Sustaining the Famiy Farm at the Rural Urban Interface

Home , Hobby farm

SUSTAINING THE FAMIY FARM AT THE RURAL URBAN INTERFACE – A

COMPARISION OF THE FARM REPRODUCTION PROCESSES AMONG

COMMODITY AND ALTERNATIVE FOOD AND AGRICULTURAL

ENTERPRISES

Presented in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy

in the Graduate School of The Ohio State University

Shoshanah Miriam Inwood, B.A., M.S.

* * * * *

The Ohio State University

2008

Dissertation Committee: Professor Jeff Sharp, Advisor Approved by

Professor Linda Lobao ______Advisor Professor Richard Moore Graduate Program in Rural Sociology

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ABSTRACT

The public debate over the future of agriculture in Rural Urban Interface (RUI)

communities has been characterized by a sense of fatalism. Despite assumptions that

agriculture will automatically decline in the face of growth and development pressures,

official statistics suggest that agriculture as a whole remains a strong (and in some cases a growing) industry in many RUI counties. While RUI scholars acknowledge internal household dynamics can significantly influence farm persistence and adaptation strategies, few studies have sought to empirically document the role of succession, lifecycle, and household goals and values on farm structure at the RUI. Further, in

recent years entrepreneurial agriculture with an emphasis on direct marketing and value

adding has been promoted as a strategy for preserving agriculture, especially at the RUI.

However, understanding the degree to which household dynamics are associated with the

adaptation and implementation of alternative food and agricultural enterprises (AFAEs)

remains an additional gap in the literature.

For this dissertation, three research objectives were tested to assess how

household goals and values, succession, life cycle effects, farm structure, and land use

policy affect the reproduction of the farming enterprise and ultimately the successful

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persistence of farming at the RUI. The first two questions are examined through a

quantitative analysis of land owners in eight case study counties across the United States.

The first question asks how household dynamics, household values and farm structure

variables are associated with Commercial farm persistence at the RUI. The second

research question compares the influence of household dynamics, values and farm

structure among the Commercial (AFAE and Non-AFAE) and Rural Residential farmers.

Comparison of means testing and a multinomial logistic regression model found lifecycle

effects, availability of an heir, ability to afford retirement, education, substantive and

instrumental values and to a more moderate degree farm type do influence farm persistence and adaptation strategies.

The third research question is a qualitative analysis examining the influence household factors and farm structure have on different farm types (First-generation

AFAEs; Multi-generation AFAEs; Commodity, and Mixed type farms) at the RUI.

When no heir could be identified farms either fell into a state of decline and

disinvestment or opted to put their land into some form of preservation. When an heir could be identified, families engaged in four distinct types of adaptation strategies: the expanders; the intensifiers; the stackers; and the entrepreneurial stackers. The interviews also brought forward the different types of AFAE farmers on the landscape. First- generation AFAE, Multi-generation AFAE and Mixed type farms demonstrate that while farmers across the RUI landscape appear to be adapting and implementing AFAE

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strategies their reasons for doing so are embedded in widely varying motivations.

Taken together the results of this study demonstrate the complex interplay between internal household dynamics, farm structure and land use policy that influences the persistence of farms on the RUI landscape. The heterogeneity of household goals and values and motivations for land use identified in this research contributes to the resilience and persistence of agriculture at the RUI.

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Dedicated To

Albert and Catherine Freeman

My Research Assistants

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ACKNOWLEDGEMENTS

I am indebted to my advisor Jeff Sharp for his guidance and support throughout

the PhD process. Jeff has generously shared his wisdom, experience and insights and has

been a role model as a sociologist who simultaneously directs an exceptional research

program while also generating knowledge that is relevant to policy makers and

community members. I also want to thank Jeff for providing me with the opportunity to

travel around the country to learn how classroom community sociology and the sociology

of agriculture operate in the everyday lives of farmers, county officials and local

residents.

I would like to thank Linda Lobao for serving on my dissertation committee and

for first sparking my interest in Rural Sociology. Linda is always there to encourage and

inspire students, challenging us intellectually so that we realize our potential as

academics, professionals and citizens.

I am grateful to Bill Flinn for serving on my committee, and for his generosity, wisdom and humor. Bill has been a role model both as an academic and also in his commitment to fostering the next generation of rural sociologists. I would also like to thank Richard Moore for serving on my committee.

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I’d like to extend a special acknowledgment to Greta Wyrick for her assistance

over the course of my graduate career. Greta’s editorial, organizational skills and humor

have been invaluable.

I would like to extend my sincere appreciation to Doug Jackson-Smith, who in

many ways has been my unofficial fourth committee member. Doug freely gave advice

and guidance on survey design, theoretical insights into the influence of household

dynamics on farm persistence at the RUI, in addition to career and professional

development recommendations. I also want to thank Doug for his encouragement and for

the opportunity to work on the NRI project.

I would like to thank Jill Clark for her encouragement, invaluable insights and for

always having an open door. I continue to admire and learn from the ways in which Jill

successfully and gracefully blends research and outreach through the Center for Farmland

Policy Innovation. I am also grateful to Molly Bean Smith for the guidance and advice she has so generously imparted. Throughout the PhD process I have been fortunate to have the professional and personal support of my fellow graduate students. I would like to thank: Nimu Mwangi, Lazarus Adua, Shauna Sowga, Chris Owens, Martha Nieset, and

Terry Steinbauer.

My deepest appreciation goes to my husband Jason Parker who has been a patient and steadfast champion along this road. Thank you for always being there and for helping

me to see the forest through the trees.

Thank you to my grandparents Albert and Catherine Freeman who as my

volunteer research assistants have never failed to scour the Philadelphia Inquirer in order

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to clip articles on food and agricultural issues. I am grateful for the continued support and love of my Grandma Elaine. This dissertation is also dedicated to the memories of my grandparents Evelyn and Louis Inwood who would be proud to see another Dr.

Inwood in the family.

The focus of this dissertation is on the influence of the household, I would be remiss if I did not acknowledge the importance of and influence my own household had on my own intellectual pursuits. I would like to thank my parents Linda and David, for inspiring me with their commitment to helping individuals and families improve their own lives through the mental health field. Their dedication and commitment to social issues has inspired my own academic and professional work. They have also been incredibly supportive throughout this process, diligently sending articles from the New

York Times and from an array of magazines that continued to reassure me of the relevance of this work. I’d also like to acknowledge my brothers Jonathan and

Benjamin, sister-in-law Erin, and nephew Ari as being some of my greatest supporters.

Finally, I am indebted to the farm and ranch families, agricultural professionals, academics and community members in Central Ohio; Kent County, MI; Frederick

County, MD; Hall and Forsythe Counties, GA; Spencer and Shelby Counties, GA;

Yamhill County, OR, and ; Cache County, UT who so willingly welcomed me into their homes and pick-up trucks to share their experiences and knowledge.

Funding for this project was provided by the following agencies: NC-SARE

Graduate Student Research Grant, OSU-OARDC Graduate Student Seed Grant, OSU-

Social Responsibility Initiative Graduate Student Grant, and The National Research

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Initiative of the Cooperative State Research, Education and Extension Service, USDA,

Grant # 2005-35401-15272.

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VITA

November 18, 1977……………..…………………….. Born – Brooklyn, NY

1999…………………………………………………… B.A., Biology, Oberlin College

2001-2004…………………………………………..… Graduate Research Associate, Agroecosystems Management Program and the Organic Food Farming Education and Research Program, The Ohio Agricultural Research and Development Center, The Ohio State University

2004…………………….……………………………… M.S., Environmental Science Graduate Program, The Ohio State University

2004 – Present...... ……………………………………. Graduate Research Associate, Rural Sociology Program, The Ohio State University

PUBLICATIONS

Inwood, S.M., J.S. Sharp, D. Stinner, and R. Moore. 2008. “Restaurants, Chefs and Local Foods: Insights Drawn from a Diffusion of Innovation Framework.” Journal of Agriculture and Human Values.

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Clark. J., S.M. Inwood, J.S. Sharp, D.B. Jackson-Smith. 2008. “Community-level Influences on Agricultural Trajectories: Seven Cases Across the Exurban U.S.” Eastern Washington University Press: The Sixth Quadrennial Conference of British, Canadian, and American Rural Geographers.

Inwood, S.M. 2005. “Good Growing: Why Organic Farming Works.” Book Review. Rural Sociology. 70(4): 595-597.

EXTENSION PUBLICATIONS

Inwood, S.M. 2003. “Resources for Building A Local Food System.” OSU Organic Food Farming Education and Research (OFFER). 2 pages.

Inwood, S.M. and J.S. Sharp. 2004. “Tips for Selling to Restaurants.” OSU Social Responsibility Initiative Extension Hand Out. 2 pages.

Inwood, S.M. and M. Balint 2004. “Farmer-Chef Frequently Asked Questions.” OSU Social Responsibility Initiative Extension Hand Out. 2 pages.

Inwood, S.M. 2004. “Packaging and Storage Conditions for Fruits and Vegetables.” OSU Social Responsibility Initiative Extension Hand Out. 3 pages.

Inwood, S.M. 2004. “Produce, Meat and Dairy Products Desired by Central Ohio Chefs.” OSU Social Responsibility Initiative Extension Hand Out. 2 pages.

Inwood, S.M. 2004. “Central Ohio Farmer Chef Network.” Brochure.

Inwood, S.M., L. Bergman, and D. Stinner. 2003. Building Capacity for Local and Organic Ohio Proud Foods for Retail and Restaurant Distribution in Ohio. (Invited into the USDA’s National Agricultural Library permanent collection).

Inwood, S.M. 2003. “Resources for Marketing and Distributing Organic Produce.” OSU Organic Food Farming Education and Research (OFFER). 2 pages.

Inwood, S.M. and H. Roehling. 2003. “Resources for Organic Production.” OSU Organic Food Farming Education and Research (OFFER). 2 pages.

FIELDS OF STUDY Major Field: Rural Sociology

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS...... vi VITA...... x EXTENSION PUBLICATIONS ...... xi CHAPTER 1 ...... 1 INTRODUCTION ...... 1 Problem Statement and Significance ...... 1 Farm Persistence – Current Issues in Historical Context...... 2 Agriculture at the RUI ...... 4 Counties ...... 5 Explanations for Farm Persistence and Change at the RUI...... 8 Succession – Maintaining the Continuity of American Agriculture...... 8 Household Values and Goals – Embedding Land Use Decision Making within the Economic Sociology Literature ...... 10 Succession as a Driver of Entrepreneurship, Enterprise Adaptation and Change ...... 11 Research Objectives and Significance...... 13 Organization of Remaining Chapter ...... 16 CHAPTER 2 ...... 18 LITERATURE REVIEW ...... 18 THE CHANGING STRUCTURE OF AGRICULTURE AT THE RUI...... 19 Defining the RUI...... 20 Factors Influencing the Spatial Pattern of Agriculture at the RUI...... 22 The Structure of Agriculture at the RUI ...... 23 Multidimensional Models of Agricultural Adaptation and Change at the RUI...... 27 THE STRUCTURE OF AGRICULTURE ...... 35 Structural Changes in Agriculture...... 36 Influence of Global and Local Policy ...... 39 Local Land Use Policy...... 42 Debates with the Sociology of Agriculture: Capital Penetration into Agriculture and the Fate of the Family Farm...... 45 The Persistence of the Family Farmer ...... 46 Theoretical Foundations...... 46 Marxist and Neo-Marxist Theoretical Explanations for Family Farm Persistence...... 49 Neo-Weberian Theoretical Explanations for Family Farm Persistence ...... 52 Connections Between Farm Persistence and Larger Questions in Economic Sociology ...... 58 FARM DIVERSIFICATION STRATEGIES...... 61 xii xi

Household Survival Strategies...... 62 Off-Farm Employment...... 64 MODELS OF DIVERSIFICATION STRATEGIES...... 67 Models of Farm Diversification and Pluriactivity ...... 68 Alternative Farm Enterprises as Diversification Strategies...... 73 Diversification through Food Labeling...... 77 SUCCESSION: PASSING DOWN THE FAMILY FARM ...... 80 The Current State of Succession in the U.S...... 81 Farm Succession Research...... 85 Land Access ...... 85 Culture, Succession, Land Tenure and Land Ethics ...... 87 The Process of Succession...... 88 Socialization...... 92 Lifecycle and Farm Cycle Influences on the Family Farm...... 96 Lifecycle Influences on Farm Adaptation and Change...... 98 Succession and Opportunities for On-Farm Entrepreneurship ...... 102 Rural Entrepreneurship ...... 103 Entrepreneurship, Succession, Farm Management and Diversity of Household Values ...... 105 Theoretical Link Between Entrepreneurship and Farm Adaptation and Persistence at the RUI...... 108 Theories of Entrepreneurship – Weber and Schumpeter...... 109 Critiques of Entrepreneurship in Agrofood System Studies ...... 111 OVERALL MODEL AND RESEARCH QUESTIONS...... 113 Research Questions and Hypothesis ...... 115 CHAPTER 3 ...... 120 METHODOLOGY ...... 120 QUANTIATIVE SURVEY INSTRUMENT AND DATA COLLECTION...... 120 Sampling Frame and Sample Selection ...... 123 Landowner Samples:...... 124 Commercial Farm Samples:...... 124 Survey Administration...... 125 Kent Drop Off Pick Up Subproject...... 127 Response Rates ...... 128 Landowner Sample: ...... 128 Commercial Farmer Sample: ...... 129 DATA ANALYSIS STRATEGY...... 135 Protocol for Categorizing Commercial and Rural Residential Farmers ...... 135 Sub Sample Characteristics...... 138 Household Dynamics...... 145 xiii xi

Demographic Variables: Age, Education and Sex...... 145 Age...... 145 Sex...... 146 Operator Health and Optimism...... 148 Family Contributions to Labor ...... 149 Off farm work ...... 150 Succession...... 152 Values and Goals Motivating Land Use Decision Making ...... 153 Instrumental Rationality...... 155 Substantive Rationality ...... 157 Stewardship...... 159 Future Plans for Land Use ...... 161 Likeliness to Develop Land ...... 162 Farm Structure ...... 163 Debt...... 164 Soil Quality ...... 165 Acres, Farm Income and Household Income...... 166 Acres ...... 166 Farm Receipts ...... 167 Household Income ...... 167 Production Systems...... 167 Past and Future Changes to the Enterprise ...... 169 Past Capital Investments in Farm ...... 170 Past Alternative Marketing Activities...... 172 Future Capital Investments in Farm...... 174 Future Marketing and Production Diversification Plans ...... 175 Marketing Strategies and AFAE Adaptations...... 177 Farm Business Trajectory ...... 179 Control Variables – Factors Associated With Farming at the RUI ...... 180 Development Pressure ...... 181 Neighbor Effects ...... 183 Availability of Labor...... 185 Availability of Local Infrastructure ...... 187 Weather Effects...... 189 Cost of Health Insurance...... 190 Effectiveness of Land Use Policy on Maintaining Agriculture in the County ...... 191 Support of Nonfarm Institutions and Groups...... 193 Community Support for Agriculture...... 195 Local Government Support for Land Use Policy ...... 196 Optimism for Future of Agriculture in the County...... 198 xiv xi

Farm Economics and Global Competition...... 200 Other controls...... 202 CHAPTER 4 ...... 204 COMMERCIAL FARM PERSISTENCE AND ADAPTATION AT THE RUI...... 204 COMPARISON OF MEANS - ONE WAY ANOVA AND CHI SQUARE TESTS ...... 204 Length of Time an Individual Expects to Continue Farming ...... 205 Household Characteristics ...... 205 Household Values ...... 208 Instrumental Values ...... 208 Substantive Values...... 210 Stewardship Values...... 210 Future Plans for Land Use ...... 213 FARM STRUCTURE VARIABLES ...... 214 Structural Variables ...... 214 Past and Future Changes Planned for the Enterprise...... 216 Marketing Strategies ...... 218 Past and Future Enterprise Trajectory...... 218 Length of Time Respondents Expect Their Enterprise to Persist...... 227 Household Characteristics ...... 227 Instrumental Values ...... 229 Substantive Values ...... 230 Stewardship Values...... 231 MULTIVARIATE ANALYSIS...... 247 Years Individual Expects to Continue Farming...... 248 Years a Respondent Expects Their Enterprise to Persist...... 254 Chapter Four Conclusions...... 260 CHAPTER 5 ...... 261 COMPARISON OF HOUSEHOLD CHARACTERISITCS, HOUSEHOLD VALUES AND FARM STRUCTURE AMONG AFAE, NON-AFAE AND RURAL RESIDENTIAL FARM TYPES...... 261 COMPARISON OF MEANS - ONE WAY ANOVA AND CHI SQUARE TESTS 261 Household Characteristics ...... 262 Household Characteristic Findings and Hypotheses ...... 265 Household Values ...... 266 Instrumental Values ...... 266 Substantive Values ...... 268 Stewardship Values...... 269 Household Values Findings and Hypotheses...... 270 Future Plans for Land Use ...... 270 xv x

Future Plans for Land Use and Hypotheses...... 271 Structural Variables...... 272 Farm Structure and Hypotheses...... 274 Crop Production and Sales Outlets ...... 274 Crop Production and Sales Outlets and Hypotheses...... 276 Past and Future Changes Planned for the Enterprise...... 276 Past and Future Enterprise Trajectory...... 279 Farm Persistence ...... 280 Business Trajectory and Farm Persistence and Hypotheses...... 281 CONTROLS ...... 282 Sample Frame and Survey County Origin...... 286 Discussion and Conclusion...... 287 CHAPTER 6 ...... 289 THE INFLUENCE OF HOUSEHOLD LEVEL DECISION MAKING FACTORS ON FARM ENTERPRISE ADAPTATION AND PERSISTENCE AT THE RUI: A QUALITATIVE EXPLORATION...... 289 Case Study Sites and Methods...... 289 Description of Qualitative Sample...... 292 Demographic Data of Respondents ...... 294 Influence of Household Dynamics and Farm Structure on Enterprise Adaptation .... 297 First-generation AFAE...... 297 Household Goals and Values...... 297 Farm Structure and Business Development...... 299 Structure of Agriculture ...... 301 Multi-generation AFAEs ...... 303 Household Goals and Values...... 303 Structure of Agriculture ...... 304 Farm Structure and Business Development...... 304 Commodity Farmers ...... 308 Household Values and Goals...... 308 Structure of Agriculture ...... 310 Farm Structure and Business Development...... 312 Mixed Farms ...... 314 Household Goals and Values...... 314 Farm Structure and Business Development...... 316 The Structure of Agriculture...... 319 Commonalities Across the Groups ...... 323 Summary Comparison ...... 325 Influence of Succession On Enterprise Adaptation ...... 328 No Heir Identified...... 329 xvi x

Heir Identified...... 330 The Expanders ...... 330 The Intensifiers ...... 330 The Stackers...... 332 The Entrepreneurial Stackers...... 333 Summary...... 334 Conclusions and Discussion Related to Qualitative Analysis ...... 335 CHAPTER 7 ...... 338 DISCUSSION AND CONCLUSIONS ...... 338 CHAPTERS 4-6 – A SYNTHESIS ...... 339 Commercial Farm Persistence at the RUI...... 339 Adaptation and Persistence Strategies – A Comparison of AFAE, Non-AFAE and Rural Residential Farms...... 345 The Influence of Household Values and Land Use Motivations on Enterprise Adaptation and Persistence – A Qualitative Analysis ...... 348 The Influence of Life Cycle Effects, Household Values, and Goals...... 349 The Role of Succession in Enterprise Adaptation and Persistence...... 352 CONCLUSIONS...... 355 Social Sustainability: The Connection Between the Diversity of Household Goals and Values and Farm Persistence at the RUI...... 355 The Tension between Entrepreneurship, Free-Market Policies and Farm Persistence at the RUI...... 357 A New Policy Agenda for Maintaining Family Farms at the RUI ...... 358 APPENDIX A...... 363 BIBLIOGRAPHY...... 379

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TABLE OF TABLES

Table 1.1 Frequencies and Percentages of County Distributions ...... 5 Table 1.2 Geographic Distribution of U.S. Agricultural Sales 2002...... 6 Table 3.1 Mail-out Schedule for NRI Land Owner Survey...... 126 Table 3.2 Sampling and Response Rate Summary for Landowner Samples...... 131 Table 3.3 Sampling and Response Rate Summary for Commercial Farmer Samples.... 132 Table 3.4 Comparison of Landowner Sample and Commercial Farm Sample to Census Data...... 133 Table 3.5 Percentage of Commercial Farmer and Rural Residential Respondents ...... 138 Table 3.6 Commercial and Rural Residential Farmers Descriptive Statistics...... 140 Table 3.7 Commercial and Rural Residential Farmers Descriptive Statistics –Income and Survey County ...... 141 Table 3.8 Descriptive Statistics Dependent Variable Commercial Farmer Sub-sample 144 Table 3.9: Comparison of Dependent Variables: Years Individual Expects to Continue Farming and Years Expect Enterprise to Continue...... 144 Table 3.10 Descriptive Statistics of Household Variables...... 149 Table 3.11 Descriptive Statistics of Family Contributions to Labor and Off-Farm Work ...... 150 Table 3.12 Descriptive Statistics of Succession Indicators ...... 153 Table 3.13 Individual Items Values and Goals Motivating Land Use...... 155 Table 3.14 Bivariate Correlations for Motivations for Land Use - Instrumental ...... 156 Table 3.15 Factor Analysis Matrix for Motivations for Land Use - Instrumental...... 156 Table 3.16 Descriptive Statistics for Motivations for Land Use - Instrumental...... 157 Table 3.17 Bivariate Correlations for Motivations for Land Use – Substantive ...... 158 Table 3.18 Descriptive Statistics for Motivations for Land Use - Substantive ...... 158 Table 3.19 Descriptive Statistics for Motivations for Land Use - Substantive ...... 159 Table 3.20 Bivariate Correlations for Motivations for Land Use – Stewardship ...... 159 Table 3.21 Factor Analysis Matrix for Motivations for Land Use - Stewardship ...... 160 Table 3.22 Descriptive Statistics for Motivations for Land Use - Stewardship ...... 161 Table 3.23 Descriptive Statistics for Future Land Use Plans When Retire...... 162 Table 3.24 Bivariate Correlations for Likeliness to Sell Land for Development in the Next Five Years ...... 162 Table 3.25 Factor Analysis Matrix for Likeliness to Sell Land for Development in the Next Five Years ...... 163 xviii x

Table 3.26 Descriptive Statistics for Likeliness to Sell Land for Development in the Next Five Years ...... 163 Table 3.27 Farm Structure Descriptive Statistics ...... 165 Table 3.28 Production System and Marketing Strategies...... 169 Table 3.29 Descriptive Statistics of Past and Future Changes to Production and Marketing Strategies ...... 170 Table 3.30 Bivariate Correlations for Past Capital Investments (Last Five Years)...... 171 Table 3.31 Factor Analysis Matrix for Past Capital Investments (Last Five Years) ...... 171 Table 3.32 Descriptive Statistics for Past Capital Investments (Last Five Years) ...... 172 Table 3.33 Bivariate Correlations for Past Alternative Market Development (Last Five Years)...... 172 Table 3.34 Factor Analysis Matrix for Past Alternative Market Development (Last Five Years)...... 173 Table 3.35 Descriptive Statistics for Past Alternative Market Development (Last Five Years)...... 173 Table 3.36 Bivariate Correlations for Future Capital Investments (Next Five Years) ... 174 Table 3.37 Factor Analysis Matrix for Future Capital Investments (Next Five Years) . 174 Table 3.38 Descriptive Statistics for Future Capital Investments (Next Five Years)..... 175 Table 3.39 Bivariate Correlations for Future Alternative Market Development (Next Five Years)...... 176 Table 3.40 Factor Analysis Matrix for Future Alternative Market Development (Next Five Years)...... 176 Table 3.41 Descriptive Statistics for Future Alternative Market Development (Next Five Years)...... 177 Table 3.42 Marketing Strategies...... 178 Table 3.43 Past and Future Business Trajectory...... 180 Table 3.44 Bivariate Correlations for Development Pressure ...... 182 Table 3.45 Factor Analysis Matrix for Development Pressure...... 182 Table 3.46 Descriptive Statistics for Development Pressure...... 183 Table 3.47 Bivariate Correlations for Neighbor Effects...... 184 Table 3.48 Factor Analysis Matrix for Neighbor Effects ...... 184 Table 3.49 Descriptive Statistics for Neighbor Effects ...... 185 Table 3.50 Bivariate Correlations for Availability of Labor ...... 186 Table 3.51 Factor Analysis Matrix for Availability of Labor...... 186 Table 3.52 Descriptive Statistics for Availability of Labor...... 187 Table 3.53 Bivariate Correlations for Availability of Local Infrastructure...... 187 Table 3.54 Factor Analysis Matrix for Availability of Local Infrastructure ...... 188 Table 3.55 Descriptive Statistics for Availability of Local Infrastructure...... 188 Table 3.56 Bivariate Correlations for Weather Effects ...... 189 Table 3.57 Factor Analysis Matrix for Weather Effects...... 189 xix xi

Table 3.58 Descriptive Statistics for Weather Effects...... 190 Table 3.59. Descriptive Statistics for Health Insurance...... 191 Table 3.60 Bivariate Correlations for Effectiveness of Land Use Policy...... 191 Table 3.61 Descriptive Statistics for Effectiveness of Land Use Policy ...... 192 Table 3.62 Descriptive Statistics for Effectiveness of Land Use Policy ...... 192 Table 3.63 Bivariate Correlations for Non-Farm Institutional and Group Support for Agriculture ...... 193 Table 3.64 Factor Analysis Matrix for Non-Farm Institutional and Group Support for Agriculture ...... 193 Table 3.65 Descriptive Statistics for Non-Farm Institutional and Group Support for Agriculture ...... 194 Table 3.66 Bivariate Correlations for Community Support For Agriculture...... 195 Table 3.67 Factor Analysis Matrix for Community Support for Agriculture...... 195 Table 3.68 Descriptive Statistics for NonFarm Institutional and Group Support for Agriculture ...... 196 Table 3.69 Bivariate Correlations for Government Support for Land Use Policy ...... 197 Table 3.70 Factor Analysis Matrix for Government Support for Land Use Policy...... 197 Table 3.71 Descriptive Statistics for Government Support for Land Use Policy...... 198 Table 3.72 Bivariate Correlations for Optimism for Future of Agriculture in County... 199 Table 3.73 Factor Analysis Matrix for Optimism for Future of Agriculture in County. 199 Table 3.74 Descriptive Statistics for Optimism for Future of Agriculture in County .... 200 Table 3.75 Bivariate Correlations for Farm Economics and Global Competition ...... 201 Table 3.76 Factor Analysis Matrix for Farm Economics and Global Competition...... 201 Table 3.77 Descriptive Statistics for Farm Economics and Global Competition...... 202 Table 3.78 Descriptive Statistics Respondent Sample Frame and Survey County Origin ...... 203 Table 4.1 Comparison of Household Characteristics- Length of Time an Individual Expects to Continue Farming...... 207 Table 4.2 Household Instrumental Values- Length of Time an Individual Expects to Continue Farming ...... 209 Table 4.3 Household Substantive Values- Length of Time an Individual Expects to Continue Farming ...... 211 Table 4.4 Household Stewardship Values - Length of Time an Individual Expects to Continue Farming ...... 212 Table 4.5 Future Land Use Plans- Length of Time an Individual Expects to Continue Farming...... 214 Table 4.6 Farm Structure Variables - Length of Time an Individual Expects to Continue Farming...... 215 Table 4.7 Past and Future Planned Changes to the enterprise - Length of Time an Individual Expects to Continue Farming ...... 217 xx x

Table 4.8 Marketing Strategies- Length of Time an Individual Expects to Continue Farming...... 218 Table 4.9 Past and Future Business Trajectory - Length of Time an Individual Expects to Continue Farming ...... 219 Table 4.10 Sample Frame and Survey County Origin - Length of Time an Individual Expects to Continue Farming...... 221 Table 4.11 Control Variables - Length of Time an Individual Expects to Continue Farming...... 222 Table 4.12 Hypothesis in relationship to Comparison of Means Testing of Years Individuals Expect to Continue Farming ...... 224 Table 4.13 Comparison of Household Characteristics - Time Respondents Expect Their Enterprise to Persist ...... 228 Table 4.14 Household Instrumental Values - Time Respondents Expect Their Enterprise to Persist...... 230 Table 4.15 Household Substantive Values - Time Respondents Expect Their Enterprise to Persist...... 231 Table 4.16 Household Stewardship Values - Time Respondents Expect Their Enterprise to Persist...... 232 Table 4.17 Future Land Use Plans - Time Respondents Expect Their Enterprise to Persist ...... 233 Table 4.18 Past and Future Planned Changes to the Enterprise - Time Respondents Expect Their Enterprise to Persist...... 235 Table 4.19 Marketing Strategies - Time Respondents Expect Their Enterprise to Persist ...... 236 Table 4.20 Past and Future Business Trajectory - Time Respondents Expect Their Enterprise to Persist ...... 237 Table 4.21 Farm Structure Variables - Time Respondents Expect Their Enterprise to Persist...... 238 Table 4.22 Control Variables- Time Respondents Expect Their Enterprise to Persist... 240 Table 4.23 Dummy Sample Frame and Survey County Origin - Time Respondents Expect Their Enterprise to Persist...... 241 Table 4.24 Hypothesis in relationship to Comparison of Means Testing of Years Respondents Expect Their Enterprise to Persist ...... 243 Table 4.25 Multinomial Logit Regression - Years Individual Expects to Continue Farming...... 250 Table 4.26 Respondent Characteristics - Years Individual Expects to Continue Farming ...... 252 Table 4.27 Hypothesis in relationship to Multinomial Logit Regression Model - Years Individual Expects to Continue Farming ...... 253

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Table 4.28 Multinomial Logit Regression Model - Years Expect Enterprise to Continue ...... 256 Table 4.29 Respondent Characteristics - Years Expect Enterprise to Persist...... 258 Table 4.30 Hypothesis in relationship to Multinomial Logit Regression of Years Respondents Expect Their Enterprise to Persist ...... 259 Table 5.1 Comparison of Household Characteristics ...... 263 Table 5.2 Household Instrumental Values...... 268 Table 5.3 Household Substantive Values ...... 269 Table 5.4 Household Stewardship Values ...... 270 Table 5.5 Future Land Use Plans...... 271 Table 5.6 Farm Structure Variables...... 273 Table 5.7 Production Type and Sales Outlets...... 275 Table 5.8 Past and Future Planned Changes to the Enterprise ...... 277 Table 5.9 Past and Future Business Trajectory...... 280 Table 5.10 Years Individual Expects to Continue Farming and Years Respondent Expects Enterprise to Persist...... 281 Table 5.11 Control Variables...... 285 Table 5.12 Dummy Sample Frame and Survey County Origin...... 286 Table 5.13 Hypothesis in relationship to Comparison of Means Testing of AFAE, Non- AFAE, and Rural Residential Farm Type...... 288 Table 6.1 Farm and Population Demographics in Columbus and Grand Rapids Metro Regions...... 291 Table 6.2 Qualitative Analysis - Respondent Region of Origin ...... 292 Table 6.3 Qualitative Analysis – Respondent Type and Region of Origin ...... 294 Table 6.4 Farm Family Demographics ...... 295 Table 6.5 Individual Farm Family Demographics...... 296 Table 6.6 Summary Comparison of Household Values and Influence of Farm Structure on Enterprise Adaptation Strategies...... 326

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TABLE OF FIGURES

Figure 1.1 Spatial Distribution of Rural-Urban Interface Counties (Clark 2007)...... 6 Figure 1.2 Spatial Distribution of Agriculturally Important (AI) Counties at RUI (Clark 2007)...... 7 Figure 1.3 Spatial Distribution of Agriculturally Important Counties at the Rural-Urban Interface (Clark 2007)...... 7 Figure 1.4 Model of Factors Influencing Farm Persistence and Adaptation at the RUI . 15 Figure 2.1 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – RUI Pressures...... 19 Figure 2.2 Bryant and Johnston (1992) Conceptual Framework for Farm Adjustment in Near-Urban Areas...... 29 Figure 2.3 Heimlich and Brooks (1989) model of agricultural adaptation to urbanization...... 30 Figure 2.4 Smithers and Johnson (2004) Forces and contexts of change in family farming...... 32 Figure 2.5 Bryant and Johnston (1992) farm decision system and the external environment model...... 33 Figure 2.6 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI- Farm Structure...... 35 Figure 2.7 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Global and Local Policy ...... 39 Figure 2.8 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Motivations for Land Use...... 45 Figure 2.9 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Household & Farm Resources...... 61 Figure 2.10 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Availability of an Heir...... 80 Figure 2.11 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Lifecycle Stage...... 96 Figure 2.12 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Enterprise Persistence and Adaptation...... 102 Figure 2.13 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI...... 115 Figure 3.1 Map of U.S. Highlighting Case Study County Locations ...... 122 Figure 6.1 Schematic Diagram of Influence Farm Succession on Enterprise Adaptation and Persistence...... 329

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Figure 6.2 Model of Expansion, Intensification and Entrepreneurial Stacking at the RUI ...... 335

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CHAPTER 1

INTRODUCTION

Problem Statement and Significance

A pressing concern across rural, peri-urban and urban locals exists over who will be the next generation of farmers. The presence or absence of a farm succession plan or an identified heir has implications for the future of family farming and the communities that depend on them. Shifting population, migration and settlement patterns are reshaping

America’s rural landscape and economy. At the rural-urban interface (RUI), farmers in metropolitan influenced areas, or exurban areas, exist in a zone of mixed land-uses, distinguished by a diversity of farm and nonfarm land uses, with varying rates of farmland being converted to non-farm purposes (Ilbery 1985); creating tenuous conditions for family farms. Concerns over farm transition are exacerbated at the RUI, where the structure of agriculture is simultaneously influenced by macro-scale political and economic processes, pressures from population growth and development, and individual household decision making (Heimlich and Anderson 1987; Johnston and

Bryant 1987). Household decision making and farm succession are critical factors influencing farm enterprise decision making and can be significant mediators in determining farm entry and exit rates and enterprise adaptation strategies.

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The purpose of this dissertation is to examine how household dynamics moderate farm enterprise persistence and adaptation at the RUI. As farming is as much a lifestyle as it is an occupation, farm households engage in a range of on- and off-farm diversification strategies to ensure survival as the family farm moderates the influences of capital penetration and American culture to achieve intergenerational succession goals. I am interested in understanding how household dynamics including succession influence farm persistence patterns relative to macro and local level political, economic and land based pressures. In recent years entrepreneurial agriculture with an emphasis on direct marketing and value adding has been promoted as a strategy for preserving agriculture, especially at the RUI. I am therefore also interested in understanding how the opportunities and constraints presented at the RUI influence enterprise adaptation strategies, and intend to characterize the relationship between farm business and household structure, and entrepreneurial farming activities.

In this chapter, I present an overview of three distinct bodies of literature that form the foundation of this dissertation. These literatures cover farm persistence

strategies, the structure of agriculture at the RUI, and the role succession has in both

maintaining the continuity of the family farm and as a driver of enterprise adaptation and

change. The first half of this chapter informs my research justification and objectives

outlined in the second half.

Farm Persistence – Current Issues in Historical Context

Rural sociologists and agricultural economists have long been occupied with

understanding the fate of family farms under advanced capitalist economies. The 1980s

2 2

Farm Crisis produced a vast literature examining family farm persistence under periods of structural change and economic uncertainty (largely driven by debt) that favored a bifurcation of farm scale into small scale and large scale capital intensive farms.

Examining the decreasing numbers of farms, bifurcation of scale, and the transformation of social relations of labor, land and capital as agriculture increasingly came to be organized along industrial capitalist logic lines.

Utilizing theories rooted in Marxism, Neo-Marxism, and Neo-Weberinism, researchers and academics have sought to understand and explain the persistence of the family farm and the uneven nature by which capitalism has penetrated into agriculture.

These studies have included the influence of: household survival strategies (Friedmann

1978b; Friedmann 1978a; Schulman and Greene 1986; Bartlett 1986e; Reinhardt and

Barlett 1989; Lasley et al. 1995);off-farm employment (Bonanno 1987; Goetz and

Debertin 2001); the role of values and household goals (Mooney 1983; Salamon 1992;

Barlett 1993); and gender and household labor (Sachs 1996). The primary question within this line of inquiry was to define a farmer and farm family given the unique farm structure produced by capitalist economies.

The debates regarding the persistence of family farms within the sociology of agriculture have evolved to examine how household agency, culture, farm structure,

economics and policy affect farm adaptation and persistence. These questions are

particularly salient at the RUI where farm persistence is threatened by growth and

development pressure.

3 3

Agriculture at the RUI

The public debate over the future of agriculture in RUI communities has been characterized by a sense of fatalism (Daniels 1999). Farmers and non-farmers alike tend to believe that traditional “family farming” is no longer viable in the modern U.S. economy, particularly with the visual sprawling non-farm development across the landscape (Jackson-Smith 1999). While agricultural operations in these areas certainly face great challenges from deteriorating macroeconomic conditions in farming and local development pressures, official statistics suggest that agriculture as a whole remains a strong (and in some cases growing) industry in many RUI counties (Jackson-Smith

1999; Butler and Maronek 2002). For example, just under half of total agricultural crop sales, 79% of U.S. fruit production and 68% of the nation’s vegetable production originated in metropolitan counties in 1997 (Butler and Maronek 2002). Jackson-Smith et al. (forthcoming) demonstrate that RUI counties account for 49.6% of all U.S. counties and contribute half of all U.S. agricultural sales in 2002 (Table 1.1 and Table 1.2).

Counties considered to be agriculturally important 1 and located at the RUI2 account for

1 Agriculturally important (AI) counties are in the top quartile of sales in 1987. An additional criterion includes geographically small counties with intensive agriculture but because of their size are unable to qualify in the top quartile of agricultural sales. This group would include counties that fell into the second quartile of overall sales, but were in the top quartile for both sales per acre (a measure of intensity) of farmland and cropland. Finally, counties that satisfied either one of the above criteria had to have greater than 50 farms in 1987. This criterion is used to remove counties that potentially will have data disclosure problems in the US Census of Agriculture (see Jackson-Smith and Jensen n.d. for more details on this methodology) (Jackson-Smith et al. forthcoming).

2 RUI counties are defined through the USDA’s Economic Research Service Urban Influence Codes (UIC) (Parker 2003). Counties that fall in the first four categories of UIC are exurban, which includes all metropolitan counties and select nonmetro counties adjacent to large metro areas. To capture4 smaller counties adjacent to metro 4

45.15% of U.S. agricultural sales in 2002 (Table 1.1 and Table 1.2). The spatial

distribution of AI, RUI and AI-RUI counties across the U.S. is shown in Figures 1.1, 1.2,

and 1.3. At the RUI farmers have the option to exit agriculture through the sale of land

for nonfarm development, yet many are choosing to remain in agriculture despite the

challenges of farming at the RUI and the opportunity to achieve large profits through land

sales. As a whole these statistics demonstrate the significance of agriculture at the RUI

and suggest that many farmers and ranchers have developed household and business

strategies that allow them to remain in operation despite growing non-farm populations

around them.

Counties % of U.S. Counties* Metropolitan 1054 34.4% Non-Metropolitan 2013 65.6% RUI 1,522 49.6% Agriculturally Important 1,946 63.4% AI-RUI 619 20.2% *lower 48 states

Table 1.1 Frequencies and Percentages of County Distributions

areas experiencing relatively high growth, those counties with UIC codes of 5-7 and whose growth between 1990 and 2000 was greater the national median of 13.15 percent were selected. To acknowledge that the agricultural importance criteria may be too restrictive in that some metropolitan counties may be relatively small but that farmland preservation or agricultural vitality may be of great local relevance, counties that were coded UIC=1 (counties that were part of large metro areas of more than one million people) and which had over 50% of their county land base in agricultural use were included (Clark et al. 2008). 5 5

Percent of 2002 Agricultural Agricultural Sales (1,000s) Sales Agriculturally Important Counties 155,206,092 77.58% Rural Urban Interface Counties 109,055,754 54.51% Ag Important + RUI Counties 90,322,185 45.15%

All US 200,049,060 Counties*

*lower 48 states

Table 1.2 Geographic Distribution of U.S. Agricultural Sales 2002.

Figure 1.1 Spatial Distribution of Rural-Urban Interface Counties (Clark 2007).

6 6

Figure 1.2 Spatial Distribution of Agriculturally Important (AI) Counties at RUI (Clark 2007).

Figure 1.3 Spatial Distribution of Agriculturally Important Counties at the Rural-Urban Interface (Clark 2007).

7 7

Explanations for Farm Persistence and Change at the RUI

A number of geographers have focused on alternative models to account for the variety of factors affecting agricultural change and adaptation at the RUI through the development of holistic ‘farmer decision-making’ models (Ilbery 1985; Johnston and

Bryant 1987; Bryant and Johnston 1992). Recognizing the dynamics affecting farm adaptation and change are not limited to the economic realm, these models incorporate elements of culture, land tenure, succession, community, agroecology, biophysical resources, and macroeconomic structural influences operating at global, national, regional and local scales (Johnston and Bryant 1987; Smithers and Johnson 2004). However, few studies have pursued an in-depth analysis of the influence household dynamics have on farm adaptation and persistence at the RUI. This dissertation aims to fill this gap in the literature by testing these holistic farmer decision making models through an empirical and qualitative analysis.

Succession – Maintaining the Continuity of American Agriculture

The question of who will be the next generation of farmers is a pressing issue facing U.S. agriculture. In recent years, the popular press (Des Moines Sunday Register

(2005) government reports (ERS 2005; CSREES 2008) and academic papers (Jervell

1999) have documented an aging farm population, a lack of succession planning, and the existence of fewer heirs choosing farming as an occupation. The continuity of the family farm sector is highly dependent on the intergenerational transfer of land following the retirement of a farm operator (Gale 2002; Small 2005). Recognizing the “future of U.S. agriculture depends on the ability of new generations to establish successful farms and

ranches” the USDA-CSREES program funded The FarmLasts Project (2008) to address

farmland access, succession, tenure and stewardship. The Project asserts

“Farm and ranch land access and transfer are particularly important for small

and medium-size farms and ranches that currently control over 80 percent of U.S

agricultural land. In the balance are the quality of life and economic vitality in

agricultural communities and the use, protection and enhancement of the nation's

working lands.” (FarmLasts Project 2008).

Regardless of land use pressures farm succession is a tenuous, long-term,

complex multi-staged process. According to the USDA, over 70% of U.S. farm and ranch

lands will change hands over the next 20 years (McAleer 2008; Ruhf 2008). The limited

land base at the RUI places new stresses on the intergenerational transfer of land and

maintenance of working agricultural landscape. Access to farmland is even more

constrained in these geographic areas and the succession process can become especially

vulnerable to familial conflict. In previous decades and in low rent rural areas, a potential

successor could purchase or rent land near their parents’ farm, eventually taking over the

parents’ operation upon their retirement. However, at the RUI lower commodity prices

and higher land prices require farm heirs to rely heavily on parents and siblings to ensure their entry into agriculture (Lyson 1986). The process of succession at the RUI becomes increasingly complex as parents try to distribute inheritance equitably among farming and non-farming heirs, while ensuring their own retirement (Jonovic and Messick 1986). At the RUI, failure of the succession process can impact whether farmland remains in agricultural use or is developed for nonfarm purposes.

