intergenerational inheritance and long or short-term
loaning of land. The Tiwi of Australia determine their hunting and tenure and
hunting rights based on seasonal growth. The seasonality of the Australian
“Outback” cycles through periods of abundance and drought causing the Tiwi to
regulate their intentional burning of lands, which establish use and expose prey,
95 to times when their subsistence activities will have minimal long-term ecological
impact (Goodale 1959).
Land use is but a small portion of land tenure (Netting 1987).
Manipulation, modification and use of the environment is land use, but land
tenure combines “landscape modifications and land use histories [which] are
important in producing and transforming the social relations which comprise any
system of land tenure” (Adler 1996:340).
Land tenure and land use in the Midwestern United States were shaped
by the history of migration and the attitudes that came with the early European
settlers. Relationships that result in reciprocal support, both within and between
farm and neighbors, contribute to stable, sustainable and equitable production
(Salamon 1992, Lobao 1990, Goldschmidt 1978). The ethnic heritage of
immigrant settlers of the Midwest persist today in land use, transfers of use rights, tenure arrangements, and management patterns.
Differences between these groups are not caused by the ethnicity per se
but rather the forms of social organization that developed in the early Midwest
among immigrant ancestors are a result of people using these patterns and
interacting with the new social environment. For example, the type of farm succession is a major contributor to sustainability of farm and community, both by
reducing transitions from agricultural to non-agricultural land use and in
encouraging management styles that focus on long-term productivity over short-
term gain (Bennett 1982, Salamon 1992).
96 Historically, farming communities in the Midwest have developed under two philosophical approaches to land use. Current landscape patterns in America represent a continuum of attitudes ranging from viewing land as a commodity, to valuing the intrinsic qualities of land as a source of existence and a legacy to pass on to future generations. These are exemplified, respectively, by the heuristic types Yankee and yeoman. Salamon uses Weber’s concept of “ideal- types” in constructing this dichotomous, heuristic typology (Salamon 1992:92-
95). Yankee tenure treats land for profit with little investment in conservation as a result of its early abundance in the Midwest, and the entrepreneurial practices brought from the British Isle. The agrarian yeoman settlers, lacking the capital resources, responded to this approach by purchasing and rehabilitating the former Yankee lands at a reduced cost.
Diversification was a hallmark of early Midwestern farms in general, but more so, among those who farmed primarily for subsistence. Contemporary descendants exhibit similar patterns of use in varying degrees from the large- scale industrial (Yankee) dairy to the smallholder (yeoman) Amish (Salamon
1992, Netting 1993, Salamon et al 1997). These patterns are found on a
continuum of higher levels of socio-cultural integration similar to Goldschmidt’s and Salamon’s findings, in that the degree of integration with social networks outside of the local community is correlated with scale and farm size and the greater the degree of integration, the less ethnicity is a factor. The passing of several Homestead Acts promised to preserve equity in farmland for future generations, but subsequent generations saw, as Moran (1993) Moran and
97 Brondizio (1998) and Collins (1986) found in Brazil, less equity and less access
to farmland than previous generations. This was due to income and commodity
prices becoming the focus of government efforts rather than land equity, which
led to an unintentional cycle of support-price dependent farmers of increasing
scale (Salter 1943:14, Gilbert and O’Connor 1996). In fact, tenancy has been on
the rise since 1930 with two of every five acres in production being rented
(Kloppenburg and Geisler 1985) noting that neighbors and relatives can be
tenants for owners in very positive ways, such as being stewards of the land as
the owner enters a new stage in life. For example, an Amish farmer may lease
land to a son as the son develops the capacity for its purchase. In this case, the
land is cared for by the son; and, the son receives access to the land without
having to purchase it beforehand.
There are several factors that shape land tenure and land use related to
the history of migration and the attitudes that came with the people who settled the Midwest. There is a growing polarization in farm size, referred to by Buttel and Gillespie (1984) as the “disappearing middle” in which there are relatively few medium-sized family farms and a large number of larger-industrial and smaller-family farms. A recent study of nine dairy farm communities in seven states reveals that demographic variables are a greater predictor of community involvement than farm structure variables implying that who operates the farm is at least as important as the size of the farm (Jackson-Smith and Gillespie 2003).
Relationships that result in reciprocal support, both within and between farm and neighbors, contribute to stable, sustainable and equitable production (Salamon
98 1992, Lobao 1990, Goldschmidt 1978). Specifically, Goldschmidt hypothesized a
relationship between increasing industrialization of agriculture and the decay of rural social institutions (1978). Ethnic heritages of immigrant settlers of the
Midwest persist today in land use, transfers of use rights, tenure arrangements,
and management patterns (Fischer 1984, Salamon 1992). Most immigrant
decedents fit into two general categories based on: the motivations early
immigrants had for migrating, their social organization, and outlook on farming
and land ethic. These characteristics influence land use and inheritance practices
that impact farm size and tenure and adoption of conservation practices
(Salamon 1992, 2002; Salamon et al 1997).
Scholars point to Jeffersonian Agrarianism as values that are the
foundation of the farmer “ideal” and are supportive of American national strength
(Goldschmidt 1978; Salamon 1992, 1998; Lobao 1990; Durrenberger and Thu
1998). These values, expressed at different levels of social integration, are where
issues of land rights and commons become relevant. Jefferson, and other
authors to the present (Wes Jackson 1980, Wendell Berry 1996) have equated
involvement in farming and ownership of the land with good morality and
citizenship by making the connections among being a land owner, an interested
or engaged citizen, and having vested interests in ensuring a just and moral
society based on common social values and standards. A synergistic relationship
among different value sources, such as agrarian and Christian values among the
Amish (Moore et al 1999), act similarly in this context.
99 Building on the concept of ethnicity or cultural heritage shaping notions of land tenure, Salamon (1997) points out that, as previously stated, land tenure is more than the sum of ownership or tenancy of a parcel. There are social constraints that are imposed on the use of land through what I refer to as
“extended tenure”, tenure that goes beyond simple ownership. For example, the
designated successor is unable to install a grass waterway BMP because his
father-in-law, from whom he and his wife are acquiring the farm, does not think it
is economically feasible to take land out of production for such a practice (e.g.
there is no return on “that” kind of BMP). Another example of “extended tenure”
has to do with past ownership or occupancy of land. Past ownership carries with
it legacy status in which the current owners have a perceived responsibility as
being the symbolic “caretaker” of the farm, and at times avoid making radical
changes in the management practices. This occurs frequently when a farm that is
not the “home farm” of a family is purchased by them from a prestigious family. I
believe this happens to compensate for the lack of continuity in the community;
structurally there is a void in this family not continuing to farm. Similarly, I believe
that the new owner and surrounding neighbors perceive the discontinuation of a farm as socially unacceptable and thus foster the idea of it continuing. In this situation, the present farmer and their neighbors refer to the farm by the previous owner and sometimes the farmer will follow the same management practices.
In the Sugar Creek watershed, it is common for families whose ancestors
owned and operated a particular farm to want to visit and “tour the old farmstead”
during occasions such as summer/fall family reunions. Such activities are
100 common and, near Smithville, occur at least once a year. For instance, as one
local family conducts their annual family reunion, they often want to visit the
farmstead of an ancestral patriarch, which is owned by a household that began
farming after acquiring land upon the death of the patriarch. For this occasion,
the Millers prepare the farm for the visit.
3.2.4 Farm Succession
“Farming remains the most hereditary of professions” (Potter and Lobley
1996:286). Potter and Lobley (1996) cite several studies that explore the great
efficiency with which farmland is passed through intergenerational transfer to a
successor (Harrison 1975, Hastings 1984, Hutson 1987 in Britain; Commins and
Kelleher 1973, in Ireland). John Bennett (1982:299) states that farm families
have cyclical histories that may be viewed as, using Roy Rappaport’s (1999)
terminology, a liturgical order8 (Rappaport 1999:169) in which various periods of mundane time are punctuated by ritual marking divisions of social and cultural importance.
Inheritance practices have a bearing on the number of farm households in
a community and the size of their holdings. Single heir inheritance, such as that
of the Amish and the Japanese (Moore 1990:15) provides a more stable pattern
of ownership and thus a greater continuity of families on the land than bilateral or
8 Liturgical orders, as defined by Rappaport, are more than “individual rituals” but also include “the more or less invariant sequences of rituals that make up cycles and other series as well”. It applies to “generally commensurable processes [that] are governed by common principles and rules” and thus constitute the basis of temporal ordering of human activity and perceptions of those activities (Rappaport 1999:169). In the context of this research, the liturgical order is the longer repeating sequence of farm household replication through intergenerational farm succession. 101 equal heir practices of the non-Amish/German as describes by Salamon (1992,
1997). Post-WWII Korean (Brandt 1971, Sorensen 1988) patterns of farm
succession grant most of the land to the eldest son upon the death of the
patriarch while other sons also are each granted part of the remaining land.
Because of this arrangement, the incentive to remain on the farm and contribute
labor is strong (Sorensen 1981). Most Midwestern immigrant decedents fit into
two general categories based on the motivations for migrating, their social
organization, and outlook on farming and disposition towards property. These
characteristics influence land use and inheritance practices that impact farm size
and tenure and adoption of conservation practices (Salamon 1992, 1998). These
cases are examples of variations in patterns observed in farm succession in
Illinois and other Midwestern states (Salamon 1984, 1992, 1997) in which
households of German decent predominantly practice single heir inheritance and households fitting the “Yankee” type practice an equal heir model.
Potter and Lobley (1996) state that farm succession depends on two
factors when an heir is present: first, the ability and readiness of the successor to
take over the operation; and, secondly, the willingness of the current farmer to
cede control of the operation and allow the successor to take over. They also find
that farm transfers are most successful when the selected heir is present on the
farm and working alongside the current operator. These factors vary in many
combinations across cultures.
English rules of primogeniture are not often followed in Midwestern
farming communities but single-heir inheritance is often practiced among the
102 descendents of early British pioneers. Rather than equal-heir inheritance as is
common among farmers of German descent, the eldest son is assumed to be the
heir (Salamon 1992). However, since 1950, it has become less of an expectation
that the eldest son will enter farming as family farm numbers decrease, farm sizes increase, and educational opportunities provide other options beyond farming for children (Hart 1991). And, equal-heir inheritance has increased in usage, creating problems of continuity of farm holdings during intergenerational
transfer where the successor is granted a portion of the farm and the rest divided
as estate assets among the other non-farming children. Farmers of British decent often have a less smooth transfer as the older generation often retains control of the operation until retirement, sometimes relinquishing control to the successor.
However, in many instances a power struggle evolves as the older generation is unwilling to convey decision-making and responsibility to the successor.
In the Amish case, families commonly have six or more children, and as a
result by the time the farm couple retires and moves to the “doti house” the eldest
child has often made other plans that preclude being the farm successor. The
younger child, the chosen successor, is present as the parents enter the later
stages of the household cycle in which they begin to make preparations for
retirement and farm transfer. The liturgical order of the family farm and household cycle is tied to this cycle of family growth and maturation as well as farm succession. In the Amish family farm system, the heir is on the farm, working alongside the parent and, through a definite series of gradual purchases,
103 the son acquires the farm first by purchasing the equipment, animals and other capital, the final purchase being the land (Moore et al 1999).
Korean farm succession begins much earlier in the family life cycle, through primogeniture. All of the children know early on that, barring major complications of unsuitability, the eldest son will inherit the farm and assume responsibility of care for the parents as they age. While the eldest son and daughter-in-law remain home in preparation to succeed the parents and begin their own nuclear families, the other siblings marry and leave the household to establish their own branch households and either find their own land to farm, or enter other occupations (Sorensen 1984).
Aside from the cultural differences in farm succession, structural elements of the farm that include size and income potential as well as land fertility – marginal land has a lower success rate for transfer – (Bryden et al 1992, Fennell
1981, respectively, in Potter and Lobley 1996) influence the successful farm transfer. In this respect, farm transfer is also a factor of economic and market factors. According to Buttel (1983), a bi-modal distribution of farms are found across the United States; farm scale is becoming increasingly large and small with very few mid-sized farms that historically were the backbone of rural communities. It is suggested by Potter and Lobley (ibid.) that this bi-modal pattern may be a product of tied to the structural characteristics, or limitations, of farms. Despite what sounds like “Social Darwinism” in this approach to succession, they acknowledge the strong role that government policy plays in overall farm success, and thus farm succession. As Bennett (1992) points out,
104 the succession of family farms is greatly due to its resilience through flexibility of
the agrifamily system (family and farm enterprise joined by location and labor
input) in adjusting to internal life cycle changes and outside economic and
political perturbations. Bennett (ibid.) describes household “life-cycles” of reproduction and succession and the interplay of this cycle with farm enterprise type, development, expansion (size) and reproduction. Zollinger and Krannich
(2002) find five factors affecting the successful intergenerational transfer of farms. Specifically, there are five factors that determine a farmer’s tendency to
sell their land. These are a perception that increasing local non agricultural land
use will affect the farm negatively, that it is difficult to rent or purchase land to
farm, belief “that the operation will become unviable”, the farm does not generate
enough profit, and the absence of an heir.
3.2.5 Time and Liturgical Order
Household life cycle and farm succession are linked through the liturgical
order (Rappaport 1999) demarcating intergenerational succession at one point in
time, rather than a gradual transition of ownership from one generation to the
next. Rappaport states that liturgical orders relate the “never changing to the
ever-changing”. Rappaport also discusses the repetition of symbolically
connected speech and action leading to the reification of deeply held beliefs
related to worldview and that these repetitions, often performed during long
periods of immersion, are done in a timely manner, the duration and temporal
distance between them being based on the degree of conformity to prescribed
norms that is required. This is to say that strict observance of a set of rules may
105 be more difficult for a group surrounded by a society of “others”. In the case of a
farming community, the internalization of belief systems is reified in daily practice
in which the farmer and family participate in a constant dialogue with their
environment, a dialogue predicated on their cognitive models but nevertheless
there is constant feedback. Potentially contrary to this is the office worker or
urban resident who is much further removed from the natural resources that sustain society. It is assumed that people who live in rural areas but do not farm there, and work “in town”, are generally less likely to make cognitive connections with the land around them. The farmer’s strong sense of stewardship comes from the beliefs that are reified through daily interaction – these beliefs are acknowledged in their self-described explanations of individual farming behavior.
3.2.6 Settlement Pattern
The relationship between settlement pattern and land use is well
documented in the literature (Ingold 1991, Batterbury and Bebbington 1999,
Haberl et al 2001). Logan and Zhang (2004) show strength and direction of
relationships between spatial and aspatial attributes of geographic objects using
Moran’s–I9 in a study of Los Angeles Chinese and Filipino ethnic neighborhoods
in 1990. Moore et al (1999) document the settlement pattern of Old Order Amish
in the South Fork Subwatershed finding that Amish have a rapidly growing
population, and in this location there is little urbanization or residential housing
growth but instead extended family members often cluster on farm sites
9 Moran’s –I is a spatial autocorrelation technique used to identify the magnitude and direction of spatial relationships between data points. 106 producing multigenerational farmsteads. Land use in the forms of diversification
and intensification are closely related to a healthy land tenure system (Netting
1993) that can lead to multigenerational farm succession. In the Amish areas, it
is common for farms to have been in the same family for three to five
generations.
3.2.7 Conservation and Land Tenure
Conservation is described as a matter of moral concern (Moore et al
unpublished) and not simply an economic or rational choice component. Michael
Paolisso and Shawn Maloney (2000) found while working with Maryland farmers to find solutions to the toxic algal bloom in the Chesapeake Bay, that their self- image is that of good stewards and conservationists of the land and they feel their management practices reflect these values.
Garrett Hardin’s “Tragedy of the Commons” has been held in high regard
by those proponents of privatization and regulation who assert that through privatization will emerge society’s most efficient use of a resource, and through
“mutual coercion” will it be protected (Burke 2001). Other authors (McCay 1995,
Ostrom 1990) provide evidence of successful cooperation in managing common areas outside of privatization. Bryan Burke suggests that there are social dimensions of the commons that Hardin ignores in the assumptions of his rational choice analysis and that the real dilemma is not in shared costs and lack of individual risk but “is actually a conditional set of perceptions and beliefs about
resources, resource use, and other resource users.” Burke believes that a
107 systematic study of perceptions of resource users is necessary before asserting
such a tragedy exists. Perceptions are distilled into two reasons a user may not
see the consequences of their actions with regard to resource degradation.
These reasons are: 1) Users have fatalistic attitudes regarding resource use and
believe that their actions or the actions of others cannot affect its quality or
quantity. 2) Modern environmental issues are of such complexity requiring
specialized knowledge that average people have a difficult time understanding
the costs when they are presented in abstract terms or probabilities instead of concrete examples. As cited above, Paolisso and Maloney (2000) found that farmers’ self-image of being “stewards of their land” may lead them to believe that they are misinformed by state agencies regarding what practices lead to conservation.
Some researchers consider coercion, or regulation, as the only way to put
sustainable practices on the land because they view the current methods of
information dissemination used by local Soil and Water Conservation Districts
and other government agencies are ineffective (Merrigan 1997, Napier and
Bridges 2002). Contemporary conservation adoption strategies rely on the
Diffusion of Innovation Model (Rogers 1982) using large sums of taxpayer dollars
to fund educational initiatives that are seen by some as failing the cost/benefit
analysis because these programs do not address the more relevant issues of
farm structure (Camboni and Napier 1993, Sommers and Napier 1993).
Researcher findings indicate that farm subsidies are detrimental to the long-term
sustainability of farms because subsidy dependency keeps some farmers from
108 implementing conservation practices because of the need to maximize use of
resources, such as acreage (Sommers and Napier 1993). This can lead to a
focus on short-term gain rather than a long-term strategy. From the farmer’s perspective, results of an agriculture focus group (Child 2001) showed that farmers felt federal farm policies hurt small-scale family farmers by providing subsidies to larger industrial farms and promoting artificially low food prices. This approach to the dissemination of information is not effective. It is not because voluntary programs are incapable of working but rather, as Aldo Leopold states in
A Sand County Almanac, government agencies have focused on the quantity
over the quality of information thereby making it irrelevant to many farmers.
3.3 Understanding Conservation Adoption
Much of the conservation literature approaches the question of farmer
adoption of conservation practices with assumptions similar to those of
neoclassical economics. That is to say it is assumed that farmers base their conservation decisions on rational choice and self-interest and that social networks and kinship obligations are external to this decision-making process.
Such assumptions do not account for socially embedded behaviors that influence conservation practices.
Innovation Diffusion models have become the dominant mode of
understanding technology transfer and adoption in the Land Grant University and
Extension system. These models have been used to demonstrate that access to
information by farmers will result in adoption phases of the new technology in
109 which the principle variables in farmer adoption are rational choice, self-interest, and personality type. Rogers (1962) was the first to develop Innovation Diffusion theory as it is commonly used today. This adopter-centered, technology-neutral model assumes that the decision to adopt a new or alternative technology occurs as a result of rational choice depending on farmers using new information provided to them by “experts” in their decision-making process and then acting based on that new information, which is assumed in their best interest. Later applications of this model by Brown (1981) focus less on the adopter’s attributes and more on access to information, cost, and infrastructure associated with innovations.
Farm Structure models emphasize farm size and income indicators as
independent variables in determining impact on technology adoption. Studies
have shown that neither the Innovation Diffusion nor the Farm Structure models
are efficacious in explaining farmer conservation behavior (Tucker and Napier
2002, Napier et al 1984, Napier et al 1986, Sommers and Napier 1993). The
conclusion of many of these studies is that farmers will adopt if they perceive it is
profitable to do so. Likewise, the variables that predict technology adoption are
not adequate predictors of farmer conservation adoption. It is clear that there are
more issues attached to the adoption of conservation than are explained by
these models and there is a need to expand the indicators used for predicting
adoption behavior. For instance, according to Brown (1981), farmers, identified
as laggards by the Innovation Diffusion model, may in fact not be “laggards” but
rather lack access to information to assist them in their decision. Other research
110 such as aesthetics focuses on the type of conservation being employed –woodlot
owners in southeastern Michigan emphasize aesthetics and environment over
economics (Erickson et al 2002). In Iowa, Bultena and Hoiberg (1983) found
adoption of certain conservation practices were dependent upon the farmer’s
perception of neighbor attitudes towards a practice. Household life cycle and
organization, tenure and farm type are all variables that have proven effective in
understanding farmer adoption of conservation or attitudes towards conservation
(Salamon et al 1997). The one thing that seems common across all studies is
that they are from different cultural and geographic areas and most of them measure general variables instead of specifics missing important local factors.
Studies of rural communities in the United States follow the pattern of
using detailed community data but describe community structure from a distance
and in a top-down approach (Goldschmidt 1978, Napier and Tucker 2001b,
Napier and Bridges 2002, Napier et al 2002, Camboni and Napier 1993,
McIntosh and Lee 2003). Such approaches provide great insight into factors that
affect community social and economic conditions, but do little to portray the
motivations of the members of that society or how they interact with their
environment (Salamon 2003:208-209). Others use statistical generalizations
(Napier and Bridges 2002, Napier et al 2002, Krupa and Vesterby 2003) as both
data sources and results providing a good overview of a problem without
providing specific or explanatory details about the lives of the people or factors
that guide their decisions.
111 William Lockeretz (1990) states that it is time to reinvent the wheel for research methodology and explanatory models designed for understanding farmer conservation behavior. He states that there is a need to move beyond simple multivariate products like “age and education as factors in conservation adoption”, which seem axiomatic or at least readily explainable, and find other variables to investigate. Watershed and conservation projects rarely investigate land tenure arrangements as explanatory variables in evaluating project success.
Although some researchers use such variables for descriptive purposes
(Camboni and Napier 1993, Napier and Johnson 1998), they present them as statistics that are not incorporated into the analysis of understanding conservation behavior.
The conclusions of Lockeretz’s broad study of research findings shows that quantitative techniques such as regression analysis and other forms of generalized data do not work, when unaccompanied by other qualitative data, to explain farmers’ conservation behavior. These models rarely produce results that can be acted on or operationalized into solutions. This statement corresponds with anthropologist Clifford Geertz’s assertion that general approaches will only get you general results, and for more precise, pertinent information, you must work locally, and in the specifics of the local society and culture (1963:3).
An alternative to these approaches would look at social and cultural as well as economic and structural factors in finding motivations for adoption of conservation practices. Salamon et al (1997) found that household life cycle and organization, tenure and farm type are variables that have proven effective in
112 understanding farmer adoption of conservation or attitudes towards conservation in Illinois and the Midwest. Likewise, she also states that family communication channels, or lack thereof, will affect the adoption of conservation strategies and practices. Research among farmers in the Chesapeake Bay Watershed found that farmers resisted new conservation initiatives because of their strong sense of stewardship stemming from their self-image of feeding and serving their community, and protecting the environment through good stewardship.
Consequently, the farmers see efforts by the state agencies as ill informed, excessive or “just bad science” when recommendations seemingly change from year-to-year and without explanation (Paolisso and Maloney 2000). These kinds of approaches necessitate the integration of more ethnographic methods in conservation adoption research to compliment census and survey-based techniques.
Pierre Bourdieu’s (1977) conceptualizes human behavior as habitus, capital and field, stating that human behavior is habitual (habitus) in nature and through resources (capital) humans act in their environment (field). That is to say that human behavior is modified and adjusted based on past experiences and actions that influence and shape current beliefs and perceptions thus informing current action. These behaviors interact with resources in a socially constructed environment that includes human material culture and the physical environment.
Bourdieu is keen to assert that he is not a cultural determinist but insists that humans have the ability to adjust their culture and perceptions and modify their habitus. Carolan (2005) applies Bourdieu’s Theory of Practice to farm tenure and
113 conservation in the following manners. Farmers, like all humans, perform daily
tasks based on past actions and the insights that are gained from them. These
insights inform future planning and affect the outcome of future decisions as well as guide daily activities on the farm. Insertion of new forms of action is difficult for many because of the numerous social and psychological barriers that exist to its
adoption, including material constraints such as the need for new types of
equipment.
Habitus makes right. By that is meant that human actions, if they are
informed through habitual performance and reification of their perceptions
through daily action those perceptions will be recognized as efficacious and thus
correct.
3.3.1 Participatory Process
The participatory development program that uses public participation
generated through trust relationships created through local collaboration is a new
direction for support of environmental protection programs (Rhoades 2001,
Moore and Weaver 2001, Pretty and Chambers 1993, Scherer 1990). Nonpoint
issues are difficult to rally people around because there is no group or person to attribute the source (Scherer 1990) and most people are familiar with the causes
(Scherer 1990, Sharp 2001). Salamon (1998) found that farm families perceive risk differently than non-farm families; similar results are found in the Sugar
Creek Watershed (Moore et al, unpublished). Self-reliant participatory development is a process rather than a product, an end in itself rather than a
114 means to an end because it establishes a framework for action on which future
social change is possible (Burkey 1993). A participatory methodology becomes
an “end” per se. It is an approach to empower local people by providing the
framework in which they become aware of their collective status and plight that
will assist them to develop local capacity to solve their own problems and avoid
external class exploitation. This is done by building a project around the needs of
local people that integrates local knowledge and aspirations (Ibid.) and adds
emphasis to the need for indicators that make sense to local people (Nazarea et al 1997) if relevant conservation programs and information are to reach the people who need it. In short, the task of researchers is to “demystify” ideas of the reality of rural life for policy makers and natural scientists; showing that rural communities are not harmonious, homogenous enclaves whose residents share similar interests, aspirations and goals.
3.3.2 Effects of Policy on Conservation
State policy, land grant university research findings, and Extension
information dissemination to agricultural communities are often associated with
United States’ Federal farm policies promoting agribusiness, factory-farming systems and agricultural export markets in place of more sustainable systems, which may produce healthy and safe food, environmental and social accountability, and support rural communities (Delind 1992, Prugh et al 1995,
Clancy 1997, Bonanno et al 2000). Frequently there is a lack of understanding between the “language” used among these government and university agents
115 and the “language” used by farmers (Williams and Matheny 1995, Child 2001).
Land grant institutions and state agencies are not successful in providing
necessary information because they have not decoded their findings in terms
accessible to the general public (Scherer 1990) or it is kept from constituents
through “blockages” in the political process (Durrenberger and Thu 1997).
Access to information is not a matter of lack of education or a Marxist explanation
of the dominant elite controlling the way people think, as debunked by James
Scott (1985), but rather “a strategic manipulation of channels of complex signals
and leverage points in the state system” (Thu 2001:5).
Most Ohio farmers get their information from sources that include
government agents and agriculture consultants (Tucker and Napier 2002).
Because of this, it is important that those providing the information understand
the farmer’s perspectives and needs when deciding what is researched, how
such research should be conducted and how to disseminate the results
(Rhoades 1982).
Soule et al (2000) suggests the incentive to invest in conservation oriented land management lies in ownership of the land. Because the person who leases
has no investment in the land except the crop and has no guarantee to future
benefits of the land there is little economic incentive for conservation. But policy
implications present many obstacles, as found in an Ohio study that showed
farmers who are dependent on state subsidies for operation success are less
likely to implement changes in practices to solve environmental problems. Those
farmers that are more likely to participate are those that are less dependent on
116 subsidies (Sommers and Napier 1993). Related to this is another Ohio study
(Napier and Brown 1998) showing that larger farm sizes were correlated with
higher debt-to-asset ratios where grain was the primary product.
Making matters more difficult for local conservation agents, geographically
arbitrary county political units are the major vehicle to water quality remediation,
a practice that hampers a whole watershed approach because county
boundaries do not coincide with watershed units (Foster and Rogers 1988).
As previously states, some researchers agree in advocating government
regulation to solve environmental problems. While I agree in principle that this
approach to dissemination of information is not effective, it is not because
voluntary programs are not capable of working but rather government agencies
have focused on the quantity over the quality of information thereby making it
irrelevant to many farmers. I think farm subsidies are detrimental to the long-term
sustainability of farms because it keeps some from conservation-minded
operations (Sommers and Napier 1993). Consistent with Scotts (1985) statement
about people knowing who to “point the finger at” with regard to creating a
difficult environment, results of an agriculture focus group (Child 2001) showed
that farmers felt that federal farm policies hurt small-scale, family farmers by
providing subsidies to larger industrial farms and promoting artificially low food
prices. These combined factors, among other criticisms, lead to a belief among family farmers that Federal farm policy is not helping them. Whether this is fact or fiction, I think the general perception of government agency efficacy in promoting
the adoption of conservation programs is not positive. However, had alternative
117 variables been investigated there might have been some support for a
hypothesis that includes tenure arrangements, farm size, and type, and farmer
sense of morality. When farmers have greater control over their resources and
are consulted in planning processes as demonstrated in the Sugar Creek participatory watershed project, they are more likely to adopt conservation practices and take an active role in their communities.
Emilio Moran extends concepts of cultural ecology beyond a focus on
culture or social organization as his key concepts, recognizing the importance
and plasticity of culture to allow for adjustments in maintaining social systems, to
addressing specific local ecological issues. Moran emphasizes understanding the
connections between land use, social systems (including land rights) and the
ecology of an area. In his work in the Brazilian Amazon, Moran looks at the
relationships among government tax policy and agricultural incentives and how
they operate at the local level and their connection with deforestation (Moran
1993). Beginning with indigenous land rights that emphasized common use
rights and among people in a society, he concluded, as has Conklin (1954), that
practices and use rights associated with swidden agriculture were more
compatible with the health of the Amazonian system than private property right in
a market system. Changing land use and subsequent deforestation is attributed
to changes in land rights and a blurring of the commons that is concomitant with
government policies that give preference to less sustainable land uses, such as
ranching, and large land holdings through “tax holidays” (Moran 1993, Walker et
al 2000). New colonists seeking a better life move and establish private property
118 rights and enter their land in another level of ecological transition of a market
economy. McCracken et al (1999) found that many of the colonists who come to
the forest contribute to deforestation initially, but as the lifecycle of their family progresses and they are able to accumulate capital, they often transition into more sustainable forms of agriculture. Through government policy and incentives that are irrelevant to small-scale agriculture, large corporate farms are able to dominate and further contribute to deforestation. In the United States, small-scale farmers in the Sugar Creek also struggle with the technology treadmill that government policy emphasizes.
Some questions that remain to be answered include asking if the upbringing of the farmer acts as a factor in future adoption? This may be true of
conservation adoption if affected by the understanding the farmer has of the
ecology and how the farm fits into the agroecosystem. If the farmer has little past experience with conservation in the stages of formal training (Bennett 1993) then
it is likely that the farmer will not develop conservation-minded strategies. This is
similar to Burke’s (2001) rejoinder to Garret Hardin and the “Tragedy of the
Commons” literature in which he concludes that the tragedy may not be in the abuse of the commons per se. Instead, the tragedy is in the inability of those who possess specialized knowledge regarding the ecological consequences of land use to convey adequately that information to land owners and operators.
Sometimes adequate information is available for dissemination, but that there are other blockages to signal broadcasting within the state system preventing
119 reception and or transmission; this may be the case when power and influence
are involved and the regulator is strongly influenced by the regulated.
Are there problems inherent in how EPA, USDA and NRCS conceptualize
“conservation”? As in Kathrine Jellison’s account of the modernization of rural
America, in “Entitled to Power” (1993), the survey sponsored by the Smith-Lever
Act of 1914 was conducted with the purpose of collecting data in order to asses the needs and address the burdens of rural women turned out supporting technological solutions rather than addressing the underlying social issues it
uncovered. Researchers discovered that the most important issue for women
was power; yet, the governmental system could not deal with such a complex
social issue with so many competing interests, so officials choose to research
and promote technological solutions instead. The current approaches to
conservation are based on this similar past example so that, today, the United
States government addresses its environmental problems with technology and
over-rationalized solutions that ignore the root of the problem (e.g. a need for
change in the social structure). The environmental crisis will never be addressed
until the issues of equity, sustainability are dealt with. Conservation adoption
tends to focus on externalities of the agricultural system and does not adequately
deal with the constraints, or perceived constraints, of the traditional agribusiness
paradigm. Ted Napier and various collaborating authors (Tucker and Napier
2002, Napier et al 1984, Napier et al 1986, Sommers and Napier 1993)
demonstrate in their studies that innovation diffusion models are not efficacious
as predictors of conservation behavior. Additionally, they find that larger farms
120 tend to participate in government subsidy programs, and concomitantly farms
that participate in government subsidy programs are less likely to participate in
conservation programs and adopt conservation strategies on their farm.
Does NRCS allow for creativity in the remediation process? Farmers are
generally problem solvers who excel at finding solutions. Key informant
interviews and interactions with farmers in Wayne and Holmes Counties
suggests that the NRCS BMP process stifles creativity through rigid bureaucratization of the conservation process that restricts implementation through formal and inflexible standards in which local knowledge is overlooked
for regulations and guidelines that often result in solutions that are more
complicated and expensive than necessary. The process emphasizes
government payments with no room for input from the farmer. This leaves the
common perception that in order to get program funding for conservation
implementation “the government ends up running your farm” leaving the operator
with little perceived room for innovation and decision-making.
3.3.3 Local Knowledge and Stewardship
The contributions to understanding land tenure and commons of the Sugar
Creek from Harold Conklin is something that can easily be taken for granted, but
the authority of which necessitates discussion. Conklin (1954) describes an
intricate and interconnected relationship based on local knowledge and
perception of ecological process, subsistence, land tenure and commons.
Conklin is a champion of ethnoscience that emphasizes learning local
121 classification and terminology before making any ethnographic conclusions to avoid confusion and misconception. This approach allowed Conklin to learn how
the Hanunóo managed their crops, not the land, in an intensive intercropping and
at times rotational, shifting cultivation. Valuing local knowledge made it possible
for Conklin to connect the preference for secondary forest growth to the
understanding that commons were protected through deference to the forest
spirits in which the Hanunóo would avoid the clearing of old growth tree stands.
The basic premise of the Hanunóo’s approach to planting was to preserve and
sustain the ability of the land to produce for them and they accomplished this by managing the delicate balance of crops and their mutual benefit that were
derived from combinations of intercropped species (Conklin 1954:167-171). This
dovetails Sugar Creek land tenure as seen in the close relationship the Amish
have to their land. And, this is also found in the emerging recognition among the
groups of farmers throughout the Sugar Creek who are managing the commons
of the stream with deference to their downstream neighbors. Farmers’
diversification and local knowledge is that source of understanding that is a
primary key to the success of conservation initiatives; be it local knowledge of
perceptions and categories, social relationships, or political processes.
3.3.4 Morality and Conservation
Concepts of morality and farmer self-identity of being good “stewards of the land” are an aspect of the conservation project process that is rarely taken
into consideration by researchers and planners. Instead of analyzing
122 conservation behavior based on neoclassical assumptions that decisions are made based on rational choice and self-interest alone, the concept of the moral economy provide a basis for investigating the social networks and kinship obligations that shape moral values of a community to which local farm households identify (Sahlins 1972, Scott 1976, Moore 1990). In the Jeffersonian description of an agrarian way of life, there are also moral contributions from secular and religious institutions. Farming is more than an occupation and is seen as a way of life since many farmers identify themselves with the morality and connections to the land and community with which they are associated
(Salamon 1992, Rosenblatt 1990). According to Paolisso and Maloney (2000),
“The cultural schema of morality is a very powerful, emotional, and motivating force for countless farming communities.” Paolisso and Maloney cite several authors, whose contributions have reflected this insight, including Bartlett (1993),
Comstock (1987), Dudley (2000). The concept of morality is not limited to the values and beliefs of farmers, as they perceive the world, but also includes their self-image that is shaped through their role in society and their interaction with the land (i.e. many farmers see themselves as providers).
Dudley states, from her research among Minnesota farmers during the farm crisis of the 1980s, that farmers are often trapped in a process in which they feel they have little control over, but at the same time try to “make the best of a bad situation” (Dudley 2000:95). Under these conditions it is possible to discern aspects of farmer individuality and “go it aloneness” that sees government subsidies as a “handout” or part of a “welfare mentality” that they look upon with
123 “disgust”. One aspect of farmer self-image is the ability to work through difficult
economic climates, to “make it through the tough times by tightening your belt”.
Some farmers feel that if you cannot do that then you should not be farming. To
demand a higher standard of living than can be earned by farming is viewed
negatively among the farmers interviewed. As one of Dudley’s farmer informants stated, ”when you got champagne taste and a beer budget, [making ends meet]
gets a little tough” (Dudley 2000:96). Another attitude regarding ownership and
morality comes from the belief that being invested in something (e.g. using your
own resources to support something, like a farm) leads to caring more about the
outcome, which is shown in the planning and decision-making that a farmer
makes.
Understanding local social networks through the concept of social
embeddedness, as described by Mark Granovetter (1985), reveals relationships
in which people make economic decisions without referencing only economic
goals and rational choice. Wayne County, in general, and the Upper Sugar Creek
subwatershed in particular, is an area where the large-scale, industrial agriculture
model is not a dominating force. Giddons (in Granovetter 1985) describes a
reversal of this concept in which disembeddedness takes place when the
economic activities of a community become separated from local social networks
allowing the external, industrial networks, to erode the social embeddedness of
the community. This changes the impact of social conformity to local norms and
expectations of appropriate agricultural practices, leaving entities in a community
124 that are lacking the social conscious fostered in areas, like the Sugar Creek
Watershed, where social embeddedness is still common.
Walter Goldschmidt was perhaps the first to recognize, with a scientific lens, how conceptions of land rights and commons are affected by changes in scale and the embeddedness of social relationships. In his 1940s study of agricultural communities in California, Goldschmidt recognized the impact that the growing agribusiness economy would have on small communities, the base of the family farm, and rural quality of life in general. In his study he attributed the increase in scale of farms to government policy that fostered increased production and profit without considering the social ramifications of such policies.
Incentives that favored getting bigger made it difficult for smaller farms to compete for market share and operating costs in the form of loans. Attributed to this shift in ownership from the family farmer to the corporate farm are various social conditions: Large corporations are less likely to be involved in community activities and to contribute to community needs because of a lack of interconnectedness leading to disinterest in rural affairs. Money from the community that would generally cycle several times from the farm to one business or another, leaves a community dominated by corporate agriculture because most of these farms have outside suppliers and other arrangements in other states. A decrease in local dollars to be spent means a decrease in local businesses and employment opportunities. All this can lead to a decrease in the average living wage because of poor job opportunities and low local involvement of the people (Goldschmidt 1978).
125
CHAPTER 4
METHODOLOGY
4.1 Institutional Arrangements
The author conducted the data collection and analysis for this research while employed as a Research Associate affiliated with the Sugar Creek Project of the Ohio Agricultural Research and Development Center’s (OARDC)
Agroecosystems Management Program (AMP). Support for the Sugar Creek
Project, through which the present research is funded, comes from three main sources: a United States Department of Agriculture’s Sustainable Agriculture
Research and Education (SARE) grant (#NCR019-01, Richard Moore, PI) that funded preliminary survey and archival data collection; the Ohio EPA 319(h) Non
Point Source Grant (#02(h)EPA11, Richard Moore, PI) funded additional survey work and provided resources for community activities allowing the researcher to engage in participatory observation; and a National Science Foundation (NSF)
Bio-complexity planning grant that funded the in-depth interviews with key informants regarding land tenure, farming attitudes and conservation awareness and preferences.
The OARDC served as the base of operations for this study providing office space, computing support, and library resources. Support for statistical 126 analysis is provided by the OARDC’s Office of Computing and Statistical
Services. The Wayne County Auditor’s office shared digital copies of the land parcel database and tax maps for creating the Geographic Information System.
The Wayne County Soil and Water Conservation District, National Resource
Conservation Service, and Ohio State University Extension offices provided technical support related to Best Management Practices and acted as gatekeepers to the community.
4.2 Community Selection
The Sugar Creek Watershed was selected because it possessed several
key characteristics that make it an appropriate and attractive location in which to
conduct this research. First, the ethnic diversity of the watershed, as a factor of
its settlement by people of varying ethnicities and under different state-sponsored programs, follows the geographic extent of the watershed spanning the north-
south gradient of heterogeneous agricultural communities of the four-county
region. This provides unique opportunities to interact with distinct American
subcultures within a relatively small geographic area. This area also represents
two distinct geological processes: glaciated and unglaciated. These subcultures
persist through different patterns of social organization, settlement, and land
tenure despite being in close proximity to urban areas and integrated into the
same state system as the urban residents. Secondly, as a result of being listed
the second most impaired watershed in the state, the Sugar Creek was one of
the first in Ohio to be targeted for Total Maximum Daily Load (TMDL) planning.
127 Third, the watershed is in close proximity to the OARDC that provides a
supportive institutional affiliation through the Ohio State University and access to
library, computing, and statistical services. It is also the location where an
interdisciplinary research team is leading a participatory watershed remediation
project that makes possible the opportunity to work with a community
development program while conducting dissertation research.
4.3 Data Collection Techniques
4.3.1 Qualitative and Quantitative Methods
Julian Steward acknowledged that ethnographic methods were not suited
for research at all levels of sociocultural integration and that other research methods beyond participatory observation need to be used to investigate social
phenomena beyond the local households and village (Steward 1955). In his
investigation of Puerto Rican subcultures (1953), Steward states that complex
societies with millions of people make it impossible to generate understandings
of their collective behavior through extrapolation from ethnographic interviews.
To accomplish this, he applied documentary and historical research and
investigated the institutional framework in order to contextualize his findings. In
the case of this project, it necessitated a pluralistic methodology that used surveys, case studies, archival research, interviews and existing population census data.
Salamon (2003) points out that many rural studies follow the pattern of
using detailed community data but describe community structure from a distance 128 and in a top-down approach (Goldschmidt 1978, Lobao 1990, Napier and Tucker
2001, Napier and Bridges 2002, Napier et al 2002, Camboni and Napier 1993,
McIntosh and Lee 2003). Such approaches provide great insight into factors that affect community social and economic conditions but do little to portray the perceptions and motivations of the members of that society and how they interact with their environment (Salamon 2003:208-209). Others use statistical generalizations (Napier and Bridges 2002, Napier et al 2002, Krupa and Vesterby
2003) as both data sources and results, which provides generalizations about an area and the people who live there that may mask the reality and overlook specific or explanatory details about their lives or factors that guide their
decisions (Geertz 1963, Lockeretz 1990). Moran (1990) warns of the difficulty
encountered when shifting between levels of analysis and generalizing data
collected at one level as explanation for another.
Qualitative studies offer a rich dataset; however, such studies are
generally more localized and are not easily generalized because of their smaller sample size and the localized extent of the study. This limits their broader applicability. A compromise can be found in studies using qualitative and
quantitative data offering better crosschecking for reliability (Guba and Lincoln
1981). Triangulation of data (collecting data from multiple sources) and methods
(using multiple collection methods) are the best way to ensure reliability and
validity of analysis (Janesick 1994:215, Huberman and Miles 1994:438). To
accomplish this goal, this research makes use of four data collection methods as
129 follows: participant observation, survey and interview, Geographic Information
Systems (GIS), and contemporary and historical document research.
In 1990, William Lockeretz appealed to academics researching farmer
conservation behavior to reassess their research methodologies stating that the
current approaches are ineffectual in producing relevant conclusions about any
variables that could be acted on locally (Lockeretz 1990). It was clear that new
variables need to be investigated. Instead of attempting to understand why a
farmer behaves in a particular way, there is a movement for researchers to
partner with farmers in the conceptualization and design of research problems,
thus including the farmer’s needs and recommendations in the process rather
than providing him or her with the results to adopt (Rhoades 1982). Since
Lockeretz’s call for change, there have been numerous studies that have
investigated at regional levels that have made some useful, if only preliminary
findings. Researchers in Michigan found that aesthetics was a determining factor
of conservation adoption in southeastern Michigan and was based on the type of
conservation being employed – woodlots owners emphasize aesthetics and
environment over economics in conservation decisions (Erickson et al 2002).
Prior to this, Bultena and Hoiberg (1983) found, in Iowa, adoptions of certain conservation practices were dependent upon the farmers’ perception of neighbor attitudes towards a practice. The one thing that seems common across all
studies is that they are from different cultural and geographic areas and most of
them measure general variables instead of specifics thereby potentially missing
important local factors. An alternative is the work of Sonya Salamon et al (1997)
130 in which she found that household life cycle and organization, tenure and farm
type are variables that have proven effective in understanding farmer adoption of
conservation or attitudes towards conservation in Illinois and the Midwest.
4.3.2 Units of Analysis
As described by Emilio Moran (1990:279-297), the unit of analysis selected for inquiry is critical to the questions being addressed, the data collection methods employed, and the type of data sought. Within a nested hierarchy, it is necessary to delineate the level and scale at which research will
be conducted while making an accounting of the interconnections and influences
of units “above” and “below”. The units of analysis, as outlined in the Introduction,
were selected because they are the three main elements in the interaction
between human and natural systems. These units fit well to answer questions of
land tenure and conservation.
The first unit of analysis is the geographical range of the subwatershed
hydrologic unit. The Sugar Creek Watershed, itself being a sub-unit of the
Tuscarawas that in turn is part of the Muskingum Basin, is comprised of several
subwatersheds. The second is the population of agriculturalists who own land along the Sugar Creek and its “blue line”10 tributaries in three subwatersheds,
Upper Sugar Creek, North Fork, and Little Sugar Creek. The farm household, consisting of kin and non-kin whose labor efforts contribute to the success of the
10 “Blue Line” refers to the Environmental Protection Agency’s use of USGS surface water maps to identify streams and rivers that fall under their jurisdiction for protection. These appear as blue lines on the maps. Those streams that are not on the map currently are unregulated. However, the Primary Headwaters Initiative at the Ohio EPA is moving towards establishing metrics for regulation of these “unmarked” streams. 131 household, is the basic organizational unit forming the basis of the agrifamily system. The final unit of analysis is the land parcel that comprises, when aggregated with all holdings of the individual’s tenure rights, the land resources from which the agrifamily system derives their living. As a social system, the agrifamily has access to land through social structures that define land tenure arrangements which are bounded by arbitrary surveys that delineate, with exactitude, parcel size.
Because one of the components of this research addresses conservation adoption, the subwatershed provides a discrete unit of analysis within which to ask questions regarding conservation and water quality remediation. Although social and other terrestrial systems transcend subwatershed and watershed boundaries, surface waters and groundwater aquifers that are directly affected by agricultural practices are typically isolated to the watershed. The population of landowners along the stream is similarly germane to organizing a social response in addressing the immediate problems associated with stream impairment. Human social systems, like other natural systems, transcend subwatershed boundaries, at higher levels of sociocultural integration (church, community, state, international), but most Sugar Creek agrifamily systems are contained within these boundaries. Moran has warned that shifting analysis among hierarchical levels of analysis in ecosystems approaches is problematic because “the processes at one level obscure relationships at other levels”
(1990:280).
132 Additionally, Moran states that:
If one started [an analysis] with the ‘state’ or national level it might appear that these external forces shape the life of local communities in relatively similar ways… [but] the focus on the community… shows individuals responding actively to actually subvert or alter these external forces, not passively accepting them”. (1990:283)
Furthermore, the parcel, as a unit of analysis of land tenure, forms the basis of
the United States’ system of land ownership in general, and the Midwestern settlement and farm structure, specifically.
Many studies use multiple measures to determine farm size including total acreage and farm income. I acknowledge that farm size based on total farm area is not always an adequate measure11 but because of the nature of this analysis,
it is a good fit for examination. Financial data was not collected because the
information it would provide for the project was determined to be unnecessary and it would not be essential to this study of land tenure. Consequently, and due
to the sensitive nature of farm financial data that could be a determining factor in
participant compliance, the total acreage owned or leased is used as the
indicator of farm size. This form of farm size can be ascertained in a variety of
ways. However, since agricultural census data produces only average farm size at an aggregated county level, as well as only providing evidence of the holdings
(ownership) and not the total farm size (ownership and other tenure such as leasing) (Burton and Walford 2005), farm size was calculated using data provided from surveys.
11 An overview is presented in Gasson 1969, Lund and Price 1998. 133 A note on comparative biophysical data: The researcher has attempted to
maintain minimum data standards for comparative research, as described in
Emilio Moran’s edited volume, The Comparative Analysis of Human Societies, in
an effort of full disclosure and to allow the potential of referencing this research
and the results in future works. Because many of the categories of data are not
used in this research, many of them are presented in the Appendices, such as geophysical and topographic maps (Appendix C) and climate (Appendix D), are used for descriptive purposes to establish regional setting and context for research. Organization of household labor is not described in detail as there are a number of studies that investigate this dynamic structure of Midwestern
(Bennett 1982, Salamon 1992) and Amish (Long 2003, Hostetler 1993, Kraybill and Hostetler 2001) farm households.
4.3.3 Drop-off and Pick-up Surveys
Data for this study were collected from a variety of sources including
surveys using mail (Dillman 1991) and “drop-off, pick-up” (Riley 2002) methods,
interviews, participatory observation, and interaction at farmer meetings and
community activities. Informants were selected from an address list created in a
GIS using ArcView 3.2 and the Wayne County Auditor’s parcel database to select farmers and non-farmers owning land on the stream. Using a population
(N=726) of land owners with land on or adjacent to the stream, a household survey was conducted utilizing a drop-off/pickup method, along with interviews and ethnographic data collection, to facilitate the understanding of social
134 organization, community continuity, and land tenure and use, and their contributions to peoples views of conservation and stream ecology. Initially surveys were mailed using Dillman’s (Ibid.) method, but because of a low response rate, the strategy was changed to follow Riley’s method. In the context of the research, this method is far more effective because Riley’s method provides opportunities for face-to-face interaction with the informants during the
“drop-off” phase with potential for further interaction in the “pick-up” creating a familiarity between researcher and informant that may lead to future contact.
In the development of the surveys, it was important to collect as much detail from the participant regarding their perceptions and attitudes to conservation, specifics regarding land use and personal vision for the local ecology of their community, and, for farm respondents, details from their farm operation and conservation use. An attempt was made to balance all of these data needs with the reasonable expectation of time required to complete the survey and sufficient opportunity for open-ended responses and freedom of the respondent to provide their feelings regarding specific questions, as well as issues brought up during the course of the survey. Question types included open- and closed-ended responses, multiple selection for preferences and uses, and perceptions and opinions were measured using 5-point, unidirectional, Likert scales (see Appendices A and B for the full survey text).
Of 498 residences surveyed during the 2001-2003 data collection period, there are 159 farm responses. Table 4.1 shows the response rates by subwatershed.
135
Total Total %Contact %Total List Completed Farm refused Response* Response Upper Sugar Creek 195 164 52 19 89.62 84.10 North Fork 136 90 27 13 87.38 66.18 Mainstem 236 146 43 4 97.33 61.86 Little Sugar Creek 159 98 37 2 98.00 61.64 159 68.59 Totals 726 498 38 Source: Sugar Creek Social Survey data. *Contact was attempted but not made for every potential participant.
Table 4.1 Subwatershed Response Rates
4.3.4 Geographic Information Systems
Before Geographic Information Systems were accessible to researchers outside of the military and fortunate geography departments, Roy Ellen (1984) spoke of the benefits that mapping a research area provides. Spatial relationships that are not evident from field notes may be readily apparent when visualized in the two-dimensional presentations of a map. Fields, farmsteads, housing developments, streams, wood lots and other land use resources can be analyzed and interpreted at multiple scales using GIS. Because the watershed is a spatial concept, which offers unique opportunities to study land tenure within an ecological system, contextual analysis of the spatial dimensions of social systems are important. In order to accomplish this, Geographic Information
Systems (GIS) were used for spatial analysis and trend seeking with regard to land tenure and land use. Spatial dimensions of social structure, including land tenure are important not only for visualization of the problem, but also to allow for
136 a contextual analysis of the spatial attributes of land tenure that are best examined using Geographic Information System (GIS) tools (Liverman et al
1998).
A GIS was created for this dissertation research that includes topographic, physiographic and social environmental data layers (see Appendix F for a complete listing). Using GIS parcel data from the Wayne County Auditor a database was created for storing and linking the interview and survey responses,
2002 Census of Agriculture, and 2000 Population Census data using the parcel identification and postal address as the common fields. Data was coded and entered into a Microsoft Access 2000 database that is linked to the GIS making it available for combining multiple data layers, including LandSat TM data, aerial photographs, and soil data, for analysis at the parcel or watershed level.
Correlations among land tenure, demographic, and land use variables with those of physical attributes of the land are possible using these methods (Wood and
Skole 1998).
GIS plays a central role in the methods used in data collection because many aspects of land tenure and social relationships have spatial components to them. For instance, the Amish threshing-rings have spatial elements in that the distance between member farmers cannot exceed an average days traveling distance for horse and buggy. Other components that take on spatial attributes include technology used by the ring, church district, and kinship, and distances from farmstead to fields.
137 Maps also play a key role in interacting with informants and interview and
survey participants because they are a medium that provide visual spatial
relationships. This makes it easier for the researcher to convey question
meanings and elicit other forms of open-ended responses regarding particular
elements of the map and how the informant and the people associated with map elements (e.g. owners, renters, users) are related spatially through networks in their environments.
4.4 Data Collection Problems
4.4.1 Under Representation of New Order Amish
“New Order Amish” was a category available for selection in the heritage section of the survey. I was initially surprised to learn that there were no
responses from any members of this group when reviewing the descriptive
statistics. This option was offered because I was unaware that there were no
New Order Amish in this part of Wayne County. To confirm this, I mapped the
Amish Church Districts of Wayne County with Arc View 3.2 GIS software using
data from the 2000 Ohio Amish Directory, and then checked the 1974 Ohio
Amish Directory to compare membership of New Order districts then and now. In doing this, I confirmed that in fact there are no New Order Church Districts listed
for the Sugar Creek area of Wayne County.
138 4.4.2 Lack of Adequate Measures of Farm Income
Although farm income was not necessary for this analysis, it would have
complimented the data analysis by making it possible for us to separate farms
based on size and income where large farms may be indicated by either the
amount of income (greater than $500,000 annual income) or acreage (greater
then 500 acres), or both. In this research, farm income is only reported as a
percent of off-farm, no real numbers given. According to Burton and Walford
(2005:335), farm size based on the total acreage farmed is an adequate measure
but others state that farm income is an important indicator (Gasson 1969, Lund
and Price 1998). In addition, Britton and Ingersent (1964)12 “observe [that] large
businesses also tend to have large overall farm areas”. Ilberly and Bowler
(1998)13 state that as a result of this former relationship, “farm size is often used
as a proxy for the size of the farm business”. The lack of farm income limits the
direct generalization of findings beyond the Sugar Creek Watershed because the
findings are not explicit in the inclusion of farm income that may be a determinant
factor in farm size for those farms that are designated Confined Animal Feeding
Operations (CAFOs), requiring a National Pollutant Discharge Elimination
System (NPDES) permit. Thus, although this watershed has few CAFO permit
holding farms and there are other regions of the United States of America that
have similar situations, the CAFO as a farm organization strategy is increasingly common in others.
12 Cited in Burton and Walford (2005:335). 13 Cited in Burton and Walford (2005:335). Bateman and Ray (1994) state the point similarly. 139 4.4.3 Measuring the Incidence of Amish Leasing
I suspect that it is difficult to assess the real rate of leasing from the
Amish. Based on experiences from interviews and surveys, the concept of
leasing differs slightly from that of the non-Amish, a distinction that gets at the
heart of the topic of farm succession. Amish leasing patterns are often
intergenerational parent-child arrangements that are problematic to assess from
the survey data because the dominant method of farmland succession is through
the sale of the farm, piecemeal (e.g. temporally different times for purchasing
chattel and eventually the land), to the next generation. A practice that may be referred to as “leasing” may occur even when the farmland is being purchased.
There are incidents of true leasing, which is why this is such a difficult area to
ascertain. This problem of differentiation between Amish and non-Amish leasing and purchasing distinctions was encountered in various survey and interview formats.
4.4.4 Reconciling Survey and Census of Agriculture Data
The land tenure data collected by the survey consists of owned and
leased land arrangements as described in Chapter 8. Although the descriptive
numbers from both census and survey data are consistent in the trends they
provide, they are not an exact match because the Wayne County Auditor’s
database, the basis of the farm size calculations for non respondents, does not
discriminate between land over ten acres, which is agricultural but not farmed,
with that which is farmed. This makes a direct comparison difficult.
140 4.5 Data Analysis and Hypothesis Testing
Data analysis used interview transcriptions and survey data coded
cleaned and formatted for use in SPSS v.11.5 software package on a Windows
XP computer. SPSS is available through site license at the Ohio Agricultural
Research and Development Center. Of the 159 farm cases, 159 are usable
cases for land tenure. The parity between the case numbers and the number of
farms is an artifact of selection criteria and good data collection methods.
Hypothesis 1: Ethnicity, social relationships, and attitudes toward farming will condition contemporary land tenure arrangements.
In testing this first hypothesis, I used historical documents from the Wayne
County Library, the Wayne County Historical Society, and the Ohio Historical
Society to trace land ownership of residents in the three subwatersheds paying
particular attention to place of origin and in some cases using surname
identification as an indicator of ethnicity. Interview transcripts, surveys and oral
histories were also consulted. Using tax records in the form of plat maps, I
measured continuity of land tenure through successive generations in
combination with interviews and oral histories noting changes in ownership and
divisions of property. The first plat directory from 1820 and several others up to
the present (1826, 1856, 1873, 1897, 1922, 1939, 1950, 1979, 2000) was used
to trace continuity or change in ownership through time. The 2000 parcel
database of Wayne County was used to construct the contemporary ownership.
141 Single heir inheritance and equal heir inheritance are taken into account and
generally associated with a particular ethnicity. Circumstances in which
daughters inherit the family farm are common. Careful elicitation of familial
ownership patterns was observed during the interview and oral history portions of
the data collection to account for such transactions.
Hypothesis 2 & 3
In order to test hypotheses 2 and 3, I used survey and interview data as
outlined in their respective descriptions. Data from the survey was used for the following variables: household demographics, farm type and structure, data describing general land tenure, integration of farm and residential homeowners into local social networks, and maintenance of social support among community members.
Hypothesis 2: Ethnicity and level of socio-cultural integration of the farm household, as independent variables, will affect farm size, land use and tenure, and use and preferences for conservation in which more traditional and less socioculturally integrated groups will have smaller farms, diversified land uses, more secure land tenure and greater preferences and use of Best Management Practices.
Data collected to satisfy testing needs for Hypothesis 2 include mapping
the degree to which kin and non-kin hierarchical and egalitarian networks are correlated with land tenure and farm operation types within and between
142 subwatersheds. Surveys of the scale of farm operation, demographics of the family and plans for intergenerational or other farm transfer, actual location of each parcel rented or leased out, off and on-farm income, conservation use, and intent to use BMPs other conservation measures such as Conservation Reserve
Program. Ethnic descriptors and assumptions from Salamon’s (1992) research were initially used by dividing populations into two research categories, Yankee and yeoman, using self-identified affiliation from survey and interview data for classification. These ethnicity categories were then subdivided along Kraybill &
Hostetler’s (2001) continuum of traditionalism among Anabaptist groups in which groups are ranked by degree of conservatism to progressivism based on their adherence to traditional standards of belief and practice, and interaction with the larger society. For the purposes of testing this hypothesis, five categories of ethnicity/heritage were created and ranked along the traditionalism continuum.
They are, from least to most traditional: “English” (category 1); Brethren,
Apostolic and Mennonite (category 2); New Order Amish (category 3); Old Order
Amish (category 4); Old Order Swartzentruber Amish (category 5). As noted previously, there are no New Order Amish in the study area.
Hypothesis 3: Farm size, farm succession and inheritance, and enterprise type will correlate with land tenure and preferences for conservation where positive relationships will be found between medium-sized farms, higher levels of farm succession, and non-grain farm types with more secure land tenure and positive preferences for conservation practices and use.
143 Based on a sample of existing surveys, interviews were conducted to
ascertain more detailed land tenure information from selected participants.
Information collected included: the size of the land that is rented/leased/owned,
the spatial distribution of these parcels, in the case of rental and leased land, the
relationship between the landlord and tenant, and the current farm succession
status. Questions were structured to elicit responses that can be used for
Analysis of Variance (ANOVA) and discriminant analysis in determining the
relationships among the independent variables and, separately, the dependent
variables of land tenure and preference and use of conservation. Discriminant
analysis was used to investigate the differences among land tenure
arrangements (own only, both own and lease, lease only) using the
discriminating (independent) variables and how well they discriminate the
dependent variables. This same statistical procedure was used for conservation,
The discriminant functions created in the model follow a linear equation
similar to multiple regression:
D = (a) * (X1) + (b) * (X2) + (c) * (X3) + (d) * (X4) + … Xn/20 or (n-1)
Where a is a constant and b1 through bm are coefficients. Interpretation of the
results follows the logic of regression formulas in which the largest standardized
coefficients contribute the most in determining group membership.
4.6 Fieldwork Timetable
Survey data was collected by the author and research assistants, working
under the supervision of the author, during the 2001-2002 field seasons. Initially,
144 structured mail surveys (for the first 20) and were used, later a “drop-off and
pickup” method was used. In-depth interviews were conducted by trained
research assistants in the spring and summer of 2004. Archival research began
in the winter of 2001 and was conducted as an on-going part of the research when weather and time precluded other aspects of the project. In December
2004, archival research concluded. Analysis, interpretation and findings were conducted in winter and spring of 2005.
4.7 Limitations of Methods
Although I believe the response rate indicates that the survey results are
representative of the watershed population, the types of questions asked and the
extent of Best Management Practices addressed are limiting factors. The survey only asked for conservation adoption and preferences based on four, and five options, respectively. Additionally, conservation questions dichotomously assess behavior as a “presence” or “absence” of use. A more detailed understanding of
conservation behavior would include the extent and frequency and duration of
use. However, the results of this study provide a promising basis upon which to
conduct future research. As addressed in the preceding sections, the analysis of
farm size is based on total farm size and does not include economic data.
Furthermore, surveys assessed the conservation practices used for the entire
farm but did not assess how the use of BMPs varied by parcel according to
tenure. Because land tenure was not taken into account, it is not possible to
separate conservation measures implemented on owned from leased land.
145 4.8 Length of Study
This study occurred over a period of four years, with preliminary and primary data collection beginning in 2001, using interviews to establish the baseline of the surveys, and later collecting data by way of additional interviews.
The length of time is long in comparison to other dissertation research projects, which I attribute to a number of factors including having been employed full-time as a Research Associate of the Agroecosystems Management Program for the duration of this research. This experience as researcher and resident for the four- year period deeply connected me to the people through daily interactions both at the OARDC and in the community at farms, local businesses and community public spaces including produce and livestock auctions, fairgrounds, town plazas, business “open houses”, the in-town diner, and hardware stores.
146
CHAPTER 5
SOCIAL AND POPULATION CHARACTERISTICS OF THE SUBWATERSHEDS
5.1 Introduction
This research incorporates levels of analysis that encompass the
household, local community and state levels of contemporary American society.
As such, there are various levels of social and political organization within the
context of which the household and local community exist and organize activities.
Many of the political units will be familiar (i.e. State, County, Township) but others
deserve explanation. Although the watershed is a physiographic unit that
characterizes a drainage pattern for a specified area of land, it is managed
through human effort within a social system. Because of this distinction, in this
analysis, the watershed is treated as a social unit.
5.2 Political Unit Descriptors
The Sugar Creek Watershed is located in Northeastern Ohio just south of
the continental divide, predominantly in the counties of Wayne, Holmes, Stark
and Tuscarawas. A very small portion (569.84 acres or 0.89 mi2) is in northeastern Coshocton County, directly south of Holmes County. The county political units are subdivided, as discussed in Chapter 2, into townships, by 147 default, and municipalities in areas of higher population density. Although two of the counties in which the watershed is located are considered “metropolitan”
(Wayne and Stark) by definition (Sharp 2001) because they are in geographic proximity to the large cities of Akron and Canton. Although there are a few small urban centers (from north to south: Wooster, Orrville, Brewster, Dover and New
Philadelphia), with small populations relative to those in Akron or Canton, the area that comprises the Sugar Creek is predominantly rural in character. Table
5.1 shows the population frequencies and spatial extent of the four county regions.
Density County Population Area (mi2) (people/mi2) Holmes County 38,943 423.00 92 Stark County 378,098 576.20 656 Tuscarawas County 90,914 576.60 158 Wayne County 111,564 555.40 201 County Total 619,519 2,131.20 277 Sugar Creek Watershed 77,270 356.40 217 Source: U.S. Census of Population 2000, U.S. Census TIGER/Line Files 2000
Table 5.1 Population and Spatial Extent of the Four County Regions and the Sugar Creek Watershed
Comparing the Sugar Creek Watershed population to Holmes and
Tuscarawas counties, which have relatively low population densities relative to
Stark and other metropolitan counties like Cuyahoga and Franklin, it is clear that the Sugar Creek has a much more rural population than other neighboring
148 watersheds to the north, east and perhaps the west. The rural character of the watershed is more dramatic when you calculate population density excluding the far south and southeastern Census Blocks of the Villages of Sugar Creek and
Dover. The Townships within which the Sugar Creek flows are more rural than those of the middle of Wayne and Holmes Counties and eastern Stark and
Tuscarawas.
The northern headwaters of the watershed begin just north of the City of
Wooster and runs southeast between the cities of Wooster and Orrville, population 24,811 and 8,551, respectively. The only urban areas in this extent of the watershed are the Villages of Smithville, population 979, and Mount Eaton, population 310. The remainder of the watershed is characterized by low population densities (217 people/mi2). Land use and cover in the region lends to the rural character with agriculture as the predominant land use occupying more than 70% of available acreage. There are differences among counties as to the dominant type of agriculture that is practiced in this region that includes all varieties found in the Midwest including dairy, corn and soybeans, swine, beef, layer and broiler hens, as well as vegetable and other fruit crops. Most notably however, is that these four counties that frame the political boundaries within which the watershed is located are in the top five dairy producing counties in
Ohio (US Census of Agriculture 2002), with Wayne ranking first, and Holmes,
Tuscarawas, and Stark as the third, fourth and fifth, respectively.