While communities attempt to implement policies, economic development activities and community actions to maintain agriculture on the landscape, it is essential to understand how individual household and demographic factors influence the trajectory of individual farms at the RUI. Research has indicated identified that succession has an even greater influence on the persistence of farming at the RUI than does local growth and development pressures (Hirschl and Long 1993; Sharp and Smith 2004). Research by Sharp and Smith (2004) identified the intergenerational succession of farmers in RUI settings to be a significant matter, as almost half of all part-time, hobby, and retired farmers at the RUI had not identified an heir or were unsure who would be farming their operation in ten years. Sharp and Smith (2004) assert that failure to account for successful intergenerational transfer of farmland at the RUI increases the vulnerability of farmland to be converted to non-farm uses.

Household Values and Goals – Embedding Land Use Decision Making within the Economic Sociology Literature

As noted earlier, the literature examining farm adaptation and persistence at the

RUI has recognized the importance of internal household processes (Bryant and Johnston

1992; Smithers and Johnson 2004), however, there has been little in-depth sociological investigation into the role of sociocultural values. Economic sociology provides a useful theoretical framework to explore internal family dynamics so often treated as a black box within the RUI literature. The concepts of substantive and formal rationalities refer to the varying weight economic and non-economic values can have in influencing land use decision making (Mooney 1982a; Mooney 1982b; McGehee and Kim 2004; Krippner and Alvarez 2007). Additionally, rural sociologists have a long standing tradition of

examining how stewardship values influence land use decision making (Salamon et al.

1998; Paolisso and Maloney 2000). This research incorporates these three sets of household values and goals in order to gain a better understanding of the role the internal

household plays in farm enterprise adaptation and persistence at the RUI.

Succession as a Driver of Entrepreneurship, Enterprise Adaptation and Change

The process of succession presents an opportunity for farm transformation, as

enterprises must grow and adapt to provide for additional family members. Succession is

a primary driver of farm adaptation. Farm enterprise growth and adaptation climax as the

farm prepares to create additional revenue streams to support an additional family

member (Bennett 1982). The farm business can utilize horizontal or vertical strategies to

grow. The farm can grow horizontally through land expansion, purchasing or renting more acres for commodity markets. Alternatively, the farm business can grow vertically, producing more valuable crops on the same amount of acreage (intensification) or diversifying its on-farm operation to include complimentary businesses for additional family members.

Although succession studies hold a prominent place in rural and agrarian studies, very little empirical research has examined the processes of succession in enterprise adaptation at the RUI. A critical question currently unexamined in the literature is the degree to which reproduction goals lend themselves to particular adaptive strategies for ensuring the successful reproduction of the family farm. The emphasis in this study is on succession as a key variable in understanding how farm households make decisions about enterprise adaptation in light of increasing population and development pressures.

At the RUI, farmers have been encouraged to transition from traditional

commodity production (corn, beans, dairy) into entrepreneurial agriculture ventures

termed Alternative Food and Agriculture Enterprises (AFAE), with an emphasis on direct

marketing and value adding as a mechanism for enterprise regeneration and as a viable

strategy for preserving farmland, farming and agricultural landscapes at the RUI. A third

type of ‘Mixed’ farm includes enterprises that engage in both commodity and AFAE

production. A series of studies have documented that farms are implementing AFAE

activities (McNally 2001; Barbieri et al. 2008), however, how family dynamics and

succession influence the specific types of entrepreneurial adaptations pursued has not

been fully explored and remains a gap in the literature.

Entrepreneurship as a rural development strategy has been critiqued as a

neoliberal market based solution to farmland protection (Allen 1999; Guthman 2008a;

Guthman 2008b) that refocuses agrofood system debates and solutions into the market

sphere based on private free market enterprise rather then on public democratic solutions

(Allen 1999; Guthman 2008a; Guthman 2008b). Market based approaches including

The critiques of neoliberal market based solutions to sustaining farms provide a framework for exploring the ways in which capitalism continues to transform the family farm as increasing numbers of households transition into AFAEs. The effects of

neoliberal policies and capital penetration into the family farm are uneven and can be

moderated by the ways in which these farm families choose to adopt, adapt and implement specific management and production strategies as they balance instrumental and substantive goals with farm reproduction goals. A sub-theme running through this dissertation is an examination of the influence household goals and values have on the decision to pursue specific adaptation strategies including entrepreneurial agricultural ventures in order to achieve livelihood, lifestyle and enterprise reproduction objectives.

Research Objectives and Significance

Researchers have assumed that alternative farming strategies such as AFAEs are able to generate higher economic returns per acre, which are hoped to offset the challenges of rising land values at the RUI enabling family farms to persist across time. A key feature of many successful AFAEs is entrepreneurship as farmers and their families develop interpersonal skills, products, and marketing networks, and initiate value-added activities to become ‘price makers’ rather then ‘price takers’ (Vesala and Peura 2005).

As a diversification strategy, AFAEs have become increasingly institutionalized through academic arguments (Feenstra 1997; Hinrichs 2003; Marsden and Smith 2005),

Extension programming (Ellerman et al. 2001), farm organizations and NGOs (OEFFA

2005; PASA 2005) and government funding through SARE (Armstrong-Cummings

1997; Keilty 1998; Hipp 2004). While the RUI and sustainable agriculture literature acknowledge barriers to adaptation and change, little research has been undertaken to link theories of sustainable rural development to their empirical realities (Sonnino 2004). The failure of AFAE’s to persist across generations may result in these enterprises simply being transitional forms of farming before conversion to urban uses. Additionally, very

few studies have examined the rates at which farm families are able to successfully transition their operations into AFAEs or how the process of succession influences the

adoption of specific vertical and horizontal growth strategies.

Many AFAEs at the RUI have been promoted as manifesting the social, economic and environmental principals of sustainability (Lapping and Pfeffer 1997; Lyson and

Green 1999). While such enterprises may be more environmentally sustainable and capable of generating reasonable economic returns, their social sustainability is an

important consideration if they are to be truly sustainable. In particular, household

decision making and farm succession processes at the RUI can be directly linked to

enterprise persistence as well as whether farmland remains in production in areas of high

nonfarm growth and development. The strategies family farms employ when adapting to the constraints of farming at the RUI may be an ultimate determinant as to whether they are able to meet the goals of the sustainable agriculture and farmland preservation movements. Retaining a profitable and sustainable working agricultural landscape in the face of development is an issue ubiquitous throughout the U.S. and many industrialized nations. In light of these pressures, this project seeks to contribute a better understanding of how agriculture, more specifically working farms, can persist at the RUI.

The central goal of this research is to examine how succession, household goals and values, life cycle effects, farm structure, land use policy, and household level adaptations affect the reproduction of the farming enterprise and ultimately the successful persistence of farming at the RUI. The primary objectives of the research are to: 1) understand how household dynamics with emphasis on succession moderate enterprise trajectory at the RUI; and 2) examine how the RUI influences enterprise farm adaptation 14

strategies (by characterizing farms that pursue Commodity, AFAE and Mixed strategies)

(Figure 1.4).

Figure 1.4 Model of Factors Influencing Farm Persistence and Adaptation at the RUI

In this study, particular attention is given to household variables (e.g. availability of an heir, substantive, instrumental and stewardship motivations for land use, life cycle effects, and optimism) farm succession, farm structure variables (e.g. land, labor, agroecology, commodities, and marketing outlets), local and global constraints (e.g. availability of farm inputs and markets, weather conditions, and global market competition), local land use policy and community support for agriculture and farming

(e.g. effectiveness of planning and zoning policies, and local government and community support). An examination of the interrelationship between individual farm structure, land use policy, succession, and household goals among AFAE, Commodity and Mixed type farms sheds light on the structural transformation of agriculture at the RUI, as farm families adapt their operations through decisions related to land, labor and capital.

Organization of Remaining Chapter

This dissertation is organized into several chapters. This chapter provided an introduction and overview of the issues examined in this research. Chapter 2 presents the theoretical background and justification for this study and concludes with a presentation of the research questions and hypothesis guiding this dissertation. Chapter 3 presents the methodological background for study. Chapter 4 presents the empirical results of a landowner survey sent to eight counties examining farm persistence of Commercial farmers, while Chapter 5 compares persistence and adaptation strategies among

Commercial farmers (AFAE and Non-AFAE) and Rural Residential farmers. Chapter 6 presents an in depth qualitative analysis of interviews with 35 farm families examining the role household level variables in influencing farm persistence and adaptation at the

RUI. Chapter 7 synthesizes the results presented in chapters 4, 5 and 6 and concludes

with several policy recommendations for enhancing and retaining agriculture at the RUI.

CHAPTER 2

LITERATURE REVIEW

The focus of this dissertation is to examine how household dynamics relative to global and local pressures and local policy influences affect agriculture at the RUI. This chapter reviews the literature supporting my model of factors influencing farm persistence and adaptation at the RUI presented in the previous chapter. This literature review first examines the structure of agriculture at the RUI and the challenges of farming there; the second section examines the overall structure of U.S. agriculture, farm persistence strategies both within the household and broader farm production and marketing diversification adaptations; the third section reviews the literature on farm succession with a focus on the household dynamics related to enterprise regeneration and the link between entrepreneurship and farm persistence at the RUI; the final section presents the proposed research questions and hypothesis. Throughout this chapter I build a model demonstrating the factors farm households take into account when making decisions about enterprise persistence and adaptation. I begin by first describing the influence of the RUI on agriculture (Figure 2.1).

RUI Pressures

Figure 2.1 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – RUI Pressures.

THE CHANGING STRUCTURE OF AGRICULTURE AT THE RUI

In the United States the public and academic debate over the changing structure of

agriculture has amplified over the last 20 years as farmers are increasingly operating on

lands falling under an ever-expanding urban shadow. The rural-urban fringe is a zone of

intermingling land-uses, characterized by an irregular transition from farm to non-farm

land. The structure and character of agriculture in these regions is impacted both by nonmetropolitan forces (changes affecting farmers everywhere, e.g. commodity markets) and by metropolitan growth and development pressures (Ilbery 1985; Shumway and

Otterstrom 2001; Shumway and Otterstrom 2004). The combined effects stemming from these two sets of pressures have often forced farmers to adapt and change production strategies or exit farming. Despite these challenges agriculture continues to persist at the

RUI (Jackson-Smith and Sharp 2008). The following section examines the literature defining the underlying causes and characteristics of the RUI, followed by a brief review of the strategies designed to protect agriculture at the RUI, and concludes with a review of the structural characteristics of agriculture in these areas. 19

Defining the RUI

Much of the RUI literature has focused on demographic trends affecting land use and population composition. These studies focus on: the pattern and causes of late 20th century migration from urban to rural areas (Fuguitt and Beale 1978; Lichter et al. 1985;

Fuguitt and Beale 1996; Heubusch 1998; Morrill et al. 1999 ; Otterstrom and Shumway

2003), the impact of shifting demographics on local government and the demand for resources (Morrill 1992; Lonsdale and Archer 1998), the direct influence of population on farming (Berry 1979; Lockeretz 1987 ; Andrews et al. 1988 ; Lockeretz 1989 ; Hirschl and Bills 1994; Krupa and Vesterby 2002; Paquette and Domon 2003), farm scale and size at the RUI (Smith 1987; Lockeretz 1988); part-time employment opportunities; and quality of life indicators for farmers at the RUI (Heffernan and Elder 1987; Goetz and

Debertin 2001).

Researchers studying the RUI agree a defining characteristic demarcating these geographic areas is the presence of urban spatial structures that become increasingly diffuse as development is channeled into rural areas. Healy and Short (1981) note three trends in rural land markets: 1) the increasing demand and diversity of rural land uses; 2) the changing distribution of land holdings (increasing parcellization); and 3) increasing price for rural lands. These changes are reflected in the processes of growth at the RUI.

Historically metro areas were characterized by a mononucleaic model, where there was one central business district to which all attention was directed (Irwin and Bockstael

2006). However, metro areas have become increasingly characterized by a polynucleaic spatial pattern with multiple employment centers often economically specialized and located proximately distant from the once central business district (Irwin and Bockstael

2006). The effect of these subcenters is to push out the commuter shed even further, thereby fostering more growth in adjacent rural areas.

Characterizing and defining the RUI has generated a great deal of debate in the literature. A critical question researchers have tried to answer is whether or not the RUI is a unique settlement pattern or if it is merely a precursor to suburban development

(Sharp and Clark 2008). Daniels (1999) defines the RUI as having a set of land and population characteristics that place it along the continuum from urban inner city to rural.

He notes RUI can be defined as the ‘rural urban fringe’ or rural urban interface (RUI) and can be characterized by a population density of less than 1,000 people per square mile and often less than 500 people per square mile. Ilbery (1985) uses four zones to define the spatial phenomena/continuum between urban and rural areas. Outside the central city appears the inner fringe rural land under development or with planning permission, and the outer fringe is characterized by rural land uses along with urban elements such as housing ribbons along route ways. The inner and outer fringe form the rural-urban fringe

(6-10 miles outside the edge of the city). The third zone is the urban shadow; here the urban presence is felt through commuting patterns, non-farm residences and non-farm ownership of land. The fourth zone, the rural hinterlands, has urban influences through second home ownership. In addition to RUI definitions based on population density and land use patterns Sharp and Clark (2008) find that fringe areas exhibit distinct ecological, occupational and sociocultural attributes that differentiate these areas from the suburbs and rural areas. As a whole these studies demonstrate the RUI is a distinct geographic area characterized by distinct land use and social patterns.

Factors Influencing the Spatial Pattern of Agriculture at the RUI

The role of land markets and land rents is intrinsic to understanding the spatial

pattern of agriculture at the RUI. O’Sullivan (1993) argues land is a scarce resource and thus land is allocated to the highest bidder. At the RUI land sold for development is worth more than land sold for farming, and as development pressures drive up the price of land farmers are unable to expand or compete with high land prices (Ilbery 1985). The value of land is determined by the private land value (the private market value) and the

social land value (the private market value plus externalities). Land rents are the

economic surplus associated with the production of a good that uses land as an input in

production. A number of different explanations have been offered to explain the

relationship of land rents to the spatial pattern of agriculture.

Ricardo (1817 cited in (William 1960) argued that the value of land is determined

by the quality of land. Thus for agriculture, fertile land was more expensive, while for urban areas topography (flat land is easier to develop) and proximity to infrastructure were the determining factors in land prices. On the other hand Von Thunnen (cited in

Sinclair 1967) argued that transportation was the driving factor of land rents. The more

perishable and harder to transport a crop the closer to the transportation networks and

markets it must be located. Therefore according to this logic, berries, which are highly

perishable, would be produced closer to the city center while grain and livestock, which

are easier to transport, would be locate further away. Sinclair (1967) modified Von

Thunnen’s observations by incorporating land rents as a factor in landscape patterns.

Sinclair argued that land rents were more expensive closer to the city center, thus crops

that commanded more economic value were able to compete with these land rents and

were thus located closer to the city. So high value fruit and vegetable crops were located

closer to the city than were grain crops. According to these models, equilibrium is

maintained as individuals differentially pay for land based on where they reside.

Exploring the impact of urban influence on agriculture Barnard et al.(2001) found

that urban influence is different from the actual urban footprint or presence of urban

development and land use. Urban influence is the effect of urban activities influencing

the change in value of farmland and changes in the resource allocation decisions of

farmers and landowners. Since the income from agricultural production is less than the

income from non-agriculture development, it is very difficult for agriculture to compete

with non-farm land uses. Since land prices for development are worth so much more

then agricultural production, these values are incorporated into the land value of

agricultural lands. At the RUI it is very difficult for farmers to afford the cost of land

based on their production output from agriculture, especially for those selling into low

price commodity markets. Therefore it is necessary to more closely examine how exactly

growth and development pressures influence the structure of agriculture at the RUI. The

next section reviews the literature documenting the spatial patterns of agriculture at the

RUI and farm structure at the RUI.

The Structure of Agriculture at the RUI

Population growth and development in relatively rural areas adjacent to large

urbanized areas results in farming amidst increasingly populated areas at the RUI

(Heimlich and Anderson 2001). While some models predict a demise of agriculture at the

RUI (Berry 1979), agriculture has proven to be resilient in the face of various pressures

from population growth, urbanization, industrialization and land speculation (Ilbery

1985; Welsh 1996). Agriculture has been found to persist in these areas as a result of

farmer and rancher adaptations that maintain economic viability and concomitantly

through local, regional and state policies that preserve farmland or manage where

nonfarm growth occurs (Lisansky and Clark 1987; Hines and Rhoades 1994; Abdalla and

Kelsey. 1996; Ayres and Hutcheson 1999). The following section examines how pressure

from urbanization and land fragmentation affects the spatial pattern and structure of agriculture at the RUI.

Generally, in metro areas there is a shift from a range of farm sizes to a more compact structure characterized by a higher number of small land intensive operations

(Krupa and Vesterby 2002). Compared to nonmetro counties, in metro counties the number of farms, size of farms and amount of farmland decline as agricultural activity is shifted to smaller operations (Lockeretz 1988; Hines and Rhoades 1994). Metro farms are also less likely to rely on government supports and subsidies compared to nonmetro farmers (Heimlich 1989). Compared to transition, adjacent and remote rural counties

Lockeretz (1989 ) found metro counties are losing the most farmland however, they also exhibited the largest increase in harvested land as a fraction of farmland. Parallel to

Kurpa and Vesterbay (2002), Lockeretz (1989) found metro farms were shifting into higher value, more intensive forms of production. Indeed smaller operations may be more compatible with conditions at the RUI and are able to take advantage of specialized

intensive niche production. Lapping and Pfeffer (1997) note that some communities

experience a steady decline in the number of farms and the amount of farmland, while others experience actual growth in the number of farms and lower levels of farmland loss

coupled with widespread development of alternative agricultural enterprises more suited to the suburban environment.

A prime area of research in the RUI literature attempts to assess shifting commodity production patterns by proximity to urban areas (Heimlich and Anderson

1987; Heimlich 1989; Hines and Rhoades 1994; Hart 1998; Block and DuPuis 2001;

Heimlich and Anderson 2001). A variety of empirical analyses (primarily bivariate contrasts of U.S. Census of Agriculture data by metropolitan character of counties) support the central place, geographical zonal model of agricultural production anticipated by Von Thünen (Nelson 1999) and Sinclair (1967) (Heimlich 1989; Hines and Rhoades

1994; Thomas et al. 1996; Hart 1998; Hart 2003; Thomas and Howell 2003). In the aggregate, this research does show that urban proximity is associated with greater agricultural intensity and the existence of a disproportionate amount of higher value production at the RUI. For example 61% of U.S. vegetable production is located in metro areas, but accounts for less then 1% of cropland (Heimlich and Anderson 2001).

Fruit, nuts, poultry and dairy are negatively affected by urbanization, while vegetables, nursery/greenhouse and field crops are able to persist and in some cases increase (Hines and Rhoades 1994). As one moves away from the urban core more land-extensive forms of agriculture tend to predominate.

Thomas and Howell’s (2003) analysis of agricultural productivity relative to metropolitan proximity for the period 1978 through 1997 reveals that while overall commodity sales decreased in each sector over time, the pattern of change does not follow a linear gradient based on the degree of urbanization. Significant differences were noticed within commodity sectors across the rural-urban continuum. Metro counties led

in the sale of cotton/cottonseed, fruits/vegetables, nursery/greenhouse and other crop products. The majority of poultry dairy and other livestock were found in fringe metro counties, while non-adjacent non-metro counties dominated in sales of grains, cattle, and hog products. Sharp et al. (2002) demonstrate that structural changes in livestock not

only vary by sector but also by urban proximity and scale. For the time period 1987 to

1997, Sharp et al. (2002) found farmers located in the more metro and urban areas of the

Corn Belt are generally less likely to adopt large scale intensive livestock operations

(especially hogs) that can be potential sources of tension with nonfarming neighbors.

Hart (2003) employs the analogy of a standing wave in front of a bow while

moving through water to describe the shifting structure of agriculture at the RUI. This

‘bow wave’ is created as four bands of production (greenhouse, nursery, vegetable, and

dairy) move out from the urban metro area. Hart (2003) argues the greenhouse industry is the last rural use that remains in an urbanizing county, as this production type can take advantage of new residential customers. Hart claims farmers on the bow wave will either: purchase new farms further out; take off-farm jobs; shift production strategies; or

rent their land to other farming neighbors. Contending farmers will trade up to more intensive and profitable production strategies such as greenhouse; Hart is unable to address if the remaining presence of the greenhouse industry in urbanizing areas is a actually the result of farms shifting from commodity production to greenhouses or if

these are new entrants altogether.

The literature reviewed above document the unique spatial pattern and structure

of agriculture observed at the RUI. Growth and development pressures simultaneously create an increase in land rents and potential for neighbor conflicts. These conditions

appear to favor certain types of farms – smaller more intensive operations that produce

higher value fruit, vegetable and nursery green house crops. Large scale farms raising livestock and grain crops are more likely to be located further away from the urban core in more rural and remote areas characterized by lower land rents and fewer neighbors.

The majority of studies examining the spatial pattern of agriculture at the RUI reviewed above have approached the topic from an economic-structural framework and have failed to include how larger macro economic and political factors and social dimensions

(including household goals and values) can influence the structure of agriculture at the

RUI. The next section reviews the literature that expands the understanding of agriculture at the RUI out from economic geography to account for the multidimensional set of influences affecting farm families at the RUI.

Multidimensional Models of Agricultural Adaptation and Change at the RUI

A second body of literature has focused on alternative models findings that a great diversity of farm structures can exist at the RUI. This literature has moved beyond economic land rent theories (Sinclair 1967) in order to account for the variety of factors affecting agricultural change and adaptation at the RUI through the development of holistic ‘farmer decision-making’ models. Recognizing the dynamics affecting farm adaptation and change are not limited to the economic realm these models incorporate elements of culture, land tenure, community, agroecology, biophysical resources, and

external forces operating at global, national, regional and local scales. Within each of

these model exists a broad range of adaptive strategies that can vary across different farms and communities. A dominant theme in this literature is the potential for urban

oriented food and fiber production and access to high wage employment opportunities as

mechanisms for maintaining working agricultural landscapes at the RUI (Fennell and

Weaver 1997; Bowler 1999; Heimlich and Anderson 2001).

Johnston and Bryant (1987) propose a model anticipating three distinct types of

farm change at the RUI, including: 1) adaptations enhancing farm production (e.g.

adding nontraditional enterprises, or intensifying traditional production); 2) normal or

managerial adjustments (changes consistent with changes occurring across the entire

agricultural sector including, adoption of new agricultural technologies to increase

efficiency); and 3) negative adaptations (an exit from farming or reduction in production intensity in anticipation of future sale to developers) (Figure 2.2). This latter type of adaptation is referred to as the impermanence syndrome (Berry 1978; Clancy 1997;

Daniels and Bowers 1997; Lyson and Green 1999), and occurs when farmers experiencing rising land rants in the RUI begin to anticipate their land being converted

into non-agricultural use, leading farmers to disinvest in their farms and eventually exit

farming.

Figure 2.2 Bryant and Johnston (1992) Conceptual Framework for Farm Adjustment in Near-Urban Areas.

Complementing the Johnston-Bryant model, Heimlich and Brooks (1989) present

a model identifying three different types of farms that can be found at the RUI. This

typology includes: 1) alternative enterprises (small in size with high value outputs); 2)

recreational enterprises (very small scale, operated by hobby farmers); and 3) traditional enterprises (large operations engaged in conventional commodity production) (Figure

2.3). Research has found a relationship between production strategies and farm retention in the RUI. Alternative farms in metropolitan counties were more likely to persist between 1978 and 1997 than either traditional or recreational enterprises (Heimlich and

Anderson 2001; Hoppe and Korb 2001).

Figure 2.3 Heimlich and Brooks (1989) model of agricultural adaptation to urbanization.

Smithers and Johnson (2004) expand the Johnston Bryant (1987) model by

acknowledging farmers may undertake multiple strategies simultaneously on the same

farm (Figure 2.4). As in the case of Bryant and Johnson (1987), Smithers and Johnson

(2004) recognize farmer agency, and give credence to the farm household acting and

contributing to farm decision making. The lifecycle of the farm business and lifecycle of

the farm household are critical for understanding how farm households negotiate farm adaptation (Jackson-Smith 1999). As two overlapping spheres the farm business and farm household jointly consider how to allocate internal household resources (lifecycle

stage, age and health of the farm operator, personal goals, attitudes, organization of the

farm, farming philosophy, and intergenerational succession goals), internal business

resources (land quality, debt, climate, topography, and access to capital) along with

pressures exerting from local and macro level forces (trade conditions, commodity prices,

land use policy etc.) (Bryant and Johnson 1992; Smithers and Johnson 2004). Smithers

and Johnson provide a unique contribution by building a model that allows for farm families to pursue multiple paths and multiple trajectories.

Figure 2.4 Smithers and Johnson (2004) Forces and contexts of change in family farming.

These holistic farmer decision making models acknowledge and emphasize the farm

household is actively influenced by both external and internal forces as the household

attempts to balance family goals with market trends, capital resources, conditions within

each commodity sector and labor resources (Figure 2.5) (Bryant and Johnston 1992).

Figure 2.5 Bryant and Johnston (1992) farm decision system and the external environment model.

However, while both Ilbery (1985), Bryant and Johnston (1992) and Smithers and

Johnson (2004) acknowledge the role of household dynamics in farm adaptation at the

RUI, none have empirically tested the degree to which household dynamics relative to

larger global and local problems (e.g. availability of local infrastructure, global

competition and consolidation in the farm sector, farm economics, biophysical conditions, development pressure, neighbor effects, community support for agriculture and local land use policy) affect farm persistence. Recognizing the importance of household dynamics (particularly factors related to succession) at the RUI led Bryant and

Johnston (1992) to note “an aspect of farmers’ motivation that has yet to be explored by geographers in any detail concerns the aspirations of other family members, particularly children” (p 107). Several studies have alluded to the significant role succession can play in enterprise adaptation and intensification at the RUI (Bryant 1973; Johnston 1989) however, none have taken into account the whole set of dynamics operating within the household relative to other pressures at the RUI. Additionally, while these studies have acknowledged the importance of goals and values in household decision making none have attempted to overlay any theoretical conditions onto testing how varying decision making motivations will influence farm adaptation and land use management. The goals and values of the household combined with farm structural characteristics will influence the type of adaptations farms make, the degree to which they are able to exercise agency in the decisions they make, and the ways in which they adapt.

All farmers regardless if they are located at the RUI or not are influenced by both household and structural forces. How individual farms react to these forces and adapt their operations depends on the unique set of local conditions (local land use policy, community support, etc.) they are embedded in and the decisions radiating out from household values and family dynamics. To better understand the conditions under which farmers make decisions it is necessary to review the historical structure of agriculture farmers in the United States are entrenched in and how specific policies, economic

conditions and cultural mores influence the adaptations farmers make and the persistence of the family farm across rural, urban and peri-urban locales (Figure 2.6).

Farm Structure

Figure 2.6 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI- Farm Structure

THE STRUCTURE OF AGRICULTURE

Beginning in the middle part of the 20th century the American farm sector began

to experience radical changes. Lobao and Meyer (1995 p 578) observe that “from 1940 to

1990 the number of farms declined by two thirds and the farm population shrank from

nearly a quarter of all Americans to about two percent.” During this same time period

the structure of agriculture has been increasingly characterized by a bi-modal distribution of small and large farms. The disappearing mid-sized family farm has generated significant volumes of research as scholars contemplate the effects of this phenomenon on: community quality of life; inequalities within the farming sector; and the path of industrial agriculture (Goldschmidt 1978; Lobao 1990; Lobao and Meyer 1995; Lobao and Meyer 2001; Lobao 2004; Domhoff 2006).

Early Jeffersonian agrarian idealism has been the foundation for the imagery

surrounding the family farm. In this scenario the family owns its land, relied on family

labor, makes all management decisions and is self sufficient (Salamon and Davis-Brown

1986; Pile 1990). However, the 19th and 20th centuries witnessed a shift among family

farms from self sufficiency to market orientation, post World War II the family farm

needed only to control the operation and be able to earn a livelihood from it. By the

1970s family farms were no longer required to provide a livelihood, Reinhart and Barlett

(1989) cite the changing vision of the family farm through the USDA’s 1970 redefinition

of a family farm “as primarily an agricultural business in which the operator is a risk

taking manager, who with his family does most of the farm work and performs most of the

managerial activities” (p 208). This new criteria was a response to the increasing concentration, decreasing number of farms, but increasing number of small part time farming operations. This new definition allowed the structure of the family farm to evolve yet remain the dominant form of agricultural production in the U.S.

Understanding how family farms evolve in a capitalist economic system has been a cornerstone of the sociology of agriculture. This section reviews the economic, sociological and anthropological literature examining the structural changes in American

agriculture and the underlying global and local policies driving these transformations.

Structural Changes in Agriculture

Since 1945 agriculture has become increasingly specialized, centralized and

concentrated. Changes in technology (e.g. fertilizers, pesticides, hybrid seeds, and genetic engineering) and increased mechanization have allowed farmers to substitute

capital for labor and produce more food on less land. These changes were supported by federal and state policies, and most notably from Land Grant University research and

Extension programs. However, mechanization has primarily benefited middle and high- income farms, as low income and small farms are unable to keep up with the cost of

production relative to output. Thus while the mechanization of labor meant the reduction

of stoop field labor, it also drove small farmers out of business and decreased the size of

the farm population (Hightower 1973).

Cochrane’s (1958; 1979) ‘treadmill of technology’ theory is embedded in the

diffusion of innovations framework and explains how entrepreneurial innovators and

early adopter farmers can take advantage of new agricultural technology to increase

profits. However, as other farms catch up and begin to adopt this new technology and productivity increases across the board, the price per unit decreases, and income remains the same or decreases. Declining prices force non-adopters to utilize these new technologies; however these subsequent adopters gain few benefits. The late adopters and non-adopters are forced out of business because they can no longer compete on their high average cost of production. The shift into capital intensive farming systems required farmers to constantly reinvest in new and more expensive agricultural technologies and management systems in order to stay afloat, even while the majority received little or no benefit. To afford these more expensive technologies farmers have had to expand and incur more debt to afford them. Farmers unable to compete were forced out of agriculture. This process created a self intensifying unsustainable loop as the cycle of increased productivity, less income per unit, steady or less total income reifies itself

(Durrenberger and Erem 2007).

This cycle based on technological competition has been a factor in the increasing

concentration and centralization of agriculture (Goodman and Redclift 1985; Lyson and

Guptill 2004). Since WWII, the overall trend in agriculture has been a decrease in the

number of farms and an increase in the size of farms in the U.S. The increased

concentration of resources and high rates of horizontal and vertical integration have significantly decreased the number of firms that control both agricultural inputs and

outputs and has effectively contributed to the decline of the family farm structure

(Goodman and Redclift 1985; Welsh 1996; Heffernan 2000; Lewontin 2000; McMichael

2000). From 1950 to 1962 the number of farms declined by half, while average farm size

increased by 60% and mean sales per farm tripled (Lobao and Meyer 1995; Lobao and

Meyer 2001). As of the last U.S. agricultural census in 2002 the average size farm was

441 acres compared to 146 acres in 1900 (Dimitri et al. 2005; Parker 2008). The shift

from hand labor to mechanization and capital intensive nature of farming, combined with

low profit margins, has meant fewer young farmers are entering into agriculture. This

reorganization of labor beginning in the mid 20th century has created a demographic shift

characterized by fewer and older farmers (Gale 2002). Today the average age of a farm

operator is 54.3 years old (ERS 2005). As the cohort of farmers who were able to survive

the farm crisis ages and the consistent decrease in the number of farmers under age 65

continues, America’s farm population is graying.

The USDA CSREES in an effort to address this oncoming crisis of a potentially

absent next generation of farmers has sponsored webinars and funding for farm

transitions projects (CSREES 2008). These questions are particularly salient at the RUI

given the significantly high volume of agricultural sales originating from these locales

and their fragility given their vulnerability to development pressure (Jackson-Smith and

Sharp 2008). However, in order to better understand the vulnerabilities farmers at the

RUI face, and the underlying reasons for a shrinking and aging farm population, it is

necessary to explore the influence of global and local policy in more detail (Figure 2.7).

Global and

Local Policy

Figure 2.7 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Global and Local Policy

Influence of Global and Local Policy

In the United States the agricultural landscape has been formed as much by

government policy as it has been by topography. Family farms have been trapped in a

paradox, as two distinct sets of policies both select for and against their interests and

persistence. Land grant colleges, the Extension service, the USDA, government policies

and programs have both led to the increasing vulnerability of family farms, while federal

relief and subsidy programs provide financial assistance to assist with enterprise survival

(Lobao and Meyer 2001). Government subsidy supports have had a critical role in

influencing the structure of agriculture. In 1900 farmers received no subsidies; in 1997

they received $5 billion. These trends have selected for certain types of farms and crops 39

(Strange 2008). The following section reviews how global and local policies have influenced the structure of agriculture at the RUI.

Global Policy

The development of subsidized industrial agriculture in the United States created

spheres of production – the Midwest as the “bread basket” produces the majority of

commodity corn, bean and other grain crops, while California, Texas, Arizona, New

Mexico and Florida have become the “fruit and vegetable basket” of America. Public

policies including Public Law 480, the Food Security Act of 1985, trade liberalization,

and tariffs both encourage and support commodity crop production in the U.S.

(Goodman and Redclift 1985; McMichael and Raynolds 1994; McMichael 2000). U.S.

government agricultural policy has emphasized an export strategy (supported through

subsidies) to influence foreign diets centered on political and economic motivations that

benefit domestic producers and larger hegemonic goals (Moore 1990). Mooney (1986)

and McMichael (1987) have documented how domestic tax policies and state sponsored

credit systems such as the FMHA, CCC and FCS push family farmers out of business and

on the road to industrial agriculture. Likewise, McMichael (2004) demonstrates how

policy makers have been able to engineer social change through international institutions

(i.e. the United Nations, the Food and Agricultural Organization (FAO), the World Bank,

and the International Monetary Fund (IMF) to affect the political, economic and social

trajectory of family farms in the U.S. and abroad. These larger macro-level policies impact the structure of agriculture at the national, regional, local and individual farm

level. As Smithers and Johnson (2004) note in their holistic farmer decision making

model, the decisions farm families make regarding their enterprises are in part shaped by these larger influences.

A uniting theme within the sociology of agriculture is the uneven nature of capital penetration. As agriculture evolved from a subsistence form of production into a commercial venture where the family unit produces for the market, the distance between farmers and consumers has grown. Since World War II the food system has increasingly been characterized by consolidation, concentration, coordination and the globalization of food production, processing and distribution (Lobao and Meyer 2001; Buttel et al. 1990;

Welsh 1996 ; Heffernan 1999; Heffernan 2000). The ways in which capital has penetrated into agriculture led Lobao and Meyer (2000) to observe: “Farmers are sandwiched in between large concentrated firms as input industries such as petrochemicals, machinery and seeds and output industries such as food processing, transportation and retailing” (p 25). These conditions also influence the ways in which families structure their farms and ultimately their ability to persist.

In agriculture, horizontal integration occurs when firms concentrate power across stages of production, processing and distribution, while vertical integration occurs as a firm increases control and ownership throughout the various phases of production to control both input and outputs (Heffernan 1999; Heffernan 2000). These processes are readily visible in certain commodity sectors such as meat processing. In beef slaughtering 87% of the beef cattle are slaughtered by the four largest firms (IBP,

ConAgra, Cargill and Farmland Industries), in crops the four largest firms (including

Archer Daniels Midland, Cargill, ConAgra) process 57% to 76% of the corn, wheat and soybeans in the U.S. (Heffernan 2000). These two processes of horizontal and vertical

integration place decision making constraints on individual farm families as operators find themselves in servitude to these companies in order to survive.

These larger trends have influenced American agriculture at the macro scale, producing large national trends. However, it is necessary to look beyond macro-level statistics to explore how local land use policy exercised at the state, county and township

level create uneven production patterns across regions (Lobao 1990). The following section explores how local land use policies respond to and influence farm structure at the

RUI given the unique pressures of farming in these localities.

Local Land Use Policy

Local land use and economic development policies can affect farm structure and the viability of agriculture at the RUI. Farming at the RUI has distinct locational disadvantages. Drawbacks include conflicts with non-farm neighbors over odor, manure

management, runoff, noise, transportation, vandalism, ethnicity of workers, reduced

access to suppliers, capital and traditional markets such as grain elevators (Lopez et al.

1988; Hines and Rhoades 1994; Kelsey and Singletary 1996; Kelsey 1998). Increasing

population places non-farming residents adjacent to farming residents where tensions

from land use and perceived irritants from farming may become a source of tension

which are factored into farmer decision making (Lifemann et al. 2000). Livestock,

particularly intensive large scale operators, have generated a great deal of environmental

and social conflict (Thu and Durrenberger 1998; Sharp et al. 2002).

To protect farmers some communities and states have passed right to farm laws as

a means to protect farmers from litigious or legislative action (Lisansky and Clark 1987).

Although agricultural zoning was designed to protect farmers, it has also been used as a

tool for exclusionary zoning to lock out low income homeowners as was the case in Mt.

Laurell, New Jersey (Pizor 1987).

Farmland preservation and protection policies include: agricultural zoning; differential taxation; and purchase or transfer of development rights programs (AFT

1997; Daniels 2000; Daniels 2001). Two important subsets of land use policy receiving

are growth management and farmland preservation programs (Daniels and Bowers 1997).

The first group of policies are clustered around ‘growth management’ programs, which

encompass a range of policies seeking to control land use directly (e.g., land use zoning

ordinances or urban growth boundaries) or to influence the amount and location of urban

land development in some way (e.g., urban service boundaries or impact fees) (Daniels

and Bowers 1997; Daniels 2001). Urban service boundaries are known as market based

solutions, where a locality subsidizes development by assuming the cost of developing

infrastructure in certain areas, to encourage development in specific locations and not in

fragile areas. Urban growth boundaries are known as command and control policies,

where a boundary is drawn, outside of which development cannot occur. These types of

policies could be categorized as sanctioning, as they depend on the consent of voters and

politicians for their success.

To appreciate the degree to which strategies designed to protect agriculture at the

RUI can be effective it is necessary to distinguish between open space policies versus

those that protect working agricultural landscapes. According to Daniels (2000) open

space can be attained through the private-public ownership of land and is seen to enhance

the quality of life for the community as a whole; working landscapes are privately held

properties often associated with resource extraction industries and are part of the local economy.

Abdalla and Kelsey (1996) suggest mediation, community dialogue and open planning processes as tools for maintaining healthy relationships between the farm and non-farm community. Others have suggested farmers can reduce conflict and tension by

reaching out to non-farm neighbors through social events, participating on planning

boards and acting as agricultural ambassadors to dispel myths about agriculture and

educate new residents about farming (Ayers and Hutcheson 1992; Wall 1997; Che and

Hutcheson 2005).

Another way communities might influence agricultural change at the RUI is

through economic development policies or programs seeking to strategically improve the

viability of the local agricultural sector. Some have recommended farmer and

community development efforts to transition out of commodity agriculture and capitalize

on a variety of new agricultural opportunities associated with proximity to urban

consumers (e.g., farmers markets, locally processed foods; community supported

agriculture) (Blobaum 1987; Lockeretz 1987 ; Gale 1997; Lapping and Pfeffer 1997).

Similar to traditional land-use policy focused on planning and zoning regulations,

proponents expect these various local development activities to impact the trajectory of

agricultural change in the community, however, there has been little careful empirical

evaluation of their impact on retaining working agricultural landscapes, or the conditions

under which farm families are most likely to adopt and implement them. When

examining the influence of global and local policy it is often easy to overlook the role of

agency exhibited by farm families, the diversity of motivations individuals have for

farming, and the innovative strategies households engineer in order to retain farming as a livelihood and lifestyle. To explore these issues the following section reviews the debates within the sociology of agriculture regarding the sociocultural dynamics and motivations influencing land use and enterprise decision making in advanced capitalist economies

(Figure 2.8).

Motivations for

Land Use

Figure 2.8 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Motivations for Land Use.

Debates with the Sociology of Agriculture: Capital Penetration into Agriculture and the Fate of the Family Farm

The U.S. Farm Crisis of the late 1970s and 1980s raised significant questions about the effects of capitalism on the persistence of the family farm and the structural effects of agricultural policy and technology. To understand the new trend of

disappearing medium sized family farms and bimodal distribution of large and small

farms a new research agenda emerged within the sociology of agriculture (Newby 1983).

Rural Sociologists observing Marx’s theoretical prediction for the complete

industrialization of agriculture had not materialized and perplexed by the persistence of

family farms, and mix of industrial/corporate farms, sharecropping, small, medium and

large scale farms on the landscape, launched a new line of inquiry into the trajectory of

agriculture in advanced capitalist societies (Friedland 1991).

The first part of the following section reviews the debate within the sociology of

agriculture literature examining the structural, cultural, and social organization of the

family farm that drives its persistence. This body of literature is then situated within the

larger field of economic sociology to examine the motivations underlining farm

adaptation and persistence. The second half of this section reviews how the debate over

the persistence of the family farm has evolved, from discussions centered on the bimodal distribution of farm size and questions related to the impact of off-farm work to a line of inquiry and research examining questions regarding adaptation strategies that allow the

family farm to persist.

The Persistence of the Family Farmer

Theoretical Foundations

Neoclassical economics founded in the writings of Adam Smith predict the family

farm is doomed to elimination due to its failure to attain economies of scale. Larger scale

units are more efficient because they can specialize, use large scale equipment and more

easily employ managerial talent. The ultimate assumption of neoclassical economists is

that both large and small firms follow the same behavior logic, the goal of profit

maximization (Reinhardt and Barlett 1989; Buttel et al. 1990; Durrenberger and Erem

2007). However, as Dorner and Kankel (1971) point out by their own analysis,

neoclassical economists have demonstrated that both empirically and theoretically small

scale production (in manufacturing) can remain economically competitive.

Theories rooted in Marxism, Neo-Marxism, and Neo-Weberinism have been put

forward to explain the persistence of the family farm and the uneven nature by which

capitalism has penetrated into agriculture. The following section reviews the variances in

these theories in the sociology of agriculture and examines the debate to understand: if

the family farm is a non capitalist unit that will ultimately be eliminated under capitalism;

or if it is a non capitalist unit whose unique characteristics and operating features allow it

to persist; or if is actually a capitalist unit that due to its peculiar efficiencies and

inefficiencies will eventually disappear (Reinhardt and Barlett 1989).

The laws of capitalist development dictate all sectors of the economy should

differentiate into two main classes: the capitalists who own the means of production; and

the proletariat hired workers, who sell their labor power (Wright 1978). However,

agriculture has not followed this direct path. To understand the relationship between

capitalism and agriculture sociologists have returned to the classic works of Marx, Lenin,

and Katausky (Lobao 1990). Farmers are distinctly different from peasants. Under the

feudal system, peasants could survive by producing their own food and fiber, not needing

to produce for exchange, as they do not sell their labor. In a modern capitalist system

farmers produce for the market, and sell their labor. In Marxist theory hired labor is a

primary criteria for establishing class. Family farm systems appear to be in a

contradictory class location as the family is both the primary labor unit and also owns the

means of production.

Citing the works of Katusky and Lenin, Marxist scholars including de Janvry

(1980) and Newby (1978 ) predicted the eventual demise of the family farm in advanced

capitalist societies. Although the process of destruction would be slow, the institution of

the family farm will eventually be eroded as farmers experience polarization and are

differentiated into antagonistic social classes through larger changes in the political

economic structures of society. Examining farming systems in England, Marx (Marx

1976) predicated the destruction of the peasantry, as agriculture would eventually come

be organized along the same lines as industry. Landholdings would become highly

concentrated and farm labor would be in the form of wage labor. Steeped in his contempt

for the backwardness of the peasantry, Marx saw their doom in capitals more efficient use of scale and advanced technology (Reinhart and Barlett 1989). He predicted a three-

tiered system of wage laborers, tenant farmers and capitalist land owners. Eventually

agriculture would be subject to centralization and concentration parallel to industry, and

peasants attaining class-consciousness would become a part of the revolution against the

bourgeoisie. Lenin (1967; 1974) formed a similar but more nuanced conclusion.

Examining U.S. agricultural systems and the Russian peasantry, Lenin concluded the

centralizing and concentrating forces of capitalist systems would inevitably transform the

family farm (and peasantry) to be organized along the same lines (and logic) as industry;

as independent producers would be transformed into wage laborers or capital land

owner/producers. However, Lenin also recognized the process of differentiation would be

gradual, complex and uneven.

A more refined analysis of the fate of family farms in industrialized societies was

presented by Katausky (1980). Katausky argued that capital is only able to penetrate

certain parts of agriculture and therefore family farms operate under a unique set of principles under capitalism that are different from industry. Capital is able to penetrate into agriculture through inputs, markets and distribution processes as opposed to directly through labor itself. Examining the German peasantry, Katausky predicted farmers would eventually be differentiated into worker-peasants and the household itself would

be differentiated through the adoption of part-time farming supplemented by part-time off farm wage work. Katausky also foresaw the tendency of farmers to become absorbed by larger and more efficient production units. These observations laid the foundation for neo-Marixst approaches for examining the persistence of the family farm and the uneven nature of capital penetration into agriculture.

Marxist and Neo-Marxist Theoretical Explanations for Family Farm Persistence

The Marxist and Neo-Marxist theoretical tradition are steeped in a structuralist approach to explain the fate of family farms in capitalist economic systems. The following section reviews this literature to understand how the biological nature of farming, the competitive ability of household units and state policy influence the farming systems in advanced capitalist societies.

To understand the persistence of the family farm Mann and Dickenson (1978) argue it is necessary to distinguish between farming (the inputs of seed, fertilizer that are transformed into products of wheat and cattle via sun, labor and soil) and agriculture

(the inputs, production, marketing and distribution of farm products). Building on

Kautsky, Mann and Dickenson (1978) note that capital has been more effective at penetrating agriculture over the farming aspects of production through the reliance on

purchased inputs, marketing and distribution channels. Mann and Dickenson (1978) argue the persistence of the family farm is primarily due to the unique biological nature of farming. The biological cycles and seasonality of farming mean that production time cannot be shortened as it can for industrial goods. The production-labor time disjuncture prohibits the ability to accelerate the cycle of capital (as can be done in factory settings) and therefore restricts the profit potential and rate of accumulation in farming (Jackson

Smith 1995).

Family farms offer a flexible labor supply able to adapt to fluctuations in the farming season (Reinhardt and Barlett 1989). Furthermore, the farming season carries risk from unpredictable weather patterns and natural disasters and profits are only realized at harvest time; these uncertainties create conditions unattractive to capital investment. Additionally, on larger farms economies of scale become less efficient, as managing labor over a large area 24 hours a day, seven days a week is unwieldy. Large scale units create diseconomies of scale and therefore it is more attractive for capital to invest in agriculture (the inputs, marketing and distribution channels) while the farmer is left to confront the risk and disjuncture between production time, labor time, cash flow,

and labor uncertainty.

Friedman (1978b; 1978a) also cites the biological nature of farming to explain the

persistence of family farms, but argues the persistence of household forms of production

or ‘simple commodity production’ is due to their ability to out compete capitalist forms

of agriculture. Friedman focuses on the ability of these units to reduce consumption

levels, especially in economic downturns, in order to ensure the persistence of the

enterprise, while capitalists will simply liquidate their assets if production no longer

generates an average rate of profit. These simple commodity producers are not required

to earn a profit in order to reproduce the enterprise, they only need to accomplish ‘simple

reproduction’ while capitalists (who operate by the logic of competition) must earn an

average rate of profit or else their firms are marginalized and forced out of business.

Other Marxist scholars such as Bonanno (1987) claim the persistence of small

farms benefits industrial capital. Manufacturing and industrial firms often prefer to move

into rural areas as they benefit from the lack of unions and low worker wages. Workers

who have a small farm are able to substitute their low income from the firm with farm

sales, and can turn to the farm for primary income earnings in economic downturns.

Mottura and Pugliesi (1980) trace the reliance of industrial capital on part-time farming

small holdings in Italy. They find farming is a back up in periods of industrial

contraction and high unemployment rates, and displaced workers with small farms can

subsist from the farm thereby retaining a reserve labor force, ready to reenter the market

when industrial conditions improve. Bonono (1987) argues federal government farm

programs serve a legitimation function to protect and perpetuate small farms to ensure

they remain a “keeper of surplus labor, providing low cost labor for industry in the face

of tenuous employment” (p 120). Bonano goes on to argue capitalists can exploit all

members of the farm household as production on family based units is organized around

obligations of kinship that link the domestic and productionist sphere. This domestic

reserve of labor subsidizes capital both directly and indirectly through unpaid household

labor, and off-farm work contributions to the household; these processes ultimately work

to lower the cost of food for other working class families, allowing them to spend greater

proportions of their income on manufactured consumer goods.

Neo-Weberian Theoretical Explanations for Family Farm Persistence

Neo-Marxists have been critiqued for their linear nature and failure to recognize

the social and cultural influences in moderating the trajectory of family farms. Buttel et

al. (1990 p. 92) cite Vandergeest’s (1988) critique that Marxist and neo-Marxist “studies

as too uni-linear, too macro-structural, too denying of human agency and too deferent to

the role of the state in the process and too unconnected with practice.” Vandergeest

(1988) argues for an examination of neo-Weberian theory rooted in the works of

Bourdieu that would expand models beyond the deductive theory of simple commodity production and that would allow for different and historically contingent principals that can only be investigated through empirical research.

Building on Wright’s (1978) observations of contradictory class locations (both inside and outside of agriculture) and the diversity of farm types existing within capitalist economic systems, Mooney (1983) counters Marxist and Neo-Marxist structuralist theories by offering a Neo-Weberian explanation for capital penetration into agriculture.

Wright (1978) points to three contradictory class locations: 1) between capitalist and proletarian e.g. managers and supervisors; 2) between petty bourgeoisie and capitalist e.g.

small employers;, and 3) between petty bourgeoisie and proletariat e.g. semi-autonomous

employees. As noted earlier, exploitation of farm wage workers by agrarian capital is

only one form of capital penetration of agriculture. Mooney (1983) makes the case that

“the alternative ways capital strips actors of surplus value may be more significant then

the full blown capitalist labor relations at the point of agricultural production” (Buttel et

al. 1990 p 87). To avoid polarization or being forced out of agriculture, farmers will enter into alternative production arrangements including: tenancy; contract farming; part time farming, and debt. In these relationships farmers are exploited by nonagricultural capital as there is no capital-labor connection at the point of agricultural production.

Within each path, different actors exploit the farmer: landlords exploit farmers in tenancy associations; agribusiness exploits farmers in contract farming arrangements; off- farm capitalists exploit part-time farmers; and finance capital exploits farmers through debt.

In Mooney’s (1983) class analysis farmers are not passive actors. Rather, his interviews with Wisconsin farmers revealed divergent goal orientations. For example,

Mooney found farmers entered into debt with the hope of gaining autonomy. To explain these divergent attitudes and behaviors that produce a contradictory class location,

Mooney roots his analysis in Weber’s distinction between formal (or instrumental) and substantive rationality. Mooney injects a subjectivist component into Marxist and neoclassical economic theories of economic behavior or what Weber calls ‘formal rationality. Substantive rationality operates simultaneously within the same individual, and is guided by values not calculated on economic factors alone. The influence of substantive rationality explains why farmers will tenaciously hold onto their farms and farming lifestyles, and will potentially adopt relationships with outside capital in order to remain on the farm. Mooney expands these social processes to the family farm by building on C. Wright Mill’s ideal types of craftsmanship. Buttel et al. (1990 p 90) state

Mooney’s key contribution “was the presumption of a predictably patterned and necessary differentiation of farmers into the capitalist class proletariat is erroneous and

that subjectively meaningful human action affects the process of differentiation

substantially.”

Mooney does not argue family farms are immune from the forces of capitalism

and formal rationality. Rather he argues that substantive rationality continues to operate

even as the family farm is increasingly rationalized and socialized into a capitalist logic, the process of transformation described by Kautasky is still progressing as family farms shift from their distinct substantive (non capitalist) internal logic to a more capitalist rational one (Reinhart and Barlett 1989). The real world theoretical manifestations of

Mooney’s argument can be seen in Salamon’s (1992) entrepreneurial English farmers who tend to be larger in size, more specialized and more reliant on capital intensive equipment, and carry higher loads of debt, thereby making these farms more vulnerable to economic downturns. Likewise, Barlett (1993) found Georgia farmers who expanded and invested along the lines of capitalist goals and methods were able to yield higher profits in average years, but the farm unit was not sufficiently protected from cash flow difficulties in the lean years. As ‘rationalized’ farmers, this group is vulnerable to economic downturns.

Family farms are a complex configuration of management, ownership and labor as the family is both the production and consumption unit (Lobao and Meyer 1995). The influence of Neo-Weberian theories and Bourdieu is pushed further with the contribution of anthropologists who layer the importance of ethnicity and culture on top of state and local level farm structure (Buttel et al. 1990). Reinhart and Barlett (1989) note the

peculiar persistence of the family farm is in part due to the fact that they are oriented

towards a range of goals, including lifestyle values and not simply profit maximization.

They see the family farm as distinct from capitalist production, yet share Friedman’s

belief that the family farm has adapted in various ways to a new economic environment.

Reinhart and Barlett (2001) argue the difference between the family farm and a

capitalist farm lies in the organization of labor and the farm’s goals and motivations. The motivation and goals of a capitalist farm are investment and profit. While the goals of profit also characterize the motivations of a family farm, they are moderated by lifestyle and other intrinsic goals. The types of goals pursued in a family farm are more complex

than simply earning the average rate of profit. Farms of all types may be interested in

maximizing net revenue and factor in the non monetary aspects of farming and rural life

in their long term decision making. While all farm types may share these overlapping

interests, the emphasis on decisions will vary. Primarily, family farms are able to defer

income for asset appreciation.

The organization of labor on both capitalist and family farms is characterized by a

division of labor, often marked by a male manager. Capitalist farms employ a manager

and workers for wage labor, owners rarely if at all participate in the day-to-day

management and labor activities on the farm. Family farms may experience a division of

labor but workers are connected by kinship networks and owners are engaged in day to

day operations of the farm (Salamon 1992; Barlett 1993). Additionally, farm households are not restricted to the nuclear family; farm persistence may be as much an intergenerational goal as it is an intragenerational goal while workers in both types of farms may have a desire to rear their children to be hardworking responsible adults,

Reinhart and Barlett (1989) argue capitalist farms make no attempt to incorporate this

goal into the family operation as workers must leave their children at home. While the

family farm is committed to socializing children it will accept lower productivity at

certain times in order to teach children farm skills and work ethics. Since farming is as

much a lifestyle as an occupation farm households engage in a range of on- and off-farm

diversification strategies to ensure survival (Hennon and Hilderbrand 2005).

Sachs (1996) argues most theories examining the failure of capital to fully

penetrate into agriculture have missed the role women have in subsidizing the farm

through their informal and formal labor contributions. Tisdall (1999) argues the persistent

undercounting and undervaluing of work is due to the nature of capitalism. In capitalism, only work that is done for wages and formal exchange is valued. Since women’s work occurs both inside and outside the home for paid and unpaid exchange, much of their work is not valued. Omari and Mbilinyi (2000) also suggest the fact women and men are not socialized to value women’s work and contributions, thereby exacerbating the undervaluing of women’s work and contributions to farm survival.

The insights anthropologists have brought into the debate have been influenced by the observations of Chayanov, a Russian economist who argued the family farm is in fact an efficient, competitive agricultural unit that works on its own unique behavioral logic

(Durrenberger and Erem 2007; Reinhart and Barlett 1989). Counter to the theories presented by Marx, Lenin and Kautusky, Chayanov argued the goal of the production unit is determined by the consumption needs of the household, rather then by profit goals.

According to Chayanov household peasant economics are distinctly different from the logic of the business firm and wage labor, because the household is tied to the land. Both factories and large scale agricultural systems that employ wage labor, must generate a profit in order to pay the wage rate. In neither case do the workers make a direct living

from the land, they work for wages. Stalin attempted to create an inexpensive food

source for the city centers by increasing agricultural production without paying the

peasants any more. Since the household is the economic unit (peasants and family

farming systems) and only has to produce enough to meet household needs, they will

only produce beyond those needs if additional revenue is to be made. In these systems

the household can intensify or deintensify labor allocations as need be through a process

of self exploitation to ensure enterprise survival. To increase production levels, Chayanov

advocated for giving peasants greater autonomy over price setting (or developing a form

of cooperative marketing), however, Stalin disagreed and had Chayanov executed.

Applying Chayanov’s theories to modern family farms helps explain why the

family farm unit is willing to forgo profit and even accept a return on labor lower than the

market wages in market downturn allowing them to effectively compete with larger scale

capital units (Durrenberger and Erem 2007). Furthermore, the flexibility to allocate

household labor by either intensifying family labor to expand farm output, or by

substituting labor for capital during economic downturns, or increasing off farm

employment of family members demonstrates that part time farming is a strategy family

farms use to survive and is not a sign of erosion (Reinhart and Barlett 1989; Buttel et al.

1990). However, Chayanov parallel to Kautasky did in fact see the family farm being

adversely affected by its integration into the broader economy, not through direct

competition with larger farm units, but through vertical concentration of agricultural

production (Reinhardt and Barlett 1989).

Taken together these theories and findings demonstrate the complex organization

of the family farm. Individual farm structure is a product not only of larger global

production policies and local land use policies but reflects the varying internal household

values, goals, kinship networks, availability of labor, and gender roles operating within

each farm family. The family farm as both a production and consumption unit is

embedded within a larger cultural context that further moderates their actions, and

contributes to the dynamic and multidimensional nature of farm persistence and

evolution. Economic sociology provides a theoretical bridge to exploring how internal

household processes (including family goals and values) can enhance our understanding of why agriculture continues to persist at the RUI and how it continues to adapt.

Connections Between Farm Persistence and Larger Questions in Economic Sociology

The farm crisis first provoked questions about farm persistence in relationship to

farm scale, ownership structure, off-farm income, part time farming, hours spent on the

farm and the division of labor. This first generation of research examining farm

persistence in advanced capitalist societies laid the foundation for further inquiry that moves beyond definitions of a farmer based in farm structure to examine how household agency, culture, farm structure, economics and policy affect farm adaptation and persistence. Situating farm persistence within the field of economic sociology expands our theoretical insights into the way farm households engage in decision making processes that ultimately influence the persistence of agriculture and the farm enterprise at the RUI.

As noted previously the impermanance syndrome is commonly taken for granted and accepted at face value as many assume farmers will exit out of unprofitable agricultural enterprises to cash in on more lucrative land sales. These assumptions follow

a neoclassical economic logic where individuals are seen as rational actors aiming to maximize profit. However as evidenced by Jackson-Smith and Sharp (2008) agriculture

continues to persist at the RUI even in the face of high land values, indicating farm families are engaged in a complex process of decision making that neoclassical economic theory is unable to capture alone.

Economic sociology is a useful tool for understanding farm persistence at the RUI as it interjects that economic action is socially situated. Where neoclassical economic

models maintain individuals are motivated by self interest and engage in decision making processes in isolation from other social actors, economic sociology counter argues economic action is embedded in social action, in social networks and that non-economic values weigh heavily in economic decision making processes (Granovetter and Swedberg

1992; Granovetter 2005; Krippner and Alvarez 2007). Economic sociology is also a useful tool for understanding economic decision making in capitalist systems because of the attention it pays to connection formation between and across individuals, firms, industries, non profit organizations and governments (Smelser and Swedberg 1994;

Krippner and Alvarez 2007). As family farms and farm households in the United States are embedded within the larger capitalist economy and culture, economic sociology provides a useful analytical tool for examining how modern farm families negotiate economic and social pressures as they make decisions regarding farm adaptation and enterprise persistence.

Economic sociology finds its theoretical roots in the work of Weber, documenting the ways in which individual actors take others behavior and thoughts into consideration when making economic decisions. The two traditions that have dominated in economic

sociology have been the work of Karl Polyani and Mark Granovetter. The Polyani

tradition focuses on the integration of the economy into broader social systems (Polyani

1992) while the Granovetter tradition is occupied with identifying the relational basis of social action in economic contexts using the concepts of embeddedness and social capital

and employing network theory as a primary methodology (Grannovetter 1992; Krippner

and Alvarez 2007). Economic sociology has primarily been applied to larger macro level

analyses examining economic decision making at the community and institution level and

rarely engaging directly in micro level work at the household level. While Weberian

themes of instrumental and substantive rationality run throughout economic sociology,

few have applied these concepts to the household and individual level as Mooney (1983)

had. Critiques of economic sociology (Beamish 2007; Swedberg 2007) argue that in

order for economic sociology to advance there is a need to more explicitly theorize on the

role of individual agency; where neoclassical economics has over emphasized the role of the individual, economic sociologists have neglected the role of agency.

Beamish (2007) argues that network analysis has dominated economic sociology at the micro level, however “network analysis is limited and cannot give a fully developed theory of market place settings as social ones inhabited by intelligent actors who reflect on their own actions and actions of others in inherently cultural and political contexts.” Beamish argues for a return to Weber’s work on interpretation and Joseph

Schumpeter’s analysis of entrepreneurship to inspire a new view of agency that is embedded in social relationships and thereby places economic action in a social structure

(Schumpeter’s work on entrepreneurship as it relates to this dissertation will be discussed in the second half of the literature review).

As discussed earlier the farm household occupies a contradictory class location,

embedded in unique economic, social and cultural structures. This position offers a unique venue for applying neo-Weberian concepts of instrumental and substantive value rationality to extend theories intrinsically institutionalized in economic sociology to the household level and thereby provide a greater accounting of the micro-processes overlooked by mainstream economic sociology. The following section reviews the literature examining farm diversification strategies undertaken by farm families to ensure enterprise persistence. Although not explicitly stated in these studies, it is possible to see how instrumental and substantive values are brought into play within farm households as they utilize both household and farm resources in an attempt to accomplish the overlapping goals of farm persistence and reproduction (Figure 2.9).

Household & Farm Resources

Figure 2.9 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Household & Farm Resources.

FARM DIVERSIFICATION STRATEGIES

The farm crisis of the 1970s and 1980s jettisoned a re-evaluation of strategies family farms utilize to remain profitable and viable. A confluence of depressed

commodity markets, shifting production patterns, and increasing land costs generated an

interest in farm diversification strategies (also referred to as pluriactivity in the European

literature) (Jervell 1999). There are two primary modes of diversification, on-farm and

off-farm. On-farm diversification strategies include varying combinations of crop and

livestock production as an instrument for protecting the farm and household from

fluctuations in the market. Transitioning into high-value crops, or an alternative food and

agricultural enterprise at the RUI is a diversification strategy that allows farm families to

earn more income on less land by catering to urban consumers (Lobley and Potter 2004;

Machum 2005). Off-farm diversification strategies generally refer to the primary

operator, spouse or other family member contributing off-farm work earnings to the

household (Mishra et al. 2002). These diversification strategies represent the variety of

ways farm families seek to simultaneously earn a livelihood while also maintaining a way

of life. The following section reviews household survival strategies, the role of off-farm

income, and models of diversification strategies in the European and North American

Context.

Household Survival Strategies

As noted earlier, to ensure farm survival the farm family can engage in a variety

of adaptive behaviors at the household level to reduce consumption and production costs.

Farm families may reduce consumption and expenditures both within the household and

farm business all enacted with the intent to ensure the survival of the farm enterprise

(Lobao and Lasley 1995). Within the farm household farm families may: produce their

own food; postpone major household purchases; alter food shopping or eating habits;

change transportation patterns; reduce utility usage; save less money for children’s education; postpone medical or dental care; cash in insurance premiums; sell household possessions; take off farm employment, or; rely on charitable contributions (Schulman and Greene 1986; Barlett 1986a; Lasley et al. 1995). Within the farm business families may: pay more attention to record keeping and marketing; reduce their use of credit; shift into part time farming by increasing off farm work; share equipment; choose low risk and low profit operations; utilize informal exchange systems; adjust farm size either by selling land to reduce debt loads or buying land to increase production; add new crops and livestock; start a new non-farm business; reduce reliance on hired labor; or exit farming altogether (Schulman and Greene 1986; Barlett 1986a; Barlett 1986c; Schulman et al. 1994; Johnson and Vlasin 1995; Lobao and Lasley 1995; Olson and Saupe 1995).

A lingering effect of the 1980s farm crisis was the emphasis placed on farmer marketing and financial record keeping abilities. In a survey of Midwestern farm operators Olson and Saupe (1995) found that 67% plan to ‘pay closer attention to marketing’; 57% plan to ‘keep more complete financial records’; 54% plan to ‘reduce long term debt’, and; 52% plan to ‘reduce short term debt’. These results led Olson and

Saupe (1995) to conclude, “marketing, record keeping and debt reduction have became high priorities for many farms [and] they will be important to farmers, and thus to educators and the agricultural service industries as well” (p 91). This new market orientation has become a significant influence on the ways in which farm families manage their operations and choose to diversify their income streams and continues to persist into present day management decisions.

Off-Farm Employment

At one time farmers who worked an off-farm job were considered to be ‘part-time farmers’, however, off-farm income is increasingly recognized as an important strategy full time farmers utilize in order to maintain household hold income levels and satisfy family member interests and desires (Barlett 1986c; Barlett 1993; Evans and Ilbery 1993;

Jackson-Smith 1999; Smithers and Johnson 2004). The following section reviews the influence of off-farm work on farm structure.

Off farm employment is an important dimension when studying adaptation and change at the RUI. Goetz and Debertin (2001) note that in the U.S. off-farm employment

contributes 53% to the household income of an average farm operator. However, the

researchers question if non-farm employment is able to buoy farm income and maintain

farms by supplementing income, or if off-farm income in fact facilitates the loss of farms.

Examining farm loss rates across counties between 1987 to1997 Goetz and Debertin

(2001) observed two opposing affects. Off-farm work accelerates the exit from

production agriculture, but only after counties start to experience a net loss of farmers.

They found that higher levels of off-farm employment reduced odds that a county lost

farms due to the stabilizing effect of off-farm income, but in counties already losing

farms off-farm employment accelerates exits from farming. As economists the authors

posit their research findings are due to the reduced transaction costs of finding a job, as

the experience of farmers with off-farm employment increases the flow of information

about available jobs and thereby lowers the risk for farmers looking for off-farm work.

Goetz and Debertin (2001) also found the physical and structural characteristics

of a farm influence exit rates at the county level. Family farmers and/or farmers who

operate their own farms exited from production agriculture at lower rates then did nonfamily farmers. A higher average value of land and buildings, a greater use of irrigated land, higher levels of farm program payments, a greater share of land devoted to farms in a county and a higher local unemployment rate each reduced the odds that a county lost farms. However, in counties already losing farms, these same characteristics plus an increased population density accelerated the loss. Counties producing poultry, cattle- wheat, sorghum, fruits, vegetable and nursery crops, and sheep lost farms at lower rates then did counties dominated by corn-soybean, hog, and tobacco commodities. Although

Goetz and Debertin mention the effects of population density, they don’t distinguish their analysis by rural vs. metro vs. adjacent-metro county analysis.

The rich research tradition of inequality in sociology has established income is correlated to education level, earnings and wealth (Ganzeeboom et al. 1991). Studies conducted by Mishra et al. (2002) have found this pattern to hold within the farm sector too. However, the caveat in the farming sector is that farmers with more education tend to work more off the farm. Therefore as a farmer’s education level increases, income from farming decreases and income from off-farm sources increases, and this is particularly true for farmers located in urban areas (Mishra et al. 2002). These results suggest that farm operators tend to allocate more time to jobs that can improve their earnings capability and less time to farm operations and management. Off-farm work can maintain a farm through hard times, provide a farm family with health insurance benefits, and allow a farm to pursue the intrinsic reasons it has for farming while allowing the family to maintain a certain standard of living that correlates to the larger general population.

Examining the adaptive strategies southern small holder tobacco farmers utilize to adjust to macro structural pressures, Gladwin and Zabawa (1986) and Schulman and

Green (1986) find farmers pursue three options. They either get larger, get out or, find off-farm work. The probability of farm survival is linked to land tenure, on-farm and off farm labor, and total farm income. Although off-farm work increases total household income, it decreases time available for labor intensive crops (such as tobacco) thereby producing an unstable part time farmer and reducing the chances for long term farm survival (Schulman and Green 1986). One of the touted benefits for farmers at the RUI is increased access to off farm wage labor that can supplement farm and household income

(Heimlich and Anderson 2001), however, the experience of small holder tobacco farmers may provide a cautionary tale for farmers at the RUI. Many of the alternative crop production strategies promoted as being well suited to the RUI are labor intensive (e.g. fruit, nut, vegetable, nursery and greenhouse production). To maintain the desired standard of living farmers may choose to pursue off-farm work which in turn will structure the types of production and management systems they adopt, and ultimately affect farm survival.

Income prospects have been found to affect an heir’s decision to take over the farm. Hennessy (2002) found that heirs with a higher level of education were less likely to take over the family farm compared to heirs with lower levels of education whose potential earnings were comparable both in- and outside of agriculture. While the potential for income generation is a key determinant in ensuring the successful intergenerational transfer of farmland, the way in which that income is derived has implications for land management decisions. The effect of education on farm

management at the RUI is not well understood. Generally farm operators with higher

education levels are more likely to adopt new technology and management strategies in

order to improve production and earnings. However, at the RUI the question becomes do

farm operators and farm households with more education use their skills to create new

alternative food and agriculture enterprises or do they use these skills to find off-farm

high wage labor, or some combination of the two?

MODELS OF DIVERSIFICATION STRATEGIES

The following section reviews the empirical research examining the intersection

of on-farm and off-farm diversification strategies. The majority of studies are based on primary data collected in the European context (in the midst of European Union Common

Agriculture Policy reform) and examine farm adjustment in rural and peri-urban locals.

Although these studies are not focused on farm adaptation at the RUI, diversification or

‘pluriactivity’ as a livelihood strategy is a structural phenomena of late industrial society that is frequently utilized across rural, urban and peri-urban locals (Kinsella et al. 2000).

To maintain the farm business, the enterprise may go through a process of restructuring where resources are deployed and redeployed within the farm and through diversified businesses on or off-the farm (Bowler 1999; Lobley et al. 2002). Rural livelihood strategies are a complex interplay between human, physical, natural and social assets, and the wider economic, political and technological climate (Kinsella et al. 2002) and these strategies become more complex as farms seek to incorporate post-productionist strategies (Marsden 1998; Evans et al. 2002; Marsden et al. 2002) into their enterprises.

As such, these studies add complexity and depth to the Johnston and Bryant (1987)

Heimlich and Anderson (2001), and Smithers and Johnston (2004) models by expanding on farmer agency to examine the varying combinations of adaptations farm families may pursue to ensure farm survival.

Models of Farm Diversification and Pluriactivity

Lobley and Potter (2004) note that the persistence of family farms in the U.K. is

accompanied by changing relations to capital and changing land ownership patterns.

While family farmers continue to be integrated into vertical supply chains they simultaneously retain flexibility and freedom in adapting to the changing demands of

markets and technology. A core of professionalizing farmers confident in their ability to

fine tune their businesses to changing markets and retail demands demonstrate the ability to adapt to structural changes in the sector. Land ownership and use is becoming increasingly complex and differentiated reflecting the number of increasing farm types

and ways one can claim to be a farmer. The authors predict that for many disengagement

from farming is inevitable, but it is a subtler, prolonged and spatially differentiated process compared to the radical abrupt mass exit from farming academics once envisioned. Disengagement encompasses multiple forms ranging from on-farm and off- farm income diversification, to the exit of traditional farmers followed by their replacement with lifestyle farmers marginally dependent on agriculture for income.

Lobely and Potter (2004) survey six different localities with varying access to metropolitan areas, and different economic bases in order to understand the types of adjustments (both structural and management oriented) and track evidence of

disengagement among the dominant farming types in rural England. Out of 255 respondents, over 84% of the sample was at least second-generation family farms.

Overall, localities exhibited relatively low levels of change in relation to entries and exits

from farming; however the sample did exhibit significant shifts in enterprise mix, levels

of diversification and on-farm employment. The majority of the sample was committed to

maintaining agriculture as a dominant income source far into the future. Small and

medium farms predominantly utilized strategies that reduced labor and liquidated existing

assets in order to invest in more diversified income streams.

Lobely and Potter (2004) create a typology to outline the distinct levels of

disengagement from mainstream agriculture. Respondents ranged from ‘traditional

agricultural restructurers’ to ‘leavers’ who plan to exit farming in the next five years due

to a lack of an heir. The types include: 1) Traditional Agriculture Restructurers - full

time farmers, this group was most likely to expand farm size, intensify and emphasize

‘efficiency’ and have repositioned their businesses to cope with future challenges; 2)

Diversifiers – the farm strategy is to reduce economic centrality of agriculture within the

household and diversify income sources via on- or off-farm diversification; 3)

Agricultural Integrators - diversify into activities closely related to agriculture, e.g.

consulting or contracting, these farms tend to be managed by younger, more educated

operators; 4) On-farm Diversifiers- these farmers are actively converting agricultural land

and buildings into non-farming uses, e.g. camp sites, bed & breakfast; 4) Off-farm

Diversifiers – are actively simplifying and downsizing the farm business in order to

facilitate off farm working and off farm businesses; 5) Capital Consumers- this group is

actively withdrawing assets from farming, reducing size of land and non-land assets. 69

Typically these farmers are characterized as elderly or retired, uncertain of succession but

unable or unwilling to give up farming entirely, and; 6) Leavers – these farmers plan to

leave farming within five years due to lack of a successor.

Similar to Potter and Lobley (2004), Shucksmith and Herrmann (2002a) examine

pluriactivity among farmers in the Grampian Region of the U.K. Their analysis is

grounded in the theoretical concept of habitus (Bourdieu 1977) which allows for an

individual to engage in a range of (sometimes opposing) actions and acknowledges a

farmers’ disposition and therefore their actions may change slowly over time. As a

whole habitus provides a framework for understanding different adjustment adaptations

that may or may not make sense from an objective standpoint as family farm values are a

merging of modern (economic) and traditional (land ethic) values. A cluster analysis of

60 farms reveals six distinct groupings distinguished by decisions regarding land, labor,

capital and succession priorities.

Shucksmith and Herrmann (2002a) define their typology accordingly: 1)

Contented Monoactives – large, multi-generation, prosperous farms almost exclusively

reliant on farm income for household income. Many carry high debt loads, and prioritize

the intergenerational transfer of the farm. This group is least likely to pursue pluriactive

activities other than other farm-related work; 2) Potential Diversifiers – slightly bigger

farms, these farmers were more likely to purchase a farm. With a strong farmer identity, they place value on intergenerational succession. As risk takers this group has greater

occupational mobility, they are more likely to adapt and engage in on-farm

experimentation. In search of maximum profit, this group is most likely to diversify into

new post-productivist agricultural enterprises; 3) Hobby Farmers – are largely small 70

farms, operators are generally new entrants with off farm jobs. Farm receipts contribute

little to houshold income and few operators regard themselves as a farmers, only half considered family succession important; 4)The Agribusinessmen – are a small group of farmers with large farms, high debt levels, who obtain the majority of their income from farming, and are more likely to rent than own farmland. These farmers think of themselves as businessmen as opposed to farmers, having a higher level of agricultural education, they are more likely to experiment and utilize capital intensive modern conventional management systems; 5) Struggling Monoactive- largely medium sized farms, carrying low levels of debt, and obtain a moderate amount of household income from farming activities (the remainder originating from social transfers or family off- farm employment). Although operators have a strong farmer identity many would have preferred another type of job but saw no alternatives to farming. These attitudes tended to hinder the development of survival strategies and the socialization of children into farming; and 6) Pluriactive Successors –smaller farms, operators have a stronger farm identity and farming background than hobby farmers, off-farm jobs are taken to maintain household income while fulfilling the desire to farm.

In a similar vein, Kinsella et al. (2000) examine pluriactivity in Ireland in a remote parish with poor public transport, bad soil and topography for agriculture, yet characterized with unique scenery ideal for tourism. The study finds pluriactivity is the rule for farms in this region due to economic push factors (low income from farming and few off-farm employment opportunities) and the sociocultural pull factors.

Differentiating households based on historical developments over time, the authors identify three types of pluiactive farms: 1) Old pluriactivity - these farmers have been

engaging in off-farm employment for more then one generation, they are generally older farmers continuing the family tradition; 2) Modern Pluriactivity – off-farm employment is a new phenomena for these farmers. They are generally younger, have young children and either the operator or spouse works off-farm; and 3) New Pluriactivity – as new

entrants, this group tends to be younger, exhibits higher education levels and began

farming either through inheritance or the purchase of land, farming provides a ‘green and

pleasant lifestyle’and ideological considerations have primacy (Kinsella et al. 2000).

Smithers and Johnson (2004; Smithers et al. 2004) and Smithers et al. (2004) find

a similar pattern among farmers in Ontario, Canada operating in an urbanizing area.

Farmers fell into three broad groups -- expanding, stable-persistent state, and in the

process of contracting. Within each broad group, Smithers and Johnson were able to

classify farmers into six distinct typologies. Expanding farms included those who were 1)

farm focused, and 2) assisted growth. Among both groups production is the primary

goal, and enterprise expansion was occurring both with and without outside sources of

income. Stable farms consisted of farmers characterized as 3) persisting, and 4) hobby

farms. Farm income is inadequate to support the farm, the majority of household income

is from non-farm activities and expansion is not a goal. Contracting farms included those

on a trajectory of 5) forced down, and 6) winding down. In both cases farmers are

moving to a state of disinvestment and eventual exit.

Diversification strategies are both economic and cultural in nature (Hinrichs 1998;

Auken 2006). The different types of diversification strategies identified by Potter and

Lobley(2004), Shucksmith and Herrman (2002b), Kinsella et al. (2000) and Smithers and

Johnson (2004) suggest rural development strategies, economic development and rural

land use policies will affect each group of farmers differently. Policy aiming to create

structural change, production expansion or adoption of alternative types of enterprises as

a means to ensuring working farms persist at the RUI will need to take into account the

varying farm structure, goals and priorities of each group in order to create meaningful

development policies.

Alternative Farm Enterprises as Diversification Strategies

Alternative rural development strategies are being explored as countries and

governments pay closer attention to the relationship between the environment and the economy. European and North American academics such as Hall (2004), van der Ploeg et

al. (2000), Feher (2002), Goetz (2001), Gorz and Kurek (1998), Jaksch (2001), Kostov

and Lingard (2002), Kovach (2000), Ray (2000) and Siebert (2001) have suggested

building multifunctional rural economies based on balancing organic agriculture, low

input farming, direct marketing and agri-tourism featuring recreational opportunities in pristine environments. Debates focused on strategies to save farmland and working agricultural landscapes have concentrated their arguments on finding strategies that

produce profitable farms to ensure farming remains an attractive employment option for

future generations of farmers (Lobely and Potter 2004; Hennessey 2002). In Europe

these strategies are in line with the European Union’s development patterns, shifting

away from a sectorial approach (agriculture) to a territorial approach (rural) (Shucksmith

and Chapman 1998). In North America these strategies are promoted in a larger body of

entrepreneurial farming strategies as an active attempt to maintain and often save the

small and medium sized family farm. The primary focus has been on the potential for

alternative food and agricultural enterprises to create new sources of profit for farmers as

they move production out of low-profit bulk commodity crops.

Although a range of academics (Kloppenburg et al. 1996; Feenstra 1997; Gale

1997; Hinrichs 2000; Hinrichs 2003), farm organizations and NGOs (OEFFA 2005;

PASA 2005) and to an extent government agencies (SARE 2004) have advocated for

active adoption of these strategies, little empirical work has been undertaken to examine

how these approaches manifest themselves on the landscape. Sonnino (2004) points out

these development policies have been discussed at an abstract global level and “there has

been little progress in the attempt to link the theory of sustainable rural development to

its empirical dimension” ( p 285). The following section outlines the literature

examining how alternative agriculture in the form of organic, quality production and

labeling, direct marketing, agritourism, and multifunctionality have been promoted and

evaluated in providing farmers with alternative and profitable income streams.

To further refine diversification strategies in an era of post-productivist transition

Bowler et al. (1996) focus on the development of ‘alternative farm enterprises’ or (AFE)

in marginal agricultural lands in the North Pennies region of England. The authors define

the term ‘alternative’ as “the introduction of a non-traditional source of income into the

pre-existing farm business, a process widely recognized in the published literature as

‘farm diversification’” (p 285). The “AFE is a new (innovative) on farm enterprise that

involves the conversion, diversification or extensificaiton of the farm business” (Ilbery et

al. 1998 p 357). Bowler et al. (1996) distinguish between external stimuli (regulations of

the state, institutions, physical environment, macro level processes) and internal stimuli

(changes in farm profitability, family stage and life course, etc.) that can lead to a 74

restructuring of the farm business. Citing Marsden et al. (1992) the authors assert farm families can adopt three broad strategies to these stimuli including: capital accumulation; economic survival; or no change.

To examine adjustment strategies in a post productionist transition Bowler et al.

(1996) utilize a discriminate analysis of 200 farms to analyze farm business, farm and

farm household characteristics in order to develop a model of adaptation paths. The seven paths identified included: 1) the industrial model of production agricultural development,

typified by an increase in scale, intensification, and specialization that relies on

traditional farm products or services; 2)AFE type I, a reallocation of farm resources into

new agricultural products or services on the farm; 3) AFE type II, a recombination of

farm resources into new non-agricultural products or services on the farm; 4) Other

gainful employment is the phenomena of off-farm employment; 5) maintaining the

traditional model of conventional farm production or services; 6) winding down to semi

retired or hobby farming; and 7) retirement or an exit from farming. Although there are

seven distinct paths, farm families may be pursuing multiple strategies simultaneously,

and it may be difficult to impossible to isolate only one practice.

Bowler et al. (1996) find that external (not internal) stimuli are the primary reason

farms search for AFEs. The need to maintain income followed by market opportunities,

developing underutilized farm resources, creating employment for family or non-family

members are the reasons for pursuing alternative enterprises (primarily off-farm income

and AFE type II). Overall they find the contribution of AFE income varies, 32% of the

AFE activity contribution less then 10% of total profit to the farm, while 40% of AFE

farms obtained more then 60% of farm income from these activities, while food retailing

and processing tended to contribute more towards farm profits relative to other types of

activities. Demographic characteristics (farmer education level, farm size, proportion of

income from dairying) and geographical location (distance to a large town) also

influenced the success of an AFE. Although Bowler et al. (1996) do not examine the

particular circumstances surrounding the decision to enter or leave a particular business

development pathway, they suggest the application of life course theory at the farm level

would enhance our understanding of the changes occurring.

Building on Bowler et al. (1996) Ilbery et al. (1998) explore the dynamics behind

farm based tourism (FBT) among the AFE type II in the Pennies region in England. As a

development strategy FBT may be particularly important for more agriculturally marginal

areas that can shift into enterprises that take advantage of their unique scenery to build up

tourism and recreation opportunities. The authors find the adoption of FBTs are

moderated by the size and type of farm, gender relations within the farm family, family

life cycle, succession, and educational and occupational experiences of family members.

AFE-FBTs can be categorized into those primarily oriented to accommodation (serviced,

self catering, and camping/caravans) and recreational (livery/pony trekking and sport

leisure). Ilbery et al. (1998) find that serviced accommodations account for the largest

growth area, as couples will use underutilized space (bedrooms children have grown out

of) to develop small bed and breakfasts. However, examining the economic profits

derived from the AFE farms in the sample, the authors conclude these enterprises do not truly “transform the economic situation” of farms already experiencing low profits, however they do seem to increase the chances of farm survival. In a similar study

Fennell and Weaver (1997) found vacation farms contributed less than 1% to farm

income and could not be considered a long term strategy for farm economic development.

These studies suggest the adoption and implementation of AFAE type strategies

do not necessarily guarantee farm viability. Rather the decision to engage in AFAEs is

the result of a complex set of household decision making factors that weigh labor

availability, life cycle affects, and location.

Diversification through Food Labeling

Local food system advocates and environmental groups have simultaneously tried

to increase farm profitability and encourage sustainable consumption by creating labels

and advertising campaigns that project a firm’s ethical and environmental behavior, thus

instituting a market system that attempts to reward and regulate fair labor practices and

responsible land stewardship (DeLind 2000; Goldstein and Bensel 2000; Raynolds 2000;

Raynolds et al. 2007).

The pressure to find alternative sources of income from farm products has

encouraged increased participation in alternative markets that return more of the food

dollar to the producer rather then to the middleman. Renting et al. (2003) and Sage

(2003) examine ‘alternative food networks’ (AFNs) to understand the effectiveness of

these strategies as rural development tools in the European context. More specifically

Renting et al. (2003) focuses on the production ‘codes’ of organic, regional, and artisanal,

to identify three categories of ‘short food supply chains’ (SFSCs) able to create social

relations of regard. Although these SFSCs and AFNs were initially promoted as a way for

more marginal rural areas to compete in a climate of market liberalization, they have in

fact been primarily adopted by core agricultural areas. These findings raise questions

about the ability of these alternatives to be effective new rural development strategies.

Renting et al. (2003) contends these SFSCs have the ability to short circuit long

anonymous food chains to construct a transparent exchange weighted by value laden

information. The authors go on to differentiate quality markers into two categories

distinguished by 1) place of production or the producer, and 2) quality in terms of bio-

processes. Due to data limitations the authors use proxies for SFSCs by examining

organic, quality production, and direct sales in Europe. They find on aggregate, 20% of

farmers the in Europe are engaged in direct selling, 12% use quality production and 1.5%

are organic, however, they observe substantial differences between countries. In Italy,

Spain and France quality production and direct selling rest on the long held gastronomic

and cultural traditions while in the UK, Netherlands, and Germany quality standards are

based on modern definitions of environmental sustainability and animal welfare. The

economic benefits of these strategies are not equally distributed, the countries most likely

to exhibit meaningful socio-economic returns from these activities are Germany, Italy

and France. These findings reinforce Sonnino’s (2004) critique of the uneven

distribution of benefits emanating from alternative production and marketing systems.

These SFSCs are part of an economic development strategy Marsden and Smith

(2005) term “ecological entrepreneurship.” Part of the eco-modernization movement,

these strategies are intended to be a form of sustainable wealth creation or ‘value-capture’

where entrepreneurship is developed in response to ecological, human, social and

manufactured capital. Working on the principals of capitalism, this type of

entrepreneurship should allow for the disposal of wealth that satisfies consumption needs

while re-investing in local people, institutions and the environment to create long-term sustainable economic engines.

In light of changing economic structures, entrepreneurship has become a popular economic development stimulus promoted across territorial spaces (rural, urban and peri-

urban) and across sectors (agriculture, manufacturing, and services) (Partridge et al.