The predominance of agricultural enterprise types in this area is as much a factor of geography as it is the historical development of the state as a whole,
149 including the immigrant families that first settled the lands after the indigenous
population was forcefully removed or annihilated. According to the Ohio EPA,
there is only one registered National Pollution Discharge Elimination System
(NPDES) permitted CAFO in the watershed, which is in the East Branch in the far
southeastern subwatershed. As discussed in Chapters Two and Three, the
impact of this historical development influences contemporary social structure,
land use and land tenure.
5.3 Watersheds and Hydrologic Unit Codes (HUCs)
Subwatershed Spatial Description
Explanations of hydrologic units are presented in this section because this
study uses the watershed as its spatial unit of analysis. Both the United States
Geological Survey (USGS) and the United States Environmental Protection
Agency (USEPA) have corresponding systems for delineating and labeling drainage units at larger scales but differ in detail at smaller scales. The USGS orders stream segments into watersheds that act in aggregate fashion to form
drainage basins that are incorporated into larger hydrologic systems. These ordered drainage areas are first defined by Major Basin (i.e. the Mississippi) and then by Region (i.e. Ohio). Subunits of the Region are called Hydrologic Units
(HU) and each unit has a unique identifier that is the Hydrologic Unit Code
(HUC). The HUCs are a numeric code consisting two digits representing the hydrologic region and an additional 2 to 14 digits indicating both size and hierarchical level of the drainage area to which it refers. They are composed of a
150 sequence of numbers that are combined in increasingly longer HUCs as the
scale decreases from Hydrologic Regions down to the more local subwatershed,
each part pertaining to these reference points. The progression from the largest
scale to smallest is as follows: Region, Subregion, Accounting Unit, Hydrologic
Unit, Watershed, and Subwatershed (see Figures 5.1, 5.2 and 5.3). The HUC
numbers are added in numeric sequence, from Region to Subwatershed, to form
a single number that acts as a physical address and identifier for the larger scale
14-digit HUCs as described in Figure 5.3 using the Sugar Creek Watershed
example. A two-digit Hydrologic Region refers to a drainage area at the scale of
the Ohio Region (Region 05) that drains the lands south of the Continental
Divide, east of the Mississippi and north of the Ohio River, and contains the
Sugar Creek Watershed; while an 11-digit HUC refers to a drainage area the size
of the Upper Sugar Creek Subwatershed in the Northwestern portion of the
Sugar Creek Watershed. It is this code that is commonly used when referencing
a drainage basin.
The USEPA uses a similar system that only differs from the USGS’s at the
smaller, local scale. Instead of 8-digit and 11-digit watersheds, they subdivide 11-
digit subwatersheds into major tributaries that equate to a 14-digit HUC, but use different code strings as shown in Figure 5.3.
In the case of the Sugar Creek Watershed, the Tuscarawas Watershed (8-
digit) is subdivided into several smaller watersheds including Sugar Creek. There
are three 11-digit watersheds within the Sugar Creek (Upper Sugar Creek,
Middle Fork and South Fork) that are subdivided into several subwatersheds
151 based on major tributaries. For this study, the Upper Sugar Creek, Little Sugar
Creek and North Fork subwatersheds are major tributaries of the Upper Sugar
Creek 11-digit HUC, as shown in Figure 5.2 and Figure 5.3.
Source: U.S.G.S.
Figure 5.1 United States Geological Survey (USGS) Hydrologic Unit Codes (HUCs) for the Mississippi Drainage System.
152
Major Hydrologic Basins of Ohio: 1. Muskingum Basin, outlined in black (4-digit HUC 05-04) 2. Tuscarawas Accounting Unit, shaded medium gray (8-digit HUC 05040001) 3. Upper Sugar Creek subwatershed, shaded dark gray (11-digit HUC 50404001100) Source: U.S.G.S.
Figure 5.2 United States Geological Survey (USGS) HUCs of the Muskingum Sub-Basin. Sugar Creek Watershed (HUC 0504001100) shaded dark gray.
153
Source: U.S.G.S., U.S.EPA
Figure 5.3 U.S. Environmental Protection Agency (USEPA) 14-digit HUCs of the Sugar Creek Watershed. Upper Sugar Creek (2308), North Fork (2313), and Little Sugar Creek (2310) are shaded dark gray.
154 5.4 Land Cover and Land Use
Agriculture is the dominant land cover type of the Sugar Creek Watershed followed by wooded areas and urban development. The Ohio Department of
Natural Resources (ODNR) defines agricultural land cover as consisting of “row crop” and “pasture” land uses (OEPA 2002). Wooded land cover consists of trees from woodlots and riparian zones in addition to forest and is not species specific; it also includes forested wetlands. Urban land cover includes the categories of residential, commercial and industrial land use. Barren land cover comprises land devoid of any major type of cover (i.e. wooded, agricultural, urban etc) and also includes land use activities such as strip mines and quarries.
5.4.1 Land Use Description
Land cover data is available for the entire areas of Holmes, Stark,
Tuscarawas and Wayne Counties from the 2000 revision of the 1992 USGS survey of land cover using digital image processing of Landsat Thematic Mapper
Data (Landsat TM). This process uses electromagnetic radiation emitted from the earth’s surface as an indicator of specific land cover categories based on a unique spectral signature of each. Units of data are 30x30 meter pixels. This data is limited to seven categories of land cover; whereas other processing techniques of Landsat TM yield many more that can be separated further to identify land use types. Of the four counties, only Wayne has more detailed land use data available, with over 30 land use classes, which is available from a 1994 survey.
155 Watershed and subwatershed land cover and land use data are created
by importing the Landsat TM coverages into the GIS and using a watershed layer
to delineate the boundaries. Acreage is calculated using the Area Tools script
(source: http://www.esri.com) and converted to square miles in an Excel spreadsheet. Land cover data is calculated for the Sugar Creek Watershed and each subwatershed. Because of the lack of access of the detailed land use data
for all of the Sugar Creek, only land cover is presented.
5.4.2 Sugar Creek Land Use and Land Cover
Covering greater than 356 square miles (228,062 acres), the Sugar Creek
Watershed is characteristic of this region of Ohio; its predominant land use is
agriculture with over 70% of the land use in cropland (51.33%) and pasture
(19.90%). Forested areas comprise 23.75% of the land use and residential
2.19%. Figure 5.4 shows the spatial distribution of land use/land cover classes
and Figure 5.5 provides a percentage breakdown of each, while Figure 5.6
shows a similar breakdown of the land in a 30-foot buffer of the streams.
Residential, industrial and commercial (red and orange pixels) are most
commonly clustered in urban areas and along the major transportation routes
such as the state and interstate road systems. The road networks increase
capacity and density to the north and east of the study area. Visualization of the
land use by GIS reveals settlement patterns and land use at a macro-level scale
showing most of the deforestation in proximity to urban centers, but in the level of
156 the Sugar Creek, the opposite is the case in which most of the deforestation follows farm and agricultural parcel boundaries.
157
Figure 5.4 Survey of Sugar Creek Watershed Land Cover
158 Sugar Creek Watershed Percent Land Cover
0.35% 19.90% 0.37% 0.90% 0.14% 51.33% 5.92% 2.05%
0.45% 0.58% 0.70% 22.85% 0.38%
Commercial/Industrial/Transportation Deciduous Forest Emergent Herbaceous Wetlands Evergreen Forest High Intensity Residential Low Intensity Residential Mixed Forest Open Water Pasture/Hay Row Crops Woody Wetlands Quarries/Strip Mines/Gravel Pits
Figure 5.5 Sugar Creek Percent Land Cover
159
Sugar Creek Watershed Forest Cover of a 30-Feet Buffer as a Percent of Total Land Cover
5.49% 1.55%
17.24% 33.41% All Forest Types Pasture/Hay Cropland Total Residential Total Other
42.31%
Figure 5.6 Land Cover within 30-Feet of the Stream
5.4.3 Upper Sugar Creek Subwatershed
Land cover in the Upper Sugar Creek is consistent with the watershed in general in that agriculture is the dominant land use/land cover (Figure 5.7, Figure
5.8, and Figure 5.9). However, the data shows that agriculture is noticeably greater in acreage than the watershed in general, and forested areas are fewer.
This is partly due to the classifications used because “agriculture” as a category for land cover is not limited to cropland and pasture, but may also include misclassified successional areas where small grains, on farms, and grasses, in
160 non-agricultural fields, have spectral signatures that are easily confused and
require ground-truthing in order to distinguish between them. The relative
percentage of acres classified as wooded (land cover) or in various land use
classifications is dramatically less than other subwatersheds when considering
that over 23% of the entire watershed is categorized as “forested”. From detailed
1994 land use data, farmstead numbers are also lower due to the larger farm
sizes and smaller number of farms in the Upper Sugar Creek. Because of the
greater number of row-crop farms in the subwatershed, there are fewer acres of
pasture. There is a higher degree of residential land use because of the proximity to larger urban centers of Wooster and Orrville, which each have a 3-mile zoning radius extending from the west (Wooster) and east (Orrville) into Green
Township, the heart of Upper Sugar Creek. Land use classes and nutrient loadings of the stream were correlated using redundancy analysis showing spatial relationships among the classes with temporal water quality data (Holmes
2004). Hydrologic parameters and nutrient loading in the Upper Sugar Creek has also been investigated (Prasad et al 2005c).
161
Figure 5.7 Survey of Upper Sugar Creek Land Use
162 Major categories of land cover are shown in the following pie chart of land cover in the Upper Sugar Creek subwatershed (Figure 5.8). As discussed above, pasture and hay and row crops are the dominant land covers. Forested and shrub are less than 8%.
Upper Sugar Creek Subwatershed Percent Land Cover
36.22%
0.65% 0.08% 1.51% 0.14%
7.65% 0.21% 0.06% 3.18% 0.23% 51.44% 0.14% 0.00%
Commercial/Industrial/Transportation Deciduous Forest Emergent Herbaceous Wetlands Evergreen Forest High Intensity Residential Low Intensity Residential Mixed Fores t Open Water Pasture/Hay Row Crops Woody Wetlands Quarries/Strip Mines/Gravel Pits
Figure 5.8 Upper Sugar Creek Land Use
163 Land cover along the stream (Figure 5.9) is, again, dominated by pasture
and hay and row crops.
Upper Sugar Creek Subwatershed Forest Cover of a 30-Feet Buffer as a Percent of Total Land Cover
3.70% 18.06% 4.65%
All Forest Types Pasture/Hay 29.09% Cropland Total Residential Total Other
44.51%
Figure 5.9 Land Cover within 30-Feet of the Stream
5.4.4 North Fork Subwatershed
North Fork Subwatershed land use (Figure 5.10, Figure 5.11 and Figure
5.12) varies from the whole in a few key areas, noticeably the acreage dedicated to agriculture is greater than 85% (cropland and pasture comprise 85.81%), a significantly larger proportion than the watershed as a whole. This agricultural land use differs from the Upper Sugar Creek in several important areas. The 164 reliability of this data is supported by the social organization and demographic
data that exhibit a trend towards larger families (see below) and smaller farms in
the North Fork when compared to the Upper Sugar Creek. There exist slightly more forested acres, due to a balance of row-crop and pasture because of a
higher concentration of dairy farms than the Upper Sugar Creek. Land use data
from the 1994 ODNR classification reveal that farmstead acreage is higher in the
North Fork, at 4.64% of total acreage, when compared to Upper Sugar Creek, with 2.17%, for two reasons: there are more, smaller farms each having it’s own farmstead; And, less obvious but also likely, the presence of multiple generations on Amish farms using more land for homes and gardens.
165
Figure 5.10 Survey of North Fork Land Use
166 Major categories of land cover are shown in the following pie chart of land cover in the North Fork subwatershed (Figure 5.11). As discussed above, pasture and hay and row crops are the dominant land covers. Forested and shrub areas are less than 13%.
Noth Fork Subwatershed Percent Land Cover
21.22% 0.26% 0.08% 0.55% 0.14% 2.60% 1.24% 64.59% 11.58% 0.19% 0.06% 0.09% 0.00% Commercial/Industrial/Transportation Deciduous Forest Emergent Herbaceous Wetlands Evergreen Forest High Intensity Residential Low Intensity Residential Mixed Fores t Open Water Pasture/Hay Row Crops Woody Wetlands Quarries/Strip Mines/Gravel Pits
Figure 5.11 North Fork Subwatershed Land Use
167 Land cover along the stream (Figure 5.12) is, again, dominated by pasture and hay and row crops. There is almost 6% more forest cover along streams than the Upper Sugar Creek.
North Fork Subwatershed Forest Cover of a 30-Feet Buffer as a Percent of Total Land Cover
0.99% 0.78%
8.40% 23.86%
All Forest Types Pasture/Hay Cropland Total Residential Total Other
65.98%
Figure 5.12 Land Cover within 30-Feet of the Stream
168 5.4.5 Little Sugar Creek Subwatershed
Land use in the Little Sugar Creek Subwatershed (Figure 5.13, Figure
5.14 and Figure 5.15) varies from the whole in a few key areas, noticeably the acreage dedicated to agriculture is greater than 86% (cropland and pasture comprise 86.45%), a significantly larger proportion than the watershed as a whole, and very similar to the North Fork. There exist slightly fewer forested acres when compared to the North Fork, but higher than the Upper Sugar Creek.
Most all of the urban and built-up areas of the subwatershed are in the north, along State Route 30, and a few in the south running diagonal along State Route
250; the area in between is agriculture and consist of Amish farm families.
169
Figure 5.13 Survey of Little Sugar Creek Land Cover
170 Major categories of land cover are shown in the following pie chart of land cover in the Little Sugar Creek subwatershed (Figure 5.14). As discussed above, pasture and hay and row crops are the dominant land covers. Forested and shrub areas are less than 12%.
Little Sugar Creek Subwatershed Percent Land Cover
0.21% 32.42% 0.10% 0.16% 0.18%
2.58% 1.61%
10.97% 0.10% 54.03% 0.09% 0.11% 0.00%
Commercial/Industrial/Transportation Deciduous Forest Emergent Herbaceous Wetlands Evergreen Forest High Intensity Residential Low Intensity Residential Mixed Forest Open Water Pasture/Hay Row Crops Woody Wetlands Quarries/Strip Mines/Gravel Pits
Figure 5.14 Little Sugar Creek Subwatershed Land Cover
171 Land cover along the stream (Figure 5.15) is, again, dominated by pasture
and hay and row crops. There is almost 2% more forest cover along streams
than the Upper Sugar Creek, and approximately 4% less than the North Fork.
Little Sugar Creek Subwatershed Forest Cover of a 30-Feet Buffer as a Percent of Total Land Cover
1.34%
2.19% 19.69% 15.25% All Forest Types Pasture/Hay Cropland Total Residential Total Other
61.53%
Figure 5.15 Land Cover within 30-Feet of the Stream
5.5 Population Characteristics
5.5.1 Census Data
In this section, the demographic and spatial characteristics of the Sugar
Creek Watershed as a whole are described, as well as the three subwatersheds
172 that are investigated and analyzed for this research. Since the United States
Census 2000 is the most recent population record, the data for this section comes from the U.S. Census Fact Finder website (http://www.census.gov) and the U.S. Census TIGER/Line Files and relational database Summary File 1
(SF1). These contain general population descriptors based on Census 2000
“short form” responses, and Summary File 4 (SF4), which has detailed population demographics, collected using the Census 2000 “long form”. Both the
SF1 and SF4 databases provide information from the 2000 Census at the smallest level, the Census Block.
The Sugar Creek Watershed and its population of approximately 77,270 people live in areas that form 2,454 Census Blocks (US Census 2000). Within- block populations range from 0 persons to 574, with a mean of 31 per census block. County populations relative to the Sugar Creek are presented above
(Table 5.1). Subwatershed populations and population densities are presented in
Table 5.2. The Upper Sugar Creek is the largest of the three subwatersheds and has the highest population with a density greater than the other two subwatersheds. This is due to the dense settlement patterns found in urban areas of Smithville and southwestern Orrville. The Little Sugar Creek is second largest in population, land size and density. Its density may be attributed to the multigenerational Amish farmsteads found throughout the subwatershed. The
North Fork is the least populated of the three subwatersheds due to the presence of just two small communities, Kidron and Mount Eaton and being the smallest
173 subwatershed in the study but also having a larger number of large farms than
the Little Sugar Creek.
Density County Population Area (mi2) (people/mi2) Sugar Creek Watershed 77,270 356.40 217 Upper Sugar Creek Subwatershed 5,547 28.17 197 North Fork Subwatershed 2,373 17.25 138 Little Sugar Creek 3,356 18.09 185 Totals for the Subwatersheds 11,276 63.51 173
Table 5.2 Sugar Creek and Subwatershed Population and Population Density
Census Data may not be accurate because of the smallness of scale and the high Amish population that has a history of non-participation with government
initiatives.
Age distributions in the subwatersheds show the Little Sugar Creek as
having a bimodal population of old and young residents. Dependency ratios are,
in increasing order, the North Fork (35.52 person per 100) Upper Sugar Creek
(39.38 persons per 100), and the Little Sugar Creek (41.24 persons per 100). As
discussed in subsequent sections, the dependency ratio was calculated based
on 2000 Census data using age groups greater then 65 years and less than 18
years as the dependency group.
174 Age Cohort Distribution by Subwatershed
Age 65 +
Age 50-64
Age 40-49
Age 30-39 Little Sugar Creek
Age 22-29 North Fork Age Cohort
Upper Sugar Creek Age 18-21
Age 5-17
Under 5
0.00% 5.00% 10.00% 15.00% 20.00%
Percent Under Age 5- Age 18-Age 22-Age 30-Age 40-Age 50-Age 65 5 17 21 29 39 49 64 + Little Sugar Creek 6.11% 19.55% 4.77% 7.99% 14.48% 16.48% 15.05% 15.58% North Fork 5.56% 16.94% 11.59% 11.72% 14.12% 13.57% 13.49% 13.02% Upper Sugar Creek 6.80% 19.33% 7.34% 10.28% 14.46% 14.80% 13.76% 13.25%
Source: 2000 U.S. Census TIGER/Line Files
Figure 5.16 Age Distributions by Subwatershed
175 5.5.2 Upper Sugar Creek
The Upper Sugar Creek or headwater of the watershed is in Wayne
County, Ohio, between the cities of Wooster and Orrville, with the Village of
Smithville at its center. It is predominantly farmed by residents of Apostolic and
Mennonite faith who are of German and Swiss decent with a minority of French
Catholics, Methodists and Lutherans, and no Amish. Labor is more difficult to recruit in the Upper Sugar Creek because of demographic and economic reasons
(e.g. family size, labor costs). As a result, tractors and combines are used. Grain farming is the dominant agricultural type using a two-year corn-soybean rotation, followed by dairy and other animal husbandry. The average total farm size, including leased land, is approximately 287 acres. Almost two-thirds of the farmers lease some land for production. All of the Sugar Creek farmers are part of social systems that are intensifying production and decreasing fallow cycles in response to external market pressures. The general trend in Upper Sugar Creek has been for farmers to increase their scale of operations, and as a consequence, average acreages have almost doubled in the last twenty years.
The dependency ratio14 of residents in the Upper Sugar Creek is 39.38
dependents per 100 people. Male/Female populations are proportionate to each other. The mean median age from 2000 Census Block data is 29.3, making it the highest of the three subwatersheds. Family and household numbers and sizes per census block, in Table 5.3, reflect average families of 2.17 person and
14 Dependency ratio is calculated using 2000 Population Census data in which dependents are classified as non-working people under age 18 and age 65 and older. 176 average household sizes of 1.95. There is a much higher degree of housing
ownership in the Upper Sugar Creek than the North Fork that may hint at other less obvious characteristics of the respective populations residing there.
Upper Sugar Creek (Census Blocks N=166) SUM MEAN RANGE VAR SD Population 5547 33 238 1873 43 Number of Females 2831 17 151 554 24 Number of Males 2716 16 96 409 20 Median Age ---- 29.3 82.5 405.6 20.1 Number of Families 1456 9 58 124 11 Mean Family Size ---- 2.17 6.2 2.1 1.45 Number of Households 2143 13 84 279 17 Mean Household Size ---- 1.95 6.4 1.7 1.3 Owner Occupied Housing 1568 9 80 182 13 Renter Occupied Housing 575 3 61 64 8
Table 5.3 Upper Sugar Creek Subwatershed Population Profile by Census Block
5.5.3 North Fork
The North Fork, southeast of the Upper Sugar Creek, is close to the
largest non-English speaking and “English as a Second Language” sections of
Wayne County (1990 Census) and encompasses a local center of commerce, an
agricultural wholesale auction house in the town of Kidron. In this subwatershed
is a mixture of Amish, Old Order and conservative Swartzentruber Amish, and
non-Amish farms with a mixture of grain and dairy farming. Preliminary survey
results show the average total farm size is around 228 acres; the mixture of
leasing and ownership is predicted to be less than the Upper Sugar Creek.
177 The population of the North Fork is slightly younger than the Upper Sugar
Creek based on the mean median age of Census Blocks. The dependency ratio is the lowest of the subwatersheds with 35.52 dependents per 100. However,
age cohort groupings (Figure 5.16, above) show that 54.2% of the population is
30 years of age or older, younger than the Upper Sugar Creek and Little Sugar
Creek whose populations age 30 and older are at 56.3% and 58.5%,
respectively. Although the age groups of “5 and under” and 5-17 are the lowest,
the “18-21” and “22-29” groups are much higher with 24% of the population
within those age groupings. Slightly more females than males live in the North
Fork (Table 6) and the mean family size is 2.21 while the mean household size is
1.85. Renter occupied housing outnumbers owner occupied by a ratio of 2:1.
Amish demographics from the Ohio Amish Directory (2000) show a much larger family and household size, which would be expected in the North Fork and Little
Sugar Creek; larger than that which is provided by the U.S. Census data in Table
5.4. This may be due in part to the settlement patterns where multiple Amish
families reside on the same farmstead but in different housing units. Conceivably
census takers may only attempt to enumerate the residents of one dwelling and
not others on the same farmstead. These multiple housing units on a single
parcel of land may also account for the rise in renter occupied housing when
compared to the Upper Sugar Creek.
178
North Fork (Census Blocks N=64) SUM MEAN RANGE VAR SD Population 2373 37 238 2484 50 Number of Females 1236 19 151 795 28 Number of Males 1137 18 87 497 22 Median Age ---- 28.9 63.8 379.8 19.5 Number of Families 591 9 52 142 12 Mean Family Size ---- 2.21 5 1.95 1.4 Number of Households 939 15 84 383 20 Mean Household Size ---- 1.85 4 1.35 1.16 Owner Occupied Housing 292 5 61 117 11 Renter Occupied Housing 647 10 80 211 15
Table 5.4 North Fork Subwatershed Population Profile by Census Block
5.5.4 Little Sugar Creek
The cultural configuration and social organization of the Little Sugar Creek
Subwatershed is almost all Old Order and very conservative Swartzentruber
Amish. Animal husbandry of many kinds (dairy, beef, hogs, and both layer and
broiler poultry) mixed with a rotation of corn, various small grains including spelt,
wheat and oats, hay, and pasture (some fixed, some rotationally grazed). The average farm size is approximately 96 acres there is minimal leasing. An abundance of local labor exists in the Little Sugar Creek that provides positive feedback for supporting labor-intensive small dairy operations using draft horses.
Another key difference between the Amish and non-Amish systems is that crop rotation is much more common in Amish areas that commonly rotate each parcel within the farm over 4 years using 5 different crops. As change comes to this part
179 of the Sugar Creek, there is a recent trend for Amish to invest in relatively small
farms for intensive vegetable crop production of a few acres. This may potentially be explained by farmers who want to farm but face growing land pressures. Such pressures have lead to declining numbers of Amish farmers where less than 20% of Amish work on a farm (Donnermeyer and Cooksey 2004, Ohio Amish
Directory 2000, Lowry and Noble 2000).
Age cohorts of the Little Sugar Creek are fairly uniform compared to the
other two subwatersheds. However, the dependency ratio is highest with 41.42
dependents per 100 people, and 61.6% of the population is age 30 or older,
making the population slightly older than the others are. Younger age groups are
uniform across subwatersheds except for the 18-21 age group is noticeably lower
at less than 5% of the population (North Fork is 12% and Upper Sugar Creek
7%). Despite the age group ranges, the data is skewed because the mean
median-age of 27.5 from the Census Blocks makes the Little Sugar Creek the
youngest of the three subwatersheds. Mean family and household sizes rank
lowest (Table 5.5). This is counter to what would be expected from a predominantly Amish area. One explanation for this phenomenon, as stated by
Donnermeyer and Cooksey (2004), is that the Amish have a low participation rate in the population enumeration surveys. Renter occupied housing is greater than owner occupied by a ratio of 4.3:1. Further research needs to be done to understand these population anomalies.
180
Little Sugar Creek (Census Blocks N=82) SUM MEAN RANGE VAR SD Population 3356 41 198 2575 51 Number of Females 1702 21 102 680 26 Number of Males 1654 20 96 620 25 Median Age 2258 27.5 82.5 442.7 21 Number of Families 969 12 58 225 15 Mean Family Size 159.8 1.95 4.29 2.2 1.48 Number of Households 1274 16 74 382 20 Mean Household Size 146.4 1.78 3.92 1.74 1.32 Owner Occupied Housing 1065 13 65 299 17 Renter Occupied Housing 209 3 20 24 5
Table 5.5 Little Sugar Creek Subwatershed Population Profile By Census Block
These statistics highlight the difficulty of using U.S. Census data for
demographic analysis in principally Amish areas. Is it that the Amish do not
participate or that the census employee does not approach all homes in an area?
As of this writing, I do not have answers to these questions.
5.6 Amish Church Districts
Unlike most other ethnic groups in the United States, the Amish have their
own level of social organization that is distinct from other groups. They organize
themselves by family first, then Church Districts that generally comprise around
30-40 families that worship, socialize, and for those that farm, often share in-kind
labor together, and finally as a settlement that is the aggregation of all Amish in a
defined geographic area (Hostetler 1993:91-92). The Amish refer to the area in
which their Church Districts cluster as a settlement; the Amish in the Sugar
Creek Watershed share membership in the Holmes County Settlement. Church 181 Districts are not necessarily delineated by a central location but rather is a composition of its members. However, there is a spatial continuum of location for members of Church Districts in the Holmes County Settlement that ranges from central places of land tenure to dispersed land tenure patterns (see Figure 5.18
and Figure 5.19 in the next sections). And, to the extent that most roads in the
hilly landscape of Holmes and southern Wayne County, many church district boundaries tend to follow subwatershed boundaries (OARDC Matching Grant, R.
Moore, PI, 2005), with a correlate of the roads falling within watershed
boundaries, as they often are associated with valleys and streams, being the
desire among the Amish to be within walking distance to those with whom they
are in fellowship – the members of their Church District. Following this logic, it
makes sense that the shortest traveling distance for a buggy or walking, and not
“as the crow flies”, is along the roads. This is a factor of the social organization among an agrarian people who value high levels of interaction and confer a great
deal of social order on the sense of place as witnessed by the clustered
settlement patterns that are found throughout the Holmes County Settlement.
5.6.1 North Fork Amish Church Districts
The church districts in the northern part of the settlement are more
dispersed and heterogeneous and land tenure intermixed with non-Amish is common. Southeast-central Wayne County has similar contiguous patterns of settlement as the South Fork, but many districts of Wayne County, including the
North Fork have dispersed settlement patterns.
182 There is a relationship between general occupation of Church District
members, size of holdings, and the degree of clustering or dispersal of
settlement pattern. For example, non-farm employment and smaller parcel sizes tend to be found among districts with dispersed settlement patterns (Figure 5.17), while larger parcel size and agricultural occupations are associated with clustered settlement patterns.
Figure 5.17 North Fork Dispersed Amish Church District Patterns*
183 5.6.2 Little Sugar Creek Amish Church Districts
The Little Sugar Creek farm population is almost exclusively Amish. The
growing Amish population and centralized road network make it attainable for
church district members to live within proximity of one another. Additionally, a
line-of-site, or viewshed of neighbors’ farmsteads result from the geography adds
to the increasing presence of the Amish in this particular subwatershed. (Figure
5.18).
184
Figure 5.18 Little Sugar Creek Clustered Amish Church District Patterns
185 A continuum of dispersed-to-clustered land tenure for Church Districts is found throughout the watershed and in Wayne County in particular. Figure 5.19 compares the settlement patterns between the Upper Sugar Creek and the Little
Sugar Creek in which the settlement patterns of the Old Order Amish dominant
Little Sugar Creek differs from the conventional farms in the Upper Sugar Creek.
Upper Sugar Creek has a mixture of farmers from German, English and French heritage with many residential housing clusters of mostly non-related families.
Though the Amish have a rapidly growing population, there is little urbanization or residential housing growth in the Little Sugar Creek aside from a few dispersed residential lots. Instead, extended family members often cluster on farm sites producing multigenerational housing and farmsteads (Moore et al
1999). Similar patterns are found among Amish whose main source of income is non-farm related labor or production. In these cases, residential land is subdivided creating another parcel on which a new house is built for an extended family member. Other non-Amish residential housing clusters (in purple polygons) are commonly found near farmsteads (orange circles) in the Upper
Sugar Creek, but are uncommon in the Little Sugar Creek. Exurban residents with capital and access to cheap and rapid transportation promote the transition of agricultural land to housing by purchasing homes built on “road frontage” and in “cluster developments” on subdivided farms. This results in a changing rural landscape in which new residents lack local connections and ties to their neighbors that in turn lead to misunderstanding and distrust in the community, mainly between farm and new non-farm residents. In the Upper Sugar Creek,
186 and to a much lesser extent the North Fork, new housing has accelerated in the last decade creating tension that finds its roots in farmer access to farmland,
ideas about land use and management development rights.
Residential settlement pattern in the Upper Sugar Creek are often clustered into developments consisting of individual parcel sizes ranging from 0.1 to 5 acres on former agricultural land “road frontage” and in “culdesac” or cluster developments in former fields. Little Sugar Creek patterns follow farmstead development consisting of more intensive housing arranged on one or more existing land parcels that makeup the farmstead with a few scattered “high-end” residential houses.
Image adapted from a graphic presented in an NSF Biocomplexity Planning Grant #0308464 (R.Moore, PI, 2003)
Figure 5.19 Farmstead and Residential Settlement Patterns in the Upper Sugar Creek and Little Sugar Creek Subwatersheds
187
CHAPTERS 6–8
DATA ANALYSES AND INTERPRETATION
The literature review of Chapter 3 presents a detailed review of the concepts, ideas and methods used in the conduct of this research and subsequent analyses. Although I occasionally offer citations in the analyses chapters (Chapters 6, 7 and 8), to simplify these chapters and limit redundancy of citations, all of the concepts and ideas discussed in these three chapters are found in the literature review. As such, the reader will refer to Chapter 3 to make clear any concepts, ideas and methods found in these chapters.
Land in the Midwestern United States is a commodity in the larger national capitalist system; however, it is treated differently at lower levels of sociocultural integration. At the grassroots and community level, farmland in the Sugar Creek is a source of community identity as well as one of the sources of reproduction of the agrifamily system. It was discussed in the literature review that agriculturalists, in general, are at the nexus of the environment and human social systems that, in combination, form the ecological framework of this dissertation.
The agrifamily systems form larger social networks that operate within a larger socioeconomic system and at multiple levels of interaction in which individuals have differential access to information and an expanded social network. It is
188 these expanded networks that can foster or constrain the success of a farm
enterprise. However, these external networks are not the only source of success
for a farmer. The local embeddedness of social ties, sometimes operating
exclusively towards the benefit of those who are less connected to external networks, can work against external economic pressures that would otherwise disenfranchise a farmer based on enterprise size and type (e.g. the size and type of a farm in combination with other structural factors can exclude the operation
from participation in government programs thus making them less competitive
with larger farm structures).
As family farmers continue in the new millennium, it is important that they
be viewed as active participants in directing their futures and that researchers
and policymakers understand their vision to ensure that they are not relegated as
victims and passive participants in larger global processes. Agency is important
in the discussion of land tenure. Groups of people are not actors in this research;
individuals working collectively with common interests are the actors organized
and working to make a living directly from the land.
Chapter 6 explores the qualitative and quantitative dimensions of land
tenure and offer proof for hypothesis 1: “Ethnicity, social relationships, and
attitudes toward farming will condition contemporary land tenure arrangements”.
In Chapter 7, I test hypothesis 2 in which I describe the “Ethnicity and level of
socio-cultural integration of the farm household, as independent variables, will affect farm size, land use and tenure, and use and preferences for conservation
in which more traditional and less socioculturally integrated groups will have
189 smaller farms, diversified land uses, more secure land tenure and greater preferences and use of Best Management Practices.” The last hypothesis tested, in Chapter 8, “farm size, farm succession and inheritance, and enterprise type will correlate with land tenure and preferences for conservation where positive relationships will be found between medium-sized farms, higher levels of farm succession, and non-grain farm types with more secure land tenure and positive preferences for conservation practices and use.” Although this work represents three subwatersheds, the statistical analyses sections for each of the following chapters include data for each of the four subwatersheds surveyed.
190
CHAPTER 6
ETHNICITY, SOCIAL RELATIONSHIPS, FARMING ATTITUDES, AND LAND TENURE BY SUBWATERSHED
6.1 Introduction
The goal of this chapter is to investigate relationships between land tenure variables in human social organization in the Sugar Creek using the biophysical concept of watershed as a focus in the unit of analysis. This is operationalized in the second hypothesis of this dissertation, which is that Ethnicity, social relationships, and attitudes toward farming will condition contemporary land tenure arrangements. This is tested in this chapter by examining historical documents and interview transcripts to investigate the effect that social networks and ethnicity have in regulating land tenure arrangements. Analysis included bringing together various data for several case studies of land tenure and using survey data in an Analysis of Variance (ANOVA) of land tenure between the subwatersheds.
Land tenure encompasses social aspects of land regarding use and
access to land by individuals and groups. In the Sugar Creek, heritage is not just
ethnic or religious in nature but also community and place-based. Many
participants in the various surveys and interviews describe their connection to the
191 history and locality of the Mennonite communities surrounding Smithville and
Kidron even if they are not themselves Mennonite. Length of time in the
community is one of the first descriptive characteristics mentioned as well as
their own criteria for their family’s connection to this local heritage. Amish and
members of other Anabaptist faiths, and non-Anabaptists, in the Sugar Creek
Watershed share commonalities of private property and exclusivity of formal land
ownership. However, it is hypothesized that there are differences regarding land
tenure and ethnicity with regard to informal land tenure systems. Social networks
also play roles in land tenure determining to a degree access to particular parcels
of land and, although not covered in this study, possibly the cost of that access.
Finally, attitudes regarding farming are less concretely measurable using the
data collected but come from inferences I made while using survey and interview
responses.
A continuity of local heritage exists in this area that belongs to all
residents. In fact, several Mennonite Congregations in the subwatershed share
common heritage with the Wayne-Holmes County Amish Settlement and
residents speak of ancestors moving from “down there” (referring to Holmes
County) to the Smithville area. The Oak Grove Mennonite Church was originally
the Oak Grove Amish and listed as an “Aumish (sic) Church” in Caldwell’s Atlas
(1897), as were other churches in the area. Isolation from the core settlement
192 and proximity to urban trade centers may have been a factor in their transition
towards Amish-Mennonites and eventually leaving the “Old Order”.15
Land tenure in the Sugar Creek Watershed varies in several ways. Access
to land and passing of those access rights to future generations form the basis of
land tenure. Familial relations and historical interfamily connections through
social networks extending spatially and temporally form the foundation upon
which contemporary social networks persist and provide the flexibility for them to
adjust to future conditions. Social networks provide access or information
regarding farmland and opportunities for successful intergenerational farmland
transfer in the lifecycle of a family farm household. Conversely, a poor network
may preclude a successful farm transfer. These variations include the
intergenerational passing of the family farm from parent to son and daughter-in-
law in a traditional single-heir inheritance. Non-traditional single heir inheritance
is also practiced when a parent generation passes the farm to their daughter to
be operated by the daughter and son-in-law. Other forms of succession include
variations of equal-heir inheritance in which the farm is divided by the parent
generation among multiple children. In the case of the Sugar Creek farmers,
examples of this practice include the division of the farm based on total assets (in
an equal asset division) and the division of the farm based on features of the
15 For a time, the Amish who became worldlier identified themselves as Amish-Mennonite. Later, as the distinction between they and the Amish grew, resulting from their acceptance of new technologies and modes of social organization that included increased interaction with the “English”, they dropped the name Amish and became Mennonite. Contemporary church members still remain distanced from national Mennonite affiliations, preferring their own independence from church hierarchy. 193 farm operation wherein the farm components are managed individually but the farm is operated as a single unit.
The concept of “commons” is addressed in the context of social networks
and management of drainage systems through both legal and informal
mechanisms. Tiles, ditches and streams in this watershed have all been
incorporated into successively complex networks of social organization to
implement their up-keep and management. These networks convey rights and
responsibilities of use regarding common use of water-conveyance resources.
The organization of the chapter is as follows. The first section outlines the
framework for analysis and discusses connections among land tenure, social
relationships and ethnicity in the Sugar Creek. The second section is an analysis
of variance (ANOVA) between subwatersheds using survey data. The results of
the statistically significant mean differences are presented. The third section
includes several case studies of varying detail16 that are presented as examples
of spatial and temporal interconnections among households in each
subwatershed and offer support for the ANOVA findings. Finally, a discussion
and conclusions drawn from this research component are presented.
6.2 Analytical Perspective
Farming and family in the Midwestern United States are interconnected
and tied to the land in a liturgical order of household, intergenerational land
transfers and open market sales. Land is a necessity in farming. Access to land
16 Data for the case studies comes from a pluralistic research methodology. The critical information is presented for each. However, as a result of survey, interview and historical document techniques being used, as presented in Chapter 4, cases have varying depth and additional detail. 194 allows for the reproduction of the social unit, the agrifamily system, and the
spatial and temporal continuity of ethnic communities built on aggregates of
these smaller systems (Salamon 1992:96). Ethnicity is a factor in social networks
because it creates reliable expectations of reciprocal behavior especially when
members of an ethnic community interact among themselves more so than with
people of a different ethnicity. This is contingent on a number of factors that
include the population density of people of the same ethnic background that
provides a critical mass of members to form ethnic enclaves, real and perceived
differences in social norms, values and behaviors of people of the in-group
versus out-group and socially constructed restrictions specifying the degree of permitted interaction. Enclaves are contingent on access to land and as such, land tenure plays a critical role in the development of ethnic communities and the spatial attachments they create (Salamon 1992). Out-group disposition towards an ethnic minority vis-à-vis restricted interaction levels is another factor of in- group cohesiveness. Economic factors, such as land availability and use, are an extension of social integration that is conducive to interaction 17 . Chapter 3
(Literature Review) and Chapter 5 (Demographics) provide the details of local
ethnicity and heritage.
Although I quantitatively address intergenerational farm succession in
subsequent chapters, social networks and land tenure cannot be discussed in
isolation from farm succession. Although these three are conceptually separate
17 Access to farmland is unlikely if there is little land available due to intense competition. As a consequence, the sale of individual labor for income becomes prevalent, and available employment requires interaction with out-group people. 195 variables in my research, they are interconnected in practice and kinship is
regularly ignored in most of the network literature. Intergenerational farm
succession is not an event per se or a performance on an occasion. It is a
process that begins in the youth of a potential successor and ends in the final,
often at middle age, transfer of the farm and responsibility from the farmer to the
successor (Salamon et al 1986). By whatever means this is accomplished 18 ,
though the passing of time is continuous, the moment of farm succession may be
punctuated by a transfer of a deed or a sales receipt; in this sense, there is an
occasion that recognizes a single moment of ritualized transfer in the liturgical
order. Even so, this process began years ago during the rearing of the successor
through positive images and interactive experiences with the farm and family, the
establishment of a viable farm enterprise, and the general agricultural
environment within which the successor will operate (Potter and Lobley 1996) all
of which shape attitudes and patterns of farming behavior (Salamon et al 1986).
An environment favorable to succession implies that the successor will be operating in a social environment that provides access to land and other resources, is supportive of farming, and affords adequate support of psychological and physical health. Such a social environment can provide early influences on the personal development of potential heirs inculcating them with the knowledge and integrity that will help them to be willing to work hard and foster a spirit of innovativeness and problem solving (Salamon et al 1986). The
18 In the Sugar Creek, this is generally accomplished through single-heir inheritance practiced among many Amish (Long 2003, Moore et al 1999), or equal-heir inheritance common among larger scale “Yankee” farmers (Salamon 1992). 196 viability of the farm enterprise depends on many factors that include the personal characteristics of the farm family and the chosen successor as well as a sound business strategy and future vision for the farm. A good match between the successor’s personal qualities, vision and business sense with the scale and type of farm production, available labor and technology are necessary for a successful transfer. Of course, good luck is often offered as one of the most important factors in this success given the ability of external market forces, weather and diseases to influence a farmer’s operational success (Potter and Lobley 1996).
6.3 Land Tenure, Social Relationships and Ethnicity
In the following sections, correlation analysis results and case studies of farm family land tenure arrangements are presented. The correlations analysis introduces the major variables involved in the statistical procedures (ANOVA) and explores the direction and intensity of the basic relationships between each variable. A representative set of farm families and their farms is described to offer their accounts of historical and contemporary land tenure in the watershed and to highlight some of the adjustment processes of farm succession and sale that occur over time. In these ways, many families act to maintain the farm and their sense of place.
Generally speaking, farmland tenure in the Sugar Creek cycles through subdivisions and aggregations as families grow and decline in farming; families have demographic peaks and valleys as membership fluctuates during their
197 cycles that are shown in their accumulation of land for farming.19 It is possible to
observe these expansions and contractions of tenure in the land ownership
records. While the nuclear and extended families fluctuate in size and
membership is dynamic through time, the household remains static in the sense
that its connection to the farm is constant – change occurs in the individuals who
are economically connected to the household as a unit of production. However, it
is difficult to ascertain the true nature of what is transpiring in the transfer of land
by looking at the record alone; interviews with surviving relatives and
investigation of other historical documents are the only way accurately to
describe the circumstances of land transfer. Increased urban expansion and
growing exurban development has placed new constraints on the reproduction of
the agrifamily system.
Salamon’s method (1992:159-164) of accounting for interfamily and
intrafamily land transfers by taking account of surname changes assigned to
parcel ownership in historical land records is effective in delineating change in
rural Illinois20. However, I found that in using a method in which historical records
are used to trace incidents of farm succession the results are ambiguous
because intra-family transfers may be misconstrued as inter-family if the transfer
is to a married daughter and spouse thus keeping the farm within the same
19 This is not expressed in any form of linearity or inevitability of stages of growth, climax and decline. 20 Salamon (1992) found single male heir inheritance almost exclusively practiced among farmers of German descent, thus making the patrilineal intergenerational transfer easily traceable through historical records. 198 household. 21 This patrilineal-centric view of land transfer is not practiced exclusively in the area of the Sugar Creek. The English concept of primogeniture
is rarely followed among farmers I encountered in my research, nor was the
German equal heir inheritance strictly followed. Among Amish farmers, single-
heir inheritance is practiced without proscription of an heir’s sex. Instead, they identify a suitable heir through a number of strategies that depends on farm structure and family cycle allowing flexibility and a degree of pragmatism. In the
former Congress Lands of Ohio, intergenerational land transfer has become
bilateral among many families, including Amish and Mennonite. This is because
of a number of factors associated with family cycle and the timing of farm
succession that includes, among other factors, the number of children, age of
parents at first birth, age of parent and children at the time of retirement, and the
viability of the farm.
Strategies for intergenerational farm transfer derived from interviews begin
with identification of a suitable heir and subsequent training. In this sense, the
heir is either a relative, spouse of a relative, or a potential non-family buyer.
Training is often formal through master-apprentice style work in which the heir
works alongside the farmer to learn the trade; this may be used in combination
with other formal training at a technical college. Or, if a farmer has already
proven able to farm, there is a “trial period” during which time the farmer is
conditionally permitted to operate the farm as the owner(s) observe the heir’s
behavior and select for what they deem to be appropriate practices.
21 Only in the last half of the 19th Century has it become socially acceptable in urban areas for married women to maintain their maiden name. This practice is rarely followed in rural areas. 199 Since the heir may be endogenous or exogenous to the household, and various conceptions of household are found22, the process of farm transfer is
dynamic and has several approaches that may be taken in combination or used
exclusively. Those encountered in this research include:
1. The sale of the farmland from a superordinate to a subordinate generation
involving a loan from a banking institution (dominant practice among
Upper Sugar Creek Apostolic and Mennonite and some North Fork
Mennonite farmers);
2. Gradual purchase of the farm from the superordinate by the subordinate
generation that is sometimes referred to as leasing because of the length
of time associated with the purchase but does not involve a bank loan
(common among Amish farm families);
3. Leasing farmland from the superordinate by a subordinate generation
farmer of another family in a long-term arrangement wherein the
superordinate generation is able to observe and finally approve or deny
the sale of the farm;
4. Creating a partnership in which the farm is incorporated, for liability
reasons, and shares of the farm are individually owned by each family
member; Limited Liability Corporations (LLC) are becoming a common
form of partnership without full incorporation.
5. Entering the farmland into a trust during which time the family does not
have a member interested in farming but the farm is retained by them and
22 For example, a son-in-law may be considered part of the household among some farm families, and seen as an outsider among others. 200 leased to another farmer with the hope that a family member of the next
generation will assume operation of the farm.
These are not exclusive approaches to farm succession, but they are the
dominant types encountered in the watershed.
One final note on the analysis and data presentation is the focus of the successional relationship. Kinship and households are often conflated or used interchangeably, however, it is important to distinguish between then. Kinship is a matter of relatedness through biological ties. In the Midwestern nuclear family, kinship networks involve affine and consanguine relations. The household, on the other hand, is the economic unit of individuals whose work or labor contribute to group maintenance and stability, this is Bennett’s agrifamily system (1982). While
families are dynamic and members move in and out of the domestic unit, the
household is less dynamic and focused on a location and economic relationships
among members living on the farm. Figure 6.1 depicts the relationship between
household and family in relation to the farm through time, with punctuated period
of intergenerational succession.
201
Figure 6.1 Static Nature of Farm Household and Dynamic Nature of Associated Families Over Time
Realizing that households function based on the composition of various kin and non-kin members, each of whom contribute to the total production of the farm, in this research, the emphasis of succession is the dyadic relationship between superordinate and subordinate generations of male farmers. The reasons for this are threefold. The first is the dichotomy of gender roles present among Midwestern farm families in which the male is prescribed the role of working “in the field” and the female is often prescribed the role of working “in the home” (Salamon 1992). Although these two ideal roles are often not seen as clearly in practice, (e.g. many families have contributions from males and females within and without the home) as in theory, they are present in many farm communities. Secondly, in order to construct coherent narratives for each case, I found it necessary to explain tenure relations from a single perspective for
202 simplicity of presentation due to the large number of cases, and individual
household members presented. Being a male observer, I chose the male perspective.
6.3.1 General Correlations
Results of SPSS analysis for associations (Spearman’s ρ) and correlations (Kendall’s τb and Pearson’s r) for variables aggregated at the
watershed level are displayed in the Correlations Matrix in Table 6.123. They confirm that several of the variables used in testing the hypothesis of this chapter are related at statistically significant levels that vary in strength and direction24.
The following relationships between farm size, farm type, land tenure,
conservation adoption and heritage are established through correlation analysis.
Degree of traditionalism is not included in the Correlations Matrix because no
statistically significant measures of association or correlation exist with any of the
other measures.
23 Indexes and other variables are detailed in Chapter 7. 24 Adjectives describing strength (i.e. “very strong”, “substantial” etc) of associations and correlations are based on Davis (1971) and Bartz (1999), respectively. 203
Off- Farm Farm Conservat- Conservat Lease-Out Farm Size Type Tenure ion Use -ion Index All Land Income Farm Size Cor. 1.000 sig. N 159 Farm Type -.060 Cor. (ρ) 1.000 sig. .519 N 117 117 Land Tenure .155** -.268** Cor. (τb) (ρ) 1.000 sig. .010 .004 N 159 117 159 Conservation .292** -.063 .099 Use Cor. (r) (ρ) (τb) 1.000 sig. .000 .498 .126 N 159 117 159 159 Conservation .277** .359** .597** Index Cor. (r) .126 (ρ) (τb) (r) 1.000 sig. .000 .175 .000 .000 N 159 117 159 159 159 Lease-Out -.417** .244** -.900** -.242** -.507** All Land Cor. (ρ) (ρ) (ρ) (ρ) (ρ) 1.000 sig. .000 .008 .000 .002 .000 N 159 117 159 159 159 159 Off-Farm .212** .288** -.215** -.166* .134 .305** Income Cor. (τb) (ρ) (τb) (τb) (τb) (ρ) 1.00 sig. .000 .003 .002 .015 .063 .000 N 159 106 139 139 139 139 159 Farm .237** -.283** .471** .079 .262** -.678** -.159* Succession Cor. (τb) (ρ) (τb) (τb) (τb) (ρ) (τb) Index sig. .000 .003 .000 .281 .001 .000 .043 N 139 109 139 139 139 139 123 ** correlation is significant at the .01 level (2-tailed) * correlation is significant at the .05 level (2-tailed)
Table 6.1 Chapter 6 Correlations Matrix
204 Multiple correlations and associations are found among the variables that
demonstrate the nuanced real-life interconnectedness of the land tenure
variables with conservation behavior. As predicted, both the Conservation Use
and Conservation Index scores correlate with several of the land tenure
variables. Conservation Use correlates with farm size (r=.292**), and is
consistent with the literature stating larger farms have greater flexibility to take
land out of production for conservation, and has a low negative association with
Lease-out all land (ρ =-.242**) showing that farms that are leased out tend to not
have BMPs implemented on them. It also has a low negative correlation with off-
farm income. The Conservation Index is positively correlated with farm size
(r=.277**), a moderate positive correlation with Land Tenure (τb =.359), and a
substantial negative association with Lease-Out All Land (ρ=-.507**). And, the
Conservation Index has a low positive correlation with Farm Succession Index
(τb =.262**). Both conservation measures show no associations with Farm Type.
Farm Type has a low negative association with land tenure (ρ=-.268**), a low negative association with Lease-Out All Land (ρ=.244**), and a low negative association with Farm Succession Index (ρ=-.283**).
Other notable relationships include the following: Farm Size and Land
Tenure have a low correlation (τb =.155**) because, as Hart (1991) has shown,
larger farms correlate with higher levels of land leasing, which helps explain the
moderate positive association between farm size and lease-out (ρ=-.417**) in which farm size decreases with land that is leased-out. This leads to the
205 relationship between Land tenure and farm type that have a low negative association (ρ =-.268**); farmers of smaller farms generally own more of their
land and an important difference in this watershed is the inclusion of Amish farmers who tend to own more of the land they farm and have smaller farms.
Farm Succession Index is correlated or associated with all variables with
the exception of Conservation Use. The moderate positive correlation between
Farm Succession Index and Land Tenure (τb =.471**) and the substantial
negative association with Leased-out All Land (ρ=-.687**) indicate relationships between leasing and farm succession. There are no correlations between these variables and ethnicity as measured by degree of traditionalism.
These associations and correlations indicate broad connections between
variables as discrete units of analysis, but they do not provide analysis of
variable combinations contributing to variance or as aspects of a dimension of
the variable.
6.3.2 ANOVA of Ethnicity and Land Tenure Variables by Subwatersheds An analysis of variance among the four Wayne County Sugar Creek
subwatersheds reveals that there are few distinct differences shared among all subwatersheds in which one is dominant for a variable when compared to the other three. However, there are significant differences between pair comparisons of subwatersheds. In the following section, ANOVA data is presented highlighting those variables that express significant differences between the watershed groups.
206 In testing for statistically significant differences, two models were run in which α =.05, in the first, and α=.01, in the second. The statistical hypothesis
(Ho) of equal population means (no difference between them) for the variables of the four Sugar Creek subwatersheds is rejected in the instances in which there are significant differences in the means. The population sizes of the subwatersheds are not equal in size due to the differences in geographic size of the areas they cover as well as the total number of farms operating within these natural boundaries. The sample population breakdown by subwatersheds is as follows: Upper Sugar Creek N=52, North Fork N=25, Mainstem N=43 and Little
Sugar Creek N=39. In total, there are 159 cases in the analysis. Yet, this number may vary based on the completeness of the data provided by the participants and as such, some of the variables have lower populations than others. Table 6.2 describes these statistics for each variable in the analysis.
207
Water- Std. Std. 95% Confidence shed* N Mean Deviation Error Interval for Mean Min Max Lower Upper Bound Bound Tenure- 1 USC 52 176.13 182.510 25.310 125.32 226.95 0 800 Acres 2 NF 27 115.19 77.556 14.926 84.51 145.87 0 300 Owned 3 MS 43 79.58 93.095 14.197 50.93 108.23 6 440 4 LSC 37 70.22 48.134 7.913 54.17 86.26 0 200 Total 159 115.03 128.898 10.222 94.84 135.22 0 800 Heritage 1 USC 52 1.56 .502 .070 1.42 1.70 1 2 Index 2 NF 27 2.37 1.245 .240 1.88 2.86 1 5 3 MS 43 1.77 .684 .104 1.56 1.98 1 5 4 LSC 37 2.86 1.357 .223 2.41 3.32 1 5 Total 159 2.06 1.075 .085 1.89 2.22 1 5 Education 1 USC 52 2.79 1.109 .154 2.48 3.10 2 5 Level 2 NF 26 2.12 1.107 .217 1.67 2.56 1 5 3 MS 43 2.60 1.116 .170 2.26 2.95 0 5 4 LSC 35 1.94 1.349 .228 1.48 2.41 1 5 Total 156 2.44 1.208 .097 2.24 2.63 0 5 Implemented 1 USC 52 .63 .486 .067 .50 .77 0 1 No-Till 2 NF 27 .33 .480 .092 .14 .52 0 1 3 MS 43 .28 .454 .069 .14 .42 0 1 4 LSC 37 .35 .484 .080 .19 .51 0 1 Total 159 .42 .495 .039 .34 .50 0 1 Installed 1 USC 52 .56 .502 .070 .42 .70 0 1 Grass 2 NF 27 .93 .267 .051 .82 1.03 0 1 Waterways 3 MS 43 .84 .374 .057 .72 .95 0 1 4 LSC 37 .65 .484 .080 .49 .81 0 1 Total 159 .72 .452 .036 .65 .79 0 1 Trust EPA 1 USC 48 1.33 1.260 .182 .97 1.70 0 5 2 NF 23 2.35 1.112 .232 1.87 2.83 1 5 3 MS 42 2.17 1.057 .163 1.84 2.50 0 5 4 LSC 35 2.14 .944 .160 1.82 2.47 1 4 Total 148 1.92 1.175 .097 1.73 2.11 0 5 Farm Size 1 USC 52 286.69 350.716 48.636 189.05 384.33 11 2000 2 NF 27 227.59 228.224 43.922 137.31 317.88 32 800 3 MS 43 156.42 320.435 48.866 57.80 255.03 8 1340 4 LSC 37 96.43 80.470 13.229 69.60 123.26 12 400 Total 159 197.15 287.521 22.802 152.12 242.19 8 2000 * USC = Upper Sugar Creek, NF = North Fork, MS = Mainstem, LSC = Little Sugar Creek
Table 6.2 Descriptive Statistics for Subwatershed ANOVA Variables
208
Sum of Squares df Mean Square F Sig. Tenure - Acres Between Groups 322497.032 3 107499.011 7.236 .000 Owned Within Groups 2302606.867 155 14855.528 Total 2625103.899 158 Heritage Index Between Groups 43.369 3 14.456 16.106 .000 Within Groups 139.122 155 .898 Total 182.491 158 Education Between Groups 18.867 3 6.289 4.607 .004 Within Groups 207.492 152 1.365 Total 226.359 155 Implemented Between Groups 3.626 3 1.209 5.331 .002 No-Till Within Groups 35.141 155 .227 Total 38.767 158 Installed Grass Between Groups 3.292 3 1.097 5.872 .001 Waterways Within Groups 28.972 155 .187 Total 32.264 158 Trust EPA Between Groups 25.024 3 8.341 6.748 .000 Within Groups 178.003 144 1.236 Total 203.027 147 Farm Size Between Groups 888617.236 3 296205.745 3.772 .012 Within Groups 12172945.142 155 78535.130 Total 13061562.377 158
Table 6.3 ANOVA of Statistically Significant Variables
209 6.3.3 Mean Difference Between Groups
(I) (J) Dependent Subwat Subwater Mean 95% Confidence Variable ershed shed Difference (I-J) Std. Error Sig. Interval Lower Upper Bound Bound Tenure - 1 USC 2 NF 60.95 28.912 .155 -14.14 136.04 Acres 3 MS 96.55(**) 25.123 .001 31.31 161.80 Owned 4 LSC 105.92(**) 26.214 .000 37.84 174.00 2 NF 1 USC -60.95 28.912 .155 -136.04 14.14 3 MS 35.60 29.928 .634 -42.12 113.33 4 LSC 44.97 30.850 .466 -35.15 125.09 3 MS 1 USC -96.55(**) 25.123 .001 -161.80 -31.31 2 NF -35.60 29.928 .634 -113.33 42.12 4 LSC 9.37 27.331 .986 -61.62 80.35 4 LSC 1 USC -105.92(**) 26.214 .000 -174.00 -37.84 2 NF -44.97 30.850 .466 -125.09 35.15 3 MS -9.37 27.331 .986 -80.35 61.62 Heritage 1 USC 2 NF -.81(**) .225 .002 -1.40 -.23 Index 3 MS -.21 .195 .706 -.72 .30 4 LSC -1.31(**) .204 .000 -1.84 -.78 2 NF 1 USC .81(**) .225 .002 .23 1.40 3 MS .60 .233 .051 .00 1.21 4 LSC -.49 .240 .170 -1.12 .13 3 MS 1 USC .21 .195 .706 -.30 .72 2 NF -.60 .233 .051 -1.21 .00 4 LSC -1.10(**) .212 .000 -1.65 -.55 4 LSC 1 USC 1.31(**) .204 .000 .78 1.84 2 NF .49 .240 .170 -.13 1.12 3 MS 1.10(**) .212 .000 .55 1.65 Education 1 USC 2 NF .67 .281 .082 -.06 1.40 Level 3 MS .18 .241 .871 -.44 .81 4 LSC .85(**) .255 .006 .18 1.51 2 NF 1 USC -.67 .281 .082 -1.40 .06 3 MS -.49 .290 .335 -1.24 .26 4 LSC .17 .302 .941 -.61 .96 3 MS 1 USC -.18 .241 .871 -.81 .44 2 NF .49 .290 .335 -.26 1.24 4 LSC .66 .266 .066 -.03 1.35 4 LSC 1 USC -.85(**) .255 .006 -1.51 -.18 2 NF -.17 .302 .941 -.96 .61 3 MS -.66 .266 .066 -1.35 .03 Implement 1 USC 2 NF .30(*) .113 .042 .01 .59 No-Till 3 MS .36(**) .098 .002 .10 .61 4 LSC .28(*) .102 .032 .02 .55 2 NF 1 USC -.30(*) .113 .042 -.59 -.01 3 MS .05 .117 .967 -.25 .36 4 LSC -.02 .121 .999 -.33 .29
Table 6.4 ANOVA Subwatersheds Tukey HSD Multiple Comparisons
Continued 210 Table 6.4 (continued)
3 MS 1 USC -.36(**) .098 .002 -.61 -.10 2 NF -.05 .117 .967 -.36 .25 4 LSC -.07 .107 .906 -.35 .21 4 LSC 1 USC -.28(*) .102 .032 -.55 -.02 2 NF .02 .121 .999 -.29 .33 3 MS .07 .107 .906 -.21 .35 Installed 1 USC 2 NF -.37(**) .103 .002 -.63 -.10 Grass 3 MS -.28(*) .089 .011 -.51 -.05 Waterway 4 LSC -.09 .093 .762 -.33 .15 2 NF 1 USC .37(**) .103 .002 .10 .63 3 MS .09 .106 .837 -.19 .36 4 LSC .28 .109 .059 -.01 .56 3 MS 1 USC .28(*) .089 .011 .05 .51 2 NF -.09 .106 .837 -.36 .19 4 LSC .19 .097 .214 -.06 .44 4 LSC 1 USC .09 .093 .762 -.15 .33 2 NF -.28 .109 .059 -.56 .01 3 MS -.19 .097 .214 -.44 .06 Trust EPA 1 USC 2 NF -1.01(**) .282 .002 -1.75 -.28 3 MS -.83(**) .235 .003 -1.44 -.22 4 LSC -.81(**) .247 .007 -1.45 -.17 2 NF 1 USC 1.01(**) .282 .002 .28 1.75 3 MS .18 .288 .923 -.57 .93 4 LSC .20 .298 .902 -.57 .98 3 MS 1 USC .83(**) .235 .003 .22 1.44 2 NF -.18 .288 .923 -.93 .57 4 LSC .02 .254 1.000 -.64 .69 4 LSC 1 USC .81(**) .247 .007 .17 1.45 2 NF -.20 .298 .902 -.98 .57 3 MS -.02 .254 1.000 -.69 .64 Farm Size 1 USC 2 NF 59.10 66.476 .811 -113.55 231.74 3 MS 130.27 57.764 .113 -19.75 280.29 4 LSC 190.26(*) 60.273 .010 33.72 346.80 2 NF 1 USC -59.10 66.476 .811 -231.74 113.55 3 MS 71.17 68.812 .730 -107.54 249.89 4 LSC 131.16 70.932 .255 -53.06 315.38 3 MS 1 USC -130.27 57.764 .113 -280.29 19.75 2 NF -71.17 68.812 .730 -249.89 107.54 4 LSC 59.99 62.841 .775 -103.22 223.19 4 LSC 1 USC -190.26(*) 60.273 .010 -346.80 -33.72 2 NF -131.16 70.932 .255 -315.38 53.06 3 MS -59.99 62.841 .775 -223.19 103.22 * The mean difference is significant at the .05 level. ** The mean difference is significant at the .01 level.
211 The total number of acres of farmland that is reported as owned (Figure
6.2) varies by subwatershed. Two sets of statistically significant relationships between the Upper Sugar Creek and the Mainstem (96.55**) and the Little Sugar
Creek (105.92**) are reported.
200
180 176 160
140
120 115 100
80 80 70 60
40 Mean of Tenure - Acres Owned Tenure of -Mean Acres Upper Sugar Creek North Fork Mains tem Little Sugar Creek
Subwatershed#
Figure 6.2 Tenure - Acres Owned
212 Heritage mean differences (Figure 6.3) are significant between several paired subwatersheds, including more traditionalism in the North Fork (.81**) compared to the Upper Sugar Creek, the Little Sugar Creek and the Upper Sugar
Creek (1.31**) and the Mainstem (1.10**). This quantitative fact is readily apparent in the subwatershed descriptive statistics as well as driving through the area and observing the farmsteads.
3.0
2.8 2.9
2.6
2.4 2.4 2.2
2.0
1.8 1.8 1.6 1.6 1.4 Mean Index ofHeritage Upper Sugar Creek North Fork Mains tem Little Sugar Creek
Subwatershed#
Figure 6.3 Heritage Index
213 Figure 6.3 is the inverse proportion of Figure 6.4 and is readily apparent from the survey data. Because of the large proportion of Amish in the Little Sugar
Creek and lack of them in the Upper Sugar Creek, the mean difference between them is (.85**).
3.0
2.8 2.8
2.6 2.6
2.4
2.2
2.1 2.0
1.9 1.8 Mean of Education Level Education of Mean Upper Sugar Creek North Fork Mains tem Little Sugar Creek
Subwatershed#
Figure 6.4 Education Level
214 No-till implementation (Figure 6.5) is greatest in the Upper Sugar Creek, the mean differences between it and the other subwatersheds are statistically significant.
.7
.6 .6
.5
.4
.4 .3 .3
.3
.2 Mean of Implemented No-Till Implemented of Mean Upper Sugar Creek North Fork Mains tem Little Sugar Creek
Subwatershed#
Figure 6.5 Implemented No-till
215 Grass waterway installation (Figure 6.6) is lowest in the Upper Sugar
Creek, with the largest amount installed in the North Fork. Mean differences between the Upper Sugar Creek and the North Fork (.37**) and the Mainstem
(.28*) are both statistically significant.
1.0
.9 .9
.8 .8
.7
.6 .6
.6 .5 Mean of Installed Grass Waterways Grass Installed of Mean Upper Sugar Creek North Fork Mains tem Little Sugar Creek
Subwatershed#
Figure 6.6 Installed Grass Waterways
216 The Upper Sugar Creek also exhibits the lowest amount of trust for the
Environmental Protection Agency (Figure 6.7), while the North Fork reports the greatest trust. Farmers in the North Fork (1.01**), the Mainstem (.83**), and the
Little Sugar Creek (.81**) report statistically significant larger trust scores than the Upper Sugar Creek.
2.6
2.4 2.3 2.2 2.2 2.1 2.0
1.8
1.6
1.4
1.3 1.2 Mean of Trust EPA Trust of Mean Upper Sugar Creek North Fork Mains tem Little Sugar Creek
Subwatershed#
Figure 6.7 Trust EPA
217 The Upper Sugar Creek farmers report the largest farm sizes (Figure 6.8) while the Little Sugar Creek farmers report the smallest. The mean difference between the Upper Sugar Creek and the Little Sugar Creek is statistically significant (190.26*).