2008). Food and farm entrepreneurship is generally focused on strategies catering to a

more urban oriented customer. The North American Direct Marketing Association

(2007) proclaims on their web site: “The consumer today is not only looking for quality

products, but also a quality experience. Our members have developed entertaining ideas

to attract more guests and keep them on their farms longer.” Farms located at the RUI in

particular are primed to take advantage of entrepreneurial farming activities (Bryant and

Johnston 1992; Paquette and Domon 2003; James 2006) however, little empirical work

has been undertaken to examine how household factors affect the decision to adopt these

types of enterprises and in turn how these practices will contribute to the long term

persistence of working agricultural landscapes at the RUI. The next section reviews the

literature examining farm household dynamics (including succession) that can affect farm

adaptation decisions and examines how the turn towards entrepreneurship reflects

broader cultural changes in the family farm (Figure 2.10).

Availability of an Heir

Figure 2.10 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Availability of an Heir.

SUCCESSION: PASSING DOWN THE FAMILY FARM

There is a rich research tradition in rural studies examining the link between intergenerational transfer of farmland and the persistence of family farms (Keating and

Munro 1989; Kennedy 1991; Burton and Walford 2005a). Succession has cross cultural significance, Bennet (1982 p 368) states that “every agricultural system has to devise a way to protect the continuity and integrity of the enterprise and its family if that’s the social group in charge.” Gasson and Errington (1993 ) define three distinct but related processes in the intergenerational transfer of the family farm business, which are: inheritance, the legal transfer of ownership of business assets; succession, the transfer of managerial control over the use of these assets; and retirement, the withdrawal of the current manager from active managerial control or farm work. This research project focuses on succession, as a multi-stage process influenced by socialization (Dumas et al.

1995; Keating and Little 1997), ethnicity (Salaman 1992), values (Glauben et al. 2002;

Glauben et al. 2005), lifecycle effects (Bennett 1982; Potter and Lobley 1996b), family 80

structure (Burton and Walford 2005b; Burton 2006), and personality/business orientation

(Taylore et al. 1998). The following section reviews the literature examining the significance of household level variables and succession in farm enterprise adaptation and reproduction in the U.S. and Western farming systems.

The Current State of Succession in the U.S.

In light of an aging and shrinking farm population the importance of succession and the successful intergenerational transfer of farmland and working farms has been a

matter of government concern. In recent years the USDA has published a series of

reports examining the consequences of an aging farm population (Gale 2002) and

succession planning (Mishra et al. 2002; Allen and Harris 2005; Mishra et al. 2005a;

Mishra et al. 2005b) for the American farm population. These reports state

“Retired and retiring farm operators account for over a fourth of the principal operators of U.S. farm businesses. Their succession decisions and retirement plans are of considerable importance to the farming community and the future structure of agriculture. Continuity of the family farm and the family farm sector is highly dependent on successful intergenerational transfer following the retirement of a farm operator.” Mishra et al. (2005a pg 7)

In the U.S. one quarter of principle or primary farm operators are retired or will retire in next five years, representing a population of 558,000 farmers who operate 87 million acres of land and control over a third of all farm assets (Mishra et al. 2002). The average age of this group of farmers is 62, they generally have over 31 years of experience farming compared to the national average of 23 years of farming experiences.

(Gale 2002; Mishra et al. 2002). Understanding the fate of these farms and agricultural lands is critical for developing future American farm and food systems.

The continuity of farm reproduction is challenged by the eminent transition of a shrinking, aging and experienced farm population to a new generation. Only 6% of farmers are under 35 and half of these are beginning farmers. Half of all farmers are between 45 and 65 years old, and 24% plan to retire in the next five years (Mishra et al.

2002). Over a quarter of all farmers and half of all agricultural landowners are age 65 or older. This is in stark comparison to the general labor force where those 65 and older cohort account for only 3% of the population (Gale 2002). This older group of farm operators and landowners are staying in farming longer then previous generations.

In 2003 the annual USDA Agricultural Resource Management Survey (ARMS) included specific questions designed to examine succession decisions and retirement income among farm households. Mishra et al. (2005b) report that among farmers planning to retire in the next five years, a fifth claim they plan to rent out their farm and a fifth plan to sell the farm. The remainder plan to turn over their operations to others or convert their land to other uses. The 42% of operators who plan to either rent or sell their land points to a substantial portion of the 87 million acres currently operated by this age group of farmers will become available on the market in the next few years. The majority of farmers (68%) indicating a planned retirement were geographically concentrated in the

Heartland, Eastern Uplands, Northern Crescent and Fruitful Rim, with the greatest concentration existing in the Heartland (21.2%) and the Northern Crescent (19.1%).

These operators produced $82,267 worth of the agricultural output in 2003 compared with $76,788 for all U.S. farmers thereby accounting for 7% of the value of agricultural production. These farmers tend to specialize in beef cattle, cash grains and oilseeds.

Mishra et al. (2005) found succession planning differed by farmer background, farm wealth and farm type. Farm operators most likely to transfer their farm operation to another family member were raised on a farm, and neither they nor their spouse worked an off-farm job. Farmers who declared an off-farm job to be their primary occupation, tended to operate smaller farms and were more likely to rent or sell their farm than to have a succession plan in place. Nationally, only 27% of farm operators indicated they have a succession plan. Among farmers who did, 87% had identified a successor, most often in the family. A small majority of these farmers (53%) indicated their successor participates in farm business activities, and 38% of successors participate in management activities and decisions. Only 34% of farm operators retiring in the next five years had a succession plan, and 80% of these households have a family member taking over the farm. Examining the relationship between farm wealth and succession planning, Mishra et al. (2005) found the larger the net worth, the more likely a household had a succession plan. For instance 44% of households with a net worth over $1 million had a succession plan compared to 31% of households with a net worth between $500,000-$999,999 versus only 16% of households with a net worth between $50,000-$99,999.

To date the U.S. Census of Agriculture has not included any succession survey questions. To compensate for the lack of direct indicators the USDA has attempted to understand farm succession planning using secondary data analysis. Allen and Harris

(2005) analyzed 2002 census of agriculture data in an attempt to identify farm operations that appear to have a succession plan in place. Variables utilized included: the age structure of principal, second and third farm operators; primary and secondary operator sex; tenure; type of farming operation; off farm work; number of households sharing in

farm income for operations with single and multiple farm operators; and the difference between ages of principal operators and second operators by gender. They found that

37.7% of all farms reported multiple farm operators – a point indicating succession potential, however further investigation revealed that the majority of additional operators were most likely spouses. Their core methodology rested on counting multiple operators with at least a 20 year age difference among reported multiple farm operators as evidence of a possible succession plan. Over 60% of male principle operators less than 25 years

of age and almost 50% of male operators between 25 and 34 years of age with a male

second operator have a second or third operator who is at least 20 years old.

Allen and Harris (2005) also find the potential for a succession plan varied by

state, income sales classes, and type of farm. Farms with multiple operators and sales of

$250,000 or more were nearly twice as likely (38.8%) to have multiple generational

operators than those farms with less than $100,000 in sales (21.4%). Dairy, cotton,

tobacco, grain and oilseed farms were most likely to have multi-generation operators.

Allen and Harris (2005) conclude only 9.1% of the 2,128,982 farm operations analyzed

qualified as demonstrating evidence of possible succession planning under the criteria of

having multiple generations presently reported as farm operators. Given these findings,

Allen and Harris (2005) assume there “must be many other succession approaches in

place- ones that do not require a successor to be presently in place as an operator.”

Allen and Harris acknowledge their methodology does not accurately account for the actual number or nature of succession planning in the U.S., however their initial estimates

do raise serious concerns about the transition of active farm operations from one

generation to the next in the midst of a rapidly aging and shrinking farm population. The following section reviews the literature examining the process of succession.

Farm Succession Research

The survival of the farm and number of farms on the landscape is dependent on the successful intergenerational transfer of land (Hennessy 2002; Gale 2002). The way a family farm reproduces and adapts to changing local and super local conditions is highly dependent on internal household dynamics. A significant body of research examining internal farm household processes helps explain how micro level processes affect macro level changes. A significant point of emphasis is the need to not only understand the influence of economics but more importantly how access to land and the effect social and cultural factors have on influencing the types of farming styles, land management

decisions and ultimately the decision to farm. Succession affects individual farm

trajectories and can facilitate or constrain farm decisions and affect the way capital is

accumulated and maintained (Gasson 1988). This section reviews the influence of land tenure and succession as social processes influenced by culture, socialization, and

lifecycle effects.

Land Access

Access to land is a precondition for farm succession and reproduction. This

section reviews the connection between land tenure and succession as socially

constructed systems. Anthropologists emphasize land tenure is not a natural system;

rather it is a socially constructed system of rights and privilege that human groups use to

protect their resources and resource areas. As a social system land tenure helps to reduce

and avoid conflict, it creates stability by replacing environmental uncertainty with institutionalized systems of resources (Adler 1996; Carter and Yao 2002).

Salamon (1998) notes “those who control land in a community act within a cultural system derived from national, community and family layers.” A community shares a particular repertoire of practices based on beliefs surrounding kinship membership, farming as a way of life, and the value of an individual person, all of which influence how land should be handled. These views mediate if the community sees the family or individual as a rightful owner or heir, and culturally determines who is responsible for assigning intergenerational land transfer, and for the appropriate division of land (Salamon and Davis-Brown 1986; Salamon et al. 1998). Thereby land is a component of local identity and is a symbolic vehicle able to perpetuate identity, “land provides an indestructible material symbol for family and community attachment”

(Simmel 1898 as cited by Salamon 1998 et al. p 167).

Adler (1996) continues to reinforce the argument that land tenure is a social

system by describing four major characteristics of land tenure grounded in social

systems. The four characteristics are: 1) human access to important resources is

generally accomplished through social mediation. The flexibility of human systems is an

outgrowth of the flexibility of natural systems, which allow groups and individuals to

determine which strategies, e.g. cooperation vs. exclusion are most appropriate in a given situation; 2) Resource access systems are multifaceted and resource rights vary depending on the types of resources being exploited. Land systems are never either purely communal or purely privatized, nor is there necessarily a one to one relationship

between the size of a resource area and the number of people who have access to it; 3)

Resource access rights can vary, not only with respect to the type of resources being exploited, but also the temporal duration over which rights are recognized. Land tenure is not a static concept; it is a malleable concept that can change with each generation; 4) the anthropological concept of land tenure is not synonymous with the archeological concept of land use. Land use refers to the physical manipulation, utilization, and modification of the landscape.

At the RUI land is a scare resource, therefore as Salamon (1986) and Adler (1996) indicate, the way farm families achieve access to and utilize their land is a reflection of larger household processes, lifecycle effects, motivations and values. These values and motivations can be based in substantive, instrumental and stewardship ethics.

Culture, Succession, Land Tenure and Land Ethics

In North America., research conducted by Salamon (1992, 1998) demonstrates culture has direct implications for how agricultural systems and communities evolve. By conducting case studies throughout rural Illinois, Salamon created a typology of farmer types based on ethnicity and distinct agricultural/land ethics. The Yeoman farmers descended from German immigrants, for them land is “a sacred trust maintained by achieving continuity of family land ownership and an agrarian way of life in a particular ethnic community” (1992 p 93). This group of farmers can be contrasted to the

Entrepreneurs, decedents of English immigrants, as these farmers see “land as a commodity, and farming is a business in which accumulation of land is a means to

increase family wealth and power” (1992 p 94). These discrete land ethics create different goals for each group.

While the yeoman aim to reproduce at least one viable farm and farmer in each generation, the entrepreneurs see their operations as businesses that prioritize short run profits over intergenerational farm succession. Following these innate patterns, the strategies the two groups utilize to fulfill their goals and expectations diverge as well.

Yeoman tend to have smaller more diversified farms as they prefer to own their land, expand only to the capabilities of the family, maximize kin involvement, and avoid debt.

In comparison the Entrepreneurs tend to manage larger then average specialized farms, and combine owned and rented land. Expansion is limited only by access to capital, efficiency and a willingness to take risks (such as debt) to meet business goals.

Studies have found the desire to farm as an occupation, life-style and the desire to pass these values down to ensure the successful intergenerational transfer of farmland is just as important as strategic business behavior (Jackson-Smith 1999) and often

outweighs neoclassical theory defining farmers as pure profit maximizers (Hennessy

2002). These cultural characteristics and submicro-level household dynamics have long-

term implications for the persistence and resilience of agriculture at the RUI.

The Process of Succession

The core of the Johnston and Bryant (1987) model focuses on the farm operator as

the change agent influenced by local, non-local and farm based factors. Farm and

personal characteristics, including a farmer’s management ability and identification of an

heir are seen as key factors mediating an operator’s decision-making processes. The

research on farm succession demonstrates that a farm business cannot be understood without understanding the family that created it. As Salamon demonstrates, it is misleading to concentrate only on the primary farm operator in explaining changing farm production, marketing and survival strategies, when these processes result from internal household dynamics reflecting the negotiations between spouses, children, and extended kin. Bennet (1982) employ the term ‘agrifamily system’ to contextualize the need for a comprehensive inclusion of the entire family as part of the farm decision making system.

Bennet’s study of farm families in Saskatchawan found it was in fact rare for a male farmer to act independently as a single operator, rather farm decision making is characterized by a complex system of cooperation, competition and interchange between husbands, wives and children. The process of resource allocation in the farm business and household is an ongoing process of collaboration, conflict, and tension management as individual and household needs are deferred, granted or sublimated. He finds the more successful farmers were not individual ventures, but were built on partnerships between women and men.

As noted earlier, family farms are unique enterprises where the household and farm business are intimately linked and where the “economic and social activities of the farm family or family farm are a behavioral continuum separable on some purposes and not on others” (Bennet 1982 p 128). Bennet’s analysis of farm reproduction found that the most important variables are not necessarily related to mode of production, income, quality of enterprise, or style of management; rather the social relationships within the farm family are of utmost importance for the succession process. Gasson and Errington

(1993 ) point out succession is not just the transfer of manual work, but of management

control. Bennet (1982) identifies six key predominantly social variables for the succession process including: 1) the socio-economic status of the household at or shortly after marriage (household dependency); 2) the mothers aspirations and expectations for their sons careers (especially probable successor); 3) the fathers aspirations and expectations for sons careers (especially probable successor); 4) the sons actual career choice (heir’s fate); 5) the type of agreement present operator had with their father; and

6) the type of agreement the present operator has with their son. Bennet found that the succession process is most successful (that is most likely to result in the intergenerational transfer of the farm enterprise to the next generation) in households that have high quality businesses, where the operating couple has a good relationship with the husbands’ parents, and a positive relationship exists between a parent’s desire for their children and their son’s (heir) fate.

Gasson et al. (1988) note “inheritance takes a few seconds, succession can be

over decades” (pg 23). The rate at which heirs transition into full operators has been to

be fairly uniform across western agricultural systems (Commins and Kelleher 1973).

Hastings (1984) as cited in Gasson and Errington (1993) outlines five stages in the

succession process. First, socialization begins at birth; throughout their lives children

develop their personality and attitudes towards their family and farm life. The ‘technical

apprenticeship’ is the second phase that begins when children start to work on the farm and responsibilities expand into general farm work, day to day planning and supervision

of staff. This stage ends when the younger generation achieves approximately 30-40%

involvement in the management (generally after working at home for four to six years, at

which point the father is in his mid-50s). The third phase is split into two distinct

partnership stages. In partnership A, the son is involved with the technical management decisions related to crop and livestock and in the planning of long term projects. In partnership B sons take more responsibility for staffing, crop planning and enterprise

balance, and are in charge of 65% of managerial responsibilities. In the fifth phase the

son is actively involved in buying and selling as the age of his father increases and his

health declines (generally in his late 60s). The final transfer is unlikely to occur until age

and health concerns require the father to retire. Hutson (1987) adds an additional sixth

stage where a new venture is planned and then implemented by the son in order to take

advantage of the additional managerial capacity of the farm and to ensure adequate

income is available to support two generations. Sons graduate as they transition out of

just providing information and advice to using their own judgment to make decisions

themselves (Hastings 1984).

The stages outlined by Hastings (1984) and Hutson (1987) are identified by

Gasson and Errington (1993 p 212) as a succession “ladder” of responsibility which “the

successor will climb, gradually becoming more and more involved in the management of

the farm.” There is a curvilinear relationship between ascent up the ladder and farm size.

Gasson and Errington (1993) found successors on the smallest farms (less then 100 acres)

and the largest farms (greater then 400 acres) had delegated successors greater

responsibilities. They also found enterprise mix can be significant, as highly diversified

farms (with multiple production lines) present more opportunities for potential heirs to

work and exert responsibility compared to highly specialized farms (with minimal

production lines). Additionally, children may climb the rung at different rates based on

the characteristics of the father and son and heirs can also be forced up the rung faster

given an unforeseen illness, accident or marriage.

These combined findings point to two important social influences in the succession process: socialization and lifecycle effects. The way in which socialization and lifecycle effects influence farm persistence and adaptation will be discussed below.

Socialization

The importance of succession begins years before an heir officially takes over the farm, it begins with the socialization process (Bennet 1982; Kennedy 1991; Salamon

1992; Gasson and Errington 1993; Taylore et al. 1998). Socialization processes begin in early childhood, and in the case of family farm systems, are integral for developing commitment to the farm enterprise among heir and nonfarming siblings (Jonovic and

Messick 1986; Salamon 1992 ; Salatin 2001). Two observations are significant regarding socialization. One is its importance in creating an interest in the heir to return to the farm and succeed their parents and the other is the role of gender as parents decide which child to socialize into the role of heir.

The messages parents transmit to children are critical in socializing an heir to the farm. Studying family farms in New Zealand, Keating and Little (1997) identify key attributes parents look for in identifying a potential successor. Parents first watch for interest by evaluating actions and attitudes among their children, then reduce the pool of

eligible successors most often on qualifications of gender, health and commitment to the

farm. Parents must then determine how best to compensate non-farming heirs as they place the successor on the farm. Succession generally preferences sons, birth order

(oldest son or youngest) and commitment (willingness to work hard). Gasson and

Errington (1993) found a quarter of English farmers had identified a successor by the

time the oldest son was 10 years old and half by the time the oldest son is 16, while on

farms with only daughters successor identification often comes much later. Keating and

Little (1997) note how parents are often unaware of the ways in which the messages they

send facilitate or impede the succession processes. Older children generally were

cognizant that their farming skills, work habits and willingness to return to the farm when

needed influenced whether they would be chosen as the farm heir. At same time, girls knew they weren’t destined to be successors. Likewise, Gasson and Errington (1993) note

succession was more likely to occur among pre-identified heirs as parents gave

preferential treatment to certain children by assisting them with hobby farm enterprises

while still in school.

Farm heir socialization can include both positive and negative messages and feedback loops. Both Jonovic and Messick (1986) and Salatin (2001) emphasize the

importance of involving the whole family in farm management decisions and in giving

children opportunities for meaningfully contributing to farm enterprise development and

to experiment and establish their own niches on the farm. They argue children need to be

seen as more than free labor in order to ensure potential heirs see a place for themselves

in the operation and will return to the farm. Commins and Keleher (1973) found the lack

of managerial responsibility was the most common source of dissatisfaction among sons

working alongside their fathers in addition to a lack of financial independence. Similarly

Keating and Munrow (1989) found fathers refusing to relinquish control and the absence

of equal partnerships posed serious obstacles to the succession process.

The importance of alternative off-farm experiences is also a component to the socialization process. Jonovic and Messick (1986) suggest allowing children to work on

other farms and in other settings is an important revitalization strategy for farm

enterprises as these experiences can generate innovation and alternative business

adaptations. Support for these assertions can be found in studies of farm succession and lifecycle effects in Germany and Austria. Glauben et al. (2005) found that full time farmers are more likely to have identified a successor compared to part time farmers, and the successors exhibit greater participation in the farm operation earlier, are more involved in farm management decision making and tend to have more managerial and entrepreneurial experience.

Succession patterns are distinguished by a consistent predilection for traditional father-son dyad while daughters have traditionally been socialized out of agriculture.

(Salaman 1992; Bennet 1982). The difference in gender patterning is distinguished by the preference given to sons as destined inheritors who must accept the control and decisions of their parents while girls are trained to accept marriage as their primary objective in addition to the opportunities afforded to them in female sanctioned occupations such as nursing and education.

Historically a formal understanding of women’s roles on the farm has been limited due to the structure of U.S. government surveys (Jellison 1993). The influence of feminist epistemologies into sociology and rural sociology catalyzed a series of studies attempting to analyze and construct a more comprehensive understanding of the role women have in agriculture. These studies examined gendered attitudes towards the farm and the sexual division of labor (Buttel and Gilbert W. Gillespie 1984; Bokemeier and

Garkovich 1987; Tigges 1987; Rogers and Vandeman 1993; Meyer and Lobao 1997;

Chiappe and Flora 1998) and consistently found agriculture to be dominated by men with

women’s influence over management decisions to be marginalized. However, Cornell

University’s Farm Family Project found “that a distinctive feature of farming families is the desire of women, as well as men to effect intergenerational transfer of the family farm” (Coleman and Elbert p. 65). Yet, studies investigating the role of women and men on farms generally find inflexible gender roles established in farm households that guide the intergenerational transfer of farmland. Father and son teams do the majority of farm work, and sons or male heirs are the dominant inheritance group (Simpson et al. 1992;

Wilson et al. 1994). As a result, women have been socialized out of the role of farm heir, and often take on the role of taking off-farm employment or engage in informal labor activities to supplement farm income (Wells 1998).

Ironically Bennet (1982) found the socialization of women out of agriculture afforded them the opportunity to increase their education levels and become invaluable partners to their farm operator husbands. He also found many farmers were functionally illiterate yet could operate million dollar operations only with the assistance of their more educated wives who were responsible for corresponding with agencies controlling access to land, water and credit. This differential in literacy and bookkeeping was directly related to a woman’s exclusion from the farm succession process, the paradox being these women develop the managerial skills necessary for contemporary operations through exclusionary processes.

These findings reinforce the importance of understanding the farm family in order to understand the farm business. Individual family member characteristics moderate farm

production, marketing, and succession strategies. The influences of individual family

members on enterprise persistence and adaptation are further moderated by lifecycle and farm cycle business effects. These influences are explored in the following section

(Figure 2.11).

Lifecycle Stage

Figure 2.11 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Lifecycle Stage.

Lifecycle and Farm Cycle Influences on the Family Farm

Farm families are dynamic entities: they negotiate the social relationships of production and work within the context of the farm wives’, husbands’ and children’s life cycles, family cycles and farm cycles (Coleman and Elbert 1984). The effects of life cycle have been found to significantly influence farm management styles, enterprise growth, adaptation and stages of growth, persistence and decline (Bennet 1982; Potter and Lobley 1992; Gasson and Errington 1993).

Bennet (1982) examines the merging of the household and enterprise cycles that occur as operator age, age of children, skills and labor required for the enterprise coalesce. A manager’s behavior can fluctuate in cyclical alternations between innovative and conservative strategies as the enterprise subsystem redevelops at periodic intervals,

especially at the time of succession. Bennet (1982) identified three distinct (but cyclical) stages in enterprise development: 1) Starting - the enterprise experiences significant development activity and growth; 2) Establishing – the enterprise continues to be developed but at a less active level; and 3) Transmitting – characterized by the cessation

of development activity and the exit or retirement of the elder generation as the enterprise

is passed on to the next generation. These stages are mediated by four key interactions:

the period of enterprise development, an aging operator, the starting phase of a young

operator and early management.

Farm management is a behavioral adaptation mediated by household and kinship.

The influence of children has a significant effect on enterprise cycles. The majority of

active management styles occur in young couples with children as both the enterprise and

family need to generate income to achieve their reproductive potential. As an enterprise

matures it is characterized by a calmer management style, while an older phase generally

exhibits inactive management. Bennet (1982) outlined four major stages in the farm

enterprise lifecycle: 1) in the establishment stage a young couple (generally with no

children) begin to establish a viable farm or ranch operation through a complex

succession process from an older generation; 2) during the development stage a young

couple with dependent children must coordinate firm and family needs, young children require greater family income and labor at the same time the enterprise requires capital labor to develop and expand; 3) at a mature stage the family and firm needs exist in a complimentary relationship, the firms operations are well established and the couple’s children are old enough to meaningfully contribute to the enterprise. The firm enters a stage of persistence where the enterprise may simply be maintained, although it is also

common to have a redevelopment of the enterprise in anticipation of the upcoming

succession; and 4) a transition stage occurs as depending on the availability of a likely

successor an elderly couple either i. continues to operate the operation, ii. moves to town

or iii. retires on the farm and are involved in the management of the operation to the

irritation of the son and wife.

Reinhart and Barlett (1989) argue the cyclical process of development,

maintenance and redevelopment identified by Bennet (1982) increases the ability of the

family farm to survive and persist on the landscape in two distinct ways. First it creates

periodic pressure for innovation, thereby retarding the enterprise from stagnating.

Secondly it creates periods where the enterprise is resistant to innovation, where debts

and risk are minimized. Therefore at no one point in time is it likely that the majority of

all firms will try a new technology or adopt radical changes in production methods and

inputs. This ‘cyclical conservation’ actually serves to protect one group of farmers from

the fall out of potentially inappropriate or disastrous innovations. As Barlett (1993)

found during the 1980s U.S. farm crisis, farmers in Georgia who had entered into a

development phase were severely handicapped by high interest rates, rising costs and low

profits; while farmers in the maintenance phase were able to sustain themselves and

weather these adverse conditions without the threat of foreclosure and bankruptcy.

Lifecycle Influences on Farm Adaptation and Change

The slow, drawn out process of succession is further elaborated by a multi phase

model identified by Burton and Walford (2005) and Hutson (1987). In the first phase, the

child leaves school to work under their parents supervision. In the second phase parents

and children make a go of it by expanding and intensifying production systems. In the third phase new ideas or methods are implemented, children are delegated more responsibility within the family business. In the fourth phase the incumbent farmer retires and management of the farm is transferred to the succeeding generation. As the farm is transitioned from one generation to the next, towards the end of the succession process elder farmers withdraw from the day to day management decisions and lastly from the financial decisions and strategic planning. Additionally, Bennet (1982) acknowledges the importance of larger economic conditions on enterprise development, noting that in an economic downturn, the enterprise often adopts a management style characterized by a holding pattern to avoid risk and maintain the farm until a period of economic growth returns.

The family cycle has an empirical relationship to enterprise management, and the

influence of succession on farm growth and decline has been widely documented. There

is a positive relationship between the probability of a farm successor and evidence of

farm diversification while farmers without a successor are more likely to reduce their

enterprise mix (Bennett 1982; Gasson and Errington 1993 ; Potter and Lobley 1996b;

Stinglauer and Weiss 2000). Bennet (1982) found support for his hypothesis that

enterprises run by young farmers with children and enterprises that have to support a large number of people should have very active management patterns in order to increase production as a means for supporting a larger number of individuals. In anticipation of

succession, the enterprise prepares to support another family by engaging in growth

behaviors marked by innovation and expansion as operators adopt new management

strategies to grow the farm business. Johnsen (2004 p 429) states

“It appears that the resilience and entrepreneurial tendencies of family farmers…are most visible during times of dramatic restructuring, when the very ability of a farm family to ‘survive’ may hinge upon its willingness to modify farm operation.”

However, Johnsen (2004) goes on to note the ability to exercise this form of entrepreneurship is geographically uneven.

These findings were further supported by research conducted by Calus et al.

(2008) examining the economic conditions of Belgian farm households (total farm assets) as a mechanism for identifying farms with a high probability of intergenerational farm transfer. The authors use the theory of asset fixity and transaction cost theory to explain how higher total farm assets indicate a greater intention of transferring the farm to the next generation independent of farm type. Conversely, they find that lower total farm asserts often results in farm discontinuation. Calus et al. (2008) therefore propose total farm assets can be an indicator for targeting succession and retirement policies as in

Europe. They suggest funds could be used for “investment policies for those farms with possibilities for succession, and early retirement or related schemes for those that are not viable in the long run” (Calus et al. 2008 p 41).

The lack of an identified successor has been linked to a trajectory of disinvestment and decline (Gasson et al. 1988). Lobley and Potter(2004) argue farmers facing uncertain or unlikely succession withdraw from active farming, and engage in a process of capital consumption in advance of leaving the farm sector. Older farmers without a successor are more likely to engage in a process of withdrawal and eventual exit as they reduce their working hours and engage in activities less stressful and strenuous (Gasson et al. 1988). The most clear cut cases of disengagement from

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mainstream farming occur in situations preceding or following a transfer of land. Parallel to findings observed by Mishra et al. (2005) elderly British farmers without an heir were observed withdrawing from active farming, choosing to rent or lease their land in a process of capital consumption and have a more pessimistic outlook compared to those with a successor (Lobley and Potter 2004). Gasson et al. (1988) identify both elderly and younger farmers without an identified successor tend to have smaller farm holdings compared to those with a successor. However, the cause and effect of the association between small landholdings and no successor is in question, and may be a tautology.

Citing the finding that older respondents with a successor tend to have higher incomes leads to the question if absence of a successor is due to the farm being too small and under capitalized, thereby retarding the potential for a livelihood to be earned from the land, or is there a reduction of investments and land because there is no successor. Both younger and elderly farmers without a successor are more likely to have more simplified enterprise structures.

Researchers in multiple disciplines have demonstrated the connection between family lifecycle, land tenure and farming styles. At the RUI where land is more finite,

Bryant and Johnston (1992) argue the availability of land will also moderate the point at which potential successors have access to their inherited land base. A large percentage of

(non-part time) farmers in the Montreal metro region reported a previous occupation before taking over the farm. Bryant and Johnston (1992) suggest the reason behind the trend is “the smaller farms…are less able to support two families. Hence, younger would be farmers spend time off the farm early on and only take over the family farm when the older generation is close to retiring completely from agriculture” (1992 p 108).

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These trends point to the importance of succession, land tenure and reproduction goals in determining the types of livelihood strategies a farm undertakes in order to persist on the landscape. While the absence of an heir often leads to a process of decline and disengagement, the availability of an heir will lead to enterprise redevelopment

(Figure 2.12). The following section examines how succession can influence enterprise persistence and adaptation strategies.

Enterprise Persistence and Adaptation

Figure 2.12 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI – Enterprise Persistence and Adaptation.

Succession and Opportunities for On-Farm Entrepreneurship

Entrepreneurship as a means for enterprise regeneration is a dominant theme in the studies cited. On farm entrepreneurship is increasingly being linked not only to the farm business itself but also to broader more regionally based economic and community development strategies. The fluidity of capital, and out-migration of manufacturing to overseas locations has left many rural communities that previously relied on industrial manufacturing and food processing employment to search for new alternatives (Dobson et al. 2003). For many rural communities, the ‘chasing the smokestack’ strategy has 102

failed to bring long-term meaningful employment (Green et al. 1990; Dabson 2000). In this new era of community based economic development entrepreneurship is the “new socioeconomic frontier for many rural communities [and] policy makers at the federal and state levels who see rural entrepreneurship as a strategy to seize opportunity in the global economy” (Sattler Webber 2007 p 426). Many communities have initiated or are exploring agriculturally based economic development projects that view productive farmland as a central stimulus to generating economic activity as opposed to viewing farmland as open space waiting to be developed (Bastin 2007; Brasier et al. 2007; Morton and Miller 2007; Robinson et al. 2007). These strategies are also promoted as a way to increase farm economic viability and ensure young farmers remain on the land.

The purpose of this section is to review the literature examining the link between farm persistence strategies, rural and on-farm entrepreneurship and theories of entrepreneurship. The second half of this section situates entrepreneurship in larger economic and political neoliberal policy agendas in order to discuss how capital continues to evolve and influence the evolution and persistence of the family farm.

Rural Entrepreneurship

The majority of literature examining rural entrepreneurship has focused on nonfarm enterprises such as rural enterprise zones and small scale industry service centers.

These studies include definitions of entrepreneurship that range from large scale high growth firms to micro-entrepreneurs who employ fewer than five people (often family businesses) (Max S. Wortman 1990a; Max S. Wortman 1990b; Tigges and Green 1994).

Recognizing the different context rural entrepreneurs operate in compared to urban 103

entrepreneurs, Wortman (1990a) defines rural entrepreneurship as the “creation of a new organization that introduces a new product, services or create new markets, or utilizes new technology in a rural environment” (p 222). Studies profiling the differences in entrepreneurs in rural and urban locals note rural entrepreneurial ventures are often initiated as a survival strategy to provide family income (Anderson 2000), tend to reflect an individuals cultural values (Webber 2007), are more homogenous and include a wider network of kin in their social groups compared to urban entrepreneurs who tend to start businesses for personal gain and intellectual stimulation (Merrett and Gruidl 2000). A great deal of attention has also been given to the role of women (Tigges and Green 1994;

Alston 2003; Bock 2004; Weber 2007), psychological traits (Babb and Babb 1992), cultural values (Hawley and Hamilton 1996), and the influence of community values

(Miller and Besser 2000) in how rural entrepreneurship is perceived and practiced.

With a few notable exceptions (e.g. (Bock 2004) the majority of research accounting for rural entrepreneurship has focused on non-farm rural entrepreneurship and relies on the use of patents as a proxy measures for entrepreneurship and innovation (Low

2007; Partridge et al. 2008). Recognizing the absence of good on-farm entrepreneurship accounting measures, the USDA has actively begun to consider survey indicators of farm household activities to assess on-farm entrepreneurship (Vogel 2007). However, a number of studies have tried to characterize how on farm entrepreneurship can intersect with farm management and succession goals. The following section reviews this vein of research.

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Entrepreneurship, Succession, Farm Management and Diversity of Household Values As noted earlier, succession is based on heir and operator personal goals and

values which can lead to a diversity of relationships, management strategies and

succession processes that ensure enterprise persistence. To fully comprehend how

entrepreneurial AFAE strategies lend themselves to the successful reproduction of the

farm at the RUI it is necessary to understand how these new marketing and production

relationships are simultaneously influenced and molded by household values. A common

theme running through each of the studies reviewed below is the way each author attempts to typologize and categorize different groups of farmers and small business

owners in order to demonstrate the diversity and heterogeneity of values and goals across

the landscape.

To expand our understanding of small firm entrepreneurship, Stanworth and

Curran (1981) developed a typology of small firm entrepreneur ideal types. The ‘classical

entrepreneur’ focuses on earnings and profits, although profit maximization is by no

means their only goal. In contrast the ‘artisan’ values intrinsic satisfaction such as

autonomy at work, status and finds contentment by producing goods or services. The

‘manager’ is centered on others (especially those outside the farm) recognizing their

achievements. Interestingly, it is the managerial type of entrepreneur who is most

concerned with ensuring their children will receive the benefits of the enterprise.

Stanworth and Curran (1981) suggest an entrepreneur’s self perception based on these

three identities can help detriment the growth rate of a firm.

Examining family farmers in New Zealand, Fairweather and Keating (1990)

identify three management types (the dedicated producers, the flexible strategist and the

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lifestyler) each distinguished by their goals, strategies and criteria for success. All three groups of farmers value family goals to a degree, but interpret and give meaning to

‘family’ in different ways. The dedicated producer believes the family should be involved on the farm, while the flexible strategist prioritizes enjoying off-farm pursuits with their family, and the lifestyler prefers the family to work together on the farm as a lifestyle.

Taylore et al. (1998) overlaps the influence of ethnicity on business orientation to examine how succession influences active management strategies. Citing Bennet’s

(1982) finding that the influence of ethnic traditions on active management strategies

tends to diminish and are essentially absent by the third generation, Taylore et al. (1998)

found interviews with 36 Canadian farm fathers and sons yielded results more complex

then Salamon’s ‘yeoman’ and ‘yankee’ classifications. In this study, ethnicity has a

smaller influence on farm continuity and profit optimization goals then did attitudes toward business management. Participants could be classified as “expanders”

(individuals who have expanded and diversified their parent’s farm to support two

generations and are characterized by entrepreneurial drive, ambition, vision, risk taking,

skilled management or resources, independence, and a need for control) and

“conservators” (hard workers but exhibit a cautious approach to debt and expansion,

which is usually pursued on a small scale and with less frequent borrowing. In general

conservators prioritize family harmony over profit generation, and are more contentious

of intergenerational succession and retirement options).

In the Taylore et al. (1998) study, combinations of expander and conservators are

exhibited in senior and junior generations to form the basis of farm succession patterns.

The four distinct combinations include: 1) senior farmer expander, successor expander;

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2) senior farmer expander, successor conservator; 3) senior farmer conservator, successor

expander; and 4) senior farmer conservator, successor conservator. Each pattern has

different intergenerational working relationships; different stages for negotiating roles

during the transition, different succession strategies, and different conflict issues, leading

Taylore et al. (1998) to conclude that particular types of issues experienced in the

succession process can be tied to particular forms of succession patterns, which may be

useful in identifying and resolving specific conflict issues.

These three studies by Stanworth and Curran (1981) Fairweather and Keating

(1990) and Taylore et al. (1998) bring forward the varying goals and motivations not only

of different enterprise types but also different members within the same household can

exhibit and how these differences can affect management, inheritance and succession

strategies.

To compare the influence of family and culture to economic profit maximization

in decisions related to farm marketing channels, Gilg and Battershiell (1998) analyze the

household dynamics (farmer’s age, family lifecycle, farming history, education, attitude

towards profits, other income sources and labor relations) of 63 Vente Directe farms and

vineyards, or farms that directly sell fruits and vegetables vis-à-vis 60 conventional farms

not engaged in direct selling in northwest France. While age, family life cycle,

succession, and labor relations were not found to be significant in understanding farm

decision making, in this study farming background, education and attitudes toward profit

were found to significantly influence decisions about direct marketing. Overall a greater

number of Vente Direct operators or spouses had influential experiences outside of

agriculture that influenced their more urbane outlook and confident approach to farming

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and farm marketing to a more urban clientele (thereby supporting Janovic and Messick’s

[1996] assertions regarding the importance of off-farm experience for future successors and farm operators). Additionally, unlike the Taylore et al. (1998) study, retirement

policies in France encourage older farmers to actively retire from farming allowing

children to become fully independent farmers exempting them from a prolonged and

overly drawn out farm transfer and succession process.

The attitude toward profit maximization also differed significantly between Vente

Direct and conventional farms. Conventional farms were more likely to rate economic processes of greater importance than Vente Direct farmers, who emphasized personal and family motivations (e.g. spending time with family, avoiding industrial farming, producing quality food products, and, protecting the environment) as top considerations.

These findings re-emphasize the role culture, goals and household values (including substantive and instrumental rationality and stewardship values) can play in land and business management decisions, as noted by Gasson and Errington (1993) and Barlett

(1993).

Theoretical Link Between Entrepreneurship and Farm Adaptation and Persistence at the RUI

The theoretical underpinnings linking entrepreneurship to rural development are

rooted in the early 20th century works of Joseph Schumpeter (2003) whose research

legacy centered on entrepreneurship based in smaller firms as the catalyst for innovation and economic growth (Low 2007). This section reviews Weber’s and Schumpeter’s

assertions regarding the entrepreneurial function in capitalism, and then links the

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critiques regarding the recent promotion of entrepreneurship within agro-food system activism to the evolution of family farms in capitalist systems.

Theories of Entrepreneurship – Weber and Schumpeter

For Weber the center of the capitalist entrepreneur rested on their ‘instrumental rationality’ and is distinguished by their “rational and systematic pursuit of economic gain, reliance on calculation measured in relation to this economic criterion, the extension of trust through credit and the subordination of consumption in the interest of accumulation” (Martinelli 1984 p 478). According to Weber the expansion of instrumental rationality through the daily actions of individuals and groups created modern institutions disembedding individuals from their relationship to nature, religion and tradition. These assertions have also been debated as Neo-Weberian theorists have pointed out the cultural and social contexts individuals are embedded in create an interplay between instrumental and substantive rationalities. However, Weberian theories on entrepreneurship inform our understanding of the social and economic transformation that can occur as entrepreneurship is widely adopted.

Considered to be the leading scholar on theories of entrepreneurship Schumpeter employed sociological, psychological and economic forms of inquiry to study the role and function of entrepreneurialism in capitalist societies. In Schumpeter’s examination of class structure he sought to understand how the leading bourgeoisie class came to dominate the functions of innovation and leadership in the economy as they acquired, consolidated and transferred prestige, power and wealth to future generations

(Schumpeter 2003 ). However, this role also led to the destruction of the bourgeoisie as

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the same processes that made the bourgeoisie so successful also routinized innovations

into larger organizations that weakened the entrepreneurial function and undermined the

bourgeoisie entrepreneurial base. Schumpeter (2003) predicted the success of the

capitalist firm would also undermine the system, causing both the collapse of the

bourgeoisie and capitalism.

While Schumpeter’s analysis failed to account for transformative properties and

adaptability of capitalism that allow different forms of capital to co-exist with a variety of firm sizes, Schumpeter was able to provide a sociological frame for studying agency as he saw the entrepreneur as a revolutionary actor able to change the conditions of supply to recombine existing resources in new ways and set new production functions

(Martinelli 1984). Schumpeter’s work laid the basis for ongoing studies of entrepreneurship that now recognize the complexity of entrepreneurship due to its dependence on economic factors (the availability of labor, capital, technology, mobility, access to markets) and non-economic factors (cultural norms, beliefs, class relations, collective action, state intervention, organizational structures, bounded solidarity and trust, marginality status, motivations for achievement) in addition to the need to account for different historical and geographical settings that produce unique forms of entrepreneurship (Martinelli 1984).

Weber and Schumpeter remind us that for actors engaged in entrepreneurship bounded in a capitalist system there are social, economic and cultural forces that both influence the individual and the institutions they help to create through their everyday actions. This theoretical insight provides a useful launching point for examining how family farms both influence and are influenced by larger structures as they adopt

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entrepreneurial AFAE strategies. The trend towards AFAEs expands questions regarding

capital penetration into agriculture (which until recently have been primarily referenced

through horizontal and vertical integration) to examine new forms of capital penetration

emanating from entrepreneurship. The following section reviews some of the critiques regarding entrepreneurship as a neoliberal economic reform and ties these arguments to larger questions about the continued transformation of family farms in capitalist systems.

Critiques of Entrepreneurship in Agrofood System Studies

While entrepreneurship has increasingly become popular as a rural development

and agrofood system development strategy (Korsching and Allen 2004b; Korsching and

Allen 2004a; Beaulieu and Jordan 2007) it has simultaneously been critiqued as a

neoliberal solution that refocuses agrofood system debates and solutions into the market

sphere based on private free market enterprise rather then on public democratic solutions

(Allen 1999; Guthman 2008a; Guthman 2008b). Market based approaches including

entrepreneurship have been suggested as a means for rebuilding local food system infrastructure and increasing the profitability of family farms. A closer examination of the politics behind and implications of entrepreneurship as a market based approach for maintaining family farms is warranted. Guthman (2008) critiques the current trend in agrofood system activism to embrace neoliberal market based strategies embedded in capitalism as a means for rebuilding locally based environmentally and socially just food systems. Guthman (2008) cites Harvey (2005) to define neoliberalism as

“a theory of political economic practices that proposes that human well-being can best be advanced by liberating individual entrepreneurial freedoms and skills

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within an institutional framework characterized by strong private property rights, free markets, and free trade.”

Guthman goes on to elaborate:

“as a practical political economic project, neoliberalsim claims to involve the privatization of public resources and spaces, minimization of labor costs, reductions of public expenditures, the elimination of regulations seen as unfriendly to business, and the displacement of governance responsibilities away from the nation-state” (2008 p 1172).

As a market based approach neoliberalism seeks to promote entrepreneurialism and

competition, and in the context of the local foods movement these attributes are

“localism, consumer choice and value capture”(Guthman 2008a). In an era of

globalization, free trade and government devolution local food system actors have looked

to privatization and the market to create opportunities for family farms and local

agriculture based in value added labeling and certification schemes (i.e. organic, fair

trade). Guthman critiques the movement for reproducing ‘neoliberal mentalities’ rather

then lobbying for better regulations regarding worker health and safety, improving access

to land and capital, and better social welfare programs. Instead the movement has

focused on consumer choice, self improvement and green businesses that replicate the

dominant system that has failed to produce a socially, economically and environmentally

just food system (Allen 1999). However, the application of neoliberal policies has been

uneven as there remain pockets of space where resistance to the neoliberal agenda is

present and other places where neoliberal policies are manifested in contradictory ways

(Harrison 2008; Morris 2008).

The critiques Guthman (2008) and Allen (1999) levy against entrepreneurialism

as a neoliberal policy can also be applied to a more thorough analysis of the ways in

which capitalism continues to transform the family farm as more farm families are 112

encouraged to follow the call to entrepreneurship. As Barlett (1993) demonstrated,

farmers reflect the societies and cultures they are embedded in. How farm families

choose to adopt, adapt and implement specific management and production strategies as they balance instrumental and substantive goals informs the uneven nature by which capital continues to evolve and penetrate the family farm.

OVERALL MODEL AND RESEARCH QUESTIONS

As reviewed above there have been a large number of studies investigating farm persistence across urban, rural and peri-urban locals, however, few have examined the influence of household level variables on farm persistence at the RUI empirically. The aim of this dissertation is to quantitatively and qualitatively test the assertions made in the holistic farmer decision making models (Bryant and Johnston 1992; Smithers and

Johnson 2004) in order to better understand how household dynamics contribute to decisions of persistence or disinvestment relative to the influence of farm structure, local land use policy and development pressure at the RUI. Within the household there are a variety of demographic and value based factors. By definition the household is an economic unit, where individuals whose work and labor contribute to group maintenance and stability (Bennet 1982). The household can be distinguished from the ‘family’ which is defined by a biological relationship through kinship ties where individual members play different roles within the household (Salamon 1992 ). In a farm family the enterprise (or farm) provides a physical locale and financial subsidy for the household.

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Figure 2.13 presents a model representing the influence of household dynamics on farm persistence both in terms of the number of years an individual expects to continue farming and the number of years one expects their farm enterprise to continue. At the

RUI there are a diversity of farm types including those that engage in AFAE adaptations, those that only sell into Commodity markets and those that are Mixed (engaging in both

AFAE and commodity marketing). All of these farm types must balance household goals and values in addition to farm structure, perceived effects of local and global policy, and perceived development pressure as they make farm management decisions. The remainder of this section outlines my research questions and hypothesis relative to this model.

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Figure 2.13 Model of Household Decision Making Factors Influencing Farm Enterprise Persistence and Adaptation at the RUI.

Research Questions and Hypothesis

Based on the above review of the theoretical and empirical literature examining the influence of household dynamics on farm persistence at the RUI the following research questions and hypothesis are proposed.

Research Question 1:

How are household dynamics, household values and farm structure variables associated

with commercial farm persistence at the RUI?

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Hypothesis R1.1: I hypothesize household characteristics, values and farm

structure variables will be associated with the number of years commercial farmers expect to continue farming controlling for the effects of global and local problems and county land use policies. More specifically I anticipate:

R1.1.1: Farming for a longer time period will be negatively associated

with age;

R1.1.2: Farming for a longer time period will be positively associated

with education, the availability of a successor, availability of family labor

and optimism for the future of one’s farm more or less off-farm income;

R1.1.3: Farming for a longer time period will be negatively associated

with the need to sell land in order to afford retirement;

R1.1.4: Respondents expecting to farm for longer periods of time will

place greater importance on substantive, instrumental and stewardship

values compared to those who expect to farm a shorter period of time;

R1.1.5: Farming for a longer time period will be positively associated

with soil quality, acres operated, smaller debt to asset ratios, farm receipts,

household income, plans for increasing future capital investments and

alternative marketing streams;

R1.1.6: Farming for a longer time period will be positively associated

with producing high value crops;

R1.1.7: Farming for a longer time period will be negatively associated

with dairy farming and livestock production; and

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R1.1.8: Farming for a longer time period will be positively associated

with AFAE adaptations.

Hypothesis R1.2: I hypothesize household characteristics, values and farm

structure variables will be associated with the number of years commercial farmers expect their enterprise to continue controlling for the effects of global and local problems and county land use policies. More specifically I anticipate:

R1.2.1: The enterprise continuing for a longer time period will be

negatively associated with age;

R1.2.2: The enterprise to persist for a longer time period will be positively

associated with education, the availability of a successor, availability of

family labor and optimism for the future of one’s farm more or less off-

farm income;

R1.2.3: The enterprise to persist for a longer time period will be

negatively associated with the need to sell land in order to afford

retirement;

R1.2.4: Respondents expecting their enterprise to persist farm for longer

periods of time will place greater importance on substantive, instrumental

and stewardship values compared to those who expect to farm a shorter

period of time;

R1.2.5: The enterprise to persist for a longer time period will be positively

associated with soil quality, acres operated, smaller debt to asset ratios,

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farm receipts, household income, plans for increasing future capital

investments and alternative marketing streams;

R1.2.6: The enterprise to persist for a longer time period will be positively

associated with producing high value crops;

R1.2.7: The enterprise to persist for a longer time period will be

negatively associated with dairy farming and livestock production; and

R1.2.8: The enterprise to persist for a longer time period will be positively

associated with AFAE adaptations.

Research Question 2:

What factors are associated with AFAE adaptations?

Hypothesis R2.1: I hypothesize household dynamics and farm structure variables will vary across AFAE, Non-AFAE and Rural Residential farm types controlling for the effects of global and local problems and county land use policies. More specifically I expect:

R.2.1.1: Commodity farmers will be older compared to AFAE and Rural Residential

farmers;

R.2.1.2: Rural Residential farmers will have higher education levels and rely on

family labor to a greater extent than AFAE and Non-AFAE farmers;

R2.1.3: AFAE farmers will report fewer problems associated with operator health,

availability of a successor, have the largest farm income, and be more optimistic for

the future of their farm compared to Non-AFAE and Rural Residential farmers;

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R.2.1.4: AFAE will place greater importance on substantive, instrumental and

stewardship values compared to Non-AFAE and Rural Residential Farmers;

R.2.1.5: Non-AFAEs will be more likely to sell land for development in the future;

R.2.1.6: Non-AFAEs will operate the largest acreage, carry more debt and farm on

better soil compared to AFAE and Rural Residential;

R.2.1.7: Non-AFAE farmers are more likely to be associated with dairy and grain

operations and commodity sales;

R.2.1.8: AFAE and Rural Residential farmers are more likely to be associated with

high value crop production and direct marketing initiatives; and

R.2.1.9: AFAE are more likely to be on a growth trajectory and anticipate their

enterprises lasting indefinitely.

Research Question 3:

How do household level decision making factors including values and land use motivations influence enterprise adaptation and persistence at the RUI? How do household level decision making factors vary among distinct farm types (First generation

AFAE’s; Multigeneration AFAE’s; Mixed; Commodity and Nonfarming Heirs) at the

RUI? This is an exploratory research question.

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CHAPTER 3

METHODOLOGY

This chapter describes the quantitative data collection and analysis methodologies. I describe the survey instrument, sampling method and data collection techniques. I then discuss how the dependent and independent variables were operationalized, measured and their associated descriptive statistics. Finally, I conclude with a table outlining the hypothesized relationships.

QUANTIATIVE SURVEY INSTRUMENT AND DATA COLLECTION

The quantitative data for this research is from a 2007 national landowner survey administered in eight case study counties. The 2007 Agriculture at the Rural-Urban

Interface: A National Study of Trends and Adaptive Strategies is a collaborative project between the Department of Human and Community Resource Development at The Ohio

State University and The Department of Anthropology, Sociology and Social Work at

Utah State University and was funded by the USDA National Research Initiative. The survey was jointly designed by a team of faculty and graduate students at both institutions and administered by The Utah State University team members. The survey design was modeled after landowner surveys administered by other researchers across the United

States.

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The survey is a 16 page booklet with 135 questions (see Appendix A to view a

copy of the survey) and space on the back cover for respondents to share comments or

request survey results. The purpose of the survey was to examine agricultural change in

urbanizing areas. Farmers and rural landowners were asked to describe their current

attitudes, activities and land use plans for the future.

The data for this research is embedded in a larger project: Agricultural Adaptation

at the Rural-Urban Interface: Can Communities Make a Difference?, which aims to

identify how and under what conditions local communities are able to influence the

trajectory of agricultural change and adaptation at the rural-urban interface. The first phase of the project was to construct and analyze a dataset of census data focused on agricultural, population, land-use indicators for all U.S. counties in the lower 48 states with significant agricultural sectors during the time period 1987-2001. Using this information, we constructed a typology of agriculturally important counties at the rural- urban interface, characterizing counties into five types, those who whose agriculture was going into a mode of: decline, deintensification, stability, intensification and growth.

Building on Phase 1, Phase 2 was an in depth study of the case study communities that represent the different trajectory types identified in Phase 1. The case study counties were representative of agricultural trends in the different census regions, and exhibited a diversity of production systems. From 2006 through 2008 a team of researchers from

The Ohio State University and Utah State University visited eight case study counties across the United States to conduct in-depth interviews with key informants representing local farmers, county officials, agricultural professionals and community members. The case study counties included: Frederick County Maryland, Yamhill County, Oregon, 121

Cache County Utah, Kent County Michigan, Forsythe and Hall Counties in Georgia, and

Spencer and Shelby Counties in Kentucky (Figure 3.1).

Figure 3.1 Map of U.S. Highlighting Case Study County Locations

In each county the researchers focused on three components including: 1) Face-to-

face interviews with local farmers with questions focusing on the challenges,

opportunities, and influences on their farming decisions, and; 2) Face-to-face interviews

with community leaders, county agricultural professionals, and local, nonfarm residents

to better understand some of the demographic, social, community and ecological factors

influencing local farm decision-making. Those persons interviewed were identified by

first contacting each county’s Agricultural Extension Agent and land use policy leaders,

additional respondents were identified through a snow ball sampling methodology (Berg

2004). The interviews were transcribed, qualitative data coding and analysis was

conducted to identify dominant themes and patterns (Lofland and Lofland 1995). The

results from the qualitative data analysis were then used to construct a land owner survey that was mailed to a total of 2,651 farm and nonfarm landowners in the unincorporated

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areas of the study counties. The sampling frame, sample size and response rates are

discussed below.

Sampling Frame and Sample Selection

Our overall study design involved sampling 300 potential respondents (combined) from the two main sample frames in each county: landowner tax parcel lists and commercial farmer lists.3 Initially, we sought a complete list of landowners who owned

agricultural land parcels at least 5 acres in size in the county. This first list served as the

basis for our ‘landowner sample’ and was obtained from county tax assessment offices in

each of the eight study counties. Second, we obtained lists of all known commercial

farming operations raising commodities that were particularly important in the particular

study county4. These supplemental lists were provided by local agricultural extension or

grower association lists. The particular focus of these supplemental farmer samples

focused on the following commodities in each particular county:

County Particular Farm Types Sampled Forsyth, GA Poultry farms Hall, GA Poultry farms Frederick, MD Dairy farms, Alternative Marketing farms Kent, MI Nursery/Greenhouse farms, Fruit farms, Dairy farms, Alternative Marketing farms Shelby, KY Horse farms Yamhill, OR Nursery/Greenhouse farms, Alternative Marketing farms

3 Much of the quantitative survey instrument, site selection and data collection procedures was originally written up by Doug Jackson-Smith (2008) and was reformatted for this dissertation. 4 Commercial farmer lists were not developed for Spencer County, KY (due to the absence of a dominant commodity group) or for Cache County, UT (due to time limitations). 123

Landowner Samples:

Initially, we randomly sampled 330 landowners in each study county from the tax parcel lists provided by county assessors. In one special case (Yamhill county), we discovered after our first mailing was developed that the original sample frame had included a very high proportion of non-qualifying parcels (e.g., those with less than 5

acres of land). To compensate for this high rate of disqualification, we sampled an

additional 185 new (non-duplicate) names from the original tax parcel list to replace all of those original sample points that we believed should not have received a survey in the first place. These replacement sample points were all from parcels larger than 5 acres in size. The original sample points with less than 5 acres of land were disqualified from the study and did not receive supplemental mailings.

The number of landowner parcels in each of the study counties is listed in Table

3.2. Overall, we sent surveys to a total of 2,176 randomly selected owners of agricultural parcels across the 8 counties. These sample points represent an unbiased and representative pool of owners of more than 5 acres of farmland. Because many of these landowners are also active commercial farmers, the landowner sample frame is the best source for estimating population characteristics of the farm enterprises present in each county.

Commercial Farm Samples:

After selecting our landowner sample frame, we then randomly sampled from the

‘supplemental’ lists of commercial farms provided by agricultural extension agents, local farmer associations, or other expert sources in 6 of the 8 counties. In each county, if

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there were more than 100 commercial farmers in a county on those lists, we sampled randomly from the lists to ensure (a) no more than 100 total commercial farmer list sample points per county, and (b) roughly even numbers of each of the commodities represented in a given county. If there were less than 100 farms on these lists, we sampled all of the names on the lists. Depending on the total number of sampled commercial farmer list names (which ranged between 0 and 100), we shrank the size of the landowner sample to cap the total sample size at 300 in each study county.

One exception to this approach was in the case of Kent County, Michigan. In this county, late in the research process we had an opportunity to add a separate wave of data collection using an innovative ‘drop-off/pick-up’ survey methodology (Steele et al.

2001). To support this supplemental wave of data collection, we sampled randomly from three of the four ‘commercial farmer lists’ that had not yet been saturated. This resulted in an additional 67 commercial farms beign added to the master sample frame.

The number of commercial farmer list sample points in each study county is

summarized in Table 3.3. Overall, we ended up with sample frames that included

between 23 and 167 commercial farms in the six study counties that provided us with

lists of commercial farmers. Across all 8 study sites, we sent surveys to a total of 475

operators of commercial farms.

Survey Administration

Regardless of the source of the sampled address (e.g., landowner parcel list vs.

commercial farmer list), we pooled our sampled by county and sent identical

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questionnaires and survey materials to 2,651 potential respondents in the study counties.

With the exception of Kent County, MI the majority of surveys were administered

through the mail. Our standard mail survey administration procedures followed guidelines outlined in Dillman (2000). Specifically, we contacted each potential respondent up to four times. The type of mailing and the approximate timing for each mailing is outlined in Table 3.1.

Item Day Advance letter Day 0 Survey mailing #1 Day 4 Cover letter Copy of survey Brochure describing project Prepaid return envelope Reminder postcard Day 11 Survey mailing #2 Day 25 New cover letter New copy of survey Insert noting “even if you are not a farmer, we still want to hear from you” New prepaid return envelope Survey mailing #3 Day 50 New cover letter New copy of survey New prepaid return envelope Survey mailing #4 Day 90 New cover letter Copy of (4 pg.) SHORTER VERSION of survey Prepaid return envelope

Table 3.1 Mail-out Schedule for NRI Land Owner Survey

All materials were mailed from the Institute for Social Science Research on Natural

Resources at Utah State University (using ISSRNR/USU letterhead).

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Respondents were sent a standardized 16-page survey booklet (with 13 pages of questions) (Appendix A). The name of the county printed in survey titles, subheadings, and question wordings was customized to be appropriate for each study site. Survey packets were mailed out in batches by county. Our first mailings went out in mid-May

2007, first surveys in late May, second surveys in July, with the third standard survey packets mailed out in late July and early August 2007.

All mailing materials were customized with the name and address information for each respondents. The surveys included a small identification number sticker in the lower right corner of the back page of the cover. This ID was uniquely assigned to a particular individual on our sample frame. We kept track of responses using this ID and only sent successive mailings only to people who had yet to respond to our surveys.

Kent Drop Off Pick Up Subproject

In addition to these standardized mail survey procedures, we also had an opportunity to administer additional surveys using a standardized Drop-Off/Pick-Up (DOPU) methodology in Kent County, Michigan (Bourke et al. 2001). DOPU methods involve more personal contact with the respondents and typically result in much higher response rates.

The DOPU effort was used in October 2007 to collect information from a new sample of commercial farms who had not been included in our original sample frame. As noted above, a total of 67 new farms were contacted using this approach in Kent County.

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In this case, the DOPU method involved mailing initial advance letters from a local extension office to these 67 producers. These advance letters provided background about our project and informed respondents that we would soon be contacting them by phone to see if we could arrange a time to meet face-to-face. Two trained local staff members then contacted each farmer to ask if they were willing to participate and to arrange a meeting time. At the first face-to-face meeting, the our research staff explained the reasons for the project, obtained signed informed consent forms, and left a hard copy of the survey with the respondent after making plans for a date and time to pick the completed survey back up. In most cases, the surveys were collected in person by our research staff. In some cases, the respondent was not available at the scheduled ‘pick-up’ time, and prepaid envelopes were left to allow the respondent to mail their surveys back.

Details about participation and response rates for these DOPU farmers are discussed below.

Response Rates

The number of usable responses (and the status of other contacts with sampled persons) are summarized for the landowner sample frame in Table 3.2 and for the commercial farmer sample frame in Table 3.3.

Landowner Sample:

Overall, the landowner sample response rate was 48.9% of eligible respondents.

This number is calculated by taking the total number of usable survey responses, and dividing it by the number of potentially eligible respondents (e.g., the total number in the sample minus the number disqualified from the study for various reasons). A total of 853 128

useable responses were obtained from randomly sampled landowners in our study counties. This includes 767 full length surveys, and an additional 86 short-version survey responses.

Response rates varied by county and state. The best response rate (62.5 percent) was achieved in Cache County, Utah (perhaps because the survey had more immediate credibility and relevance since it was mailed from Utah State University, which is located in that county). Response rates were over 50 percent in Frederick, MD and Yamhill, OR.

Overall response rates were particularly low in the two Georgia counties (34.2 and 39.4

percent, respectively).

The disqualification rate in most counties ranged from 12 to 17 percent – largely

due to the fact that many addresses were undeliverable and some parcels turned out to be

below our 5 acre farmland threshold (despite information to the contrary on the tax role

list). The high rate of disqualification in Yamhill County (42 percent) is primarily due to

the inadvertent inclusion of a large number of landowners who owned less than 5 acres in

our initial sample frame. As described above, upon realizing this error our research team

quickly pulled the remaining names of small non-qualified landowners from our sample

frame and replaced them with 185 newly selected names of owners of qualified parcels.

Commercial Farmer Sample:

We obtained lists of commercial farmers in 6 of our 8 study counties. Overall, the

response rate for the commercial farmer sample was 51.1 percent of eligible respondents.

The highest response rate was found in Frederick County (59.3 percent), while the

response rate for Hall County was extremely low (16.7 percent). Most of the other

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county-level response rates were close to 50 percent. Overall, we received 180 usable surveys from individuals in the commercial farm samples. More than half of the respondents were from two counties: Frederick and Kent.

Interestingly, the rate of disqualification (25.8%) was higher in the commercial farmer sample frame than in the general landowner parcel lists. This suggests that lists provided by local organizations or institutions might not be as accurate, complete, or current as lists maintained for tax assessment purposes. Most commercial farmers were disqualified because they were no longer farming (n=27), had duplicate addresses (n=26) or did not own 5 acres of farmland (n=47).

A comparison of the landowner and commercial farm samples with the 2002

Census of Agriculture data statistics for the case study counties is presented in Table 3. 4.

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Fred- Cache erick Forsyth Hall Kent Shelby Spencer Yamhill Com- UT MD GA GA MI KY KY OR bined

Original size of sampling frame 2,727 4,258 2,565 5,033 5,807 3,640 1,329 8,593 33,952

Original size of sample 301 200 200 276 200 275 300 424 2,176

Total with some type of contact 217 130 103 123 116 150 175 314 1,328 Contact rate (crude) 72.1% 65.0% 51.5% 44.6% 58.0% 54.5% 58.3% 74.1% 61.0% Total sample points with no contact 84 70 97 153 84 125 125 110 848

Disqualified1 50 25 35 36 34 32 40 179 431 Disqualification rate 16.6% 12.5% 17.5% 13.0% 17.0% 11.6% 13.3% 42.2% 19.8% 130 Disqualified as % of contacts 23.0% 19.2% 34.0% 29.3% 29.3% 21.3% 22.9% 57.0% 32.5%

Adjusted size of sample2 251 175 165 240 166 243 260 245 1,745

Responded (usable full length survey) 147 87 57 72 74 105 113 112 767 Responded (usable short survey) 10 14 8 10 4 10 13 17 86 Total Responded 157 101 65 82 78 115 126 129 853

Overall Response Rate3 62.5% 57.7% 39.4% 34.2% 47.0% 47.3% 48.5% 52.7% 48.9% 1 Disqualification due to undeliverable address, duplicate address (same operation), not an owner of 5 acre parcel, deceased, or publicly-owned property; 2 Potentially eligible respondents; 3 Percent of potentially eligible respondents.

Table 3.2 Sampling and Response Rate Summary for Landowner Samples.

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Fred- Cache erick Forsyth Hall Kent Shelby Spencer Yamhill Com- UT MD GA GA MI KY KY OR bined

Original size of sampling frame none 116 113 23 252 25 none 60 589

Original size of sample 100 100 23 167 25 60 475

Total with some type of contact 66 68 13 136 16 39 338 Contact rate (crude) 66.0% 68.0% 56.5% 81.4% 64.0% 65.0% 71.2% Total sample points with no contact 34 32 10 31 9 21 137

1 Disqualified 14 33 11 36 8 21 123

131 Disqualification rate 14.0% 33.0% 47.8% 21.6% 32.0% 35.0% 25.9% Disqualified as % of contacts 21.2% 48.5% 84.6% 26.5% 50.0% 53.8% 38.3%

Adjusted size of sample2 86 67 12 131 17 39 352

Responded (usable full length survey) 45 28 2 65 8 15 163 Responded (usable short survey) 6 5 0 4 0 2 17 Total Responded 51 33 2 69 8 17 180

Overall Response Rate3 59.3% 49.3% 16.7% 52.7% 47.1% 43.6% 51.1% 1 Disqualification due to undeliverable address, duplicate address (same operation), not an owner of 5 acre parcel, deceased, or publicly-owned property; 2 Potentially eligible respondents; 3 Percent of potentially eligible respondents. Table 3.3 Sampling and Response Rate Summary for Commercial Farmer Samples.

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Landowner Commercial Farmer Census (2002) Sample (2007)* Sample (2007)* Farm Size

Farms, number 9,550 312 132

Acres operated, total 1,186,989 93,236 41,285

Acres operated, mean 124 299 313

Acres operated, median*** 61 100 200 Farms by acre class, percentage Under 10 acres 15% 5% 5% 10-49 acres 41% 29% 17% 50-69 acres 8% 9% 5% 70-99 acres 8% 6% 5% 100-139 acres 7% 9% 7% 140-269 acres 10% 15% 23% 270-500 acres 6% 10% 20% 500 acres or more 5% 16% 18% On-farm production, percentage Farms with livestock sales 52% 58% 55% Farms with crop sales 46% 59% 68% *Four surveys had ripped labels and therefore the sample type is unknown. **Valid response in the 2002 Census of Agriculture, ***average or the median per county

Continued

Table 3.4 Comparison of Landowner Sample and Commercial Farm Sample to Census Data

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Table 3.4 Continued

Landowner Commercial Farmer Census (2002) Sample (2007)* Sample (2007)* Business organization form, percentage Sole proprietorship 88% 60.9% 56.1% Partnership 7% 28.2% 22.7% Family corporation 4% 9.3% 18.2% Nonfamily partnership or corp. 1% 1.6% 3.0% Other** 1% Total farm receipts, percentage Under $10,000 66% 40.4% 7.6% $10,000 to $24,999 11% 19.6% 3.8% $25,000 to $49,999 6% 10.9% 8.3% $50,000 to $99,999 5% 7.1% 13.6% $100,000 to $249,000 5% 7.7% 22.0% $250,000 to $499,999 4% 6.1% 21.2% $500,000 and above 3% 8.3% 23.5% Land tenure, percentage Full owner 80% 71% 45% Part owner 20% 29% 55% Age classes, percentage Under 45 22% 11.2% 15.2% 45-64 54% 52.9% 66.7% Over 65 24% 35.9% 18.2% Principal operator characteristics, percentage Principal occupation - farmer 51% 32.7% 75.8% Works off-farm 49% 49.7% 24.2% *Four surveys had ripped labels and therefore the sample type is unknown. **Valid response in the 2002 Census of Agriculture, ***average or the median per county

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DATA ANALYSIS STRATEGY

To assess how household dynamics, farm structure and RUI pressures influence farm persistence a series of bivariate and multivariate analysis were conducted.

Recognizing the heterogeneity of farm types at the RUI (Bryant and Johnson YEAR) two

separate sets of analysis were conducted in order to distinguish between the factors

influencing commercial farmers and those influencing rural residential farmers. To

assess the factors influencing production agriculture, chapter 4 presents a series of

bivariate analyses (One-Way ANOVA and Chi Square tests) and a multivariate

multinomial regression model that includes only the commercial farm subsample

(n=293). Chapter 5 presents a bivariate (One-Way ANOVA and Chi Square tests) and

qualitative analysis of the factors affecting both commercial and rural residential farmers

at the RUI (n= 426), and further distinguishes the commercial farmers by AFAE

adaptations.

The remainder of this chapter will describe the protocol for categorizing

commercial and rural residential farmer subsamples, followed by a discussion of how the

dependent variables in chapter 4 were operationalized and how the independent variables

in both chapter 4 and 5 were operationalized. Missing data was imputed by using both

mean and mode substitution and multiple imputation methodology strategies (grounded

in multiple regression analysis) (Acock 2005).

Protocol for Categorizing Commercial and Rural Residential Farmers

As the focus of this dissertation is on farm persistence and the farm business as

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opposed to general land use, it was necessary to distinguish between working farms and rural residential or hobby farms. Following USDA definitions (Heimlich and Anderson

2001; (Banker and Hoppe 2005) respondents were categorized as either “commercial” farms and “rural residential” farms. Creating a collapsed farm typology Banker and

Hoppe (2005) identify three types of farms based on whether farming was the primary occupation and sales. Their definitions are: (i) Intermediate farms which include producers with a farming occupation and lower-sales or farming occupation and higher- farm sales; (ii) Commercial farms include large, very large, and nonfamily farms, and;

(iii) Rural residence farms include limited-resource, retirement, and residential lifestyle farms. I collapsed these into two categories: 1) Commercial Farms – these include the intermediate farms and commercial farms defined by Banker and Hoppe (2005), and 2)

Rural Residential Farms – that include the rural residence farms.

To classify respondents as commercial farmers or rural residential farmers I used three primary indicators: 2006 farm receipts, total household income, and the amount

(proportion) of household income from farm sources. In addition I was able to contextualize respondents using: farm size (acreage), acreage owned vs. rented, land use

(crops, grazing, forest cover), types of products raised (grains, crops livestock, fruit, vegetable, nursery and greenhouse), family contributions to labor, off farm employment

(operator and spouse), occupational self identification, sample frame origin, if the respondent had ever operated a farm, and if they currently operate a farm. The following example illustrates the usefulness of incorporating these additional variables into the classification methodology. Certain types of production systems (e.g. corn, beans, and

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some livestock operations) lend themselves more readily to part time farming operations

(Hallberg et al. 1991), therefore a respondent reporting a full time off farm job and who indicated farm receipts contribute less then half to their household income, and was running a corn and bean operation would be classified as a commercial farmer.

Heimlich and Anderson (2001) contend farms grossing less then $10,000 a year do not meaningfully contribute to household income. Other studies characterize hobby

farms as enterprises where the majority of income is from off the farm, farm size is less

then 50 acres and farming is often seen as a form of recreation over employment (Daniels

1986). Therefore, in this study farms reporting less then $10,000 in receipts were

classified as rural residential or hobby farms unless household income was reported to be

very low and it appeared respondents were engaged in subsistence farming (this occurred

in at least one case). However, respondents were categorized as commercial farmers if

they claimed that none of the farm income contributed to their household income, but

reported more then $20,000 in farm receipts. The assumption in this case was that the

farm enterprise itself was a viable self sustaining business but was not central to the farm

household compared to other sources of income.

The quantitative data analysis for this dissertation is based on the commercial

farmer (n= 293) and rural residential farmer (n= 133) subpopulations (Table 3.5). Below

I describe the differences between the two subpopulations.

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Frequency Percent Commercial Farmer 293 68.8% Rural Residential 133 31.2% Total 426 100.0%

Table 3.5 Percentage of Commercial Farmer and Rural Residential Respondents

Sub Sample Characteristics

This section describes the different characteristics of the commercial farmers from

the rural residential farmers using a comparison of means test with significance tested at

the .05 level (Table 3.6 and Table 3.7). The commercial farmers operate more land an

average of 420.97 acres compared to the rural residential farmers who operate an average

of 55.89 acres both in terms of owned land (251.97 vs. 47.54 acres) and rented land

(140.17 vs.7.17 acres). Commercial farmers were more likely to operating dairy and high

value crops compared to rural residential farmers. Both groups were as likely to raise

grains (corn and/or soy) and livestock. The commercial farmers exhibited a greater

reliance on hired labor and a larger range of family contributions to farm labor compared to the rural residential farmers who tended to rely exclusively on family labor. The commercial farmers exhibit a greater reliance on farm income for household income compared to the rural residential.

Rural residential farmers were more likely to indicate the primary operator and other adults are more likely to be employed (either part time or full time) off the farm compared to commercial farmers. In terms of farm economics, the rural residential farmers were almost exclusively generating less then $10,000 a year in farm receipts, and

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indicated very little income from the farm contributed to household income, they also

tended to have higher education levels compared to the commercial farmers. There were

no significant differences between the average age and total household income of the two

groups.

The rural residential farmers were more likely to originate from the random

landowner sample frame while commercial farmer respondents were captured fairly

equally in both the commercial and random landowner samples. There were also

significant differences across the survey counties. The majority of rural residential respondents were from Spencer County (20.30%) followed by Shelby (19.50%) and

Yamhill County (16.50%), with a smaller percentage of respondents coming from Cache

County (12.80%), Frederick County (12.00%), Forsythe County (9.00%), Hall County

(7.50%) and finally Kent County (2.30%). The majority of commercial farmers were from Kent (25.30%), Frederick (19.50%), and Cache (16.70%); followed by Shelby

(12.30%) and Yamhill (11.90%), with the smallest number of commercial farmer respondents coming from Spencer (8.20%), Hall (3.10%) and Forsyth (3.10%).

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Commercial Rural Farmer Residential Age (mean) 58.6 56.3 Land Operated (mean) 421.0 55.9 ** Land Operated (median) 165.0 26.0 Owned Land (mean) 252.0 47.5 ** Owned Land (median) 7.0 0.0 Rented Land (mean) 140.2 7.2 ** Rented Land (median) High value crops (veg, nursery, fruit, etc) 28.7% 17.3% ** Corn and/or Soy 3.8% 1.5% Livestock 45.1% 42.1% Dairy 19.1% 0.8% ** Family Labor Contributions ** Family labor contributions to farm - less then half 25.9% 8.3% Family labor contributions to farm - more then half 74.1% 91.7% Operator Off-farm Job ** No 68.3% 30.8% Yes- full time 22.9% 59.4% Yes- part-time 8.9% 9.8% Off-farm Work Other adult members of household ** No 58.4% 33.8% Yes 41.6% 66.2% Farm debts to assets ratio Farm debts are 40% or below assets 93.2% 91.0% Farm debts are 40% above assets 6.8% 9.0% Education * Some high schools, but no diploma 4.4% 0.8% High school graduate 28.0% 23.3%

Some college, associate degree 28.0% 24.8% Bachelors degree 25.6% 25.6% Graduate or professional degree 14.0% 25.6% *p<.05 two tailed t-test **p<.01 two tailed t-test

Table 3.6 Commercial and Rural Residential Farmers Descriptive Statistics 140

Commercial Rural Farmer Residential

Proportion of total household income from farm source ** All income is from farm sources 30.4% 0.0% More than half of income is from farm sources 21.2% 0.0%

Household income is evenly split between farm and off farm 6.8% 0.8% Less than half is from the farm 22.2% 8.3% Very little is from the farm 19.5% 91.0% Total farm receipts in 2006 ** less than $ 10,000 4.4% 86.5% $ 10,000 to 24,000 14.0% 13.5% $25,000 to 49,000 15.4% 0.0% $50,000 to $90,000 13.0% 0.0% $100,000 to 249,999 18.1% 0.0% $250,000 to 499,999 15.4% 0.0% $500,000 to above 19.8% 0.0% Total household income in 2006 less than $15,000 2.7% 4.5% $15,000 to $24,000 6.5% 3.8% $25,000 to $34,000 9.6% 4.5% $35,000 to $49,000 11.9% 11.3% $50,000 to $74,000 16.4% 18.0% $75,000 to $99,000 14.3% 16.5% $100,000 and over 38.6% 41.4% * Pearson Chi-square significant at .05 level, ** Pearson Chi-square significant at .01 level

Continued

Table 3.7 Commercial and Rural Residential Farmers Descriptive Statistics – Income and Survey County

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Table 3.7 continued

Commercial Rural Farmer Residential Sample Frame ** Landowner random sample frame 57.0% 92.5% Commercial farm sample frame 43.0% 7.5% Survey County ** Cache 16.7% 12.8% Frederick 19.5% 12.0% Kent 25.3% 2.3% Forsyth 3.1% 9.0% Hall 3.1% 7.5% Shelby 12.3% 19.5% Spencer 8.2% 20.3% Yamhill 11.9% 16.5% * Pearson Chi-square significant at .05 level, ** Pearson Chi-square significant at .01 level

OPERATIONALIZING THE DEPENDENT VARIABLES

Two dependent variables are operationlized to measure farm persistence at the

RUI (Table 3.8). Farm persistence is measured through two discrete variables: the first is the length of time an individual respondent expects to continue to farm, the second variable is the length of time a respondent expects their farm enterprise to continue. With both questions respondents were asked to estimate the actual number of years they expected to either continue farming or the number of years they expect their business to persist, they were given the option to check ‘indefinitely’ or ‘not sure’. Many respondents provided a numerical estimate to these questions, to examine this data I ran frequency checks and examined the quartiles responses fell into in order to identify

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natural break points within the data

In response to the question how many years an individual expected to continue

farming, respondents were categorized into one of four discrete groups 1-11 Years (n=

60), 12-30 Years (n= 49), Indefinitely (n= 79) and Unsure (n= 105). In regards to the

number of years one expected their farm enterprise to remain in business respondents fell

into one of three categories, 1-30 Years (n= 62), Indefinitely (n= 122) and Unsure (n=

109). As a category 1- 30 Years may seem like a lengthy time frame, however this

category represents respondents who had identified a terminal end date for their

enterprises as opposed to those who had indicated indefinitely or unsure (which were

both the majority of cases). Identification of a terminal end date suggests the respondents expect their enterprise will cease to exist at a given point in time. Since this study is primarily concerned with farm persistence rather then the reasons farmers exit out of agriculture at the RUI, having a category with a terminal end date is useful for comparing across groups. The majority within the category (38 cases or 12% of the sample) indicated they expected their enterprise to continue only another 1 to 15 years, only 31 cases (10% of the total sample) indicated their enterprise would last between 15 and 30 years,. Additionally, had the groups been broken out into 1-15 Years and 16-30 Years the number of cases would have been so small in each category the multivariate statistics would not be reliable.

Those who expect their enterprise to continue for 1-30 years were also likely to indicate they only expect to farm for an additional 1-10 years and 12-30 years (Table

3.9). The majority of respondents who expect their enterprise to continue Indefinitely

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also expect to continue to farm Indefinitely. Likewise the majority of respondents indicating they are Unsure how long their enterprise will continue also indicate they are

Unsure how long they will continue to farm.

Frequency Percent

Years Individual Expects to Continue Farming 1-11 Years 60 20.5% 12-30 Years 49 16.7% Indefinitely 79 27.0% Unsure 105 35.8%

Years Expect Enterprise to Continue 1-30 Years 62 21.2% Indefinitely 122 41.6% Unsure 109 37.2%

Table 3.8 Descriptive Statistics Dependent Variable Commercial Farmer Sub- sample

Years Expect Enterprise to Continue 1-30 Years Indefinitely Unsure Pearson χ2

Years Individual Expects to Continue Farming 1-10 Years 45.0% 25.0% 30.0% 144.4 ** 12-30 Years 44.9% 32.7% 22.4% Indefinitely 7.6% 75.2% 17.1% Unsure 6.3% 15.2% 78.5% **Pearson Chi-square significant at .01 level

Table 3.9: Comparison of Dependent Variables: Years Individual Expects to Continue Farming and Years Expect Enterprise to Continue

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OPERATIONALIZING THE INDEPENDENT VARIABLES

A number of factors associated with farm persistence and adaptation have been identified in the literature, hypothesis related to these factors were outlined earlier. In the

following section I describe how the variables accounting for household dynamics, farm

structure and RUI pressures were operationalized.

Household Dynamics

A number of household level factors identified in the literature have been found to

be associated with farm persistence including: age, education, health of key operator,

availability of farm successor, optimism for the future of your farm, gender, family

contributions to farm labor, and off farm work5.

Demographic Variables: Age, Education and Sex

Age

As reviewed in chapter two lifecycle effects can influence farm persistence and

adaptation strategies (Bennett 1982; Gasson and Errington 1993 ; Potter and Lobley

1996b). For example Ilbery (1991) found that 70% of farmers engaged in diversification

strategies were over 45 years old. Furthermore the decision to pursue off-farm versus on-

farm diversification was further moderated by the age of children in the home; as those

with children under 16 years of age pursued off-farm income generating activities while

those who had children over 16 tended to develop on-farm diversification strategies

(Ilbery 1991). However, age has so far not been linked to specific types of on-farm

5 Data for this dissertation was collected from a cross sectional survey as opposed to panel data, I recognize a number of these variables are related to lifecycle effects and may change over time. 145

diversification strategies in the literature (Damianos and Skuras 1996; McNally 2001).

Gilg and Battershill (1998) found no direct relationship between the age distributions of

French farmers engaged in direct marketing and those that were not. In this study, age is

used as a proxy for lifecycle stage. Age was measured using respondents self reported

age. The age of respondents ranged from 26 to 92 years old (Table 3.10).

Education

Operator education levels have been linked to farm enterprise structure. Higher

levels of education are associated with increased opportunity for higher wage off-farm

employment (Stinglauer and Weiss 2000), less traditional views of farming, more

participation in and adoption of alternative farming systems, and a less profit-oriented approach to business management(Benjamin 1994; Gilg and Battershill 1998).

Furthermore in some studies higher levels of education increases the probability of family

succession (Stinglauer and Weiss 2000).

In this study respondents were asked to identify the highest number of years of education

completed. Response categories included: Some high school, but no diploma; High

school graduate (includes equivalency); Some college (no degree), associate degree, or

completed technical school program; Bachelors degree; Graduate or professional degree.

The majority of respondents had at least some college education (mean 3.8) (Table 3.10).

Sex

There has been considerable interest in understanding the gendered dimension of

farm adaptation and production strategies particularly as it relates to alternative

agriculture (Feldman and Welsh 1995; Sachs 1996; Chiappe and Flora 1998). Allen and

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Sachs (1991) criticize the sustainable agriculture movement for perpetuating visions of

the family farm based on patriarchal relations as the ideal organizational structure for the

future of farming. They argue the role of women within the sustainable agriculture

movement has been marginalized to essentialist roles as food purchasers and child care

providers that influence the agricultural system through their purchasing activities rather

then through direct action. However, women have contested their place both in the

sustainable agriculture paradigm and the traditional commodity sector through the

development of bottom-up female farm movements such as The Women, Food and

Agriculture Network (WFAN) and the Agricultural Women’s Leadership Network

(AWLN) (Wells and Tanner 1994; Wells 1998). Direct marketing and value adding

activities create spaces for women to occupy distinctly different roles in the production

system of AFAEs than they do on traditional farm operations. However, even as more

attention is directed to understanding women’s role in farming, the role of gender is still

not well understood in the alternative agriculture literature.

Women in agriculture movements often emphasize that sustainable agriculture

goes beyond sustainable environmental and economic systems, but extends to- and is tightly linked to sustainable communities (Bells 1998), and these attitudes have been

empirically substantiated among the wider female farm population (Feldman and Welsh

1995; Chiappe and Flora 1998). The weight women accord to social sustainability has

led them to favor craft development, cooperative farm markets, and AFAEs over the continuation of large-scale commodity agriculture. McNally (2001) found when women participated in the active management of the farm operation the probability of observing

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on-farm retailing and recreation enterprises increased by 12%. In order to examine if

there were differences between males and females, respondents in this study were asked

to identify their sex. The majority of respondents were male (79.80%) compared to only

20.20% of the sample which was female (Table 3.10).

Operator Health and Optimism

Additional life cycle effects including health of the key operator have been linked to farm persistence and adaptation strategies. Poor physical and mental health of an operator has been associated with disinvestment and plans for an eventual withdrawal from farming (Bennett 1982; Potter 1992). To asses the influences of health on the farm enterprise respondents were asked to indicate how much of a problem the health of the

key operator was to their business. Responses ranged from a severe problem (coded as 1) to not a problem (coded as 5) and had a mean of 3.59 (Table 3.10).

An additional factor associated with farm persistence is the optimism an operator

has for the future of their farm. Battershill and Gilg (1998) found a positive correlation

between increased optimism and delayed plans for retirement. In this study respondents

were asked to indicate how optimistic or pessimistic they were about the future of their

farm, responses ranged from very pessimistic (coded as 1) to very optimistic (coded as 7).

Overall respondents exhibited a mixed attitude leaning toward optimism (mean 4.53) in

regards to the future of their farm (Table 3.10).

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Mean or % Min Max SD Age 57.9 26 92 12.1 Education 3.3 1 5 1.1 Male 79.8% Female 20.2% Health of key operator 3.6 1 5 1.5 Optimism for future of farm 4.5 1 7 1.5

Table 3.10 Descriptive Statistics of Household Variables

Family Contributions to Labor

The household is both a production and consumption unit. Family labor can be

accounted for both as formal full-time, regular part-time, seasonal or casual paid work or

it can be ad hoc through informal assistance with chores and errand running (Bennett

1982; Salamon and Davis-Brown 1986; Gasson 1988; Salamon 1992 ; Gasson and

Errington 1993 ). The flexible supply of labor that families provide and their willingness

to engage in self-exploitive behaviors enables the family farm to persist and survive

negative turns in the farm economy (Barlett 1984; Reinhardt and Barlett 1989; Barlett

1993). Family labor contributions can vary with lifecycle and business cycle effects and

can illuminate how much of the farm business is connected to the household versus the

degree to which it is independent from the family (Barlett 1993). Family member

participation in farm activities is moderated by individual interest and can also vary by

farm type. Farms producing labor intensive crops and those direct marketing may need more assistance then the family alone is able to provide. Barbieri et al. (2008) found

highly diversified farms employ more full time employees year round. However, Gilg

and Battershill (1998) found the vast majority (60%) of French farmers selling direct to

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customers had at least two to three employees, the majority of these were family

members who not only were more flexible in their time availability but could also

discount their time to the business.

To assess family contributions to labor respondents were asked how much of the

total labor on their farming operation was provided by the respondent or a family

member, response categories included: All; Almost all; More than half; Less then half

(most labor done by paid, nonfamily workers), and; None (all labor done by paid,

nonfamily workers). A dummy variable was created with 1= half or more of the labor on

the farm is provided by the family and 0 indicating less then half of the labor on the farm

was provided by the family. In this sample the majority of respondents (79.60%)

indicated more than half of the labor on their farm came from family members (Table

3.11).