300
287
228 200
156
100 96
0 Mean of Farm of Mean Size Upper Sugar Creek North Fork Mains tem Little Sugar Creek
Subwatershed#
Figure 6.8 Farm Size
218 6.4 Upper Sugar Creek
6.4.1 Social Networks
Social dimensions of the Upper Sugar Creek subwatershed offer a
complex interaction of factors (including cultural, economic and geographic) that
create a network of interrelated and interacting families. The history of this area
of Wayne County includes settlement of German and Swiss immigrants of
various Christian denominations that are of predominantly Anabaptists affiliations
of Apostolic, Brethren or Mennonite identity, with a few members of other
denominations and ethnicities (i.e. French Catholics, German Lutherans and
various other denominations of German and British Protestants). There is a
geographic as well as cultural divide in the watershed among these religious
groups.
Sense of community is derived from the social networks in which people are integrated, or embedded. These networks are found in all forms of social action including work, recreation, residence, religious experience, birth and death
(being tied to some degree to religion and worldview, but also action).
The Apostolic community is mainly in the northern part of the
subwatershed and correspondingly is part of what some Mennonites refer to
“Apostolic Country”, which is the area of land spanning the north central and
northeastern part of Wayne County in which the majority of Apostolic Christians
in this area live. They are affiliated with one of two churches: Smithville Apostolic
Church and Rittman Apostolic Church. Community solidarity is intense as seen in
219 remarks from non-Apostolic farmers who anecdotally state that they would not
attempt to purchase farmland, or “go head-to head with an Apostolic” because of
the ability of Apostolic members to use social networks for support and
consequently “outbid the outsiders” at a farm sale or auction.
This spatial dimension culturally separates Apostolic, Mennonite and
Brethren farmers. This is not a physical separation, since there are churches of
Brethren or Mennonite affiliation above this geographic line. This is a perceived division that exists among the people in the area. Below are Brethren and
Mennonite and above are Apostolic Christians. See Figure 6.9 for church locations and proximities to local towns.
220 "± B$ K' x| (LQ'"± "¸Ox|% "± Churches Creston "± K' Rittman "Mx| D& Apostolic x| "± Baptist "MC% "¸ D& (L Brethren (L"± "² Christian Scientist x} "± (F 'K Church of God 'E Conservative Mennonite D& "±x| (FL "M "¶ Episcopal (L 'Q Evangelical "¸ Evangelical Lutheran Synod Smithville x} (F J& Grace Brethren ' K "Mx|'(L"¸ "M Orrville P& Holdeman Mennonite (F x|"¸ "±K (F (F J& "M I% Jehovah's Witnesses x|x} (R Jewish (L"M"M x} "± C%(F x}"¸"M "M "º Lutheran "± I% (L (L B$ K' (F Mennonite "º J& (F x}x} x| Methodist "´ (R"²(F (F Dalton (F C% O% Methodist Episcopal C%B$ $ Mormon x%x|(F"¶±¸x}y$"± (L x| y "M "±K' Wooster "¸ "M (F"± "±C% "´ Nazarene 221 "±x} "M Non Denominational E' $H Pentecostal C% Presbyterian x} H$"M Sonnenberg H$ Mennonite x} Reformed Church y$ (F(F Q' x|x} (F $B Roman Catholic P& % Unitarian Universalist Apple Creek x "G Wesleyan O% "M Kidron (F O% Subwatersheds x| Little Sugar Creek North Fork "M x}x} Upper Sugar Creek x|C%"M Mount Eaton Mainstem C%x} E' Roads (F Places N Townships 202468Miles W E
S
Figure 6.9 Church Locations and Proximities to Local Towns 221 As seen in this setting, spatial dimensions of social organization based on physical location or locus of “sense of community” can focus on an area as small as a neighborhood or village or as large as a city or region. In this case, the focus is on village and city: for most Apostolic Christians in the Upper Sugar Creek, community is created and maintained in Creston and Rittman is one center and for the Mennonite and Brethren, Smithville is the other.
The Brethren and Mennonite, unlike most Apostolic Christians in the
subwatershed, share a common sense of community around the Village of
Smithville that is located in west-central Green Township. This is principally
because of their churches and residences being in proximity to, or within the
Village. Consequently, their children attend the same schools that are located in
Smithville. Community members shop at the same locally owned grocery and
hardware store25 and farmers purchase materials and deliver their products to
Tyler’s or the Town and Country grain elevator26. Residents of the northern part
of the Upper Sugar Creek are not in proximity to Smithville and generally live outside Green Township. Because many townships tend to correspond to school districts, many of them identify with the villages of Creston and Sterling, and the
city of Rittman that are located north of the watershed and in the townships of
Canaan and Milton, respectively, because this is where their children are
educated and they possibly were, too.
25 Rather than shopping locally, some residents drive the distance to the new Wal Mart west of Smithville, north of Wooster. 26 For many of the farmers in this area, the logo of “their” grain elevator, in this case Tylers, is often found on ball caps and shirt pockets, which are worn with a sense or pride. 222 As part of the yearly farming cycle, farm communities from around the
area come together before planting in early spring to socialize, investigate new
machinery and farming techniques, and share plans for the coming growing
season. This is performed at “open houses” that are hosted by local farm equipment businesses, or “dealers”. Open houses are not just about serving economic interests of the hosts. People generally do not make plans to purchase
new equipment based on attending one open house, but rather focus their attention on socializing with others. Unsurprisingly, there is a little “shop talk”
regarding the newest “New Holland bailer” among farmers and with the
employees, but mostly the machinery acts as a backdrop, the scenery and
setting for these activities.
In 2004 and 2005, I visited three open houses each year in different
locations in relation to the Sugar Creek, one to the north, one to the west and
one in the southern part of the Upper Sugar Creek. At each of them, I saw
familiar faces of farmers in the communities in which I worked; some went to all
the open houses, others just to the ones nearby.
These occasions, as part of the liturgical order in this farm community, are
informal markers of the start of a new growing season, detached from the
formality of the calendrical year27 and centers on themes of farm successes and
new beginnings. Opportunities for improvement after the end of the fall harvest
and long winter are created by offering a time for socialization centering on the
family and their place in the community. The scale of farming in this part of the
27 In which the new year is formalized on January 1 of each successive year. 223 county and the balance of labor and machinery make it difficult for these kinds of
interactions throughout the year because of time and labor demands28, but in the
spring, there is time and place for this to occur. A casual observer will see the interaction of farmers, known and unknown to the observer, updating one
another regarding their farm and family, neighboring farms and families, market
prices and plans for the next year. Many “deals” are brokered at these meetings
including information exchanged about farmland availability for purchase or rent,
or land that is being lost to development.
New Social Networks: Sugar Creek Partners
The community around Smithville, Ohio, was not involved in water quality
issues until one farmer in the community contacted the researchers with the
Agroecosystems Management Program (AMP) to discuss methods of solving the
impairment using community-based solutions. Prior to this, Ohio EPA hosted a
“town hall” style meeting to discuss their TMDL approach and gain input from
community stakeholders. However, this open forum was, as described by one of
the three farmers who attended, an expert-driven exchange of ideas in which
Ohio EPA simply told residents what they planned, essentially allowing no input
from those attending. Subsequently, a participatory approach was decided upon
by the researchers and farmer that would begin efforts to remediate the water
quality without direct collaboration with Ohio EPA. The farmer then contacted
28 Pressing time and labor demands has resulted from successive generations expanding operation size and replacing human labor with machinery. This replacement of human labor makes the tasks on the farm easier for one person to accomplish, but at the expense of extending the work one person can accomplish. Marshall Sahlins, in Stone Age Economics discusses his concept of the “original affluent society” (1972) in which he references John Stuart Mill’s belief that mechanization does not reduce labor for agriculture; it just makes more labor possible for each person. 224 three neighbors who in turn contacted up to three others. This self-selected
group met for the first time in the fall of 2000 with the purpose of discussing the
water quality in the Sugar Creek and deciding on a plan of action for their
community.
The decision to work as a group to solve the water quality problem came
easily but with skepticism of those who levied the charges against the farming
community, namely the Ohio Environmental Protection Agency (OEPA). Initially,
the farm group had several skeptical members who questioned the validity of
OEPA’s data because they were not consulted in the collection of the data and felt that the sites were too few and the collection process not long enough (OEPA collected from four sites in the Upper Sugar Creek, once per month for five months). With assistance and support from the OARDC, they decided to conduct their own testing, but at a much finer scale than the coarse four sample points of
OEPA. As they took ownership of their subwatershed, the farmers selected twenty-one sites in the Upper Sugar Creek with a focus on the land near their farms and homes. This decision was based on local values of private property and of not interfering in the business of others – the farmers did not want to be in the position of making decisions for others, especially those outside of their subwatershed. The subwatershed as a concept began to take on meaning for these participants symbolizing connectedness and local decision making as they envisioned alternatives for their common resource (i.e. hunting ground, community forested buffer, joint wetland, etc). As the local farming community coalesced around the issue of water quality, a key decision was made based on
225 firm beliefs embedded in a land ethic and the values of rural Mennonite and
Apostolic communities that even if the OEPA was wrong the team would
continue to meet with the purpose of proposing and discussing solutions to the
impaired state of the watershed. Since the formation of this first subwatershed
group, three other groups, each in a separate subwatershed, in the Sugar Creek
have started to work together.
6.4.2 Strategies for Intergenerational Farm Succession
Farming by Western Europeans in the Sugar Creek began in the early 19th
Century with the first pioneers and later settlers. Migration, wars and mechanization would change the composition of rural communities over the course of the next century. Much of the farming community experienced changes in family composition resulting from the U.S. Civil War of the 1860s (Hart 1991).
Post-Civil War Ohio brought new forms of mechanization and chemical inputs to farm communities as replacements for the lost labor of those who perished in the war.
Spatial distribution of the farm households described in this section is shown in Figure 6.10. Farm size differences among the subwatersheds are evident by the parcel sizes (multiple parcels comprising one farm are dissolved to form one parcel). Parcels of same farm ownership are similarly colored. In order to maintain a level of privacy for the families involved in this part of the analysis, family and individual names of the households presented in this section have been coded in the following format. Each household has been assigned a
226 number formatted as “HH#” (i.e. household three = HH3). Each member of the household has been assigned a number that corresponds to the household of membership, the generation they represent in this study (i.e. G1, G2), and their sex with a corresponding number for each (i.e. M1 = male one, F3 = female three) that represents the number of the person, not necessarily birth order. For example, the fourth person of the second generation of four males in household twelve is coded: 12-G2-M4. For simplicity, spouses marrying into a household assume that households number while their family retains their own. Related households are designated by the same household number with the addition of a letter identifying them as a different but related household (i.e. HH6 and HH6a are related through a common ancestor).
227
*Household ownership of parcels may extend beyond the watershed boundary.
Figure 6.10 Case Study Farm Household Participants*
228 Patrilocal Residence and Patrilineal Intergenerational Farm Transfers
Perhaps the most common manner in which farms are transferred, or at
least the most commonly evoked image of farm transfer, occurs between a father and son. This agnatic relationship works to maintain the farm as a part of familiar heritage to be passed to future generations. Despite the images of fathers passing the family farm to their sons, this is an idealized portrayal of farm succession because farm families operating in the last 100 years have done so in an environment of rapid industrial change affecting community demographics and ways of life that pushed families to develop adjustment strategies during these periods of transition.
In recent history, World War II and the death of 1a-G2-M2 the patriarch of
t6he largest land owning household created opportunities for change in the farm
tenure of the Upper Sugar Creek. Several families “got their start” from land sold
by 1a-G2-M2’s sons (1a-G3-M3 and 1a-G3-M4) in the later half of the 1940s as
they transitioned from apprentices to operators. The following three cases
demonstrate some of the subtle details of this patrilineal farm transfer.
Succession Case 1. HH1a Potato Farms: Two generations transitioning to agri-
tourism without a third-generation heir.
1a-G4-M5, son of 1a-G3-M3, is one of two brothers that are the original
owners of HH1a Potato Farms. He says that his paternal great-grandfather, 1a-
G1-M1, purchased the farm in 1908. 1a-G4-M5 is the third generation operating
229 the farm. This land originally included what is now 1b-G2-M2’s (of HH1b Farms) farm for which 1a-G1-M1 built the original barn. This farm also included the HH6a
Farm of 6a-G2-M2 and 6a-G2-F2 and 6a-G3-M3 and 6a-G3-F3. 1a-G1-M1 came from Switzerland and purchased his first farm that is now owned by the HH6a.
The family farm was originally a 150-head dairy that bottled their milk, on-farm, and marketed it under the HH1a name. In 1938, a fire destroyed the barn and killed all the animals. At that point, 1a-G1-M1, 1a-G4-M5’s grandfather, decided to start a potato farm rather than rebuild the dairy operation and by 1943, they were farming potatoes and had expanded to 2000 acres, one of the largest farms in the area. He owned land spanning the city of Wooster to Boys Village and over to what is today HH22’s Potato Farm.
In the late 30s and early 40s, potato chip manufacturers asked Wayne
County farmers to grow potatoes for their operations. Gradually potato sales shifted from table stock to chipping potatoes. Currently, HH1a potatoes are sold to potato chip producers in North Carolina, Ohio, Pennsylvania, and West
Virginia. 1a-G2-M2 died suddenly from a cardiac arrest in 1948 while 1a-G3-M3 was in College and 1a-G3-M4 still in high school. The farm was too large for the sons to handle but they wanted to continue farming. To accomplish this, they sold half the farm to several other new farmers in the area, scaling back to 1000 acres by 1950. They divided the farm in 1952-53 when 1a-G3-M4 was out of high school and old enough to operate on his own. 1a-G4-M5 began to take over the farm in 1996, and his cousin 1a-G4-M6 took over for 1a-G3-M3.
230 Due to the immensity of the original HH1a farm (1a-G2-M2’s ~2000 acres), the land tenure is better presented in a horizontal table that shows the tenure connections among them and other contemporary households.
Acre Twp Sec Qtr 1820 1826 1856 1873 1897 1939 1950 1979 2000 Rng 204 T17 7 SW Jacob none Kieffer JS Kieffer A Weimer 1b-G1-M1 1b-G1-M1 1b Farms 1b Farms R12 Kiefer (121), Jonas (121), M & & (150.5), Burkholder Smoker (82) 1b-G1-F1 1b-G1-F1 Wayne Cty (82) Commissioner s (50) 83 T17 7 SE Jacob Michael Baker C Stauffer M 1c-G2-M1 1c-G2-M1 1c-G2-F1 1c-G2-F1 R12 Kiefer Kiefer Lichtenwalter & & (61.7),
231 1c-G2-F1 1c-G2-F1 1c-G3-M2 & 1c-G3-F2 (20) 103 T17 7 NW David David A Weimer A Weimer A Weimer 1b-G1-M1 1b-G1-M1 Lester W & Lester W & R12 Antles Antles (125) (123) & & Olga Ruth Olga Ruth 1b-G1-F1 1b-G1-F1 Geiser Geiser (124) (124) 165 T17 16 SW none none D Smoker George 1d-G1-M1 1d-G2-F1 I1d-G2-F1 1d-G2-F1 4-G3-M8 & R12 Smiley 4-G3-F4 (145), Ben Gerig (20) 60 T17 17 NW John Peter R Robert PS 1a-G1-M1 1b-G1-M1 HH1b HH1b Farms R12 Schrock Schrock Hutchinson Hutchison Greenermyre & Farms (110) (72) 1b-G1-F1
Figure 6.11 HH1a Tenure History
Continued 231 Figure 6.11 (continued)
Acre Twp Sec Qtr 1820 1826 1856 1873 1897 1939 1950 1979 2000 Rng 118 T17 18 NW John Wade Adam Ch Christopher C Brenner 1a-G2-M2 6a-G1-M1 6a-G2-M2 6a-G2-M2 & R12 Kiefer Brenner Brenner & 6a-G2-F2 6a-G2-F2 74 T17 18 NE Abraham Abraham JM Samuel 1a-G1-M1 1b-G1-M1 1b-G1-M1 & HH1b HH1b Farms R12 Flickinger Flickinger Flickinger Longenecker & 1b-G1-F1 Farms (124) 1b-G1-F1 72 T17 18 SW Wm Ewing Thomas Eberly Peter Eberly S Eberly (73), 1a-G2-M2 1a-G3-M3 1a-G3-M4 1a-G3-M4 & R12 Boydston Stutzman (40) Trustee 1a-G4-M6 66 T17 19 NW John Wade John Wade J Grenier Peter Eberly S Eberly (45), 1a-G2-M2 1a-G3-M3 1a-G3-M3 1a-G4-M5 R12 S Staman (23) & Trustee 1b-G1-F1 75 T17 19 NE George John S Breziner Samuel S Mowery (62), Olive I 1a-G3-M3 1a-G3-M3 1a-G3-M3
232 R12 Boydston Hartzler Brenizer S Staman (13) Slater, Trustee Trustee 55 T17 19 SW David David J Duffy John Funk J Funk Dan I 1a-G3-M3 1a-G3-M3 1a-G3-M3 R12 McConahay McConahay McClelland Trustee 60 T17 19 SW David David W John Funk J Funk 1a-G2-M2 1a-G3-M3 1a-G3-M3 1a-G3-M3 R12 McConahay McConahay Livingston Trustee 159 T17 20 SW Jacob Jacob D Weiler Wm Weiler's John W Miller 1a-G2-M2 1a-G3-M3 1a-G3-M3 1a-G3-M3 R12 Brackbiel Brakefield Heirs (150) & Trustee 1b-G1-F1 202 T17 22 NE Joseph Daniel JK Yoder John D Yoder 1d-G1-M1 1a-G2-M2 Abner & Abner & HH22 Farms R12 Stibbs Conrad (142), Ben (126), S Yoder Grace Troyer Grace Troyer (73.7), Willis & Gerig (60) (12), J Yoder (108.80) / John (105) / John Phyllis Troyer (4), D Schrock B & Cora B & Cora (62.8), Peterson (60) Leichty (145) Leichty Holding, Ltd (100) (25)
Continued
232
Figure 6.11 (continued)
Acre Twp Sec Qtr 1820 1826 1856 1873 1897 1939 1950 1979 2000 Rng 68 T17 30 NW Joseph David J Bricker John Funk J Funk (66) 1a-G2-M2 23-G1 (94.77) 1a-G3-M3 23-G2 R12 Stibbs McConahay 93 T17 30 NE George George S Yoder Samuel J SJ Yoder (137) 1a-G2-M2 1a-G3-M3 1a-G3-M3 1a-G3-M3 R12 Boydston Boydston Yoder Trustee 214 T16 13 NE Edward Edward C Brenner Elias Schrock JC Longdorf 1a-G2-M2 6a-G1-M1 6a-G2-M2 6a-G2-M2 R13 Gallagher Gallagher (84), C Brenner (40), E Schrock & & (58), Henry (84), SL Brenner Shellenberger (58), HD Wills 6a-G2-F2 6a-G2-F2 (40), Samuel (20) Baker (20) 41 T16 13 SE Edward Edward M J Scher (25), J Scher (25), SL 1a-G2-M2 1a-G3-M3 1a-G3-M4 1a-G3-M4 R13 Gallagher Gallagher Gallagher Brenner (15) Trustee & 1a-G4-M6 233 314 T16 24 S Reasin Beall Reasin Beall P Eberly Peter Eberly J Fetter (80), CJ 1a-G2-M2 1a-G3-M3 1a-G3-M4 1a-G3-M4 R13 Miller (80), LB Trustee & Eberly (76), A Brenner (77) 1a-G4-M6
233 Currently, 1a-G4-M5 does not have plans for intergenerational farm
succession; all five of his children (two stepchildren and three biological) are
starting careers in other occupation: four in art and one in business. 1a-G4-M5 is
not concerned about the future though because he thinks that his two daughters
may find husbands who are interested in farming; adding that he did not always
want to farm, “You know, when I went to college, [farming] was the last thing I
was going to do was come back here to farm”. He received a business degree
from Bluffton College and had an office job after graduation – he knew then that
sitting behind as desk was not for him and returned to the farm “’cause I grew up doing this all the time. I enjoy the outside too much; Being my own boss and doing what I want to do.” The HH1a have collaborated with the HH23s in raising and marketing their potatoes for a number of years.
1a-G4-M5’s cousin 1a-G4-M6 and uncle 1a-G3-M4 have similar
succession situation and are no longer exclusively farming commodity potatoes.
Their farm consists of potatoes, wheat, corn, soybeans, and pumpkins. 29 Of
these, they predominantly emphasize fall pumpkin harvests and corn mazes as
they continue transitioning to agro-tourism. Fall Festivals (each Saturday) and a
Potato Festival, u-pick pumpkins, and hayrides are their main source of farm
income. As a result of this new strategy, their neighboring potato farm partners,
the HH23s, had to seek other farms to grow for them, which include 13-G1-M1’s
farm.
29 The Ramseyer’s have an agro-tourism web site: http://www.ramseyerfarms.com/potatoes.htm 234
1a-G4-M5’s family is affiliated with the Oak Grove Mennonite Church
where he serves as a board member and sings in the Church choir. He does not
have time for other local activities because of his work with several other state
and national farm organizations that include: the Ohio Potato Growers
Association Board, the National Potato Board and is currently the president of the
Ohio Vegetable and Potato Growers Association. He says he’s been approached by several local organizations including the Lions and Ruritans but “I’ve turned down all those guys ‘cause there’s only so much you can do”. He wouldn’t mind working with the residents along his stream, but feels “they have a whole different opinion about things. They would be the last [group of people] to work with.”
Succession Case 2. HH2: Patrilineal farm succession.
The HH2 Farm is a family farm passed to 2-G3-M3 and 2-G3-F3 from 2-
G3-M3’s parents, 2-G2-M2 and 2-G2-F2. There are two historical farms that make up most of the contemporary HH2 Farm. This family has practiced a single- heir succession pattern that includes integration of local social networks for farm expansion. The home farm (southeast quarter, Section 20 Green Twp.) has been in 2-G3-M3’s family for three generations, and prior to that it was owned by
Millers who received it from a Boyneston; However, Michael Thomas was the
original owner (ca. 1820). One of the oldest homes in this part of Wayne County,
235 dating to the 1830s and built by J. Miller, the first owner, is located there. The
HH2 Farm was later sold to Jacob Miller (approx. 1873) who eventually sold it to
2-G3-M3’s grandfather in the 1920s. The 168-acre home farm was later purchased by 2-G3-M3 and 2-G3-F3 in 1984. 2-G3-M3 is a distant paternal cousin to 7-G5-F4, through 7-G2-M2 & 7-G2-F1, making the kinship connection through his cousin, 7b-G5-M3. The newest addition to the farm is known as the
HH24 Farm (southwest quarter, Section 21 Green Twp). It was originally owned by C. Spink and Mary Burgan, and later J.D. Yoder. It then sold to Samuel
Longenecker (before 1973). The HH24 farm was sold to the HH2 in 1997 when
the decided to expand their enterprise.
Farm 1 HH2 Farm Plat Acres Farm 2 HH24 Farm Plat Acres Date Date Michael Thomas 1820 160 C. Spink & Burgan 1820 160 Ben Boynston 1826 160 Mary Burgan 1826 160 J. Miller 1856 --- J. D. Yoder 1856 --- Jacob Miller 1873 163 Samuel Longenecker 1873 167 J. Miller 1897§ 83.5 Samuel Longenecker 1897 172 A.M. Miller 80 2-G1-M1 & 2-G1-F1 1939 163.4 Bernadine Daly 1939 139.5 2-G2-M2 & 2-G2-F2 1950 163.4 24-G1-M1 & 24-G1-F1 1950 127.1 2-G3-M4 & 2-G3-M3 1979 163.4 24-G2-M2 & 24-G2-F2 1979 182 2-G3-M3 & 2-G3-F3 2000 163.4 2-G3-M3 & 2-G3-F3 2000 177.8 § indicates subdivision
Figure 6.12 HH2 Tenure History
Patrilineal Intergenerational and Extended Family Cooperative Farm Expansion
Since the 1980s farm crisis and the shrinking opportunities for land
acquisition in the Wayne/Holmes county area resulting from population and
development pressures, an increasing strategy of farm expansion and labor 236 allocation is the extension of kinship networks to mediate the issues of single-
and equal-heir inheritance. Several instances of equal-heir inheritance in the
Upper Sugar Creek result in the division of farm labor, rather than the farm, in which components of the farm are divided among the heirs, and not the arbitrary division of the entire farm that remains intact. That is to say, as an example, two sons will take over the farm enterprise from their father and operate the enterprise as a single unit with a logical division of labor and capital, assigning ownership and/or responsibility of farm components to each (i.e. one son take charge of the field crops, the other son has charge over the dairy herd).
Farm expansion on the exurban fringe of the Akron/Canton and Massillon
metropolitan area is difficult because of increasing land prices resulting from
development pressures. In order to adjust an enterprise to this new environment,
family members in the Sugar Creek extend their farm enterprise to include
extended family members by incorporating their farm as a corporation. In this
way, they share ownership, and risk, among the participating (and often retired)
family members. In doing so, family members can pool their various resources
(money, land, capital equipment and buildings, and labor etc) for the common
good of the farm. In this fashion, farmers are able to negotiate the various scale
limitations in contemporary agriculture on the urban fringe.
237 Succession Case 3: The HH3 Family: Successional division of parts while
maintaining the whole
This is a case of patrilocal residence in which the farm has been
successfully transferred from 3-G1-M1 and 3-G1-F1 to his two sons 3-G2-M2
and 3-G2-M3 and their spouses, 3-G2-F2 and 3-G2-F3, in 1997. However, unlike
other examples where strategies are used to successfully transfer the farm and
minimize conflict, the latter has not been accomplished.
Several historical farms form the HH3 Farm. The land was originally
brought into the HH3 family when 3-G1-M1 and 3-G1-F1 purchased the farm
30 from 6b-G2-F2’s parents in 1960s. The land that comprises this farm comes
from several farms knit together from land originally purchased by George
Boynston, and sold to Pete Yoder. It later went to J.D. Yoder (later owned by
John Freeman, 1873, and D. Culler, 1897) and C. Hoover (later owned by I.
Hoover, 1897), S. Lanz (later owned by George Hestand, 1873) and J. Myers
(later owned by S. Bowser, 1873, and S. Bauser heirs, 1897), each in Section 29
of Green Township. The other farm is the “HH28 Farm” that has passed through
a few generations since 1900 and prior to purchase by the HH3s.
The HH3s operate a 350 acre dairy farm. Richard wanted the sons to be
active in the Sugar Creek Partners but from the first team meeting, they wanted
nothing to do with the team. In the last year (2005) the brothers have started to
be more interested in what the Sugar Creek Partners are doing as they find that
30 6b-G2-F2 is the mother of 6a-G3-M3, dairy farmer who married into the HH6a agrifamily. 238 the only agenda of the group is to create a better environment and a more optimistic future for their families and farms.
Farm 1 HH3 Home Farm Plat Acres Farm 2 HH28 Farm Plat Acres Date Date George Boynston 1820 160 Thomas Taylor 1820 160 Pete Yoder 1826 160 Sam Lane 1826 160 J. D. Yoder 1856§ --- S. Lanz 1856§ --- C. Hoover J. Myers John Freeman 1873 66 George Hestand 1873 149 Cyrus Hoover 144 S. Bowser 16 D. Culler 1897 66 G. Hestand 1897§ 100 I. Hoover 143 H. Gerber 45 S. Bauser 20 Elmer C Buckwalter 1939 58.8 HH28 et al 1939 127.8 A. Hoover, 6b-G1-M1 1939§ 44, 100 D. & M. Brubaker 37.5 Elmer C. Buckwalter 1950 58.8 HH28 et al 1950€ 164.3 Elmer H. & Verba Buchwalter, 100 6b-G1-M 44 3-G1-M1 and 3-G1-F1 1979€ 200 HH28 et al 1979 157.9 HH3 Farms, Inc. 2000 200 HH3 Farms, Inc. 2000 158 § indicates subdivision € indicates consolidation
Figure 6.13 HH3 Tenure History
The HH3s operate a 350 acre dairy farm. 3-G1-M1 wanted the sons to be active in the Sugar Creek Partners but from the first team meeting, they wanted nothing to do with the team. In the last year (2005) the brothers have started to be more interested in what the Sugar Creek Partners are doing as they find that the only agenda of the group is to create a better environment and a more optimistic future for their families and farms.
One step the brothers have taken to remedy their animal to land ratio is to pool resources through the farm and purchase more land for manure spreading.
In 2002, they purchased the 25-G1-M1 and 25-G1-F1 Farm north of Smithville.
239 Succession Case 4. The HH4s: Patrilineal succession and extended family corporate ownership.
The HH4 family hog farms are difficult to describe and briefly characterize because of the intricacy of tenure arrangements that operate in maintaining the
success of this farm. The original HH4 farm is located in southeastern Green
Township (southwester quarter, Section 26 Green Twp.), but the entire enterprise
is comprised of roughly 1200 acres spread throughout Green and East Union
Townships. The original acreage will be the focus of this presentation.
Originally owned by David Hartzler (1820, 1826), this land was later
transferred to Charles Walter whose sons, Cyrus and Samuel, received the land
by 1973. It was later transferred to M.O. Brennen and C. Wilderson. By 1939, the
HH4 family owned this land as it was farmed by 4-G1-M1 and 4-G1-M2. While 4-
G1-M1 maintained most of his land, he transferred part to 4-G2-M3 and 4-G2-F1.
4-G2-F2 received the land of 4-G1-M2. As the farm expanded prior to the 1970s,
4-G2-M4 and 4-G2-F2 consolidated this land into one farm, and were joined by
4-G2-M5 and 4-G2-F3. 4-G2-M6 and 4-G3-M8 and 4-G3-F4 began farming in the
1980s and expanded the operation further to include land was formerly Shrock and HH1d family farmland.
240
Owner Plat Date Acres David Hartzler 1820 360 David Hartzler 1826 360 Cha. Walter 1856 --- Cyrus Walter 1873§¥ 160 Samuel Walter 100 P. Leichty 162.5 M.O. Brennen 1897 175 C. Wilderson 82 4-G1-M1 1939 112.2 4-G1-M2 112.2 4-G1-M1 1950 112.2 4-G1-M2 156 4-G1-M1 1979¥ 64.3 4-G2-M3 & 4-G2-F1 47.9 4-G2-F2 156 4-G2-M4 & 4-G2-F2 2000€¥ 158 + various other parcels § indicates subdivision € indicates consolidation ¥ indicates expansion
Figure 6.14 HH4 Tenure History
4-G3-M8, the son of 4-G2-M5 and 4-G2-F3, is the successor of the
Williams hog farms, which is a patchwork of dispersed farms that are owned by several extended family members in Green and East Union Townships. Through kin networks, the HH4s (including 4-G2-M4 (4-G3-M8’s Uncle), 4-G2-M5 (4-G3-
M8’s father), 4-G2-M6 and 4-G2-M7 maintain and grow their hog enterprise in the face of a depressed hog market as well as increasing land pressures. Unlike most other farms in the area, the farms are operated as a unit but managed separately, with different components of the operation on different family member farms. In this manner, they mediate the need for expansion and access to land.
241 Succession Case 5. HH5: Incorporated family dairy processing and marketing of products from independently held family farms.
Originally owned by George Boynston and Nicolas Smith, then sold to
Christian Lance, the HH5 farm was settled by 26-G1-M1 of the HH26 Lumber
Company family (before 1856), the HH26 farm (northeast and northwest quarter,
Section 28 Green) stayed in that family as it passed to a daughter who was married to 26-G2-M2. Two generations later, it was purchased by 27-G1-M1 prior to 1939, who is unrelated to the HH26 Lumber Company family.
According to 5-G2-M5, there are five families of the same HH5 surname in the Wayne County area. He refers to his family as being the “interloper” family because they were the last to move to the area – his paternal grandfather moved from West Liberty to what is now his son’s, 5-G1-M1, farm (northeast quarter,
Section 36 Milton). Since 5-G2-M5’s older brothers are operating the family farm, he purchased his farm from 26-G4-M4 and 26-G4-F4 in 1994 and now manages it as a forty-head uncertified organic dairy farm.
5-G2-M5 is the sixth of eight children, two females and six males, of 5-G1-
M1 and 5-G1-F1. The entire HH5 family work together in a family dairy business that includes their spouses and everyone has a part in the operation. Four of 5-
G2-M7’s brothers (5-G2-M2, 5-G2-M3, 5-G2-M4, and 5-G2-M7) operate their own dairy farms of varying sizes (5-G2-M2 operates the family farm of 5-G1-M1 and 5-G1-F1) and 5-G2-M6, 5-G2-F3 and 5-G2-F4 operate the diary and store.
5-G2-M5’s parents and siblings pool their resources to produce milk, operate and
242 manage a processing and bottling facility, and run a storefront dairy where customers can buy their products in various forms that include ice cream and
chocolate milk.
Owner Plat Date Acres George Boynston & Nicolas Smith 1820 160 Christian Lance 1826 160 26-G1-M1 1856 --- 26-G2-M2 orphanage 1873 122 26-G2-M2 1897 122 26-G3-M3 1939 126 26-G3-M3 1950 126 26-G4-M4 & 26-G4-F4 1979 123.7 5-G2-M5& 5-G2-F2 2000§ 122.8 § indicates subdivision
Figure 6.15 HH5 Tenure History
5-G2-M5 is a leading member of a local community watershed
organization, the Sugar Creek Partners, and a catalyst for community
involvement and change. He and 5-G2-F2 are active in their community as
members of the Crown Hill Mennonite Church. 5-G2-M5 and 5-G2-F2 feel the future of their farm is secure, but are not certain who will continue the operation once they retire. 5-G2-M5 is a past President of the Innovative Farmers of Ohio, a grassroots farm organization that works to promote family farms and alternative agriculture.
Uxorilocal Intergenerational Farm Transfers
Variations in tenure arrangements based on the nature of relationship that exists between the leaser and the lessee are found in the Sugar Creek. The 243 details of these relationships can affect the lease rate as well as prescribed or
proscribed management practices that are beyond the scope of a survey. In this
section are two examples of farm succession strategies that have adapted to
changing demographics and employment opportunities resulting in a farm
transfer in which a daughter and son-in-law rather than a “traditional” agnatic
transfer to a son occurs.
One example comes from ongoing interviews and observation of a father-
in-law and son-in-law farm partnership in which the farm operation is divided into two components31. Both men have different management styles and different
conceptions of conservation and stewardship that reflect in the practices
implemented in their respective operations on the farm. Intergenerational conflict
resulting from differences in views and management styles is minimized in this
way because the father-in-law maintains control over the dairy while the son-in-
law assumes control of the dry-heifer component. The younger generation is
allowed to implement change in that operation without conflict.
The other case is of father-in-law and son-in-law who both work towards a
common goal of balancing environmental impact and not compromising
productivity. The son-in-law has somewhat limited freedom to make changes in
operations and alter management strategies without conferring with his father-in-
law. In this way, the senior generation maintains a level of control over the entire
operation. However, conflict is minimized because both generations have
common goals for the farm.
31 At the time of survey and interview, both generations operated their respective components of the dairy farm. Since then, the son-in-law and daughter have assumed both roles from her retired parents. 244 Succession Case 6. HH6a: Bilateral inheritance and uxorilocal residence with
affinal succession.
6a-G3-M3 and 6a-G3-F3 operate the HH6a Farm that is a combination of two historically separate farms: the HH6a Farm and the Geiser Farm. The Geiser
Farm (northeast quarter section, Section 12 Wayne Twp.) was first owned by
Henry Geeting and later settled by S. Kieffer before 1856 and later by Solomon
Keifer prior to 1873. After the death of Solomon Keifer, the property was divided five ways among four descendants and assumed non-kin: C.J, E.B., S.P. and
H.M. Kieffer with 33 acres being sold to E. Hutchinson prior to 1897. By 1922, the remaining family land was consolidated by H. Kiefer and S.P. Kiefer into two parcels. The Hutchinson land was retitled to E.E. Shisler. By 2000, the Kiefer farm was owned by Ruth Geiser, the sole heir of the farm before it was sold to
6a-G3-M3 and 6a-G3-F3 who leased the land prior to purchase.
The HH6a Farm (northeast quarter section, Section 13 Wayne Twp. and northwest quarter section, Section 18 Green Twp.) was originally owned by
Edward Galagher and then settled by Charles Brenner prior to 1856. The land was subdivided prior to 1873 into three parcels, the largest equaled fifty-percent of the total and was owned by Christopher Brenner, the remaining half was equally divided between Elias Shrock and Samuel Baker. By 1897, Elias Shrock had purchased half of the Baker and Brenner farms, with the remaining Brenner land in Section 13 of Wayne Township being owned by S. L. Brenner. By 1922,
245 ownership of all the parcels changed hands as the Brenner parcel was purchased by 1a-G2-M2 and the Shrock parcels by James Miller.
The Miller Farm was purchased by Heidi’s maternal grandfather from 1a-
G3-M3 and 1a-G3-M4 after the death of their father 1a-G2-M2, after World War
II. To this day, 1a-G3-M3 and his family stop in once a year during their family
reunion to ask if they “can view the old farmstead?” As a result of Kathy’s father
being killed in Europe during WWII, and her stepfather was not interested in
farming, the land was leased-out until 6a-G2-M2 married 6a-G2-F2 and began to
farm. At that point, 6a-G1-M1 then sold the farm to 6a-G2-M2 and 6a-G2-F2.
Farm 1: Geiser Farm Plat Acres Farm 2: HH6a Farm (two parcels) Plat Acres Date Date Henry Geeting 1820 160 Edward Galagher 1820 160 Henry Geeting 1826 160 Edward Galagher 1826¥ 360 S. Kieffer 1856 --- Christopher Brenner 1856§ --- Solomon Keifer 1873 163 Christopher Brenner (Green) 1873 118 (Wayne) 58 E. Hutchinson* 1897§ 33 C. Brenner (Green) 1897§ 118 C.J. Kiefer 33 S.L. Brenner (Wayne) 58 E.B. Kiefer 33 S.P. Kiefer 33 H.M. Kiefer 33 H. Kieffer 1922 33 1a-G2-M2 (Wayne) 1922 58 S.P. Kieffer 98.5 Amos & Leah 1939 131.44 1a-G3-M3 (Green) 1939¥ 118 Steiner (Wayne) 213.95 Allen Steiner etal. 1950 131.44 6a-G1-M1 (Green) 1950§ 118 6a-G1-M1 (Wayne) 173.56 Lester W. Geiser 1979 131.4 6a-G2-M2 & 6a-G2-F2 (Green) 1979 118 6a-G2-M2 & 6a-G2-F2 (Wayne) 173.6 Ruth Geiser Trustee 2000 131.4 6a-G2-M2 & 6a-G2-F2 (Green) 2000 118 6a-G2-M2 & 6a-G2-F2 (Wayne) 173.6 § indicates subdivision *no longer part of the farm ¥ indicates expansion
Figure 6.16 HH6a Tenure History
246 6a-G3-M3 and 6a-G3-F3 purchased the Geiser farm in 2001 and are in
the process of acquiring half of the 260-acre family farm from Heidi’s parents, 6a-
G2-M2 and 6a-G2-F2, who recently retired as 6a-G3-M3 and 6a-G3-F3 assumed responsibility. The HH6a’s are practicing equal-heir inheritance between their two daughters, of which 6a-G3-F3 is the only one interested in continuing to work the farm; her sister, 6a-G3-F4, is not interested in the farm but wants her share of the
enterprise. Currently, the 6a-G3-M3 and 6a-G3-F3 have purchased 93 acres from the 6a-G2-M2 and 6a-G2-F2, and the rest of the farm has been entered into a trust for 6a-G3-F4. But the entire enterprise is operated by 6a-G3-M3 and 6a-
G3-F3.
6a-G3-M3 was raised near his mother’s family farm in Weilersville, a small intersection just south of Smithville. Since they did not want to farm, his parents sold their family farm to the HH3. After attending Ohio State University’s
Agricultural Technical Institute, and later OSU’s main campus, he graduated with two degrees in animal and horticulture sciences. After working on 26-G4-M4’s
(currently 5-G2-M7’s) farm while in college, he came to work on the HH6a farm
(where he met 6a-G3-F3) after 26-G4-M4 retired. 6a-G3-F3’s family has close ties to the area, and many of their relatives are buried locally in the Paradise
Cemetery; 6a-G3-M3’s family is also closely tied to the area having family in the
Smithville Cemetery.
The complications with 6a-G3-F3’s sister consequently led the 6a-G3-M3 and 6a-G3-F3 to seek outside sources of land to lease. They currently lease up to 200 acres from nearby retired farm families including 20 acres from Roger
247 Ramseyer, and other acreage from Obermillers, Henshaws and Burkeys, of which the long-term access to the Obermillers is uncertain due to its proposed development that will transition into housing.
6a-G3-M3 is working along side his father-in-law, 6a-G2-M2, and gradually assuming more of the farm decision-making responsibilities. Yet, as the responsibilities are currently arranged, 6a-G3-M3 is the primary decision-maker for the dry-heifer farm, while the 6a-G2-M2 operates the dairy barn. This separation of responsibility has lead to two distinct management patterns based on the differences in management styles of these farmers. 6a-G2-M2 follows and intensive rotation of corn and soy beans on a farm not adjacent to the 6a-G3-
M3’s, whose own farm follows a four crop, five-year rotation of corn, soy beans, wheat, and two years of hay.
Because of his actions and attitudes regarding conservation, 6a-G3-M3 recently (2004) was honored with the Farmer Conservation award from the local
Soil and Water Conservation District and from the state (2005), is a member of the Sugar Creek Partners, and is very conscious of his operation’s impact on water quality. This honor was made after 6a-G3-M3 and 6a-G3-F3, implemented numerous Best Management Practices, including grass waterways, contour strip cropping, stream erosion control using exclusionary fencing and flow control drop-structures, manure application to hay fields in a conservation cropping rotation of corn-soy-wheat-hay, and riparian buffer strips. 6a-G2-M2 operates the dairy following the dominant management style of the area in which additional
248 effort with water quality and conservation in mind are not generally taken and a
strict corn and soybean rotation is used along with animal confinement.32
The future of the HH6a farms are not certain because of the current
agricultural climate and the young age of their children, but 6a-G3-M3 and 6a-
G3-F3 think that 6a-G4-M5, their middle child, is the most likely successor. 6a-
G3-F3 sees the farm as “a source of their identity” and as their heritage and believes that it should be preserved as a farm for any child that wants to enter into a partnership with them as the successor. She says that the farm will go to whichever child wants to farm “[a]nd the other two, they can have whatever’s personal that belongs to me” as their inheritance.
Succession Case 7. HH7: Purchasing farm shares from the senior generation
and uxorilocal residence.
The HH7, Inc., farm is currently operated by 7-G5-F4 and 7-G5-M5, with
assistance from 7-G5-F4’s father, 7-G4-M4. The land was originally owned by
Jacob Brackbiel but sold shortly thereafter and settled by 7-G1-M1 (before 1856)
who farmed land south and southeast of Smithville and operated the Smithville
Brewery. His descendants, the HH7s, have been successors of the farm in its
entirety, while adding to the holdings over time. The current generation of
farmers, 7-G5-F4 and 7-G5-M5, are continuing the family legacy by operating the
farm. 7-G5-F4 states that she “feel[s] more attached to my family through the
32 This was in 2002-2003. At the present time, 2005, 6a-G2-M2 and 6a-G2-F2’s farm has been partially transferred to the 6a –G3-M3 and 6a-G3-F3. 249 farm than what I do through a cemetery or anything [else]”. Aside from legal
matters, the farm was incorporated to maintain familial continuity and provide
economic support for all shareholders in the family.
Owner Plat Date Acres Jacob Brackbiel 1820 160 7-G1-M1 1826 80 7-G1-M1 1856 --- 7-G1-M1 1873 246 7-G1-M1 1897 246 7-G2-M2 & 7-G2-F1 1939§ 111.6 7-G3-M3 & 7-G3-F2 112 7-G2-M2 & 7-G2-F1 1950 111.6 7-G3-M3 & 7-G3-F2 112 7-G4-M4 & 7-G4-F3, HH7, Inc 1979€ 234.6 7-G4-M4 & 7-G4-F3, HH7, Inc 2000 234.6 § indicates subdivision € indicates consolidation
Figure 6.17 HH7 Tenure History
Farm share structures are as follows of HH7 Inc.: 7-G4-M4 & 7-G4-F3 are the majority shareowners (95%), 7-G4-M4’s parents 7-G3-M3 & 7-G3-F2 are minority holders (5%). 7-G3-M3 is a brother of 7b-G3-M1 (father of 7b-G4-M2 and grandfather of 7b-G5-M3). Recently, 7-G5-F4 and 7-G5-M5 have incorporated the Sweet Water Farms, each having an equal share (50/50), as a separate farm through which they plan to purchase the HH7 Inc. farm. Currently, the purchase paperwork has been completed but not executed.
This is a case in which farm inheritance is bilateral and residence patterns are uxorilocal whereby the father-in-law is selling the farm to his son-in-law and daughter. 7-G5-M5 and 7-G4-M4, working together, planned and installed a 250 riparian buffer and planted trees along the stream as well as modified other farming practices. In both of these examples, family networks acted as a factor in land tenure arrangements as the farm was passed to the next generation. 7-G5-
F4’s brother (7-G5-M6) lives but does not work on the farm. There are still some restrictions to the speed of which changes can be made, but 7-G4-M4 was the first in the group to adopt new BMPs on the farm prior to turning most of the management over to 7-G5-F4 and 7-G5-M5 who have a genuine sharing of farm responsibilities and benefits. 22-G2-M2 used to farm the Leighty Road fields of
HH7, Inc.
Uncertain Futures: Strategies for Saving the Farm
I present six farms in this section, each having a different barrier to farm succession. One of the largest reasons, aside from economic and policy directions, for farm failure is the overall health or stability and resilience of the farm enterprise. Many farmers speak of a “sacrifice” and “working hard” to ensure that their farm will be available for their children, should they choose to farm.
Though this is not a model exclusive to the watershed, families have many values and perceptions that work together to provide motivation and purpose for them, which motivate them to keep the farm as a healthy and viable enterprise in the Sugar Creek.
251 Succession Case 8. HH8: Patrilineal succession and pessimism of community
potential to support agriculture.
The HH8 Farm (southeast quarter, Section 1 Wayne Twp.) was owned by
Robert Matthews but later settled by M. Wolf before 1856. It was then purchased
prior to 1873 by Simon Switzer. By 1922, the farm was owned by J.L. Koons. 8-
G2-M2’s family came to this part of Ohio after migrating from an Apostolic community in Sardis County, in southern Ohio. The HH8 farm was purchased by
8-G2-M2’s paternal grandfather in 1960 and soon given to his parents, Leo and
Joyce in 1962. 8-G2-M2 lived and worked on the farm most of his life. He spent four years off the farm, but living about a mile down the road on another relative’s farm. He purchased 42 acres of the family farm in 1993 and moved in 1996 to the farm once he succeeded his father as the farm operator.
Owner Plat Date Acres Robert Mathews 1820 160 no owner recorded 1826 160 M. Wolf 1856 --- Simon Switzer 1873/1897 --- J.L. Koons 1922 --- Elias E. Shisler 1939/1950 60.43 Lois Shisler 1979 60.4 8-G1-M1 & 8-G1-F1 1998 60.4 8-G2-M2 & 8-G2-F2 2000 47
Figure 6.18 HH8 Tenure History
8-G2-M2 was born in southern Wayne County in 1965 and started
operating his father’s farm eight years ago. With help from the Soil and Water 252 Conservation District’s staff, 8-G2-M2 has implemented a number of conservation measures that include grass-waterways, contour strip-cropping and
run-off management as well as using no-till conservation tillage. He owns 47
acres of land and rents the rest from his family in the area as well as the farm of
14-G1-M1 and 14-G1-F1.
The geographic extent of 8-G2-M2’s social networks is generally limited to northern Wayne County. He is unfamiliar with the farms and families south of
Smithville, but has many closely related relatives and many neighbors with whom he has frequent contact in the area north of Smithville and continuing towards
Creston and Rittman. 8-G2-M2’s family has weekly visits with relatives around
this part of the county. Regarding his sense of community, he is a member of the
Rittman Apostolic Church, located in Rittman, Ohio. He “believe[s] that the non- farming community nowadays are two generations out of a farm. They do not
have the same knowledge [as farmers with regard to rural lifestyles and farm
operation]”. Because of this, he states that most farmer social networks tend to
be separate from non-farmers.
When considering conservation adoption, 8-G2-M2 believes that “it takes
a balance of nature to keep things going” and that he is in favor of conservation
measures being implemented in the watershed but has reservations as to how
they are done. What concerns him the most is “how they accomplish their goals”
(emphasis added) and the impact that will have on farmers being able to make a
living from the land. This includes concerns about an potential overabundance of
wildlife if conservation efforts do not include considerations for farmers.
253 8-G2-M2’s parents (8-G1-M1 & 8-G1-F1) now live about a half-mile down the road where 8-G2-M2 once lived. Because of the slow, but characteristically steady growth of new housing developments in northern Wayne County, 8-G2-
M2 thinks that housing will “swallow up” much of the farmland and that there will
be “farming on a small scale, but [it won’t remain] a farming community”.
Although his sons are active in helping operate the farm, because of this self-
described “pessimistic” view of the future of farming, he does not have firm plans
for an intergenerational farm transfer since he believes that agriculture will not be
viable in the county for the next generation. He thinks the land should be there for them to do whatever they want, adding that there is no family trust because it is unfair “to dictate what will happen” in the farm to future generations of Marty’s if farming is not viable.
Succession Case 9. HH9: Family trust to “skip a generation” in hopes future
succession.
The 99 acres of land that consist of HH9 family farm (northwest quarter,
Section 16 Green Twp.) was originally parceled as part of the school lands of
Green Township but was later sold to F. Zeitler (approx 1856) after the Village of
Smithville was created. It was later sold to Eli Kauffman (date approx. 1873) as a
complete 160 acre quarter section in Green Township. By 1897, the parcel was
subdivided into two parcels, one consisting of 100 acres on the east and the
other 60 acres on the west, and was owned by H.R. Hurst and B. Gerig,
254 respectively. From 1940 to 1974 there was a series of owners and renters of the
farm, none of whom farmed for very long as it changed hands almost every four
years with only one period of ownership lasting more than 10 years.
Owner Plat Date Acres School lands 1820 640 School lands 1826 640 F. Zeitler 1856§ --- Eli Kauffman 1873 166 R. Hurst 1897§ 100 John A Hurst 1939 100 John Jr. & K. Edgar 1950 100 9-G1-M1 & 9-G1-F1 1979§ 99 9-G1-M1 & 9-G1-F1 2000 99 HH9 Trust 2002 99 § indicates subdivision
Figure 6.19 HH9 Tenure History
The HH9’s have owned the farm for over 30 years but since their children
have no current interest in farming the land, it was entered into a trust in 1999,
potentially saving it for the next generation. The HH9’s took this step as a long-
term plan for the future of the farm in case something happened to 9-G1-M1 & 9-
G1-F1 to avoid the land being sold for some use other than farming.
Until five years ago, 9-G1-M1 & 9-G1-F1 leased the adjacent farm and
farmed the entire quarter section, but since 1999, they have only maintained their
own, stating that “the farm itself isn’t such that it is sustainable from an income-
production” standpoint. The farm income has been supplemented with their part-
time wood and corn stove business sine the 1970s. They have three children
who are “in different directions”, but none are interested in farming.
255 They attend Smithville Apostolic Church and were very active in local
organizations while their children were in school. These included the Smithville
Booster Club, the local Farm Bureau and Wooster Chamber of Commerce, as
well as participating in an informal exchange of farm products for charity. Their
personal philosophy regarding nature and wildlife is that
“G-d made them for all of us. And if we confine areas as far as development and housing, why hey, those people need places to go and to share this type of thing. And putting a house on five acres won’t do it as far as having much wildlife, but if we have areas that we can go through and so forth and a place for people to walk or hike, that’s nice and meaningful and it gives people an opportunity to reach out with nature and allow all of us to share in what’s been given.”
Succession Case 10. HH1b: Multiple non-Farm Heirs Potentially Causing
Terminal Farm Succession.
The HH1bs own the land that was once part of the Keifer and later the
Ramseyer farms. The remnants of the Kiefer farm is now owned by the county
and used for the airport, while the HH1bs own the rest. The HH1bs are related to
the HH1as through 1b-G1-M1 and 1b-G1-F1 – 1b-G1-F1 is the namesake of the
Farm (southwest and northeast quarters, Section 7 Green Twp.).
HH1b Farms is in an ambiguous succession period. The son is in
partnership with his father to operate this predominantly row-crop farm. However,
there are no plans for the next generation as a number of family members want a
share of the farm, but are not involved in farming. This tension in the family may
256 lead to the subdivision and subsequent sale of the land because a potential
buyout of non-farm relatives is, as in most cases, unaffordable.
Owner Plat Date Acres Jacob Kieffer 1820 160 Mich. Kieffer 1826 160 A. Greiner 1856 --- D. Hermon F. Griner 1873§ 160 J.S. Kieffer 121 Jonathan Burkholder 82 S. & S. Griner 1897 161 A. Weimer 121 M. Smoker heirs 82 1b-G1-M1 and 1b-G1-F1 1939€ 306.8 1b-G1-M1 and 1b-G1-F1 1950 306.8 HH1b, Inc. 1979 306.8 HH1b, Inc. 2000 306.8 § indicates subdivision € indicates consolidation
Figure 6.20 HH1b Tenure History
Succession Case 11. HH11: Farm is in a trust and is being leased as a process
to find an heir.
11-G3-M3 and 11-G3-F2 jointly own the property located near the corner
of Chippewa and Smucker Roads as well as land at the intersections of Smucker
and Egypt Roads (the home farm) and at Five Points and Chippewa Roads.
These farms are near Dean Horst to the north and his sister-in-law and brother,
11-G3-F3 and 11-G3-M4 to the east and north. Lila also owns some property near the Agromark Town and Country in Smithville, the Sugar Creek also runs through that property. 257 The home farm (eastern half of southwest quarter, Section 15 Green
Twp.) has a long history dating to the pioneer period of Ohio. The farm house, a
source of pride for the HH11s, was built in the 1830s by a man named Moses
Rutt, who, incidentally is buried in the cemetery near the Chester School and was the original owner of the parcel (before 1856). After Moses Rutt, David
Zimmerman (date approx. 1873) owned the farm until sometime before 1897
when, by that time, it was sold to Jonas Smoker, an absentee landlord whose
family farmed much of the land in Section 15 and whose relative, David Smoker,
farmed adjacent land in the 1856 Plat listing. 11-G3-M3’s family has owned the
farm since approximately 1900 when his paternal great-grandfather purchased
the land for his grandfather (11-G1-M1) to farm; the land was purchased Jonas
Smoker who previously rented to tenants. The land was depleted by the tenant
farmers prior to Ralph’s grandfathers rehabilitation of the farm.
Owner Plat Date Acres Henry Ruble 1820 160 Jack Oxenrider 1826§ 160 Moses Rutt 1856 --- David Zimmerman 1873 --- Jonas Smoker 1897 --- 11-G1-M1 1922/1939 74 11-G2-M2 & 11-G2-F1 1950 73 11-G3-M3 & 11-G3-F2 1979 73 11-G3-M3 & 11-G3-F2 2000 73 § indicates subdivision
Figure 6.21 HH11 Tenure History
258 Adjacent land owned by 11-G3-F3, 11-G3-M3’s sister-in-law who married his brother (now deceased) 11-G3-M4, was recently sold to 4-G3-M9, but she still owns 75 acres east of the home farm. 11-G3-F2 is first cousin to 7-G4-F3, the mother of 7-G5-F4 OF Sweet Water Farms. 11-G3-F2’s family is also local and mainly to the southeast near Kidron where she has strong ties. 11-G3-M3 jokingly states that she has “five-hundred cousins” there.
Regarding conservation, the HH11’s biggest concerns are the “big stall
farms and all their manure” which is a growing problem that, in a heavy rain, can
cause “a tremendous amount of pollution”. They have had their land in one form
of conservation or another since the 1950s when 11-G3-M3 first installed contour strips. Since then, he’s also utilized green manure and cover crops as well as no- till conservation tillage, taking more than one suggestion from Louis Bromfield’s
“Plowman’s Folly”. 11-G3-M3 believes that if you can find a way to retain or hold water on the land, without flooding it, you can keep the soil in place and provide good conservation coverage by reducing run-off.
The long-term future of the HH11 farm is currently uncertain because 11-
G3-M3 does not have a successor and has been retired from farming for twelve
years. However, the mid-term future is more certain: currently the farm is in a
trust to protect it from sale and division for other uses that are not related to
farming. This is not the optimal scenario for 11-G3-M3, but his son is a banker
and is not interested in taking over, so the farm will remain a farm, just not
owner-operated. Currently, and since 11-G3-M3 retired from farming, 5-G2-M7, the younger brother of 5-G2-M5, leases and operates the farm to whom 11-G3-
259 M3 and 11-G3-F2 lend their labor where they can and to their ability. 11-G3-M3
recently retired from his post-farming occupation of twelve years, which was the
sale of insurance. Interestingly, farm enterprises are not the only occupations
with chosen successors in this community – just as some doctors chose their
successor to inherit the practice, 11-G3-M3 was handpicked to as successor to
the previous insurance salesperson in Smithville because of his prestige and
general perception of trustworthiness that others have of him.
Locally, 11-G3-M3 is a member of the Smithville Ruritans, a voluntary
association of community booster that, in 11-G3-M3’s words, promotes
“fellowship, goodwill and community service” at the Westwood club, having
chapters in twenty-six states. Both he and 11-G3-F2 attend church at the Oak
Grove Mennonite Church.
Succession Case 12. HH7a: Succession in doubt because of the tragic death of
the successor.
7a-G4-M4 is the older surviving brother of two sons of 7a-G3-M3; 7a-G4-
M5, who is the second son, operated the land until his death in 2001. The land of
the HH7a farm (southeast quarter, Section 16 Green Twp.) was pioneered by
Peter Zeigler (before 1856) and later settled by 7-G1-M1 before 1897, the man
who operated the Smithville Brewery and was the owner of HH7 Farms and the paternal great-great grandfather of 7-G4-M4, 7-G5-F4. There is some confusion
regarding ownership of the farm in this period because contemporary family
260 records show the land being purchased by 7a-G2-M2, a local farmer and blacksmith, in 1889 for back taxes. Yet, plat records show continuity between 7-
G1-M1 and 7a-G2-M2, it is possible that 7a-G2-M2purchased the farm for 7-G1-
M1 back taxes and that there is no family relation between them. 7a-G2-M2’s son, 7a-G3-M3, purchased the farm in the 1930’s and worked it part-time while employed at the OARDC.
Owner Plat Date Acres School lands 1820 640 School lands 1826 640 Peter Zeigler 1856§ --- Peter Zeigler 1873 80 7-G1-M1 1897 81.75 7a-G2-M2 1939 81.7 7a-G3-M3 & 7a-G3-F1 1950 81.7 7a-G3-M3 & 7a-G3-F1 1979§ 74 7a-G4-M5 2000 74 7a-G4-M4 2002 74 § indicates subdivision
Figure 6.22 HH7a Tenure History
The present day operation of the farm is through lease contract because
7a-G4-M4, the eldest and farm successor to 7a-G4-M5, is not a farmer. 7a-G4-
M4 returned to Wayne County to purchase the family farm in 2002 from 7a-G4-
M5’s estate. Before Loren owned the farm, he worked with his father until 7a-G3-
M3’s death in 1989, when he purchased the farm from his parent’s estate in
1990. The future of this farm is uncertain but for the present time it will be
farmed.
261 Succession Case 13. HH13: Terminal farm generation with potential future sale.
This case is presented last for this section because it bridges both the
farms that have uncertain futures as well as the current farmer being a
“newcomer”, or the first generation of his family on this farm, and in this case the
area, too. 13-G1-M1 and 13-G1-F1 moved to the present farm in 1960 (southeast
quarter, Section 32 Green Twp.), leaving his family farm in Stark County near the
north Canton/Belden Village area (55th street), because of development pressures. He raises potatoes and hay for sale to HH23 Potato Farms and local dairies around Smithville. As a part of their arrangement, 13-G1-M1 uses his
tractor to pull HH23’s planting and harvesting equipment for the potatoes. The
HH13 have no farming children or potential heirs and the future of their farm is
not certain.
Owner Plat Date Acres Samuel Casebeer 1820 160 John Yoder 1826 160 Ch. Hoy 1856 --- Cyrus Hoover 1873§ 125 Spicher & Ramseyr 1897 125 Solomon S. Woods et al 1939 177.5 Daniel M. & Ella E. Troyer 1950 177.5 13-G1-M1 and 13-G1-F1 1979 176 13-G1-M1 and 13-G1-F1 2000 176 § indicates subdivision
Figure 6.23 HH13 Tenure History
262 13-G1-M1 was an active proponent for the new “State Route 30” bypass
that was constructed between 2004 and 2005. This bypass intersected part of his
farm, which he wanted the option to sell more land adjacent to the bypass as a
way to sustain the farm and fund part of their future retirement.
Newcomers to Old Farms
Several new farms have started in the past three decades in the Sugar
Creek. This section examines these new farms and the challenges they face with
respect to land tenure and farm succession.
Succession Case 14. HH14: New non-farming owners leasing the farm as a way
to preserve the land in agriculture.
The 34.4 acre parcel owned by 14-G1-M1 and 14-G1-F1 (part of northwest quarter section, Section 7 Green Twp.) has a history of secure tenure in which the land has changed infrequently since being settled prior to 1856 by A.
Weimer. It was sold to the Smiths and leased to HH6a after the Geisers decided there was no heir to continue the operation.
14-G1-M1 and 14-G1-F1 have owned their 35 acre farm for 11 years,
since 1993, and for that much time most of it has been leased to another farmer,
8-G2-M2, who uses it for raising replacement heifers.
263
Owner Plat Date Acres David Ankle 1820 160 David Antles 1826 160 A. Weiner 1856§ --- A. Weimer 1873 125 A. Weimer 1897 123 S.J.& A.L. Smucker 1939 123 Lester W. & Olga R. Geiser 1950 123 Lester W. & Olga R. Geiser 1979 124.1 14-G1-M1 & 14-G1-F1 2000§ 34.4 Rodney E. & Earl Morrison 66.3 § indicates subdivision
Figure 6.24 HH14 Tenure History
14-G1-M1 and 14-G1-F1 see themselves as hobby farmers and feel that
there are some good managers nearby that he looks to for advice, namely the
HH6as and HH1bs. Both 14-G1-M1 and 14-G1-F1 have farming backgrounds,
though neither have operated their own farm, having four generations on a family
farm between Moreland and Fredericksburg starting in 1845. 14-G1-M was
raised on a farm and his family lives within four miles of their farmstead, 14-G1-
F1 spent time on her grandparent’s (who farmed part-time) farm as a child.
Though not farmers, they are active in the wider community, but not locally
in Smithville, as members of the local Farm Bureau and attending church in
Wooster at the Church of the Savior where they are socially active. They both
visit with family ever-other week alternating family visits between 14-G1-M1 and
14-G1-F1’s.
264 6.5 North Fork and Little Sugar Creek
6.5.1 Social Networks
Continuing the theme of subwatershed variation in social organization and
networks, the North Fork, Little Sugar Creek, and southern portion of the
Mainstem vary from the Upper Sugar Creek in the structure of social networks in
relation to land tenure. Much of this area is part of the historic Swiss-Mennonite
Sonnenberg Settlement as well as the site of the first Sam Yoder, or
Swartzentruber, Amish Church Districts.
The North Fork Subwatershed is unique from other Sugar Creek
subwatersheds in Wayne County because of its diversity of farming communities.
Like the Upper Sugar Creek, the farmers share a common heritage, but unlike
the Upper Sugar Creek communities, the North Fork includes Amish farmers that
have maintained their cultural identity and forged interconnected Church
Districts, where some member-owned lands are contiguous, and others
interspersed with Mennonites.
The Little Sugar Creek is similar to some Holmes County settlement areas
because of its contiguous Church Districts resulting from it being exclusively settled by Amish farmers. The geography of the Little Sugar Creek is unique among the subwatersheds of the Sugar Creek because of its irregularly long, narrow drainage pattern that is less than 1.5 miles from west to east, and 9.2
miles running from south to north.
265 Two main town centers act as the focus of social and economic activity in
this subwatershed: Kidron in the northwest and Mount Eaton in the south. Kidron
has been the center of farming and economic activity for much of the farming
communities in the surrounding area inside and outside of the subwatershed.
Each Thursday and Saturday there are livestock auctions within the main auction
house; and, in the rear lot, miscellaneous auctions occur such as hay and farm
equipment. This area has also become a center for the exchange of used good
(e.g. a flea market) where people are permitted to set up a table and sell
everything from used farm tools to Barbie Dolls and old-style washing machines.
Nearby is Lehman’s Hardware, the regions largest retailer of non-electric
household items (i.e. propane powered refrigerators, mechanical apple peelers, and composting toilets etc). Mount Eaton is a residential town with small businesses, one traffic light, a grain elevator, and a local bank. Many Amish in the area retire to this small town and many Amish and non-Amish businesses operate there.
Sonnenberg History
As briefly stated above, a combination of Amish and Swiss Mennonite
farmers, some of whom share a common heritage in the original Sonnenberg
settlement, live in these subwatersheds. The geographic center of this community has shifted from the area around the Sonnenberg Mennonite Church,
on Hackett Road northeast of Kidron, to the village of Kidron that is at the
crossroads of Kidron and Emerson Roads. Swiss Mennonites make up a large
part of the community and in addition there is a large Amish component. Old
266 Order and Swartzentruber Amish compose the majority of the Amish in the community – it is important to note that there are no New Order Amish in the northern extent of the settlement 33 . Strong intra-group social ties exist in the subwatershed, with the most inter-group contact being between the Old Order
Amish and the Mennonites. The Swartzentruber Amish, characterized as the
“least worldly” (Hostetler 1993) or most “traditional” (Kraybill & Hostetler 2001) – the most conservative among the Ohio Amish – have a tendency towards high levels of intra-group social capital (discussed in more detail in Chapter 7) which creates a barrier that is perceived by outsiders and other community members
(i.e. Ohio State Extension Agents and some Old Order Amish farmers) and is expressed through, among other symbolic indicators of group identity, a lack of trust with those outside their social unit (e.g. the Church District).