% Min Max Family labor contributions to farm - less then half 20.4% Family labor contributions to farm - more then half 79.6% Off-Farm Work No off farm income 36.4% Operator No Off Farm, Spouse Off farm Income 20.2% Operator Off farm income, Spouse No Off Farm Income 14.3% Spouse and Operator Off farm Income 29.1%

Table 3.11 Descriptive Statistics of Family Contributions to Labor and Off-Farm Work

Off farm work

Earlier debates focused on the transformative effects of off-farm work on the

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family farm established that part time farming does not necessarily lead to an exit from agriculture or the complete subsumption of the farm to capital. Indeed off-farm employment, both part time and full time, is a survival strategy farm families can implement to buffer the household through difficult downturns in the farm economy

(Barlett 1986c; Reinhardt and Barlett 1989; Barlett 1993; Kinsella et al. 2000). Bryant and Johnson (1992) found farmers at the RUI are a heterogeneous group motivated by a variety of lifestyle and economic goals, with proximity to off-farm work at the RUI enabling a larger number of farmers to pursue both their interest in agriculture and to maintain a specific standard of living via off-farm work. Kinsella et al. (2000) demonstrated off-farm work may be used to support the household while farm income is reinvested into the farm enterprise thereby minimizing risk to the household. Studies have repeatedly found that farmers identify access to well paying jobs at the RUI as a benefit to farming and living in these areas (Lockeretz 1987 ; Heimlich and Anderson

2001).

To measure off-farm income contributions to total house income, respondents were asked about both operator and other adult household members off-farm employment. Operators were asked if they work any off farm jobs either full time or part- time. Response categories included: No; Yes-Full time; Yes-Part Time. A dummy variable was created where 1 = operator works off farm either full time or part time and 0 indicating operator has no off farm job. Less then half (43.40%) of operators indicating they were employed off the farm either part time or full time (Table 3.8). Respondents were also asked if any other adult members in the household was working any regular off

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farm jobs where 1= yes, they had an off farm job and 0 indicating they do not have any

other outside jobs. Just under half of the respondents (49.30%) indicated there was

another adult in the household working off the farm (Table 3.11).

Succession

Based on the literature reviewed in chapter two the presence of a successor is

hypothesized as a factor relating to farm persistence and adaptation at the RUI.

Identification and availability of a successor has been strongly correlated with farm

enterprise redevelopment and investment (Bennett 1982; Keating and Munro 1989; Potter

1992; Salamon 1992 ; Potter and Lobley 1996b; Potter and Lobley 1996a). While the lack of a successor has been correlated with disinvestment and a withdrawal from agriculture (Potter and Lobley 1996b).

To understand how succession influences enterprise persistence and adaptation at the RUI two indicators of succession are utilized in this study (Table 3.9). The first is the availability of a successor. Respondents were asked to indicate how much of a problem the availability of a farm successor was for their operation. The question allowed five possible choices, ranging from a severe problem (coded as 1) to not a problem (coded as

5). Overall respondents reported availability was not a serious problem with a mean of

4.0 (Table 3.12) The second measure of farm succession was derived from a question asking respondents to identify if a relative will take over the operation when they retire.

Respondents could indicate: It’s too early to tell; Don’t know; A relative will take over the operation; I will keep the land, but will idle it; I will keep the land, but rent the farm

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to another farmer; I will sell to another farmer (not a relative); I will sell to a developer,

Other. A dummy variable was created to measure farm succession plans upon retirement with 1=a relative will take over the operation upon retirement and 0 indicating a relative will not take over the operation upon retirement. Less then a quarter of respondents

(21.80%) indicated their successor was a relative (Table 3.12).

Mean or % Min Max SD Availability of a farm successor 4.0 1 5 1.2 Successor is relative when retire 21.8%

Table 3.12 Descriptive Statistics of Succession Indicators

Values and Goals Motivating Land Use Decision Making

As noted in chapter two, values and goals heavily influence land use decisions. In

this study instrumental, substantive and stewardship values are hypothesized to influence

land management decisions.

Academic studies examining values and goals have found objectives vary with

farm structure and family objectives, and can change throughout the lifecycle. Studies

have found smaller family farms tend to emphasize substantive values, while larger

farmers emphasize instrumental values; however, these values can change with age as earlier in the lifecycle financial objectives are weighted with greater importance and then

decline with age as more substantive goals take prominence (Gasson and Errington

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1993). Goals and values can also vary by farm type, Gilg and Battershill (1998) found conventional farmers prioritized economic decisions while those farmers engaged in direct sales ranked personal and family considerations over economic goals. As farmers reflect the culture they are embedded in, their interest in profit and growth may become

more prominent in capitalist settings (Barlett 1993; Gasson and Errington 1993 ). Some

have argued that in the long term the increasing separation of farm from family in

industrialized nations would lead to a hardening of attitudes towards employing family

members as economic pressures on the business would favor succession based on skill

rather then kinship alone (Pile 1990). However, studies have continued to find a diversity

of reasons motivating individuals to stay in agriculture including the consistent ranking

among many groups of farmers that farming and ranching as a way of life is of greater

importance than profit motivations (Rowe et al. 2001).

Household life cycle, household organization, land tenure and farm type are

associated with stewardship values and therefore also influence enterprise adaptations

(Salamon 1992 ; Salamon et al. 1998; Parker et al. 2007). A number of studies have

found a relationship between stewardship attributes (soil and water conservation, lower

levels of nitrogen and agrochemical herbicide, fungicide and insecticide applications) to

be associated with on-farm diversification and alternative direct marketing enterprises

(Battershill and Gilg 1998; Gilg and Battershill 1998; Barbieri et al. 2008).

The following three sections review how the instrumental, substantive and

stewardship values examined in this study were operationalized. The individual

indicators associated with each type of value are presented in Table 3.13, which is

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followed by a discussion of the scales created to represent each value type.

Mean Min Max SD Instrumental Rationality Values Importance of minimizing debt 3.90 1 5 1.33 Importance of ensuring household income is adequate 3.77 1 5 1.28 Importance of maximizing net farm income 3.76 1 5 1.25 Importance of maximizing sale value of farmland 2.98 1 5 1.45

Substantive Rationality Values Desire to keep living in a rural area 4.33 1 5 1.02 Desire to keep this farm in the family 3.95 1 5 1.25 Desire to spend more time with family 3.99 1 5 1.14 Desire to be my own boss 4.01 1 5 1.17

Stewardship Values Importance of maintaining and improving quality of my soil 4.16 1 5 1.03 Importance of being a good steward of the land 4.37 1 5 0.90 Importance of minimizing nutrient & chemical runoff from farm 4.09 1 5 1.11 Importance of protecting scenic quality of the property 4.07 1 5 1.08 Desire to stay on good terms with neighbors 3.95 1 5 1.10

Table 3.13 Individual Items Values and Goals Motivating Land Use

Instrumental Rationality

An instrumental rationality scale was created to measure how economically based motives and values affect the way in which individuals manage their land. To create this scale, bivariate correlations were examined to assess levels of association among each of the variables proposed for the scales. Bivariate correlations significant at the .01 level were identified for all the variables included in this scale. Table 3.14 presents these

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correlations. A factor analysis was performed to further assess its distinctiveness. The

items comprising this scale loaded together on a single factor (Table 3.15).

123 4 Maximize net farm income 1 Ensure household income is adequate .691** 1 Minimize debt .431** .548** 1 Stay ahead of the competition .451** .403** .371** 1 ** Correlation is significant at the 0.01 level

Table 3.14 Bivariate Correlations for Motivations for Land Use - Instrumental

Factor 1 Maximize net farm income 0.779 Ensure household income is adequate 0.838 Minimize debt 0.557 Stay ahead of the competition 0.516 Cronbach's Alpha 0.788 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.15 Factor Analysis Matrix for Motivations for Land Use - Instrumental

To create this scale, respondents were asked to indicate on a scale of 1 to 5 (1=not

important; 5=extremely important) the degree to which a series of questions related to

economic rationality influences land management decisions. The items measuring

instrumental rationality included: Maximize net farm income; Ensure household income

is adequate; Minimize debt, and; Stay ahead of the competition, even if it entails risk.

The scale was created by summing the response to each individual question and had a

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mean of 13.73 and a range of 4 to 20 with a standard deviation of 3.99 and resulted in a

Cronbach’s alpha of 0.79 (Table 3.16). To interpret this scale, lower scores indicate instrumental rationality values were less important, and higher scores indicates instrumental rationality values were more important.

Mean Min Max SD Motivation for Land Use-Economic (Alpha=.79) 13.7 4 20 4.0 Maximize net farm income 3.7 1 5 1.2 Ensure household income is adequate 3.8 1 5 1.3 Minimize debt 3.9 1 5 1.3 Stay ahead of the competition 2.4 1 5 1.2

Table 3.16 Descriptive Statistics for Motivations for Land Use - Instrumental

Substantive Rationality

A substantive scale was created to measure how cultural and social motives and

values affect the way in which individuals manage their land. To create this scale,

bivariate correlations were examined to assess levels of association among each of the

variables proposed for the scales. Bivariate correlations significant at the .01 level were

identified for all the variables included in this scale. Table 3.17 presents these

correlations. A factor analysis was performed to further assess its distinctiveness. The

items comprising this scale loaded together on a single factor (Table 3.18).

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12 3 4 Desire to keep living in a rural area 1 Desire to keep this farm in the family .468** 1 Desire to be my own boss .397** .317** 1 To spend more time with family .440** .409** .404** 1 ** Correlation is significant at the 0.01 level **.

Table 3.17 Bivariate Correlations for Motivations for Land Use – Substantive

Factor 1 Desire to keep living in a rural area 0.732 Desire to keep this farm in the family 0.578 Desire to be my own boss 0.445 To spend more time with family 0.508 Cronbach's Alpha 0.728 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.18 Descriptive Statistics for Motivations for Land Use - Substantive

To create this scale, respondents were asked to indicate on a scale of 1 to 5 (1=not important; 5=extremely important) the degree to which a series of questions related to cultural and social motivations affects land management decisions. The items measuring substantive rationality included: Desire to keep living in rural area; Desire to keep this farm in the family; Desire to be my own boss; To spend more time with family, and;

Desire to stay on good terms with neighbors. The scale was created by summing the response to each individual question and had a mean of 16.27 and a range of 4 to 20 with a standard deviation of 3.42 and resulted in a Cronbach’s alpha of 0.73 (Table 3.19). To interpret this scale, lower scores indicate substantive values were less important, and

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higher scores indicates substantive values were more important.

Mean Min Max SD Motivation for Land Use- Social (Alpha=.73) 16.3 4 20 3.4 Desire to keep living in a rural area 4.3 1 5 1.0 Desire to keep this farm in the family 4.0 1 5 1.2 Desire to be my own boss 4.0 1 5 1.2 To spend more time with family 4.0 1 5 1.2

Table 3.19 Descriptive Statistics for Motivations for Land Use - Substantive

Stewardship

A stewardship scale was created to measure how different motives and values

affect the way in which individuals manage their land. To create this scale, bivariate

correlations were examined to assess levels of association among each of the variables

proposed for the scales. Bivariate correlations significant at the .01 level were identified

for all the variables included in this scale. Table 3.20 presents these correlations. A

factor analysis was performed to further assess its distinctiveness. The items comprising

this scale loaded together on a single factor (Table 3.21).

12 3 4 Being a good steward of the land 1 Maintain and improve quality of my soil .688** 1 Minimize nutrient & chemical runoff from farm .629** .684** 1 Protect scenic quality of the property .614** .481** .542** 1 ** Correlation is significant at the 0.01 level **.

Table 3.20 Bivariate Correlations for Motivations for Land Use – Stewardship

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Factor 1 Being a good steward of the land 0.718 Maintain and improve quality of my soil 0.719 Minimize nutrient & chemical runoff from farm 0.742 Protect scenic quality of the property 0.555 Cronbach's Alpha 0.856 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.21 Factor Analysis Matrix for Motivations for Land Use - Stewardship

To create this scale, respondents were asked to indicate on a scale of 1 to 5 (1=not important; 5=extremely important) the degree to which a series of questions related to the environment and natural resources affects land management decisions. The items measuring stewardship included: Being a good steward of the land; Maintain or improve quality of my soil; Minimize nutrient and chemical runoff from farm, and; Protect scenic quality of the property.

The scale was created by summing the response to each individual question and had a mean of 16.7 and a range of 4 to 20 with a standard deviation of 3.44 and resulted in a Cronbach’s alpha of 0.86 (Table 3.22). To interpret this scale, lower scores indicate stewardship values were less important, and higher scores indicates stewardship values were more important.

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Mean Min Max SD Motivation for Land Use-Stewardship (Alpha=.86) 16.7 4 20 3.4 Being a good steward of the land 4.4 1 5 0.9 Maintain and improve quality of my soil 4.2 1 5 1.0 Minimize nutrient & chemical runoff from farm 4.1 1 5 1.1 Protect scenic quality of the property 4.1 1 5 1.1

Table 3.22 Descriptive Statistics for Motivations for Land Use - Stewardship

Future Plans for Land Use

Land use plans made in anticipation of retirement have direct implications for

farm wealth, industry structure and production output (Mishra et al. 2003; Sharp and

Smith 2004). Three questions (the importance of selling farmland to afford retirement,

plans to sell land to a developer on retirement and likeliness to develop land) are used to

assess future land use plans in this study. The first question asked respondents to indicate

on a scale of 1 to 4 the importance of selling farmland to afford retirement with 1 = not important and 4= very important. On average respondents reported selling farmland to afford retirement was of moderate importance (mean 2.08) (Table 3.23). A dummy variable was created to measure if respondents would sell land to a developer on retirement with 1= I will sell to a developer when I retire and 0 indicating they would not sell to a developer when they retire. Less then 15% (14.80%) of respondents indicated they would sell to a developer (Table 3.23).

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Mean or % Min Max SD Importance of selling farmland to afford retirement 2.1 1 4 1.1 Will sell to a developer when retire 14.8%

Table 3.23 Descriptive Statistics for Future Land Use Plans When Retire

Likeliness to Develop Land

A likeliness to develop land scale was created to measure how likely respondents

were to develop their land for nonfarm/non-agricultural purposes. To create this scale,

bivariate correlations were examined to assess levels of association among each of the

variables proposed for the scales. Bivariate correlations significant at the .01 level were

identified for all the variables included in this scale. Table 3.24 presents these

correlations. A factor analysis was performed to further assess its distinctiveness. The

items comprising this scale loaded together on a single factor (Table 3.25).

12 3 Likeliness to sell housing lots directly to individuals 1 Likeliness to sell any land to a developer .656** 1 Likeliness to develop own land for housing .672** .521** 1 ** Correlation is significant at the 0.01 level **.

Table 3.24 Bivariate Correlations for Likeliness to Sell Land for Development in the Next Five Years

Factor 1 Likeliness to sell housing lots directly to individuals 0.889 Likeliness to sell any land to a developer 0.737 Likeliness to develop own land for housing 0.738 Cronbach's Alpha 0.827 Extraction Method Maximum Likelihood and Varimax Rotation 162

Table 3.25 Factor Analysis Matrix for Likeliness to Sell Land for Development in the Next Five Years

To create this scale, respondents were asked to indicate on a scale of -2 to +2 (-

2=definitely won’t; +2=definitely will) how likely they were to develop their land or sell to nonfarmers. The items in the likeliness to develop land include: Sell housing lots directly to individuals; Sell any of my land to a developer, and; Develop my own land for housing. The scale was created by summing the response to each individual question and had a mean of 5.41 and a range of 3 to 15 with a standard deviation of 2.93 and resulted in a Cronbach’s alpha of 0.83 (Table 3.26). To interpret this scale, lower scores indicate respondents are less likely to develop their land, and higher scores indicate respondents are more likely to develop their land for nonfarm purposes.

Mean Min Max SD Optimism for Likeliness to Sell Land for Development (Alpha=.83) 5.4 3 15 2.9 Likeliness to sell housing lots directly to individuals 1.8 1 5 1.1 Likeliness to sell any land to a developer 1.9 1 5 1.2 Likeliness to develop own land for housing 1.7 1 5 1.1

Table 3.26 Descriptive Statistics for Likeliness to Sell Land for Development in the Next Five Years

Farm Structure

As discussed in chapter 2 farm structure can influence farm persistence and

adaptation especially at the RUI where population growth and development exert

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additional pressures on the farm enterprise. The probability of farm succession has been shown to increase significantly when exhibiting positive relationships with farm characteristics including: farm size, previous farm growth and on farm diversification

(Stinglauer and Weiss 2000). To assess the influence of farm characteristics this study

incorporates several factors into the analysis including: debt to asset ratio, acres,

production type, soil quality, farm receipts, household income, capital investments made

into the farm enterprise, AFAE adaptations, and the degree to which more alternative

marketing strategies are being implemented. The following section reviews how each of

these variables are operationalized.

Debt

The debt to asset ratio is used as a measure of financial stress. Leistritz and

Barnard (1995) citing USDA publications indicate a debt-asset ratio under 40% is a sign the farmer has no apparent financial problems, while a debt-asset ratio above 40%

suggests significant financial stress. More specifically a debt-asset ratio of 40%-70%

indicates serious financial problems; 71%-100% is associated with extreme financial

problems, and; a debt-asset ratio over 100% indicates the enterprise is technically insolvent. In this survey debt-asset ratio response categories included: we have no

outstanding debts; our farm debts are below 10% of our assets; our farm debts are

between 10 to 40% of our assets; and our farm debts are above 40% of our assets. A dummy variable was created to measure farm debt with 1 = debt is 40% or above assets

and 0 indicating debts are 40% or below assets. The vast majority of respondents

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(92.50%) indicated their debt to asset ratio was less then 40% suggesting they are in good

financial health (Table 3.27).

Mean or % Min Max S.D. Acres Operated 307.0 1 8,200 651.1 Acres Owned 188.1 0 5,600 377.2 Acres Rented 98.7 0 3,200 306.1 Soil Quality 3.2 1 5 0.8 2006 Farm Receipts 3.5 1 7 2.2 2006 Household Income 5.4 1 7 1.8 Farm debts are 40% or below assets 92.5% Farm debts are 40% above assets 7.5%

Table 3.27 Farm Structure Descriptive Statistics

Soil Quality

The European literature has examined the relationship between areas exhibiting

poor growing conditions (generally biophysical in nature), also referred to as ‘less

favored areas,’ and farm persistence and adaptation (Potter and Lobley 1996a; Renting

2003). Poor growing conditions do not necessarily predetermine an exit from agriculture but can influence the types of adaptations an enterprise is able to pursue. In this study soil quality was included as an indicator representing biophysical influences on farm adaptation and persistence. Respondents were asked to compare their farm soil quality to the average in their county, response categories included: Much worse than average;

Worse than average; About average; Better than average; Much better than average. On average respondents indicated their soil quality was about average (mean 3.23) (Table

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3.27).

Acres, Farm Income and Household Income

Acres and farm sales are both indicators of farm size. Research examining the

relationship between acres and on-farm diversification is mixed. Damianos and Skuras

(1996) found a negative relationship between acres and a farmer’s likeliness to adopt alternative farm enterprise, Barbieri et al. (2008) found no relationship between acres and on-farm diversification. These results suggest that farm size measures should include a measure of farm income in order to understand how farm size and profitability influence production and marketing decisions. Stinglauer and Weiss (2000) found bigger farms are able to create economies of scale, able to provide greater returns then small farms, thereby decreasing the need for off-farm employment and create the conditions that will enable a potential successor to earn a reasonable and secure income. Furthermore

Barbieri et al. (2008) found higher farm incomes are associated with higher levels of diversification. Measures of household income are included to parse out the degree to which household income is tied to farm persistence and adaptation

Acres

The variable acres was measured through respondents self reported number of total acres operated, and of those operate acres, how many acres were owned and how many were rented. Overall respondents reported operating an average of 307 acres; owning an average of 188 acres and renting an average of 99 acres (Table 3.27).

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Farm Receipts

Respondents were asked to identify the category representing total farm receipts in 2006. Response categories included: Less then $10,000; $10,000 to $24,999; $25,000 to $49,999; $50,000 to $99,999; $100,000 to $249,999; $250,000 to $499,999, and;

$500,00 and above. These categories were coded from 1 (Less then $10,000) to 7

($550,000 and above), overall respondents reported a mean farm income of 3.46 (Table

3.27).

Household Income

Respondents were asked to identify the approximate range of total household income in 2006. Response categories included: Less than $15,000; $15,000 to $24,999;

$25,000 to $34,999; $35,000 to $49,999; $50,000 to $74,999; $75,000 to $99,999; and,

$100,00 and over. These categories were coded from 1 (Less then $15,000) to 7

($100,000 and over), overall respondents reported a mean income of 5.36 (Table 3.27).

Production Systems

The unique pressures exerted at the RUI tend to favor certain production systems

over others. Research examining farm structure at the RUI has consistently found fruit,

vegetable, nursery greenhouse and grain crops are better able to persist at the RUI while

dairy, hogs, and livestock farms are more likely to disappear from the landscape (Barbieri

et al. 2008). Hoppe and Korb (2001) found high value crops are better able to compete

with higher land rents at the RUI and are also more likely to be associated with

diversification strategies that take advantage of new urban customers.

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To classify farmers into production system type, two variables were used.

Respondents were given a list of livestock and crop commodities and were asked to

check all the types that were grown in 2006. The livestock list included: milk; dairy cattle

(breeding stock); hogs; beef; sheep or goats; poultry; horses, and; other. The crops list

included: corn (either grain or silage); hay or haylage; soybeans; small grains (oats,

barley, etc.); vegetables (fresh or processing); tobacco; nursery or greenhouse crops; fruit,

nut or orchard crops, and; other. Respondents were provided room to write in any other

livestock and crops they produced. Respondents were then asked to circle the single

commodity that contributed the most gross farm income in 2006. Other livestock and

crops were recoded to fit into one of the provided categories. Respondents were also

asked to write in the percentage of 2006 total gross farm receipts that came from

livestock, crops and other sources. The responses to these two questions were compared

and used to classify respondents into one of four categories, those who primarily raise: 1)

high value crops (fruit, vegetable, nut, nursery and greenhouse crops); 2) grain (corn and

soy); 3) livestock, and 4) dairy. Almost half of the respondents (44.10%) are engaged in

livestock production and a quarter (25.10%) are raising high value crops, a very small

minority of respondents are engaged in dairy production (3.10%) or grain production

(3.10%) (Table 3.28).

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Percent

Production System

High value crops (veg, tobacco, nursery, green, fruit, nut, orchard, etc) 25.1% Corn and/or soy 3.1% Livestock 44.1% Dairy 3.1%

Table 3.28 Production System and Marketing Strategies

Past and Future Changes to the Enterprise

Past and future changes made to the farm enterprise provide insight into the future plans operators are making for their farm. According to the impermanence syndrome, farmers preparing to exit agriculture should exhibit signs of disinvestment (Berry 1979).

To evaluate the degree to which respondents are investing or disinvesting in their enterprises two indicators are examined: past and future capital investments made in the farm. Engaging in direct marketing strategies that take advantage of new urban markets which should theoretically be able to compete with higher land rents is also a sign of positive change and investment in the farm (Bryant and Johnston 1992). To measure the degree to which respondents were actively pursuing these strategies respondents were asked to indicate if they were making changes on their farm operation over the last five years and again what changes they were anticipating making over the next five years.

Responses ranged from “Decreased a Lot” (coded as 1) to “Remain the Same” (coded as

3) to “Increased a Lot” (coded as 5). Respondents were also given the option of checking

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NA. NA responses were recoded into “remain the same” as these were interpreted to mean there was neither a decrease or increase that had been implemented or was anticipated. The variables and scales measuring past and future capital investments and alternative marketing activities are described below.

To examine past and future changes to the farm enterprise respondents were asked to indicate the: Changes to the number of commodities produced; Sales directly to consumers; and, Value added processing of farm products (Table 3.29). On average respondents indicated relatively little change had been made in relation to these adaptations, the greatest increase was observed among past and future sales direct to consumers.

Mean Min Max SD Last 5 Years, Changes to number of commodities produced 3.1 1 5 0.5 Last 5 Years, Sales directly to consumers 3.2 1 5 0.6 Last 5 Years, Value added processing of farm products 3.1 1 5 0.4 Next 5 Years, Changes to number of commodities produced 3.1 1 5 0.6 Next 5 Years, Sales directly to consumers 3.3 1 5 0.6 Next 5 Years, Value added processing of farm products 3.1 1 5 0.5

Table 3.29 Descriptive Statistics of Past and Future Changes to Production and Marketing Strategies

Past Capital Investments in Farm

A past capital investments in farm scale was created to measure the degree to which respondents were investing in farm equipment and farm buildings over the last five years. To create this scale, bivariate correlations were examined to assess levels of

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association among each of the variables proposed for the scales. Bivariate correlations

significant at the .01 level were identified for all the variables included in this scale.

Table 3.30 presents these correlations. A factor analysis was performed to further assess

its distinctiveness. The items comprising this scale loaded together on a single factor

(Table 3.31).

1 2 Past capital investment in farm buildings 1 Past investment in farm equipment .520** 1 ** Correlation is significant at the 0.01 level **.

Table 3.30 Bivariate Correlations for Past Capital Investments (Last Five Years)

Factor 1 Past capital investment in farm buildings 0.493 Past investment in farm equipment 0.590 Cronbach's Alpha 0.683 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.31 Factor Analysis Matrix for Past Capital Investments (Last Five Years)

To create this scale, respondents were asked to indicate on a scale of -2 to +2 (-

2=decrease a lot; +2=increase a lot) how likely they were to make capital investments in

their farm over the last five years. The items in the past capital investments scale

include: Capital investment in farm buildings, and; Investment in farm equipment. The

scale was created by summing the response to each individual question and had a mean

of 6.97 and a range of 2 to 10 with a standard deviation of 1.34 and resulted in a

Cronbach’s alpha of 0.68 (Table 3.32). To interpret this scale, lower scores indicate a

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respondent decreased the amount of capital investments they were making into their

farm, and higher scores indicate a respondent increased the amount of capital investments

they were making into their farm.

Mean Min Max SD Optimism for Past Capital Investments (Alpha=.68) 7.0 2 10 1.3 Past capital investment in farm buildings 3.4 1 5 0.7 Past investment in farm equipment 3.6 1 5 0.8

Table 3.32 Descriptive Statistics for Past Capital Investments (Last Five Years)

Past Alternative Marketing Activities

A past alternative marketing activities scale was created to measure the degree to

which respondents were changing marketing streams over the last five years. To create

this scale, bivariate correlations were examined to assess levels of association among

each of the variables proposed for the scales. Bivariate correlations significant at the .01

level were identified for all the variables included in this scale. Table 3.33 presents these

correlations. A factor analysis was performed to further assess its distinctiveness. The

items comprising this scale loaded together on a single factor (Table 3.34).

12 Past sales of products directly to consumers 1 Past on-farm(value added) processing of farm products .458** 1 ** Correlation is significant at the 0.01 level **.

Table 3.33 Bivariate Correlations for Past Alternative Market Development (Last Five Years)

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Factor 1 Past sales of products directly to consumers 0.976 Past on-farm(value added) processing of farm products 0.450 Cronbach's Alpha 0.607 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.34 Factor Analysis Matrix for Past Alternative Market Development (Last Five Years)

To create this scale, respondents were asked to indicate on a scale of -2 to +2 (-

2=decrease a lot; +2=increase a lot) how likely they were to change the types of products

they grew and who they sold these products to. The items in the past alternative

marketing activities scale include: Sales of products directly to consumers and Past on-

farm (value added) processing of farm products. The scale was created by summing the

response to each individual question and had a mean of 6.26 and a range of 2 to 10 with a

standard deviation of 0.94 and resulted in a Cronbach’s alpha of 0.61 (Table 3.35). To

interpret this scale, lower scores indicate a respondent decreased their alternative marketing activities, and higher scores indicate a respondent increased their alternative

marketing activities over the last five years.

Mean Min Max SD Past Alternative Market Development (Alpha=.61) 6.3 2 10 0.9 Past sales of products directly to consumers 3.2 1 5 0.6 Past on-farm(value added) processing of farm products 3.1 1 5 0.4

Table 3.35 Descriptive Statistics for Past Alternative Market Development (Last Five Years)

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Future Capital Investments in Farm

A future capital investments in farm scale was created to measure the degree to which respondents anticipated investing in farm equipment and farm buildings over the

next five years. To create this scale, bivariate correlations were examined to assess levels

of association among each of the variables proposed for the scales. Bivariate correlations significant at the .01 level were identified for all the variables included in this scale.

Table 3.36 presents these correlations. A factor analysis was performed to further assess

its distinctiveness. The items comprising this scale loaded together on one factor (Table

3.37).

12 Future capital investment in farm buildings 1 Future investment in farm equipment .622** 1 ** Correlation is significant at the 0.01 level **.

Table 3.36 Bivariate Correlations for Future Capital Investments (Next Five Years)

Factor 1 Future capital investment in farm buildings 0.522 Future investment in farm equipment 0.961 Cronbach's Alpha 0.767 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.37 Factor Analysis Matrix for Future Capital Investments (Next Five Years)

To create this scale, respondents were asked to indicate on a scale of -2 to +2 (-

2=decrease a lot; +2=increase a lot) how likely they were to make capital investments in their farm over the next five years. The items in the future capital investments scale

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include: Future capital investment in farm buildings, and; Future investment in farm

equipment. The scale was created by summing the response to each individual question

and had a mean of 6.64 and a range of 2 to 10 with a standard deviation of 1.40 and

resulted in a Cronbach’s alpha of 0.77 (Table 3.38). To interpret this scale, lower scores

indicate respondents anticipate decreasing the amount of capital investments they were

making into their farm, and higher scores indicate respondents anticipate increasing the amount of capital investments they were making into their farm over the next five years.

Mean Min Max SD Future Capital Investments (Alpha=.77) 6.6 2 10 1.4 Past sales of products directly to consumers 3.3 1 5 0.8 Past on-farm(value added) processing of farm products 3.4 1 5 0.8

Table 3.38 Descriptive Statistics for Future Capital Investments (Next Five Years)

Future Marketing and Production Diversification Plans

A future marketing and production diversification scale was created to measure

the degree to which respondents anticipated making changes to their marketing and production systems over the next five years. To create this scale, bivariate correlations

were examined to assess levels of association among each of the variables proposed for

the scales. Bivariate correlations significant at the .01 level were identified for all the

variables included in this scale. Table 3.39 presents these correlations. A factor analysis

was performed to further assess its distinctiveness. The items comprising this scale

loaded together on a single factor (Table 3.40).

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1 2 3 Futurenumber of distinct commodities produced 1 Future sales of product directly to consumers .534** 1 Future on-farm (value-added) processing of farm products .430** .496** 1 ** Correlation is significant at the 0.01 level **.

Table 3.39 Bivariate Correlations for Future Alternative Market Development (Next Five Years)

Factor 1 Futurenumber of distinct commodities produced 0.621 Future sales of product directly to consumers 0.800 Future on-farm (value-added) processing of farm products 0.558 Cronbach's Alpha 0.735 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.40 Factor Analysis Matrix for Future Alternative Market Development (Next Five Years)

To create this scale, respondents were asked to indicate on a scale of -2 to +2 (-

2=decrease a lot; +2=increase a lot) how likely they were to change the types of products they grew and who they sold these products to in the next five years. The items in the future alternative market development scale: Future number of distinct commodities produced; Future sales of products directly to consumers, and; On-farm (value-added) processing of farm products. The scale was created by summing the response to each individual question and had a mean of 9.45 and a range of 3 to 15 with a standard deviation of 1.43 and resulted in a Cronbach’s alpha of 0.74 (Table 3.41). To interpret this scale, lower scores indicate a respondent anticipates decreasing their alternative marketing and production activities, and higher scores indicate a respondent anticipates

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increasing their alternative marketing and production activities over the next five years.

Mean Min Max SD Future Alternative Market Development (Alpha=.74) 9.5 3 15 1.4 Future number of distinct commodities produced 3.1 1 5 0.6 Future sales of product directly to consumers 3.3 1 5 0.7 Future on-farm (value-added) processing of farm products 3.1 1 5 0.5

Table 3.41 Descriptive Statistics for Future Alternative Market Development (Next Five Years)

Marketing Strategies and AFAE Adaptations

To examine the types of marketing and AFAE adaptations farmers at the RUI are

engaged in three types of marketing variables are examined: AFAE, value adding and

labeling. Reviewing the literature examining alternative food enterprises Barbieri et al.

(2008) identify eight distinct types of farm diversification strategies. These eight include:

1) non-traditional corps and livestock or alternative agricultural production systems (e.g.

organic or holistic grazing); 2) recreation, tourism and hospitality enterprises; 3) value

added processing and packaging; 4) alternative marketing and distribution outlets (e.g.

direct and internet sales); 5) passive diversification as land and buildings are rented for

non-farm activities (e.g. weddings and reunions); 6) contracting services (e.g. animal

boarding and crop harvesting contracts); 7)cultural and historic preservation, and; 8)

education focused farms. Many of these alternative food enterprises require a proximate

consumer base, thereby making them especially suited to the RUI.

In this analysis the definition for an AFAE was based on marketing and not production criteria. Variables were coded “1” as an AFAE if they exhibited at least one

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type of direct marketing stream or value added production. These attributes included:

Direct sales to consumers from the farm (e.g. Farmstand, U-pick etc); Direct sales to

consumers at a Farmers’ Market or through a CSA; Sales to local institutions or

businesses (e.g. restaurants, school or grocery store), and; Agritainment (e.g. mazes, hay rides, petting zoos, or farm events); hunting, fishing, wildlife viewing; animal boarding,

breeding and training. A dummy variable was created with 1= AFAE and 0 indicating

there was no AFAE activity. Almost half of all respondents (49.40%) indicated they

were engaged in an AFAE (Table 3.42).

Percent Marketing Strategies AFAE 49.3% Value Added 32.9% Labeling Products 31.0%

Table 3.42 Marketing Strategies

To assess the degree to which farmers were using any sort of labeling on their

products respondents were asked to check any attributes they use to market their

products. Response categories included: Locally Grown; Fresh-in-Season; Natural; State

Product (e.g. Utah Grown); Family-farm raised, and; Organic. A dummy variable was

created with 1 = at least one label is utilized and 0 indicating no label is used. Less then a

third of respondents (31.00) indicated they use some form of labeling to demarcate their

products (Table 3.42).

Respondents were also asked if they in any value added processing of farm

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products. A dummy variable was created with 1 = value added processing occurring and

0 indicating there was no value added processing activity. About a third of all

respondents (32.90%) indicated they were actively processing and adding value to farm products (Table 3.42).

Farm Business Trajectory

The trajectory of the farm business is an indicator of persistence, plans for

expansion or contraction (Bennett 1982; Gasson and Errington 1993 ; Jackson-Smith and

Sharp 2008). Two variables were created to measure farm trajectory: past farm trajectory

and future farm trajectory. Both variables are rooted in changes to land and sales as an

indicator of the intention to grow, remain the same or retract the enterprise. Each

respondent was coded into one of three categories: growth (coded as 1), stable (coded as

2) or decline (coded as 3). To assess both past and future farm trajectory, respondents

were asked to comment on changes made over the last 5 years to land (farmland owned

and farmland rented) and value of commodities sold (total gross sales). Responses ranged

from decrease a lot (coded as 1) to remain the same (coded as 3) increased a lot (coded as

5). Respondents were also given the option to check Not Applicable, NA responses were

recoded into remain the same as no change was indicated. Respondents indicating a

process of intensification, where the land base remained stable but sales increased were

also coded as growth. Respondents indicating no change across all three categories were

coded as stable. Finally respondents indicating a decline or deintensificaiton in sales or

land were coded as decline. Due to missing data, some respondents could not be clearly

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defined by these three criteria. In these cases additional factors were utilized to categorize respondents, these variables included: years expect farm to continue, years expect farm business to continue, past and future capital investments, past and future alternative marketing changes, and, past and future changes to the number of commodities sold. In the recent past, a slight majority of farms were on a decline trajectory (46.00%) followed by those on a growth trajectory (43.20%), the minority of respondents were classified as

being stable (10.80%) (Table 3.43). A slightly different story emerges in regards to the

future, here the vast majority of farms (57.50%) are on a growth trajectory, followed by

those anticipating a decline (30.80%) and a minority following a stable trajectory

(11.70%).

Percent Past Five Year Trajectory Decline 46.0% Stable 10.8% Growth 43.2%

Future Five Year Trajectory Decline 30.8% Stable 11.7% Growth 57.5%

Table 3.43 Past and Future Business Trajectory

Control Variables – Factors Associated With Farming at the RUI

The primary focus of this study is on the influence of household dynamics and

farm structure on enterprise adaptation and persistence; however I recognize other factors

unique to the RUI can also influence farm change. To control for the types of problems

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farmers at the RUI face a number of variables were included as control variables in this analysis including: cost of health insurance, perceived development pressure, perceived neighbor effects, availability of labor, availability of local infrastructure, weather effects, and, farm economics and global competition. Daniels (2000) identified five goals for ensuring the protection of working agricultural landscapes. These include: 1) protecting a critical mass of farmland to support farms and prevent fragmentation; 2) ensuring affordable land prices for expansion; 3) creating a culture of permanence; 4) programs should be cost effective to ensure public support, and; 5) having sustained social and political capital that emanates from the general public and elected officials. Four scales were created to capture the degree to which respondents identify the presence or absence of these key ingredients for protecting agricultural landscapes (effectiveness of local land use policy; nonfarm group support; community support for agriculture; local government support for land use policy and optimism for the future of agriculture in the county). The following section describes how these variables were operationalized.

Development Pressure

Farms at the RUI have been documented as experiencing an increase in

complaints about odors, pesticides, noise, reduced markets for traditional crops, increases

in taxes, increased pressure from water and land use restrictions (Heimlich and Anderson

2001). A development pressure scale was created to measure how respondents perceived

factors associated with increasing development as influencing their farm businesses. To

create this scale, bivariate correlations were examined to assess levels of association

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among each of the variables proposed for the scales. Bivariate correlations significant at the .01 level were identified for all the variables included in this scale. Table 3.44 presents these correlations. A factor analysis was performed to further assess its distinctiveness. The items comprising this scale loaded together on a single factor (Table

3.45).

1 2 3 Cost of farmland 1 New housing development near my farm .512** 1 Land-use policies in this county .474** .531** 1 ** Correlation is significant at the 0.01 level **

Table 3.44 Bivariate Correlations for Development Pressure

Factor 1 Cost of farmland 0.621 New housing development near my farm 0.671 Land-use policies in this county 0.556 Cronbach's Alpha 0.753 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.45 Factor Analysis Matrix for Development Pressure

To create this scale, respondents were asked to indicate on a scale of 0 to 4 (0=not a

problem; 4=severe problem) the extent to which a number of local conditions represented

a problem for their farm business. The items measuring development pressure included:

Traffic congestion; Cost of farmland; New housing development near my farm, and;

Land-use policies in this county. The scale was created by summing the response to each individual question and had a mean of 8.82 and a range of 3 to 15 with a standard

deviation of 3.75 and resulted in a Cronbach’s alpha of 0.75 (Table 3.46). To interpret

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this scale, lower scores indicate development pressures pose less of a problem to the farm

business, and higher scores indicate development pressures pose more of a problem to the

farm business.

Mean Min Max SD Development Pressure (Alpha=.75) 8.8 3 15 3.7 Cost of farmland 3.3 1 5 1.6 New housing development near my farm 2.7 1 5 1.5 Land-use policies in this county 2.8 1 5 1.5

Table 3.46 Descriptive Statistics for Development Pressure

Neighbor Effects

Neighbors complaining about odor, noise, runoff and ethnicity of workers can

create conflict between farm and nonfarm neighbors (Kelsey and Singletary 1996). At the

RUI tensions can be especially elevated as the increasing number of new nonfarm

residents not embedded in social relationships with their farm neighbors exacerbates not

only the incidence of neighbor conflicts but also the nature by which conflicts are

resolved (Kelsey 1998). Research in New Jersey conducted by Lisansky and Clark

(1987) found different types of farmers exhibit different types of problem (nuisance

issues, municipal ordinances, and trespass and vandalism) based on size and enterprise

type. In this study the largest farmers (those grossing more then $250,000) had the most

problems with nuisance issues compared to large farms (those grossing between

$100,000-$250,000) while the smallest farms (grossing less then $100,000) had the

fewest problems. Lisansky and Clark (1987) also found that compared to hog and

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livestock farms, the fruit, vegetable, dairy, grain and nursery crop farms experienced the

highest number of trespass and vandalism incidents. Livestock, hogs and poultry farms

are also more likely to catalyze conflict over odor and manure management (Sharp et al.

2002).

In order to account for the ways in which perceived neighbor conflicts and neighbor attitudes influence the farm business a neighbor effects scale was created. To create this scale, bivariate correlations were examined to assess levels of association among each of the variables proposed for the scales. Bivariate correlations significant at the .01 level were identified for all the variables included in this scale. Table 3.47 presents these correlations. A factor analysis was performed to further assess its distinctiveness. The items comprising this scale loaded together on a single factor (Table

3.48).

12 3 Difficulty applying agricultural chemicals due to nearby houses 1 Neighbor concerns about fieldwork .694** 1 Neighbor concerns about livestock .474** .606** 1 ** Correlation is significant at the 0.01 level **.

Table 3.47 Bivariate Correlations for Neighbor Effects

Factor 1 Neighbor concerns about fieldwork 0.905 Neighbor concerns about livestock 0.540 Difficulty applying ag chemicals due to nearby houses 0.642 Cronbach's Alpha 0.804 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.48 Factor Analysis Matrix for Neighbor Effects

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To create this scale, respondents were asked to indicate on a scale of 0 to 4 (0=not

a problem; 4=severe problem) the extent to which a number of local conditions

represented a problem for their farm business. The items measuring neighbor effects

included: Difficulty applying agricultural chemicals due to nearby houses; Neighbor

concerns about fieldwork, and; Neighbor concerns about livestock. The scale was

created by summing the response to each individual question and had a mean of 4.99 and

a range of 3 to 15 with a standard deviation of 2.47 and resulted in a Cronbach’s alpha of

0.80 (Table 3.49). To interpret this scale, lower scores indicate neighbors pose less of a

problem to the farm business, and higher scores indicate neighbors pose more of a

problem to the farm business.

Mean Min Max SD Neighbor Constraints (Alpha=.80) 5.0 3 15 2.5 Neighbor concerns about fieldwork 1.5 1 5 0.8 Neighbor concerns about livestock 1.7 1 5 1.0 Difficulty applying ag chemicals due to nearby houses 1.8 1 5 1.0

Table 3.49 Descriptive Statistics for Neighbor Effects

Availability of Labor

A labor availability scale was created to measure how respondents perceived factors associated with the availability and cost of labor to influence their farm businesses. To create this scale, bivariate correlations were examined to assess levels of association among each of the variables proposed for the scales. Bivariate correlations

significant at the .01 level were identified for all the variables included in this scale.

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Table 3.50 presents these correlations. A factor analysis was performed to further assess its distinctiveness.