New Social Networks: North Fork Task Force
Resulting from concern regarding surface water pollution in this subwatershed, the North Fork Technical Advisory Committee (TAC) was formed by members of the Wayne Soil and Water Conservation District (Wayne SWCD),
Natural Resources Conservation Service (NRCS), Ohio State University
Extension (OSUE), and Wayne County Environmental Services. The North Fork subwatershed was targeted by the state and local conservation agents because of the impaired state of the Sugar Creek, Ohio EPA’s prioritization of the Sugar
Creek for TMDL assessment, and their judgment that much of the water quality
33 For more on this topic, see Chapter 4 Section 4.4.1. 267 impairment in the North Fork resulted from non-point agricultural sources. In
addition, local agency personnel were committed to the idea that a subwatershed
approach was superior because it allowed for the focus of resources to achieve
concrete results in a specific area and because it incorporated a grassroots
rather than expert-driven approach that would focus on practical conservation
strategies that would be achievable by working with local community members. It
was important to work in an area that included members of the Amish community
because they have a strong presence in the watershed, and in the North Fork
“The Amish community occupies approximately 44% of the acreage” (Schultz,
2002: 1).
Community leaders in the subwatershed were recruited by the North Fork
TAC so that they would include “farmers (both Amish and English), businessmen,
township trustees, community council members, schoolteachers and administrators, and any other concerned landowner who wishes to attend the meetings” (Schultz, 2002:13). They recruited eight key community leaders to lead the watershed group known as the North Fork Task Force (NFTF). Additionally, they planned to hold regular meetings that were open to the public and to encourage the participation of any concerned landowner in the North Fork. Since inception, this new social network has been responsible for bringing water quality issues forward in community discussions; a wastewater treatment system has been installed in the village of Kidron34; and residents in the community have
34 Kidron has been a source of surface water impairment resulting from failing home sewage treatment systems in this part of Wayne County. 268 participated in a well-water testing and education program sponsored by the
OARDC and Wayne SWCD.
6.5.2 Strategies for Intergenerational Farm
Succession Case 15. HH15: Land contract upon retirement then transfer of land
and skills to an apprentice.
Originally owned by C. Dahlheimer (approx. 1856) and later Lewis Burich
(1873), the land at 16082 Withrich Rd, Dalton (southeast quarter, Section 14
Sugar Creek Twp.) was purchased by 15-G1-M1 (approx. 1897) and passed
through the family to 15-G3-M3. 15-G3-M3 has lived in the house west of his father’s farm since 1976; this was originally owned by G.W. Zeigler (approx.
1897) and Jacob Garmon (approx. 1873) before him; he moved from Dalton to work on that farm in conjunction with his family’s farm. One last parcel was owned by the Gerber family and was purchased by the HH15s as they expanded their enterprise in the 1970s.
269
Owner Plat Acres Date Jeptha Powell 1820 160 James Lilly 1826 160 C. Dalheimer 1856 --- Lewis Burich 1873 64 15-G1-M1 1897 65 15-G2-M2 & 15-G2-F1 1939§ 54.2 15-G2-M2 & 15-G2-F1 1950 54.2 15-G3-F2 1979¥ 82.9¥ 15-G3-M3 27.9 15-G3-M3 & 15-G3-F2 (from Arner Farm) 33.6 15-G3-M3 & 15-G3-F2 (from Arner Farm) 34.9 15-G3-M3 & 15-G3-F2 2000§¥ 82.9 15-G3-M3 & 15-G3-F2 27.9 15-G3-M3 & 15-G3-F2 34.9 15-G3-M3 & 15-G3-F2 33.6 § indicates subdivision ¥ indicates expansion
Figure 6.25 HH15 Tenure History
The last piece of the HH15 farm came from the Arners. 15-G3-M3 purchased the farm after renters decided to quit farming it and the owners became concerned about the future handling of the farm. 15-G3-M3 began renting from the Arners and after a period of observation, they decided he was a suitable person to whom they could sell the farm. Since both sides of this tenure arrangement were regarding this agreement as a long-term solution to their tenure problems, 15-G3-M3 made an offer to purchase the farm during an evening visit one night; the Arners accepted because it was exactly what they were hoping would happen. They were owners of land with unsecured tenure with a history of high rental turnover. An agreement was made that after an informal “break-in” period, the Arners would sell the farm. Since that day, 15-G3-
M3 said he would visit with them almost daily and when he did, he always came 270 in the kitchen door off the side of the house and Mr. and Mrs. Arner would be sitting at the kitchen table, a third chair there for him, with coffee and a slice of pie waiting.
An additional component to the agreement was that they Arners could stay in the house as long as they needed. Years later, when their family moved them to a retirement home, there was another agreement regarding the disposition of their belongings that stated the family was to take from the house what they wanted and Jerry would purchase the rest. 15-G3-M3 believed this to be a good arrangement because the Arner family dreaded the process of dividing up the estate and selling things to strangers, and 15-G3-M3 and 15-G3-F2 needed to furnish their new home. So, 15-G3-M3 purchased everything in the house for $400, site unseen since he had only seen the dining room and kitchen in all his years of visiting. Arner family members acquired mostly family photos and other wall hangings; most of the furniture was left behind. To this day, many of the furnishings in the living room and bedrooms remain the same.
At the same time as they were paying on the newly acquired farm, the farm next to 15-G3-M3’s home farm (where he grew up) went on the market. He secured a loan from Wooster Bank in order to purchase it. His son now lives in the family house, which was sold and then repurchased by them, with 15-G3-
M3’s assistance. The son is a teacher in Dalton and a football coach. He helps on the farm when he has time.
271 Succession Case 16. HH16: Leasing the farm until a successor is found.
The HH16 farm (northeast quarter, Section 6 Paint Twp.) is a fourth generation dairy farm located near Kidron. It was settled by Charles Begley
th before 1856 and later purchased around the mid-19 Century by 16-G3-M4’s
paternal great-grandfather, 16-G1-M1 as verified by Caldwell’s Atlas of Wayne
County (1873). The farm is another example of secured family tenure and
successful intergenerational farmland transfer without continuity of
intergenerational family operation. 16-G3-M4’s grandfather died when his dad
was 5 years old and his mother kept the farm in the family by moving to her
neighboring sister’s farm, with his siblings, and leasing-out the family farm. Once
16-G3-M4’s father was old enough to work the farm, he took over the operation
and later passed the farm to 16-G3-M4, the youngest son. 16-G3-M4’s’s older
brother, 16-G3-M3 (his spouse is 16-G3-F4’s name is on the deed of their farm)
purchased another adjacent farm from Susan Welty (the Welty Farm has been in
that family for several generations) and also operates a dairy.
272
Owner Plat Date Acres Daniel Roderick 1820 160 Daniel Roderick 1826 160 Charles Begley 1856 --- 16-G1-M1& 16-G1-F1 1873 100 16-G1-M1& 16-G1-F1 1897§ 82 16-G1-F1 1922¥ 100 16-G2-M2 & 16-G2-F2 1939 100 16-G2-M2 & 16-G2-F2 1950 --- 16-G3-M3 & 16-G3-F3 1979§ 96.6 16-G3-M3 & 16-G3-F3 2000 96.6 § indicates subdivision ¥ indicates expansion
Figure 6.26 HH16 Family Farm
Uxorilocal Farm Residence and Farm Succession
Succession Case 17. HH17: Single-heir inheritance and uxorilocal residence.
The HH17 Farm (southwest quarter, Section 28 Sugar Creek Twp,) was first farmed by 17-G1-M1 as listed in the 1856 directory of landowners. 17-G1-M1 later passed it to 17-G2-M2 prior to 1873. The farm has remained in the HH17 household to the present day. A lack of a male successor and the presence of an interested daughter and son-in-law have provided 17-G4-M4 and 17-G4-F2, parents of (daughter), with a suitable heir for the farm. 17-G5-M5 and 17-G5-F3 purchased the farm from 17-G4-M4 and 17-G4-F2 in 2005, after several years of farming with 17-G5-F3’s parents.
273
Owner Plat Date Acres Richard Burl 1820 160 17-G1-M1 1826 160 17-G1-M1 1856 --- 17-G2-M2 1873 80 17-G2-M2 1897 80 17-G3-M3 & 17-G3-F1 1939 79 17-G3-M3 & 17-G3-F1 1950 79 17-G4-M4 and 17-G4-F2 1979 78.9 17-G4-M4 and 17-G4-F2 2000 78.9 17-G5-M5 and 17-G5-F3 2005 78.9 § indicates subdivision
Figure 6.27 HH17 Tenure History
Amish Farm Succession
According to Long (2003), Amish farm succession follows a pattern of single-heir inheritance to the first-born male. This order is punctuated when the parents move to a smaller home adjacent to the family home. However, I have
found variation in the birth order selection in Amish single-heir inheritance in
Wayne County. Amish farmers, like other farming families make accommodations to the traditional ways of doing things based on family life cycle.
This area of the county is also historically a Swartzentruber settlement
community, established by Sam Yoder around 1913 when he split away from the
Old Order Amish because of disputes that included the Older Order moderation of the practice of meidung (shunning). This closed community makes it difficult to
disseminate or transfer conservation knowledge since part of the conservative
ethos of the group is avoidance of outside ideas. There are no Swartzentruber
Amish in local community organizations, including the North Fork Taskforce, the
274 organization of community leaders who were organized by the Soil and Water
Conservation District to provide local solutions to the surface water impairment
problem. A slightly less conservative group is the Andy Weaver Amish. These
Amish were not afforded a response option on the survey out of omission due to
my lack of knowledge regarding their geographic locations. Despite there
traditionalism, they are listed among the other Old and New Order Amish in the
Ohio Amish Directory (2000) so it is possible to locate them. The cloistered
nature of these groups has interesting implications for issues of environmental
pollution and impairment.
Old Order Amish of the watershed are more socially liberal than the
Swartzentruber35, and interact, albeit on a limited basis, with other non-Amish for
employment, transportation and “for the good of the broader community”
(Wengerd, personal communication), in comparison to less conservative Amish.
Non-Traditional Traditional Amish Farm Succession
In the following part, I present two cases of traditional single-heir farm
succession that follows a non-traditional birth order. In both cases, the eldest son
had already started another vocation before the father was ready to begin
training and transferring the farm to the next generation.
35 Liberal in their application of “the ban” that proscribes contact with people unaffiliated with their communion, and modes of dress, occupation, and Biblical interpretations for these practices. 275 Succession Case 18. HH18: Single-heir inheritance and younger sibling
succession with land subdivisions.
18-G2-M2 AND 18-G2-F2’s family farm is an example of non-overlapping
family life cycle and farm succession. 18-G1-M1 and 18-G1-F1, of Maysville
Northeast Church District, passed their farm to the youngest son, 18-G3-M4, also
of the same Church. 18-G2-M2, the eldest son, moved to the east of Kidron,
purchasing the Daniel Yoder farm.
Owner Plat Date Acres Wm. Elliot 1820 130 Wm. Gareston 1826 130 Henry Zartman 1856 --- Henry Zartman 1873 130 Henry Zartman 1897 130 P. and J.A. Zimmerly 1922§ 83 W.I. Gerber 47 R. A. & L. Troyer 1939 83 Eli D. & Susie Yoder 1950 83 Daniel Yoder 1979 83 18-G2-M2 AND 18-G2-F2 2000§ 73.1 § indicates subdivision
Figure 6.28 HH18 Tenure History
The farm of 18-G2-M2 AND 18-G2-F2 (northeast quarter, Section 34
Sugar Creek Twp.) was settled by Henry Zartman who is listed as the owner as late as 1897. Prior to 1922, the farm was subdivided and sold with 83 acres to P.
and J.A. Zimmerly, and 47 acres to W.I. Gerber. By 1974, the farm was owned
by Daniel Yoder. This farmstead follows a traditional Amish settlement pattern
276 with multigenerational residences at the main farmstead offering room for the farm successor’s family, and satellite residences on the periphery for the other
married adult children.
Succession Case 19. HH19: Single-heir inheritance and younger sibling
succession.
The HH19 farm (northeast quarter, Section 3 Paint Twp.) was owned by
M. Buchwalter family (approx. 1856) and later purchased by Philip Saure (1922).
19-G1-M1 purchased the farm from Solomon Saurer, and is the father of 12
children (now all adults), 5 of them boys. As a result of circumstances
encountered during the family life cycle, the last-born male, and the eleventh
child, of the family was chosen to succeed 19-G1-M1, 19-G2-M7.
Owner Plat Date Acres Abraham Beals 1820 160 John Leyda 1826 160 M. Buchwalter 1856 --- M. Buchwalter heirs 1873 144 D. Bookwalter 1897 144 Philip Saure 1922 148.8 Solomon Saurer 1939 148.8 Solomon Saurer 1950 148.8 19-G1-M1 & 19-G1-F1 1979 148.4 19-G2-M7 & 19-G2-F7 2000 146 § indicates subdivision
Figure 6.29 HH19 Tenure History
277 19-G2-M7’s four older brothers had moved off the home farm and found
work in manufacturing wrought-iron implements, farm equipment and other
agriculturally self-employed occupations. 19-G1-M1 chose 19-G2-M7 as the
successor because of timing: 19-G2-M7 was at the age to begin helping his
father farm at the same time that 19-G1-M1 and 19-G1-F1 were making decisions regarding the future of the farm, while the older brothers were already involved in other occupations. 19-G2-M7 also showed an interest in farming. The family farm was purchased by 19-G1-M1 father and has been in the family since.
19-G1-M1 and 19-G1-F1, and 19-G1-M1’s younger brother 19-G1-M2 live on the family farm with their parents. The eldest brother, Wayne, employs a number of
Amish in the Church District and surrounding Amish community at his family owned horse-drawn farm implements factory just north of the family farm.
The land upon which 19-G2-M7’s older brother’s, 19-G2-M3, Farm
Implement Company is located was pioneered by C. Beals and later sold to N.H.
Snyder and D. Blackstone prior to 1897. The farm was later reconsolidated and
transferred to W.H. Snyder by 1922. In 1992, Charles J. Snyder sold the family
farm to 19-G2-M3. 19-G2-M3 states that he would have liked to farm, and did so
until he was 21 years old, but that there was no available land at the time he was
old enough. Because of this, he decided to do the next best thing and start his
own business supporting small-scale, horse-drawn agriculture.
278 As 19-G2-M3 says,
“I got a job in a machine shop, and got my taste for steal. At that time, we would walk behind the plowed and the only [implements] to use were leftover from John Deere; the supply was dwindling. So we made one and tinkered with it. The neighbors saw it and liked it and ordered some and asked for ride on plows, as the company in Indiana that made them wasn’t coming through. I was doing this on the side with Dad, decided to make a business out of it in 1978.”
His company plays a role in supporting the agricultural community and off-
farm Amish community by producing equipment and providing local jobs.
Non-Farming Amish Farm Tenure
Historically, the Amish settlement patterns have been that of clustered
residential housing on multigenerational farmstead (at one time it is possible for
four or more generations to live in such proximity: grandparents, parents, married
adult children who are successors to the farm, and their young children) exhibiting patrilineal transfers of land and patrilocal residence. In recent decades, the expansion of the Amish population has lead many to off-farm occupation and in search of land for housing. In an effort to perhaps create spatial patterns of settlement comparable to the Holmes County end of the Settlement, Amish in the
Southern part of Wayne County purchase large blocks of land for their own housing and work sites (e.g. houses, workshops, storefronts etc.). Often times these parcels were previously farmland that went to auction. More investigation into this trend needs to be done, but speculation leads me to believe that most of who practice this form of tenure are non-farmers who work off-farm and want to
279 have a family environment in which work and family life are interconnected and in close proximity that maintains the rural character of their communities. I believe
this approach to land acquisition and tenure can help make this possible.
Another significant land tenure practice that demonstrates the foresight
and planning with regard to community changes36 occurs among Amish families
who are living and working off-farm but seeking housing in close proximity to their families, church members and community. Although this is a topic that warrants its own research per se, I believe it is essential for understanding how future generations of Amish will adjust to the growing scarcity of land, dwindling proportion of families working on farms and population growth. Due to the agrarian nature of Amish households and the dependence on horse drawn buggies for transportation, there is a need for pasture, even the smallest amount, for horses that require ample land to meet this need. In this section, I discuss two examples of Amish households in transition from farm to off-farm lifestyles.
Succession Case 20. HH20: Single-heir inheritance and youngest female
succession.
This case is presented from the perspective of 20-G2-M2, son of 20-G1-
M1 and 20-G1-F1, Bishop of the Kidron North Church District. 20-G1-M1 and 20-
G1-F1 were farmers in the Mt. Eaton Church District. Their three sons entered
other occupations outside of farming while 20-G1-M1 was still the primary
36 Changes include a growing population, increasing number of members employed off-farm, and land tenure. 280 operator. As such, the results of these circumstances of family life cycle, the family farm (northwest quarter, Section 3 Paint Twp.) was passed to the youngest child, 20-G2-M2’s sister 20-G2-F3 and her husband 20-G2-M3, son of
20b-G1-M1 and 20b-G1-F1 of the Elm Grove Church (#55). 20-G2-M3’s family farm, which is located south of Fredericksburg, is operated by his younger brother, 20b-G2-M1.
Owner Plat Date Acres Abraham Beals 1820 160 David Beals 1826 160 J. Beals 1856 --- D. Beals 1873§ 56 S.P. Beals 137 S. P Beals 1897§¥ 218 G.W. Galletia 28 Horace Beals 1922§ 118 D.D. Yoder 77.18 D.D. Yoder 1939 77.18 D.D. Yoder 1950 77.18 20-G1-F1 1979 79 20-G2-F3 & 20-G2-M3 2000 77.2 § indicates subdivision ¥ indicates expansion
Figure 6.30 HH20 Tenure History
Although 20-G2-M2 is not a resident of Kidron and lives on Dover Road, he is compelled to participate in activities and community decisions that affect his family’s farm. 20-G2-M2 has been involved in the concrete business and later woodworking, owning a shop near Maysville that his son now operates, and currently works as an excavator as part of the septic system tie-ins and other
281 related construction in the town of Kidron. He is viewed by neighbors inside and outside of the Amish community as a very influential man.
Succession Case 21. HH21: Bilateral inheritance and farm transitioning to non- farm use.
21-G1-M1, an Amish woodworker of the Kidron Middle Church District, purchased this farm (southeast quarter, Section 6 Paint Twp.) in its entirety, but never farmed it. The farm was originally owed by one of several members of the
Lehman family (originally I.C. Lehman, but at the time of purchase, Peter
Lehman) that have since left farming as the community membership transitioned from one set of dominant families to another.
Owner Plat Date Acres Fred Roderick 1820 160 Fred Roderick 1826 160 I. Lehman 1856 --- I.C. Lehman 1873 80 P. Lehman 1897§ 40 I.C. Lehman 40 D.P. Lehman 1939€ 81.3 D.P. Lehman 1950 81.3 21-G1-M1 & 21-G1-F1 1979§ 74.8 21-G2-F2 & 21-G2-M2 2000 69.1 § indicates subdivision € indicates consolidation
Figure 6.31 HH21 Tenure History
The current land tenure that the HH21 family exercises with this holding is an example of a recent Amish strategy for land acquisition in which, as the Amish
282 community expands, large families will purchase an entire farm, even though
they are not themselves farmers, and save the land for future home development
for successive generations. This farm was passed to 21-G1-M1 and 21-G1-M1’s
younger daughter, 21-G2-F2, and their son-in-law, 21-G2-M2. 21-G2-F2 and 21-
G2-M2 have continued the same strategy in assisting their children in developing
other parts of the old farm into homes and garden space. This is done as another
way of acquiring secured tenure for land in an area where agriculture is not
accessible to everyone (e.g. there is an inadequate amount of land at an
affordable price for farming to put together a viable farm given the timing of each
generation of potential farmer).
21-G2-F2 and 21-G2-M2 live ambilocally (in this case matrilocally),
originally near 21-G2-F2’s mother and father, but currently near her mother since
the passing of her father. This follows a pattern of non-farm residency in which, in
the absence of a farm to transfer to the available male heir, emphasis on
patrilineal intergenerational land transfers is diminished in favor of a pragmatic
approach to residency based on availability of land.
I think that this strategy enables families to pool there resources for the
purchase of land that would otherwise be unattainable because of high prices
sought by sellers in this part of this county (one informant told me that land in his
area sold or $7-$15,000/acre depending on road frontage and slope). By doing
so, Amish are able to put together a sizeable piece of land that can be reserved for a future generation to farm, used as an extension of the family farm, and offer
283 site locations for the next generation’s housing. It is becoming more common for
females to become successors when an operating farm is not transferred.
Little Sugar Creek
There is no formal center of social activity outside of the farm in the Little
Sugar Creek. This long and narrow subwatershed spans a valley east of Apple
Creek, West of Kidron and North of Fredericksburg (a community of mixed
heritage, both Amish and English). The uniqueness of the subwatershed extends
to its population as well. This narrow drainage provides the valley that Kansas
Road follows making it convenient for Amish to extend their Church Districts
along its contours and be mostly contained within its boundaries. The majority of
farmers increasingly are Swartzentruber and Old Order Amish interspersed
among mostly non-farming residents.
6.6 Commons
Irrigation is one form of commons that acts as a factor in social
organization and farm structure in many western regions of the United States
(Atherton 2004) leading farm families into cooperative tenure arrangements
regarding common water resources that act as local management strategies and
structure daily activities as well as the overall farm enterprise. However, in the
temperate rainforests and hills of the Allegheny Plateau and Lake Erie Till Plains,
water abundance is a problem rather than a goal. Seasonal rain and snowfall require cooperative community strategies to drain the excess water from the land
284 so that it can be farmed. Cooperative drainage, like irrigation, requires
information of methods, capital and time investments and social networks strong enough to construct and maintain a vast network of interconnected field tiles and drainage ditches that move water below and/or through (by means of intersecting
drainage tiles and ditches) various farm fields, as water travels to creeks and
streams. The maintenance of those drainage channels, which run alongside and
parallel to roads, are organized by county and township leaders. They also rely
on social networks that provide cooperative and collaborative structures to
maintain local tile and ditch lines.
The history of land drainage in Ohio has been intimately connected to
other hydraulic projects in the Mississippi drainage basin because of the
concurrent settlement of interconnected lands requiring drainage. In a period of
less than one hundred years, the temperament of the settlers drifted from one of
land conquest through drainage to land reclamation and protection due to the
former causing downstream flooding and the latter as a response and effort to
control. Drainage patterns that began out of necessity to settle and farm wetland
areas, became a matter of convenience as some farmers and local county
governments straightened streams to contour and protect farm field boundaries
and allow farming land up to the edge of the stream bank. Once the use of channelization was common in most places, it was needed everywhere to stem the diminishing capacity of downstream lands to retain water and prevent flooding. Overall, the rapid draining of the land caused immense and unforeseen problems, which, in the Muskingum Basin of Ohio, culminated in the Flood of
285 1913, then later, and local to the research area, the 1969 Flood (MWCD
Drainage Leaflet). It is estimated that there were 20,000 miles of drainage ditches in Ohio by 1884, draining 11 million acres (Atherton et al 2004). This flooding coalesced public opinion that demanded a resolution to this problem.
Today, a history of channelization permeates the region. Large-scale public works, constructed during the 1930s, have replaced the natural capacity of water retention with reservoirs and flood dams throughout the Muskingum Basin.
Continued urban growth and exurban sprawl has amplified the presence of impervious surfaces that generate increased water run-off into streams and rivers. This process has led to sedimentation stress in the MWCD system at places like Beach City Dam, where waters from the Sugar Creek are retained
during periods of flood.
Drainage networks are artifacts of this social history of drainage through
which contemporary residents continue to act collectively in modifying their
environment. At every intersection of sloped land and winding or straightened
stream are found networks of tiles and ditches in the landscape representing
complex social relationships and networks of management for water drainage,
which “prior to 1947, was the responsibility of benefiting landowners” (Atherton et al 2004). While western states of the U.S. were and are preoccupied with irrigation, social organization regarding water rights and access also included drainage to a large degree. Swamps or, to use today’s term, wetlands, prohibited settlement in a large part of the Ohio Valley, specifically most of Northwest Ohio,
286 but also smaller areas throughout most of Ohio were saturated, including parts of
the counties of the Sugar Creek Watershed.
Initially, lands classified as swamp was owned by the Federal Government
and early settlers were prohibited from making use of these “swamp” or
marshlands until the 1850 Swamp Land Act (Pavelis 1987:17). This allowed the
sale of the swamp to individuals. Shortly after this, networks of drainage ditches
and later drainage tiles were installed with the assistance of engineering
expertise from state and federal government agencies (County Engineers and
Soil and Water Conservation Districts). In the present time period, residents are
concerned about drainage as seen in the survey data. Survey respondents
reported drainage as a high concern – of the 159 farms surveyed, 45.9% reported concern for adequate drainage.
6.7 Conclusion
In this chapter, I presented the hypothesis that land tenure was affected
by a combination of attributes related to ethnicity, heritage social networks and
farming attitudes. Social networks (or simply social relationships) are a product of
history and heritage in an area and consequently have spatial and temporal
dimensions. I emphasize the concept of area in this statement because of the
strong sense of place experienced among most people. The farmers in Wayne
and Holmes County are intergenerationally connected among themselves and
with the land through household structures and networks of family and neighbors
that are experienced through social interactions including marriage, spiritually
287 through church, economically as participants in the local agroecosystem, and physically through ritual and shared labor.
Recently purchased farms are referred to by their previous owner’s name until the second generation of the current family. This is true unless the previous family had much prestige and high standing in the community; in this case, the current family and surrounding community will continue to refer to this farm by the previous owners name for two or more generations. This is an aspect of the spatial and temporal ordering of community life and how people relate to each other as individuals and family units and to the land on which they work and live.
The continuity of social elements in land tenure spans generations and emerges as a social structure making the farm more than the sum of its parts (e.g. people, equipment and land).
Land tenure in the Sugar Creek, like most parts of the Midwest with a brief history of less than 200 years of European settlement, is intricately interwoven through four, sometimes five generations of farm families. These families, some descendants of the first pioneers, have adjusted their operations in response to multiple levels of external pressures in a number of ways. Some families have expanded their operations to accommodate the need for increased income; others have contracted their land resource base to the point of complete sale.
Farms have grown and declined, while others have remained constant. As the agrifamily cycle has vacillated, some farms have a history of insecure tenure while others remained within the same families for generations. It is within these dynamics of land tenure that the families in the Sugar Creek Watershed are
288 contextualized. It should come as no surprise that land changes ownership within
and between families through time. To many, this mundane aspect of life is
unworthy of notice, yet it is in this mundane aspect of life that a longer ritual cycle
of intergenerational farm succession unfolds within and between families and
through the farm household; succession that either continues continuity of the
family farm, or fails and the farm either is passed to another person to farm, or
enters non-agricultural use.
Returning to the concept of a sense of place, farmers are often tied,
voluntarily, to their land, and farm families will act in the interest of maintaining
these ties to the extent that they make many decisions regarding the future
status of the farm with the end goal of continuity in tenure in mind. Temporally,
farm families are a snapshot of the farm household as it acts to provide continuity
between generations on the farm through time. The family’s sense of place is
rooted in a greater cycle, or liturgical order, in which the mundane passage of
time in the enduring and often lifelong process of intergenerational farm transfer
is punctuated by a single event signifying the transfer of land from one generation to the next. For some families, this event comes through inheritance, for others it is the outright purchase of land from their parents, or the “buying out” of other siblings and other members of their extended family cohort. These processes also occur in families in which the heir is not a farmer. In this research, a few examples of this are found in the Sugar Creek in which land is transferred to the next generation who are themselves not farmers, and held in trust with the
289 hope or expectation to skip a generation and preserve the farm for a future
generation of potential farmers.
Land tenure overlaps both Amish and non-Amish farming social networks,
but also non-farming networks as farmland is incorporated into residential tenure
among all groups. Through these networks, land access rights are held and
transferred for permanent (ownership) or temporary long-term (lease) and short- term (rent) use. The security of this tenure is dependent on the relationship between the leaser and the lessee. Land tenure strategies include: purchasing more land, leasing more land (various farms), renting from a relative, consolidating with other family members to spread the burden of expansion among a larger extended family farm.
The land tenure correlations and associations demonstrate how ownership of land is related to conservation adoption. It is important to be aware that the
conservation index includes both reported current conservation practices used on the farm and preferences for new practices. Current use of conservation practices does not significantly correlate with land tenure categories and is an example of the difference between word and deed or possibly an inadequacy in the measurement.
Access to land for members comes from within existing kinship networks
instead of purchasing individually. Two farmers reported in this chapter gained
access to farmland bilaterally through their spouse’s family farm, living
uxorilocally, while others access land through other nuclear and extended
agnatic family networks. Of the 35 farms interviewed, relationships of farm
290 tenure, succession and size are related to conservation adoption. Interviewed farms that lacked plans for an intergenerational farm transfer and/or were leased out and not operated by the owner generally were less likely to implement BMPs; farms that are leased out also have a tendency to lack farm heirs.
A common theme of farming in this community is the necessity for adjustment by the farm operators to current agricultural trends and needs.
Throughout the history of this region, farmers have come and gone with many planning and succeeding in passing the farm to the next generation. A look at land records will reveal a great deal of transfer of land within and among families.
However, as Salamon (1992) noted, there is a great deal of land that transfers to a daughter and son-in-law, keeping the farm in the family, but under a new surname at the auditor’s office. Other ways of adjusting to present circumstances is shifting production practices. It is a misunderstanding to believe that farms are static entities that do not change. Change comes in many forms as farmers adjust their operations for new products. Wheat, once a number one crop in
Wayne and Holmes Counties, is still grown but sells a distant fifth to dairy, corn, soybeans and hay. It used to be that every farm in Ohio had a mandated orchard that included apple trees, however, much of that land has since been put into corn and beans, or pasture, and only a handful of fruit farms exist in the area as external sources of fruit have become commonplace. Likewise, the adoption of new farm technologies has caused many farms to expand production to meet the needs of added equipment expenses and concomitantly taking advantage of potential of these new implements.
291 Yet, in periods of rapid social, technological and land use changes,
increasing external economic pressures, and information by-way-of
misinformation from agribusiness, many farmers adopt a pessimistic attitude
regarding their forecast of farming locally. These farmers feel the local social
environment is losing its supportive capacity. Although a time may come when
farming is not economically viable in Wayne County, surely a negative outlook
makes an impression on the next generation of potential farmers and acts to
redirect their ambitions to other vocations. The future of these farms is uncertain and the larger question to address is what is necessary for these farmers to view agriculture similarly to other farmers who are adjusting, persisting and doing it
interdependently with other local farmers.
In agreement with Salamon’s (1992) and Salamon and Rogers (1983)
findings that most Yankee, or British (English, Irish, Scottish, or Welsh) families
have potential farm heirs that are more likely to have occupations out of
agriculture and rent the farmland to a farmer. This is due to the expectation that
only one child will continue farming while others will be integrated into other
occupations through education. Concurrently, German, or yeoman farmers are
more likely to have children who plan to continue farming and thus will continue
to operate the farm as it is assumed that most children will remain in farming or
at least in an agricultural trade. Although there is a degree of agreement in the
findings from the Sugar Creek Watershed surveys and interviews, however, as is
the case with most research, these categories are not exclusive.
292 Ethnicity and heritage are closely connected in the small communities of
this study. Perhaps widely diverse groups of people from diverse parts of the
world coming together in the same place would create a divergence between
these two attributes, but among the people of the Sugar Creek, there is plenty of
room for overlap and sharing of one or both. Many of the German and Swiss
farmers share in a common Apostolic, Brethren or Mennonite faiths, which are all
Anabaptist, while others ethnicities are represented among Methodist, Lutheran
or Baptist religions. Yet, though religious and ethnic characteristics are starting points for a common heritage, the history of interaction and cooperation among
members of these rural communities is another, perhaps stronger component of
their heritage in which most of the community members share.
293
CHAPTER 7
ETHNICITY AND LEVEL OF SOCIOCULTURAL INTEGRATION OF FARM HOUSEHOLDS BY SUBWATERSHED
7.1 Introduction
In the ensuing chapter, social networks and community in the Sugar Creek
Watershed are analyzed in the context of levels of sociocultural integration
(LSCI). In using Steward’s concept of LSCI, I integrate the sociological concepts of social capital (Flora 1995, Putnam 1995, Bourdieu 1990) and embeddedness
(Grannovetter 1985) by emphasizing household participation in particular
LSCIs37. Flora (1995) parses the concept of capital into seven distinct types in which social capital describes networks of social relationships among people38.
The hypothesis tested in this chapter is that ethnicity and level of socio-cultural integration of the farm household, as independent variables, will affect farm size, land use and tenure, and use and preferences for conservation in which more traditional and less socioculturally integrated groups will have smaller farms, diversified land uses, more secure land tenure and greater preferences and use of Best Management Practices.
37 The Amish, for example, are integrated into the larger sociocultural system in the United States, integrating them into higher LSCIs, but they consciously construct their society to act and think locally. 38 Flora’s capital types include social, human, cultural, financial, built, natural, and political. I think these sociological explanations are analogous to itemizing Steward’s culture core as explained in Section 7.3.2. 294 This chapter is organized into five subsequent sections that pursue a discussion of Steward’s concept of LSCI as applied to farm communities in the
Sugar Creek Watershed. First a brief discussion of the analytical perspective taken in this chapter is presented followed by relevant literature and methods used. Ethnographic evidence from the watershed is then provided to contextualize the research. Statistical analysis of the survey data was performed using SPSS v11.5 and the results of correlation and ANOVA procedures are presented with plausible explanations for their occurrence. Finally, discussion and conclusions drawn from this research are presented.
7.2 Analytical Perspective
Data from the social surveys of the Sugar Creek subwatersheds are used in the following manner to construct metrics for hypothesis testing. An ordinal ranking of ethnicity and LSCI is constructed based on the concept of traditionalism39 (Kraybill and Hostetler 2001), farm size is calculated using total acres farmed. Indexes of conservation behavior (conservation attitudes and current conservation use) are created, which focus on both attitudes and reported behavior. Key informant interviews contextualize the data as ethnographic examples of the relationships found among the dependent and independent variables.
39 Kraybill and Hostetler (2001) use the term traditionalism in ranking Anabaptist affiliations based on their contemporary adherence to historically accepted cultural practices and interaction with non society people. In this ranking, the Old Order Amish are the most “traditional” and certain affiliations of Apostolic, Mennonite and Brethren rank the least traditional. John Hostetler (1993) uses a similar ranking of worldliness that is inversely ordered. 295 Initially, Salamon’s dichotomy of Yankee and yeoman farmer was used to formulate a typology of ethnicity and heritage distinctions among groups of farmers. This method worked heuristically to aid in understanding the differences found among these groups while analyzing the data, but was of little statistical significance when applied in a One-way Analysis of Variance (ANOVA) and other statistical modeling techniques used in subsequent chapters. I believe this was an artifact of the small scale at which the data was collected and, most likely, due to the diversity that is represented among the Anabaptist groups who live in the watershed 40 . However, using Kraybill and Hostetler’s traditionalism ranking is
both a good typology for understanding differences and proved to be statistically
significant while providing the basis for ordinal ranking of ethnicity and heritage.
7.3 Levels of Sociocultural Integration and Social Networks
In the Sugar Creek, sociocultural integration occurs at multiple levels such
as the household/family, county or state and national level. A person’s integration
into one or more levels depends on multiple factors that include social networks
encompassing local and non-local people as well as networks that include upper
socioeconomic classes near and far. Rather than stick with a strict definition of
ethnicity, the survey elicited responses that are ethnic categories (i.e. German,
French) and heritage and religious descriptions (i.e. Amish, Swiss Mennonite) which I combined to create a measure of ethnicity referred to as heritage group. I
40 Salamon’s (1992) typology is applied to several communities across the large expanse of southern Illinois and not confined to one watershed. 296 chose this strategy because these attributes fit well with the logic found in
participant responses to earlier surveys in another nearby research area41. The measure of this variable ranks heritage groups by degree of traditionalism permitting quantitative analysis of a rank-ordered variable.
7.3.1 Context: Family Farms as Firms
Research suggests that family run businesses are more adaptable to environmental 42 change because of their smaller scale has less momentum,
which allows for more rapid changes in the direction the business is going (Jones
2005). Lobley and Potter (2004) discuss Gasson and Errington’s (1993)
presentation of the apparent contradiction of family farm portrayal in
contemporary agricultural markets stating that, for example, Britain’s family farms
are both “subsumed” and “surviving” transformations in capitalist agriculture, and
that
“farm families are inevitably penetrated by external sources of capital and thus increasingly integrated within vertical supply chains, they also retain significant flexibility and freedom of movement in adapting to the changing demands of markets, technology, and the shifting relations of production.” (Lobley & Potter 2004:499).
Yet, United States Federal farm policy, through incentives and subsidies, make
the persistence of smaller scale family farms difficult (Delind 1992, Prugh et al
1995, Clancy 1997, Bonanno et al 2000). Increasingly, family farms succumb to
41 The researcher has experience working in the adjacent Apple Creek watershed as part of an agroecosystems health assessment project (Prasad et al in revision) during which time respondents offered a variety of religious, ethnic and heritage descriptions when asked to write in their heritage. 42 Environment is referred to here in the holistic sense of social (including economic) and physical environment. 297 external market pressures, pushing families to sell their farms for development or
to larger neighboring farms whose owners are increasing scale in an expansion/technological treadmill driven system. This is done in an effort to remain competitive. Alternatively, they might diversify their enterprises and remove themselves, partly or completely, from commodity-based competition.
Concurrently, most firms in the United States are owned and operated by families with few non-family laborers. Because of this, these family firms tend to be less profit-driven instead, they focus their efforts to provide for family needs
(Jones 2005). Work comes from family members and goals are driven by family
needs, as in Sahlin’s domestic mode of production (DMP). In this manner, work
is not always optimized to return the maximum benefit but rather is applied to
meet the needs of the individuals of the household; in this respect, family labor
follows the law of diminishing returns (Sahlins 1972). This emphasis on family
support removes the family from the “cut-throat” and predatory economics of
many larger corporations who are shareholder driven with an emphasis only on
profit (Cronk 1981, Jones 2005). In 2002, Wayne County had 1,702 family farms,
which are 89.9% of all county farms and an increase from 1,576 farms in 1997
(2002 U.S. Census of Agriculture). I believe the strength of these family farms is
in their capacity to work within and act to facilitate social networks that are an
integral part of building community. By bringing residents of a localized area into
298 frequent face-to-face interaction, social bonds are developed43 and a sense of place is created.
7.3.2 Social Networks and Social Capital
Level of sociocultural integration is the third concept of Julian Steward’s cultural ecology methodology in which he envisions multiple levels of integration for cultural groups in a society, for example, the household, community, and state, wherein various dependencies and responsibilities are created through functional specialization. Steward saw human survival as not only dependent on the adaptation to the immediate physical environment, but that survival for many
who are interconnected with multiple levels of sociocultural integration was
dependent upon social networks not affected by and necessarily removed from
the local environment. In this interconnected social network, environmental
factors in distant places may have an immediate impact on the lives of those
physically removed, and in which subsistence does not necessarily extend from
such places (i.e. a drought in Iowa may lead to better grain prices at the Chicago
market for Ohio farmers). Steward’s statements regarding level of sociocultural
integration distinguishes between two collections of cultural features: features
that function and should be analyzed at the national (or international) level, and
features analyzed at the levels of groups or subcomponents of society.
Accompanying these levels of society are different methods of analysis. In most
of contemporary Europe, for example, cultural features operated at multiple
43 Others refer to this as social capital (Putnam 1995, Flora et al 1997) or social embeddedness (Granovetter 1985). 299 levels of integration: societies cooperated in work parties, organized public
works, served in the national army etc (Steward 1955:43-63).
LSCIs are to be distinguished from the spatial hierarchies (Olson and
Lyson 1999:6-7) of economic and political manifestations of social organizations
in which all people in a geographic area are nested. All residents of Sugar Creek
Township and the vicinity of the village of Kidron are nested in the same spatial
hierarchical social structures of census block, township, county, state and nation
as their neighbors, however, the degree of participation in these levels is the
determining factor in the LSCI. To “opt out” of participation is a de facto level of
participation, that of non-participant. Yet, simply being a non-participant does not
imply unaffected by decision and policies that emanate from uppers levels of the
hierarchy.
Pierre Bourdieu (1984) states that social networks are socially constructed
and must be maintained through interaction in order to be used. His concept of
habitus may be applied here in understanding the relationships among people and their environment, a relationship that engenders specific understandings
based on a subjective reality, an individual’s perception, that is shaped by the
objective physical and social structures of his/her surrounding world. This
approach is mirrored in Rappaport’s (1979) cognitive models, and counters some
arguments of free-will by asserting that all humans, at any given time, do not
have an infinite number of possibilities for action, but rather have only those
possibilities provided them by their habitus, creativity, and resources. Sherry
Ortner states (1984) of culture: through past experiences, it shapes actions and
300 ways of thinking, in the absence of innovation, while also limiting future
alternative ways of acting and thinking. According to (Foley and Edwards 1999),
Putnam (1995) presents social capital as if society were an autonomous whole, a
superorganic, in which groups possess social capital and this capital is equitably
distributed. Durrenberger (2002) warns against this form of social capital stating
that it blurs class divisions and presents these networks as if they are open to
anyone who wants to participate. Additionally, social capital is simply another
term that includes social networks and social relationships and the degree of
trust found among them. Yet according to (Foley and Edwards 1999), Bourdieu’s
(1991) concept of social capital takes into account class divisions and includes
the relative degree of concentration of social capital directed towards individual
interests. In Bourdieu’s capital, there are no group actors, but only individuals
with interests that work collectively and are in conflict with others of differing
class interests in achieving their goals. In my analysis, there are no group actors
possessing social capital. I agree with Durrenberger and approach social capital
from the perspective of social relationships while acknowledging Bourdieu’s
emphasis on class divisions in society and the role of social capital to bring
together groups of individuals to work towards their common interests.
All people work and live within social networks; some networks are tightly
embedded social relationships consisting of family and neighbors in close
geographic proximity, while others are more diffused networks that extend beyond the local community and are influenced by decisions made based on information that may be perceived as irrelevant or become detrimental to that
301 farmer’s local community. Farmers that are active in conservation on an
individual basis are sometimes not involved in community or local level
participation where cooperation in such efforts may yield greater results (i.e.
towards water quality remediation).
Of the many positive aspects of social capital, Annen (2001), and others
(Portes & Lanholt 1996, Flora & Flora 2004) discuss negative forms of social capital in which community cohesiveness proscribes contact or exchange of ideas, technology and/or material culture with outside groups. Along a continuum of rural community cohesiveness, social capital may be exhibited strongly or weakly by households. This is associated with various levels of sociocultural integration in which less embedded households have weaker degrees of social capital within a small community while more embedded households, operating at lower LSCIs, have greater degrees of localized social capital44.
7.3.3 Place, Interaction and Embeddedness
A common component of rural localities is the idea or conceptualization of
community (i.e. how it is defined, and its perceived decline) acting as the social
glue of society. Wilkinson (1991) states that there are two attributes that are
necessary for community to exist; they are place and interaction. He emphasizes
that interaction among residents of a localized geographic area emerges
44 “Bedroom communities” suffer from a lack of social capital as many members of the community merely live in the area they own housing while most of their daily activities (e.g. working, socializing, recreating, and shopping) occur elsewhere (Salamon 2003). Social embeddedness is low because there are few ties beyond economic ownership of land with which people can connect. In other parts of rural communities, social capital is so strong that an outsider is regarded with suspicion and outside information is near impossible to convey. These areas of strong social capital show high degrees of local embeddedness because residents are bonded together in many aspects of their lives.
302 symbolically in the form of community, and a sense of place emerges for residents through structuring of activities and work towards a common goal. Bell
(1992) states that it is the sense of place that provides a local identity thereby connecting people with the land. In Newcomers to Old Towns, Salamon (2003:5) adds that Midwestern “agrarian communities”, exhibiting gemeinschaft, or an agrarian ideal, offer a strong sense of community because they are “cultural systems densely connected by social networks that link families in functional and emotional ways”. This agrarian ideal is structured around the idea that farming communities are tied to the land through tenure arrangements designed around social relationships with multiple generations of family members and neighbors in a localized geographic area; and, all aspects of their daily life (social, spiritual, economic etc) are interconnected and interdependent with their family and neighbors (Salamon 2003:14-15). People throughout the Midwest often share common heritages of immigration and ethnicity as early immigrants formed ethnic enclaves (Salamon 1992). Additionally, Salamon (2003) writes that the Midwest has entered a post-agrarian era in which community ties are weakened through the lessening of localized interaction and sense of place. Such an example would be new, non-agricultural, residents in a small town who view themselves as being from Wayne County, or even Northeast Ohio instead of Smithville, or Kidron.
Newcomers to these rural towns bring with them cultural norms and assumptions based on individuality, or gesellschaft, where enforcement is grounded in rule of law and formal regulation rather than the informal control of social networks and rural interdependence.
303 Multiple levels of social organization have connections that are socially
and spatially overlapping, but vary in the extent to which these boundaries
correspond. Networks of family, friends and acquaintances are found in various
structural settings such as school districts, municipal and political boundaries
(villages, cities, townships and counties), continuity, contiguity and extent of
Amish Church Districts or other denominational affiliations, and farmer
information exchange networks, among others, are just a small sample of the diversity and potential complexity of networks that families and households of the
Sugar Creek construct and reinforce on a daily basis.
7.3.4 LSCI and Liturgical Order among the Sugar Creek Farming
Communities
Midwestern farming communities are coupled to levels of sociocultural
integration spanning local and transnational levels of integration. In the Sugar
Creek, agrarian communities are found throughout in varying degrees. Family
farms composed of Amish, Apostolic or Mennonite families act as exemplars of
this point. The Amish largely offer the strongest example of agrarian community;
however, there are examples of discontentment within select congregations
(Donnermeyer course notes). Cronk states of the Amish community orientation is
that
[t]hrough both self-yielding and community-building qualities of work, the economic system is transformed from a potentially competitive, self-aggrandizing process into a way of caring about others (1981:9).
304 Amish and Mennonite liturgical orders (Rappaport 1999) emphasize moral
order in daily life rather than just strict adherence to periodic rituals (Cronk
1981:11); that is to say they place value on high levels of interaction and a sense
of localized place as the ideal living condition. Further, “Amish ritual takes place
in the ordinary spheres of everyday life” (ibid.:6) that are enacted in patterns of
dress, speech and use of language, and mutual aid that create symbols of
community in everyday life (Hostetler 1993). This emphasis on building
community through shared work and face-to-face interaction in daily life creates
awareness of and works to fulfill community members’ needs. Hostetler states
(1993:251) that “[t]he meaning inherent in Amish symbols keeps the group from deteriorating into a sentimental community with a precarious existence” and by displaying symbolic emblems of their community they “express unity in material form.” Among the Old Order Amish, these material forms are codified in the
Ordnung, the oral tradition of social order that is operationalized through ritual regulation which is agreed to by all members of the congregation (Ibid.).
Ritualized activity in daily life is not exclusive to Amish family farms, though it may be said that they act more strongly in producing community oriented behavior. Such activities are found among other farm families within and without the Anabaptist Orders. I believe that, to an uncertain extent, this is an aspect of the smallholder type (Netting 1993) as well as a factor of living and operating an enterprise in such a community. Interviews and oral histories of families in the watershed reveal that residents of faiths other than Anabaptist have strong local connections and strongly identify with the local cultural heritage
305 of their community. Several participants in the oral history project shared their
own connections to the community sharing stories of community solidarity in
times of crisis45.
In the larger faming community, gemeinschaft is found among activities such as Friday night high school football, community chicken barbeques, Fourth
of July parades, morning coffee at the local diner, Sunday church services and
church socials. Although many aspects of social order in this complex society
rely on various elements of gesellschaft, these and other events act to solidify
bonds of mutual aid and economic support in support of common goals. At these
activities, bonds are reaffirmed as information about the weather, agricultural
markets, production strategies, other members of social networks and their
activities are shared. Face-to-face interaction is not as strong among community
members in the Upper Sugar Creek when compared to Amish practices in the
North Fork or Little Sugar Creek due in some part to increasing mechanization of
farm labor and reliance on crop consultants and external information sources, but
these gatherings and activities still act to buffer changes to community structures.
7.3.5 LSCI and Conservation
Among the farmers in the Upper Sugar Creek subwatershed are many small and medium-sized farms that are active in conservation adoption. At the same time, larger-scale farmers tend not to be involved in voluntary group cooperative efforts for conservation adoption nor do they favor many individual
45 For example, residents often speak of neighbors helping during illness, floods, poor crop season and times when the community is able to “pull together” to meet any challenge. 306 initiatives. Two separate examples of farmers provide ethnographic evidence of
large-scale farmers who chose not to participate in community conservation
efforts.
The first two farms were initially involved in the efforts of the Upper Sugar
Creek partners to find local solutions to the water quality problems in the watershed. A father and son (1b-G2-M2 and 1b-G2-M3), farming a 950-acre corn and soybean farm, showed some interest by attending the first few meetings but were disillusioned by team efforts because of the topic of riparian zone protection and tree planting. They stated that they felt the team’s goals were not in line with the goals of their farm and that the group (farmers and researchers) were attempting to push an environmental agenda that was not compatible with their management style. It was recently revealed that the succession of this farm is in jeopardy and it may be sold for development because of the exorbitant costs associated with inheritance payments to non-farming family by the farm successors.
The HH22 Farm, managed by two brothers and hired labor, operate as a
partnership of over 800 acres, producing grain and potatoes, came to the first
meeting and decided not to come to others. This was because they were not interested in participating in a lengthy process of remediation. However, they said they were tentatively interested in cooperating with the team if an easy solution was found but were “too busy” to participate in regular meetings and activities because the amount of stream that actually runs through their farm is small.
307 As mentioned, these farms were initially interested in participating, but
later perceived incompatibilities with the goals of the Partners. Since this initial
rejection of the community-led conservation efforts, I have learned that both
farms are also encountering difficulty with intergenerational farm transfer. In the case of the father and son, the problem is a matter of there being too many non- farm kin who want to participate in the transfer, thereby making it too expensive for the farming kin to “buy-out” the others. The potato farmers have separated the farm into potato and dairy operations for which neither has an heir who will continue the operations.
The second two farms are the HH23s and HH1as. These two farm families
are the largest farms in the subwatershed and represent farms from the highest
LSCI because of their strong ties to state and national agricultural commodity
groups (the HH1a ties are discussed in Chapter 6). There was no interest initially
and is none currently in participating in locally led conservation initiatives. Having
said that, it is necessary to point out that a member of HH23, is active at the
county and state level as a member of the Wayne County Soil and Water
Conservation District and a participant in on-farm research trials with the Ohio
Agricultural Research and Development Center’s Integrated Pest Management
(IPM) program.
Farmers in the North Fork subwatershed vary in their LSCI because of
community diversity. Two main communities are found in the subwatershed, the
Swiss Mennonites whose members trace their heritage to the historical
Sonnenberg settlement and earlier to Bern, Switzerland, and the Old Order and
308 Swartzentruber Amish many of whose ancestors migrated from Alsace-Lorain or
Eastern Pennsylvania, in which between-group interaction is frequent, outside of communion46 which is in-group focused, and interaction with non-Amish groups is minimal and predominantly only to the extent necessitated by economics.
Little Sugar Creek is dominated by Old Order and some Swartzentruber
Amish farmers. This subwatershed is characterized by high embeddedness due
to the exclusivity of Amish society. Several Church Districts are contiguously
organized along the nine-mile narrow subwatershed, with Kansas Road running
north to south as the central road. Amish members of the Green Fields Farm
organic Amish farm cooperative are found in both subwatersheds. This is a new social network and form of integration with non-Amish that focuses on marketing
Amish organic products while still maintaining an economic system as one that cares about its members thereby protecting their way of life for future farmers.
Local Integration and Conservation
Successes of the Partners have come in many forms including social and
environmental. To date, they have installed three miles of Conservation Reserve
Program (CRP) buffers consisting of trees and/or grasses separating the fields
from the streams by a minimum of thirty feet. There is an aspect of community in
this buffer in which farmers now relate to one another based on their location on
the stream and where their buffers connect. Aside from the riparian buffers, many
of the Partners have implemented several new “best management practices”
46 Communion is used in the broad sense of church worship and adherence to precepts of the Ordnung. 309 (BMPs) such as milk house waste elimination, livestock exclusion and prescribed grazing fencing, and grass waterways. Another idea the group is considering is joining parts of several properties in a wetland.
“Upper Sugar Creek Family Day” occurs in July, before farm labor
intensifies in late summer, and involves all of the team members and their
families. Friends and neighbors are also invited. At this gathering, there is a
picnic in the local Smithville Park that is located along the mainstem of the stream. Educational and fun activities are planned for all age groups with the expressed goal of celebrating the team’s participation in water quality remediation. Pride in the stream extends to the farm of each of the farmers as they come together in the early fall to host or attend the annual farm tour.
Neighboring farmers and friends of the Partners have also started to attend.
These tours focus on the changes that the host family has instituted on their farm as well as highlighting family history and their historical and social connections to the community. The farmers also take the opportunity to highlight their operation and unique aspects of their management style.
The strength of the Upper Sugar Creek Partners may come from the manner in which the team formed. It began when farmers recruited from family and neighbors using existing kinship and social networks in the community in the following manner: one farmer chose three other farmers who each chose two new members to initiate dialogue regarding the Sugar Creek. This is much different from other community groups that attempt a top-down “democratic” pattern of selecting representatives from among different segments of society.
310 Another area of strength is that team members do not operate using by-laws or
other formal rules that prescribe membership or other regulations, which allows for flexibility in decision-making. Through this approach, the group is able to build temporary agreements for issues for which they disagree instead of consensus
regarding water quality, new members, outside visitors, and future directions.
In the North Fork, a tributary along Mount Eaton Road has been improved
through collective action of the farmers owning the land through which the stream
flows. The farmers are all Amish and members of the same contiguous Church
District. Using this existing social structure, members are able to achieve water
quality remediation through social cooperation and, in some cases, pressure
from within the Church District.
7.4 Data Construction and Analysis
Specific examples are discussed in this section in which LSCI and land
tenure, farm type and farm size, and heritage group interact. Then, the results of
a correlations analysis are explored for corroborating evidence. Finally, two
analysis of variance sequences are run to explore the variance of categories for
heritage group and farm type and compare the mean differences.
As described in Chapter 4, two surveys were administered in the Sugar
Creek Watershed: one in the Upper Sugar Creek in 2000-2001, and another to residents of the Mainstem, Little Sugar Creek and the North Fork subwatersheds
in 2002-2003. The second survey collected the same data as the initial
311 procedure, with changes being made in the format to improve the participants
experience and easier coding and data entry.
Inferences can be made from the survey variables and constructed
indexes (described below) that provide information on human social networks
and what Steward (1955) refers to as levels of sociocultural integration (LSCI).
These variables include responses to questions ascertaining demographic
characteristics of persons in the household such as heritage, religion,
characteristics of land tenure 47 and operation type. Correlation analysis and
Analysis of Variance (ANOVA) are used to test the hypothesis by emphasizing differences in various measures of: conservation practice adoption preferences, land tenure, farm size, level of off-farm income, and intergenerational farm success.
In this analysis heritage group, farm size, land tenure and preferences for conservation adoption are used as indicators of household levels of sociocultural integration. I predicted that those farms that are larger, non-diversified row- cropping, have higher rental rates and have less preferences for conservation adoption. Subsequently, these households are also incorporated into higher levels of sociocultural integration.
Among the Amish, levels of social capital are strong within all heritage
communities, but are extremely high within the Swartzentruber Amish, closing
their community to most all outside contact. Swartzentruber Amish are mostly
47 The social networks involved and obligations incurred through them and ratio of ownership to leasing. 312 farmers and the dissemination of new conservation ideas difficult among the
people of this community.
Ethnicity and heritage, operationalized in broader terms as heritage group,
is used as a distinction among members of the watershed populations as well as
an indicator of sociocultural integration. Ranking heritage classifications along a
continuum of traditionalism (Kraybill and Hostetler 2001:56-65) 48 , I created
heritage defined LSCIs based on characteristics of the population of each group.
These characteristics include degree of farm income, farm size, proximity to
family and church member, symbols of in-group identification, all of which are
characteristics found in reference to Anabaptist groups, specifically Amish groups
(Hostetler 1993, Kraybill and Hostetler 2001, Donnermeyer and Cooksey 2004).
Five categories of traditionalism are ranked as follows from the highest
degree of traditionalism, inversely characterized as acting in the lowest LSCIs:
Swartzentruber (SA, rank 5), Old Order Amish (OOA, rank 4), New Order Amish
(NOA, rank 3), Apostolic-Mennonite-Brethren (AMB, rank 2), and “English” (E,
rank 1). Correlations and Analysis of Variance reveal that these characterizations
of traditionalism show certain relationships regarding a group’s integration into
the larger society. What is more striking is that there is a range of the LSCIs, the
48In this ranking, Anabaptist groups are ordered from conservative to progressive. Along this continuum, the groups are classified as traditional, transitional and transformational. The authors are clear in stating that this continuum is in no way a linear process of change or fixed progression. Rather, change is dynamic and there is no fixed progression since historically, groups have both moved towards being more conservative while others become more progressive, and other group members often moving between groups to satisfy their own dispositions. Group characteristics are based on such ideas as the degree to which a group emphasizes the preservation of tradition (cultural and religious) and attempts to exclude the surrounding exogenous society. One could just as easily use Hostetler’s (1993) term worldliness that is the inverse of traditionalism. 313 middle range of AMB and OOA, which are most likely to participate in
conservation or express interest in conservation adoption.
In the cross-tabulations table, farm type is composed of two nominal
categories49 based on the primary product of the farm as derived from surveys.
Then farms are coded dichotomously as grain, coded “1” and other, coded “0”.
Incidentally, descriptive statistics also show this coding to be consistent with farm
size since most large farms are grain operations and smaller farmers are animal
based and “other”. Farm categories are expanded to include grain, dairy, other
animal and other for the ANOVA.
Statistical relationships among the variables were investigated using a
correlations analysis and one-way analysis of variance (ANOVA) procedures in
SPSS (v11.5) to investigate the strength and direction of differences among variables across subwatersheds.
7.5 Results: Correlations and ANOVA
In the following three sections, results of the correlations analysis and
analysis of variance operations are presented. The correlations analysis
introduces the major variables involved in the statistical procedures and explores
the direction and intensity of the basic relationships between each variable
(Table 7.1). The ANOVA procedures explore the variance that exists within and between categories of the dependent variables of heritage group in the first
analysis, and farm type in the second. Each of the two ANOVA sections
49 In other chapters, farm type is ranked by farm size calculated as the total area of land farmed, leased and owned. 314 introduces the concepts and reasons for variable inclusion. Interactions among
the categories of each variable are explored and explained based on the
previous theoretical and ethnographic presentations. Graphs showing means of
each variable in relation to the dependent variables under investigation are
included for reference in visualizing statistically significant mean differences
among the categories.
7.5.1 Correlations
Results of SPSS analysis for associations (Spearman’s ρ, Kendall’s τb)
and correlations (Pearson’s r) are displayed in Table 7.1, the Correlations Matrix.
They show that several of the variables used in testing the hypothesis of this chapter are related at statistically significant levels that vary in strength and
direction. The following relationships between farm size, farm type, land tenure,
conservation adoption and heritage are established through correlation analysis.
Traditionalism is not included in the Correlations Matrix because no statistically significant relationships exist with any of the other measures on a case-by-case basis; However, Chapter 8 explains how, in combination, these variables help to explain dimensions of land tenure and conservation behavior.
315
Farm Farm Tenure Conservation Conservation Lease-Out Off-Farm Size Type Use Index All Land Income
Farm Size Cor. 1.000 sig. N 159 Farm Type -.060
Cor. (ρ) 1.000 sig. .519 N 117 117 Land Tenure .155** -.268**
Cor. (τb) (ρ) 1.000 sig. .010 .004 N 159 117 159 Conservation .292** -.063 .099 Use Cor. (r) (ρ) (τb) 1.000 sig. .000 .498 .126 N 159 117 159 159 Conservation .277** .126 .359** .597** Index Cor. (r) (ρ) (τb) (r) 1.000 sig. .000 .175 .000 .000 N 159 117 159 159 159 Lease-Out All -.417** .244** -.900** -.242** -.507** Land Cor. (ρ) (ρ) (ρ) (ρ) (ρ) 1.000 sig. .000 .008 .000 .002 .000 N 159 117 159 159 159 159 Off-Farm .212** .288** -.215** -.166* .134 .305** Income Cor. (τb) (ρ) (τb) (τb) (τb) (ρ) 1.00 sig. .000 .003 .002 .015 .063 .000 N 159 106 139 139 139 139 159 Farm .237** -.283** .471** .079 .262** -.678** -.159* Succession Index Cor. (τb) (ρ) (τb) (τb) (τb) (ρ) (τb) sig. .000 .003 .000 .281 .001 .000 .043 N 139 109 139 139 139 139 123 ** correlation is significant at the .01 level (2-tailed) * correlation is significant at the .05 level (2-tailed)
Table 7.1 Correlations Matrix
316 So that this chapter can stand alone, this brief section is repeated from
Chapter 6. Multiple correlations and associations are found among the variables
that demonstrate the nuanced real-life interconnectedness of the land tenure
variables with conservation behavior. As predicted, both the Conservation Use
and Conservation Index scores correlate with several of the land tenure
variables. Conservation Use correlates with farm size (r=.292**), and is
consistent with the literature stating larger farms have greater flexibility to take
land out of production for conservation, and has a low negative association with
Lease-out all land (ρ =-.242**) showing that farms that are leased out tend to not
have BMPs implemented on them. It also has a low negative correlation with off-
farm income. The Conservation Index is positively correlated with farm size
(r=.277**), a moderate positive correlation with Land Tenure (τb =.359), and a
substantial negative association with Lease-Out All Land (ρ=-.507**). And, the
Conservation Index has a low positive correlation with Farm Succession Index (τb
=.262**). Both conservation measures show no associations with Farm Type.
Farm Type has a low negative association with land tenure (ρ=-.268**), a low negative association with Lease-Out All Land (ρ=.244**), and a low negative association with Farm Succession Index (ρ=-.283**).
Other notable relationships include the following: Farm Size and Land
Tenure have a low correlation (τb =.155**) because, as Hart (1991) has shown,
larger farms correlate with higher levels of land leasing, which helps explain the
moderate positive association between farm size and lease-out (ρ=-.417**) in
317 which farm size decreases with land that is leased-out. This leads to the
relationship between Land tenure and farm type that have a low negative association (ρ =-.268**); farmers of smaller farms generally own more of their
land and an important difference in this watershed is the inclusion of Amish farmers who tend to own more of the land they farm and have smaller farms.
Farm Succession Index is correlated or associated with all variables with
the exception of Conservation Use. The moderate positive correlation between
Farm Succession Index and Land Tenure (τb =.471**) and the substantial
negative association with Leased-out All Land (ρ=-.687**) indicate relationships between leasing and farm succession. There are no correlations between these variables and ethnicity as measured by degree of traditionalism.
These associations and correlations indicate broad connections between
variables as discrete units of analysis, but they do not provide analysis of
variable combinations contributing to variance or as aspects of a dimension of
the variable.
7.5.2 ANOVA of LSCI
Heritage Group ANOVA Part 1
As discussed in Chapter Six, heritage group membership in the Sugar
Creek varies by subwatershed in which the most traditional groups are found in
the Little Sugar Creek, a balance of traditional/worldly groups are found in the
North Fork, slightly more worldly groups in the Mainstem, and none of the highly traditional groups in the Upper Sugar Creek. Differentiation of groups ranked
318 along Kraybill & Hostetler’s (2001) continuum of traditionalism provides
uniqueness to each subwatershed. Our understanding of these differences is
enhanced by ANOVA of a number of statistically significant differences in the
following measured variables: farm type, subwatershed (covered in Chapter 6),
number of years a person has farmed, current use of conservation practices,
desired recreational uses of the stream, average education level of attainment,
overall conservation index scores, no-till adoption, aesthetic value of the stream,
and perception of pollution in the stream. Other variables were analyzed for this
section, however, only those that are statistically significant (α =.05, and α =.01) are included in this chapter and the discussion. Descriptive statistics are provided in Table 7.2 for each variable in the analysis.
319
Heritage Std. Std. 95% Confidence Group* N Mean Deviation Error Interval for Mean Min Max Lower Upper Bound Bound Farm Type 1 E 38 2.26 .978 .159 1.94 2.58 1 4 Index 2 AMB 59 1.85 .761 .099 1.65 2.05 1 4 4 OOA 14 2.43 1.016 .272 1.84 3.02 1 4 5 SA 4 2.50 1.000 .500 .91 4.09 2 4 Total 115 2.08 .900 .084 1.91 2.24 1 4 Sub- 1 E 50 2.12 1.172 .166 1.79 2.45 1 4 Watershed 2 AMB 82 2.29 1.105 .122 2.05 2.54 1 4 4 OOA 22 3.55 .858 .183 3.17 3.93 2 4 5 SA 5 3.00 1.000 .447 1.76 4.24 2 4 Total 159 2.43 1.183 .094 2.25 2.62 1 4 Number of 1 E 46 25.46 18.939 2.792 19.83 31.08 0 80 Years 2 AMB 75 27.95 13.823 1.596 24.77 31.13 0 60 Farmed 4 OOA 22 18.45 11.673 2.489 13.28 23.63 1 45 5 SA 5 38.60 31.769 14.208 -.85 78.05 7 90 Total 148 26.12 16.397 1.348 23.46 28.79 0 90 Conservation 1 E 50 1.96 .755 .107 1.75 2.17 1 3 Use 2 AMB 82 2.17 .663 .073 2.03 2.32 1 3 4 OOA 22 1.68 .568 .121 1.43 1.93 1 3 5 SA 5 2.20 .837 .374 1.16 3.24 1 3 Total 159 2.04 .702 .056 1.93 2.15 1 3 Desired Use 1 E 50 .60 .833 .118 .36 .84 0 3 Recreational 2 AMB 82 .39 .797 .088 .22 .57 0 4 Score 4 OOA 22 1.00 1.480 .316 .34 1.66 0 4 5 SA 5 .20 .447 .200 -.36 .76 0 1 Total 159 .53 .940 .075 .39 .68 0 4 Education 1 E 50 3.06 1.185 .168 2.72 3.40 2 5 Level 2 AMB 79 2.53 1.023 .115 2.30 2.76 0 5 4 OOA 22 1.00 .000 .000 1.00 1.00 1 1 5 SA 5 1.00 .000 .000 1.00 1.00 1 1 Total 156 2.44 1.208 .097 2.24 2.63 0 5 Implemented 1 E 50 .46 .503 .071 .32 .60 0 1 No-Till 2 AMB 82 .49 .503 .056 .38 .60 0 1 4 OOA 22 .18 .395 .084 .01 .36 0 1 5 SA 5 .00 .000 .000 .00 .00 0 0 Total 159 .42 .495 .039 .34 .50 0 1 Fishing Use 1 E 50 .16 .370 .052 .05 .27 0 1 2 AMB 82 .13 .343 .038 .06 .21 0 1 4 OOA 22 .41 .503 .107 .19 .63 0 1 5 SA 5 .20 .447 .200 -.36 .76 0 1 Total 159 .18 .387 .031 .12 .24 0 1
Table 7.2 Heritage Group ANOVA Descriptive Statistics
Continued
320 Table 7.2 (continued)
Local 1 E 50 .46 .503 .071 .32 .60 0 1 Government 2 AMB 82 .22 .416 .046 .13 .31 0 1 Decision 4 OOA 22 .27 .456 .097 .07 .47 0 1 Makers 5 SA 5 .20 .447 .200 -.36 .76 0 1 Total 159 .30 .461 .037 .23 .37 0 1 Pollution in 1 E 49 3.16 .898 .128 2.91 3.42 1 5 Sugar Creek 2 AMB 80 3.46 .871 .097 3.27 3.66 2 5 4 OOA 21 3.24 1.044 .228 2.76 3.71 1 5 5 SA 5 2.20 1.304 .583 .58 3.82 0 3 Total 155 3.30 .941 .076 3.15 3.45 0 5 Sugar Creek 1 E 49 2.84 1.106 .158 2.52 3.15 1 5 Aesthetics 2 AMB 81 3.00 1.107 .123 2.76 3.24 1 5 4 OOA 22 3.32 1.171 .250 2.80 3.84 1 5 5 SA 5 1.60 1.140 .510 .18 3.02 0 3 Total 157 2.95 1.142 .091 2.77 3.13 0 5 Off-Farm 1 E 41 2.15 .823 .129 1.89 2.41 1 3 Income 2 AMB 74 1.85 .855 .099 1.65 2.05 1 3 Index 4 OOA 19 2.16 .765 .175 1.79 2.53 1 3 5 SA 5 1.20 .447 .200 .64 1.76 1 2 Total 139 1.96 .842 .071 1.82 2.10 1 3 Conservation 1 E 50 2.14 .808 .114 1.91 2.37 1 3 Index 2 AMB 82 2.10 .764 .084 1.93 2.27 1 3 4 OOA 22 2.00 .816 .174 1.64 2.36 1 3 5 SA 5 2.00 1.000 .447 .76 3.24 1 3 Total 159 2.09 .786 .062 1.97 2.22 1 3 * E=English, AMB=Apostolic-Mennonite-Brethren, OOA=Old Order Amish, SW=Swartzentruber Amish
321 Thirteen variables were statistically significant in the analysis of variance for heritage group membership. The variables are listed in Table 7.3 with the respective F-values and level of statistical significance. They are:
1. Farm type,
2. Subwatershed,
3. The number of years a participant has farmed,
4. Current land use score measuring conservation practices that are
currently implemented,
5. Desire for recreational uses of the stream,
6. Highest level of education,
7. Implementation of no-till conservation tillage on the farm,
8. Desire for fishing activities in the stream,
9. Opinion on whether local government officials should make water quality
decisions regarding clean-up in the Sugar Creek,
10. Perception or extent to which they feel the Sugar Creek is polluted,
11. Do participants feel that farmers should change their practices to protect
the scenic beauty of the watershed,
12. The percent of off-farm income the household receives,
13. And, a conservation index which measures conservation attitudes and
behavior.
322
Sum of Mean Squares Df Square F Sig. Farm Type Between Groups 6.872 3 2.291 2.976 .035 Index Within Groups 85.424 111 .770 Total 92.296 114 Subwatershed Between Groups 36.571 3 12.190 10.506 .000 Within Groups 179.856 155 1.160 Total 216.428 158 Number of Between Groups 2341.957 3 780.652 3.024 .032 Years Farmed Within Groups 37179.854 144 258.193 Total 39521.811 147 Conservation Between Groups 4.671 3 1.557 3.301 .022 Use Within Groups 73.102 155 .472 Total 77.774 158 Desired Use Between Groups 7.248 3 2.416 2.830 .040 Recreational Within Groups 132.312 155 .854 Score Total 139.560 158 Education Between Groups 75.868 3 25.289 25.543 .000 Level Within Groups 150.491 152 .990 Total 226.359 155 Implemented Between Groups 2.587 3 .862 3.694 .013 No-Till Within Groups 36.181 155 .233 Total 38.767 158 Fishing Use Between Groups 1.348 3 .449 3.115 .028 Within Groups 22.363 155 .144 Total 23.711 158 Local Gov’t Between Groups 1.877 3 .626 3.066 .030 Decision Within Groups 31.632 155 .204 Makers Total 33.509 158 Pollution in Between Groups 9.157 3 3.052 3.624 .015 Sugar Creek Within Groups 127.191 151 .842 Total 136.348 154 Aesthetics Between Groups 12.926 3 4.309 3.457 .018 More Within Groups 190.667 153 1.246 Important Total 203.592 156 Off-Farm Between Groups 5.928 3 1.976 2.905 .037 Income Index Within Groups 91.813 135 .680 Total 97.741 138 Conservation Between Groups .345 3 .115 .184 .907 Index Within Groups 97.240 155 .627 Total 97.585 158
Table 7.3 ANOVA of Heritage Groups
323 7.5.3 Mean Differences Between Groups
(I) (J) Mean Dependent Heritage Heritage Difference Variable Index Index (I-J) Std. Error Sig. 95% Confidence Interval Lower Upper Bound Bound Farm Type 1 E 2 AMB .38 .187 .184 -.11 .87 Index 4 OOA -.27 .276 .761 -.99 .45 5 SA -.24 .475 .959 -1.48 1.00 2 AMB 1 E -.38 .187 .184 -.87 .11 4 OOA -.65 .261 .067 -1.33 .03 5 SA -.62 .467 .551 -1.83 .60 4 OOA 1 E .27 .276 .761 -.45 .99 2 AMB .65 .261 .067 -.03 1.33 5 SA .03 .509 1.000 -1.29 1.36 5 SA 1 E .24 .475 .959 -1.00 1.48 2 AMB .62 .467 .551 -.60 1.83 4 OOA -.03 .509 1.000 -1.36 1.29 Sub- 1 E 2 AMB -.12 .193 .919 -.63 .38 watershed 4 OOA -1.43(**) .276 .000 -2.14 -.71 5 SA -.88 .505 .306 -2.19 .43 2 AMB 1 E .12 .193 .919 -.38 .63 4 OOA -1.30(**) .259 .000 -1.97 -.63 5 SA -.76 .496 .426 -2.04 .53 4 OOA 1 E 1.43(**) .276 .000 .71 2.14 2 AMB 1.30(**) .259 .000 .63 1.97 5 SA .55 .534 .737 -.84 1.93 5 SA 1 E .88 .505 .306 -.43 2.19 2 AMB .76 .496 .426 -.53 2.04 4 OOA -.55 .534 .737 -1.93 .84 Number of 1 E 2 AMB -2.49 3.009 .841 -10.31 5.33 Years 4 OOA 7.00 4.165 .337 -3.82 17.83 Farmed 5 SA -13.14 7.566 .308 -32.81 6.52 2 AMB 1 E 2.49 3.009 .841 -5.33 10.31 4 OOA 9.49 3.896 .075 -.63 19.62 5 SA -10.65 7.422 .479 -29.94 8.64 4 OOA 1 E -7.00 4.165 .337 -17.83 3.82 2 AMB -9.49 3.896 .075 -19.62 .63 5 SA -20.15 7.961 .059 -40.84 .55 5 SA 1 E 13.14 7.566 .308 -6.52 32.81 2 AMB 10.65 7.422 .479 -8.64 29.94 4 OOA 20.15 7.961 .059 -.55 40.84 Conservati 1 E 2 AMB -.21 .123 .322 -.53 .11 on Use 4 OOA .28 .176 .391 -.18 .73 5 SA -.24 .322 .879 -1.08 .60 2 AMB 1 E .21 .123 .322 -.11 .53 4 OOA .49(*) .165 .018 .06 .92 5 SA -.03 .316 1.000 -.85 .79
Table 7.4 Tukey HSD Multiple Comparisons
Continued
324 Table 7.4 (continued)
4 OOA 1 E -.28 .176 .391 -.73 .18 2 AMB -.49(*) .165 .018 -.92 -.06 5 SA -.52 .340 .426 -1.40 .37 5 SA 1 E .24 .322 .879 -.60 1.08 2 AMB .03 .316 1.000 -.79 .85 4 OOA .52 .340 .426 -.37 1.40 Desired 1 E 2 AMB .21 .166 .586 -.22 .64 Recreational 4 OOA -.40 .236 .331 -1.01 .21 Use Score 5 SA .40 .433 .793 -.73 1.53 2 AMB 1 E -.21 .166 .586 -.64 .22 4 OOA -.61(*) .222 .034 -1.19 -.03 5 SA .19 .426 .970 -.92 1.30 4 OOA 1 E .40 .236 .331 -.21 1.01 2 AMB .61(*) .222 .034 .03 1.19 5 SA .80 .458 .303 -.39 1.99 5 SA 1 E -.40 .433 .793 -1.53 .73 2 AMB -.19 .426 .970 -1.30 .92 4 OOA -.80 .458 .303 -1.99 .39 Education 1 E 2 AMB .53(*) .180 .020 .06 1.00 Level 4 OOA 2.06(**) .255 .000 1.40 2.72 5 SA 2.06(**) .467 .000 .85 3.27 2 AMB 1 E -.53(*) .180 .020 -1.00 -.06 4 OOA 1.53(**) .240 .000 .91 2.15 5 SA 1.53(**) .459 .006 .34 2.72 4 OOA 1 E -2.06(**) .255 .000 -2.72 -1.40 2 AMB -1.53(**) .240 .000 -2.15 -.91 5 SA .00 .493 1.000 -1.28 1.28 5 SA 1 E -2.06(**) .467 .000 -3.27 -.85 2 AMB -1.53(**) .459 .006 -2.72 -.34 4 OOA .00 .493 1.000 -1.28 1.28 Implemented 1 E 2 AMB -.03 .087 .989 -.25 .20 No-Till 4 OOA .28 .124 .114 -.04 .60 5 SA .46 .227 .182 -.13 1.05 2 AMB 1 E .03 .087 .989 -.20 .25 4 OOA .31(*) .116 .045 .00 .61 5 SA .49 .223 .130 -.09 1.07 4 OOA 1 E -.28 .124 .114 -.60 .04 2 AMB -.31(*) .116 .045 -.61 .00 5 SA .18 .239 .872 -.44 .80 5 SA 1 E -.46 .227 .182 -1.05 .13 2 AMB -.49 .223 .130 -1.07 .09 4 OOA -.18 .239 .872 -.80 .44 Fishing 1 E 2 AMB .03 .068 .981 -.15 .20 Use 4 OOA -.25 .097 .055 -.50 .00 5 SA -.04 .178 .996 -.50 .42 2 AMB 1 E -.03 .068 .981 -.20 .15 4 OOA -.27(*) .091 .016 -.51 -.04 5 SA -.07 .175 .982 -.52 .39 4 OOA 1 E .25 .097 .055 .00 .50 2 AMB .27(*) .091 .016 .04 .51 5 SA .21 .188 .683 -.28 .70 5 SA 1 E .04 .178 .996 -.42 .50
Continued 325 Table 7.4 (continued)
2 AMB .07 .175 .982 -.39 .52 4 OOA -.21 .188 .683 -.70 .28 Sugar Creek 1 E 2 AMB -.30 .166 .279 -.73 .13 is Polluted? 4 OOA -.07 .239 .989 -.70 .55 5 SA .96 .431 .118 -.16 2.08 2 AMB 1 E .30 .166 .279 -.13 .73 4 OOA .22 .225 .751 -.36 .81 5 SA 1.26(*) .423 .017 .16 2.36 4 OOA 1 E .07 .239 .989 -.55 .70 2 AMB -.22 .225 .751 -.81 .36 5 SA 1.04 .457 .109 -.15 2.22 5 SA 1 E -.96 .431 .118 -2.08 .16 2 AMB -1.26(*) .423 .017 -2.36 -.16 4 OOA -1.04 .457 .109 -2.22 .15 Aesthetics 1 E 2 AMB -.16 .202 .851 -.69 .36 More 4 OOA -.48 .286 .338 -1.23 .26 Important 5 SA 1.24 .524 .089 -.12 2.60 2 AMB 1 E .16 .202 .851 -.36 .69 4 OOA -.32 .268 .637 -1.02 .38 5 SA 1.40(*) .514 .036 .06 2.74 4 OOA 1 E .48 .286 .338 -.26 1.23 2 AMB .32 .268 .637 -.38 1.02 5 SA 1.72(*) .553 .012 .28 3.15 5 SA 1 E -1.24 .524 .089 -2.60 .12 2 AMB -1.40(*) .514 .036 -2.74 -.06 4 OOA -1.72(*) .553 .012 -3.15 -.28 Off-Farm 1 E 2 AMB .29 .161 .261 -.12 .71 Income 4 OOA -.01 .229 1.000 -.61 .58 Index 5 SA .95 .391 .078 -.07 1.96 2 AMB 1 E -.29 .161 .261 -.71 .12 4 OOA -.31 .212 .474 -.86 .25 5 SA .65 .381 .323 -.34 1.64 4 OOA 1 E .01 .229 1.000 -.58 .61 2 AMB .31 .212 .474 -.25 .86 5 SA .96 .415 .101 -.12 2.04 5 SA 1 E -.95 .391 .078 -1.96 .07 2 AMB -.65 .381 .323 -1.64 .34 4 OOA -.96 .415 .101 -2.04 .12 Conservation 1 E 2 AMB .04 .142 .991 -.33 .41 Index 4 OOA .14 .203 .900 -.39 .67 5 SA .14 .372 .982 -.82 1.10 2 AMB 1 E -.04 .142 .991 -.41 .33 4 OOA .10 .190 .956 -.40 .59 5 SA .10 .365 .993 -.85 1.05 4 OOA 1 E -.14 .203 .900 -.67 .39 2 AMB -.10 .190 .956 -.59 .40 5 SA .00 .392 1.000 -1.02 1.02 5 SA 1 E -.14 .372 .982 -1.10 .82 2 AMB -.10 .365 .993 -1.05 .85 4 OOA .00 .392 1.000 -1.02 1.02 * The mean difference is significant at the .05 level. ** The mean difference is significant at the .01 level.
326 The following variables from Table 7.4 have statistically significant differences between heritage groups in the Sugar Creek as presented below.
The category means are rounded to the nearest tenth. Figure 7.1 shows the Old
Order and Swartzentruber Amish having the most non-grain farms; these relationships are not statistically significant.
3.0
2.5 2.5 2.5 2.3 2.0 1.9
1.5
1.0
.5
0.0 Mean of FarmType Index English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.1 Farm Type
327 Subwatershed is a nominal variable, but Figure 7.2 does indicate that there is significant difference between each of the means of the Amish groups with those of the other two.
4.0
3.5 3.5
3.0 3.0
2.5
2.2 2.0 2.1
1.5 Mean of Subwatershed# of Mean English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.2 Subwatershed
328 There are no significant differences among heritage group means for the number of years a farmer has been farming (Figure 7.3), but there is a great deal of variance. The Swartzentruber Amish have the highest mean years of farming; the Old Order Amish have the lowest mean.
40
39
30
28
25
20
18
10 Mean of Number of Years Farmed Years of Number of Mean English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.3 Number of Years Farmed
329 There are several differences among the groups’ conservation use scores
(Figure 7.4). The AMB group has the second highest overall conservation score
mean differences with one statistically significant mean difference (.49*) that is greater than the conservation score of the Old Order Amish. Of the four groups, the Old Order Amish rank lowest in reported implementation of conservation practices followed by the English. The Swartzentruber Amish, though not statistically significant, report the highest conservation use.
2.3
2.2 2.2 2.2 2.1
2.0
2.0 1.9
1.8
1.7 1.7 1.6 Mean of Conservation Use Mean of Conservation English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.4 Conservation Use
330 Respondents were asked to select a number of activities that occur in or near the stream that were classified as recreational (Figure 7.5) that included hunting, fishing, hiking, bird watching, swimming/wading, and picnicking. These are summed for each case and presented as a “desired recreational use” score that is viewed as a cultural connection to the stream. Of the four groups, the Old
Order Amish scored highest in their desire for stream recreation activities. The mean differences are greatest between the Old Order and the Swartzentruber
Amish. The Old Order Amish mean difference with the AMB group is statistically significant (0.61*).
1.2
1.0 1.0
.8
.6 .6
.4 .4
.2 .2
0.0 Mean of Desired Recreational Use Score Use Recreational Mean of Desired English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.5 Desired Recreational Use
331 Education levels (Figure 7.6) are reported based on responses to a
question which asks participants to select their highest attained level of
education. Options included in a five-point ordinal scale ranking are Eighth
Grade, High School, Some College, Bachelor Degree, and Graduate Degree. As
expected, the Amish participants ranked below the AMB and English groups, with no statistically significant mean differences between the Old Order and
Swartzentruber Amish. There are statistically significant mean differences between the English and AMB (0.53*), between the English and Old Order &
Swartzentruber Amish (2.06**), between the AMB and the Old Order and
Swartzentruber Amish (1.53**). These differences reflect the value the Amish
place on life experience as education versus the formalized training that is often required of English and AMB farmers whose farms are larger scale and require the operators to have a greater understanding of agricultural systems beyond their farm. This formalized training also provides entry and indoctrination into industrial agriculture and the larger capitalist farming system.
332
3.5
3.0 3.1
2.5 2.5
2.0
1.5
1.0 1.0 1.0
.5 Mean of Education Level Mean of Education English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.6 Education Level
333 Implementation of no-till conservation tillage (Figure 7.7) is a marker of
adoption of conservation practices in which a farmer who implements no-till may
also implement other practices. AMB farmers represent the largest group of no-
till adopters followed by the English, Old Order Amish and Swartzentruber Amish.
The AMB farmers have a statistically significant mean difference (0.31*) that is higher than the Old Order Amish for this practice.
.6
.5 .5 .5 .4
.3
.2 .2
.1
0.0 Mean of ImplementedNo-Till English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.7 Implemented No-Till
334 Desired recreational use of the stream is highest among the Old Order
Amish, and fishing (Figure 7.8) is one of the activities in which they have
statistically significant mean difference (0.27*) compared to the AMB group.
Fishing ranks highest among the Old Order Amish, Swartzentruber Amish,
English with the Apostolic, Mennonite and Brethren group ranking lowest.
.5
.4 .4
.3
.2 .2
.2 .1 .1 Mean of Fishing Use Mean of Fishing English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.8 Fishing Use
335 Perception of the water quality of a stream (Figure 7.9) is a good indication of the level of education and type of approach that is needed to collaborate with local community residents in stream remediation projects.
Pollution was perceived to be the greatest among the AMB group, followed by
Old Order Amish and English, then Swartzentruber. This variable also shows a statistically significant mean difference (1.26*) between the AMB and the
Swartzentruber Amish.
3.6
3.4 3.5
3.2 3.2 3.2 3.0
2.8
2.6
2.4
2.2 2.2 2.0 Mean of Sugar Creek is Polluted? is Mean Creek of Sugar English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.9 Perception of Pollution in the Sugar Creek
336 The scenic beauty, or aesthetics, of the Sugar Creek (Figure 7.10) is important to most groups; however, the Swartzentruber Amish place the least amount of value in this, feeling that farmers in the watershed should not have to change practices to achieve beauty. Responses show differences among the groups, with the Old Order Amish group showing a greater appreciation for aesthetics, followed by AMB and the English. Mean differences are statistically significant between two group pairs: the Old Order Amish (1.72*) and the AMB
(1.40*) means are greater than the Swartzentruber Amish.
3.5
3.3
3.0 3.0 2.8
2.5
2.0
1.5 1.6
1.0 Mean of Aesthetics More Important Mean of Aesthetics English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.10 Aesthetics More Important
337 Although off-farm income differences exist among the groups, there are no
statistically significant mean differences among them. Interestingly, the Old Order
Amish whose recent trend is towards off-farm occupations have the highest
mean off-farm incomes, followed by the English and the AMB, respectively. The
Swartzentruber Amish report the lowest off-farm incomes. Figure 7.11 presents
the mean differences among the groups.
2.4
2.2 2.1 2.2 2.0
1.8 1.9
1.6
1.4
1.2 1.2
1.0 Mean of Off-Farm Income Index English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.11 Off-farm Income Index
338 Even though conservation use scores are statistically significant in their
variance among the heritage groups the same is not true of the Conservation
Index. The conservation index is a combination of actual practices that are
adopted and implemented combined with the reported desires of new
conservation practices. ANOVA reveals no statistically significant mean
differences among the groups. Though there are no significant differences the
ranking of the groups is consistent with actual practices implemented (Figure
7.12) with the AMB group ranking highest, the English second, and Old Order
and Swartzentruber Amish lowest in overall conservation scores. The
Swartzentruber score does not correspond with their reported conservation use.
This is an interesting example in which measurements of perceptions do not predict behavior.
339
2.2
2.1
2.1 2.1
2.0 2.0 2.0
1.9 Mean of Conservation Index Mean of Conservation English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.12 Conservation Index
Heritage Group ANOVA Part 2
The second section of the ANOVA looks at responses to survey questions regarding local water quality decision-making and 1) the level at which local residents deem appropriate to make these decisions (individual land owners, local, state or federal government, etc), and 2) the levels of trust that residents have of multiple levels of government represented by agencies with agriculture, conservation and/or health missions.
340
Heritage Std. 95% Confidence Group N Mean Deviation Std. Error Interval for Mean Min. Max. Lower Upper Bound Bound Federal 1 E 50 .04 .198 .028 -.02 .10 0 1 Government 2 AMB 82 .04 .189 .021 .00 .08 0 1 Decision 4 OOA 22 .00 .000 .000 .00 .00 0 0 Makers 5 SA 5 .00 .000 .000 .00 .00 0 0 Total 159 .03 .175 .014 .00 .06 0 1 State 1 E 50 .12 .328 .046 .03 .21 0 1 Government 2 AMB 82 .06 .241 .027 .01 .11 0 1 Decision 4 OOA 22 .05 .213 .045 -.05 .14 0 1 Makers 5 SA 5 .00 .000 .000 .00 .00 0 0 Total 159 .08 .265 .021 .03 .12 0 1 Local 1 E 50 .46 .503 .071 .32 .60 0 1 Government 2 AMB 82 .22 .416 .046 .13 .31 0 1 Decision 4 OOA 22 .27 .456 .097 .07 .47 0 1 Makers 5 SA 5 .20 .447 .200 -.36 .76 0 1 Total 159 .30 .461 .037 .23 .37 0 1 Coalition of 1 E 50 .60 .495 .070 .46 .74 0 1 Decision 2 AMB 82 .71 .458 .051 .61 .81 0 1 Makers 4 OOA 22 .73 .456 .097 .53 .93 0 1 5 SA 5 .40 .548 .245 -.28 1.08 0 1 Total 159 .67 .473 .038 .59 .74 0 1 Individual 1 E 50 .72 .454 .064 .59 .85 0 1 Owners as 2 AMB 82 .63 .485 .054 .53 .74 0 1 Decision 4 OOA 22 .64 .492 .105 .42 .85 0 1 Makers 5 SA 5 .80 .447 .200 .24 1.36 0 1 Total 159 .67 .473 .038 .59 .74 0 1 Trust EPA 1 E 46 1.67 1.055 .156 1.36 1.99 0 4 2 AMB 77 1.96 1.251 .143 1.68 2.24 0 5 4 OOA 21 2.29 .956 .209 1.85 2.72 1 5 5 SA 4 2.00 1.826 .913 -.91 4.91 0 4 Total 148 1.92 1.175 .097 1.73 2.11 0 5 Trust USDA 1 E 47 3.00 .885 .129 2.74 3.26 1 5 2 AMB 75 3.01 1.020 .118 2.78 3.25 0 5 4 OOA 21 3.14 1.014 .221 2.68 3.60 1 5 5 SA 4 2.75 1.258 .629 .75 4.75 1 4 Total 147 3.02 .976 .080 2.86 3.18 0 5 Trust ODA 1 E 47 3.23 1.026 .150 2.93 3.54 1 5 2 AMB 76 3.42 1.499 .172 3.08 3.76 0 13 4 OOA 21 3.38 1.071 .234 2.89 3.87 1 5 5 SA 4 2.75 1.258 .629 .75 4.75 1 4 Total 148 3.34 1.297 .107 3.13 3.55 0 13 Trust SWCD 1 E 47 4.38 4.446 .648 3.08 5.69 0 33 2 AMB 77 3.79 1.080 .123 3.55 4.04 0 5 4 OOA 20 4.15 .988 .221 3.69 4.61 2 5
Table 7.5 Heritage Group ANOVA Descriptive Statistics for Water Quality Decisions and Agency Trust Scores
Continued 341 Table 7.5 (continued)
5 SA 4 2.75 1.258 .629 .75 4.75 1 4 Total 148 4.00 2.657 .218 3.57 4.43 0 33 Trust MWCD 1 E 44 2.91 1.053 .159 2.59 3.23 0 5 2 AMB 69 3.35 1.027 .124 3.10 3.59 0 5 4 OOA 20 3.50 .827 .185 3.11 3.89 2 5 5 SA 4 2.00 1.826 .913 -.91 4.91 0 4 Total 137 3.19 1.068 .091 3.01 3.37 0 5 Trust OSU 1 E 46 3.39 1.308 .193 3.00 3.78 1 5 Extension 2 AMB 74 3.42 .993 .115 3.19 3.65 0 5 4 OOA 20 3.40 1.142 .255 2.87 3.93 1 5 5 SA 4 2.00 1.826 .913 -.91 4.91 0 4 Total 144 3.37 1.157 .096 3.18 3.56 0 5 Trust Farm 1 E 46 3.46 1.277 .188 3.08 3.84 0 5 Bureau 2 AMB 77 3.69 1.067 .122 3.45 3.93 0 5 4 OOA 20 3.35 1.182 .264 2.80 3.90 1 5 5 SA 4 2.75 1.258 .629 .75 4.75 1 4 Total 147 3.54 1.160 .096 3.36 3.73 0 5 Trust ODNR 1 E 46 2.91 .985 .145 2.62 3.21 1 5 2 AMB 73 3.63 4.794 .561 2.51 4.75 0 43 4 OOA 20 3.60 .995 .222 3.13 4.07 2 5 5 SA 4 2.00 1.826 .913 -.91 4.91 0 4 Total 143 3.35 3.511 .294 2.77 3.93 0 43 Trust WHD 1 E 47 2.57 1.137 .166 2.24 2.91 0 5 2 AMB 73 3.00 1.280 .150 2.70 3.30 0 5 4 OOA 21 2.81 1.078 .235 2.32 3.30 1 5 5 SA 4 2.75 1.258 .629 .75 4.75 1 4 Total 145 2.83 1.210 .100 2.63 3.03 0 5 Trust County 1 E 25 2.36 1.186 .237 1.87 2.85 1 4 Commissioners 2 AMB 47 3.21 .977 .142 2.93 3.50 0 5 4 OOA 22 3.50 1.012 .216 3.05 3.95 1 5 5 SA 4 2.00 1.826 .913 -.91 4.91 0 4 Total 98 3.01 1.162 .117 2.78 3.24 0 5 Trust North 1 E 20 2.95 1.276 .285 2.35 3.55 0 5 Fork Taskforce 2 AMB 45 3.16 1.127 .168 2.82 3.49 0 5 4 OOA 16 3.69 1.014 .254 3.15 4.23 2 5 5 SA 4 2.00 1.826 .913 -.91 4.91 0 4 Total 85 3.15 1.210 .131 2.89 3.41 0 5 Trust Township 1 E 25 3.16 1.405 .281 2.58 3.74 1 5 Trustees 2 AMB 49 3.67 1.197 .171 3.33 4.02 0 5 4 OOA 20 3.65 .988 .221 3.19 4.11 2 5 5 SA 4 4.25 .957 .479 2.73 5.77 3 5 Total 98 3.56 1.219 .123 3.32 3.81 0 5
342 Sum of Mean Squares df Square F Sig. Federal Between Groups .033 3 .011 .349 .790 Government Within Groups 4.810 155 .031 Decision Makers Total 4.843 158 State Between Groups .165 3 .055 .778 .508 Government Within Groups 10.930 155 .071 Decision Makers Total 11.094 158 Local Between Groups 1.877 3 .626 3.066 .030 Government Within Groups 31.632 155 .204 Decision Makers Total 33.509 158 Coalition of Between Groups .794 3 .265 1.188 .316 Decision Makers Within Groups 34.539 155 .223 Total 35.333 158 Individual Owners Between Groups .338 3 .113 .499 .683 as Decision Within Groups 34.995 155 .226 Makers Total 35.333 158 Trust EPA Between Groups 5.750 3 1.917 1.399 .246 Within Groups 197.278 144 1.370 Total 203.027 147 Trust USDA Between Groups .631 3 .210 .217 .884 Within Groups 138.308 143 .967 Total 138.939 146 Trust ODA Between Groups 2.454 3 .818 .481 .696 Within Groups 244.654 144 1.699 Total 247.108 147 Trust SWCD Between Groups 16.918 3 5.639 .795 .498 Within Groups 1021.082 144 7.091 Total 1038.000 147 Trust MWCD Between Groups 12.777 3 4.259 3.981 .009 Within Groups 142.289 133 1.070 Total 155.066 136 Trust OSU Between Groups 7.723 3 2.574 1.961 .123 Extension Within Groups 183.770 140 1.313 Total 191.493 143 Trust Farm Between Groups 5.230 3 1.743 1.304 .276 Bureau Within Groups 191.233 143 1.337 Total 196.463 146 Trust ODNR Between Groups 23.052 3 7.684 .618 .604 Within Groups 1727.466 139 12.428 Total 1750.517 142 Trust WHD Between Groups 5.212 3 1.737 1.192 .315 Within Groups 205.477 141 1.457 Total 210.690 144 Trust County Between Groups 21.857 3 7.286 6.276 .001 Commissioners Within Groups 109.132 94 1.161 Total 130.990 97 Trust North Fork Between Groups 10.713 3 3.571 2.576 .060 Task Force Within Groups 112.299 81 1.386 Total 123.012 84 Trust Township Between Groups 6.697 3 2.232 1.527 .213 Trustees Within Groups 137.436 94 1.462 Total 144.133 97
Table 7.6 Heritage Group ANOVA for Decision Makers and Agency Trust
343
(I) (J) Mean Dependent Heritage Heritage Difference Std. 90% Confidence Variable Index Index (I-J) Error Sig. Interval Lower Upper Bound Bound Federal 1 E 2 AMB .00 .032 1.000 -.07 .08 Government 4 OOA .04 .045 .811 -.06 .14 Decision 5 SA .04 .083 .963 -.15 .23 Makers 2 AMB 1 E .00 .032 1.000 -.08 .07 4 OOA .04 .042 .823 -.06 .13 5 SA .04 .081 .969 -.15 .22 4 OOA 1 E -.04 .045 .811 -.14 .06 2 AMB -.04 .042 .823 -.13 .06 5 SA .00 .087 1.000 -.20 .20 5 SA 1 E -.04 .083 .963 -.23 .15 2 AMB -.04 .081 .969 -.22 .15 4 OOA .00 .087 1.000 -.20 .20 State 1 E 2 AMB .06 .048 .603 -.05 .17 Government 4 OOA .07 .068 .692 -.08 .23 Decision 5 SA .12 .125 .770 -.17 .41 Makers 2 AMB 1 E -.06 .048 .603 -.17 .05 4 OOA .02 .064 .995 -.13 .16 5 SA .06 .122 .959 -.22 .34 4 OOA 1 E -.07 .068 .692 -.23 .08 2 AMB -.02 .064 .995 -.16 .13 5 SA .05 .132 .986 -.26 .35 5 SA 1 E -.12 .125 .770 -.41 .17 2 AMB -.06 .122 .959 -.34 .22 4 OOA -.05 .132 .986 -.35 .26 Local 1 E 2 AMB .24(*) .081 .018 .05 .43 Government 4 OOA .19 .116 .370 -.08 .45 Decision 5 SA .26 .212 .611 -.23 .75 Makers 2 AMB 1 E -.24(*) .081 .018 -.43 -.05 4 OOA -.05 .108 .961 -.30 .20 5 SA .02 .208 1.000 -.46 .50 4 OOA 1 E -.19 .116 .370 -.45 .08 2 AMB .05 .108 .961 -.20 .30 5 SA .07 .224 .988 -.44 .59 5 SA 1 E -.26 .212 .611 -.75 .23 2 AMB -.02 .208 1.000 -.50 .46 4 OOA -.07 .224 .988 -.59 .44 Coalition of 1 E 2 AMB -.11 .085 .585 -.30 .09 Decision 4 OOA -.13 .121 .718 -.41 .15 Makers 5 SA .20 .221 .803 -.31 .71 2 AMB 1 E .11 .085 .585 -.09 .30 4 OOA -.02 .113 .998 -.28 .24 5 SA .31 .217 .493 -.20 .81 4 OOA 1 E .13 .121 .718 -.15 .41 2 AMB .02 .113 .998 -.24 .28 5 SA .33 .234 .502 -.21 .87 5 SA 1 E -.20 .221 .803 -.71 .31 2 AMB -.31 .217 .493 -.81 .20 4 OOA -.33 .234 .502 -.87 .21
Table 7.7 Tukey HSD Multiple Comparisons Continued 344 Table 7.7 (continued)
Individual 1 E 2 AMB .09 .085 .746 -.11 .28 Owners as 4 OOA .08 .122 .902 -.20 .36 Decision 5 SA -.08 .223 .984 -.59 .43 Makers 2 AMB 1 E -.09 .085 .746 -.28 .11 4 OOA .00 .114 1.000 -.27 .26 5 SA -.17 .219 .873 -.67 .34 4 OOA 1 E -.08 .122 .902 -.36 .20 2 AMB .00 .114 1.000 -.26 .27 5 SA -.16 .235 .899 -.71 .38 5 SA 1 E .08 .223 .984 -.43 .59 2 AMB .17 .219 .873 -.34 .67 4 OOA .16 .235 .899 -.38 .71 Trust EPA 1 E 2 AMB -.29 .218 .554 -.79 .22 4 OOA -.61 .308 .199 -1.32 .10 5 SA -.33 .610 .951 -1.74 1.08 2 AMB 1 E .29 .218 .554 -.22 .79 4 OOA -.32 .288 .674 -.99 .34 5 SA -.04 .600 1.000 -1.43 1.35 4 OOA 1 E .61 .308 .199 -.10 1.32 2 AMB .32 .288 .674 -.34 .99 5 SA .29 .639 .970 -1.19 1.76 5 SA 1 E .33 .610 .951 -1.08 1.74 2 AMB .04 .600 1.000 -1.35 1.43 4 OOA -.29 .639 .970 -1.76 1.19 Trust USDA 1 E 2 AMB -.01 .183 1.000 -.44 .41 4 OOA -.14 .258 .945 -.74 .45 5 SA .25 .512 .962 -.93 1.43 2 AMB 1 E .01 .183 1.000 -.41 .44 4 OOA -.13 .243 .951 -.69 .43 5 SA .26 .505 .954 -.90 1.43 4 OOA 1 E .14 .258 .945 -.45 .74 2 AMB .13 .243 .951 -.43 .69 5 SA .39 .537 .884 -.85 1.63 5 SA 1 E -.25 .512 .962 -1.43 .93 2 AMB -.26 .505 .954 -1.43 .90 4 OOA -.39 .537 .884 -1.63 .85 Trust ODA 1 E 2 AMB -.19 .242 .866 -.75 .37 4 OOA -.15 .342 .973 -.94 .64 5 SA .48 .679 .892 -1.09 2.05 2 AMB 1 E .19 .242 .866 -.37 .75 4 OOA .04 .321 .999 -.70 .78 5 SA .67 .669 .748 -.88 2.22 4 OOA 1 E .15 .342 .973 -.64 .94 2 AMB -.04 .321 .999 -.78 .70 5 SA .63 .711 .811 -1.01 2.28 5 SA 1 E -.48 .679 .892 -2.05 1.09 2 AMB -.67 .669 .748 -2.22 .88 4 OOA -.63 .711 .811 -2.28 1.01
Continued
345 Table 7.7 (continued)
Trust SWCD 1 E 2 AMB .59 .493 .629 -.55 1.73 4 OOA .23 .711 .988 -1.41 1.88 5 SA 1.63 1.387 .642 -1.57 4.84 2 AMB 1 E -.59 .493 .629 -1.73 .55 4 OOA -.36 .668 .950 -1.90 1.19 5 SA 1.04 1.366 .871 -2.12 4.20 4 OOA 1 E -.23 .711 .988 -1.88 1.41 2 AMB .36 .668 .950 -1.19 1.90 5 SA 1.40 1.459 .772 -1.97 4.77 5 SA 1 E -1.63 1.387 .642 -4.84 1.57 2 AMB -1.04 1.366 .871 -4.20 2.12 4 OOA -1.40 1.459 .772 -4.77 1.97 Trust MWCD 1 E 2 AMB -.44 .200 .129 -.90 .02 4 OOA -.59 .279 .153 -1.24 .05 5 SA .91 .540 .337 -.34 2.16 2 AMB 1 E .44 .200 .129 -.02 .90 4 OOA -.15 .263 .938 -.76 .46 5 SA 1.35 .532 .059 .12 2.58 4 OOA 1 E .59 .279 .153 -.05 1.24 2 AMB .15 .263 .938 -.46 .76 5 SA 1.50(*) .567 .044 .19 2.81 5 SA 1 E -.91 .540 .337 -2.16 .34 2 AMB -1.35 .532 .059 -2.58 -.12 4 OOA -1.50(*) .567 .044 -2.81 -.19 Trust OSU 1 E 2 AMB -.03 .215 .999 -.53 .47 Extension 4 OOA -.01 .307 1.000 -.72 .70 5 SA 1.39(*) .597 .096 .01 2.77 2 AMB 1 E .03 .215 .999 -.47 .53 4 OOA .02 .289 1.000 -.65 .69 5 SA 1.42(*) .588 .079 .06 2.78 4 OOA 1 E .01 .307 1.000 -.70 .72 2 AMB -.02 .289 1.000 -.69 .65 5 SA 1.40 .628 .120 -.05 2.85 5 SA 1 E -1.39(*) .597 .096 -2.77 -.01 2 AMB -1.42(*) .588 .079 -2.78 -.06 4 OOA -1.40 .628 .120 -2.85 .05 Trust Farm 1 E 2 AMB -.23 .215 .705 -.73 .27 Bureau 4 OOA .11 .310 .986 -.61 .82 5 SA .71 .603 .645 -.69 2.10 2 AMB 1 E .23 .215 .705 -.27 .73 4 OOA .34 .290 .649 -.33 1.01 5 SA .94 .593 .392 -.43 2.31 4 OOA 1 E -.11 .310 .986 -.82 .61 2 AMB -.34 .290 .649 -1.01 .33 5 SA .60 .633 .779 -.86 2.06 5 SA 1 E -.71 .603 .645 -2.10 .69 2 AMB -.94 .593 .392 -2.31 .43 4 OOA -.60 .633 .779 -2.06 .86
Continued
346 Table 7.7 (continued)
Trust ODNR 1 E 2 AMB -.72 .664 .702 -2.25 .82 4 OOA -.69 .944 .886 -2.87 1.50 5 SA .91 1.838 .960 -3.34 5.16 2 AMB 1 E .72 .664 .702 -.82 2.25 4 OOA .03 .890 1.000 -2.03 2.09 5 SA 1.63 1.810 .805 -2.56 5.82 4 OOA 1 E .69 .944 .886 -1.50 2.87 2 AMB -.03 .890 1.000 -2.09 2.03 5 SA 1.60 1.931 .841 -2.87 6.07 5 SA 1 E -.91 1.838 .960 -5.16 3.34 2 AMB -1.63 1.810 .805 -5.82 2.56 4 OOA -1.60 1.931 .841 -6.07 2.87 Trust WHD 1 E 2 AMB -.43 .226 .239 -.95 .10 4 OOA -.24 .317 .880 -.97 .50 5 SA -.18 .629 .992 -1.63 1.28 2 AMB 1 E .43 .226 .239 -.10 .95 4 OOA .19 .299 .920 -.50 .88 5 SA .25 .620 .978 -1.18 1.68 4 OOA 1 E .24 .317 .880 -.50 .97 2 AMB -.19 .299 .920 -.88 .50 5 SA .06 .659 1.000 -1.46 1.58 5 SA 1 E .18 .629 .992 -1.28 1.63 2 AMB -.25 .620 .978 -1.68 1.18 4 OOA -.06 .659 1.000 -1.58 1.46 Trust County 1 E 2 AMB -.85(*) .267 .010 -1.47 -.23 Commissioners 4 OOA -1.14(**) .315 .003 -1.87 -.41 5 SA .36 .580 .925 -.99 1.71 2 AMB 1 E .85(*) .267 .010 .23 1.47 4 OOA -.29 .278 .731 -.93 .36 5 SA 1.21 .561 .142 -.09 2.52 4 OOA 1 E 1.14(**) .315 .003 .41 1.87 2 AMB .29 .278 .731 -.36 .93 5 SA 1.50(*) .586 .057 .14 2.86 5 SA 1 E -.36 .580 .925 -1.71 .99 2 AMB -1.21 .561 .142 -2.52 .09 4 OOA -1.50(*) .586 .057 -2.86 -.14 Trust 1 E 2 AMB -.21 .316 .915 -.94 .53 North Fork 4 OOA -.74 .395 .250 -1.66 .18 Task Force 5 SA .95 .645 .458 -.55 2.45 2 AMB 1 E .21 .316 .915 -.53 .94 4 OOA -.53 .343 .412 -1.33 .27 5 SA 1.16 .614 .244 -.28 2.59 4 OOA 1 E .74 .395 .250 -.18 1.66 2 AMB .53 .343 .412 -.27 1.33 5 SA 1.69 .658 .058 .15 3.22 5 SA 1 E -.95 .645 .458 -2.45 .55 2 AMB -1.16 .614 .244 -2.59 .28 4 OOA -1.69 .658 .058 -3.22 -.15
Continued
347 Table 7.7 (continued)
Trust 1 E 2 AMB -.51 .297 .315 -1.20 .18 Township 4 OOA -.49 .363 .533 -1.33 .35 Trustees 5 SA -1.09 .651 .343 -2.60 .42 2 AMB 1 E .51 .297 .315 -.18 1.20 4 OOA .02 .321 1.000 -.72 .77 5 SA -.58 .629 .796 -2.04 .88 4 OOA 1 E .49 .363 .533 -.35 1.33 2 AMB -.02 .321 1.000 -.77 .72 5 SA -.60 .662 .802 -2.14 .94 5 SA 1 E 1.09 .651 .343 -.42 2.60 2 AMB .58 .629 .796 -.88 2.04 4 OOA .60 .662 .802 -.94 2.14 * The mean difference is significant at the .05 level. ** The mean difference is significant at the .01 level.
348 English respondents stated they had greater trust for Federal Government decision makers than other groups. However, the mean differences are not statistically significant.
.05
.04 .04 .04
.03
.02
.01
0.00 Mean of Federal Government Decision Makers Decision Government Federal of Mean English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.13 Federal Government Decision Makers
349 State government trust declines with increased traditionalism among the groups, though none are statistically significant.
.14
.12 .12
.10
.08
.06 .06
.04 .05
.02
0.00 Mean of State Government Decision Makers GovernmentDecision Mean of State English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.14 State Government Decision Makers
350 English reported greater trust of local government than all other groups, with a (0.24*) difference with the Apostolic, Mennonite and Brethren group that is statistically significant.
.5
.5
.4
.3
.3
.2 .2 .2
.1
0.0 Mean of Local Government Decision Makers GovernmentMean of Local Decision English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.15 Local Government Decision Makers
351 A coalition of decision makers is preferred more by Old Order Amish and
Apostolic, Mennonite and Brethren groups. This option was favored least by the
Swartzentruber Amish, followed by the English.
.8
.7 .7 .7
.6 .6
.5
.4 .4
.3
.2
.1
0.0 Mean of Coalition of Decision Makers of Decision Mean of Coalition English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.16 Coalition of Decision Makers
352 Responses for individuals as decision makers for watershed decisions were highest for the Swartzentruber Amish and English. These are lowest for Old
Order Amish and then Apostolic, Mennonite and Brethren. These means differences are not statistically significant
.9
.8 .8
.7 .7
.6 .6 .6
.5
.4
.3
.2
.1
0.0 Mean of Indivdual Owners as Decision Makers Decision as Owners Indivdual of Mean English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.17 Individual Owners as Decision Makers
353 The trust level for the Environmental Protection Agency is highest among the Old Order Amish and lowest for the English. Outside of the Amish, the general decline in trust is proportional to the degree of traditionalism. These mean differences are not statistically significant.
2.5
2.3 2.0 2.0 2.0
1.7 1.5
1.0
.5
0.0 Mean of Trust EPA English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.18 Trust in EPA
354 The United States Department of Agriculture enjoys higher levels of trust among most groups except the Swartzentruber Amish, and highest among the
Old Order Amish. The mean differences are not statistically significant.
3.5
3.0 3.1 3.0 3.0 2.8 2.5
2.0
1.5
1.0
.5
0.0 Mean of Trust USDA English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.19 Trust in USDA
355 Swartzentruber Amish reported the least trust in the Ohio Department of
Agriculture, while the Apostolic, Mennonite and Brethren group reports the highest, followed by slightly less high Old Order Amish, then the English.
However, none of the mean differences are statistically significant.
3.5 3.4 3.4 3.2 3.0
2.8 2.5
2.0
1.5
1.0
.5
0.0 Mean of Trust ODA English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.20 Trust in ODA
356 Reported trust levels by groups for the Soil and Water Conservation
District is highest among the English and Old Order Amish, slightly lower among
Apostolic, Mennonite and Brethren farmers and much lower among the
Swartzentruber Amish. None of these mean differences are statistically significant.
4.5 4.4 4.0 4.2
3.8 3.5
3.0
2.5 2.8
2.0
1.5
1.0
.5
0.0 Mean of Trust SWCD English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.21 Trust in SWCD
357 Old Order Amish exhibit greater trust of the Muskingum Watershed
Conservancy District than the other groups while the Swartzentruber Amish show the least. The mean difference between (1.50*) Old Order and Swartzentruber
Amish is statistically significant
4.0
3.5 3.5 3.3 3.0 2.9 2.5
2.0 2.0
1.5
1.0
.5
0.0 Mean of Trust MWCD English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.22 Trust in MWCD
358 Trust in Ohio State University Extension is fairly even across groups but is dramatically lower for Swartzentruber Amish. These mean differences are statistically significant between the Swartzentruber Amish and each of the following two groups: English (1.39*) and Apostolic, Mennonite and Brethren
(1.42*).
4.0
3.5 3.4 3.4 3.4 3.0
2.5
2.0 2.0
1.5
1.0
.5
0.0 Mean of Trust OSU Extension Mean of Trust OSU English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.23 Trust in OSU Extension
359 Levels of trust in the Farm Bureau are highest among the Apostolic,
Mennonite and Brethren farmers and lowest for the Swartzentruber Amish. There is a slight difference in the means of the Old Order Amish and English, and both
are lower than the highest group.
4.0
3.5 3.7 3.5 3.3 3.0
2.8 2.5
2.0
1.5
1.0
.5
0.0 Mean of Trust Farm Bureau English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.24 Trust in Farm Bureau
360 Old Order Amish and Apostolic, Mennonite and Brethren households reported the greatest trust levels for the Ohio Department of Natural Resources, while the Swartzentruber Amish reported the least, followed by the English.
However, none of these mean differences were statistically significant.
4.0
3.5 3.6 3.6
3.0 2.9 2.5
2.0 2.0
1.5
1.0
.5
0.0 Mean of Trust ODNR English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.25 Trust in ODNR
361 The English report the least trust of the Wayne County Health Department
followed by Swartzentruber participants. Apostolic, Mennonite and Brethren reported the highest levels of trust. None of the mean differences were
statistically significant.
3.5
3.0 3.0 2.8 2.8 2.5 2.6
2.0
1.5
1.0
.5
0.0 Mean of Trust WHD English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.26 Trust in WHD
362 The Old Order Amish reported greater trust of the County Commissioners than all other groups. The following mean differences are statistically significant:
Old Oder Amish is greater than English (1.14**) and Swartzentruber Amish
(1.50**); the AMB group is greater than the English (0.85*).
4.0
3.5 3.5
3.0 3.2
2.5 2.4 2.0 2.0
1.5
1.0
.5
0.0 Mean Commissioners of Trust County English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.27 Trust in County Commissioners
363 Reported trust in the North Fork Task Force is highest among the Old
Order Amish, and lowest for the Swartzentruber Amish; both are consistent with
the previous data in which they reported support for a coalition of people working
to solve water problems. Because the North Fork Task Force is a subwatershed
specific organization, these results may be misleading since the Amish are in the
North Fork and Little Sugar Creek, and the groups in the Upper Sugar Creek are all English and Apostolic, Mennonite and Brethren farmers.
4.0
3.5 3.7
3.0 3.2 3.0 2.5
2.0 2.0
1.5
1.0
.5
0.0 Mean of Trust Fork North Taskforce English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.28 Trust in North Fork Task Force
364 Township Trustees, the most localized and smallest political unit, have the
highest level of trust among the Swartzentruber Amish. Trust is inversely
proportional to the degree of traditionalism exhibited by the group. However,
these mean differences are not statistically significant.
4.5
4.3 4.0
3.5 3.7 3.7
3.0 3.2
2.5
2.0
1.5
1.0
.5
0.0 Mean of Trust Trustees Township English Old Order Amish Apostolic, Mennonite Sw artzentruber Amish
Heritage Index
Figure 7.29 Trust in Township Trustees
7.5.4 Discussion LSCI ANOVA
A literature review of findings reveal that Amish communities in Wayne
and Holmes counties exhibit strong degrees of embeddedness and in-group
social capital (Kraybill & Hostetler 2001, Hostetler 1993, Zide 1971, Williams
1969, Nethers 1959, Ely 1942, Leeper 1936, Fletcher 1932). The degree of
365 traditionalism is highest among the Swartzentruber Amish and lowest among the
“English”, or non-Anabaptist groups. Concern for the local environment diminishes at the opposite ends of traditionalism in some measures: among the
Swartzentruber Amish and the English heritage groups, while the less traditional orders of Anabaptists and the Old Order Amish demonstrate the highest concerns for the environment across some indexes of measurement. Yet in other measures, the less traditional Anabaptists and the English rank higher than the
Old Order. The Swartzentruber Amish rank consistently low in attitudes. This is not to say that the Swartzentruber Amish have a poor environmental ethic – they rank highest in use but due to their small sample size, more work needs to be done towards understanding their conservation behavior before a conclusion is drawn.
A profile of the heritage groups would show that the Old Order Amish are the youngest group of farmers, have the greatest amount of off-farm income (but a near tie, 0.01 mean difference, with the English), and are less likely to adopt conservation practices that do not have an immediate benefit to their farm as inferred from the practicality or concreteness of desired conservation use score.
They have the greatest desire for recreational uses of the stream. It is necessary to couple the desire of the Old Order families for recreation with their overall farming system and way of life in order to create a successful collaborative relationship between them and other agencies. To approach them solely based on the need to change farming practices in order “to improve the environment” is not sufficient enough due to the abstract nature of such approaches. An
366 alternative approach would emphasize concrete goals and results that benefit the
community, such as recreation opportunities that are social rather than individual
pursuits.
Apostolic, Mennonite and Brethren households are concerned with the
environmental quality of the Sugar Creek and are the second most active in
using conservation practices and in their desire to implement more based on available finances. These farmers rank second in the number of years farming, and are less interested in using the stream for recreational purposes. If you couple this with the general worldview in which many see themselves as good stewards of their land, it is understandable why they have a lesser degree of direct use of the stream for recreation and still have the highest conservation use and reported desires. I do not believe that the philosophical concept of agrarianism explains this pattern as much as the model of a “good Christian farmer” is coupled with their concept of stewardship. As such, it is slightly differentiated from the generalized Amish ethos that holds the land and the animals to be under the dominion of “man”.
The English farmer, a category created based on non-membership in the
other groups, is characterized as the most worldly. In this profile, group
membership has the most educated, and ranks high in several conservation
categories including no-till conservation tillage and perception of pollution in the
stream. The aesthetics of the watershed do not rank high compared to Old Order
Amish and Apostolic, Mennonite and Brethren farmers, but is much higher than
367 the Swartzentruber Amish. They are a near tie with Old Order Amish in high
levels of off-farm income.
Based on survey data analysis, Swartzentruber Amish are the most
difficult to determine conservation behavior. This is because the response rate of
the Swartzentruber’s is low compared to their projected demographics
(Donnermeyer and Cooksey 2004). This community is difficult to enter even by
the Old Order Amish who have reported being rebuffed from entry in attempts to
meet with them regarding local conservation projects such as livestock exclusion
fencing along streams. That withstanding, the evidence from the survey does
show that this group has the lowest rates of off-farm employment, the longest
terms in farming, highest rates of conservation adoption, and the lowest rates of
desired recreational use 50 . Lack of perception of the level of pollution in the
Sugar Creek is consistent with a lower value placed on aesthetics in the
watershed. Likewise, maintaining current farming practices as a tradeoff with
aesthetics is acceptable to respondents in this group. In an insular world in which access to outside information is often filtered through the local church hierarchy, the Swartzentruber Amish choose to act locally with a high level of in-group embeddedness.
The second part of the ANOVA, decision-making and trust scores of government agencies, shows a consistent trend in which trust and decision- making preferences increase with level of integration of the agency. Those agencies that work primarily at a more localized level score higher than those
50 This is likely due to the austere nature of Swartzentruber culture in which leisure is viewed as self-aggrandizing and prideful and thus should be avoided. 368 that work at the state and federal level. The more traditional a group is, the more
likely they are to support and trust involvement of local agency officials over state and federal. Trust scores improve at the local level as seen in trust levels of
County Commissioners (Figure 7.27, above) and of Township Trustees (Figure
7.29, above), both show increase in trust across the groups when compared to the Federal EPA (Figure 7.18, above).
These findings suggest that the Old Order Amish are more inclined to
collaboration with other people, and possibly non-Amish groups (as shown by
their participation in the North Fork Taskforce), in a coalition framework than
others. And, the Swartzentruber are the most group-centered in their responses
in which they preferred to work as individuals and reported low trust for most
agencies outside of the Township Trustees. Additionally, members of the English
group reported the greatest degree of trust for higher levels of government
involvement in local issues, or saw what were portrayed as local issues as non-
local issues. All of these findings are consistent with the assumptions that the
degree of traditionalism is directly related to a group’s level of sociocultural
integration.
7.6 ANOVA of Farm Type
Farm type in the Sugar Creek provides some insights into conservation
adoption behavior, though some of the findings in this section, such as a greater
degree of manure management among dairy farms, are commonsensical.
Between 104 and 115 of the 159 cases qualified for the analysis, fluctuating
369 based on the participants satisficing of responses to specific questions from the survey. Table 7.8 lists statistically significant variables and descriptive statistics used in the ANOVA.
Of the many variables resulting from the survey instrument, nine variables are statistically significant in the analysis of variance for farm type group membership. The variables are listed below in Table 7.9. They are:
1. Heritage index which ranks groups by degree of traditionalism, 2. A six-category ordinal ranking of basic land tenure relationships as a ratio of owning to leasing farmland, 3. Highest level of education attained, 4. An intergenerational farmland succession index measuring the perceived status of the participant’s farm in the next ten years, 5. Current implementation of on-farm no-till conservation tillage, 6. Current implementation of on-farm manure management conservation planning, 7. Extent to which participants feel that the economic viability of the Sugar Creek is more important than its environmental quality, 8. The total number of acres that are leased out for farming, 9. And, the percent of off-farm income contributing to the household budget.
370 Farm Type N Mean Std. Std. 95% Min Max Deviation Error Confidence Interval for Mean Lower Upper Bound Bound Heritage 1 Grain 34 1.79 .729 .125 1.54 2.05 1 4 Index 2 Dairy 46 2.30 1.152 .170 1.96 2.65 1 5 3 Other Animal 27 1.59 .844 .162 1.26 1.93 1 4 4 Other 8 2.75 1.669 .590 1.35 4.15 1 5 Total 115 2.02 1.068 .100 1.82 2.21 1 5 Tenure 1 Grain 34 3.03 1.992 .342 2.33 3.72 1 6 Category 2 Dairy 46 4.28 1.486 .219 3.84 4.72 1 6 3 Other Animal 27 4.37 2.078 .400 3.55 5.19 1 6 4 Other 8 5.00 1.414 .500 3.82 6.18 2 6 Total 115 3.98 1.882 .176 3.63 4.33 1 6 Education 1 Grain 34 2.85 1.184 .203 2.44 3.27 1 5 Level 2 Dairy 44 2.07 1.108 .167 1.73 2.41 0 5 3 Other Animal 27 2.74 1.196 .230 2.27 3.21 1 5 4 Other 8 2.50 1.690 .598 1.09 3.91 1 5 Total 113 2.50 1.233 .116 2.27 2.73 0 5 Farm 1 Grain 31 2.35 .661 .119 2.11 2.60 1 3 Succession 2 Dairy 43 2.74 .581 .089 2.57 2.92 1 3 Index 3 Other Animal 26 2.65 .485 .095 2.46 2.85 2 3 4 Other 8 2.75 .463 .164 2.36 3.14 2 3 Total 108 2.61 .593 .057 2.50 2.72 1 3 Implemented 1 Grain 34 .62 .493 .085 .45 .79 0 1 No-Till 2 Dairy 46 .43 .501 .074 .29 .58 0 1 3 Other Animal 27 .30 .465 .090 .11 .48 0 1 4 Other 8 .13 .354 .125 -.17 .42 0 1 Total 115 .43 .498 .046 .34 .53 0 1 Implemented 1 Grain 34 .41 .500 .086 .24 .59 0 1 Manure 2 Dairy 46 .89 .315 .046 .80 .98 0 1 Management 3 Other Animal 27 .63 .492 .095 .43 .82 0 1 4 Other 8 .50 .535 .189 .05 .95 0 1 Total 115 .66 .475 .044 .57 .75 0 1 Economic 1 Grain 33 3.18 .882 .154 2.87 3.49 1 5 More 2 Dairy 45 3.33 .977 .146 3.04 3.63 1 5 Important 3 Other Animal 26 3.19 1.021 .200 2.78 3.60 2 5 4 Other 8 2.13 .991 .350 1.30 2.95 1 4 Total 112 3.17 .994 .094 2.98 3.36 1 5 Tenure - 1 Grain 34 .41 .500 .086 .24 .59 0 1 Acres 2 Dairy 46 .09 .285 .042 .00 .17 0 1 Leased Out 3 Other Animal 27 .22 .424 .082 .05 .39 0 1 4 Other 8 .00 .000 .000 .00 .00 0 0 Total 115 .21 .408 .038 .13 .28 0 1 Off-Farm 1 Grain 31 2.16 .688 .124 1.91 2.41 1 3 Income 2 Dairy 38 1.42 .642 .104 1.21 1.63 1 3 Index 3 Other Animal 27 2.19 .879 .169 1.84 2.53 1 3 4 Other 8 2.13 .991 .350 1.30 2.95 1 3 Total 104 1.89 .823 .081 1.73 2.05 1 3 Table 7.8 Farm Type Descriptive Statistics
371
Sum of Mean Squares df Square F Sig. Heritage Between Groups 14.649 3 4.883 4.700 .004 Index Within Groups 115.316 111 1.039 Total 129.965 114 Tenure Between Groups 47.372 3 15.791 4.915 .003 Category Within Groups 356.593 111 3.213 Total 403.965 114 Education Between Groups 14.002 3 4.667 3.256 .024 Level Within Groups 156.245 109 1.433 Total 170.248 112 Farm Between Groups 2.999 3 1.000 2.999 .034 Succession Within Groups 34.667 104 .333 Index Total 37.667 107 Implemented Between Groups 2.422 3 .807 3.469 .019 No-Till Within Groups 25.838 111 .233 Total 28.261 114 Implemented Between Groups 4.786 3 1.595 8.437 .000 Manure Within Groups 20.988 111 .189 Management Total 25.774 114 Economic Between Groups 9.954 3 3.318 3.590 .016 More Within Groups 99.823 108 .924 Important Total 109.777 111 Tenure - Between Groups 2.437 3 .812 5.447 .002 Acres Within Groups 16.554 111 .149 Leased Out Total 18.991 114 Off-Farm Between Groups 13.431 3 4.477 7.937 .000 Income Within Groups 56.406 100 .564 Index Total 69.837 103
Table 7.9 ANOVA of Farm Type
372 7.6.1 Mean Differences Between Groups
Mean Dependent (I) Farm Type (J) Farm Type Difference Std. 95% Confidence Variable Index Index (I-J) Error Sig. Interval Lower Upper Bound Bound Heritage 1 Grain 2 Dairy -.51 .231 .126 -1.11 .09 Index 3 Other Animal .20 .263 .869 -.48 .89 4 Other -1.01(*) .367 .035 -1.96 -.05 2 Dairy 1 Grain .51 .231 .126 -.09 1.11 3 Other Animal .71(*) .247 .024 .07 1.36 4 Other -.50 .356 .506 -1.42 .43 3 Other Animal 1 Grain -.20 .263 .869 -.89 .48 2 Dairy -.71(*) .247 .024 -1.36 -.07 4 Other -1.21(**) .377 .010 -2.19 -.22 4 Other 1 Grain 1.01(*) .367 .035 .05 1.96 2 Dairy .50 .356 .506 -.43 1.42 3 Other Animal 1.21(**) .377 .010 .22 2.19 Tenure 1 Grain 2 Dairy -1.25(*) .416 .017 -2.34 -.17 Category 3 Other Animal -1.34(*) .474 .028 -2.58 -.10 4 Other -1.17 .662 .293 -2.90 .55 2 Dairy 1 Grain 1.25(*) .416 .017 .17 2.34 3 Other Animal -.09 .446 .997 -1.25 1.07 4 Other .08 .642 .999 -1.59 1.76 3 Other Animal 1 Grain 1.34(*) .474 .028 .10 2.58 2 Dairy .09 .446 .997 -1.07 1.25 4 Other .17 .681 .994 -1.60 1.95 4 Other 1 Grain 1.17 .662 .293 -.55 2.90 2 Dairy -.08 .642 .999 -1.76 1.59 3 Other Animal -.17 .681 .994 -1.95 1.60 Education 1 Grain 2 Dairy .78(*) .274 .025 .07 1.50 Level 3 Other Animal .11 .309 .984 -.69 .92 4 Other .52 .450 .656 -.65 1.69 2 Dairy 1 Grain -.78(*) .274 .025 -1.50 -.07 3 Other Animal -.67 .293 .106 -1.44 .09 4 Other -.27 .439 .931 -1.41 .88 3 Other Animal 1 Grain -.11 .309 .984 -.92 .69 2 Dairy .67 .293 .106 -.09 1.44 4 Other .41 .462 .814 -.80 1.61 4 Other 1 Grain -.52 .450 .656 -1.69 .65 2 Dairy .27 .439 .931 -.88 1.41 3 Other Animal -.41 .462 .814 -1.61 .80
Table 7.10 Tukey HSD Multiple Comparison Continued
373 Table 10 (continued)
Farm 1 Grain 2 Dairy -.39(*) .141 .033 -.76 -.02 Succession 3 Other Animal -.30 .159 .241 -.71 .12 Index 4 Other -.20 .226 .811 -.79 .39 2 Dairy 1 Grain .39(*) .141 .033 .02 .76 3 Other Animal .09 .148 .929 -.30 .48 4 Other .19 .219 .824 -.38 .76 3 Other Animal 1 Grain .30 .159 .241 -.12 .71 2 Dairy -.09 .148 .929 -.48 .30 4 Other .10 .231 .974 -.50 .70 4 Other 1 Grain .20 .226 .811 -.39 .79 2 Dairy -.19 .219 .824 -.76 .38 3 Other Animal -.10 .231 .974 -.70 .50 Implement 1 Grain 2 Dairy .18 .108 .334 -.10 .47 No-Till 3 Other Animal .32 .123 .050 .00 .64 4 Other .52(*) .172 .017 .07 .97 2 Dairy 1 Grain -.18 .108 .334 -.47 .10 3 Other Animal .14 .116 .632 -.16 .44 4 Other .33 .167 .192 -.10 .77 3 Other Animal 1 Grain -.32 .123 .050 -.64 .00 2 Dairy -.14 .116 .632 -.44 .16 4 Other .20 .177 .685 -.27 .66 4 Other 1 Grain -.52(*) .172 .017 -.97 -.07 2 Dairy -.33 .167 .192 -.77 .10 3 Other Animal -.20 .177 .685 -.66 .27 Implement 1 Grain 2 Dairy -.48(**) .099 .000 -.74 -.22 Manure 3 Other Animal -.22 .112 .218 -.51 .08 Mgmt. 4 Other -.09 .157 .943 -.50 .32 2 Dairy 1 Grain .48(**) .099 .000 .22 .74 3 Other Animal .26 .106 .069 -.01 .54 4 Other .39 .152 .055 -.01 .79 3 Other Animal 1 Grain .22 .112 .218 -.08 .51 2 Dairy -.26 .106 .069 -.54 .01 4 Other .13 .161 .853 -.29 .55 4 Other 1 Grain .09 .157 .943 -.32 .50 2 Dairy -.39 .152 .055 -.79 .01 3 Other Animal -.13 .161 .853 -.55 .29 Economic 1 Grain 2 Dairy -.15 .222 .903 -.73 .43 More 3 Other Animal -.01 .254 1.000 -.67 .65 Important 4 Other .88 .349 .062 -.03 1.79 2 Dairy 1 Grain .15 .222 .903 -.43 .73 3 Other Animal .14 .238 .934 -.48 .76 4 Other 1.03(*) .338 .015 .15 1.92
Continued
374 Table 10 (continued)
3 Other Animal 1 Grain .01 .254 1.000 -.65 .67 2 Dairy -.14 .238 .934 -.76 .48 4 Other .89 .360 .069 -.05 1.83 4 Other 1 Grain -.88 .349 .062 -1.79 .03 2 Dairy -1.03(*) .338 .015 -1.92 -.15 3 Other Animal -.89 .360 .069 -1.83 .05 Tenure - 1 Grain 2 Dairy .32(**) .091 .003 .09 .56 Acres 3 Other Animal .19 .103 .263 -.08 .46 Leased 4 Other .21 .144 .460 -.16 .59 Out 2 Dairy 1 Grain -.32(**) .091 .003 -.56 -.09 3 Other Animal -.14 .097 .507 -.39 .12 4 Other -.11 .140 .850 -.48 .25 3 Other Animal 1 Grain -.19 .103 .263 -.46 .08 2 Dairy .14 .097 .507 -.12 .39 4 Other .02 .148 .999 -.36 .41 4 Other 1 Grain -.21 .144 .460 -.59 .16 2 Dairy .11 .140 .850 -.25 .48 3 Other Animal -.02 .148 .999 -.41 .36 Off-Farm 1 Grain 2 Dairy .74(**) .182 .001 .27 1.21 Income 3 Other Animal -.02 .198 .999 -.54 .49 Index 4 Other .16 .273 .935 -.55 .87 2 Dairy 1 Grain -.74(**) .182 .001 -1.21 -.27 3 Other Animal -.76(**) .189 .001 -1.26 -.27 4 Other -.58 .267 .139 -1.28 .12 3 Other Animal 1 Grain .02 .198 .999 -.49 .54 2 Dairy .76(**) .189 .001 .27 1.26 4 Other .19 .278 .910 -.54 .91 4 Other 1 Grain -.16 .273 .935 -.87 .55 2 Dairy .58 .267 .139 -.12 1.28 3 Other Animal -.19 .278 .910 -.91 .54 * The mean difference is significant at the .05 level. ** The mean difference is significant at the .01 level.