12 Availability of farm labor 1 Cost of hiring farm labor .756** 1 ** Correlation is significant at the 0.01 level **.

Table 3.50 Bivariate Correlations for Availability of Labor

Factor 1 Availability of farm labor 0.825 Cost of hiring farm labor 0.720 Cronbach's Alpha 0.861 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.51 Factor Analysis Matrix for Availability of Labor

The items comprising this scale loaded together on one factor (Table 3.51). To create this scale, respondents were asked to indicate their extent to which a number of

local conditions represented a problem for their farm business including: Availability of

Farm Labor, and; Cost of Hiring Farm Labor. The scale was created by summing the

response to each individual question and had a mean of 5.56 and a range of 2 to 10 with a

standard deviation of 2.62 and resulted in a Cronbach’s alpha of 0.86 (Table 3.52). To

interpret this scale, lower scores indicate Labor Conditions in the farm sector pose less of

a problem to the farm business, and higher scores indicate labor conditions and

consolidation in the farm sector pose more of a problem to the farm business.

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Mean Min Max SD Labor Conditions (Alpha=.86) 5.6 2 10 2.6 Availability of farm labor 2.7 1 5 1.4 Cost of hiring farm labor 2.9 1 5 1.4

Table 3.52 Descriptive Statistics for Availability of Labor

Availability of Local Infrastructure

An availability of local infrastructure scale was created to measure how respondents perceived factors associated the availability of local marketing and processing outlets influence their farm businesses. To create this scale, bivariate

correlations were examined to assess levels of association among each of the variables

proposed for the scales. Bivariate correlations significant at the .01 level were identified

for all the variables included in this scale. Table 3.53 presents these correlations. A

factor analysis was performed to further assess its distinctiveness. The items comprising

this scale loaded together on a single factor (Table 3.54).

12 3 Availability of local farm input suppliers 1 Availability of local processors .669** 1 Availability of local marketing outlets .703** .750** 1 ** Correlation is significant at the 0.01 level **.

Table 3.53 Bivariate Correlations for Availability of Local Infrastructure

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Factor 1 Availability of local farm input suppliers 0.709 Availability of local processors 0.797 Availability of local marketing outlets 0.811 Cronbach's Alpha 0.878 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.54 Factor Analysis Matrix for Availability of Local Infrastructure

To create this scale, respondents were asked to indicate on a scale of 0 to 4 (0=not a problem; 4=severe problem) the extent to which a number of local conditions represented a problem for their farm business. The items measuring the availability of local infrastructure included: Availability of local farm input suppliers; Availability of local processors, and; Availability of local marketing outlets. The scale was created by summing the response to each individual question and had a mean of 5.83 and a range of

3 to 15 with a standard deviation of 3.26 and resulted in a Cronbach’s alpha of 0.88

(Table 3.55). To interpret this scale, lower scores indicate availability of local infrastructure pose less of a problem to the farm business, and higher scores indicate availability of local infrastructure pose more of a problem to the farm business.

Mean Min Max SD Local Infrastructure (Alpha=.88) 5.8 3 15 3.3 Availability of local farm input suppliers 1.9 1 5 1.2 Availability of local processors 2.0 1 5 1.3 Availability of local marketing outlets 2.0 1 5 1.2

Table 3.55 Descriptive Statistics for Availability of Local Infrastructure

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Weather Effects

A weather effects scale is included as an additional measure of biophysical

influences on the farm enterprise, in this case it was a measure of how respondents

perceived the weather as an influence on their farm business. To create this scale,

bivariate correlations were examined to assess levels of association among each of the

variables proposed for the scales. Bivariate correlations significant at the .01 level were

identified for all the variables included in this scale. Table 3.56 presents these

correlations. A factor analysis was performed to further assess its distinctiveness. The

items comprising this scale loaded together on a single factor (Table 3.57).

12 Long term weather issues 1 Recent weather events .684** 1 ** Correlation is significant at the 0.01 level **.

Table 3.56 Bivariate Correlations for Weather Effects

Factor 1 Long term weather issues 0.844 Recent weather events 0.697 Cronbach's Alpha 0.812 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.57 Factor Analysis Matrix for Weather Effects

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included: Long term weather issues (drought, climate change), and; Recent weather events (floods, storms, etc.).

The scale was created by summing the response to each individual question and had a mean of 4.88 and a range of 2 to 10 with a standard deviation of 2.37 and resulted in a Cronbach’s alpha of 0.81(Table 3.58). To interpret this scale, lower scores indicate weather effects pose less of a problem to the farm business, and higher scores indicate weather effects pose more of a problem to the farm business.

Mean Min Max SD Labor Conditions (Alpha=.81) 4.9 2 10 2.4 Long term weather issues 2.7 1 5 1.3 Recent weather events 2.2 1 5 1.3

Table 3.58 Descriptive Statistics for Weather Effects

Cost of Health Insurance

The need to access and ensure health insurance may direct the household to structure their enterprise to allow them to pursue off-farm work or income generating

activities that enable them to purchase private insurance. The increasing cost of health

care has been cited as a significant problem for farmers by the Ohio Rural Development

Partnership (2006). Respondents were asked to indicate how much of a problem the cost

of health insurance was for their operation based on a five point scale. Response

categories ranged from a severe problem (coded as 5) to not a problem (coded as 1).

Overall respondents reported the availability was of health insurance was more of a

problem for their operation with a mean of 3.6 (Table 3.59).

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Mean Min Max SD Cost of health insurance 3.6 1 5 1.5

Table 3.59. Descriptive Statistics for Health Insurance

Effectiveness of Land Use Policy on Maintaining Agriculture in the County

An effectiveness of land use policy scale was created to measure how respondents

perceived factors associated with land use policy impacted the ability to retain agriculture

in the county. To create this scale, bivariate correlations were examined to assess levels

of association among each of the variables proposed for the scales. Bivariate correlations

significant at the .01 level were identified for all the variables included in this scale.

Table 3.60 presents these correlations. A factor analysis was performed to further assess

its distinctiveness. The items comprising this scale loaded together on a single factor

(Table 3.61).

12 34 Keeping land in this county in farming 1 The viability of commercial farms in the county .699** 1 Enabling new farms to get started in the county .523** .605** 1 Keeping residential developments out of agricultural areas .598** .505** .550** 1 ** Correlation is significant at the 0.01 level **.

Table 3.60 Bivariate Correlations for Effectiveness of Land Use Policy

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Factor 1 Keeping land in this county in farming 0.802 The viability of commercial farms in the county 0.859 Enabling new farms to get started in the county 0.711 Keeping residential developments out of agricultural areas 0.577 Cronbach's Alpha 0.845 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.61 Descriptive Statistics for Effectiveness of Land Use Policy

To create this scale, respondents were asked to indicate on a scale of -2 to +2 (-

2=strong negative impact; +2=strong positive impact) the impact of a number of land use

policies has had on farms in the county. The items measuring effectiveness of land use

policy included: Keeping land in this county in farming; The viability of commercial

farms in the county; Enabling new farms to get started in the county; Keeping residential

development out of agricultural areas, and; Protecting the rights of property owners. The scale was created by summing the response to each individual question and had a mean of 10.08 and a range of 4 to 20 with a standard deviation of 3.84 and resulted in a

Cronbach’s alpha of 0.85 (Table 3.62). To interpret this scale, lower scores indicate the effectiveness of land use policies have a more positive impact on keeping land in the county in agriculture, and higher scores indicates land use policies have a more negative impact on keeping land in the county in agriculture.

Mean Min Max SD Impact of Land Use Policy (Alpha=.85) 10.1 4 20 3.8 Keeping land in this county in farming 2.7 1 5 1.2 The viability of commercial farms in the county 2.7 1 5 1.1 Enabling new farms to get started in the county 2.3 1 5 1.1 Keeping residential developments out of agricultural areas 2.4 1 5 1.2

Table 3.62 Descriptive Statistics for Effectiveness of Land Use Policy

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Support of Nonfarm Institutions and Groups

A support of nonfarm institutions and groups scale was created to measure how respondents perceived factors associated with increasing development to influence their farm businesses. To create this scale, bivariate correlations were examined to assess

levels of association among each of the variables proposed for the scales. Bivariate

correlations significant at the .01 level were identified for all the variables included in

this scale. Table 3.63 presents these correlations. A factor analysis was performed to

further assess its distinctiveness. The items comprising this scale loaded together on a

single factor (Table 3.64).

1234 5 County government 1 City/municipal governments .651** 1 Economic development organizations .496** .622** 1 Media .470** .511** .578** 1 General public .312** .378** .417** .554** 1 Local environmental organizations .299** .346** .362** .444** .497** ** Correlation is significant at the 0.01 level

Table 3.63 Bivariate Correlations for Non-Farm Institutional and Group Support for Agriculture

Factor 1 County government 0.682 City/municipal governments 0.763 Economic development organizations 0.753 Media 0.752 General public 0.602 Local environmental organizations 0.529 Cronbach's Alpha 0.836 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.64 Factor Analysis Matrix for Non-Farm Institutional and Group Support for Agriculture 193

To create this scale, respondents were asked to indicate on a scale of 0 to 4

(0=not at all supportive; 4=very supportive) the level of support varying institutions and groups in the county have for farming in the county. The items measuring support of nonfarm institutions and groups included: County government; City/Municipal governments; Economic development organizations; Media (such as newspapers);

General public, and; Local environmental organizations. The scale was created by summing the response to each individual question and had a mean of 16.58 and a range of 6 to 30 with a standard deviation of 4.59 and resulted in a Cronbach’s alpha of 0.84

(Table 3.65). To interpret this scale, lower scores indicate lower support for farming among nonfarm institutions and groups in the county, and higher scores indicates greater support for farming among nonfarm institutions and groups in the county.

Mean Min Max SD NonFarm Institutional and Group Support for Agriculture (Alpha=.84) 16.6 6 30 4.6 County government 3.0 1 5 1.0 City/municipal governments 2.5 1 5 1.0 Economic development organizations 2.5 1 5 1.1 Media 2.9 1 5 1.1 General public 3.0 1 5 0.9 Local environmental organizations 2.8 1 5 1.1

Table 3.65 Descriptive Statistics for Non-Farm Institutional and Group Support for Agriculture

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Community Support for Agriculture

A social capital/community support for agriculture scale was created to measure

how respondents perceived community support for local agriculture. To create this scale,

bivariate correlations were examined to assess levels of association among each of the

variables proposed for the scales. Bivariate correlations significant at the .01 level were

identified for all the variables included in this scale. Table 3.66 presents these

correlations. A factor analysis was performed to further assess its distinctiveness. The

items comprising this scale loaded together on a single factor (Table 3.67).

12 3 Mostly residents agree farming positively contributes to the quality of life in the county 1 Farmers and nonfarmers in this county get along well .560** 1 In general the citizens of county are very supportive of farming in the county .574** .605** 1 ** Correlation is significant at the 0.01 level **.

Table 3.66 Bivariate Correlations for Community Support For Agriculture

Factor 1 Mostly residents agree farming positively contributes to the quality of life in the county 0.667 Farmers and nonfarmers in this county get along well 0.706 In general the citizens of county are very supportive of farming in the county 0.694 Cronbach's Alpha 0.803 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.67 Factor Analysis Matrix for Community Support for Agriculture 195

To create this scale, respondents were asked to indicate on a scale of -2 to +2 (-

2=strongly disagree; +2=strongly agree) the degree to which the local community

supports farming. The items measuring social capital/community support for agriculture included: Most residents of the county agree that farming positively contributes to the quality of life in the county; Overall, farmers and nonfarmers in this county get along well, and; In general, the citizens of this county are very supportive of farming in the county. The scale was created by summing the response to each individual question and had a mean of 10.68 and a range of 3 to 15 with a standard deviation of 2.65 and resulted

in a Cronbach’s alpha of 0.80 (Table 3.68). To interpret this scale, lower scores indicate

less support for farming among the community, and higher scores indicates greater

support for farming among the community.

Mean Min Max SD Community Support for Agriculture (Alpha=.80) 10.7 3 15 2.7 Mostly residents agree farming positively contributes to the quality of life in the county 3.7 1 5 1.1 Farmers and nonfarmers in this county get along well 3.6 1 5 1.0 In general the citizens of county are very supportive of farming in the county 3.4 1 5 1.0

Table 3.68 Descriptive Statistics for NonFarm Institutional and Group Support for Agriculture

Local Government Support for Land Use Policy

A government support for land use policy scale was created to measure how

respondents perceived their local government and land use policies influenced farming in

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the county. To create this scale, bivariate correlations were examined to assess levels of

association among each of the variables proposed for the scales. Bivariate correlations

significant at the .01 level were identified for all the variables included in this scale.

Table 3.69 presents these correlations. A factor analysis was performed to further assess

its distinctiveness. The items comprising this scale loaded together on a single factor

(Table 3.70).

12 Local government does a good job of allowing public input into land use decisions 1 Land use policies in this county are effective at preserving farming in the county .537** 1 ** Correlation is significant at the 0.01 level **.

Table 3.69 Bivariate Correlations for Government Support for Land Use Policy

Factor 1 Local government does a good job of allowing public input into land use decisions 0.681 Land use policies in this county are effective at preserving farming in the county 0.639 Cronbach's Alpha 0.698 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.70 Factor Analysis Matrix for Government Support for Land Use Policy

To create this scale, respondents were asked to indicate on a scale of -2 to +2 (-

2=strongly disagree; +2=strongly agree) the degree to which the local community

supports farming. The items measuring social capital/community support for agriculture included: Local government does a good job of allowing public input into land use decisions in this county, and; Land use policies in this county are effective at preserving

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farming in the county. The scale was created by summing the response to each individual

question and had a mean of 5.39 and a range of 2 to 10 with a standard deviation of 1.91

and resulted in a Cronbach’s alpha of 0.70 (Table 3.71). To interpret this scale, lower

scores indicate less support for farming among the community, and higher scores

indicates greater support for farming among the community.

Mean Min Max SD Government Support for Land Use Policy (Alpha=.70) 5.4 2 10 1.9 Local government does a good job of allowing public input into land use decisions 2.8 1 5 1.1 Land use policies in this county are effective at preserving farming in the county 2.6 1 5 1.1

Table 3.71 Descriptive Statistics for Government Support for Land Use Policy

Optimism for Future of Agriculture in the County

An optimism for the future of agriculture in the county scale was created to

measure how optimistic respondents were about the long term viability of farming in

their county. To create this scale, bivariate correlations were examined to assess levels of

association among each of the variables proposed for the scales. Bivariate correlations

significant at the .01 level were identified for all the variables included in this scale.

Table 3.72 presents these correlations. A factor analysis was performed to further assess

its distinctiveness. The items comprising this scale loaded together on a single factor

(Table 3.73).

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1 2 3 Is population growth and development is having a positive or negative impact on the quality of life in the county? 1 Is population growth and development is having a positive or negative impact on farming in the county? .695** 1 Are you optimistic or pessimistic about the future of agriculture in the county? .465** .544** 1 ** Correlation is significant at the 0.01 level **.

Table 3.72 Bivariate Correlations for Optimism for Future of Agriculture in County

Factor 1 Is population growth and development is having a positive or negative impact on the quality of life in the county? 0.782 Is population growth and development is having a positive or negative impact on farming in the county? 0.874 Are you optimistic or pessimistic about the future of agriculture in the county? 0.588 Cronbach's Alpha 0.798 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.73 Factor Analysis Matrix for Optimism for Future of Agriculture in County

To create this scale, respondents were asked to indicate on a scale of 1 to 7 (1=very negative/very pessimistic; 7=very positive/very optimistic) how optimistic they were about the future of farming in the county. The items the future of agriculture in the county: Is population growth and development in the county having a positive or negative impact on farming in the county; Is population growth and development in the county having a positive or negative impact on the quality of life in the county, and; Are you optimistic or pessimistic about the future of agriculture in the county. The scale was created by summing the response to each individual question and had a mean of 8.83 and

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a range of 3 to 21 with a standard deviation of 4.16 and resulted in a Cronbach’s alpha of

0.80 (Table 3.74). To interpret this scale, lower scores indicate a more pessimistic and negative outlook, and higher scores indicates a more optimistic and positive outlook.

Mean Min Max SD Optimism for Future of Agriculture in County (Alpha=.80) 8.8 3 21 4.2 Is population growth and development is having a positive or negative impact on the quality of life in the county? 3.3 1 7 1.7 Is population growth and development is having a positive or negative impact on farming in the county? 2.5 1 7 1.6 Are you optimistic or pessimistic about the future of agriculture in the county? 3.0 1 7 1.6

Table 3.74 Descriptive Statistics for Optimism for Future of Agriculture in County

Farm Economics and Global Competition

A farm economics and global competition scale was created to measure how respondents perceived factors associated with the profitability of farming (cost of inputs

and income from farming) and factors associated with increasing consolidation, mergers

and competition influence their farm businesses To create this scale, bivariate

correlations were examined to assess levels of association among each of the variables

proposed for the scales. Bivariate correlations significant at the .01 level were identified

for all the variables included in this scale. Table 3.75 presents these correlations. A

factor analysis was performed to further assess its distinctiveness. The items comprising

this scale loaded together on a single factor (Table 3.76).

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1 2 3 4 5 6 Current prices for farm products 1 Net farm income from this farm .711** 1 Cost of farm inputs .642** .719** 1 Increased global competition in the farm sector .624** .546** .585** 1 Mergers among farm input suppliers .546** .490** .545** .683** 1 Consolidation in the farm processing sector .539** .478** .480** .658** .808** 1 **Correlation is signficant at the 0.01 level

Table 3.75 Bivariate Correlations for Farm Economics and Global Competition

Factor 1 Current prices for farm products 0.799 Net farm income from this farm 0.760 Cost of farm inputs 0.770 Increased global competition in the farm sector 0.797 Mergers among farm input suppliers 0.779 Consolidation in the farm processing sector 0.752 Cronbach's Alpha 0.921 Extraction Method Maximum Likelihood and Varimax Rotation

Table 3.76 Factor Analysis Matrix for Farm Economics and Global Competition

To create this scale, respondents were asked to indicate on a scale of 0 to 4 (0=not a problem; 4=severe problem) the extent to which a number of regional, national or international conditions represented a problem for their farm business. The items measuring farm economics and global competition included: Current prices for farm products I produce; Net farm income from this farm, Cost of farm inputs; Increased global competition in the farm sector; Mergers among farm input suppliers, and;

Consolidation in the farm processing sector. The scale was created by summing the response to each individual question and had a mean of 16.74 and a range of 6 to 30 with

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a standard deviation of 6.56 and resulted in a Cronbach’s alpha of 0.92 (Table 3.77). To

interpret this scale, lower scores indicate farm economics and global competition pose less of a problem to the farm business, and higher scores indicate farm economics and global competition pose more of a problem to the farm business.

Mean Min Max SD Farm Economics and Global Competition (Alpha=.92) 16.7 6 30 6.6 Current prices for farm products 3.0 1 5 1.4 Net farm income from this farm 3.0 1 5 1.3 Cost of farm inputs 3.3 1 5 1.4 Increased global competition in the farm sector 2.7 1 5 1.4 Mergers among farm input suppliers 2.4 1 5 1.3 Consolidation in the farm processing sector 2.3 1 5 1.3

Table 3.77 Descriptive Statistics for Farm Economics and Global Competition

Other controls

To control for sample selection bias two variables were included with survey

county of origin and sample frame. The sample frame was coded into a dummy variable

where 1= landowner random sample and 0= commercial farm sample frame. The

majority of respondents (68.10%) came from the landowner random sample frame

(Table 3.78). A categorical variable was coded from 1 to 8 to represent each of the case

study counties. The majority of respondents were from Kent (18.10%), followed by

Frederick (17.10%), Cache (15.50%), Shelby (14.60%), Yamhill (13.40%), Spencer

(12.00%), Forsythe (4.90%), and Hall (4.50%) (Table 3.72).

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Mean or % Min Max S.D. Cost of Health Insurance 3.6 1 5 1.5

Sample Frame Landowner random sample frame 68.1% Commercial farm sample frame 31.9%

Survey County Cache 15.5% Frederick 17.1% Kent 18.1% Forsyth 4.9% Hall 4.5% Shelby 14.6% Spencer 12.0% Yamhill 13.4%

Table 3.78 Descriptive Statistics Respondent Sample Frame and Survey County Origin

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CHAPTER 4

COMMERCIAL FARM PERSISTENCE AND ADAPTATION AT THE RUI

The analysis related to research question number one examining how household dynamics, intrinsic and instrumental values are associated with commercial farm persistence at the RUI are reported in this chapter. The first section describes these results and describes them in relation to the hypothesis. In the second section I evaluate the hypothesis using a multinomial regression model.

COMPARISON OF MEANS - ONE WAY ANOVA AND CHI SQUARE TESTS

Initial hypothesis testing is conducted through a comparison of means test examining the relationship between the dependent and independent variables.

Comparisons between groups along with the appropriate test statistic (F statistic when conducting analysis of variance (ANOVA) or a Pearson chi-square statistic when conducting cross-tabulations), statistical significance levels, and the pattern of difference among groups determined by post hoc tests (least significant difference) are reported to illuminate differences among survey respondents according to the length of time they expect to continue farming and the length of time respondents expect their farm enterprise to persist. For the most part, only relationships that are deemed statistically significant will be discussed and elaborated upon.

204

Length of Time an Individual Expects to Continue Farming

Household Characteristics

In terms of household characteristics, significant differences were observed

between respondents grouped according to the length of time they expect to continue

farming. Differences were observed in the case of the farmer’s age, the availability of

farm successor, optimism for future of the farm, and education (Table 4.1). The oldest

group of farmers, with a mean age of 62.5, were most likely to indicate they would only

continuing farming for 1 to 10 years into the future, while farmers who were Unsure

about how long they expect to continue farming had a mean age of 61.7. Farmers indicating they would farm Indefinitely were younger (mean 56.8), while farmers who reported they would continue to farm another 12-30 Years were the youngest with a mean age of 52.9.

The availability of a successor was least likely to be a problem for respondents intending to farm Indefinitely (mean 4.1) and for those who were Unsure how long they

will continue to farm (mean 3.9). The availability of a farm successor was identified as a

more modest problem for those who only expected to continue farming for 12-30 years

and was the most serious problem for those who only plan to farm for 1-10 additional

years.

Those who plan to farm Indefinitely were the most optimistic about the future of

their farm (mean 5.0) followed by those who plan to farm for 12-30 Years (mean 4.9).

Optimism levels were lower for those who only plan to farm for 1-10 Years (mean 4.2)

while those who were Unsure how long they would continue farming were the less

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optimistic in their outlook for the future of their farm (mean 4.0). Respondents who plan

to farm Indefinitely also had the highest education levels (mean 3.4) followed by those

who plan to farm only 1-10 additional years ( mean 3.3) and those who expect to farm for

only 12-30 additional years (mean 3.2). Respondents who are Unsure of how long they

will continue to farm had the lowest education levels (mean 2.8).

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F-test or 1-10 12-30 Pearson Post Hoc Years Years Indefinitely Unsure χ2 Tests Mean Mean Mean Mean N 60 49 105 79 Household Characteristics 12-30Y < I < U < 1- Age 62.5 52.9 56.8 61.7 8.9 ** 10Y Health of Key Operator 3.4 3.7 3.7 3.4 1.3 I, U > 12- Availability of Farm 30Y> 1- Successor 3.2 3.6 4.1 3.9 7.2 ** 10Y I > 12-30 Optimism for Future of Y > 1-10 Your Farm 4.2 4.9 5.0 4.0 8.5 ** Y > U

Male (%) 85.0% 89.8% 82.9% 81.0% 1.9 I > U > 12- 30Y, 1-10 Education 3.3 3.2 3.4 2.8 5.1 * Y

Family contributes less then half of the labor to the farm (%) 33.3% 34.7% 21.9% 20.3% 5.9 Family Contributes more then half of the labor to the farm(%) 66.7% 65.3% 78.1% 79.7%

Off-Farm Income 14.4 No off farm income (%) 45.0% 44.9% 41.0% 55.7% Operator No Off Farm, Spouse Off farm Income, (%) 23.3% 32.7% 17.1% 20.3% Operator Off farm income, Spouse No Off Farm Income 15.0% 6.1% 17.1% 6.3% Spouse and Operator Off farm Income 16.7% 16.3% 24.8% 17.7% *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.1 Comparison of Household Characteristics- Length of Time an Individual Expects to Continue Farming

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Although there were no significant differences in relationship to off-farm income

were observed, almost a quarter of farmers indicating they plan to farm Indefinitely

(24.8%) reported both the operator and spouse had off-farm income. The security of off-

farm income may contribute to respondents reporting they plan to continue to farm

Indefinitely. The typical pattern for farm families includes an operator with no-off job

and a spouse who works off the farm, this pattern was observed most frequently among

the 12-30 Year group (32.7%), the 1-10 Year group (23.3%) and the Unsure group

(20.3%). No significant differences among the health of the key operator, gender, family

contributions to labor, off-farm employment, and other adults in the household who work off the farm were observed between the different groups.

Household Values

Significant differences were observed among the groups in relation to the emphasis they placed on a number of the instrumental (Table 4.2) and substantive values

(Table 4.3) measured in this research.

Instrumental Values

Respondents who plan to continue farming for 12-30 Years placed the most emphasis on overall instrumental values (mean 15.9) followed by the Unsure group

(mean 15.4). Instrumental values were slightly less important for those who plan to farm

Indefinitely (mean 14.6) and lowest of all for those who only expect to farm an additional

1-10 Years (mean 14.0). Within the instrumental factors the importance of net farm income was most important for those who will continue farming for 12-30 Years (mean

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4.4) and for those who are Unsure (mean 4.4) while it was more moderately important for those who will farm Indefinitely (mean 4.1) and least important to those who will be

farming for only 1-10 Years (mean 3.9). Maximizing the sale value of farmland was most important for those who are Unsure of how long they will continue to farm (mean 3.3) followed by those who will be farming for 12-30 Years (mean 3.3). It was less important for the 1-10 Year group (mean 3.0) while the Indefinitely group placed the least emphasis on maximizing the sale value of farmland (mean 2.7). No significant

differences were observed among the groups in relationship to: the importance of

minimizing debt, ensuring household income is adequate and staying ahead of the competition.

F-test or 1-10 12-30 Pearson Post Hoc Years Years Indefinitely Unsure χ2 Tests Mean Mean Mean Mean N 60 49 105 79 12-30Y > Factor Instrumental U > I > 1- Values 14.0 15.9 14.6 15.4 3.6 * 10Y Importance of minimizing debt 3.8 4.2 4.0 4.3 1.8 Importance of ensuring household income is adequate 3.9 4.3 3.9 4.2 2.4 Importance of maximizing net farm 1-10Y< income 3.9 4.4 4.1 4.4 2.9 * 12-30, U Importance of maximizing sale value I < 1-10Y, of farmland 3.0 3.3 2.7 3.3 3.1 * 12-30Y Importance of staying ahead of the competition 2.4 2.9 2.5 2.6 2.0 *F-test significant at the .05 level **F-test significant at the .01 level

Table 4.2 Household Instrumental Values- Length of Time an Individual Expects to Continue Farming

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Substantive Values

Among the substantive value set, significant differences were observed among the groups in relationship to the desire to keep this farm in the family and the desire to spend

more time with family. The Indefinitely group placed the strongest emphasis on the

desire to keep the farm in the family (mean 4.2) while the Unsure (mean 4.0) and 12-30

Year group (mean 4.0) were more modest in their emphasis. Compared to the other

groups keeping the farm in the family was least important to those who only intend to

farm for an additional 1-10 Years (mean 3.6). A slightly different pattern emerges

among the groups in relation to the importance of spending time with family. The Unsure

(mean 4.4) and the 12-30 Year group placed the most emphasis on spending time with

family. While respondents who plan to farm Indefinitely had a more moderate emphasis

on spending time with family (mean 4.0) the 1-10 Year group placed the least amount of

emphasis on spending time with family (mean 3.7) as a motivation for land use. There

were no significant differences between the groups in relationship to substantive values

that included: keep living in a rural area or being one’s own boss.

Stewardship Values

There also were no differences in stewardship values among the groups (Table

4.4).

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F-test or 1-10 12-30 Pearson Post Hoc Years Years Indefinitely Unsure χ2 Tests Mean Mean Mean Mean N 60 49 105 79 Factor Substantive Values 15.6 16.8 16.8 16.9 2.4 Desire to keep living in a rural area 4.1 4.2 4.4 4.3 1.2 Desire to keep this farm in the family 3.6 4.0 4.2 4.0 2.7 * I > 1-10Y U > I, 12- 30 > 1- Desire to spend more time with family 3.7 4.2 4.0 4.4 5.5 ** 10Y Desire to be my own boss 4.1 4.3 4.2 4.2 0.3 *F-test significant at the .05 level **F-test significant at the .01 level

Table 4.3 Household Substantive Values- Length of Time an Individual Expects to Continue Farming

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F-test or 1-10 12-30 Pearson Post Hoc Years Years Indefinitely Unsure χ2 Tests Mean Mean Mean Mean N 60 49 105 79 Factor Stewardship Values 16.3 17.7 17.1 17.4 2.0 Importance of maintaining and improving quality of my soil 4.1 4.4 4.4 4.4 1.5 Importance of being a good steward of the land 4.2 4.54.5 4.5 1.5 Importance of minimizing nutrient & chemical 212 runoff from farm 4.1 4.4 4.2 4.4 1.7 Importance of protecting scenic quality of the property 3.9 4.34.1 4.1 1.5 Desire to stay on good terms with neighbors 3.9 4.2 4.0 4.1 1.5 *F-test significant at the .05 level **F-test significant at the .01 level

Table 4.4 Household Stewardship Values - Length of Time an Individual Expects to Continue Farming

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Future Plans for Land Use

Among the variables pertaining to future land use plans, several significant differences were observed between the groups (Table 4.5). Respondents who only planned to continue farming 1-10 Years were the most likely to indicate an intention to sell their land for development in the next five years (mean 6.5) followed by those who are Unsure of how long they will farm (mean 6.2) and those who will farm for an additional 12-30 Years (mean 5.7). Those planning to farm Indefinitely were least to indicate a high likelihood of selling their land for development in the next five years

(mean 4.4). When making plans for retirement those farming for 1-10 Years indicated selling farmland was more important to their ability to afford retirement (mean 2.5) compared to those who are Unsure (mean 2.3) and the 12-30 Year group (mean 2.2), while it was least important to those who plan to farm Indefinitely (mean 1.9). Parallel to these findings just under one third of those planning to farm for 1-10 Years (30.0%) and

one quarter of those planning to farm for 12-30 Years (24.5%) plan to sell to a developer

when they retire compared to 17.7% of those who are Unsure and only 4.8% of those

who plan to farm Indefinitely. While there is no statistically significant difference

between the groups in regards to whether a relative will inherit the farm, respondents who

plan to farm Indefinitely were most likely to identify a relative as a successor (32.4%)

compared to those who are Unsure (26.6%), 12-30 Year (20.4%) and the 1-10 Year group

(16.7%).

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F-test or 1-10 12-30 Pearson Post Hoc Years Years Indefinitely Unsure χ2 Tests Mean Mean Mean Mean N 60 49 105 79 Factor Likeliness to Sell I < 12- Land for Development in 30Y, U, 1- Next 5 Years 6.5 5.7 4.4 6.2 9.4 ** 10Y Future Retirement Plans Importance of selling farmland in order to I < 12-30 afford retirement 2.5 2.2 1.9 2.3 4.7 * Y, 1-10Y Relative is a Successor 16.7% 20.4% 32.4% 26.6% 5.8 Will sell to developer 30.0% 24.5% 4.8% 17.7% 20.6 ** *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.5 Future Land Use Plans- Length of Time an Individual Expects to Continue Farming

FARM STRUCTURE VARIABLES

Structural Variables

There were a number of significant differences among groups in a number of farm structural characteristics, including the level of farm receipts, debt to asset ratio, planned changes to the enterprise in the next five years, marketing strategies and future farm enterprise trajectory (Table 4.6).

Respondents planning to farm for 12-30 Years had the highest average farm receipts (mean 5.1) representing an income between $100,000 to $249,000. Those who were Unsure (mean 4.6) and the Indefinitely group (mean 4.4) earned , while those who will farm only 1-10 additional years had the lowest farm income (mean 4.1). In

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relationship to farm debt, farms planning on continuing for 12-30 Years had the largest farm debt with 16.3% reporting farm debts at 40% or above their assets. In comparison only 5.7% farmers planning to farm Indefinitely, 5% the 1-10 Year group, and 3.8% of the Unsure group were carrying debt at greater then 40% of assets.

F-test or Post 1-10 12-30 Pearson Hoc Years Years Indefinitely Unsure χ2 Tests Mean Mean Mean Mean N 60 49 105 79 Acres Operated 314.5 628.5 409.7 388.1 1.7 Acres Owned 209.0 290.6 287.4 213.5 0.8 Acres Rented 102.7 170.5 121.3 174.9 0.7 Soil Quality 3.3 3.5 3.3 3.3 0.6 12-30Y Total Farm Receipts > I, 1- in 2006 4.1 5.1 4.4 4.6 2.9 * 10Y Total Household Income in 2006 5.2 5.6 5.4 5.2 0.6 Farm Debts are 40% or Below Assets (%) 95.0% 83.7% 94.3% 96.2% 8.6 * Farm Debts are 40% or Above Assets (%) 5.0% 16.3% 5.7% 3.8% High value crops (veg, tobacco, nursery, green, fruit, nut, orchard, etc) (%) 23.3% 32.7% 33.3% 24.1% 3.2 Raises corn and/or soy (%) 3.3% 6.1% 3.8% 2.5% 1.1

Raises livestock (%) 41.7% 42.9% 43.8% 50.6% 1.4

Raises dairy (%) 15.0% 18.4% 16.2% 26.6% 4.1 *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.6 Farm Structure Variables - Length of Time an Individual Expects to Continue Farming

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Past and Future Changes Planned for the Enterprise

There were significant differences among the groups in relation to future changes that would be made to the enterprise in the next five years (Table 4.7). Farmers expecting to farm Indefinitely plan to make more capital investments in their enterprise (mean 7.0) followed by those farming for 12-30 Years (mean 6.9). Those who were Unsure indicated they would make fewer investments (mean 6.6) as did those who will only farm for 1-10 Years (mean 6.1). However, those who plan to continue farming for 12-30

Years plan to make the most investments in alternative marketing (mean 10.2) followed by those farming Indefinitely (mean 9.7). Smaller shifts into alternative marketing were observed among those will farm for 1-10 Years (mean 9.3) and the Unsure group (mean

9.1). Those who plan to farm for 12-30 Years are also most likely to increase the number of commodities they produce (mean 3.3), increase sales of products directly to consumers

(mean 3.6) and increase value added processing of farm products (mean 3.3).

Respondents planning to farm Indefinitely indicate they are planning to increase these

alternative strategies to a moderate degree: by increasing the number of commodities they

produce (mean 3.2), increasing sales of products directly to consumers (mean 3.4) and

increasing value added processing of farm products (mean 3.1). Those indicating they

would farm for 1-10 Years were less likely to indicate they would increase the number of

commodities they produce (mean 3.0), increase sales of products directly to consumers

(mean 3.2) and increase value added processing of farm products (mean 3.0). Those who

are Unsure how long they will be farming were most likely to indicate they would

decrease the number of commodities they produce, (mean 2.9), they were also least likely

216

to increase sales of products directly to consumers (mean 3.1) or increase value added processing of farm products (mean 3.0).

F-test or Post 1-10 12-30 Pearson Hoc Years Years Indefinitely Unsure χ2 Tests Mean Mean Mean Mean N 60 49 105 79 Past Changes to Enterprise (Last Five Years) Past Capital Investment Past Alternative Marketing 6.3 6.6 6.4 6.2 1.0 Last 5years changes to number of distinct commodities produced 3.1 3.2 3.1 3.1 0.6 Last 5 years sales of products directly to consumers 3.2 3.3 3.2 3.2 0.9 Last 5 years value added processing of farm products 3.0 3.2 3.2 3.1 2.2

Future Planned Changes To Enterprise (Next Five Years) I > U, Factor Future Capital 12-30Y Investment 6.1 6.9 7.0 6.6 5.7 * > 1-10Y 12-30Y Factor Future Alternative > I, 1- Market 9.3 10.2 9.7 9.1 5.9 * 10> U In next 5years changes to 12-30Y number of commodities > I > 1- produced 3.1 3.3 3.2 2.9 3.3 * 10Y > U 12-30Y >1-10Y, In next 5 years sales of U I > product directly to consumers 3.2 3.6 3.4 3.1 6.0 * U 12-30Y In next 5 years value-added > U, 1- processing of farm products 3.0 3.3 3.1 3.0 2.9 * 10Y *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.7 Past and Future Planned Changes to the enterprise - Length of Time an Individual Expects to Continue Farming

217

Marketing Strategies

In terms of marketing strategies, two thirds of those respondents who plan to farm for 12-30 Years are engaged in AFAE strategies (69.4%) followed by those farming

Indefinitely (60.0%) (Table 4.8). Just over half of the 1-10 Year group (55.0%) and a

third of the Unsure (38.1%) group are pursuing AFAE strategies. There were no

significant differences between the groups engaged in value added or labeling schemes,

however, the 12-30 year category was most likely to pursue theses strategies followed by

the Indefinitely, 1-10 Years and the Unsure categories.

F-test or 1-10 12-30 Pearson Years Years Indefinitely Unsure χ2 % % % % N 60 49 105 79 AFAE 55.0% 69.4% 60.0% 39.2% 15.0 * Non-AFAE 45.0% 30.6%40.0% 60.8% Value Added 33.3% 51.0% 38.1% 29.1% 6.7 Labeling Products 33.3% 44.9% 34.3% 31.6% 2.6 *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi- square significant at .01 level

Table 4.8 Marketing Strategies- Length of Time an Individual Expects to Continue Farming

Past and Future Enterprise Trajectory

Farm trajectory is a measure of the expansion, contraction or stability of changes

in farmland (rented and owned) and farm sales (Table 4.9). At lest half of all respondents

in each of the four groups reported being on a future growth trajectory, however a

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significantly greater number of respondents expecting to farm for a longer time frame

(12-30 Years and Indefinitely) reported a growth trajectory. Within the 1-10 Year group

50.0% of respondents are on a growth trajectory, the reaminder are fairly split between

stable (30.0%) and Decline (20.0%). A slight majority (55.7%) of Unsure respondents

reported a growth trajectory, however almost a third (31.6%) report they expect to go into

decline, while 12.7% report stable. Almost three quarters (70.5%) of the Indefinitly group

report a trajectory of growth, almost a quarter (23.8%) report decline while 5.7% expect

to stay stable. Among the 12-30 Year group, the vast majority of respondents, 79.6% are

on a growth trajectory, 18.4% report they expect to go into decline, and a small minority

(2.0%) are on a stable trajectory.

F-test or 1-10 12-30 Pearson Years Years Indefinitely Unsure χ2 % % % % N 60 49 105 79 Past Five Year Trajectory 12.1 Decline 40.0% 26.5% 28.6% 45.6% Stable 15.0% 10.2% 9.5% 13.9% Growth 45.0% 63.3% 61.9% 40.5%

Future Five Year Trajectory 32.0 ** Decline 20.0% 18.4% 23.8% 31.6% Stable 30.0% 2.0% 5.7% 12.7% Growth 50.0% 79.6% 70.5% 55.7% * Pearson Chi-square significant at .05 level ** Pearson Chi-square significant at .01 level

Table 4.9 Past and Future Business Trajectory - Length of Time an Individual Expects to Continue Farming

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No significant differences were identified in regards to the following farm sure characteristics: acres operated, acres owned, acres rented, soil quality, household receipts, the type of commodities being raised (high value crops, corn and or soy, livestock, and dairy). Past changes to the enterprise over the last five years in regards to past capital investments, past alternative marketing changes in number of distinct commodities produced, sales of products directly to consumers and changes to value added processing of farm products were also not significantly different among the groups.

CONTROL VARIABLES

As discussed in the methodology section a number of control variables were included in the analysis to account for factors affecting all farmers at the RUI. This initial comparison of means analysis examines the degree to which these variables may influence the length of time an individual expects to continue farming.

There were significant differences between the groups in regards to sampling frame origin (Table 4.10). The majority of those who plan to farm for 1-10 Years

(66.7%), and Indefinitely (64.8%) originated from the landowner random sample, while the majority of respondents in the 12-30 Year group (57.1%) and the Unsure group

(50.6%) are from the commercial farm sample.

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F-test or 1-10 12-30 Pearson Years Years Indefinitely Unsure χ2 % % % % N 60 49 105 79 Sample Frame Landowner Random Sample 66.7% 42.9% 64.8% 48.1% 12.8 * Commercial Farm Sample 31.7% 57.1% 34.3% 50.6% Unknown 1.7% 0.0% 1.0% 1.3%

Survey County 21.0 Cache 26.7% 12.2% 15.2% 13.9% Frederick 13.3% 24.5% 18.1% 22.8% Kent 23.3% 28.6% 24.8% 25.3% Shelby 11.7% 6.1% 14.3% 13.9% Forsyth 6.7% 0.0% 1.9% 3.8% Hall 3.3% 8.2% 1.9% 1.3% Spencer 6.7% 8.2% 9.5% 7.6% Yamhill 8.3% 12.2% 14.3% 11.4% *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.10 Sample Frame and Survey County Origin - Length of Time an Individual Expects to Continue Farming

There were no significant differences among the control variables in relationship to: cost of health insurance, community support, optimism for agriculture in the county, development pressure, effectiveness of land use policy, global competition and farm economics, local government support for land use policy, labor conditions, weather effects local infrastructure, neighbor pressures, nonfarm group support and the survey county origin (Table 4.11).

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F-test or Post 1-10 12-30 Pearson Hoc Years Years Indefinitely Unsure χ2 Tests Mean Mean Mean Mean N 60 49 105 79 Cost of health insurance 3.7 4.1 3.8 4.1 2.1 Factor Community Support 10.5 10.5 10.5 10.7 0.1 Factor County Optimism Development Pressure 8.9 9.4 9.5 9.8 0.7 Effectiveness of Land Use Policy 9.5 9.6 10.3 10.0 0.7 Factor Global Competition and Farm Economics 18.3 18.1 18.1 19.7 1.4 Factor Local Government Support for Land Use Policy 5.4 5.3 5.5 5.7 0.4 Factor Labor Conditions 6.0 6.1 5.8 6.5 1.2 Weather 4.9 4.8 5.1 5.4 0.9 Factor Local Infrastructure 6.2 6.0 6.3 6.5 0.2 Factor Neighbor 5.2 6.0 5.3 5.7 1.2 Factor NonFarm Group Support) 16.0 16.9 16.3 16.9 0.6 *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.11 Control Variables - Length of Time an Individual Expects to Continue Farming

Discussion and Conclusion of Years Individuals Expect to Continue Farming – Comparison of Means Testing

The purpose of this analysis was to compare household characteristics, household values and farm structure variables across the length of time commercial farm respondents expect to continue farming. The analysis provides mixed support for hypothesis 1.1, Table 4.12 summarizes the relationships observed in regards to research

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question number one. Significant findings in relation to the four groups are discussed below.