375 Heritage group traditionalism (Figure 7.30) increases among dairy and
other farm types as a result of most members being Apostolic-Mennonite-
Brethren Anabaptist Christians and most of the other farmers having affiliation with one of the Amish Orders. The other farm type category is a mixture of vegetable and fruit growers and small-scale, smallholder, mixed agriculture (corn, oats, hay and a variety of animals) and has the highest mean scores of the four farm types illustrating a greater degree of traditionalism found in this farm type.
Other animal farms are the most worldly of farm types due in part to the specialization of their operations – these farms tend to operate on smaller parcels of land and specialize in only one animal type (i.e. beef cattle, hogs, chickens). Both dairy and other farm types have statistically significant mean differences compared to other animal. Though not statistically significant, the mean differences between grain farms and dairy (0.51) is less but comparable to the mean difference between grain and other (1.01*), which shows grain farmers to be worldlier.
376
3.0
2.8 2.5
2.3 2.0
1.8 1.5 1.6
1.0
.5
0.0 Mean of Heritage Index Heritage of Mean Grain Dairy Other Animal Other
Farm Type Index
Figure 7.30 Heritage Index
377 Land ownership and leasing by farm type (Figure 7.31) reveals that grain farm have statistically significant negative mean differences in comparison to dairy (1.25*), other animal (1.34*), and other (1.97*). Ownership is highest among other farms, other animal farms are second highest, and dairy farms rank third.
4.5 4.4 4.3 4.0 4.2
3.5
3.0 3.0
2.5
2.0
1.5
1.0
.5
0.0 Mean of of TenureMean Category Grain Dairy Other Animal Other
Farm Type Index
Figure 7.31 Tenure Category
378 Education level (Figure 7.32) is highest among grain farmers, followed by
other animal and other. Dairy farmers have the lowest mean education level.
Statistically significant mean differences are found between grain (0.78*) and dairy farmers with grain farmers having a higher level of attained education.
These findings are consistent with heritage group ANOVA findings in which grain
and other animal farmers tend to be comprised of more English farmers. The
Amish lower the education means for the dairy farm type.
3.0
2.9 2.7 2.5
2.3
2.0 2.1
1.5
1.0
.5
0.0 Mean of Education Level Education of Mean Grain Dairy Other Animal Other
Farm Type Index
Figure 7.32 Educational Level
379 The intergenerational farm succession index (Figure 7.33) represents the
future stability of farmland over a future ten-year period. This variable represents
three rank-ordered categories of farmland disposition in order of increasing stability: the sale of farmland for other purposes than farming, the sale of farmland as a farm, and keeping the farm in the family. Other, then dairy, farms rank the highest for perceived future intergenerational farmland succession; other animal rank third and grain farms rank lowest for intergenerational succession.
The mean difference shows dairy farms as statistically significant (0.39*) in its higher ranking to grain farms.
3.0
2.7 2.7 2.5 2.6 2.4
2.0
1.5
1.0
.5
0.0 Mean of Farm Succession Index Farmof Mean Succession Grain Dairy Other Animal Other
Farm Type Index
Figure 7.33 Intergenerational Farm Succession Index
380 No-till conservation tillage (Figure 7.34) has statistically significant variance among the farm types, but no farm type has a statistically significant greater mean difference. As expected, the inherent needs of the farm operation order the reported usage of this practice; grain farms report using conservation tillage more than other enterprise types in descending order from dairy farms, which raise their own feed, other animal also raising their own feed, and lastly, other farms that tend towards not using the practice because of multiple factors.
Other farms, as seen in Figure 7.31, are more traditional and thus less likely
(seen in Figure 7.7) to implement this practice or have no need for the practice because of the type of operation.
.7
.6 .6
.5
.4 .4
.3 .3
.2
.1 .1
0.0 Mean of Implemented No-Till Implemented of Mean Grain Dairy Other Animal Other
Farm Type Index
Figure 7.34 No-Till Conservation Tillage
381 Manure management (Figure 7.35) is another practice that has an obvious bias towards enterprise types that include animals, which is why the difference is greatest among dairy and other animal farms when compared to grain. There is a noticeable but insignificant difference in the rate of use between these two farms that is not explained by ANOVA with this variable. Enterprises that are predominantly grain report the lowest rate of manure management followed by other farms, which are predominantly fruit and vegetable or mixed farming operations that either do not produce and thus are not required to manage manure, or have such small numbers of animals that the operators feel they do not need this type of management practice. It is also possible that the other farm operators do not participate in a formalized manure management program because their level of traditionalism bars them from participation.
382
1.0
.9 .9 .8
.7
.6 .6
.5 .5 .4 .4
.3
.2
.1
0.0 Meanof Manure Implimented Management Grain Dairy Other Animal Other
Farm Type Index
Figure 7.35 Manure Management
383 When participants were asked if the economic viability of Sugar Creek
was more important than environmental quality (Figure 7.36), farmers responded
more positively for farm types grain, dairy and other animal and negatively for
other farms. In order of increasing difference, grain (1.06*), other animal (1.07*)
and dairy (1.21*) have statistically significant mean differences than other farms.
I believe that this is a response to the type of question which is focused primarily
on economic utility and although the Amish, who comprise most of the other farm
group, do not score high overall on conservation implementation, they have reacted against this “either/or” scenario of either the economy or the environment.
3.5
3.3 3.2 3.2 3.0
2.5
2.3 2.0
1.5
1.0
.5
0.0 Mean of Economic MoreImportant Economic of Mean Grain Dairy Other Animal Other
Farm Type Index
Figure 7.36 Economic Viability of Sugar Creek
384 Tenure-Acres Leased Out (Figure 7.37) is a dichotomous variable in which respondents are coded “zero” if they operate their farm, and coded “one” if they do lease their farm out to others for farming purposes. Based on this dichotomy, the results of this ANOVA illustrate the status of grain farms as the largest source of leased land in the watershed, followed in decreasing order by other animal, dairy and other. Differences in means of the responses to leasing are statistically significant for grain having higher lease rates than dairy (0.32*) farms and other
(0.41*) farms.
.5
.4 .4
.3
.2 .2 .2
.1 .1
0.0 Mean of Tenure - Acres Leased Out Leased Tenureof - Mean Acres Grain Dairy Other Animal Other
Farm Type Index
Figure 7.37 Acres Leased Out for Farming
385 The rate of off-farm income (Figure 7.38) is greatest among other animal operations, but only slightly in comparison to grain and other operations. The
ANOVA cannot explain why this difference exists. However, I speculate that this may be a factor of the small size of some of the non dairy animal operations – some of them may be too small to support the household, or are not intended to be the sole source of income, and thus require a larger amount of off-farm income. Others may be sourcing off-farm occupations to acquire health insurance. There are two statistically significant mean difference relationships relating to dairy operations: dairy operators report lower off-farm income rates than grain (0.74*), and other animal (0.76*).
2.5
2.2 2.2 2.0 2.0
1.5 1.4
1.0
.5
0.0 Meanof Off-Farm Index Income Grain Dairy Other Animal Other
Farm Type Index
Figure 7.38 Off-farm Income
386 7.7 Conclusion
The levels of sociocultural integration in which a farm household operates does relate to farm size, type and adoption of conservation practices as well as desires for the implementation of new conservation measures. This does not operate in a linear fashion across the continuum of traditionalism in which a high or low score in one variable set at one level of traditionalism necessitates that similar or increased/decreased intensities of scores will be seen as you move up or down the levels.
In the face of commodity price declines, inflated farm subsidies and government agricultural policies that foster growth of industrial farm practices and vertical integration of farms, family farms are under pressure to adjust their enterprise through growth or diversification. It is in this context that farms in the
Sugar Creek negotiate among different levels of sociocultural integration in which planning and action are performed. In effect, this is a balancing act of family and community needs with external market pressures from higher levels of sociocultural integration that places them at the intersection of local needs and globalization processes.
I think that this adaptability of the family farm does maintain an adaptive fitness that the large-scale farms do not; they allow for feedback directly between the operator and the ecology of the farm and surroundings. This level of local control is unsurpassed in its effectiveness to maintain a sustainable system. Yet, when the owner is not the person who manages the farm or the owner or
387 operator is acting on the orders of a distant contracting firm with external production demands, the system fails to receive these local signals.
In brief, the relationships among ethnicity and LSCI, in the form of a traditionalism ranking, with land tenure and conservation behavior are elliptical in which patterns emerge based on traditionalism of the group. Some group characteristics from the opposite ends of the ranking (“English” and
“Swartzentruber Amish”) group together and are differentiated from the centrist group rankings (Apostolic-Mennonite-Brethren (AMB) and Old Order Amish).
Research exploring the differences in assumed homogeneous populations
(i.e. Old Order v. Swartzentruber Amish) is key. Knowing the nuances the findings in each category of heritage group provides background information for developing and implementing a successful conservation program strategy.
Knowing the statistical information is important, but it has little value if sufficient qualitative descriptions are not available to contextualize the data analysis and interpretation.
To understand why the Sugar Creek Partners participate in this particular group and government programs and the reasons they have become community leaders in conservation is to understand the economic and social realities of their farm enterprises and relations in the community. Most team members have diversified livestock and grain farms. This diversified farm type differentiates them from the larger grain and potato farmers in the community, who, as other research mentioned above has shown, are less likely to adopt. The only “grain-
388 only” farmer is unique to the area because his management style incorporates a
self-designed “ridge-till” system and all cultivated acres are owned by the family.
These farm families exhibit a commitment to the land and to farming that
help provide opportunities for future generations to farm. Team members
manage expansion through kinship networks, using extended family to increase farm scale and decrease risk. Access to land for members comes from within existing kinship networks instead of purchasing individually. Two team members
have access to farmland through their spouse’s family farm, living uxorilocally, while others access land through other nuclear and extended family networks.
389
CHAPTER 8
THE EFFECTS OF FARM SIZE, SUCCESSION AND ENTERPRISE TYPE ON LAND TENURE AND CONSERVATION PREFERENCES
8.1 Introduction
As discussed in Chapter 6, land tenure both influences and is influenced
by farmland succession. In saying this, I mean to articulate that the various land
tenure arrangements may indicate farmland succession insecurity as well as act
as a factor in the successful transfer of a farm. Likewise, conservation attitudes
and behaviors are also linked to tenure and the future status of the farm because
preserving the farm as part of a family’s heritage, or selling it for profit, are
related to succession. Land tenure, being a composite of several characteristics
of interconnections among people and the land acts as an indicator of these
relationships, the details of which are not always apparent in examining basic
ownership or leasing arrangements.
Walter Goldschmidt (1978) identified farm related factors that influence
quality of life in rural areas in a study presented in his book, As You Sow. He
concluded that, in general, quality of life and inequality in a rural community as defined by higher per capita incomes, better standards of health, higher quality of schools, larger numbers and better quality of locally owned businesses, and
390 greater employment opportunities, among other variables, are correlated with the
presence of medium-sized family farms. He further exposed that the presence of
large industrial farms occurred concurrently with small farms and were found
negatively correlated with social disorders.
This chapter seeks to extend Goldschmidt’s findings to include
conservation adoption and attitudes as indicators of rural quality of life that
conceptualizes relationships among land tenure and farm size and enterprise
type as influencing conservation adoption and intergenerational farm succession.
Statistical analysis of these variables, using correlations analysis, Analysis of
Variance (ANOVA), and Discriminant Analysis, shows the strength and, in some
cases, directionality of these relationships, revealing a complex and
interconnected reality that can be better understood when analyzed in the
context of qualitative analysis.
In the following sections, I use conservation behavior and attitudes and
perceptions, as stated by participants in surveys and interviews, as a benchmark
of the local environment and community well-being. These behaviors and
perceptions act as indicators or measures of quality of life in the Sugar Creek
Watershed, the second most impaired in the State of Ohio. The following
assumptions are made in this regard: positive conservation attitudes (e.g. those
that show more conservation behaviors) are viewed as positive indicators of
quality of life; positive conservation attitudes compel residents to want to improve
the current assessment of the state of the watershed leading to a healthier
environment. In this chapter, I investigated relationships among land tenure, on-
391 farm conservation practices, farm size, and intergenerational farm succession. I
hypothesized that farm size, farm succession and inheritance, and enterprise
type will correlate with land tenure and preferences for conservation where
positive relationships will be found between medium-sized farms, higher levels of
farm succession, and non-grain farm types with more secure land tenure and
positive preferences for conservation practices and use.
8.1.1 Goldschmidt’s Findings
Since Goldschmidt published his findings, and added a nationwide dataset
to them in a 1978 reprinting of As You Sow, numerous scholars have attempted to research this linkage between the family farm and quality of life. A summary of
this body of research is available in Linda Lobao’s Locality and Inequality (1990)
in which an overview of corroborating research demonstrates support for
Goldschmidt’s findings. Some findings among the studies cited by Lobao
(1990:57) suggest that large-scale agriculture is associated with a variety of
community disorders, including 51 : “lower levels of living” (Goldschmidt 1978,
Rodefeld 1974); lower income for working class labor and increases in income
inequality and poverty (Tetreau 1940, Golschmidt 1968, Head and Sonka 1974,
Rodefeld 1974, Flora et al 1977, Wheelock 1979); “greater unemployment”
(Marousek 1979); decreased community services (Tetreau 1940, Raup 1973,
Fujimoto 1977, Swanson 1980); decreases in “social participation and integration
of communities” and higher level of mental disorders (Goldschmidt 1978,
51 Each is cited in Lobao 1990. 392 Heffernan 1972, Rodefeld 1974, Martison et al 1976, Poole 1981); less diversity and fewer trade and retail centers (Goldschmidt 1968, Heady and Sonka 1974,
Rodefeld 1974, Fujimoto 1977, Marousek 1979, Swanson 1980, Skees and
Swanson 1986); and “environmental pollution, depletion of energy resources”
(Tetreau 1940, Raup 1973, Buttel and Larson 1979). There are a few dissenting opinions that, according to Lobao, result from studies that may have been
“framed narrowly in terms of theory and scope” (Lobao 1990:4).
An overview of Lobao’s research review (1990:60-64) reveals that half of
the studies (13 of 26) solidly support Goldschmidt’s finding, 9 offer mixed support, and 4 offer no support. Lobao offers as one potential source of
methodological error resulting in findings of mixed or no support is the reliance
on secondary data sources rather than primary, first-hand accounts, a practice used in all dissenting research projects52. Durrenberger and Thu (1996) provide
an example of secondary data that supports Goldschmidt’s findings. By using
census of population and agricultural data, they show a correlation between the
number of industrial hog farms with social and economic deterioration of rural
communities in Iowa; fewer farms related to increased social disorders.
Environmental quality is rarely used as a variable relating quality of life to
farm scale with the exception of Buttel and Larson (1979), which emphasized environment in the context of energy use and efficiency and not directly measure of perceptions of environmental quality. Technology adoption research has
shown that conservation behavior is not measurable in terms of innovation
52 This is not stated to devalue secondary data sources but to suggest that research can benefit from primary data collection. 393 diffusion, rational choice or economic utility models (Tucker and Napier 2002,
Napier et al 1984, Napier et al 1986, Sommers and Napier 1993). Some of this
may be due to the variables that are investigated and the generality of the
research questions asked. Instead, farmer conservation behavior is morally
based rather than purely economic and self-interested (Paolisso and Maloney
2000, Dudley 2000, Bartlett 1993, Comstock 1987, Scott 1976) and is linked to
social responsibility making it useful as an indicator of community well-being.
In testing the stated hypothesis of this chapter, several indexes were
constructed from survey response data and used (see “Variables” section for a
full explanation on their calculation and meaning) to quantitatively represent, from
the survey data and interviews, the following: land tenure (TEN_TYPX), farm
succession (FRM_SUC), enterprise type (GRAIN_FRM) and disposition towards
conservation adoption.
Four statistical procedures were conducted in ascertaining the validity of this hypothesis: an Analysis of Variance (ANOVA), and three discriminant analysis models. The ANOVA examines the variation among land tenure categories. Discriminant analysis modeling demonstrated how the various
hypothesis variables discriminate for land tenure categories of the TEN_TYPX
variable and rank-ordered conservation measure CUR_USE2. Two discriminant
analysis models, demonstrate the discriminating power of the variables in
determining group membership in both the land tenure and conservation use
variables. Only the models that are statistically significant (α=0.05) are included.
394 8.1.2 Variable Descriptions and Explanation of Indexes
The variables analyzed in this chapter are described in this section.
Several variables are indexes or scores modified from their original coding of the survey data to operationalize the concepts tested in this chapter. Table 8.1 lists the variables and gives brief descriptions. Below is a detailed accounting of each variable. Two conservation measures are included because of the requirements of the statistical procedures.
Variable & Description: Coding: FIELD_NAME: land tenure 6 rank-ordered categories of various 1=lease out all 4=own 33-65% TEN_TYPX ratios of owned and leased land 2=own 0% 5=own 66-99% 3=own 1-32% 6=own 100% farm size sum of owned and leased land total number of acres farmed FRM_SIZE farm type 2 categories 0=other, 1=grain GRAIN_FRM farm succession 3 rank-ordered categories of 10-year 1=keep in family index future farm plan 2=sell as farm FRM_SUC2 3=sell for development leasing-out all Dichotomous variable of non farming 0=farm your land land landlords 1=do not farm and lease out land TN_LEASE Conservation 3-point scale of BMP use and future 1= <3, 2= 3-5, 3 = >5 index preferences CON_INDX Conservation 3-point scale of conservation use 1= Low, 2= Medium, 3= High Use CON_USE2 off-farm 10-point scale of percentage of off- 1=0-10%, 2=11-20%, 3=21-30%, etc… income farm income Q7PERCEN
Table 8.1 Descriptions and coding of concepts
As discussed in “Data Collection Problems” section of Chapter 4, it is difficult to assess Amish leasing from the survey data. Nevertheless it is 395 represented in the variable TEN_TYPX, which is a composite of responses to four survey questions to which farmers were asked to answer the amount of land, in acres, that they: 1) owned and farmed/operated, 2) rented to farm, 3) total land owned and 4) total land leased. Based on responses from these questions, a rank-ordered array of categories was constructed into which each farm was placed and assigned a number from 1 to 6 that corresponded to the rank
(Table 8.2).
Code Category N % 1 Own and Lease Out 68 42.8 2 Own 0% 5 3.1 3 Own 1-32% 9 5.7 4 Own 33-65% 23 14.5 5 Own 66-99% 19 11.9 6 Own 100% 35 22.0 Total 159
Table 8.2 Land Tenure Categories and Frequencies
Assumptions of this ranking order are based on the relationships found in the literature between farm succession and land tenure in which conservation and farm succession are both associated with greater rates of land ownership.
As such, non-farming landowners who lease out their land to others for farming tend to lack a farm heir and are shown to adopt fewer conservation practices.
Because of these reasons, “Own Land and Lease it out for Farming” was ranked lowest in the array. Each tenure group increases in the percentage of ownership as the rank increases, placing greater weight on ownership of land.
396 The farm type (GRAIN_FRM) variable is created using responses from the percentage of gross sales that the respondent indicates for each of the product categories (i.e. corn, soybeans, dairy, vegetable etc). This variable is ranked with two categories (Table 8.3) for the purposes of the discriminant analysis. It is assumed that grain farms will have a lesser degree of land tenure security due to high levels of leasing than will dairy and other mixed faming enterprises. The interpretation of results using this dichotomous ranking can only be done to the extent that this variables construction is understood.
Code Category N % 0 Other 81 70.4 1 Grain 34 29.6 Total 115
Table 8.3 Farm Types Categories and Frequencies
Farm size (FRM_SIZE) as a variable in this analysis is represented by the total number of acres that a farmer operates during the course of a growing season. This includes all owned and rented or leased land that is farmed.
Another measure of farm size is a an interval variable indicating the percent of off-farm income coming into the farm (Q7PERCEN). Larger amounts of off-farm income is negatively correlated with smaller farms (Pearson’s r =-.233**, sig.=.006, N=139) and negatively correlated with total owned acreage (Pearson’s r =-.385**, sig.=.000, N=139). This is likely because this watershed has a large number of small-scale Amish farms that are typically owned and not rented or
397 leased53. As such, this variable acts as a proxy for real farm income, another
measure of farm size.
Farm Succession (FRM_SUC2) was measured by condensing eight rank-
ordered response options provided for a question that assesses a farmer’s
current vision of the future intergenerational transfer of the farm within the next
ten years, with the emphasis on perceived tenure security. The eight options ranged from “sell the farm for development” to continue farming by my children or spouse without selling” and “continue farming by myself”, the latter offers an
option for those families with young children and an uncertainty regarding farm
succession but currently have secured tenure; each was ranked low to high,
respectively (Table 8.4). The array of options were then distilled into three ranks
that comprised the farm succession index in which keeping the farmland in
farming and keep farm in family is viewed as the highest ranked option followed
by selling the farm as farmland and then selling the farm for development, as
seen below.
Code Category N % 1 Sell Farm 15 10.8 2 Sell as Farm 50 36.0 3 Keep in Family 74 53.2 Total 139
Table 8.4 Intergenerational Farm Success Categories and Frequencies
53 As noted in previous chapters, Amish tenure is difficult to ascertain from direct survey results due to the degree of leasing reported in connection with father/son and father-in-law/son-in-law succession. 398 Table 8.5 and Table 8.6 show all variables used in the discriminant analysis exercises; Table 8.5 includes the variables for land tenure analysis and
Table 8.6 the variables for the conservation use analysis.
Land Tenure Discriminant Analysis Variables FRM_SUC2 GRAIN_FRM FRM_SIZE Q7PERCEN Q1MANURM Q1NOTILL CON_INDX
Table 8.5 Variables for Discriminant Analysis of Land Tenure (TEN_TYPX)
Conservation Discriminant Analysis Variables FRM_SUC2 FRM_SIZE TEN_TYPX GRAIN_FRM Q7PERCEN
Table 8.6 Variables for Discriminant Analysis of Conservation Use (CON_USE2)
8.1.3 Descriptive Statistics
The tables that follow provide descriptive details of potential relationships between land tenure and farm size and farm type that have guided the model building in this chapter.
Survey respondents were asked what they would like to see on their land in the future. Using categorized responses by land tenure, results from the survey show that land tenure arrangements are associated with current adoption rates (Table 8.7) and farmer preferences for certain BMPs (Table 8.8). 399
Installed Installed Implemented % of Grass Grass Implemented Manure N Total Waterways Buffers No-Till Management Lease out 68 42.77 43 63.24 8 11.76 26 38.24 26 38.24 Own 0% 5 3.14 4 80.00 1 20.00 3 60.00 3 60.00 Own 1- 32% 9 5.66 9 100.00 3 33.33 6 66.67 9 100.00 Own 33-65% 23 14.47 20 86.96 8 34.78 16 69.57 20 86.96 Own 66-99% 19 11.95 16 84.21 6 31.58 12 63.16 10 52.63 Own 100% 35 22.01 22 62.86 7 20.00 4 11.43 22 62.86 N=159 100.00% 114 33 67 90
Table 8.7 Conservation Practice by Land Tenure Category (N, %)
Increased Adequate Erosion % of Reforested Grass Buffer Biodiversity Drainage Protection N Total Tree Vision Vision Vision Vision Vision Lease Out 68 42.77 18 26.47 17 25.00 19 27.94 28 41.18 34 50.00 Own 0% 5 3.14 2 40.00 1 20.00 2 40.00 4 80.00 2 40.00 Own 1- 32% 9 5.66 1 11.11 3 33.33 1 11.11 4 44.44 2 22.22 Own 33- 65% 23 14.47 3 13.04 10 43.48 6 26.09 8 34.78 10 43.48 Own 66- 99% 19 11.95 4 21.05 9 47.37 2 10.53 6 31.58 7 36.84 Own 100% 35 22.01 13 37.14 7 20.00 13 37.14 17 48.57 23 65.71 N=159 100.00% 41 47 43 67 78
Table 8.8 Conservation Preferences by Land Tenure Category (N, %)
400 Farm Size
Mean, median and range of farm size vary among Wayne County Farms
and the farms in the Sugar Creek Farm Partners (who are involved in watershed
wide conservation efforts, see Table 8.9). A comparison shows that the Partners’ farms consist of a mean acreage of 263.7, median acreage is 216.6, and the range is 500.76 acres. In comparison, Wayne County and non-team Upper Sugar
Creek farmers, mean farm size is 96.91 and 121.85, respectively, and the range is 1368.43 and 936.79 with a median of 72.12 and 78.23 acres, respectively.
Based on this data you can assume that, of the available farmers who could join
the Upper Sugar Creek Farm Partners, the operators of medium-scale farms of the Upper Sugar Creek are most likely to join than the small- and large-scale farm operators.
Total Acres Number of Mean Median Min. Max. Range SD Agricultural Farm Farm Farm Farm Farm Owners Wayne County 254877.59 2630 96.91 72.12 10.00 1378.42 1368.43 103.89 Upper Sugar Creek 22842.12 170 134.37 81.36 10.12 946.91 936.79 157.74 Non- Partners 18886.62 155 121.85 78.23 10.12 946.91 936.79 152.80 Sugar Creek Partners 3955.50 15 263.7 216.6 75.89 576.64 500.76 154.47 Source: Wayne County Auditor’s GIS Parcel Database.
Table 8.9 Descriptive Statistics of Owned Agricultural Land in Wayne County, the Upper Sugar Creek Watershed, and of the Upper Sugar Creek Partners
401 8.1.4 ANOVA of Dependent Variables
The concepts of land tenure and intergenerational farm succession as
they relate to the variables describing them are included in the following analysis of this chapter. Consequently, I present the results of two calculations of One-
way Analysis of Variance (ANOVA) to explore differences among categories of
the land tenure variable (TEN_TYPX) using statistically significant variables to
produce possible background explanations and directions for interpretation of the
Discriminant Analysis functions presented and described below. The same
ANOVA procedures were executed to analyze variance among categories of the
intergenerational farm succession variable (FRM_SUC2) and associated statistically significant variables 54 . Several of these variables were found to
correlate with the Traditionalism Index in Chapter 7.
54 See Appendix E, Survey Data Dictionary, for a fuller explanation of all variables, included and not included, in this chapter. 402
N Mean Std. Std. Deviation Error Conservation 1 Lease Out 68 1.63 .710 .086 Index 2 Own 0 % 5 2.20 .837 .374 3 Own 1-32% 9 2.56 .527 .176 4 Own 33-65% 23 2.65 .487 .102 5 Own 66-99% 19 2.42 .692 .159 6 Own 100% 35 2.31 .718 .121 Total 159 2.09 .786 .062 Conservation 1 Lease Out 68 1.84 .660 .080 Use 2 Own 0 % 5 2.20 .837 .374 3 Own 1-32% 9 2.44 .527 .176 4 Own 33-65% 23 2.35 .647 .135 5 Own 66-99% 19 2.11 .737 .169 6 Own 100% 35 2.06 .725 .123 Total 159 2.04 .702 .056 Desired 1 Lease Out 68 .50 .906 .110 Recreational 2 Own 0 % 5 .00 .000 .000 Use Score 3 Own 1-32% 9 .11 .333 .111 4 Own 33-65% 23 .26 .541 .113 5 Own 66-99% 19 .32 .749 .172 6 Own 100% 35 1.09 1.222 .206 Total 159 .53 .940 .075 Farm 1 Lease Out 49 1.84 .514 .073 Succession 2 Own 0 % 5 2.80 .447 .200 Index 3 Own 1-32% 9 2.78 .667 .222 4 Own 33-65% 23 2.87 .344 .072 5 Own 66-99% 18 2.61 .698 .164 6 Own 100% 35 2.71 .519 .088 Total 139 2.42 .681 .058 Implemented 1 Lease Out 68 .38 .490 .059 No-Till 2 Own 0 % 5 .60 .548 .245 3 Own 1-32% 9 .67 .500 .167 4 Own 33-65% 23 .70 .470 .098 5 Own 66-99% 19 .63 .496 .114 6 Own 100% 35 .11 .323 .055 Total 159 .42 .495 .039 Installed 1 Lease Out 68 .63 .486 .059 Grass 2 Own 0 % 5 .80 .447 .200 Waterways 3 Own 1-32% 9 1.00 .000 .000 4 Own 33-65% 23 .87 .344 .072 5 Own 66-99% 19 .84 .375 .086 6 Own 100% 35 .63 .490 .083 Total 159 .72 .452 .036 Implemented 1 Lease Out 68 .38 .490 .059 Manure 2 Own 0 % 5 .60 .548 .245
Table 8.10 Land Tenure Categories ANOVA Descriptive Statistics
Continued
403 Table 8.10 (continued)
Management 3 Own 1-32% 9 1.00 .000 .000 4 Own 33-65% 23 .87 .344 .072 5 Own 66-99% 19 .53 .513 .118 6 Own 100% 35 .63 .490 .083 Total 159 .57 .497 .039 Farm Size 1 Lease Out 68 72.72 56.022 6.794 2 Own 0 % 5 295.00 333.954 149.349 3 Own 1-32% 9 735.33 644.683 214.894 4 Own 33-65% 23 381.30 306.870 63.987 5 Own 66-99% 19 289.84 298.360 68.448 6 Own 100% 35 115.20 123.721 20.913 Total 159 197.15 287.521 22.802 Off-Farm 1 Lease Out 54 2.24 .845 .115 Income Index 2 Own 0 % 5 2.20 .447 .200 3 Own 1-32% 8 1.63 .744 .263 4 Own 33-65% 22 1.73 .767 .164 5 Own 66-99% 18 1.67 .767 .181 6 Own 100% 32 1.84 .884 .156 Total 139 1.96 .842 .071 Farm Type 1 Lease Out 26 .54 .508 .100 2 Own 0 % 5 .80 .447 .200 3 Own 1-32% 9 .33 .500 .167 4 Own 33-65% 23 .17 .388 .081 5 Own 66-99% 19 .32 .478 .110 6 Own 100% 35 .20 .406 .069 Total 117 .32 .470 .043
404
Sum of Squares df Mean Square F Sig. Conservation Between Groups 27.362 5 5.472 11.923 .000 Index Within Groups 70.223 153 .459 Total 97.585 158 Conservation Use Between Groups 6.638 5 1.328 2.856 .017 Within Groups 71.135 153 .465 Total 77.774 158 Desired Between Groups 16.388 5 3.278 4.071 .002 Recreational Use Score Within Groups 123.172 153 .805 Total 139.560 158 Farm Succession Index Between Groups 26.878 5 5.376 19.282 .000 Within Groups 37.079 133 .279 Total 63.957 138 Implemented No-Till Between Groups 6.675 5 1.335 6.365 .000 Within Groups 32.092 153 .210 Total 38.767 158 Installed Grass Waterways Between Groups 2.349 5 .470 2.403 .039 Within Groups 29.915 153 .196 Total 32.264 158 Implemented Manure Between Groups 6.281 5 1.256 5.864 .000 Management Within Groups 32.776 153 .214 Total 39.057 158 Farm Size Between Groups 4885759.690 5 977151.938 18.286 .000 Within Groups 8175802.687 153 53436.619 Total 13061562.377 158 Off-Farm Income Index Between Groups 8.613 5 1.723 2.571 .030 Within Groups 89.128 133 .670 Total 97.741 138 Farm Type Between Groups 3.387 5 .677 3.376 .007 Within Groups 22.271 111 .201 Total 25.658 116
Table 8.11 ANOVA Land Tenure Categories
405 8.1.5 Mean Differences Between Groups
Dependent (I) Tenure (J) Tenure Mean 95% Confidence Variable Category Category Difference (I-J) Std. Error Sig. Interval Lower Upper Bound Bound Conservation 1 Lease 2 0% -.57 .314 .464 -1.47 .34 Index 3 1-32% -.92(**) .240 .002 -1.62 -.23 4 33-65% -1.02(**) .163 .000 -1.49 -.55 5 66-99% -.79(**) .176 .000 -1.30 -.28 6 100% -.68(**) .141 .000 -1.09 -.28 2 0 % 1 Lease .57 .314 .464 -.34 1.47 3 1-32% -.36 .378 .935 -1.45 .74 4 33-65% -.45 .334 .755 -1.42 .51 5 66-99% -.22 .341 .987 -1.20 .76 6 100% -.11 .324 .999 -1.05 .82 3 1-32% 1 Lease .92(**) .240 .002 .23 1.62 2 0 % .36 .378 .935 -.74 1.45 4 33-65% -.10 .266 .999 -.87 .67 5 66-99% .13 .274 .996 -.66 .93 6 100% .24 .253 .932 -.49 .97 4 33-65% 1 Lease 1.02(**) .163 .000 .55 1.49 2 0 % .45 .334 .755 -.51 1.42 3 1-32% .10 .266 .999 -.67 .87 5 66-99% .23 .210 .881 -.38 .84 6 100% .34 .182 .432 -.19 .86 5 66-99% 1 Lease .79(**) .176 .000 .28 1.30 2 0 % .22 .341 .987 -.76 1.20 3 1-32% -.13 .274 .996 -.93 .66 4 33-65% -.23 .210 .881 -.84 .38 6 100% .11 .193 .994 -.45 .66 6 100% 1 Lease .68(**) .141 .000 .28 1.09 2 0 % .11 .324 .999 -.82 1.05 3 1-32% -.24 .253 .932 -.97 .49 4 33-65% -.34 .182 .432 -.86 .19 5 66-99% -.11 .193 .994 -.66 .45 Conservation 1 Lease 2 0% -.36 .316 .862 -1.27 .55 Use 3 1-32% -.61 .242 .129 -1.30 .09 4 33-65% -.51(*) .164 .028 -.98 -.03 5 66-99% -.27 .177 .659 -.78 .24 6 100% -.22 .142 .637 -.63 .19 2 0 % 1 Lease .36 .316 .862 -.55 1.27 3 1-32% -.24 .380 .988 -1.34 .85 4 33-65% -.15 .336 .998 -1.12 .82 5 66-99% .09 .343 1.000 -.89 1.08 6 100% .14 .326 .998 -.80 1.08
Table 8.12 ANOVA Land Tenure Categories Tukey HSD Multiple Comparisons
Continued
406 Table 8.12 (continued)
3 1-32% 1 Lease .61 .242 .129 -.09 1.30 2 0 % .24 .380 .988 -.85 1.34 4 33-65% .10 .268 .999 -.68 .87 5 66-99% .34 .276 .822 -.46 1.14 6 100% .39 .255 .652 -.35 1.12 4 33-65% 1 Lease .51(*) .164 .028 .03 .98 2 0 % .15 .336 .998 -.82 1.12 3 1-32% -.10 .268 .999 -.87 .68 5 66-99% .24 .211 .861 -.37 .85 6 100% .29 .183 .608 -.24 .82 5 66-99% 1 Lease .27 .177 .659 -.24 .78 2 0 % -.09 .343 1.000 -1.08 .89 3 1-32% -.34 .276 .822 -1.14 .46 4 33-65% -.24 .211 .861 -.85 .37 6 100% .05 .194 1.000 -.51 .61 6 100% 1 Lease .22 .142 .637 -.19 .63 2 0 % -.14 .326 .998 -1.08 .80 3 1-32% -.39 .255 .652 -1.12 .35 4 33-65% -.29 .183 .608 -.82 .24 5 66-99% -.05 .194 1.000 -.61 .51 Desired 1 Lease 2 0% .50 .416 .835 -.70 1.70 Recreational 3 1-32% .39 .318 .826 -.53 1.31 Use Score 4 33-65% .24 .216 .879 -.39 .86 5 66-99% .18 .233 .969 -.49 .86 6 100% -.59(*) .187 .025 -1.12 -.05 2 0 % 1 Lease -.50 .416 .835 -1.70 .70 3 1-32% -.11 .500 1.000 -1.56 1.33 4 33-65% -.26 .443 .992 -1.54 1.02 5 66-99% -.32 .451 .982 -1.62 .99 6 100% -1.09 .429 .122 -2.32 .15 3 1-32% 1 Lease -.39 .318 .826 -1.31 .53 2 0 % .11 .500 1.000 -1.33 1.56 4 33-65% -.15 .353 .998 -1.17 .87 5 66-99% -.20 .363 .993 -1.25 .84 6 100% -.97(*) .335 .047 -1.94 -.01 4 33-65% 1 Lease -.24 .216 .879 -.86 .39 2 0 % .26 .443 .992 -1.02 1.54 3 1-32% .15 .353 .998 -.87 1.17 5 66-99% -.05 .278 1.000 -.86 .75 6 100% -.82(*) .241 .010 -1.52 -.13 5 66-99% 1 Lease -.18 .233 .969 -.86 .49 2 0 % .32 .451 .982 -.99 1.62 3 1-32% .20 .363 .993 -.84 1.25 4 33-65% .05 .278 1.000 -.75 .86 6 100% -.77(*) .256 .035 -1.51 -.03 6 100% 1 Lease .59(*) .187 .025 .05 1.12 2 0 % 1.09 .429 .122 -.15 2.32
Continued 407 Table 8.12 (continued)
3 1-32% .97(*) .335 .047 .01 1.94 4 33-65% .82(*) .241 .010 .13 1.52 5 66-99% .77(*) .256 .035 .03 1.51 Farm 1 Lease 2 0% -.96(**) .248 .002 -1.68 -.25 Succession 3 1-32% -.94(**) .191 .000 -1.49 -.39 Index 4 33-65% -1.03(**) .133 .000 -1.42 -.65 5 66-99% -.77(**) .146 .000 -1.20 -.35 6 100% -.88(**) .117 .000 -1.22 -.54 2 0 % 1 Lease .96(**) .248 .002 .25 1.68 3 1-32% .02 .295 1.000 -.83 .87 4 33-65% -.07 .261 1.000 -.82 .68 5 66-99% .19 .267 .981 -.58 .96 6 100% .09 .252 .999 -.64 .82 3 1-32% 1 Lease .94(**) .191 .000 .39 1.49 2 0 % -.02 .295 1.000 -.87 .83 4 33-65% -.09 .208 .998 -.69 .51 5 66-99% .17 .216 .972 -.46 .79 6 100% .06 .197 1.000 -.51 .63 4 33-65% 1 Lease 1.03(**) .133 .000 .65 1.42 2 0 % .07 .261 1.000 -.68 .82 3 1-32% .09 .208 .998 -.51 .69 5 66-99% .26 .166 .629 -.22 .74 6 100% .16 .142 .882 -.25 .57 5 66-99% 1 Lease .77(**) .146 .000 .35 1.20 2 0 % -.19 .267 .981 -.96 .58 3 1-32% -.17 .216 .972 -.79 .46 4 33-65% -.26 .166 .629 -.74 .22 6 100% -.10 .153 .985 -.55 .34 6 100% 1 Lease .88(**) .117 .000 .54 1.22 2 0 % -.09 .252 .999 -.82 .64 3 1-32% -.06 .197 1.000 -.63 .51 4 33-65% -.16 .142 .882 -.57 .25 5 66-99% .10 .153 .985 -.34 .55 Implemented 1 Lease 2 0% -.22 .212 .909 -.83 .39 No-Till 3 1-32% -.28 .162 .501 -.75 .18 4 33-65% -.31 .110 .057 -.63 .01 5 66-99% -.25 .119 .294 -.59 .09 6 100% .27 .095 .061 -.01 .54 2 0 % 1 Lease .22 .212 .909 -.39 .83 3 1-32% -.07 .255 1.000 -.80 .67 4 33-65% -.10 .226 .998 -.75 .56 5 66-99% -.03 .230 1.000 -.70 .63 6 100% .49 .219 .235 -.15 1.12 3 1-32% 1 Lease .28 .162 .501 -.18 .75 2 0 % .07 .255 1.000 -.67 .80 4 33-65% -.03 .180 1.000 -.55 .49 5 66-99% .04 .185 1.000 -.50 .57
Continued 408 Table 8.12 (continued)
6 100% .55(*) .171 .019 .06 1.05 4 33-65% 1 Lease .31 .110 .057 -.01 .63 2 0 % .10 .226 .998 -.56 .75 3 1-32% .03 .180 1.000 -.49 .55 5 66-99% .06 .142 .998 -.35 .47 6 100% .58(**) .123 .000 .23 .94 5 66-99% 1 Lease .25 .119 .294 -.09 .59 2 0 % .03 .230 1.000 -.63 .70 3 1-32% -.04 .185 1.000 -.57 .50 4 33-65% -.06 .142 .998 -.47 .35 6 100% .52(**) .131 .002 .14 .89 6 100% 1 Lease -.27 .095 .061 -.54 .01 2 0 % -.49 .219 .235 -1.12 .15 3 1-32% -.55(*) .171 .019 -1.05 -.06 4 33-65% -.58(**) .123 .000 -.94 -.23 5 66-99% -.52(**) .131 .002 -.89 -.14 Installed 1 Lease 2 0% -.17 .205 .964 -.76 .42 Grass 3 1-32% -.37 .157 .183 -.82 .09 Waterways 4 33-65% -.24 .107 .233 -.55 .07 5 66-99% -.21 .115 .451 -.54 .12 6 100% .00 .092 1.000 -.26 .27 2 0 % 1 Lease .17 .205 .964 -.42 .76 3 1-32% -.20 .247 .965 -.91 .51 4 33-65% -.07 .218 1.000 -.70 .56 5 66-99% -.04 .222 1.000 -.68 .60 6 100% .17 .211 .965 -.44 .78 3 1-32% 1 Lease .37 .157 .183 -.09 .82 2 0 % .20 .247 .965 -.51 .91 4 33-65% .13 .174 .975 -.37 .63 5 66-99% .16 .179 .950 -.36 .67 6 100% .37 .165 .222 -.11 .85 4 33-65% 1 Lease .24 .107 .233 -.07 .55 2 0 % .07 .218 1.000 -.56 .70 3 1-32% -.13 .174 .975 -.63 .37 5 66-99% .03 .137 1.000 -.37 .42 6 100% .24 .119 .330 -.10 .58 5 66-99% 1 Lease .21 .115 .451 -.12 .54 2 0 % .04 .222 1.000 -.60 .68 3 1-32% -.16 .179 .950 -.67 .36 4 33-65% -.03 .137 1.000 -.42 .37 6 100% .21 .126 .537 -.15 .58 6 100% 1 Lease .00 .092 1.000 -.27 .26 2 0 % -.17 .211 .965 -.78 .44 3 1-32% -.37 .165 .222 -.85 .11 4 33-65% -.24 .119 .330 -.58 .10 5 66-99% -.21 .126 .537 -.58 .15
Continued
409 Table 8.12 (continued)
Implemented 1 Lease 2 0% -.22 .214 .912 -.84 .40 Manure 3 1-32% -.62(**) .164 .003 -1.09 -.14 Management 4 33-65% -.49(**) .112 .000 -.81 -.16 5 66-99% -.14 .120 .837 -.49 .20 6 100% -.25 .096 .114 -.52 .03 2 0 % 1 Lease .22 .214 .912 -.40 .84 3 1-32% -.40 .258 .633 -1.15 .35 4 33-65% -.27 .228 .846 -.93 .39 5 66-99% .07 .233 1.000 -.60 .75 6 100% -.03 .221 1.000 -.67 .61 3 1-32% 1 Lease .62(**) .164 .003 .14 1.09 2 0 % .40 .258 .633 -.35 1.15 4 33-65% .13 .182 .980 -.39 .66 5 66-99% .47 .187 .122 -.07 1.01 6 100% .37 .173 .269 -.13 .87 4 33-65% 1 Lease .49(**) .112 .000 .16 .81 2 0 % .27 .228 .846 -.39 .93 3 1-32% -.13 .182 .980 -.66 .39 5 66-99% .34 .143 .165 -.07 .76 6 100% .24 .124 .382 -.12 .60 5 66-99% 1 Lease .14 .120 .837 -.20 .49 2 0 % -.07 .233 1.000 -.75 .60 3 1-32% -.47 .187 .122 -1.01 .07 4 33-65% -.34 .143 .165 -.76 .07 6 100% -.10 .132 .971 -.48 .28 6 100% 1 Lease .25 .096 .114 -.03 .52 2 0 % .03 .221 1.000 -.61 .67 3 1-32% -.37 .173 .269 -.87 .13 4 33-65% -.24 .124 .382 -.60 .12 5 66-99% .10 .132 .971 -.28 .48 Farm Size 1 Lease 2 0% -222.28 107.113 .306 -531.43 86.87 3 1-32% -662.61(**) 81.995 .000 -899.27 -425.96 4 33-65% -308.58(**) 55.760 .000 -469.52 -147.65 5 66-99% -217.12(**) 59.986 .005 -390.25 -43.99 6 100% -42.48 48.089 .950 -181.27 96.32 2 0 % 1 Lease 222.28 107.113 .306 -86.87 531.43 3 1-32% -440.33(*) 128.937 .010 -812.47 -68.20 4 33-65% -86.30 114.064 .974 -415.51 242.90 5 66-99% 5.16 116.189 1.000 -330.18 340.50 6 100% 179.80 110.517 .582 -139.17 498.77 3 1-32% 1 Lease 662.61(**) 81.995 .000 425.96 899.27 2 0 % 440.33(*) 128.937 .010 68.20 812.47 4 33-65% 354.03(**) 90.889 .002 91.71 616.35 5 66-99% 445.49(**) 93.541 .000 175.52 715.47 6 100% 620.13(**) 86.395 .000 370.78 869.49 4 33-65% 1 Lease 308.58(**) 55.760 .000 147.65 469.52 2 0 % 86.30 114.064 .974 -242.90 415.51 3 1-32% -354.03(**) 90.889 .002 -616.35 -91.71
Continued 410 Table 8.12 (continued)
5 66-99% 91.46 71.664 .798 -115.37 298.30 6 100% 266.10(**) 62.049 .000 87.02 445.19 5 66-99% 1 Lease 217.12(**) 59.986 .005 43.99 390.25 2 0 % -5.16 116.189 1.000 -340.50 330.18 3 1-32% -445.49(**) 93.541 .000 -715.47 -175.52 4 33-65% -91.46 71.664 .798 -298.30 115.37 6 100% 174.64 65.873 .091 -15.48 364.76 6 100% 1 Lease 42.48 48.089 .950 -96.32 181.27 2 0 % -179.80 110.517 .582 -498.77 139.17 3 1-32% -620.13(**) 86.395 .000 -869.49 -370.78 4 33-65% -266.10(**) 62.049 .000 -445.19 -87.02 5 66-99% -174.64 65.873 .091 -364.76 15.48 Off-Farm 1 Lease 2 0% .04 .383 1.000 -1.07 1.15 Income 3 1-32% .62 .310 .356 -.28 1.51 Index 4 33-65% .51 .207 .138 -.09 1.11 5 66-99% .57 .223 .110 -.07 1.22 6 100% .40 .183 .257 -.13 .93 2 0 % 1 Lease -.04 .383 1.000 -1.15 1.07 3 1-32% .58 .467 .820 -.77 1.92 4 33-65% .47 .406 .852 -.70 1.65 5 66-99% .53 .414 .791 -.66 1.73 6 100% .36 .394 .945 -.78 1.49 3 1-32% 1 Lease -.62 .310 .356 -1.51 .28 2 0 % -.58 .467 .820 -1.92 .77 4 33-65% -.10 .338 1.000 -1.08 .88 5 66-99% -.04 .348 1.000 -1.05 .96 6 100% -.22 .324 .984 -1.15 .72 4 33-65% 1 Lease -.51 .207 .138 -1.11 .09 2 0 % -.47 .406 .852 -1.65 .70 3 1-32% .10 .338 1.000 -.88 1.08 5 66-99% .06 .260 1.000 -.69 .81 6 100% -.12 .227 .996 -.77 .54 5 66-99% 1 Lease -.57 .223 .110 -1.22 .07 2 0 % -.53 .414 .791 -1.73 .66 3 1-32% .04 .348 1.000 -.96 1.05 4 33-65% -.06 .260 1.000 -.81 .69 6 100% -.18 .241 .977 -.87 .52 6 100% 1 Lease -.40 .183 .257 -.93 .13 2 0 % -.36 .394 .945 -1.49 .78 3 1-32% .22 .324 .984 -.72 1.15 4 33-65% .12 .227 .996 -.54 .77 5 66-99% .18 .241 .977 -.52 .87 Farm 1 Lease 2 0% -.26 .219 .838 -.90 .37 Type 3 1-32% .21 .173 .844 -.30 .71 4 33-65% .36 .128 .058 -.01 .74 5 66-99% .22 .135 .569 -.17 .61 6 100% .34* .116 .048 .00 .67
Continued 411 Table 8.12 (continued)
2 0 % 1 Lease .26 .219 .838 -.37 .90 3 1-32% .47 .250 .427 -.26 1.19 4 33-65% .63 .221 .060 -.01 1.27 5 66-99% .48 .225 .269 -.17 1.14 6 100% .60 .214 .065 -.02 1.22 3 1-32% 1 Lease -.21 .173 .844 -.71 .30 2 0 % -.47 .250 .427 -1.19 .26 4 33-65% .16 .176 .944 -.35 .67 5 66-99% .02 .181 1.000 -.51 .54 6 100% .13 .167 .968 -.35 .62 4 33-65% 1 Lease -.36 .128 .058 -.74 .01 2 0 % -.63 .221 .060 -1.27 .01 3 1-32% -.16 .176 .944 -.67 .35 5 66-99% -.14 .139 .910 -.54 .26 6 100% -.03 .120 1.000 -.37 .32 5 66-99% 1 Lease -.22 .135 .569 -.61 .17 2 0 % -.48 .225 .269 -1.14 .17 3 1-32% -.02 .181 1.000 -.54 .51 4 33-65% .14 .139 .910 -.26 .54 6 100% .12 .128 .944 -.25 .49 6 100% 1 Lease -.34* .116 .048 -.67 .00 2 0 % -.60 .214 .065 -1.22 .02 3 1-32% -.13 .167 .968 -.62 .35 4 33-65% .03 .120 1.000 -.32 .37 5 66-99% -.12 .128 .944 -.49 .25 * The mean difference is significant at the .05 level. ** The mean difference is significant at the .01 level.
412 Referring to Table 8.12, there is variation among the tenure categories
that are specific to particular combinations of variables. Overall conservation
scores (CON_INDX, Figure 8.1) show significant variance existing between each
of the farm tenure categories and the category 1, Lease Out, except category 2,
Own 0%. Tenure category 3, Own 1-32%, was higher by (0.92**), category 4,
Own 33-65%, was higher by (1.02**), category five, Own 66-99%, was higher by
(0.79**), and category 6, Own 100% as higher by (0.68**).
2.8
2.6 2.7 2.6 2.4 2.4 2.3 2.2 2.2
2.0
1.8
1.6 1.6
1.4 Mean of Conservation Index Mean of Conservation Ow n and Lease Out Ow n 1-32% Ow n 66-99% Ow n 0 % Ow n 33-65% Ow n 100%
Tenure Category
Figure 8.1 Conservation Index
413 Likewise, the mean of the current use index (CUR_USE2, Figure 8.2) of conservation practices is also higher (0.51*) between the same two categories,
Lease Out and Own 33-65%. Both conservation variables show negative relationships for tenure category 1 and all other categories.
2.6
2.4 2.4
2.3
2.2 2.2
2.1 2.0 2.1
1.8 1.8
1.6 Mean of Conservation Use Mean of Conservation Ow n and Lease Out Ow n 1-32% Ow n 66-99% Ow n 0 % Ow n 33-65% Ow n 100%
Tenure Category
Figure 8.2 Conservation Use
414 Desired Recreational Land Use (DES_UREC, Figure 8.3) is significantly higher for category 6, Own 100%, when compared to all other categories accept category 2, Own 0%, the score of which is still higher, but the variance is not significant. The following categories have lower means than Own 100%: Lease out (0.59*), Own 1-32% (0.97*), Own 33-65% (0.82*), Own 66-99% (0.77*).
1.2
1.1 1.0
.8
.6
.5 .4
.3 .2 .3
.1 0.0 Mean of Desired Recreational Use Score Use Recreational Mean of Desired Ow n and Lease Out Ow n 1-32% Ow n 66-99% Ow n 0 % Ow n 33-65% Ow n 100%
Tenure Category
Figure 8.3 Recreational Use Score
415 Intergenerational farm succession (FRM_SUC2, Figure 8.4) is lower for category 1, Own and Lease Out, versus all other categories by nearly one full point for each: Own 0% (0.96**), Own 1-32% (0.94*), Own 33-65% (1.03**), Own
66-99% (0.77**), and Own 100% (0.88**).
3.0
2.8 2.9 2.8 2.8 2.7 2.6 2.6
2.4
2.2
2.0
1.8 1.8
1.6 Mean of FarmSuccession Index Ow n and Lease Out Ow n 1-32% Ow n 66-99% Ow n 0 % Ow n 33-65% Ow n 100%
Tenure Category
Figure 8.4 Farm Succession Index
416 No-till conservation tillage adoption (Figure 8.5) is reported as being used
more among the three leasing tenure categories than the full owner-operator
category, Own 100%, where category 3, 4 and 5, Own 1-32% (0.55*), Own 33-
65% (0.58*) and Own 66-99% (0.52*) have higher mean implementation rates than Own 100%. And, farm sizes are greatest among those farms that Own 1-
32%, supporting the idea that renters tend to have larger farm sizes to lower their
cost per area, usually in row crops, mainly corn and soybeans.
.8
.7 .7 .7 .6 .6 .6
.5
.4 .4 .3
.2
.1 .1
0.0 Mean of ImplementedNo-Till Ow n and Lease Out Ow n 1-32% Ow n 66-99% Ow n 0 % Ow n 33-65% Ow n 100%
Tenure Category
Figure 8.5 No-Till Conservation Tillage Use
417 Manure management (Figure 8.6) is used more frequently in categories three and four, Own 1-32% (0.62**) and Own 33-65% (0.49**), than category 1,
Lease out.
1.2
1.0 1.0
.9 .8
.6 .6 .6 .5
.4 .4
.2 Mean Manure of Implimented Management Ow n and Lease Out Ow n 1-32% Ow n 66-99% Ow n 0 % Ow n 33-65% Ow n 100%
Tenure Category
Figure 8.6 Manure Management Use
418 There are statistically significant differences among the means showing a trend of higher farm sizes among those farms leasing the most land. The category mean for Own 1-32% is less than that of each of the following categories: Lease Out (662.61**), Own 0% (440.33*), Own 33-65% (354.03**),
Own 66-99% (445.49**), and Own 100% (620.13**). Own 33-65% has a lower mean than categories Lease Out (308.58**) and Own 100% (266.10**). Own 66-
99% has a mean lower than category Lease Out (217.12**) reflecting the need for less land when most is owned, and possibly the older generation of land owners who have more land, in each of the two categories, respectively.
800
735
600
400 381
295 290 200
115 73 0 Mean of FarmSize Ow n and Lease Out Ow n 1-32% Ow n 66-99% Ow n 0 % Ow n 33-65% Ow n 100%
Tenure Category
Figure 8.7 Farm Size
419 All leasing tenure categories, except category 2, have statistically significant larger farms than Own 100% with Own 1-32% having the largest farm sizes. Full owners leasing out their land (tenure category 1) have higher means for farm type (Figure 8.8) than other categories except category 2, of which the difference in means with category 6, Own 100%, is statistically significant (.34*).
Owner operators in the watershed are less likely to operate grain farms than other types.
1.0
.8 .8
.6
.5
.4
.3 .3
.2 .2 .2
0.0 Mean Farms of Grain Ow n and Lease Out Ow n 1-32% Ow n 66-99% Ow n 0 % Ow n 33-65% Ow n 100%
Tenure Category
Figure 8.8 Farm Type
420
Std. Std. N Mean Deviation Error
Subwatershed 1 Low 36 2.33 1.195 .199 2 Medium 81 2.53 1.184 .132 3 High 42 2.24 1.122 .173 Total 159 2.41 1.170 .093 Farm 1 Low 31 2.32 .541 .097 Succession 2 Medium 70 2.43 .734 .088 Index 3 High 38 2.50 .688 .112 Total 139 2.42 .681 .058 Farm Type 1 Low 25 .40 .500 .100 2 Medium 56 .30 .464 .062 3 High 36 .31 .467 .078 Total 117 .32 .470 .043 Farm Size 1 Low 36 115.17 175.020 29.170 2 Medium 81 170.93 241.712 26.857 3 High 42 318.00 395.227 60.985 Total 159 197.15 287.521 22.802 Percent of 1 Low 31 6.68 3.868 .695 Off-farm 2 Medium 72 5.13 3.768 .444 Income 3 High 36 4.14 3.885 .648 Total 139 5.22 3.895 .330 Number of 1 Low 34 24.97 20.039 3.437 Years 2 Medium 74 24.22 14.340 1.667 Farmed 3 High 40 30.63 16.121 2.549 Total 148 26.12 16.397 1.348 Education 1 Low 36 2.50 1.384 .231 Level 2 Medium 80 2.42 1.188 .133 3 High 40 2.40 1.105 .175 Total 156 2.44 1.208 .097 Age 1 Low 36 54.64 15.309 2.551 2 Medium 80 54.11 15.012 1.678 3 High 37 55.51 15.345 2.523 Total 153 54.58 15.073 1.219
Table 8.13 Conservation Use ANOVA Descriptive Statistics
421 The results of ANOVA are presented in Table 8.14 that shows only two of seven variables are statistically significant for explaining variation between
Conservation Use categories. They are farm size and percent of off-farm income.
Sum of Squares df Mean Square F Sig. Farm Succession Between Groups .540 2 .270 .579 .562 Index Within Groups 63.417 136 .466 Total 63.957 138 Farm Type Between Groups .180 2 .090 .403 .670 Within Groups 25.478 114 .223 Total 25.658 116 Farm Size Between Groups 911067.822 2 455533.911 5.849 .004 Within Groups 12150494.556 156 77887.786 Total 13061562.377 158 Percent of Income Between Groups 108.570 2 54.285 3.719 .027 from Off-farm Within Groups 1984.955 136 14.595 Total 2093.525 138 Number of Years Between Groups 1124.925 2 562.462 2.124 .123 Farmed Within Groups 38396.886 145 264.806 Total 39521.811 147 Education Level Between Groups .209 2 .104 .071 .932 Within Groups 226.150 153 1.478 Total 226.359 155 Age Between Groups 49.849 2 24.925 .108 .897 Within Groups 34483.536 150 229.890 Total 34533.386 152
Table 8.14 ANOVA Conservation Use
422 (I) (J) Mean Dependent Conservation Conservation Difference Std. 95% Confidence Variable Use Use (I-J) Error Sig. Interval Lower Upper Bound Bound Farm 1 Low 2 Medium -.11 .147 .752 -.46 .24 Succession 3 High -.18 .165 .532 -.57 .21 Index 2 Medium 1 Low .11 .147 .752 -.24 .46 3 High -.07 .138 .862 -.40 .25 3 High 1 Low .18 .165 .532 -.21 .57 2 Medium .07 .138 .862 -.25 .40 Farm Type 1 Low 2 Medium .10 .114 .674 -.17 .37 3 High .09 .123 .724 -.20 .39 2 Medium 1 Low -.10 .114 .674 -.37 .17 3 High .00 .101 1.000 -.24 .24 3 High 1 Low -.09 .123 .724 -.39 .20 2 Medium .00 .101 1.000 -.24 .24 Farm Size 1 Low 2 Medium -55.76 55.903 .580 -188.04 76.52 3 High -202.83(**) 63.388 .005 -352.83 -52.84 2 Medium 1 Low 55.76 55.903 .580 -76.52 188.04 3 High -147.07(*) 53.066 .017 -272.65 -21.50 3 High 1 Low 202.83(**) 63.388 .005 52.84 352.83 2 Medium 147.07(*) 53.066 .017 21.50 272.65 Percent 1 Low 2 Medium 1.55 .821 .145 -.39 3.50 Off-farm 3 High 2.54(*) .936 .021 .32 4.76 Income 2 Medium 1 Low -1.55 .821 .145 -3.50 .39 3 High .99 .780 .418 -.86 2.83 3 High 1 Low -2.54(*) .936 .021 -4.76 -.32 2 Medium -.99 .780 .418 -2.83 .86 Number 1 Low 2 Medium .75 3.371 .973 -7.23 8.74 Years 3 High -5.65 3.796 .299 -14.64 3.33 Farmed 2 Medium 1 Low -.75 3.371 .973 -8.74 7.23 3 High -6.41 3.194 .114 -13.97 1.15 3 High 1 Low 5.65 3.796 .299 -3.33 14.64 2 Medium 6.41 3.194 .114 -1.15 13.97 Education 1 Low 2 Medium .08 .244 .949 -.50 .65 Level 3 High .10 .279 .932 -.56 .76 2 Medium 1 Low -.08 .244 .949 -.65 .50 3 High .02 .235 .994 -.53 .58 3 High 1 Low -.10 .279 .932 -.76 .56 2 Medium -.02 .235 .994 -.58 .53 Age 1 Low 2 Medium .53 3.043 .984 -6.68 7.73 3 High -.87 3.550 .967 -9.28 7.53 2 Medium 1 Low -.53 3.043 .984 -7.73 6.68 3 High -1.40 3.014 .888 -8.54 5.73 3 High 1 Low .87 3.550 .967 -7.53 9.28 2 Medium 1.40 3.014 .888 -5.73 8.54 * The mean difference is significant at the .05 level. ** The mean difference is significant at the .01 level.
Table 8.15 ANOVA Conservation Use Categories Tukey HSD Multiple Comparisons
423 Farm succession is highest among those who practice the most conservation on-farm (Figure 8.9). However, this variable was not significant in the ANOVA and none of the mean differences are statistically significant.
3.0
2.5 2.5 2.4 2.3 2.0
1.5
1.0
.5
0.0 Mean of Farm Succession Index Farmof Mean Succession Low Medium High
Conservation Use
Figure 8.9 Farm Succession Index
424 Grain farmers (Figure 8.10) tend towards low conservation adoption but this variable was not significant in the ANOVA and none of the differences are statistically significant.
.50
.40 .40
.30 .30 .31
.20
.10
0.00 Mean of Grain Farms of Grain Mean Low Medium High
Conservation Use
Figure 8.10 Farm Type
425 Medium-sized farms (Figure 8.11) implement more conservation practices; these differences are statistically significant (202.83** greater than low adopters;
147.07* greater than medium adopters). It is important to note that this is consistent with the ANOVA of heritage groups in which the AMB and
Swartzentruber Amish reported the highest levels of conservation use while the
Old Order Amish and English reported the lowest. The size of the AMB farms are smaller than the English farms but larger than Amish, thus the high conservation users have larger farms than low users, but are not the largest farms in the watershed. As such, the medium and high users are the medium-sized farms.
400
300 318
200
171
100 115
0 Mean of Farmof Mean Size Low Medium High
Conservation Use
Figure 8.11 Farm Size
426 Percent of off-farm income (Figure 8.12) is highest among low rates of adoption, and is significantly higher than adopters with high conservation implementation rates (2.54*).
7.0
6.7 6.0
5.0 5.1
4.0 4.1
3.0
2.0
1.0
0.0 Mean of Percent of Income from Off-farm Income of Percent of Mean Low Medium High
Conservation Use
Figure 8.12 Percent of Off-Farm Income
427 High adopters have farmed longer (Figure 8.13), but none of the differences between conservation use categories are significant. Interestingly, the lower rates of adoption also have higher educational attainment levels
(Figure 8.14). This is most likely due to differences in traditionalism of the people surveyed – more traditional groups have higher rates of conservation use and lower education levels. However, none of the mean differences are statistically significant.
35
30 31
25 25 24
20
15
10
5
0 Mean of Number of Years Farmed Years of Number of Mean Low Medium High
Conservation Use
Figure 8.13 Number of Years Farmed
428 Education levels are higher for low adopters relative to medium and high
adopters of conservation practices. Although the mean differences among the three groups is slight and are not statistically significant, they are consistent with the ANOVA differences among traditionalism in which the AMB and
Swartzentruber Amish, each having a lower educational attainment level than the
English groups, report higher levels of conservation use.
3.00
2.50 2.50 2.42 2.40
2.00
1.50
1.00
.50
0.00 Mean of Education Level Education of Mean Low Medium High
Conservation Use
Figure 8.14 Education Level
429 High conservation adopters are slightly older than (Figure 8.15) medium
and low conservation using adopters. However, none of the mean differences are statistically significant.
60.0
55.0 55.5 54.6 54.1 50.0
45.0
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0 0.0 Mean of of Age Mean Low Medium High
Conservation Use
Figure 8.15 Age
430 8.1.6 Discriminant Analysis
Using observed characteristics from independent, discriminating variables of each case, a predictive model is built using discriminant analysis that will determine group membership of each case to a discrete category of the dependent variable. A discriminant function, or set of functions if there are more than two categories, is created using the results of a linear combination of predictor variables that best discriminate between or among groups.
The hypothesis tested in this chapter is supported by both quantitative and qualitative findings. Using the multivariate data analysis technique Discriminant
Analysis in SPSS v11.5 statistical software package, two models are developed to test for significance of four independent variables in the first model, and seven in the second in discriminating dimensions of land tenure. The four main variables found in both models are one measure of farm type, two measures of farm size (FRM_SIZE in acres, and Q7PERCEN, the percent off-farm income), and one measure of farm succession (FRM_SUC2) in discriminating among the six categories of the dependent land tenure variable (TEN_TYPX). The additional variables used in the second model are: conservation index (CON_INDX), no-till use (Q1NOTILL) and manure management planning (Q1MANUREM). The resulting functions are compared to the land tenure “group centroids” for each to describe the discriminating strength of the significant independent variable.
For each model, the purpose is to combine the variables in a function, or set of functions to determine how well the four and seven variable combinations
431 discriminate group membership. Such a function is similar to a multiple regression equation; that is, using coefficients a, b, c, and d, the function is represented as:
D = (a) * (X1) + (b) * (X2) + (c) * (X3) + (d) * (X4) + … Xn/20 or (n-1)
Interpretation of discriminant models uses several measures generated in the
output that include for each variable55. Within-groups correlation matrix is run to
test for multicollinearity among independent variables as a means of analyzing
variables for their unique contribution and to avoid the use of variables that
measure the same dimension.
Finally, for each canonical discriminant function there are several statistics
calculated that indicate significance and contributions of the independent
variables: eigenvalues indicating the variance explained by the resulting function,
percentage of variance explained by the function, canonical correlation (Rc) indicating the strength of the function’s correlation with the independent variable means, and Wilks’ lambda (WL) and chi-square test for significance of the function. Interpretive measures generated for each function are presented as coefficients in the Standardized Canonical Discriminant Function Coefficients, which indicates strength and direction of the relationship between variables along the dimension of the function. Only those functions that are found significant
(α=.05) are interpreted in the analysis and discussion.
55 See Appendix E: Statistic Syntax and Models for more on this topic. 432 8.2 Land Tenure Discriminant Analysis Model 1
Intergenerational farm succession, farm type, farm size and percent of off-
farm income are used in this first discriminant analysis model to understand their
discriminating effects in determining specific dimensions of land tenure category
membership. Two functions are generated from the model that demonstrates
these dimensions of discriminating power. Due to the limitations of sample size,
the number of variables that I could include in the analysis was limited, thus I
could only include a maximum of seven in Model 2.56
8.2.1 Model
D = (a) * (farm success) + (b) * (grain farm) + (c) * (farm size in acres) + (d) *
(percent off-farm income)
Or,
D = (a) * (FRM_SUC2) + (b) * (GRAIN_FRM) + (c) * (FRM_SIZE) + (d) *
(Q7PERCEN)
56 Discriminant Analysis variable limitations are as follows: one independent variable per 20 cases, or one less than the number of independent variables (n-1). Thus, 159 cases allows for the inclusion of a maximum of seven independent variables. The number of functions is limited to the number of variables included or the number of categories per dependent variable, whichever is the smaller number. 433 8.2.2 Results
Std. TEN_TYPX Mean Deviation 1 FRM_SUC2 2.00 .378 GRAIN_FRM .60 .507 FRM_SIZE 55.67 30.800 Q7PERCEN 7.87 3.623 2 FRM_SUC2 2.80 .447 GRAIN_FRM .80 .447 FRM_SIZE 295.00 333.954 Q7PERCEN 5.40 3.050 3 FRM_SUC2 2.75 .707 GRAIN_FRM .38 .518 FRM_SIZE 789.75 666.733 Q7PERCEN 3.75 2.00 4 FRM_SUC2 2.86 .351 GRAIN_FRM .18 .395 FRM_SIZE 380.45 314.064 Q7PERCEN 3.91 3.393 5 FRM_SUC2 2.71 .588 GRAIN_FRM .35 .493 FRM_SIZE 247.47 238.142 Q7PERCEN 4.00 3.606 6 FRM_SUC2 2.69 .535 GRAIN_FRM .22 .420 FRM_SIZE 109.38 118.119 Q7PERCEN 4.63 4.038 Total FRM_SUC2 2.64 .562 GRAIN_FRM .33 .474 FRM_SIZE 249.55 333.456 Q7PERCEN 4.82 2.00
Table 8.16 Discriminant Analysis of Land Tenure Model 1Group Statistics
434
Wilks' Lambda F df1 df2 Sig. FRM_SUC2 .754 6.079 5 93 .000 GRAIN_FRM .859 3.052 5 93 .014 FRM_SIZE .641 10.428 5 93 .000 Q7PERCEN .875 2.647 5 93 .028
Table 8.17 Discriminant Analysis of Land Tenure Model 1 Tests of Equality of Group Means
Table 8.18 shows the pooled within-group matrices that checks for multicollinearity between variables. The correlation between independent variables is minimal and no multicollinearity exists.
Pooled Within-Groups Matrices(a)
FRM_SUC2 GRAIN_FRM FRM_SIZE Q7PERCEN Correlation FRM_SUC2 1.000 -.188 .176 -.030 GRAIN_FRM -.188 1.000 .143 .196 FRM_SIZE .176 .143 1.000 -.112 Q7PERCEN -.030 .196 -.112 1.000 a The covariance matrix has 93 degrees of freedom.
Table 8.18 Discriminant Analysis of Land Tenure Model 1 Pooled Within-Group Matrices (a)
Discriminant analysis of land tenure groups and the four independent variables produces four functions that are possible (based on N variables) in distinguishing among the six tenure groups, with an emphasis on combinations of variable and their correlated bi-directional intensities; two of these functions are significant at the .05 level and are described below (Table 8.20). The eigenvalues
435 of the discriminant functions indicate that both functions are needed to understand land tenure in this model since they indicate a high level of variance explained and a high degree of correlation between land tenure and the four discriminating variables. In the analysis, the canonical correlation (Rc) of (0.634) and (0.479) have a cumulative percent of variance explained of 91.2% indicating substantial relatedness (Table 8.19). These functions increase the explanatory ability of the model in numeric order (e.g. F1> F2> F3).
% of Cumulative Canonical Function Eigenvalue Variance % Correlation 1 .671 63.2 63.2 .634 2 .297 28.0 91.2 .479 3 .087 8.2 99.4 .283 4 .006 .6 100.0 .079 a. First 4 canonical discriminant functions were used in the analysis.
Table 8.19 Discriminant Analysis of Land Tenure Model 1 Eigenvalues
Test of Wilks' Chi- Function(s) Lambda square Df Sig. 1 through 4 .422 80.261 20 .000 2 through 4 .705 32.522 12 .001 3 through 4 .914 8.324 6 .215 4 .994 .575 2 .750
Table 8.20 Discriminant Analysis of Land Tenure Model 1 Wilks’ Lambda
According to Lobao (1990:201), “Each discriminant function represents a dimension of local characteristics” of the variables that influence a respondent’s land tenure category while the centroids (Table 8.21) indicate proximity and
436 distance among the categories along the dimension of the function in which the
six groups are located. Function 1 distinguishes land tenure categories three and
four, distinguishing them from full-ownership category one, Lease Out. Positive centroids are highest for tenure group three, Own 1-32%, (1.669) and group four,
Own 33-65%, (0.670), which are distinguished from category six (-1.349) meaning that there is greater dimensional distance between these groups.
Centroids of land tenure categories six (-0.295), two (0.000) and five (0.092), are
close to zero and as such are not relevant to this dimension of land tenure.
Function 2 emphasizes dimensions of the variables for category 3, Own 1-32%
(1.074), and to a lesser extent category one (.813) that are distinguished from
Own 100% (-0.481).
Function TEN_TYPX 1 2 3 4 1 Lease Out -1.349 .813 -.114 .016 2 Own 0% .000 .154 1.153 .115 3 Own 1-32% 1.669 1.074 -.105 -.038 4 Own 33-65% .670 -.146 -.138 .077 5 Own 66-99% .092 -.173 .163 -.146 6 Own 100% -.295 -.481 -.093 .009 Unstandardized canonical discriminant functions evaluated at group means
Table 8.21 Discriminant Analysis of Land Tenure Model 1 Functions at Group Centroids
Two functions were generated from the model that demonstrates how the
variables relate when they are statistically significant and correlated. The
Standardized Canonical Discriminant Function Coefficients (Table 8.22) illustrate
that Function 1 describes land tenure categories 3 and 4, Own 1-32% and Own
437 33-66%, when these farms are generally not grain farms, sizes are large and the intergenerational farm succession index is higher. Also, there is a negative correlation with off-farm income; so on-farm income sources are higher. Full
ownership, Lease Out, category one, is discriminated against in Function 1. The
four variables discriminate in combination along the dimension of Function 1 for
farms that own some land, but less than two-thirds. And, Function 1 reveals that full ownership, especially farms that are entirely leased out to another producer, are smaller in size than category 3 and 4 farms. This corresponds to Hart’s
(1991) findings that Midwestern farms tend to be larger if part of the land is leased than when the operator farms only land that is owned. This finding also corresponds with Salamon’s (1992) assertion that leasing arrangements in the
Midwest generally do not carry the same negative structural constraints and status implications associated with leasing in other societies. These farms have higher farm succession scores, which is expected since farm succession scores
57 are negatively correlated with age (τb=-.422**, sig.=.000, N=133), age is also positively related to leasing out land (ρ=.349**, sig.=.000, N=153), and farm succession is positively correlated with tenure type (τb=.471**, sig.=.000, N=139).
Likewise, farm succession scores are negatively correlated with off-farm income
scores (τb=-.159*, sig.=.043, N=123). Perhaps higher levels of off-farm income
are related to changes in expectations of and for future aspirations of children as
other forms of off-farm education have become more available since the 1950s
opening up possibilities for different occupations and careers.
57 All correlations are statistically significant using a 2-tailed test. The following notations refer to the level of significance: ** significant at the .01 level; * significant at the .05 level. 438 The second significant function, for which these four variables discriminate, Function 2, represents large farms that have a greater tendency to be grain operations with a higher off-farm income and low level of farm succession. This function discriminates against farms that have higher rates of ownership (categories four through six, especially six), which are smaller, have low levels of off-farm income, higher succession rates and tend not to be grain farms. In the case of Function 2 farms, I believe the farms represented in this function are characteristic of Mennonite farms but I cannot say with certainty because heritage indexes did not discriminate significantly and subsequently were removed from the model.
Function 1 2 3 4 FRM_SUC2 .373 -.675 .535 .452 GRAIN_FRM -.205 .198 1.006 -.181 FRM_SIZE .777 .668 -.191 .070 Q7PERCEN -.246 .376 -.199 .907
Table 8.22 Discriminant Analysis of Land Tenure Model 1 Standardized Canonical Discriminant Function Coefficients
Function 1 2 3 4 FRM_SIZE .842 .535 .070 .022 FRM_SUC2 .556 -.606 .318 .472 GRAIN_FRM -.212 .495 .839 -.079 Q7PERCEN -.385 .360 .004 .850 Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function
Table 8.23 Discriminant Analysis of Land Tenure Model 1 Structure Matrix 439
Predicted Group Membership TEN_TYPX 1 2 3 4 5 6 Total Original Count 1 40 6 0 0 4 18 68 2 1 2 1 1 0 0 5 3 1 0 4 3 0 1 9 4 2 2 2 11 0 6 23 5 3 4 2 2 2 6 19 6 3 5 0 5 0 22 35 % 1 58.8 8.8 .0 .0 5.9 26.5 100.0 2 20.0 40.0 20.0 20.0 .0 .0 100.0 3 11.1 .0 44.4 33.3 .0 11.1 100.0 4 8.7 8.7 8.7 47.8 .0 26.1 100.0 5 15.8 21.1 10.5 10.5 10.5 31.6 100.0 6 8.6 14.3 .0 14.3 .0 62.9 100.0 a. 50.9% of original grouped cases correctly classified.
Table 8.24 Discriminant Analysis of Land Tenure Model 1 Classification Results (a)
8.2.3 Discussion
In this exercise, two functions were created that demonstrate dimensions of the dependent variable on which the independent variables exert influence.
The two functions generated in this model explain 91.2% of the variance in the variables examined. The classification results (Table 8.24, above) show that the
model successfully classifies 50.9% of the cases indicating a much higher rate
than would be expected if the categories were randomly selected (16.66%), a
34.2% increase in predictive ability when using these four variables.
8.3 Land Tenure Discriminant Analysis Model 2
Land tenure discriminant analysis Model 2 incorporates the four independent variables from the first model and includes an additional three. In
440 Model 1, I examined the discriminating affects of farm succession, farm type, farm size, and percent of off-farm income on the land tenure variable. In exploring dimensions of the land tenure categories in this second model, I include manure management use, no-till conservation tillage use, and the conservation index. These three variables were added to investigate the contribution that conservation preferences and use have on land tenure – one general measure of conservation and two measures that are specific conservation behavior, are used. As stated previously, there are a maximum of seven independent variables that can be included in this analysis as a limitation imposed on the analysis by the number of cases available for use.
8.3.1 Model
D = (a) * (farm success) + (b) * (farm type) + (c) * (farm size in acres) + (d) *
(percent off-farm income) + (e) * (use of manure management) + (f) * (use of no-till)
+ (g) * (conservation index)
Or,
D = (a) * (FRM_SUC2) + (b) * (GRAIN_FRM) + (c) * (FRM_SIZE) + (d) *
(Q7PERCEN) + (e) * (Q1MANURM) + (f) * (Q1NOTILL + (g) * (CON_INDX)
441 8.3.2 Results Summary Statistics and Test of Significance
Table 8.25 Discriminant Analysis of Land Tenure Model 2 Group Statistics
Std. # Mean Deviation 1 FRM_SUC2 2.00 .378 GRAIN_FRM .60 .507 FRM_SIZE 55.67 30.800 Q7PERCEN 7.87 3.623 Q1MANURM .33 .488 Q1NOTILL .40 .507 CON_INDX 1.80 .676 2 FRM_SUC2 2.80 .447 GRAIN_FRM .80 .447 FRM_SIZE 295.00 333.954 Q7PERCEN 5.40 3.050 Q1MANURM .60 .548 Q1NOTILL .60 .548 CON_INDX 2.20 .837 3 FRM_SUC2 2.75 .707 GRAIN_FRM .38 .518 FRM_SIZE 789.75 666.733 Q7PERCEN 3.75 3.694 Q1MANURM 1.00 .000 Q1NOTILL .75 .463 CON_INDX 2.63 .518 4 FRM_SUC2 2.86 .351 GRAIN_FRM .18 .395 FRM_SIZE 380.45 314.064 Q7PERCEN 3.91 3.393 Q1MANURM .86 .351 Q1NOTILL .73 .456 CON_INDX 2.68 .477 5 FRM_SUC2 2.71 .588 GRAIN_FRM .35 .493 FRM_SIZE 247.47 238.142 Q7PERCEN 4.00 3.606 Q1MANURM .47 .514 Q1NOTILL .59 .507 CON_INDX 2.35 .702 6 FRM_SUC2 2.69 .535 GRAIN_FRM .22 .420 FRM_SIZE 109.38 118.119 Q7PERCEN 4.63 4.038
Continued 442 Table 8.25 (continued)
Q1MANURM .66 .483 Q1NOTILL .09 .296 CON_INDX 2.34 .701 Tot FRM_SUC2 2.64 .562 GRAIN_FRM .33 .474 FRM_SIZE 249.55 333.456 Q7PERCEN 4.82 3.850 Q1MANURM .65 .480 Q1NOTILL .44 .499 CON_INDX 2.35 .690
Wilks' Lambda F df1 df2 Sig. FRM_SUC2 .754 6.079 5 93 .000 GRAIN_FRM .859 3.052 5 93 .014 FRM_SIZE .641 10.428 5 93 .000 Q7PERCEN .875 2.647 5 93 .028 Q1MANURM .821 4.053 5 93 .002 Q1NOTILL .716 7.381 5 93 .000 CON_INDX .835 3.666 5 93 .005
Table 8.26 Discriminant Analysis of Land Tenure Model 2 Tests of Equality of Group Means
FRM_SUC2 GRAIN_FRM FRM_SIZE Q7PERCEN Correlations FRM_SUC2 1.000 -0.188 0.176 -0.030 GRAIN_FRM -0.188 1.000 0.143 0.196 FRM_SIZE 0.176 0.143 1.000 -0.112 Q7PERCEN -0.030 0.196 -0.112 1.000 Q1MANURM 0.053 -0.175 0.087 -0.292 Q1NOTILL -0.091 0.256 0.356 0.038 CON_INDX -0.043 -0.005 0.047 -0.018
Q1MANURM Q1NOTILL CON_INDX Correlations FRM_SUC2 0.053 -0.091 -0.043 GRAIN_FRM -0.175 0.256 -0.005 FRM_SIZE 0.087 0.356 0.047 Q7PERCEN -0.292 0.038 -0.018 Q1MANURM 1.000 0.039 0.312 Q1NOTILL 0.039 1.000 0.244 CON_INDX 0.312 0.244 1.000 a. The covariance matrix has 92 degrees of freedom.
Table 8.27 Discriminant Analysis of Land Tenure Model 2 Pooled-Within-Group Matrices (a) 443 Five functions are produced in the results that distinguish among the six groups, with an emphasis on combinations of variable and their correlated bi- directional intensities; three of these functions are significant at the .05 level, which are described below. The eigenvalues of the discriminant functions (Table
8.28) indicate that each significant function is needed in understanding land tenure as they indicate a high level of variance explained and a high degree of correlation between land tenure and the seven discriminating variables included in the analysis. The canonical correlation of land tenure and seven independent variables define the degree of relatedness of the variables among the six groups of land tenure patterns found in the Sugar Creek subwatersheds. The canonical correlation of (0.685), (0.526) and (0.409) have a cumulative percent of variance explained of 92.1%, indicating substantial relatedness (Table 8.29); this is slightly greater than the variance explained by the results of Model 1, and given the addition of three variables and the variance they contribute into the model, this is a good fit. These functions increase the explanatory ability of the model in descending numeric order (e.g. F1> F2> F3).
% of Cumulative Canonical Function Eigenvalue Variance % Correlation 1 .882 55.5 55.5 .685 2 .382 24.0 79.5 .526 3 .201 12.6 92.1 .409 4 .084 5.3 97.4 .278 5 .042 2.6 100.0 .200 a. First 5 canonical discriminant functions were used in the analysis.
Table 8.28 Discriminant Analysis of Land Tenure Model 2 Eigenvalues
444 Test of Wilks' Function(s) Lambda Chi-square Df Sig. 1 through 5 .284 115.275 35 .000 2 through 5 .534 57.428 24 .000 3 through 5 .738 27.846 15 .023 4 through 5 .886 11.093 8 .196 5 .960 3.742 3 .291
Table 8.29 Discriminant Analysis of Land Tenure Model 2 Wilks’ Lambda
Each discriminant function represents a dimension of the variables that
influence a respondents land tenure categorical placement while the centroids
(Table 8.30) indicate proximity and distance among each along the dimension of the function on which the tenure groups are located. Function 1 distinguishes
land tenure categories three and four with the greatest dimensional strength in
category 3. These are separated from both full-ownership categories one and six,
but mostly one, Lease Out. The positive centroids are highest for tenure group
three, Own 1-32%, (1.612) and group four, Own 33-65%, (0.954), and are
farthest from categories six (-0.376) and one (-1.560) with increasing distance
along the dimension of the function. Centroids for Function 2 represents both categories one (0.888, Lease Out) and three (0.878 Own1-32%) equally.
Indicating differences from category six, (-0.724, Own 100%). Function 3 again highlights category three (0.957, Own 1-32%) differentiating it mainly from category five (-0.602, Own 66-99%) but also two and four. This third function differentiates the leasing groups from category three.
445
Function TEN_TYPX 1 2 3 4 5 1 -1.560 0.888 0.076 -0.172 0.046 2 -0.024 0.364 -0.315 1.043 0.399 3 1.612 0.878 0.957 0.133 -0.198 4 0.954 0.037 -0.290 -0.267 0.202 5 0.098 0.012 -0.602 0.100 -0.329 6 -0.376 -0.724 0.294 0.015 0.002 Unstandardized canonical discriminant functions evaluated at group means
Table 8.30 Discriminant Analysis of Land Tenure Model 2 Functions at Group Centroids
The standardized discriminant function coefficient (Table 8.31) represents
the relative contribution of its associated variable to the net function of the other
variables. The sign of the coefficient indicates the direction and the number
indicates the magnitude of the contribution to the function. Significant
standardized coefficients are reported in the first three columns of Table 8.31
while the remaining columns contain the coefficients for each non-significant
function.
Based on group centroids, the variables with positive coefficients in
Function 1 (Rc=.685) are more descriptive of categories three and four that have
positive centroids, to the degree indicated above, and the variables with negative
coefficients describe categories one and, to a lesser extent, six. Specifically, the
Standardized Canonical Discriminant Function Coefficients (Table 8.31) of
Function 1 indicate larger Farm Sizes (.528) which is also highest for this function
(Table 32), a moderately greater amount of No Till Use (.255), higher Farm
Success Scores (.393), Manure Management Use increases (.174), and higher
446 Conservation Index Scores (.314). Farm Type (-.203) tends to not be grain only,
while Percent of Off-farm Income (-.183) is also less.
Discriminant Function 2, as noted above, has a Canonical Discriminant
Function of (Rc=.526) and an explanation of variance of 24.0%. These farms tend
to have lower Farm Succession (-.484), a slight tendency to be grain farms
(.123), larger farm sizes (.400), higher percent of off-farm incomes (.293), use
more No-Till, and otherwise score lower on conservation adoption and
preferences (-.422).
Function 3 (Rc =.409) explains 12.6% of the model’s variance and focuses
on the leasing dimension, differentiating category three farms from categories
five (-.602), two (-.305) and four (-.290), with greatest difference with category
five. Category three, Own 1-32%, of farmed land is different from the other
leasing categories in this dimension in that they have lower Farm Succession
scores (-.411), are not characterized by farm type, they have large farms (.719),
may have higher off-farm incomes (.328), use more Manure Management (.445)
and less No-till conservation tillage (-.882), and the members’ conservation Index
scores have a negligible contribution.
Function 1 2 3 4 5 FRM_SUC2 0.393 -0.484 -0.411 0.495 0.291 GRAIN_FRM -0.203 0.123 0.008 0.949 0.256 FRM_SIZE 0.528 0.400 0.719 0.086 -0.482 Q7PERCEN -0.183 0.293 0.328 -0.249 0.533 Q1MANURM 0.174 0.092 0.445 -0.083 0.889 Q1NOTILL 0.255 0.534 -0.882 -0.293 0.251 CON_INDX 0.314 -0.422 -0.009 -0.088 -0.155
Table 8.31 Discriminant Analysis of Land Tenure Model 2 Standardized Canonical Discriminant Function Coefficients 447 Function 1 2 3 4 5 FRM_SIZE 0.709* 0.478 0.335 0.222 -0.294 Q7PERCEN 0.502* -0.471 -0.191 0.365 0.173 Q1NOTILL 0.442* -0.229 -0.040 -0.203 0.138 GRAIN_FRM 0.432 0.664* -0.558 -0.099 0.136 Q1MANURM -0.204 0.452 -0.050 0.759* 0.147 CON_INDX 0.438 -0.117 0.351 -0.181 0.624* FRM_SUC2 -0.341 0.288 0.097 -0.073 0.381* Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function
Table 8.32 Discriminant Analysis of Land Tenure Model 2 Structure Matrix
Predicted Group Membership TEN_TYPX 1 2 3 4 5 6 Total Original Count 1 48 3 0 5 8 4 68 2 0 4 0 0 0 1 5 3 1 0 4 1 0 3 9 4 2 2 2 10 1 6 23 5 1 5 1 4 3 5 19 6 4 4 0 2 1 24 35 % 1 70.6 4.4 .0 7.4 11.8 5.9 100.0 2 .0 80.0 .0 .0 .0 20.0 100.0 3 11.1 .0 44.4 11.1 .0 33.3 100.0 4 8.7 8.7 8.7 43.5 4.3 26.1 100.0 5 5.3 26.3 5.3 21.1 15.8 26.3 100.0 6 11.4 11.4 .0 5.7 2.9 68.6 100.0 a. 58.5% of original grouped cases correctly classified.
Table 8.33 Discriminant Analysis of Land Tenure Model 2 Classification Results (a)
8.3.3 Discussion
In this exercise, three functions were created that demonstrate dimensions of the dependent variable on which the independent variables exert influence.
The three functions generated in this model explain 92.1% of the variance in the
448 variables examined. The classification results (Table 8.33, above) show that the
model successfully classifies 58.5% of the cases indicating a much higher rate
than would be expected if the categories were randomly selected (16.66%), a
41.8% increase in predictive ability when using the seven variables in combination. The inclusion of conservation measures improves the discriminatory power of the model and gives Model 2 a predictive advantage of
7.6% more cases correctly classified than Model 1.
8.4 Conservation Use Discriminant Analysis Model
The results of this third model have no statistically significant findings.
Perhaps this is a result of the construction of the conservation use index in which
only three categories are available, thus limiting to two the number of mathematically possible functions. Despite the lack of statistically significant
results, they may still contribute to our understanding of the variables in relation
to conservation adoption and provide the basis for future research regarding the relationships of conservation behavior and farm size, succession, type, and income. In view of this, I examine the model’s results and discuss them with this in mind.
8.4.1 Model
D = (a) * (farm success) + (b) * (farm size in acres) + (c) * (farm tenure) + (d) *
(farm type) + (e) * (percent off-farm income)
Or,
449 D = (a) * (FRM_SUC2) + (b) * (FRM_SIZE) + (c) * (TEN_TYPX) + (d) *
(GRAIN_FRM) + (e) * (Q7PERCEN)
8.4.2 Results
Conservation Use Mean Std. Deviation 1 Low FRM_SUC2 2.40 .503 FRM_SIZE 132.10 225.027 TEN_TYPX 3.65 2.207 GRAIN_FRM .40 .503 Q7PERCEN 6.25 4.089 2 Medium FRM_SUC2 2.72 .579 FRM_SIZE 228.53 297.720 TEN_TYPX 4.38 1.636 GRAIN_FRM .32 .471 Q7PERCEN 4.77 3.678 3 High FRM_SUC2 2.66 .545 FRM_SIZE 353.81 410.074 TEN_TYPX 4.22 1.621 GRAIN_FRM .31 .471 Q7PERCEN 4.00 3.810 Total FRM_SUC2 2.64 .562 FRM_SIZE 249.55 333.456 TEN_TYPX 4.18 1.763 GRAIN_FRM .33 .474 Q7PERCEN 4.82 3.850
Table 8.34 Discriminant Analysis of Conservation Use Group Statistics
The results of the tests of equality of group means (Table 8.35) show that
none of the variables used in this model offer statistically significant results. Due to this, neither of the two functions (Table 8.38) are statistically significant, even
though the Function 1 explains over 73% of the variance in the model.
450 Wilks' Lambda F df1 df2 Sig. FRM_SUC2 .952 2.424 2 96 .094 FRM_SIZE .941 3.018 2 96 .054 TEN_TYPX .975 1.228 2 96 .297 GRAIN_FRM .995 .246 2 96 .782 Q7PERCEN .957 2.160 2 96 .121
Table 8.35 Discriminant Analysis of Conservation Use Tests of Equality of Group Means
GRAIN_F FRM_SUC2 FRM_SIZE TEN_TYPX RM Q7PERCEN Correlation FRM_SUC2 1.000 .260 .291 .105 -.163 FRM_SIZE .260 1.000 -.113 -.266 -.164 TEN_TYPX .291 -.113 1.000 .235 -.224 GRAIN_FRM .105 -.266 .235 1.000 .003 Q7PERCEN -.163 -.164 -.224 .003 1.000 a The covariance matrix has 96 degrees of freedom.
Table 8.36 Discriminant Analysis of Conservation Use Pooled Within-Group Matrices (a)
Canonical Function Eigenvalue % of Variance Cumulative % Correlation 1 .104 73.6 73.6 .307 2 .037 26.4 100.0 .190 a First 2 canonical discriminant functions were used in the analysis.
Table 8.37 Discriminant Analysis of Conservation Use Eigenvalues
Wilks' Test of Function(s) Lambda Chi-square df Sig. 1 through 2 .873 12.754 10 .238 2 .964 3.444 4 .486
Table 8.38 Discriminant Analysis of Conservation Use Wilks’ Lambda
451 The centroids of Function 1 (Table 8.39) indicate that this function emphasizes “high” Conservation Use in this dimension, and differentiates it from
“low” conservation use. Function 2 focuses on “medium” conservation use and is separated from “high” and, to a lesser degree, “low”.
Function Conservation Use 1 2 1 Low -.585 -.143 2 Medium .037 .199 3 High .311 -.203 Unstandardized canonical discriminant functions evaluated at group means
Table 8.39 Discriminant Analysis of Conservation Use Functions at Group Centroids
Standardized Discriminant Function Coefficients (Table 8.40) show that high conservation use, in Function 1, is characterized by higher levels of Farm
Succession (.239), larger farm sizes (.625), lower tenure rankings (.292) and lower levels of off-farm income (-.446). Function 2 distinguishes medium conservation users as having very high levels of Farm Succession (.854), smaller farms (-.660), somewhat higher tenure rankings (.263), and a tendency towards grain farm operations (.230).
Function 1 2 FRM_SUC2 .239 .854 FRM_SIZE .625 -.660 TEN_TYPX .292 .263 GRAIN_FRM -.012 .230 Q7PERCEN -.446 .085
Table 8.40 Discriminant Analysis of Conservation Use Standardized Discriminant Function Coefficients
452
Function 1 2 FRM_SIZE .727* -.460 Q7PERCEN -.657* .054 FRM_SUC2 -.212* -.110 TEN_TYPX .563 .685* GRAIN_FRM .395 .501* Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. * Largest absolute correlation between each variable and any discriminant function
Table 8.41 Discriminant Analysis of Conservation Use Structure Matrix
Predicted Group Membership Conservation Use 1 Low 2 Medium 3 High Total Original Count 1 Low 24 7 5 36 2 Medium 41 27 13 81 3 High 15 6 21 42 % 1 Low 66.7 19.4 13.9 100.0 2 Medium 50.6 33.3 16.0 100.0 3 High 35.7 14.3 50.0 100.0 a 45.3% of original grouped cases correctly classified.
Table 8.42 Discriminant Analysis of Conservation Use Classification Results (a)
8.4.3 Discussion
This model accurately classifies 45.3% of the cases (Table 8.42, above),
indicating a 12% increase in prediction over random placement, which would yield 33.3% accuracy. The model results are inconclusive because the variables used are not statistically significant as discriminators of conservation use.
453 8.5 Conclusion
The correlations and ANOVA results in this, and from previous chapters
demonstrates the relationships among the variables tested in this chapter. But, they do not provide evidence of how these variables may be used in combination, case-by-case, in predicting land tenure ratios. The discriminant analysis models in this chapter provide evidence to support using conservation use as a factor in understanding land tenure in the Sugar Creek. I suggest that this evidence supports the assertion that conservation use can be used as an indicator of “quality of life” in testing Goldschmidt’s findings (1978). Conversely, the power of conservation use as an indicator has not been established, as
discussed below.
The ANOVA mean differences and discriminant functions presented in the
findings indicate that medium-sized farms are more likely to use conservation
practices on their farms. It is important to note that the discriminant functions are
consistent with the ANOVA of heritage groups in which the AMB and
Swartzentruber Amish reported the highest levels of conservation use while the
Old Order Amish and English reported the lowest. However, larger farm sizes in this case is relative to other groups. The size of the AMB farms are smaller than the English farms but larger than Amish, thus the high conservation users have larger farms than low users, but are not the largest farms in the watershed. As such, the medium and high users are the medium-sized farms. Among the conservation scores, the Old Order Amish reported the lowest conservation use, while the Swartzentruber Amish reported the highest, with, in order of increasing 454 use, the English and Apostolic, Mennonite and Brethren having higher scores.
Other ANOVA results show statistically significant differences between AMB
groups and Swartzentruber Amish in their perception of pollution in the Sugar
Creek as well as the importance of aesthetics (the Old Order Amish also differed
in this variable). For the Swartzentruber Amish, I believe it is their propensity to
stay within their community and “opt-out” of participation in the larger society that
mediates their perceptions of conservation. The “English” farmers’ engagement
in higher levels of sociocultural integration lead them to less local
embeddedness, but similar perceptions and lower conservation scores, as they
make farm decision based on non-local information and criteria. Additionally, the relative farm size mean differences found in the ANOVA relationships perhaps
would be smaller if the analysis was based on a per acre, per farm analysis of
conservation.
The statistical models created in this section show that ratios of ownership
to leasing are discriminated by two functions in the first model and three in the
second. In each model, the predictive ability of the model to classify farm tenure
was based on two pairs in Functions 1 and 2 that discriminate land tenure
categories based on the same criteria. Function 1, in both models, discriminates
land tenure categories three, then four, indicating that owners of 1-32% and 33-
65% have large farm sizes, generally non-grain farms, high farm succession indices, and lower levels of off-farm income and are differentiated from the Lease
Out group, which is characterized by the opposite trends that includes lower conservation usage in Model 2.
455 Function 2, in both models, discriminates land tenure categories of Lease
Out and Own 1-32% when farm sizes are large, farm success indices are low, off-farm income is higher, and they tend to be more grain farms. This is differentiated from the full-ownership farms that are smaller, non-grain farms
(dairy, other meat, produce and mixed operations) with less off-farm income and high farm succession indices.
Function 3 explains <13% of the variance in Model 2 but indicates a division between 1-32% and the other leasing/owning categories in which the category three farms are large in size, have higher off-farm income, use manure management, very little no-till, and the farm type and conservation use differences are negligible.
The advantage of Model 2, aside from the 7.6% increase in its classification ability, is that the addition of conservation measures allow for a third function to help in characterizing dimensions of land tenure categories.
Specifically that the addition of Function 3 shows another dimension and the arrangements of land tenure that would otherwise be missing from the findings.
Although further research and quantitative analysis must be performed, the findings in this chapter lead to confirming the hypothesis that farm size, farm type, percent of off-farm income and conservation measures can be used in predicting the rate of farm ownership and leasing. Although farm conservation use and preferences are correlated with the variables tested in this chapter (See
Chapter 6 for correlation analysis results), the procedure used to predict conservation adoption levels do not indicate support for the second component of
456 the hypothesis regarding farm size, farm type, land tenure, and percent of off-
farm income as predictors of conservation adoption behavior. However, the
addition of conservation measures to the second land tenure discriminant
analysis model (Model 2) reveals some discriminatory power of conservation.
Additionally, the findings of this chapter demonstrate that discriminant analysis
procedures can be used to predict land tenure and the use of conservation as an
independent variable, but conservation cannot be used, in this manner, as a dependent variable in which group membership is predicted; reasons and a discussion of this follows.
I believe that there are several contributing factors that contribute to the
inefficaciousness of the variables in the discriminant analysis of conservation
use. Principally, the database used for this analysis does not have an adequate
range of variables to determine conservation behavior. The general purpose of
the data collection instrument (e.g. the social survey) was to explore the best way
to develop local water quality remediation strategies by ascertaining conservation
attitudes and practices in order to build a broad picture of residents’ reference
points to water quality in the Sugar Creek Watershed. The goal was to
understand the ways in which residents perceived water quality in their
watershed, to assess trust of local agencies and assist in determining what form
of local organization and collaboration was best suited for each subwatershed.
While land tenure data was efficiently collected using the survey, conservation
attitudes and practices were elicited only in a general sense and in
accomplishing this goal, neither an exhaustive list of conservation practices nor a
457 complementary list of preferences was available to assess use and attitudes.
More detailed conservation data needs to be collected before this issue is resolved.
Despite these findings, understanding land tenure statuses and the social
networks in which they are embedded has enabled the author to explain the
conservation behavior of farmers in the watershed. However, the reverse still has
not been achieved, namely, the use of independent variables of land tenure to
predict conservation behavior. Other research findings have shown that there are
several mediating factors in conservation adoption among farmers that are
beyond farm structural variables that include aesthetic components that interact with a farmer’s perception of neighbor approval of a practice. Moreover, a
farmer’s perceived prestige and status in the community has an interacting affect
with neighbor approval. An example of this is the impact that corn nitrogen
application reductions will have on the rate of crop growth as well as its coloring
(light or dark green).
Based on these findings, I propose an approach to conservation planning
that recognizes the importance of land tenure and incorporates it into planning
conservation initiatives. Although prediction of conservation use is still
unavailable, the ability of conservation use to discriminate among land tenure
categories shows some promise that knowing the details of local land tenure
networks, restrictions and benefits will help adjust watershed initiatives to a local
community. This research offers another set of variables that can be incorporated
into our understanding of who may adopt conservation strategies on their farm.
458 In order to address the problem of the adequacy of the statistical methods used to test conservation use group membership, in future analysis the author proposes a simplified procedure to test the efficacy of these variables in predicting conservation behavior by employing logistic regression. In this procedure, the conservation behavior will be measured as a presence or absence (1 or 0) of use with the intent of predicting group membership using the same independent variables.
Notwithstanding the shortcomings of the available conservation data in the survey, the exploratory analysis of conservation preferences and use as indicators of “quality of life” in predicting land tenure status was moderately successful. The predictive ability of the Discriminant Analysis land tenure model was increased nearly 8% by the addition of conservation measures to the understanding of the land tenure dimensions. As such, the findings are congruent with and support Goldschmidt’s findings relating farm size to community well- being.
459
CHAPTER 9
CONCLUSIONS
9.1 Introduction
The intention of this dissertation research was an investigation of
relationships found among land tenure variables, as outlined in Chapter 1, and
the importance of understanding land tenure in conservation-based watershed initiatives. In the process of this research, the following three hypotheses were tested:
1. “Ethnicity, social relationships, and attitudes toward farming will condition contemporary land tenure arrangements” was assessed in Chapter 6. 2. “Ethnicity and level of socio-cultural integration of the farm household, as independent variables, will affect farm size, land use and tenure, and use and preferences for conservation in which more traditional and less socioculturally integrated groups will have smaller farms, diversified land uses, more secure land tenure and greater preferences and use of Best Management Practices” was examined in Chapter 7. 3. “Farm size, farm succession and inheritance, and enterprise type will correlate with land tenure and preferences for conservation where positive relationships will be found between medium-sized farms, higher levels of farm succession, and non-grain farm types with more secure land tenure and positive preferences for conservation practices and use” was analyzed in Chapter 8.
460 This dissertation research focused on local action of community members
with some emphasis on social structure as mediating factors in land tenure and
conservation adoption among farm households in the Sugar Creek Watershed.
As such, this research was undertaken and analyzed from the perspective of
farmer households and community. Whereas most conservation research
focuses on the message and effectiveness of the conservation messenger, I
chose to focus on the message receiver as an independent actor working within
the structural constraints of class and power relations found among the levels of
sociocultural integration of the United States of America. In this social structure, I
implicitly viewed the individual household members as having cognition and
agency in decision-making but I did not make an explicit account of class and
power relations of upper class decision-makers whose agency and cognition
(Durrenberger and Erem 2005) help to create the structure within which the
Sugar Creek household members conduct their lives.
Although I briefly addressed them in the preceding three data analysis
chapters, several aspects of family, household and community dynamics are not
fully explored in this research, which include gender (Coleman and Elbert 1984),
and power and structural constraints (Durrenberger and Erem 2005) that affect
decision-making related to land tenure and conservation strategies. I will attempt
to address these after a review of the findings of Chapters 6, 7 and 8, and prior to
the concluding section.
461 9.2 Chapters 6 – 8: A Synthesis
In Chapter 6, land tenure was examined in relation to the affects that
several attributes have on access to land and organization of land use and
households, such as ethnicity, heritage, social relationships (or networks) and
farming attitudes. A summary of findings confirm that social relationships are a
product of the history and heritage and consequently have spatial and temporal
dimensions. These temporal and spatial connections among individual farm
members as well as farm households are centered in intergenerational
succession among kin as well as non-kin through household structures and
networks of family and neighbors; these networks are experienced through
various forms of social action and interaction. Through the process of
succession, an intricately woven pattern of social relationships emerges that
connects community members through family alliances, founded through
marriage, and a common heritage experienced and reinforced through
interpersonal and group/community interactions.
As each farm household moves through the different stages of the
liturgical cycle of succession, patterns of ownership and household dominance in community affairs (power relations) are reflected in land tenure as witnessed in
farm size and the entrance of new families as others exit the farming community.
Operations are adjusted by households in response to multiple levels of external pressures in a number of ways ranging from expanding the enterprise to allow for increased income and/or multiple-heir intergenerational transfer of the enterprise,
462 to devolving the farm to a few acres for retirement in which the household exits farming as an occupation.
In Chapter 7, commonalities are found across ethnicities in tenure practices as the community as a whole struggles to adjust to increasing internal and external land pressures. Steward’s concept of levels of sociocultural integration was used as a way of understanding the role of power and social structure in mediating local decision-making. However, because of the scope of the project and limitations of time, as mentioned above, complete analyses of these structural components were not conducted.
The levels of sociocultural integration in which a farm household operates does relate to farm size, type and adoption of conservation practices as well as desires for the implementation of new conservation measures. This does not operate in a linear fashion across the continuum of traditionalism in which a high or low score in one variable set at one level of traditionalism necessitates that similar or increased/decreased intensities of scores will be seen among other levels. What was revealed in the ANOVA findings is that there are statistically significant differences among ethnicities, as measured by traditionalism, in conservation perceptions and use. For instance, while the Old Order Amish have a higher awareness of the water quality impairment in the Sugar Creek and place more value in the aesthetics than Swartzentruber Amish, the Old Order Amish report significantly lower levels of conservation use, and both groups report very low preferences for future conservation, as inferred from the conservation index.
The Old Order Amish’s high degree of value in aesthetics is consistent with their
463 very high reporting of recreational use of the Sugar Creek streams and tributaries
for fishing, hunting and bird watching. And, low conservation use may be a factor
of their increasing dependence on off-farm income.
Ethnicity and LSCI in the form of a traditionalism ranking, as examined in
Chapter 7, show elliptical patterns with land tenure and conservation behavior in
which patterns emerge at opposite ends of the ranking. Some groups from these
“opposite ends” (“English” and “Swartzentruber Amish”) converge and are differentiated from the centrist group rankings (Apostolic-Mennonite-Brethren
(AMB) and Old Order Amish). Analyses of variance of government agency trust
scores demonstrate the tendency of higher levels of traditionalism to value higher
levels of local embeddedness and thus express trust in local agencies over state
and federal. Similarities are shown between English and Swartzentruber
categories in preferences for group composition for the purpose of solving water
quality issues – these groups prefer land owners working individually rather than a coalition (as the Old Order Amish prefer) or local, state or federal government.
Self-imposed cultural barriers to Amish collaboration in government sponsored
conservation initiatives, though not insurmountable as witnessed in the North
Fork Task Force partnership (Weaver et al 2005), are exacerbated by conflicting
structural barriers in which NRCS standards require high cost-share financing on
lower income producing Amish farms.
The results of the third hypothesis, reported in Chapter 8, examined land
tenure and conservation through the use of ANOVA and discriminant analysis
statistical procedures. Chapters 6 and 7 built the case for including land tenure,
464 conservation, farm succession and others in the Chapter 8 analyses, but they did
not provide evidence of how these variables may be used in combination, case- by-case, in predicting ratios of ownership to leasing in land tenure arrangements.
Two models of land tenure demonstrated dimensions of ownership by revealing two functions in the first model and three in the second. Function 1, stable or intensifying farms58, in both models, discriminates land tenure categories three,
then four, indicating that owners of 1-32% and 33-65% have large farm sizes,
generally non-grain farms, high farm succession indices, and lower levels of off-
farm income and are differentiated from the Lease Out group, which is characterized by the opposite trends, which includes lower conservation usage in
Model 2.
Function 2, the deintensifying farms, in both models, discriminates land
tenure categories of Lease Out and Own 1-32% when farm sizes are large, farm
success indices are low, off-farm income is higher, and in which there tends to be
more grain farms. This is differentiated from the full-ownership farms that are
smaller, non-grain farms (dairy, other meat, produce and mixed operations) with
less off-farm income and high farm succession indices.
In the second discriminant analysis model, Model 2, the addition of
conservation measures shows that stable and intensifying farms also adopt more conservation measures, while the deintensifying farms of Function 2 and
58 The names given to the following functions are loosely based on the categorization provided by Lobley and Potter, 1996. In this context, a stable or intensifying farm is one that persists or is increasing its future capacity by having a stated plan for intergenerational farm succession for the household and a greater reliance on on-farm household income. Deintensifying farms have a greater reliance on off-farm and, more detrimental to the farms household’s future, a lack of an intergenerational succession plan. 465 Function 3 adopt fewer. Function 3 indicates a division between Own 1-32% and the other leasing/owning categories in which the category three farms are large in size, have higher off-farm income, use manure management, very little no-till, and the farm type and conservation use differences are negligible. The differences between Function 2 and Function 3 deintensifiers are that Function 2 deintensifiers tend to have more grain farmers than Function 3, as inferred through the lower coefficient for GRAIN_FRM, and varying levels of no-till
(associated with grain farms) and manure management (associated with dairy) practices.
The advantage of Model 2, aside form the 7.6% increase in its
classification ability, is that the addition of conservation measures allows for a
third function to help in characterizing dimensions of land tenure categories.
Specifically that the addition of Function 3, which further explores the range of
heterogeneity in the Own 1-32% category, shows another dimension in the
arrangements of land tenure that would otherwise be missing from the findings.
All farms represented in the dimensions of land tenure tend towards larger farms,
distinguishing, as stated earlier, the leasing categories (including Lease Out all
land, in Function 2) from the predominantly owner categories of operated farms.
The findings of the investigation for discriminants of conservation adoption are inconclusive because none of the models are statistically significant. I believe that there are several contributing factors that add to the inefficaciousness of the variables in the discriminant analysis of conservation use. Principally, the general purpose of the data collection instrument (e.g. the social survey) was to explore
466 the best way to develop local water quality remediation strategies by ascertaining
conservation attitudes and practices in order to build a broad picture of residents’
reference points to water quality in the Sugar Creek Watershed. While land
tenure data was efficiently collected using the survey, conservation attitudes and
practices were elicited only in a general sense and in accomplishing this goal,
neither an exhaustive list of conservation practices nor a complementary list of
preferences was available to assess use and attitudes. More detailed
conservation data needs to be collected before this issue is resolved.
Nonetheless, I still would like to discuss briefly the two functions that
resulted from this analysis. Each function focused on conservation adopter
categories of medium, in Function 1, and high, in Function 2. The medium
adopter function is characterized by higher farm succession, farm size, and lower
off-farm income while the high adopters have substantially higher farm
succession scores, smaller farm sizes, tend to incorporate grain into their farm,
and have a negligibly higher degree of off-farm income. The common theme
between these medium and high adopter categories is the higher farm
succession scores found among both upper adoption levels.
The findings in this chapter lead to confirming the hypothesis that farm
size, farm type, and percent of off-farm income and conservation use can be
used in predicting the rate of farm ownership and leasing. However, although
farm conservation use and preferences are correlated with the variables tested in
this chapter, the findings of Model 3 do not confirm the second component of the hypothesis that stated that conservation adoption could be predicted by using
467 farm size, farm type, land tenure, and percent of off-farm income as independent
variables. Discriminant analysis was used to explore dimensions of conservation
adoption among three categories and with four variables but perhaps a different
approach, using a different statistical model, such as logistic regression, would
be more appropriate. Using logistic regression, the emphasis would shift from an
exploration of dimensions of adoption in predicting group membership to
establishing if the independent variables have predictive power in determining
which households will and will not adopt.
Despite the results of the discriminant analysis model of conservation use,
the exploratory analysis of conservation as indicators of “quality of life” in
predicting land tenure status was moderately successful. Conservation improved
the predictive ability of the Discriminant Analysis land tenure model by almost 8%
thereby improving our understanding of the land tenure dimensions. As such, the
findings are congruent with and support Goldschmidt’s findings relating farm size
to community well-being and offer promise of further research.
9.3 Gender, Power and Structure: three unexplored factors
Gender (Coleman and Elbert 1984), power and structural constraints
(Durrenberger and Erem 2005) are dimensions of individual decision-making that are related to many domains including land tenure, intergenerational farm
transfer and conservation adoption. Despite the fact that these issues have been
acknowledged as contributing factors related to land tenure, as described
previously, these issues have not been formally hypothesized or analyzed in the
course of this dissertation. This was done mainly because this research was 468 conceptualized to investigate agency and decision-making while being sensitive to these larger issues of gender, power and structure. Likewise, farm structure models address conservation adoption from the perspective of the organizational structure present on a single farm and does not account for the overall structure of political and economic constraints in which a farm household operates.
However, I would like to look at the issues of power and structure as they relate to land tenure and the larger picture of land policy and ownership in the United
States of America.
9.3.1 Power and Structure in Decision-Making in Sugar Creek
According to Durrenberger and Erem (2005, independently and also referencing Wolf 1990), social structures mediate the cognition and agency of those within the various levels. “Class practices” and “patterns of thought” create social structures through decision-making conducted by upper classes which constrain and shape boundaries for themselves and lower classes (although not immutable) in which “people’s views of things depend on the position [in the social structure] from which they see them” (Durrenberger and Erem 2005:50).
The question of structure becomes one of identifying not only what the constraints are, but who benefits from them and who they act against. Although
Scott (1985) argues that oppressed people are aware of their status and that awareness extends to who is “responsible” for their position in society, I believe that there is a difference between awareness and being cognizant of the reality of structural positioning that imposes limitations to resources and options for decision-making.
469 In the Sugar Creek, agricultural restructuring and change are common
themes discussed by household members who emphasize the need for
adjustment of their enterprise to current agricultural trends and needs; many note that it is necessary to keep up on technology and markets. Despite this awareness, it is also common for farmers to invoke the folk model of
“individualism”59 in which a farmer “pulls themselves up by their boot-straps”, so
to speak, and emphasizes their independence and preference to “go it alone”
(Dudley 2000). At the risk of taking a privileged position as “researcher”, I think this model is antithetical to the reality of Midwestern agricultural communities that are generally enmeshed, as subordinates, in a global system of commodity agriculture as competition is used to push farmers to produce high yielding, inexpensive crops. This treadmill of production (Schnaiberg 1980, cited in Buttel
2003) benefits the buyers (i.e. commodity groups), not the farmer, and in the long term, not the consumer 60 , and in this power-relationship, farmers have little
power to increase their own profit except through increasing production, which
cumulatively leads to overproduction and depressed market prices, or by
decreasing inputs. Exceptions to this model of Midwestern farming are found in
alternative agricultural enterprises that emphasize diversification and
specialization with a focus on sustainability and/or organic agriculture by
59 This “folk model” is similar to that invoked by other Midwestern farmers (Dudley 2000), non union shrimpers (Durrenberger 1992), coal miners, and unskilled health care workers (Durrenberger and Erem 2005). In this model, the relations of power and structure are such that the worker has no power in determining his or her status because of the lack of skills and abundance of non-skilled labor and thus the worker is left to rely only on themselves. 60 Although cheaper food is a byproduct of depressed farm prices, other externalities of agricultural production (i.e. non point source pollution, air pollution, and depressed rural economies) are passed on to the tax payers, who are also consumers, in the form of corporate agricultural subsidies, corporate welfare, and government sponsored incentive programs. 470 marketing to urban and suburban residents who participate in varying degrees in
the alternative agriculture movement (Lapping and Pfeffer 1997; Lyson and
Green 1999).
Changing social conditions, technology and land use, increasing external
economic pressures and information by means of misinformation from
agribusiness are the circumstances that have led some Sugar Creek farmers to
adopt a pessimistic attitude regarding their forecast of farming. These farmers feel the local social environment is losing its supportive capacity; that the future of these farms is uncertain. This is not the case for every farmer as some are adjusting, persisting and doing so interdependently with other local farmers.
9.3.2 American Agrarianism and the Myth of a Classless Society
Jefferson, among others, has written about the “agrarian values” that serve as the strength of the United States of America and the source of that
country’s “greatness”61. Within this generalization are attitudes toward the land
and assumptions regarding a land ethic that lend to community vitality, economic
prosperity and national security rooted in the grassroots ideal of ownership and
productivity leading the individual towards the desire to participate and
cooperate. This mythologized landed citizen, contemporaneously portrayed as
the family farmer or farm family, provides the model of greatness that is ascribed to the American farmer and agricultural citizen. This agrarianism needs to be differentiated from the agrarian communities, of which Salamon studied, and the use of the term in this research. Whereas, Salamon’s use of “agrarian” refers to
61 The use of a single quoted word is meant to characterize the viewpoint of Jefferson, not to convey sarcasm as quotes may often be employed. 471 the values and disposition towards farming, social organization and the land of
various societies in the Midwest, I have taken Jeffersonian agrarianism to refer to
an idyllic class of landed citizenry.
With a broad stroke, all that is good in rural America is attributed to this
notion of Jeffersonian Agrarianism, yet rarely are such values, or a sense of
belonging to this grand historical theme, articulated as the only basis by farmers
in the Sugar Creek Watershed. More often than not, farmers provide a blended
explanation that combines this Jeffersonian myth with the ethical principles of
their community that are interconnected with the moral and value teachings of
their specific denominations of Christianity.
In the first half of the Twentieth Century, Leonard Salter (1943) called for legislation that would regulate the sale of land as a commodity, just as other commodities are regulated. The passing of several Homestead Acts promised to preserve equity in farmland for future generations, but subsequent generations saw, as Moran (1993, 1998) and Collins (1986) later found in Brazil, less equity and less access to farmland than previous generations. This state was attributed to income and commodity prices becoming the focus of government efforts rather than land equity, which led to an unintentional cycle of support-price dependent farmers of increasing scale (Salter 1943:14).
In fact, land tenure in the United States has never been equitably
distributed among socioeconomic classes and the myth of the “agricultural ladder” of farm ownership and upward mobility was created in the 1920s to account for this inconsistency between myth and reality. Since 1935, it has
472 become more apparent that the ladder does not exist. The decade prior to 1880
saw the highest percentage of land ownership with tenancy rates below 25
percent. Tenancy had dramatically increased through 1935 and later declined as
ownership became concentrated among owners, again, but among fewer owners
(Kloppenburg and Geisler 1985).
Land as a commodity creates a system in which landowners may treat
land as an investment purely for profit and with no intent to farm or to preserve
an “agrarian” capacity with equitable access to land thus further removing
citizens from the means of food production; such policies are not socially,
economically or environmentally sustainable. In 1949, Aldo Leopold lamented the
lack of a “Land Ethic” in America in which people recognized and respected the
relationship they have with the land. He said the U.S. government’s current depth
of conservation education was for citizens to
Obey the law, vote right, join some organizations, and practice what conservation is profitable on your land; the government will do the rest. (Leopold 1949:207)
I believe this holds true today. Educational efforts of this kind need to
refocus on the content and approach of the education, rather than volume.
Rappaport (1971b, 1999) offers a solution in suggesting that a new form of belief system be fostered that sanctifies aspects of ecology and the natural environment, like that found among many Amish (Bennett 1976; Moore et al
1999). Although this seems idealistic, the concept is not unattainable; appealing
to the “bottom line” and “because it is the right thing to do” are fine, but both need
473 to be understood in the context of people’s values. I agree with Bennett who
argues, in a footnote (1976:205), that complex, higher-energy societies often rely
on rationality in interactions with the environment. Such a choice will require a balance between sanctification and rationality because it is through rationality that we are able to discover those detailed interactions that occur in agroecosystems such as nutrient cycling, diversification through multi-cropping, and Best Management Practice efficacy.
Having stated that, I believe that agrarian values are generalizations and
as such, they do not lend to critical or empirical analysis but rather provide an
idealistic, if not romantic, portrayal of American farming and agriculture. To an
extent, the concept of agrarianism can be viewed as part of a structural and ideological power system in which the cultural elite pay “lip-service” to the family farm while proposing or signing legislation that provides advantages to industrial agricultural enterprises while concomitantly disadvantaging family farms. Yet, at the same time and contrasting this previous statement, many involved in the sustainable agriculture movement have adopted the values of the agrarian myth as they generate rationale for the persistence of family-based farming62.
The “Great Tradition” of Jeffersonian Agrarianism offers a simplistic
explanation for the early strength of the American system and provides a
convenient, if not misleading, “operational model” for policy-makers and
academics alike to employ when explicating the decline of rural communities by
way of linking them to the loss of the family farm. An emphasis on agrarianism
62 See Logsdon 1994, Berry 1996, and Salatin 2001 for examples of these alternative agriculture proponents. 474 overlooks farming policy, structural manipulation of information (Durrenberger
and Thu 1997) and manipulation of key cultural symbols through power
relationships (Wolf 1990).
9.3.3 Removing Barriers to Alternative Action
According to Napier (2002), regulation is a more effective approach than
the IETS (Information, Education, Technical assistance, and economic
Subsidies) that is the current focus of voluntary farmer conservation adoption; the
use of disincentives would be more cost-effective than the current incentive
structure. Napier further notes that a premise of the Diffusion of Innovation Model
is that the perception of problems associated with agricultural practices and
environmental problems is necessary for change, but not enough to prompt
farmers to adopt Best Management Practices, or any other variations of
conservation measures; that access to solutions is required.
Working within the current model of IETS, I believe that Napier is correct
in saying this model does not work, as it is currently structured, because the
current state of the system in which this approach operates does not include
alternative actions or approaches that provide support for farmers to disentangle
them from the commodity system. 63 Rather than focus on strategies that empower farmers, Napier suggests an emphasis on a change in the structure of power away from volunteerism and towards coercion and force through disincentives. I believe that the problems are barriers to cognition and not a lack
63 Conventional agriculture in the United States has been shown to entangle farmers in the commodity- oriented cycle of overproduction and subsidies wherein dependence on government payments for crop production makes it difficult to exit the system and less probable to participate in conservation measures (Sommers and Napier 1993). 475 of wanting to “do the right thing”. Bryan Burke (2001) puts forth the idea that the
“tragedy of the commons” is not one of rationale choice and self-interest, but is a
matter of perception of the problem and understanding tangible solutions.
These barriers to cognition are not impenetrable but require more than education through the transference of abstractions (Lave and Wenger 1991, cited in Durrenberger and Erem 2005). As witnessed through the Sugar Creek
Partners’ participation in community visioning and water quality remediation projects (Weaver et al 2005, Moore et al In Press), alternative actions and experiential learning is required. It has been demonstrated in this work that, in the Sugar Creek Watershed, farmers do adopt Best Management Practices when such options are available and sufficient information and collaboration are provided to them. Sufficient alternative actions include a grassroots partnership between researchers, education specialists, farmers and local and state conservation agents. In this collaborative atmosphere, farmers and agents are able to develop local solutions within the scope of the values and needs of the community. Creating opportunity through state and federal agency grants creates an atmosphere in which change is possible.
Therefore, to understand why the Sugar Creek Partners have become community leaders in conservation is to understand the economic and social realities of their farm enterprises and relations in the community. Most team members have diversified livestock and grain farms. This diversified farm type
differentiates them from the larger grain and potato farmers in the community,
who are less likely to adopt. These farm families exhibit a commitment to the land
476 and to their farm households as heritage. This cultural emphasis helps provide
opportunities for future generations to farm through local social structures that
have emerged in the last century to mediate external pressures and structural
constraints. Some examples include team members managing expansion
through kinship networks, using extended family to increase farm scale and
decrease risk. Also included is the manner in which households access land
from within existing kinship networks instead of purchasing the land individually.
The two Sugar Creek Partners team members who have access to farmland
through their spouse’s family farm, living uxorilocally, is another example of kin
networks operating to expand land access.
Using a similar approach, in the Middle Fork, a situation that would have
resulted in regulation and financial penalties for a local cheese factory, possibly reducing future production, has been turned into an opportunity for the cheese maker and farmers to collaborate. This collaboration with local agencies produced an alternative future in which local people and companies are able to distribute the economic resources, which would have been tied up in technological solutions, to improve the overall ecology of the watershed.
Hundreds of thousands of dollars that would have been used for water filtration to remove phosphorus is now available to farmers in the area. There is potential for farmers who take advantage of this project to use these funds to reduce their risk in adjusting their farming practices to include BMPs and alternative production strategies that do not include commodity farming.
477 Another example of removing barriers to prosperity and conservation is an
evolving organic Amish farm co-op, the Green Field Farms. This group is
successful, in small part as a result of the alternative approaches in this
watershed, by seeking a new direction for their community in which sustainability
is emphasized. In this approach, this Amish Coop has chosen to strike a new
balance between their community and the larger population in attempting to
preserve their heritage and way of life.
9.4 Conclusion
It is clear that agriculture is the dominant influence on a large portion of the Earth’s surface (Vitousek 1994) and this influence is proceeding through socionatural systems that are created and perpetuated as understood through
Bennett’s concept of “the ecological transition” (Bennett 1976). It is also evident to me that these problems are social in nature and require social solutions that do not require complicated and expensive technological solutions alone.
Understanding the non-linear connections among levels of political and economic influences, manifested in policy, social networks and market forces, that influence local social structure and organization (i.e. land tenure and farmer decision making, respectively) is important in addressing issues of conservation adoption and sustainability. Equally important is acquiring an understanding of the local realities of social networks and land tenure, which mediate our influence with the physical world through access to land, and that are necessary to provide pragmatic and real solutions for our ecological problems. John Bennett wrote that the task at hand is to understand how ecological systems come to be viewed 478 through economic and political values rather than biophysical properties. Further,
Bennett believed that it is important for a “more comprehensive examination of
the concept of institution, as the principal social vehicle” of the ecological
transition because it is through institutions that the majority of people mediate
their lives with the environment (1993:455). Stinner and Blair (1990:138) provide
a complementary evaluation stating that understanding and creating change in
human social systems may provide the greatest impact for the development of a
sustainable agriculture.
At present, many farmers operate in a system that is not beneficial to
them, but usually do not have the information or resources to be empowered to
change. An increase in wealth and accumulation of material goods is viewed as a
sign of progress, for progresses sake (Rappaport 1971b:356, Thu and
Durrenberger 1998:16). The reality is that “progress”, as defined, only detracts from and does not contribute “to the correction of the factors producing the anxiety and disorder” in our ecological system (Rappaport 1971a:73).
Regarding Goldschmidt’s findings (1978), the implications for conservation
use as a factor in community well-being is troubling when considering the correlations of variables, such as farm size, with land tenure and Buttel’s (1983) findings of a bi-modal farm distribution of small and large-scale farms. The implications complicate the long-term outlook for increased adoption rates of conservation practices because of the paucity of future medium-sized household
operated farms.
479 This new direction in conservation research will benefit agencies and farmers alike in striving to solve our more pressing environmental problems.
Time and money are required to investigate these relationships in a meaningful way that will provide valuable information, but the cost of inaction and continued misdirection of policy are much greater.
480
APPENDIX A
UPPER SUGAR CREEK SOCIAL SURVEY
481
Survey – Cover Letter Page 1
482
Survey – Cover Letter Page 2
483
Survey – Page 3
484
Survey – Page 4
485
Survey – Page 5
486
Survey – Page 6
487
Survey – Page 7
488
Survey – Page 8
489
Survey – Page 9
490
Survey – Page 10
491
APPENDIX B
NORTH FORK, LITTLE SUGAR CREEK AND MAINSTEM SOCIAL SURVEY
492 Dear Watershed Resident,
We are contacting you because you live in or own property within the Sugar Creek Watershed. The Sugar Creek is a river that originates just north of Smithville and empties into the Muskingum River at New Philadelphia, draining an area of land (called a “watershed”) that is approximately 97 square miles.
Recently the Ohio Environmental Protection Agency (EPA) has released a study concluding that the Sugar Creek ranks as one of the most impaired streams in the state. They are now working on watershed restoration plans for all rivers and streams in Ohio. We think that it is important to start any discussion about the future of the Sugar Creek with the views of those of us who live and work in this watershed.
Our goal is to collect information on what local residents think should be done regarding the Sugar Creek, and to assist local residents in furthering their goals, their values, and their vision of its future. We will be contacting some of you for a follow-up interview regarding your views of what we should be doing or not doing to the Sugar Creek. In addition, we will respond to all of you who might be interested in joining a local watershed group or making changes individually along your section of the stream.
Funding for this survey comes from a water quality improvement grant. Our hope is that the results will influence future policy in the State of Ohio. It is very important for you to complete this survey because it is your chance to express your views on what local issues or problems should be addressed in the Sugar Creek Watershed.
Attached is a map of the Sugar Creek; have fun finding your place in the watershed. We think that local people in this area have the creativity and ability to improve our common quality of life. Thank you very much for your help. By completing this survey, you are providing a local perspective on the issues relating to the Sugar Creek watershed. All information that you provide to us will be used for statistical analysis only and will remain strictly confidential. If you want additional information on issues of water quality in the Sugar Creek, if you want to explore what you might do as an individual living alongside the Sugar Creek, or if you are interested in joining the local watershed group, please use the enclosed postcard to contact us.
Sincerely,
Richard Moore Mark Weaver Associate Professor, Professor, Department of Human and Community Political Science, Resource College of Wooster, Development/OARDC Tel. Wooster, Ohio 44691 (330) 202-3538 Tel. (330) 263-2416.
Survey – Cover Letter 493 How to complete your survey:
1. This survey is divided into 3 parts, Part 1, Part 2 and Part 3. We ask all people to complete Part 1 & Part 2. If you are a farmer and/or own a farm, we ask that you also complete Part 3. 2. There are two columns of questions on each page; Please start with the left column and move to the right column of each page. 3. When you are finished, place your survey into the bag or envelope provided and leave it on your front porch. Please leave it sticking out from under your door, hanging from the doorknob, or somewhere convenient for you and easy for us to find.
Types of questions in the survey:
1. There are 3 kinds of questions on the survey a. Some will ask you to use a 5-point scale to “rate” your answer by selecting one of the numbers to show how strongly you feel for your answer. For Example: How important are farming magazines to you? (1=Not Important, 5=Very Important)
b. Some ask you to select from a list by checking options provided below. For Example: What activities do you perform daily? Please check all that apply.
c. Some ask you to fill-in a brief answer.
2. At any point during the survey, we ask you to add your comments on the side or the back of the survey. Your comments can be about anything you think is important for us to know, especially if you found a particular question difficult or poorly put together.
Survey – Instructions
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Survey – Page 1
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Survey – Page 2
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Survey – Page 3
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Survey – Page 4
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Survey – Page 5
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Survey – Page 6
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APPENDIX C
GEOPHYSICAL AND CLIMATE MAPS
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Map 1: ODNR Division of Geological Survey’s Composite Spectrum of Ohio Elevation based on LandSat Data. Source: http://www.ohiodnr.com/geosurvey/gen/map/map.htm
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Map 2: ODNR Division of Geological Survey’s Ohio Physiographic Regions and Districts based on glaciations and Major Land Resource Areas. Source: http://www.ohiodnr.com/geosurvey/gen/map/map.htm 503
Map 3: ODNR Division of Geological Survey’s Glacial Map of Ohio showing glacial deposition and landscape impact. Source: http://www.ohiodnr.com/geosurvey/gen/map/map.htm 504
Map 4: ODNR Division of Geological Survey’s Geologic Map Cross Section of Ohio showing the six major geologic systems that formed the bedrock of the state over the past 500 Million Years. Source: http://www.ohiodnr.com/geosurvey/gen/map/map.htm
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Map 5: ODNR Division of Geological Survey’s Drift-Thickness Map of Ohio showing the assessed thickness of glacial deposits in previously glaciated and unglaciated regions of the state. Source: http://www.ohiodnr.com/geosurvey/gen/map/map.htm
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APPENDIX D
CLIMATE DATA
507 Average daily measurements for the year 2000 are presented in entirety.
Monthly weather data averages from 1/1/2000 to 12/31/2000 at WOOSTER All measurements English. (Temp. Fahrenheit, Precip. inches, wind speed mph)
Yearly Averages Max Air Min Air Avg Air Max Rel. Min Rel. Avg. Rel. Month Precip Temp Temp Temp Hum. Hum. Hum. Avg High 60 Temp Jan 0.08 34.38 17.29 26.41 95.39 58.61 79.68 Avg Low 40 Temp Feb 0.07 43.88 26.06 34.49 97.24 57 78.93 Avg Temp 49.9 Mar 0.07 54.55 33.24 43.97 94.29 43.48 69.52 Normal Avg 49.5 Temp Apr 0.13 60.12 38.01 48.85 95.50 48.30 73.87 Avg Solar 286 Radiation May 0.15 73.47 50.85 62.34 97.45 50.42 76.29 508 32.85 Total Precip Jun 0.11 79.43 58.67 69.11 97.93 50.87 78.30 Normal 38.92 Precip Jul 0.06 79.73 57.58 68.78 99.94 52.19 80.03 Avg Wind 5.5 Speed Aug 0.11 79.94 57.79 68.65 99.90 54.71 82.87 Avg Wind 181 Dir Sep 0.09 73.60 51.33 61.90 99.77 54.37 83.07
Oct 0.06 64.57 43.07 52.45 98.81 50.65 81.71
Nov 0.04 46.40 32.17 39.18 96.90 63.67 82.93
Dec 0.11 29.37 13.75 21.41 98.06 71.65 87.45
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APPENDIX E
SURVEY DATA DICTIONARY, SYNTAX AND MODELS
509 Data Dictionary Q2FISHIN Fishing Use Measurement Level: Scale Column Width: 8 Alignment: Right List of variables on the working file64 Print Format: F11 Write Format: F11 SUB Subwatershed Name Measurement Level: Nominal Value Label Column Width: 4 Alignment: Left 0 no Print Format: A3 1 yes Write Format: A3 Q2CHILDR Play Use SUB# Subwatershed# Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 Value Label Value Label 0 no 1 Upper Sugar Creek 1 yes 2 North Fork 3 Mainstem Q2PASTUR Pasture Use 4 Little Sugar Creek Measurement Level: Scale Column Width: 8 Alignment: Right HHID Household ID Number Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 0 no 1 yes Q2HUNTIN Hunting Use Measurement Level: Scale Q2DRAIN Drainage Use Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F11 Write Format: F11 Value Label 0 no Value Label 1 yes 0 no 1 yes Q2CROPS Crop Use Measurement Level: Scale Q2BIRDS Bird Watching Use Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F11 Write Format: F11 Value Label 0 no Value Label 1 yes 0 no 1 yes
64 Variables not used in this work are excluded. 510 Q2HIKING Hiking Use Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11 Q3INDIVI Individual Owners as Decision Makers Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F11 Write Format: F11 Q2OTHER Other Use Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11 Q3OTHER Other Decision Makers Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F11 Write Format: F11 Q3FEDS Federal Government Decision Makers Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11 Q3OTHERT Value Label Measurement Level: Nominal 0 no Column Width: 50 Alignment: Left 1 yes Print Format: A50 Write Format: A50 Q3STATIE State Government Decision Makers Measurement Level: Scale Q4POLLUT Sugar Creek is Polluted? Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F11 Write Format: F11 Value Label 0 no Value Label 1 yes 1 strongly disagree 2 disagree Q3LOCALS Local Government Decision Makers 3 neither agree or disagree Measurement Level: Scale 4 agree Column Width: 8 Alignment: Right 5 strongly agree Print Format: F11 Write Format: F11 Q4HEADWA Headwater Stream Improvements Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11 Value Label Q3COALIT Coalition of Decision Makers 1 strongly disagree Measurement Level: Scale 2 disagree Column Width: 8 Alignment: Right 3 neither agree or disagree Print Format: F11 4 agree Write Format: F11 5 strongly agree 511 Q4BEAUTY Aesthetics More Important Q5WELLH2 Well Water Quality Concern Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11
Value Label Value Label 1 strongly disagree 0 no 2 disagree 1 yes 3 neither agree or disagree 4 agree Q5CHILDR Children's Health Concern 5 strongly agree Measurement Level: Scale Column Width: 8 Alignment: Right Q4VIABIL Economic More Important Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 0 no 1 yes Value Label 1 strongly disagree Q5LESSBI Decreased Biodiversity Concern 2 disagree Measurement Level: Scale 3 neither agree or disagree Column Width: 8 Alignment: Right 4 agree Print Format: F11 5 strongly agree Write Format: F11
Q5LEGALE Legal - Regulatory Concern Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F11 Write Format: F11 Q5STREAM Stream Bank Stability Concern Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11
Q5LEGALL Legal - Liability Concern Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F11 Write Format: F11 Q5GRNDH2 Groundwater Taste Concern Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11
Q5DRAIN Adequate Drainage Concern Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F11 Write Format: F11
Value Label 0 no 1 yes 512 Q5AGPOLL Agricultural Pollution Concern Q5FILLIN Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F8.2 Write Format: F11 Write Format: F8.2
Value Label Q6TREES Reforested Tree Vision 0 no Measurement Level: Scale 1 yes Column Width: 8 Alignment: Right Print Format: F11 Q5SEPTIC Failing Septic Systems Concern Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 0 no Write Format: F11 1 yes
Value Label Q6BUFFER Grass Buffer Vision 0 no Measurement Level: Scale 1 yes Column Width: 8 Alignment: Right Print Format: F11 Q5RUNOFF Lawn Runoff Concern Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 0 no Write Format: F11 1 yes
Value Label Q6MOREBI Increased Biodiversity Vision 0 no Measurement Level: Scale 1 yes Column Width: 8 Alignment: Right Print Format: F11 Q5EROSIO Stream Bank Erosion Concern Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 0 no Write Format: F11 1 yes
Value Label Q6MOREHE Increased Herd Size Vision 0 no Measurement Level: Scale 1 yes Column Width: 8 Alignment: Right Print Format: F11 Q5OTHER Other Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 0 no Write Format: F11 1 yes
Value Label Q6HIKING Hiking Vision 0 no Measurement Level: Scale 1 yes Column Width: 8 Alignment: Right Print Format: F11 Q5OTHERT Write Format: F11 Measurement Level: Nominal Column Width: 50 Alignment: Left Value Label Print Format: A50 0 no Write Format: A50 1 yes 513 Q6WETLAN Wetlands Vision Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11 Q6PICNIC Picnic Vision Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F11 Write Format: F11 Q6BIRDS Bird Watching Vision Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11 Q6OTHER Other Vision Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F11 Write Format: F11 Q6TRAPPI Trapping Vision Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11 Q6OTHERT Value Label Measurement Level: Nominal 0 no Column Width: 50 Alignment: Left 1 yes Print Format: A50 Write Format: A50 Q6DRAIN Adequate Drainage Vision Measurement Level: Scale Q6FILLIN Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F8.2 Write Format: F8.2 Value Label 0 no Q7GROUP Interest in Joining A Group 1 yes Measurement Level: Scale Column Width: 8 Alignment: Right Q6PLAY Children Play Vision Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 0 no 1 yes Value Label Q8INDIVI Interest in Working Alone 0 no Measurement Level: Scale 1 yes Column Width: 8 Alignment: Right Print Format: F11 Q6EROSIO Erosion Protection Vision Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 0 no Write Format: F11 1 yes 514 Q9BUFFER Interest in Installing a Buffer 3 neutral trust Measurement Level: Scale 4 trust Column Width: 8 Alignment: Right 5 trust very much Print Format: F11 Write Format: F11 Q10SWCD Trust SWCD Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 no Print Format: F11 1 yes Write Format: F11
Q9EXISTB Already Installed a Buffer Value Label Measurement Level: Scale 1 strongly do not trust Column Width: 8 Alignment: Right 2 do not trust Print Format: F11 3 neutral trust Write Format: F11 4 trust 5 trust very much Value Label 0 no Q10MWCD Trust MWCD 1 yes Measurement Level: Scale Column Width: 8 Alignment: Right Q10EPA Trust EPA Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 1 strongly do not trust 2 do not trust Value Label 3 neutral trust 1 strongly do not trust 4 trust 2 do not trust 5 trust very much 3 neutral trust 4 trust Q10OSUE Trust OSU Extension 5 trust very much Measurement Level: Scale Column Width: 8 Alignment: Right Q10USDA Trust USDA Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 1 strongly do not trust 2 do not trust Value Label 3 neutral trust 1 strongly do not trust 4 trust 2 do not trust 5 trust very much 3 neutral trust 4 trust Q10FB Trust Farm Bureau 5 trust very much Measurement Level: Scale Column Width: 8 Alignment: Right Q10ODA Trust ODA Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 1 strongly do not trust 2 do not trust Value Label 3 neutral trust 1 strongly do not trust 4 trust 2 do not trust 5 trust very much 515 Q10ODNR Trust ODNR Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 1 strongly do not trust 2 do not trust Value Label 3 neutral trust 1 strongly do not trust 4 trust 2 do not trust 5 trust very much 3 neutral trust 4 trust Q10FILLI 5 trust very much Measurement Level: Nominal Column Width: 50 Alignment: Left Q10WHD Trust WHD Print Format: A50 Measurement Level: Scale Write Format: A50 Column Width: 8 Alignment: Right Print Format: F11 V82 Religious Values Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 1 strongly do not trust Write Format: F11 2 do not trust 3 neutral trust Value Label 4 trust 1 very unimportant 5 trust very much 2 not important 3 neutral Q10COMMI Trust County Commissioners 4 important Measurement Level: Scale 5 very important Column Width: 8 Alignment: Right Print Format: F11 Q1ECONOM Economic Values Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 1 strongly do not trust Write Format: F11 2 do not trust 3 neutral trust Value Label 4 trust 1 very unimportant 5 trust very much 2 not important 3 neutral Q10NFTF Trust North Fork Taskforce 4 important Measurement Level: Scale 5 very important Column Width: 8 Alignment: Right Print Format: F11 Q1FAMILY Family Values Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 1 strongly do not trust Write Format: F11 2 do not trust 3 neutral trust Value Label 4 trust 1 very unimportant 5 trust very much 2 not important 3 neutral Q10TRUST Trust Township Trustees 4 important Measurement Level: Scale 5 very important Column Width: 8 Alignment: Right 516 Q1COMMUN "Good of the Community" Values Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 1 strongly disagree 2 disagree Value Label 3 neither agree or disagree 1 very unimportant 4 agree 2 not important 5 strongly agree 3 neutral 4 important Q2CRIME Less Crime and Drug Abuse 5 very important Measurement Level: Scale Column Width: 8 Alignment: Right Q1CONSER Conservation Values Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 1 strongly disagree 2 disagree Value Label 3 neither agree or disagree 1 very unimportant 4 agree 2 not important 5 strongly agree 3 neutral 4 important Q2NEIGHH Neighbors Help Each Other 5 very important Measurement Level: Scale Column Width: 8 Alignment: Right Q2SCHOOL Schools are Good Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 1 strongly disagree 2 disagree Value Label 3 neither agree or disagree 1 strongly disagree 4 agree 2 disagree 5 strongly agree 3 neither agree or disagree 4 agree Q2QUALIF Good Quality of Life 5 strongly agree Measurement Level: Scale Column Width: 8 Alignment: Right Q2SAFPLA Safe Place to Live Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right Print Format: F11 Value Label Write Format: F11 1 strongly disagree 2 disagree Value Label 3 neither agree or disagree 1 strongly disagree 4 agree 2 disagree 5 strongly agree 3 neither agree or disagree 4 agree Q2LOCALG Responsive Local Government 5 strongly agree Measurement Level: Scale Column Width: 8 Alignment: Right Q2NEIGHB Trust in Neighbor Print Format: F11 Measurement Level: Scale Write Format: F11 Column Width: 8 Alignment: Right 517 Value Label Q6.CHILD Number of Children Under 18 1 strongly disagree Measurement Level: Scale 2 disagree Column Width: 8 Alignment: Right 3 neither agree or disagree Print Format: F11 4 agree Write Format: F11 5 strongly agree Q7.YEARS Years Lived in Community Q2LOCALE Good Local Economy Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 Q8EDUC Education Level Value Label Measurement Level: Scale 1 strongly disagree Column Width: 8 Alignment: Right 2 disagree Print Format: F11 3 neither agree or disagree Write Format: F11 4 agree 5 strongly agree Value Label 1 8th Grade Q3HOHH Status of Respondent 2 High School Measurement Level: Scale 3 Some College Column Width: 8 Alignment: Right 4 College Graduate Print Format: F11 5 Graduate Degree Write Format: F11 Q9HHAGE Age Value Label Measurement Level: Scale 1 Head of Household Column Width: 8 Alignment: Right 2 Spouse of Head Print Format: F11 3 Other Write Format: F11
Q4MARRIE Marital Status Measurement Level: Scale Q10HERIT Heritage Raw Code Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F11 Write Format: F11 Value Label 0 NO Value Label 1 YES 1 African American 2 Asian American Q5SEX Sex 3 Hispanic/Latino Measurement Level: Scale 4 English Column Width: 8 Alignment: Right 5 French Print Format: F11 6 German Write Format: F11 7 Irish 8 Middle Eastern Value Label 9 Native American 1 Female 10 Scotch-Irish 2 Male 11 Swiss 3 Completed as a Couple 12 White 13 Other
518 HER_CDE Binary Heritage FRM_INCM Off-Farm Income Index Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F8 Write Format: F11 Write Format: F8
Value Label Value Label 0 Yankee 1 Low 1 Yeoman 2 Medium 3 High HER_CD3 Heritage Index Measurement Level: Scale Q1GRASS Installed Grass Waterways Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F8 Column Width: 8 Alignment: Right Write Format: F8 Print Format: F11 Write Format: F11 Value Label 1 English Value Label 2 Apostolic, Mennonite, Brethren 0 no 3 New Order Amish 1 yes 4 Old Order Amish 5 Swartzentruber Amish Q1BUFFER Installed Grass Buffers Measurement Level: Scale Q12RELIG Religion Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F11 Write Format: F11 Value Label 0 no Value Label 1 yes 1 Apostolic 2 Lutheran Q1GRAZE Implemented Prescribed Grazing 3 Methodist Measurement Level: Scale 4 Presbyterian Column Width: 8 Alignment: Right 5 Roman Catholic Print Format: F11 6 Mennonite Write Format: F11 7 New Order Amish 8 Old Order Amish Value Label 9 Swartzentruber Amish 0 no 10 Other 1 yes
Q13INCOM Household Income Q1FENCIN Installed Exclusion Fencing Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 Value Label 1 < $25K Value Label 2 $25-$50K 0 no 3 $50-$75K 1 yes 4 $75-$100K 5 $100-$150K 6 $150-$175K 7 $175-$200K 8 > $200K 519 Q1NOTILL Implemented No-Till Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 1 very unimportant Write Format: F11 2 not important 3 neutral Value Label 4 important 0 no 5 very important 1 yes Q2MANAGE A Good Farmer is a Good Manager Q1COVERC Installed a Cover Crop Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 Value Label Value Label 1 very unimportant 0 no 2 not important 1 yes 3 neutral 4 important Q1LEGUME Implemented a Grasses and Legumes 5 very important Rotation Measurement Level: Scale Q2PRESER A Good Farmer Preserve the Farm Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F11 Write Format: F11 Value Label 0 no Value Label 1 yes 1 very unimportant 2 not important Q1MANURM Implemented Manure Management 3 neutral Measurement Level: Scale 4 important Column Width: 8 Alignment: Right 5 very important Print Format: F11 Write Format: F11 Q2PROMOT A Good Farmer Promotes Community Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F11 Write Format: F11 Q1MANURT Participated in Manure Trading Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 1 very unimportant Print Format: F11 2 not important Write Format: F11 3 neutral 4 important Value Label 5 very important 0 no 1 yes Q7PERCEN Percent of Income from Off-farm Measurement Level: Scale Q2STEWAR A Good Farmer is a Good Steward Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F11 520 Q8YEARFA Number of Years Farmed TN_LEASE Tenure - Acres Leased Out Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11
Q8ACREOW Number of Acres Owned (1st) Value Label Measurement Level: Scale 0 No Lease Column Width: 8 Alignment: Right 1 Lease Print Format: F11 Write Format: F11 TN_PRCT_ Percent Owned Farmed Land Measurement Level: Scale Q9ACREFA Number of Acres Owned (2nd) Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: PCT11 Column Width: 8 Alignment: Right Write Format: PCT11 Print Format: F11 Write Format: F11 TEN_TYPX Tenure Category Measurement Level: Scale Q10ACRER Number of Rental Acres Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F8 Column Width: 8 Alignment: Right Write Format: F8 Print Format: F11 Write Format: F11 Value Label 1 Own and Lease Out Q11ACREL Number of Leased Out Acres 2 Own 0 % Measurement Level: Scale 3 Own 1-32% Column Width: 8 Alignment: Right 4 Own 33-65% Print Format: F11 5 Own 66-99% Write Format: F11 6 Own 100%
Q12ACREC Number of Cultivated Acres Q13FUTUR Future of Farm Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11
TN_OWN_C Tenure - Acres Owned FRM_SUCC Farm Succession Coded Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F8 Write Format: F11 Write Format: F8
FRM_SIZE Farm Size - Owned and Leased Land Value Label Measurement Level: Scale 1 Sell for Development Column Width: 8 Alignment: Right 2 Subdivide and Sell, Farm Some Print Format: F8 3 Sell to Family Write Format: F8 4 Sell to Another Farmer 5 Lease or Rent to Farmer TN_RENT_ Tenure - Acres Rented 6 Continue Farming by Children Measurement Level: Scale 7 Continue Farming Column Width: 8 Alignment: Right Print Format: F11 Write Format: F11
521 FRM_SUC2 Farm Succession Index Column Width: 8 Alignment: Right Measurement Level: Scale Print Format: F11 Column Width: 8 Alignment: Right Write Format: F11 Print Format: F8 Write Format: F8 Q16SHEEP Sheep Crop Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 1 Sell Farm Print Format: F11 2 Sell as Farm Write Format: F11 3 Keep in Family Q16HAY Hay Crop Q16CORN Corn Crop Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 Q16FRUIT Fruit Crop Q16SOY Soy Bean Crop Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 Q16VEGGI Vegetables Crop Q16WHEAT Wheat Crop Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 Q16OTHER Other Crop Q16OATS Oat Crop Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 FRM_TYP Farm Type Code Q16DAIRY Dairy Crop Measurement Level: Scale Measurement Level: Scale Column Width: 8 Alignment: Right Column Width: 8 Alignment: Right Print Format: F11 Print Format: F11 Write Format: F11 Write Format: F11 Value Label Q16BEEF Beef Crop 1 Grain Measurement Level: Scale 2 Hay/Small Grain Column Width: 8 Alignment: Right 3 Dairy Print Format: F11 4 Beef Write Format: F11 5 Hogs 6 Poultry Q16HOGS Hogs Crop 7 Sheep Measurement Level: Scale 8 Other Column Width: 8 Alignment: Right Print Format: F11 Write Format: F11
Q16POULT Poultry Crop Measurement Level: Scale 522 FRM_TYP2 Farm Type Index Write Format: F11 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 0 no Write Format: F11 1 yes Value Label FARM Is this A Farm? 1 Grain Measurement Level: Scale 2 Dairy Column Width: 8 Alignment: Right 3 Other Animal Print Format: F11 4 Other Write Format: F11
GRAIN_FRM Grain Farms Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F8 Write Format: F8 CON_INDX Conservation Index Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 0 Non-Grain Print Format: F11 1 Grain Write Format: F11
Q17CONTO Interest in Contour Strips Value Label Measurement Level: Scale 1 con_ind3 0-1 Column Width: 8 Alignment: Right 2 con_ind3 2-3 Print Format: F11 3 con_ind3 4-7 Write Format: F11 CUR_USE2 Conservation Use Value Label Measurement Level: Scale 0 no Column Width: 8 Alignment: Right 1 yes Print Format: F8 Write Format: F8 Q18MANUR Interest in Manure Swap Measurement Level: Scale Value Label Column Width: 8 Alignment: Right 1 Low Print Format: F11 2 Medium Write Format: F11 3 High
Value Label CON_PREF Conservation Preferences 0 no Measurement Level: Scale 1 yes Column Width: 8 Alignment: Right Print Format: F8 Q19FENCI Interest in Exclusion Fencing Write Format: F8 Measurement Level: Scale Column Width: 8 Alignment: Right Value Label Print Format: F11 1 Low Write Format: F11 2 Medium 3 High Value Label 0 no 1 yes
Q20NOTIL Interest in No-Till Measurement Level: Scale Column Width: 8 Alignment: Right Print Format: F11 523 Statistics Syntax and Models
Conservation Index CON_INDX
“IF (q1notill + q1grass + q1buffer + q1manurm + q6trees + q6buffer + q6morebi + q6wetlan + q6erosio <= 2) CON_INDX = 1 . EXECUTE .
IF (q1notill + q1grass + q1buffer + q1manurm + q6trees + q6buffer + q6morebi + q6wetlan + q6erosio >= 3 & q1notill + q1grass + q1buffer + q1manurm + q6trees + q6buffer + q6morebi + q6wetlan + q6erosio <= 5) CON_INDX = 2 . EXECUTE .
IF (q1notill + q1grass + q1buffer + q1manurm + q6trees + q6buffer + q6morebi + q6wetlan + q6erosio >= 6) CON_INDX = 3 . EXECUTE.”
Current Conservation Use CURE_USE2
“COMPUTE Cur_Use = q1notill + q1grass + q1buffer + (q2huntin or q2birds or q2fishin) + q1manurm . EXECUTE .
IF (q1buffer=1) cur_usex = q1notill + q1grass + q1buffer + q1manurm + (Q2fishin or q2huntin or q2birds) . EXECUTE .
IF (q1buffer=0) cur_usex = q1notill + q1grass + q1buffer + q1manurm + (Q2fishin or q2huntin or q2birds) - (q2crops or q2pastur) . EXECUTE.” Farm Succession FRM_SUC2
“RECODE frm_succ (7=3) (6=3) (5=2) (3=1) (4=2) (1=1) (2=1) INTO frm_suc2 . EXECUTE .”
524
APPENDIX F
BASE GIS METADATA
The following table lists the basic metadata for the GIS data used in this dissertation. The file, original data source and a description are provided.
Filename Source Description wyn_rds00 2000 U.S. Census Wayne County road layer TIGER/Line Files sc_24kstreams_only USGS Sugar Creek Watershed stream layer wyn_mcd00 2000 U.S. Census Wayne County township boundaries TIGER/Line Files ohio_counties 2000 U.S. Census Ohio County boundaries TIGER/Line Files usc_all_subwatersheds USEPA 14-digit subwatersheds of the Sugar Creek Watershed ohio_watershed_11-dig USGS 11-digit subwatersheds of the Sugar Creek Watershed ohio_watershed_8-dig USGS 8-digit Sugar Creek Watershed us_basins_mississippi USGS National drainage basin network oh_lc_mar2000 ODNR March 200 land cover of the State of Ohio updated from 1992 dataset wayneparcel00 Wayne County Auditor 2000 Parcel Data and 2003 landowner database
525
BIBLIOGRAPHY
Adler, Michael 1996 Land Tenure, Archaeology, and the Ancestral Pueblo Social Landscape. Journal of Anthropological Archaeology 15 (4): 337-371.
Annen, K. 2001 Inclusive and exclusive social capital in the small-firm sector in developing countries. Journal of Institutional and Theoretical Economics 157: 319–330.
Atherton, B.C., L.C. Brown, N.R. Fausey and F.J. Hitzhusen 2004 Maintenance of multi-Landowner Drainage Improvements in Ohio. Drainage VIII Proceedings of the Eighth International Symposium , Sacramento, CA: 21-24 March. ASAE Publication 710P0304. Not peer- reviewed.
Barfield Thomas 1997 The Dictionary of Anthropology. Oxford: Blackwell.
Barth, Fredrik 1956 Ecologic Relationships of Ethnic Groups in Swat, North Pakistan. American Anthropologist 58(6): 1079-89.
Bartz, A.E. 1999 Basic Statistical Concepts. Upper Saddle River, NJ: Prentice Hall.
Batterbury, Simon and Bebbington A. J. (Eds.) 1999 Environmental Histories, Access to Resources and Landscape Change: Special Issue. Land Degradation and Development 10 (4).
Battershill, M., and A. Gilg. 1997 Socioeconomic constraints and environmentally friendly farming in the Southwest of England. Journal of Rural Studies 13: 213-228.
Brandt, Vincent S. R. 1971 A Korean Village Between Farm And Sea. Cambridge, MA: Harvard University Press.
526 Bender, Martin H. 2003 Animal production and farm size in Holmes County, Ohio, and US agriculture. Journal of Alternative Agriculture. 18(2): 70-79. Bennett, John 1993 Human Behavior as Human Ecology: essays in environmental and development anthropology. New Brunswick, NJ: Transaction Publishers. 1982 Of Time and the Enterprise: North American farm management in a context of resource marginality. Minneapolis: University of Minnesota Press. 1976 The Ecological Transition: Cultural anthropology and human adaptation. New York: Pergamon.
Berry, Wendell 1996 The Unsettling of America: Culture and Agriculture. CA: University of California Press.
Bohannon, Paul and Mark Glazer 1988 High Points in Anthropology. 2nd Ed. McGraw Hill. 1960 Africa's Land. The Centennial Review Vol. LV.
Bonanno, Alessandro and Lawrence Busch, William H. Friedland, Lourdes Gouveia, and Enzo Mingione 2000 From Columbus to ConAgra: The Globalization of Agriculture and Food. University of Kansas Press.
Bourdieu, Pierre 1990 The Logic of Practice. London: Cambridge University Press. 1984 A Social Critique of the Judgment of Taste. Translated by Richard Nice. Cambridge, MA: Harvard University Press. 1977 Outline of a Theory of Practice. Cambridge, UK: Cambridge University Press.
Brady, Niles 1974 The Nature and Properties of Soils. 8th Edition. New York: Macmillan Publishing Company, Inc.
Brockman, C. Scott n.d. Physiographic Regions of Ohio. Map created for the ODNR, Division of Geological Survey.
Brown, Larry 1981 Innovation Diffusion: A new perspective. London, New York: Methuer.
527 Bultena, Gordon L. and Eric O. Hoiberg 1983 Factors Affecting Farmers’ Adoption of Conservation Tillage. Journal of Soil and Water Conservation, 38(3): 281-284.
Burke, Bryan E. 2001 Hardin Revisited: A critical look at perception and the logic of the commons. Human Ecology 29 (4): 449-476.
Burkey, Stan 1993 People First: A Guide to Self-reliant Participatory Rural Development. NJ: Zed Books.
Burton, J.F. and L. Walford 2005 Multiple succession and land division on family farms in the South East of England: A counterbalance to agricultural concentration? Journal of Rural Studies 21, 335-347.
Buttel, Frederick 2003 Environmental sociology and the explanation of environmental reform. Organization and Environment 16(3): 306-344. 1983 Beyond the family farm. In: Technology and Social Change in Rural Areas, G. Summers, Ed. Boulder Co: Westview Press, 87-107.
Buttel, Frederick and Gill Gillespie 1984 The sexual division of farm household labor: an exploratory study of the structure of on-farm and off-farm labor allocation among farm men and women. Rural Sociology 49:182–209
Buttel, Frederick and Oscar Larson 1979 Farm Size, Structure, and Energy Intensity: An Ecological Analysis of U.S. Agriculture. Rural Sociology 44: 471-488.
Buttel, Frederick, Gill Gillespie, Oscar Larson, and Craig Harris 1981 The Social Bases of Agrarian Environmentalism: A comparative analysis of New York and Michigan farm operators. Rural Sociology 16(3): 391- 410.
Caldwell, J. A. 1897 Caldwell’s Atlas of Wayne County. 1873 Caldwell’s Atlas of Wayne County.
Carolan, Michael S. 2005 Barriers to the Adoption of Sustainable Agriculture on Rented Land: An Examination of Contesting Social Fields. Rural Sociology 70(3): 387-413.
528 Camboni, Sylvia and Ted L. Napier 1993 Factors Affecting Use of Conservation Farming Practices in East Central Ohio. Agriculture, Ecosystem and Environment 45: 79-94.
Child, Wendy 2001 Food Routes and Vanguard Communications Focus Groups with Farmers and Consumer. Do You Know Where Your Food Comes In: Recipes for effectively communicating about American Agriculture, Food Routes.
Clancy, Kate 1997 Reconnecting Farmers and Citizens in the Food System. In: Visions of American Agriculture, William Lockeretz, Ed. 47-57.
Collins, Jane L. 1986 Smallholder Settlement of Tropical South America: The Social Causes of Ecological Destruction. Human Organization 45 (Spring).
Coleman, Gould and Sarah Elbert 1984 Farming Families: The Farm Needs Everyone. Research in Rural Sociology and Development 1:61-78.
Conklin, Harold 1954 The Relation of Hanunuo Culture to the Plant World. Doctoral Dissertation, Yale University, New Haven, CT.
Conway, Gordon 1987 The Properties of Agroecosystems. Agricultural Systems 24: 95-117.
Crocombe, Ron 1974 An Approach to the Analysis of Land Tenure Systems. In: Land Tenure in Oceania. Henry P. Lundsgarde, Ed. ASOA Monograph 2, HI: University of Hawaii Press.
Cronk, Sandra 1981 Gelassenheit: The Rites of the Redemptive Process in Old Order Amish and Old Order Mennonite Communities. Mennonite Quarterly Review 55: 5-44.
Davis, J.A. 1971 Elementary Survey Analysis. Englewood Cliffs, NJ: Prentice Hall.
529 DeLind, Laura 1992 Cheap Food: A case of mind over matter. A paper presented to the Annual Meeting of the Agriculture, Food and Human Values Society, June 4-7, Lansing, MI.
Denevan, William M. 1992 The Pristine Myth: The landscape of the Americas in 1492. Annals of the Association of American Geographers, 82(3): 369-385.
Dillman, Don A. 1991 The Design and Administration of Mail Surveys". Annual Review of Sociology 17: 225-249.
Donnermeyer, Joseph and Elizabeth Cooksey 2004 The Demographic Foundations of. Amish Society. Paper presented at the annual meeting of the Rural Sociological Society, Sacramento, California August 11-15.
Doolittle, William E. 2004 Permanent vs. shifting cultivation in the Eastern Woodlands of North American prior to European contact. Agriculture and Human Values 21: 181-189.
Douglas, Ben 1878 History of Wayne County.
Dudley, Kathryn 2000 Debt and Dispossession: Farm Loss in America’s Heartland. University of Chicago Press.
Durrenberger, Paul 2002 Why the Idea of Social Capitalism is Bad. Society for Applied Anthropology Newsletter, 13(3): 3-4, August. 1992 It’s All Politics: South Alabama’s Seafood Industry. Urbana, IL: University of Illinois Press.
Durrenberger, E. Paul and Kendall Thu 1997 Systems, Signals, and Environment in Industrial Food Production. Journal of Political Ecology 4: 27-40. 1996 The Expansion of Large Scale Hog Farming in Iowa: The Applicability of Goldschmidt's Findings Fifty Years Later. Human Organization 55 (4): 409-415.
530 Ellen, Roy 1992 Environment, Subsistence and System: The Ecology of Small-Scale Social Formations. New York: Cambridge University Press. 1984 Ethnographic Research: A Guide to General Conduct. Research Methods in Social Anthropology 1. New York: Academic Press.
Ely, Ralph 1942 A History of the Amish People of East Union Township, Wayne County, Ohio, with Special Emphasis on Educational Problems. Masters Thesis, The Ohio State University: Columbus, OH.
Erickson, D.L., R.L. Ryan, and R. De Young 2002 Woodlots in the rural landscape: Landowner motivations and management attitudes in Michigan case study. Landscape and Urban Planning 58(2-4): 101-112.
Flannery, Kent V. 1972 Cultural Evolution of Civilizations, Annual Review of Ecology and Systematics 3.
Fletcher, Lyle Rexford 1932 The Amish People of Holmes County, Ohio: A Study in Human Geography. Masters Thesis, The Ohio State University: Columbus, OH. Flora, Cornelia Butler 1995 Social Capital and Sustainability: Agriculture and Communities in the Great Plains and Corn Belt. Research in Rural Sociology and Development: A Research Annual 6: 227-246.
Flora, Jan 1998 Social Capital and Communities of Place. Rural Sociology 63(4): 481- 506.
Flora, Cornelia and Flora, Jan 2004 Rural Communities: Legacy and Change. Boulder: Westview Press.
Foley, Michael W. and Bob Edwards 1999 Is it Time to Disinvest in Social Capital. Journal of Public Policy 19(2): 141-173.
Gasson, R. 1969 The choice of farming as an occupation. Sociologia Ruralis 9: 146-166.
Gasson R. and A. J. Errington 1993 The Farm Family Business. Wallingford: CAB International.
531 Geertz, Clifford 1963 Agricultural Involution: the process of ecological change in Indonesia. Berkley, University of California Press.
Gilbert and O’Connor 1996 Leaving the Land Behind: Struggles for Land Reform in U.S. Federal Policy, 1933-1965. http://www.ies.wisc.edu/ltc/ltc156.html Last visited 01/25/06.
Gluckman, Max 1963 Order and Rebellion in Tribal Africa. London: Cohen and West.
Godelier, Maurice 1986 The Mental and the Material. New York: Verso.
Goebel, P. C. and D.M. Hix, and M.E. Semko-Duncan 2003 Identifying reference conditions for Ohio’s riparian areas. OARDC Special Circular (in review).
Goldschmidt, Walter 1998 Conclusion: The Urbanization of Rural America. In: Pigs, profits, and rural communities, Kendall M. Thu and E. Paul Durrenberger, eds. Albany: State University of New York Press 1978 As You Sow: Three studies in the social consequences of agribusiness. The Free Press.
Granovetter, Mark 1985) Economic Action and Social Structure: The problem of embeddedness, American Journal of Sociology 91 (3): 481-510.
Guba, E. and Y. Lincoln 1981 Effective Evaluation. Jossey-Bass.
Haberl, H. and S. Batterbury, Emilio Moran (Guest Eds.) 2001 Using and Shaping the Land: A long-term perspective. Special Issue of Land Use Policy 18 (1): 1-91.
Habermas, Jurgen 1971 Knowledge and Human Interests. Boston: Beacon Press.
Hansen, Michael 1997 The Ice Age in Ohio. Educational Leaflet No. 7, ODNR, Division of Geological Survey.
532 Harris. Marvin 1999 Science, Objectivity, Morality. In: Anthropological theory in North America. E.L. Cerroni-Long, Ed. Westport, CT: Bergin & Garvey, pp77- 84.
Hart, John 1991 Part-Ownership and Farm Enlargement in the Midwest. Annals of the Association of American Geographers. 81(1): 66-79.
Holmes, Kathryn L. 2004 Landscape factors influencing water quality and the development of reference conditions for riparian restoration in the headwaters of a northeast Ohio watershed. M.S. Thesis, The Ohio State University. Hostetler, John 1993 Amish Society. Baltimore, MA: Johns Hopkins University Press.
Huberman, A. M., and Miles, M. B. 1994 Data management and analysis methods. Chapter 27 (p. 428-444) in N. K. Denzin and Y. S. Lincoln (eds.). The evaluation handbook. Thousand Oaks, Calif.: Sage.
Hurt, R. Douglas 1996 The Ohio Frontier: Crucible of the Old Northwest, 1720-1830. Indiana University Press.
Ingold, Tim 1986 The Appropriation of Nature. Manchester University Press.
Jackson, Wes 1980 New Roots for Agriculture. San Francisco : Friends of the Earth.
Jackson-Smith, Douglas and Gill Gillespie 2003 Social Linkages Between Farm and Community, presented at the 66th Annual Meeting of the Rural Sociological Society, Montreal, Quebec CANADA: July 27-30, 2003.
Janesick, V. J. 1994 The dance of qualitative research design. In: N. K. Denzin & Y. S. Lincoln (Eds.) Handbook of Qualitative Research Design (pp. 209-219). Thousand Oaks: Sage.
533 Jellison, Katherine 1993 Entitled to Power : farm women and technology, 1913-1963. Chapel Hill: University of North Carolina Press.
Jones, Andrew M. 2005 The Elementary Structures of the Family Firm: An Anthropological Perspective. Human Organization Volume 64 (3): 276-285.
Kieffer, D. L. 1876 Sixty-three handwritten pages of Green Township and Smithville Area History. Transcribed by Bonnie Knox, 1988.
Kleiman, Richard and Donna Erickson 1996 Landscape change in an agricultural watershed: the effect of parcelization on riparian forest cover. Environment and Planning B 23: 25-36.
Kloppenburg and Geisler 1985 The agricultural ladder: agrarian ideology and the agriculture. Journal of Rural Studies 1:59-72. Kottak, Conrad 1999 The New Ecological Anthropology. American Anthropologist
Kraybill, David and C. Nelson Hostetler 2001 Anabaptist World USA. Scottsdale, PA: Herald Press.
Krupa, Kenneth S. and Marlow Vesterby 2003 Urbanization and Agriculture: Affect on Livestock and Poultry in 17 U.S. Fast Growth Counties from 1950 to 1997, presented at the 2003 Food Agriculture and Human Values Meetings, Austin, Texas, June12-15.
Lansing, Stephen J. and James N. Kremer 1993 Emergent Properties of Balinese Water Temples: Coadaptation on a Rugged Fitness Landscape. American Anthropologist 95 (1): 97-114.
Lapping, M.B. and M.J. Pfeffer 1997 City and Country: Forging New Connections through Agriculture. In: Visions of American Agriculture. Lockeretz (ed.). Ames, IA: Iowa State University Press: 91-104.
Lansing, Stephen J, Phillip S. Lansing and Juliet S. Erazo 1998 The Value of a River. Journal of Political Ecology 5: 1-22.
Leach, Edmund 1965 Culture and Social Cohesion: AN Anthropologists View. Daedalus 94(1): 24-38.
534 Leeper, Velma LaRue 1936 The History, Customs, and Social Life of the Amish of Ohio with Special Reference to Holmes County. Masters Thesis, Kent State University: Kent, OH.
Lehman, James O. 1969 Sonnenberg: A haven and a heritage, a sesquicentennial history of the Swiss Mennonite community of southeastern Wayne County, Ohio. Kidron, Ohio.
Liverman, D. and Emilio Moran, Ronald R. Rindfuss and Paul C. Stern. 1998 People and Pixels. Washington D.C.: National Academy Press.
Llyod, W. A., J. I. Falconer and C.E. Thorne 1918 The Agriculture of Ohio. Bulletin 326, Ohio Agriculture Experiment Station.
Lobao, Linda 1990 Locality and Inequality : farm and industry structure and socioeconomic conditions. Albany : State University of New York Press.
Lobley, Matt and Clive Potter 2004 Agricultural restructuring and state assistance: competing or complementary policy paradigms? Journal of Environmental Policy and Management 6 (1): 3-18.
Lockeretz, William 1990 What have we learned about who conserves soil? Journal of Soil and Water Conservation, Sept-Oct: 517-523.
Logan and Yang 2004 Identifying Ethnic Neighborhoods with Census Data: Group concentration and spatial clustering. In: Spatially Integrated Social Science, Michael Goodchild and Donald Janelle, Eds. Oxford: Oxfod University Press. 113- 126.
Logsdon, Gene 1994 The Contrary Farmer. Post Mills, Vt.: Chelsea Green Publishing Co.
Long, Scot 2003 The Complexity of Labor Exchange Among Amish Farm Households in Holmes County, Ohio. Dissertation, Ohio State University, Advisor: Richard H. Moore.
535 Lovell, W. George 1992 “Heavy Shadows and Black Night”: Disease and depopulation in Colonial Spanish America. Annals of the Association of American Geographers 82 (3): 426-443.
Lowry, Sean and Allen Noble 2000 The Changing Occupational Structure of the Amish of the Holmes County, Ohio, Settlement. The Great Lakes Geographer 7(1): 26-37.
Lund and Price 1998 The measurement of average farm size. Journal of Agricultural Economics 9: 100-110.
Lundsgarde, Henry P. (Ed.) 1974 Land Tenure in Oceania. ASOA Monograph 2, HI: University of Hawaii Press.
Lyson, Thomas A., and Judy Green 1999 The Agricultural Marketscape: A Framework for Sustaining Agriculture and Communities in the Northeast. Journal of Sustainable Agriculture 15 (2/3):133-150.
Manners, Robert A. 1964 Process and Pattern in Culture: Essays in Honor of Julian H. Steward. Chicago: Aldine Press
McCay, Bonnie 1995 Common and Private Concerns. Advances in Human Ecology 4: 89-116.
McCracken, Stephen D; Brondizio, Eduardo S; Moran, Emillo F; Siqueira, Andrea D; Nelson, Donald; Rodriguez-Pedraza, Carlos 1999 Remote sensing and GIS at the farm property level: demography and deforestation in the Brazilian Amazon. Photogrammetric Engineering and Remote Sensing, 65(11): 1311-1320.
McGee, R. Jon and Richard L. Warms 1996 Anthropological Theory: An Introductory History. Mt. View, CA: Mayfield.
McIntosh, W. and Z. Lee 2003 Correlates of Dairy Farm Household Expenditures, presented at the 66th Annual Meeting of the Rural Sociological Society, Montreal, Quebec CANADA * July 27-30, 2003.
536 McNally, S. 2002 Are ‘other gainful activities’ on farms good for the environment? Journal of Environmental Management 66: 57-65.
Merrigan, Kathleen 1997 Government Pathways to True Food Security. In: Visions of American agriculture. William Lockeretz, Ed. Ames : Iowa State University.
Mill, B. and G. Hazarika 2001 The Migration of Young Adults from Non-Metropolitan Counties. American Journal of Agricultural Economics 83 (2): 329-340
Moore, Jerry 1997 Julian Steward: cultural ecology and multilinear evolution. In: J. Moore, Visions of Culture. Walnut Creek, CA: Altamira Press, pp. 181-189.
Moore, H. L. 1988 Feminism and anthropology. Minneapolis: University of Minnesota Press.
Moore, Richard 1996 Sustainability and the Amish: Chasing Butterflies? Culture and Agriculture 53 (Winter). 1990 Japanese Agriculture, Westview Press.
Moore, Richard and Mark Weaver 2001 Participatory Headwaters Research in Sugar Creek Watershed, paper presented to the Environmental Policy Initiative, OSU. Nov. 20.
Moore, R., D. McCartney, L. Williams, M. Wood, J. Christner, P. Anderson, J. Kwolek, R. Ramseyer, S. Ankrom, D. Miklos, L. Grant, D. Hudgins, S. Long, J. Parker, K. Prasad, B. Stinner 2005 A Plan to Reduce Phosphorus Loading and Improve Stream Ecological Function in the Middle Fork and Adjoining Watersheds of the Sugar Creek Watershed: Joint Recommendations for the Alpine Cheese Phosphorus Nutrient Trading Plan. Alpine Cheese Company NDPES Permit Application to Ohio EPA. Ohio EPA. 49 pages. December.
Moore, Richard, Jason Parker and Mark Weaver 2005 Lessons from the Sugar Creek Farmers in Ohio. Resubmitted to Culture and Agriculture.
537 Moore, Richard, Ben Stinner, P. Charles Goebel, Jason S. Parker, Deana M. Hudgins, Dave A. McCartney, and Mark Weaver 2002 Social Creek Social Indicators: Tapping Sub-headwater TMDL Potential in the Headwaters of the Ohio River. Proceedings of EMAP (Environmental Measurement and Assessment Program—USEPA) Symposium 2002: The Condition of Our Nation’s Streams and Rivers from the Mountains to the Coast. Kansas City, MO, May 8th.
Moore, Richard and Deborah H. Stinner, David Kline, Elsie Kline 1999 Honoring Creation and Tending the Garden: Amish views of biodiversity. In: Cultural and Spiritual Values of Biodiversity, Daryl Posey, Ed. Cambridge University Press, London: 305-309.
Moran, Emilio. 1996 Transforming Societies, Transforming Anthropology. Ann Arbor, MI: University of Michigan Press.
1993 Deforestation and Land Use in the Brazilian Amazon. Human Ecology 21: 1-21. 1990 The Ecosystem Approach in Anthropology: From Concept to Practice. Ann Arbor: University of Michigan Press. 476 pp.
Moran, Emilio F. and Eduardo S. Brondizio 1998 Land-use change after deforestation in Amazonia. In: People and Pixels: Linking Remote Sensing and Social Science. D. Liverman, E.F. Mora, R.R. Rindfuss, and P.C. Stern (eds.). Washington, D.C.: National Academy Press, Pp. 94-120.
Moran, Emilio, R. Walker and Luc Anselin 2000 Deforestation and Cattle Ranching in the Brazilian Amazon: External Capital and Household Process. World Development 28(4): 683-699.
Morris and Potter. 1995 Recruiting the new conservationists: farmers’ adoption of agri- environmental schemes in the UK. Journal of Rural Studies 11: 51-63.
Muskingum Watershed Conservancy District 2005 The Idea Holds Water. Information Leaflet.
Napier, T. L. and T. Bridges 2002 Adoption of Conservation Production Systems in Two Ohio Watersheds: A Comparative Study. Journal of Soil and Water Conservation 57(4): 229- 235.
538 Napier, T. L. and E. J. Johnson 1998 Impacts of voluntary conservation initiatives in the Darby Creek Watershed of Ohio.” Journal of Soil and Water Conservation 53(1): 78-84.
Napier, Ted L. and Sylvia M. Camboni, Cameron S. Thraen 1986 Environmental concern and the adoption of farm technologies. Journal of Soil and Water Conservation March-April: 109-113.
Napier, T. L. and Cameron S. Thraen, Akia Gore, W. Richard Goe 1984 Factors affecting adoption of conventional and conservation tillage practices in Ohio. Journal of Soil and Water Conservation May-June: 205- 209.
Napier, Ted L. and Mark Tucker 2001a Factors affecting nutrient application rates within three Midwestern watersheds. Journal of Soil and Water Conservation: 220-228. 2001b Use of Soil and Water Protection Practices Among Farmers in Three Midwest Watersheds. Environmental Management 27(2): 269-279.
Napier, Ted L. and Mark Tucker, Brent Sohngen, Silvana M. Napier 2002 Weather Projections for Production Decisions-Making: A Multiple Watershed Study. Revitalization of Agriculture in the Global Environment: 13-30.
Nazarea, Virginia, E. Tison, M. Piniero, and Robert E. Rhoades 1997 Yesterday’s Ways, Tomorrow’s Treasures: Heirloom Plants and Memory Banking. Dubuque, IA: Kendall/Hunt Publishing Co.
Nethers, John Lewis 1959 A Historical Study of the Amish People in the Holmes County Area of Ohio. Masters Thesis, The Ohio State University: Columbus, OH.
Netting, Robert 1993 Smallholder, Householder: Farm families and the ecology of intensive, sustainable agriculture. Stanford, Stanford University Press. 1981 Balancing on an Alp: Ecological Change and Continuity in a Swiss Mountain Community. New York: Cambridge University Press.
Ohio Auditor of State 2002 The Official Ohio Lands Book. Columbus, Ohio.
Ohio State Board of Agriculture 1877 Thirty-First Annual Report of the Ohio State Board of Agriculture [for the year 1876], Nevins and Meyers, State Printers, Columbus, Ohio.
539 Ortner, Sherry 1984 Theory in Anthropology since the Sixties. Comparative Study in Society and History 26 (1): 126-166.
Ostrom, Elinor 1990 Governing the Commons: The Evolution of Institutions for Collective Action. New York: Cambridge University Press
Paolisso, Michael and R. Sean Maloney 2000 Farmer Morality and Maryland’s Nutrient Management Regulations. Culture and Agriculture 22(3).
Parker, Jason, Richard Moore and Mark Weaver 2006 Land Tenure as a Variable in Community Based Watershed Projects: Some Lessons from the Sugar Creek Watershed, Wayne & Holmes County, Ohio. Resubmitted to Society and Natural Resources.
Patterson and Sassaman 1988 Indian fires in the prehistory of New England. In: Holocene Human Ecology in Northeastern North America. G. P. Nicholas (Ed.). New York: Plenum Publishing, 107-135.
Pavelis, George A (Ed.) 1987 Farm Drainage in the United States: History, Status and Prospects. USDA- ERS, Miscellaneous Publication 1455.
Perrin, William Henry 1881 History of Stark County, with an Outline Sketch of Ohio. Chicago, IL: Baskin and Battey, Historical Publishers.
Pfeffer, Max 1983 Social Origins of Three Systems of Farm Production in the United States. Rural Sociology 48 (4):540-562.
Pollan, Michael 2001 The Botany of Desire: A plant’s eye view of the world. New York: Random House.
Portes, A., Landolt, P. 1996 The Downside of Social Capital. The American Prospect, 26.
Potter, Clive and Matt Lobley 1996 The farm family cycle: succession paths and environmental change in Britain’s countryside. Journal of Agricultural Economics 47: 172-190.
540 Potter, C., P. Burnham, A. Edwards, R. Gasson, and B. Green 1991 The Diversion of Land: Conservation in a period of farming contraction. Routledge, London and New York.
Prasad, V. K., J. Cardina, F. Hitzhusen, I. Ibayoh, R. Moore, J. S. Parker, B. Stinner, D. Stinner and C. Hoy 2005a Quantifying agroecosystem health at the landscape scale with combined biophysical and socioeconomic data. In revision. 2005b Correlations among spatially referenced socioeconomic and biophysical variables related to agroecosystem health. In revision.
Prasad, V. K., A. Ortiz, B. Stinner, D. McCartney, J. S. Parker, C. Hoy, and R. Moore 2005c Exploring The Relationship Between Hydrologic Parameters And Nutrient Loads Using Digital Elevation Model and GIS - A Case Study From Sugarcreek Headwaters, Ohio, USA. Environmental Monitoring and Assessment, 110:141-165.
Pretty, Jules N. and Robert Chambers 1993 Towards a learning paradigm: new professionalism and institutions for sustainable agriculture. IDS Discussion Paper DP 334. IDS, Brighton.
Prugh, Thomas and Robert Constanza, John H. Cumberland, Herman Daly, Robert Goodland, Richard B. Norgaard 1995 Natural Capital and Human Economic Survival. CRC Press.
Putnam, Robert 1995 Bowling Alone: America's Declining Social Capital. The Journal of Democracy 6 (1): 65-78. 1993 Making Democracy Work.
Rappaport, Roy A. 1999 Ritual and Religion in the Making of Humanity. Cambridge, Cambridge University Press. 1993 Distinguished Lecture in General Anthropology: The Anthropology of Trouble. American Anthropologist 95 (2): 295-303 1984 Pigs for the Ancestors. Cambridge, MA: Cambridge University Press. 1979 Ecology, Meaning, and Religion. Richmond, Calif.: North Atlantic Books. 1971a Ritual, sanctity, and cybernetics. American Anthropologist, 73(1): 59-76. 1971b The Flow of Energy in an Agricultural Society. Scientific American 225: 116-132.
Rhoades, Robert 2001 Bridging Human and Ecological Landscapes. Participatory Research and Sustainable Development in an Andean Agricultural Frontier. Dubuque, IA: Kendall/Hunt Publishing Company. 541 1997 The Participatory Multipurpose Watershed Project: Nature's salvation or Schumacher's nightmare? Global Challenges in Ecosystem Management in a Watershed Context, 52nd Annual Soil and Water Conservation Society. Toronto, Ontario, Canada.
Rhoades, R. and R. H. Booth 1982 Farmer-Back-To-Farmer: a model for generating acceptable agricultural technology. Agricultural Administration 11: 127-137.
Riley, P. J. and G. Kiger 2002 Increasing Survey Response: The Drop-Off/Pick-Up Technique. The Rural Sociologist 22(1): 6-9.
Rogers, Everett 1982 Diffusion of Innovations. New York: Free Press.
Rosenblatt, P. C. 1990 Farming is in our blood: Farm families in economic crisis. Ames: Iowa State University Press.
Ryszkowski, Lech 2002 Landscape Ecology in Agroecosystems Management. New York: CRC Press.
Sahlins, Marshal 1972 Stone Age Economics. Chicago, University of Chicago Press.
Salamon, Sonya 2003 Newcomers, Old Towns: community change in the postagrarian Midwest. Chicago, University of Chicago Press. 2002 Cultural Dimensions of Land Tenure. In: Who Owns America? H. M. Jacobs. Madison: University of Wisconsin Press, 159-181. 1992 Prairie patrimony: family, farming and community in the Midwest, University of North Carolina Press. 1984 Ethnic Origin as Explanation of Land Ownership Patterns. In: Research in Rural Sociology and Development 1, H. Schwarzweller (Ed.), Greenwich, CT: JAI Press, 161-186.
Salamon, S., R. Farnsworth, J.A. Rendziak 1998 Is locally led conservation planning working? A farm town case study. Rural Sociology 63 (2): 214-234.
Salamon, S., R. L. Farnsworth, D. G. Bullock, and R. Yusuf 1997 Family Factors Affecting Adoption of Sustainable Farming Systems. Journal of Soil and Water Conservation 52 (2): 265-271.
542 Salamon, Sonya, Kathleen Genenbacher, Dwight J. Penas 1986 Family Factors Affecting the Intergenerational Succession to Farming. Human Organization 45 (1): 24-33.
Salatin, Joel 2001 Family friendly farming: a multi-generational home-based business testament. Swoope, VA: Polyface.
Salter, Leonard 1943 Farm Property and Agricultural Policy. The Journal of Political Economy, 51(1): 13-22.
Scherer, Clifford W. 1990 Communicating Water Quality Risk, Journal of Soil and Water Conservation, 45 (2): 198-200. Schultz, Eric 2002 Sugar Creek Headwaters Watershed Action Plan. HUC 0504001-100.
Scott, James 1985 Weapons of the Weak. New Haven, Yale University Press. 1976 The Moral Economy of the Peasant: Rebellion and Subsistence in Southeast Asia. New Haven, CT: Yale University Press.
Sharp, Jeff 2001 Agricultural Change by Metropolitan Character in Ohio: 1978 to 1997. Prepared for the online Exurban Change Project, The Ohio State University, Columbus, Ohio. http://www-agecon.ag.ohio- state.edu/programs/exurbs/ Last accessed: 01/25/06.
Shipton, Parker 1994 Land and Culture in Tropical Africa: Soils, symbols, and the metaphysics of the mundane. Annual Review of Anthropology, 23: 347-377. 1984 Lineage and Locality in East African Systems of Land Tenure. Ethnology 23:117–32.
Sommers, D. G. and T. L. Napier 1993 Comparison of Amish and Non-Amish Farmers: A Diffusion/Farm Structure Perspective. Rural Sociologist 58 (1): 130-145.
Soule M. J., A. Tegene and K. D. Wiebe 2000 Land Tenure and the Adoption of Conservation Practices. American Journal of Agricultural Economics 82 (4): 993-1005.
543 Sorensen, Clark 1988 Over the Mountains are Mountains: Korean peasant households and their adaptations to rapid industrialization. Seattle : University of Washington Press. 1984 Farm Labor and Family Cycle in Traditional Korea and Japan. Journal of Anthropological Research 40 (2):306-323. 1981 Marketing and Social Structure among the Peasantry of the Yongso Region of South Korea. Journal of Korean Studies 3 :83-112.
Stewart, Julian 1977 The Concept and Method of Cultural Ecology. Evolution and Ecology. J. Steward. Urbanna, University of Illinois Press: 43-57. 1955 Theory of Culture Change: The Methodology of Multilinear Evolution. Urbana, University of Illinois 1949 Cultural Causality and Law.
Stinner, Benjamin R. and John M. Blair 1990 Ecological and Agronomic Characteristics of Innovative Cropping Systems. In Sustainable Agricultural Systems. Clive Edwards et al (Eds). Soil and Water Conservation Society. 123-140.
Stinner, D. H., M. G. Paoletti, and B. R. Stinner 1989 Amish agriculture and implications for sustainable agriculture. Agriculture Ecosystems and Environment 27: 77-90.
Thernstrom, Stephan 1980 Harvard Encyclopedia of American Ethnic Groups. MA: Belknap Press.
Thu, Kendall. M. 2001 Agriculture, the environment, and sources of state ideology and power, Culture and Agriculture, 23(1): 1-7.
Thu, Kendall and E. Paul Durrenberger 1998 Pigs, Profits, and Rural Communities. Albany: State University of New York Press.
Ting, Lisa and Ian Williamson 1999 Land Administration and Cadastral Trends: A framework for re- engineering. Presented at the UN-FIG Conference on Land Tenure and Cadastral Infrastructures for Sustainable Development, Melbourne, Australia. October 24-27, 1999. Online reference last viewed on 01-23-06: http://www.sli.unimelb.edu.au/UNConf99/sessions/session4/williamson.p df#search='Ting%20and%20Williamson%201999'
544 Tucker, Mark and Ted L. Napier 2002 Preferred sources and channels of soil and water conservation information among farmers in three Midwestern US watersheds. Agriculture Ecosystems & Environment 81: 297-313.
United States Census Bureau 2000 2000 Population Census 1990 1990 Population Census
United States Department of Agriculture 2002 Census of Agriculture. 1997 Census of Agriculture. 1950 Census of Agriculture. 1870 Census of Agriculture. 1850 Census of Agriculture.
United States Environmental Protection Agency 2000 Environmental Protection Agency: Revisions to the Water Quality Planning and Management Regulation and Revisions to the National Pollutant Discharge Elimination System Program in Support of Revisions to the Water Quality Planning and Management Regulation; Final Rules. Federal Register (July 13): 43586-43670.
Walker, R., Emilio Moran and Luc Anselin 2000 Deforestation and Cattle Ranching in the Brazilian Amazon: External Capital and Household Process. World Development 28 (4): 683-699.
Weaver, Mark, Richard Moore and Jason Parker. 2005 Understanding Grassroots Stakeholders and Grassroots Stakeholder Groups: The View from the Grassroots in the Upper Sugar Creek. Paper presented at the 2005 Annual Meeting of the American Political Science Association, September 1-4, Washington, DC.
Wengerd, Marvin 2000 Amish Home Directory. Carlisle Press. Walnut Creek, OH.
Williams, John Pennington Jr. 1969 Yesterday is Today: Cohesion and Social Distance Among the Amish. Master Thesis, University of Akron, Akron, OH.
Williams, B., and A. Matheny 1995 Democracy, Dialogue and Environmental Disputes. New Haven: Yale University Press.
545 Wilmsen, Edwin N. 1989 Land Filled With Flies: A Political Economy of the Kalahari. Chicago: The University of Chicago Press.
Wilson. G. 1997 Factors influencing farmer participation in Environmentally Sensitive Areas scheme. Journal of Environmental Management 50, 67-93. Wilson, G. and K. Hart 2000 Financial imperative or conservation concern? EU farmers’ motivations for participation in voluntary agri-environmental schemes. Environment and Planning A 32, 2161-2185.
Wolf, Eric 1990 Distinguished Lecture: Facing power – old insights, new questions. American Anthropologist 92(3): 586-596. Wood, Charles H. and David Skole 1998 Linking satellite, census and survey data to study deforestation in the Brazilian Amazon." Pp. 70-93 In: D. Liverman, E. Moran, R. Rindfuss and P. Stern (eds.), People and Pixels: Linking Remote Sensing and Social Science. Washington, D.C.: National Academy Press.
Zide, Michael 1971 Land Scarcity in an Amish Settlement: Perception and Reality. Masters Thesis, Kent State University: Kent, OH.
Grants Cited
Moore, Richard, Ben Stinner, Lance Williams, Robert Ramseyer 2005 Evaluation of Innovative Conservation Measures in a Phosphorus Nutrient Trading Program in the Sugar Creek Watershed of Ohio. OARDC Matching Grant Competition.
Moore, Richard, Ben Stinner, P.Charles Goebel, Larry Brown 2003 Improving Water Quality and Fostering a Community Vision and Action Through Participatory Farmer Groups in the Sugar Creek Headwaters, EPA 319 grant, (Lead PI: R. Moore, co-pi’s B. Stinner, P.C. Goebel, L. Brown), $475,479 (#02(h)EPA11).
Moore, Richard, Benjamin Stinner, R.A.J. Taylor, Deborah Stinner, P. Charles Goebel 2003 BE/CNH: Impact of Economics-Driven Land-Use Decisions on Watershed Health (0308464).
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546 Moore, Richard 2002 (Year 2) Improving Livestock and Grain Farms’ Contribution to Environmental Quality through Headwaters Learning Communities. USDA SARE grant, (Lead PI: R. Moore), $49,532 including OARDC match (#NCR019-01).
2001 (Year 1) Improving Livestock and Grain Farms’ Contribution to Environmental Quality through Headwaters Learning Communities. USDA SARE grant, (Lead PI: R. Moore), $49,532 including OARDC match (#NCR019-01).
Moore, Richard, and Mark Weaver 2000 Apple Creek and Little Chippewa Watersheds Participatory Headwaters Project Environmental Policy Initiative (EPI) and Agroecosystems Management Program (AMP), R. Moore and Mark Weaver, co-pi’s, $16,000.
References Not Included
Bartlett (1993), cited in Paolisso and Maloney 2000. Bauer (1987), cited in Adler 1996. Brasser (1971), cited in Doolittle 2004. Baudry (1993), cited in Kleiman and Erickson 1996. Comstock (1987), cited in Paolisso and Maloney 2000. Fischer (1984), cited in Salamon 1992. Freid (1967), cited in Durrenberger 2002. Giddens, cited in Granovetter 1985. Goodale (1959), cited in Adler 1996. Howden and Vaclay (2000), cited in Burton and Walford 2005. Ilberly and Bowler (1998), cited in Burton and Walford 2005. Newman (1988, 1993, 2000), cited in Durrenberger 2002. Schnaiberg 1980, cited in Buttel 2003 Shucksmith (1993), cited in Burton and Walford 2005. Strange 1988, cited in Salamon 1992. Watson (1988), cited in Doolittle 2004.
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