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Hypothesis Conclusion R1.1.1: Anticipation of farming for a Supported longer time period will be negatively associated with age R1.1.2: Anticipation of farming for a Mixed Support- Respondents anticipating to farm for a longer time period will be positively longer timeline had higher education levels, were more associated with education, off-farm optimistic about the future of their farm and indicated income, the availability of a successor, the availability of a successor and health of key operator health, availability of family labor operator* was less serious of a problem. The Unsure and optimism for the future of one’s farm. group reported the greatest family contribution to labor followed by the Indefinitely group however the differences across the groups were not statistically significant. There were no significant differences observed in regards to off-farm employment, however the Indefinitely group reported a higher percentage of both operator and spouse working off-farm. R1.1.3: Anticipation of farming for a Supported longer time period will be negatively with the need to sell land in order to afford retirement R1.1.4: Respondents expecting to farm for Mixed Support – the substantive value of keeping the longer periods of time will place greater farm in the family was positively associated with a importance on substantive, instrumental longer time line, Instrumental values were negatively and stewardship values compared to those associated with farming for a longer time period. There who expect to farm a shorter period of time was no significant difference among length of time respondents in regards to stewardship values. R1.1.5: Anticipation of farming for a Mixed support – A longer time line was positively longer time period will be positively associated with a smaller debt to asset ratio, and plans associated with soil quality, acres operated, for increasing capital investments and alternative smaller debt to asset ratios, farm receipts, marketing strategies. Total farm receipts was not household income, plans for increasing associated with the hypothesized direction. No future capital investments and alternative significant differences were observed across the groups marketing streams in regards to acres operated, soil quality, household income. R1.1.6: Anticipation of farming for a Not Supported longer time period will be positively associated with producing high value crops R1.1.7: Anticipation of farming for a Not Supported longer time period will be negatively associated with dairy farming and livestock production R1.1.8: Anticipation of farming for a Mixed Support- Respondents most likely to identify longer time period will be positively AFAE adaptations were expect to farm for 12-30 Years, associated with AFAE adaptations followed by Indefinitely. *Relationship is present but not significant Table 4.12 Hypothesis in relationship to Comparison of Means Testing of Years Individuals Expect to Continue Farming

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Respondents who only expect to continue farming for 1-10 Years are the oldest group of farmers and exhibit disinvesting behaviors often associated with the impermanance syndrome. This group has the most trouble identifying a successor and is making only minor capital investments into their enterprise, however they did not express the most pessimism over the future of their farm. As a group the 1-10 Year respondents are not as concerned about maximizing the sale value of farmland however, they are most likely to emphasize the importance of selling farmland for retirement, are the most likely to sell land for development in the next five years and are the most likely to sell their land to a developer when they retire.

Respondents who are Unsure of how long they will continue farming were older, had less education and were most pessimistic about the future of their farm.

The Unsure group placed the most emphasis on both instrumental values and spending more time with family. While the availability of a successor was not reported to be a serious problem and just over a quarter anticipate a relative will inherit the farm, this group is highly likely to sell land for development. The Unsure group is also least likely to be engaging in AFAE strategies and to be making future investments in alternative marketing and production techniques over the next five years. Although not significant, the Unsure group is more likely to be engaged in livestock and dairy farming which in general have a more difficult time at the RUI (Sharp et al. 2002) and are also more difficult to transition into AFAE strategies (Barbieri et al. 2008).

Those respondents who plan to farm Indefinitely are younger, have the highest education levels, are the most optimistic about the future of their farms, and

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are least likely to indicate the availability of a successor as a problem. Compared to

other categories the Indefinitely group had a more moderate farm income and were the

most likely to have off farm income. Supplementing household income with off farm work may be a strategy employed within the household to achieve both the goal of maintaining a livelihood along with a lifestyle. This group placed the most emphasis on keeping the farm in the family and placed less emphasis on instrumental values compared to the 12-30 Year and Unsure groups.

Respondents that expect to continue farming another 12-30 years one of the most dynamic groups. The 12-30 Year group places the most emphasis on instrumental values, they also have the largest farm receipts and carry the most debt. While this could be considered a sign of vulnerability the fact that this group also represents the youngest group of farmers, and are the most likely to be increasing future investments in their enterprise especially in terms of increasing alternative marketing and production strategies. Additionally, this group is most likely to indicate their business is on a future growth trajectory. Furthermore, just under a quarter of this group anticipates a relative as a successor indicating that this group is investing and expanding (with more debt) in new opportunities as they prepare for additional family members e.g. a new generation to join the farm. These indicators can be taken as a sign of agrifamily enterprise redevelopment

(Bennett 1982).

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Length of Time Respondents Expect Their Enterprise to Persist

Household Characteristics

Among household characteristics, significant differences were observed among health of key operator, availability of farm successor, optimism for future of the farm, and education (Table 4.13).

Health of the key operator was identified as a serious problem for farmers indicating they only expected their operations to continue for 1-30 Years (mean 3.3) followed by those who are Unsure (mean 3.4) and was the less of a problem for those anticipating their enterprise would continue Indefinitely (mean 3.6). Similarly the availability of a successor was a more serious problem for the 1-30 Year group (mean

3.1), the Unsure group (mean 3.7) and was less of a problem for those anticipating their enterprise will continue Indefinitely (mean 4.2).

The Indefinitely group were most optimistic about the future of their farm (mean

5.2) followed by those farming for 1-30 Years (mean 4.2) while those who are Unsure are less optimistic and report a more mixed outlook (mean 4.0). Those expecting their enterprise to continue Indefinitely had the highest education levels (mean 3.32) followed

by the 1-30 years (mean 3.3) while the Unsure had the lowest levels of education (mean

2.9). No significant differences among family contributions to labor, operator and spouse

off farm work were observed between the different groups.

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F-test or 1-30 Pearson Post Hoc Years Indefinitely Unsure χ2 Tests Mean Mean Mean N 62 122 109

Age 59.0 58.2 59.0 0.2 Health of Key Operator 3.3 3.6 3.4 5.8 * I <1-30, U

Availability of Farm Successor 3.1 4.2 3.7 19.6 ** I > 1-30 Optimism for Future of Your Farm 4.2 5.2 4.0 21.6 ** I > 1-30, U Percent Male (%) 87.1% 82.8% 83.5% 0.6 Education 3.3 3.3 2.9 4.1 * U < 1-30, I

Family contributes less then half of the labor to the farm (%) 29.0% 28.7% 21.1% 2.1

Family Contributes more then half of the labor to the farm (%) 71.0% 71.3% 78.9%

Off-Farm Income No off farm income (%) 35.5% 45.9% 53.2% 8.2 Operator No Off Farm, Spouse Off farm Income, (%) 29.0% 18.0% 22.0% Operator Off farm income, Spouse No Off Farm income (%) 11.3% 14.8% 9.2% Spouse and Operator Off farm Income (%) 24.2% 21.3% 15.6% *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.13 Comparison of Household Characteristics - Time Respondents Expect Their Enterprise to Persist

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Household Values

Significant differences were observed among the groups in the level of

importance they placed on several instrumental (Table 4.14), substantive (Table 4.15),

and stewardship (Table 4.16) values as influences on their land use decision making.

Instrumental Values

Respondents who anticipate their enterprise will persist for 1-30 Years placed the most importance on the instrumental value of maximizing the sale value of the farmland

(mean 3.5) followed by those who were Unsure (mean 3.1). Maximizing the sale value of farmland was least important to those who anticipate their enterprise will continue

Indefinitely (mean 2.7). No significant differences were observed among the groups arising from different adherence to instrumental values, or to the importance of minimizing debt, ensuring adequate household income, maximizing net farm income, and staying ahead of the competition.

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1-30 Post Hoc Years Indefinitely Unsure F-test Tests Mean Mean Mean N 62 122 109 Factor Instrumental Values 14.4 14.7 15.4 2.0

Importance of minimizing debt 4.0 4.1 4.2 0.8

Importance of ensuring household income is adequate 3.9 3.9 4.2 2.2 Importance of maximizing net farm income 4.0 4.1 4.3 1.9 Importance of maximizing sale value of farmland 3.5 2.7 3.1 6.3 * I < 1-30, U Importance of staying ahead of the competition 2.5 2.6 2.7 0.5 *F-test significant at the .05 level **F-test significant at the .01 level

Table 4.14 Household Instrumental Values - Time Respondents Expect Their Enterprise to Persist

Substantive Values

Among the substantive values set, the importance of keeping the farm in the

family was most important for those who expect to their enterprise to continue

Indefinitely (mean 4.2) followed by those who are Unsure (mean 4.0). Keeping the farm

in the family was least important for those who only expect their enterprise to continue

for another 1-30 Years (mean 3.6). No significant differences were observed among the groups in relationship to the overall substantive value scale, or to the desire to live in a

rural area, desire to spend more time with family and the desire to be ones own boss.

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1-30 Post Hoc Years Indefinitely Unsure F-test Tests Mean Mean Mean N 62 122 109 Factor Substantive Values 15.7 16.7 16.9 2.8 Desire to keep living in a rural area 4.2 4.3 4.3 0.9 Desire to keep this farm in the family 3.6 4.2 4.0 5.0 * I,U>1-30 Desire to spend more time with family 3.9 4.0 4.3 2.8 Desire to be my own boss 4.1 4.2 4.3 0.9 *F-test significant at the .05 level **F-test significant at the .01 level

Table 4.15 Household Substantive Values - Time Respondents Expect Their Enterprise to Persist

Stewardship Values

In relation to stewardship values, overall the Indefinitely group placed the most importance on the stewardship factor scale (mean 17.4) followed by the Unsure (mean

17.2), while the 1-30 Year group placed the least importance on stewardship (mean 16.3).

The significance of being a good steward of the land was most important for the

Indefinitely group (mean 4.5) and the Unsure group (mean 4.5) while the 1-30 Year group placed slightly less importance on being a steward of the land (mean 4.2). No significant differences were observed among the groups in relationship to: importance of maintaining and improving soil quality, minimizing nutrient and chemical runoff from the farm, protecting the scenic quality of the property or the desire to stay on good terms

with the neighbors.

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1-30 Post Hoc Years Indefinitely Unsure F-test Tests Mean Mean Mean N 62 122 109 Factor Stewardship Values 16.3 17.4 17.2 3.0 * I >1-30

Importance of maintaining and improving quality of my soil 4.2 4.4 4.3 1.2 Importance of being a good steward of the land 4.2 4.5 4.5 3.9 * I,U >1-30

Importance of minimizing nutrient & chemical runoff from farm 4.0 4.3 4.4 2.8

Importance of protecting scenic quality of the property 3.9 4.2 4.1 1.6 Desire to stay on good terms with neighbors 3.9 4.0 4.2 1.4 *F-test significant at the .05 level **F-test significant at the .01 level

Table 4.16 Household Stewardship Values - Time Respondents Expect Their Enterprise to Persist

Future Plans for Land Use

Among the variables measuring future land use plans, several significant differences were observed between the groups including differences in the likeliness to sell land for development in the next five years, the importance of selling farmland to afford retirement, the likeliness of selling the farm to a developer upon retirement, and the likeliness the future heir is a relative (Table 4.17). Those anticipating their enterprise will only continue another 1-30 Years were most likely to indicate an intent to sell land for development in the next five years (mean 7.2), compared to those who are Unsure

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(mean 5.9), while the Indefinitely group was least the likely to indicate an intent to sell

(mean 4.3).

Regarding future retirement plans, those in the 1-30 Year group were more likely

to report that selling farmland was important for their ability to afford retirement (mean

2.7) compared to the Unsure group (mean 2.3), while it was least important to the

Indefinitely group (mean 1.8). Following a similar pattern, almost half (48.8%) of the 1-

30 group indicated they would sell farmland to a developer upon retirement, while only

13.8% of the Unsure and 3.3% of the Indefinitely will. The Indefinitely group was most

likely (40.4%) to identify a relative as a successor upon retirement, while only 20.2% of

the Unsure and a minority of the 1-30 Years (6.5%) did as well.

F-test or 1-30 Pearson Post Hoc Years Indefinitely Unsure χ2 Tests Mean Mean Mean N 62 122 109

Factor Likeliness to Sell Land for Development in Next 5 Years 7.2 4.3 5.9 26.0 ** I<1-30, U

Future Retirement Plans

Importance of selling farmland in order to afford retirement 2.7 1.8 2.3 18.5 ** 1-30 >U >I Relative is a Successor (%) 6.5% 40.2% 20.2% 2720.0% ** Will sell to developer (%) 48.4% 3.3% 13.8% 6116.0% ** *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.17 Future Land Use Plans - Time Respondents Expect Their Enterprise to Persist

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FARM STRUCTURE

Past and Future Changes Planned for the Enterprise

Several farm structure variables including past capital investment over the last five years, planned changes to the enterprise in the next five years, and future farm enterprise trajectory were significantly different among the groups (Table 4.18).

Respondents expecting their enterprise to continue Indefinitely reportedly had made the largest increases in capital investments (mean 7.5) over the last five years. In comparison the Unsure (mean 6.9) and the 1-30 Year (mean 6.6) groups reported considerably less investments in their enterprise.

There were significant differences among the groups in relation to reported future changes that were to be made to the enterprise in the next five years. Respondents in the

Indefinitely group plan to make more capital investments in their enterprise (mean 7.0)

compared to those in the Unsure group (mean 6.6) and 1-30 Year group (mean 6.3).

Those who expect their enterprise to continue Indefinitely were most likely to increase alternative marketing strategies (mean 9.8) followed by those who anticipate farming for

1-30 Years (mean 9.7). The Unsure group reported a more modest increase in alternative

marketing strategies (mean 9.2). Those in the 1-30 Year (mean 3.4) and the Indefinitely

(mean 3.4) groups were most likely to report they planed to increase sales directly to

consumers compared to those in the Unsure category (mean 3.2).

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F-test or 1-30 Pearson Post Hoc Years Indefinitely Unsure χ2 Tests Mean Mean Mean N 62 122 109 Past Changes to Enterprise (Last Five Years) Past Capital Investment 6.6 7.5 6.9 9.5 ** I > U, 1-30 Past Alternative Marketing 6.4 6.4 6.3 0.6

Last 5years changes to number of distinct commodities produced 3.2 3.1 3.1 0.2

Last 5 years sales of products directly to consumers 3.3 3.3 3.2 0.9 Last 5 years value added processing of farm products 3.2 3.1 3.1 1.9

Future Planned Changes To Enterprise (Next Five Years)

Factor Future Capital Investment 6.3 7.0 6.6 5.4 * I > U, 1-30

Factor Future Alternative Market 9.7 9.8 9.2 4.1 * I > U

In next 5years changes to number of commodities produced 3.1 3.2 3.0 2.2

In next 5 years sales of product 1-30> I > directly to consumers 3.4 3.4 3.2 3.8 * U In next 5 years value-added processing of farm products 3.2 3.2 3.0 2.7 *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.18 Past and Future Planned Changes to the Enterprise - Time Respondents Expect Their Enterprise to Persist

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Marketing Strategies

There were no significant differences between the groups in relation to the

number of commodities produced or plans to engage in value added processing of farm

products. And while over half of each group was pursuing AFAE strategies, there were

no significant differences among marketing, value adding or labeling strategies (Table

4.19).

1-30 Pearson Years Indefinitely Unsure χ2 % % % N 62 122 109 AFAE 58.1% 57.4% 50.5% 1.4 Non-AFAE 41.9% 42.6% 49.5% Value Added 43.5% 39.3% 30.3% 3.6 Labeling Products 27.4% 34.4% 40.4% 3.6 * Pearson Chi-square significant at .05 level ** Pearson Chi-square significant at .01 level

Table 4.19 Marketing Strategies - Time Respondents Expect Their Enterprise to Persist

Past and Future Enterprise Trajectory

Past and Future Farm Trajectory

As described previously, farm trajectory is a measure of the expansion,

contraction or stability of changes in farmland (rented and owned) and farm sales. The

majority of respondents in each of the three groups were identified as being on a growth

trajectory (Table 4.20). Almost three quarters of the Indefinitely group (72.1%) are on a

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growth trajectory path while less then a quarter (23.0 %) are on a decline trajectory and a

small minority (4.90%) are stable. Among the Unsure, two thirds are on a growth path

(60.6%) while a quarter are in decline (25.7%) and 13.8% are stable. In the 1-30 Year

group just over half (53.2%) are on a growth trajectory, while the remainder are fairly

evenly divided between decline (24.2%) and stable (22.6%).

1-30 Pearson Years Indefinitely Unsure χ2 % % % N 62 122 109 Past Five Year Trajectory Decline 35.5% 29.5% 41.3% 7.8 Stable 17.7% 9.0% 11.9% Growth 46.8% 61.5% 46.8%

Future Five Year Trajectory Decline 24.2% 23.0% 25.7% 14.0 * Stable 22.6% 4.9% 13.8% Growth 53.2% 72.1% 60.6% *Pearson Chi-square significant at .05 level **Pearson Chi-square significant at .01 level

Table 4.20 Past and Future Business Trajectory - Time Respondents Expect Their Enterprise to Persist

Structural Variables

No significant differences were observed among the groups in : acres operated,

acres owned, soil quality, farm receipts, household income, farm debt, production type

(high value, the type of commodities being raised (high value crops, corn and or soy,

livestock, and dairy) (Table 4.21). There were also no significant differences in relation

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to: investments in alternative marketing, changes in number of distinct commodities produced, sales of products directly to consumers or changes to value added processing of farm products over the last five years.

F-test or 1-30 Pearson Post Hoc Years Indefinitely Unsure χ2 Tests Mean Mean Mean N 62 122 109 Acres Operated 472.4 447.7 361.9 0.6 Acres Owned 211.7 294.2 227.6 1.0 Acres Rented 127.1 152.6 133.7 0.1 Soil Quality 3.3 3.4 3.3 0.3 Total Farm Receipts in 2006 4.3 4.6 4.5 0.4 Total Household Income in 2006 5.2 5.5 5.2 1.0 Farm Debts are 40% or Below Assets (%) 93.5% 90.2% 96.3% 3.5 Farm Debts are 40% or Above Assets (%) 6.5% 9.8% 3.7%

High value crops (veg, tobacco, nursery, green, fruit, nut, orchard, etc) (%) 25.8% 27.9% 31.2% 0.6

Raises corn and/or soy (%) 4.8% 3.3% 3.7% 0.3

Raises livestock (%) 50.0% 41.0% 46.8% 1.6

Raises dairy (%) 21.0% 17.2% 20.2% 0.5 *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level Table 4.21 Farm Structure Variables - Time Respondents Expect Their Enterprise to Persist

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CONTROL VARIABLES

As noted earlier a number of control variables were included in the analysis to account for factors affecting all farmers at the RUI. This initial comparison of means analysis examines the degree to which these variables may influence the length of time individuals expect their enterprise to persist. Among the control variables there were significant differences between the groups in regards to the cost of health insurance, the global competition and farm economics scale and, local government support for land use policy (Table 4.22).

The cost of health insurance was more of a problem for respondents in the Unsure group (mean 4.1) and 1-30 Year group (mean 4.0) compared to the Indefinitely category

(mean 3.7). Global Competition and Farm Economics was a more serious problem for those who are Unsure how long their enterprise will continue (mean 19.9) and the 1-30

Year group (mean 19.1) compared to the Indefinitely category (mean 17.1). Labor was also a more serious problem for those who are Unsure (mean 6.5) and the 1-30 Year group (mean 6.5) compared to the Indefinitely group (mean 5.6). Those who expect their enterprises to continue Indefinitely were more likely to perceive local land use policy as being more effective at retaining farmland (mean 10.6) compared to the Unsure (mean

9.7) and those in the 1-30 Year group (mean 9.1).

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F-test or 1-30 Pearson Post Hoc Years Indefinitely Unsure χ2 Tests Mean Mean Mean N 62 122 109 Cost of health insurance 4.0 3.7 4.1 3.6 * I > U Factor Community Support 10.3 10.7 10.5 0.6 Factor County Optimism 9.0 9.2 9.2 0.1 Development Pressure 10.0 8.9 9.7 2.3 Effectiveness of Land Use Policy 9.1 10.6 9.7 3.7 * I > 1-30 Factor Global Competition and Farm Economics 19.1 17.1 19.9 6.7 * I< U, 1-30

Factor Local Government Support for Land Use Policy 5.3 5.6 5.5 0.6 Factor Labor Conditions 6.5 5.6 6.5 4.4 * I< U, 1-30 Weather 4.8 4.8 5.5 2.9 Factor Local Infrastructure 6.8 6.0 6.4 1.2 Factor Neighbor 5.9 5.4 5.4 1.0 Factor Non Farm Group Support 16.1 16.8 16.4 0.6 *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 4.22 Control Variables- Time Respondents Expect Their Enterprise to Persist

There were no significant differences among the groups across several other control variables, including: community support for agriculture, optimism for agriculture the county, development pressure, local government support for land use policy, weather, availability of local infrastructure, neighbor pressures, nonfarm grou8p support, sample frame and survey county origin (Table 4.23).

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1-30 Pearson Years Indefinitely Unsure χ2 % % % N 62 122 109 Sample Frame Landowner Random Sample 53.2% 60.7% 55.0% 2.6 Commercial Farm Sample 46.8% 38.5% 43.1% Unknown 0.0% 0.8% 1.8%

Survey County 21.3 Cache 21.0% 17.2% 13.8% Frederick 22.6% 18.0% 19.3% Kent 19.4% 21.3% 33.0% Shelby 8.1% 14.8% 11.9% Forsyth 6.5% 1.6% 2.8% Hall 4.8% 1.6% 3.7% Spencer 9.7% 6.6% 9.2% Yamhill 8.1% 18.9% 6.4% *Pearson Chi-square significant at .05 level **Pearson Chi-square significant at .01 level

Table 4.23 Dummy Sample Frame and Survey County Origin - Time Respondents Expect Their Enterprise to Persist

Although a number of the control variables are not significant they do provide some interesting insight into factors contributing to enterprise persistence. While perception of development pressure is not significantly different among the groups, those in the 1-30 Year category perceive development pressure to be the biggest problem

(mean 10.0) followed by those who are Unsure (mean 9.7) and the Indefinitely group

(mean 8.9). Availability of local infrastructure was considered to be the biggest problem for those in the 1-30 Year group (mean 6.8) and the Unsure (mean 6.4) followed by the

Indefinitely group (mean 6.0).

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Discussion and Conclusion of Years Respondents Expect Their Enterprise to Persist – Comparison of Means Testing The purpose of this analysis was to compare household characteristics, household values and farm structure variables across the length of time commercial farm respondents expect their enterprise to persist. The findings provide mixed support for hypothesis 1.2, Table 4.24 summarizes the relationships observed in regards to research question number one. Significant findings in relation to the three groups are discussed below.

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Hypothesis Conclusion R1.2.1: Anticipation of the enterprise Not Supported continuing for a longer time period will be negatively associated with age R1.2.2: Anticipation of the enterprise to persist Mixed Support- education, operator health, for a longer time period will be positively availability of successor, and optimism for associated with education, off-farm work, future of farm are supported. Availability of operator health the availability of a successor, family labor and off-farm work not supported. availability of family labor and optimism for the future of one’s farm. R1.2.3: Anticipation of the enterprise to persist Supported for a longer time period will be negatively associated with the need to sell land in order to afford retirement R1.2.4: Respondents expecting their enterprise Mixed Support – Indefinitely group placed to persist farm for longer periods of time will greater importance on stewardship and place greater importance on substantive, substantive values. Instrumental values were instrumental and stewardship values compared negatively associated with extended enterprise to those who expect to farm a shorter period of persistence. time R1.2.5: Anticipation of the enterprise to persist Mixed Support – Capital investments and for a longer time period will be positively alternative marketing supported. associated with soil quality, acres operated, smaller debt to asset ratios, farm receipts, Relationships present but not significant for: household income, plans for increasing future acres operated, soil quality, farm receipts, capital investments and alternative marketing household income. Indefinitely group report streams highest debt to asset ratio, but relationship not significant. R1.2.6: Anticipation of the enterprise to persist Not Supported for a longer time period will be positively associated with producing high value crops R1.2.7: Anticipation of the enterprise to persist Not Supported for a longer time period will be negatively associated with dairy farming and livestock production R1.2.8: Anticipation of the enterprise to persist Not Supported for a longer time period will be positively associated with AFAE adaptations

Table 4.24 Hypothesis in relationship to Comparison of Means Testing of Years Respondents Expect Their Enterprise to Persist

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Respondents who expect their enterprise to continue for 1-30 Years exhibit a

number of disinvestment characteristics, however, they also exhibit some contradictory and unexpected results in regards to investing in alternative marketing. As a group these respondents are the oldest group of farmers, they are most

likely to indicate the health of the key operator and the availability of a farm successor as

a problem. Farmers in the 1-30 Year category are moderately optimistic about the future

of their farm, have more moderate education levels, and though not significant are most likely to have a spouse or other adult household member working an off farm job. They place the most emphasis on maximizing the sale value of farmland, and simultaneously indicate keeping the farm in the family and stewardship values are of least importance compared to other groups.6 In regards to future land use decisions respondents who

expect their enterprise to persist for 1-30 Years are most likely to emphasize the importance of selling farmland for retirement, are the most likely to sell land for development in the next five years and are the most likely to sell their land to a developer when they retire. They are also the least likely to indicate a successor is a relative compared to the other groups. Farmers in the 1-30 Year category were the least likely to make any capital investments in their enterprise over the last five years and the next five years. However, they are somewhat more likely to engage and increase in future alternative marketing schemes and increase sales directly to customers over the next five years compared to the other groups. Just over half of those in the 1-30 Year category are on a future growth trajectory with the remainder or respondents fairly split between decline and stable trajectories. This group also indicated the cost of health insurance,

6 This finding is supported by the conservation literature documenting the negative relationship between land tenure and stewardship (Parker et al. 2007). 244

availability of labor, and global competition and farm economics to be more of a problem compared to the Indefinitely group but less so then the Unsure group. They also perceived the effectiveness of local land use policy to be the weakest compared to other groups, and while development pressure was not significant the 1-30 Year group did indicate it as a bigger problem compared to the other groups.

Enterprises in the Indefinitely category are most likely to persist and exhibit enterprise growth characteristics. Those respondents anticipating their enterprise will continue Indefinitely included the youngest group of farmers who had the highest education levels. The health of key operator and availability of farm successor were least likely to be a problem compared to the other groups. Those in the Indefinitely category were most optimistic about the future of their farms, placed the greatest importance on stewardship values and keeping the farm in the family, and were most likely to de-

emphasize maximizing the sale value of farmland.

Respondents in the Indefinitely group were least likely to sell land to a developer

in the next five years and least likely to indicate selling farmland was an integral part to

their ability to afford retirement. This group was by far the most likely to identify a

relative as a successor upon retirement. Compared to the other groups the Indefinitely category made the most capital investments into their enterprise over the last five years and plan to make the greatest increase in capital investments, alternative marketing strategies and direct sales to customers over the next five years. Furthermore, though not statistically significant, this group had the largest number of respondents reporting farm debts were 40% or above assets, suggesting these farmers were taking on debt in order to

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expand. These trends indicate continued expansion, which is supported by predominance of growth exhibited by their future trajectory. However, the future trajectory of the

Indefinitely group is somewhat characterized by a bimodal distribution with respondents clustering in growth and decline, and very few on a stable trajectory. Compared to other

groups the cost of health care, global competition and farm economics, and labor are

reported as less of problem, while this group is most likely to perceive local land use

policy as effective.

Respondents indicating they were Unsure how long their enterprise will

continue are often sandwiched in between those who expect their enterprises to

persist for 1-30 Years and the Indefinitely group. The Unsure respondents include the

oldest group of farmers, have the lowest levels of education. The health of key operator is

a problem and availability of a succor are less of a problem for the Unsure group then the

1-30 Year group but more of a problem compared to the Indefinitely group. The Unsure

respondents are the most pessimistic regarding the future of their farm and place

moderate importance on the need to maximize the sale value of farmland, keep farm in

the family and be good stewards of the land. The Unsure group is fairly likely to sell land

for development in the next five years, and indicates selling farmland is somewhat important for the ability to afford retirement. They are more likely then the Indefinitely group, but considerably less likely then the 1-30 Year group to sell their land to a

developer when they retire. The Unsure group also exhibit moderate capital investments

in their enterprise over the last five years and indicate moderate increases in new

alternative marketing strategies over the next five years. They are the least likely to

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increase sales of products directly to customers compared to the other groups. The

majority of respondents in the Unsure group are on a growth trajectory with almost a

quarter on decline and smaller percentage continuing on a stable trajectory. The Unsure

group was most likely to indicate the cost of health insurance, labor, and global

competition and farm economics as a problem compared to the other groups.

Respondents in the Unsure category did perceive land use policy to be more effective then the 1-30 Year group but less so then the Indefinitely group.

MULTIVARIATE ANALYSIS

To fully comprehend the relationship of household dynamics and farm structure and enterprise persistence and adaptation at the RUI, two multinomial logistic regression models were run. A multinomial logistic regression analysis presents the likeliness or

‘odds ratio’ of falling into one category in relationship to the reference category. Positive

values indicate the chances of being in this category are increased compared to the

chances of being in the reference category, while negative values indicate a decreased

likelihood of being in this category compared to the chances of being in the reference

category7. Table 4.25 reports the relationship between length of time a respondent

expects to continue farming and household dynamics, farm structure, and the control

variables.

7 While precise goodness of fit tests are difficult to calculate in multinomial logistic regression, a crude approximation of R2 is McFadden’s R2; while useful to compare the relative fit of models, a literal interpretation of fit (e.g., percent of variance explained) is not appropriate for this indicator. 247

Table 4.27 presents the relationship between the length of time respondents expect to their farm enterprise to persist and household dynamics, farm structure, and the control variables.

Years Individual Expects to Continue Farming

As stated above, the first model presents the variables associated with the length of time respondents expect to continue farming (Table 4.25). In this model the group of individuals expecting to continue farming for 1 to 11 Years is the reference category.

This group was chosen as the reference because this study is primarily concerned with farm persistence rather then the reasons farmers exit out of agriculture at the RUI. Having the 1-11 Year group serve as the reference group allows me to compare the factors exploring why respondents continue to farm for varying lengths of time as opposed to the reasons why they automatically exit agriculture.

Among respondents expecting to continue farming for 12 to 30 years, several variables are significant including: age, importance of selling farmland in order to afford

retirement, value placed on maximizing the sale value of farmland and raising livestock.

The older a respondent is the more likely they are to only continue farming for another 1-

11 Years rather then 12-30 Years (Beta = -0.088). Also, the more important it is for a

respondent to sell farmland in order to afford retirement the more likely they are to

expect to farm 1-11 more years rather then 12-30 Years (Beta = -0.582). The more

important it is to maximize the sale value of farmland the more likely a respondent

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expects to continue farming for 12-30 Years rather than for 1-11 Years (Beta = 0.504).

Farmers predominantly raising livestock are more likely to expect to continue to farm for

12-30 Years versus 1-11 Years (Beta = 1.422).

Age, availability of a successor, and selling farmland in order to afford retirement

are significantly associated with the likeliness of falling in the Indefinitely category. The

older a respondent is the more likely they are to expect to continue farming for 1-11

Years then to farm Indefinitely (Beta = -0.048). The less of a problem the availability of

a successor is the more likely respondents are to be farming Indefinitely then 1-11 Years

(Beta = 0.616). The more important it is for a respondent to sell farmland in order to

afford retirement the more likely they are to expect to be faring for 1-11 Years then to be

farming Indefinitely (Beta = -0.500).

Among respondents who are Unsure how long they will continue farming several

variables are significant including the availability of a successor and the desire to spend more time with family. The less of a problem the availability of a successor is the more likely respondents are to be Unsure how long they will farm then to be in the 1-11 Year group (Beta = 0.584). The more important it is to spend time with family the more likely respondents are to be Unsure how long they will farm then to be in the 1-11 Year group

(Beta = 0.692).

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1-11 12-30 Years Years Indefinitely Unsure N 60 49 79 105 Household Factors Age ref -0.088 * -0.048 * 0.014 Availability of a Successor ref 0.190 0.616 * 0.584 * When Retire Successor is a Relative ref 0.325 -0.191 0.381 Selling Farmland is Important for Affording Retirement ref -0.582 * -0.500 * -0.245 Optimism for Future of Your Farm ref 0.141 0.308 -0.311 Instrumental Values (Maximize Net Farm Income) ref 0.001 0.055 0.220 Instrumental Values (Maximize Sale Value of Farmland) ref 0.504 * 0.071 0.104 Substantive Values (Desire to Spend More Time with Family) ref 0.339 -0.023 0.692 * Substantive Values (Desire to Keep this Farm in the Family) ref 0.018 0.111 -0.012 Family Labor Contributions ref 0.145 0.576 0.479 Health of Key Operator ref -0.003 0.079 0.044 Education ref -0.089 0.126 -0.082 Off-Farm Income No off farm income ref 0.305 -0.180 -0.280 Operator No Off Farm, Spouse Off farm Income ref 0.086 -0.684 -0.534 Operator Off farm income, Spouse No Off Farm Income ref -0.935 -0.355 -1.515 Spouse and Operator Off farm Income (ref) ref 0.000 0.000 0.000 Pseudo R-Square McFadden 0.233; Cox and Snell 0.466; Nagelkerke 0.500

Continued

Table 4.25 Multinomial Logit Regression - Years Individual Expects to Continue Farming. Discussion and Conclusion of Years Individual Expects to Continue Farming – Multinomial Logit Regression

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Table 4.25 Continued 1-11 12-30 Years Years Indefinitely Unsure N 60 49 79 105 Farm Structure Variables Debt to Asset Ratio ref 1.049 0.139 -0.997 Dairy Farm ref -1.198 -0.165 0.014 Raises High Value Crops (Nursery, Fruit, Vegetable) ref 0.331 0.772 0.437 Livestock ref 1.422 * 0.730 0.195 AFAE ref 0.824 0.343 -0.709 Acres Operated ref 0.000 0.000 0.000 Soil Quality ref 0.152 -0.156 -0.228 Farm Receipts 2006 ref 0.210 0.108 0.153 Household Income ref -0.002 -0.101 -0.005 Controls Factor Development Pressure ref -0.003 0.089 0.030 Factor Effectiveness of Local Land Use Policy ref -0.028 0.041 0.042 Factor Future Capital Investment in Enterprise ref 0.132 0.178 0.174 Factor Global Competition and Farm Economic ref -0.034 0.042 0.001 Sample Frame ref -0.565 0.381 -0.997 Pseudo R-Square McFadden 0.233; Cox and Snell 0.466; Nagelkerke 0.500

The findings provide mixed support for hypothesis 1.1, Table 4.26 summarizes the results, outlining respondent characteristics in relation to the length of time a group expects to continue farming while Table 4.27 summarizes the relationships observed in

regards to research question number one. Variables controlling for growth and

development pressure at the RUI commonly thought of as forcing farmers out of

agriculture were not significant. Lifecycle and household decision making factors and to

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a lesser extent farm structure variables were better able to predict how long a respondent expects to continue to farm. Additionally, these results indicate there are some structural effects associated with raising livestock, influencing respondents who are more likely to farm for 12-30 Years then for 1-11 Years.

Years Expected to Continue Farming Respondent Characteristics 12-30 Years vs. 1-11 Years • Younger farmers • Report identifying a successor is a less serious of problem to their enterprise • Are less likely to rely on selling farmland in order to retire • Place greater emphasis on maximizing the sale value of farmland • Are more likely to raise livestock

Indefinitely vs. 1-11 Years • Younger farmers • Report identifying a successor is a less serious of problem to their enterprise • Are less likely to rely on selling farmland in order to retire

Unsure vs. 1-11 Years • Report identifying a successor is a less serious of problem to their enterprise • Place greater importance on spending time with family

Table 4.26 Respondent Characteristics - Years Individual Expects to Continue Farming

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Hypothesis Conclusion R1.1.1: Anticipation of farming for a longer Supported time period will be negatively associated with age R1.1.2: Anticipation of farming for a longer Mixed Support- availability of a successor time period will be positively associated with significant. Operator health, availability of education, off-farm income, the availability of a family labor, off farm income, and optimism successor, operator health, availability of family were not significant labor and optimism for the future of one’s farm. R1.1.3: Anticipation of farming for a longer Supported time period will be negatively with the need to sell land in order to afford retirement R1.1.4: Respondents expecting to farm for Supported longer periods of time will place greater importance on substantive, instrumental and stewardship values compared to those who expect to farm a shorter period of time R1.1.5: Anticipation of farming for a longer Not Supported time period will be positively associated with soil quality, acres operated, smaller debt to asset ratios, farm receipts, household income, plans for increasing future capital investments and alternative marketing streams R1.1.6: Anticipation of farming for a longer Not Supported time period will be positively associated with producing high value crops R1.1.7: Anticipation of farming for a longer Mixed Support – Farmers raising livestock are time period will be negatively associated with more likely to continue to farm for 12-30 dairy farming and livestock production Years vs. 1-11 Years. Results not significant for dairy farming R1.1.8: Anticipation of farming for a longer Not Supported time period will be positively associated with AFAE adaptations

Table 4.27 Hypothesis in relationship to Multinomial Logit Regression Model - Years Individual Expects to Continue Farming

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Years a Respondent Expects Their Enterprise to Persist

In model 2, Table 4.28 presents the variables associated with the length of time

respondents expect their farm enterprise to persist. In this model individuals expecting to

only continue farming for 1-30 Years are the reference category. As in Model 1, the 1-30

Year category was chosen as the reference group because this study is primarily

concerned with farm persistence rather then the reasons farms exit from agriculture at the

RUI8. Having the 1-30 Year group serve as the reference group allows me to compare

the factors exploring why respondents expect their enterprise to persist for varying

lengths of time as opposed to the reasons why they will automatically exit.

Availability of a successor, optimism for future of your farm, selling farmland in

order to afford retirement, and effectiveness of local land use policy are significantly

related to the likeliness of a respondent farming Indefinitely. The less of a problem the

availability of a successor is the more likely respondents are to be farming Indefinitely

then 1-30 Years (Beta = 0.624). The more important it is for a respondent to sell farmland

in order to afford retirement the more likely they are to expect to be faring for 1-30 Years

then to be farming Indefinitely (Beta = -0.569). The more optimistic respondents are for

the future of their farm the more likely they are to be continue farming Indefinitely then

to farm for 1-30 Years (Beta = 0.330). The more important it is to maximize the sale

8 Although While 1- 30 may seem like a long time this was chosen as a reference category as these individuals had identified a terminal end date for their enterprises as opposed to those who had indicated Indefinitely or Unsure, which were both the majority of cases. There were only 31 cases or 10% of the total sample who indicated their enterprise would last between 15 and 30 years, the majority within the category (38 cases or 12% of the sample) indicated they expected their enterprise to continue only another 1 to 15 years. Additionally, had the groups been broken out into 1-15 and 16-30 the number of cases would have been so small in each category the multivariate statistics would not be reliable. 254

value of your farmland the more likely you are to be farming for 1-30 Years then to be

farming Indefinitely (Beta = -0.296). The more effective local land use policy is

perceived to be the more likely respondents are to continue farming Indefinitely then to

farm for 1-30 Years (Beta = 0.126). This last finding suggests that in addition to internal

household characteristics, goals and values the farm family also takes the local land use

policy environment into account when considering strategies that ensure enterprise adaptation and persistence.

Among respondents who are Unsure how long they will continue farming several variables are significant including: the availability of a successor, optimism for the future of your farm, operator off farm work and farm economics and global competition. The less of a problem the availability of a successor is the more likely respondents are to be

Unsure how long they will farm then to be in the 1-30 Year group (Beta = 0.470). If a farm family has no off-farm income they are more likely to be in the Unsure category then they are in the 1-30 Year group (Beta = 1.127). The more of a problem global competition and farm economics are the more likely a respondent is to be in the Unsure group then in the 1-30 Year category (Beta = 0.086).

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1-30 Years Indefinitely Unsure N 62 122 109 Household Factors Age ref -0.030 -0.024 Availability of a Successor ref 0.624 * 0.470 * When Retire Successor is a Relative ref -1.926 -0.975 Selling Farmland is Important for Affording Retirement ref -0.569 * -0.251 Optimism for Future of Your Farm ref 0.330 * -0.261 Instrumental Values (Maximize Sale Value of Farmland) ref -0.296 * -0.251 Substantive Values (Desire to Keep this Farm in the Family) ref 0.017 0.128 Family Labor Contributions ref 0.352 0.604 Education ref 0.161 -0.092 Off-Farm Income No off farm income ref 0.974 1.127 * Operator No Off Farm, Spouse Off farm Income ref -0.134 0.544 Operator Off farm income, Spouse No Off Farm Income ref 0.227 -0.066 Spouse and Operator Off farm Income (ref) ref 0.000 0.000

Continued

Table 4.28 Multinomial Logit Regression Model - Years Expect Enterprise to Continue

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Table 4.28 Continued 1-30 Years Indefinitely Unsure N 62 122 109 Farm Structure Variables Debt to Asset Ratio ref 0.582 -1.343 Dairy Farm ref -0.140 -0.205 Raises High Value Crops (Nursery, Fruit, Vegetable) ref -0.063 0.269 Livestock ref -0.165 -0.470 AFAE ref 0.165 -0.314 Acres Operated ref 0.000 0.000 Soil Quality ref 0.212 0.041 Farm Receipts 2006 ref 0.087 0.133 Household Income ref -0.027 -0.006

Controls Factor Development Pressure ref 0.014 -0.070 Factor Effectiveness of Local Land Use Policy ref 0.126 * 0.064 Factor Future Capital Investment in Enterprise ref 0.039 0.044 Factor Global Competition and Farm Economic ref 0.056 0.086 * Sample Frame ref 0.777 0.382 Pseudo R-Square McFadden 0.232; Cox and Snell 0.442; Nagelkerke 0.389

Discussion and Conclusion of Years Expect Enterprise to Persist – Multinomial Logit Regression The findings provide mixed support for hypothesis 1.2, Table 4.29 summarizes the results, outlining respondent characteristics in relation to the length of time a group expects to their operations to persist, while Table 4.30 summarizes the relationships observed in regards to research question number one.

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Category Respondent Characteristics Indefinitely vs. 1-30 Years • Report identifying a successor is a less serious of problem to their enterprise • Are less likely to rely on selling farmland in order to afford retirement • Are more optimistic for the future of their farm • Perceive land use policy to be more effective at maintaining agriculture in the county • More likely to maximize sale value of farmland

Unsure vs. 1-30 Years • Place greater importance on spending time with family • Report identifying a successor is a less serious of problem to their enterprise • Are more likely to report the operator works off the farm • Report global competition and farm economics are a more serious problem • The more optimistic a respondent is for the future of their farm, the more likely they are to fall into the 1-30 Year category then the Unsure group

Table 4.29 Respondent Characteristics - Years Expect Enterprise to Persist

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Hypothesis Conclusion R1.2.1: Anticipation of the enterprise Not Supported continuing for a longer time period will be negatively associated with age R1.2.2: Anticipation of the enterprise to persist Mixed Support – Availability of a successor for a longer time period will be positively was associated with a longer time line. associated with education, off-farm income, the Families not relying on off-farm income are availability of a successor, availability of more likely to be Unsure how long their family labor and optimism for the future of operations will continue then to identify a one’s farm terminal end date. The influence of education and family labor was not significant R1.2.3: Anticipation of the enterprise to persist Supported for a longer time period will be negatively associated with the need to sell land in order to afford retirement R1.2.4: Respondents expecting their enterprise Mixed Support - The more important the to persist farm for longer periods of time will instrumental value of maximizing the sale place greater importance on substantive, value of farmland is, the more likely instrumental and stewardship values compared respondents are to report a terminal end then to to those who expect to farm a shorter period of farm Indefinitely. time R1.2.5: Anticipation of the enterprise to persist Not Supported for a longer time period will be positively associated with soil quality, acres operated, smaller debt to asset ratios, farm receipts, household income, plans for increasing future capital investments and alternative marketing streams R1.2.6: Anticipation of the enterprise to persist Not Supported for a longer time period will be positively associated with producing high value crops R1.2.7: Anticipation of the enterprise to persist Not Supported for a longer time period will be negatively associated with dairy farming and livestock production R1.2.8: Anticipation of the enterprise to persist Not Supported for a longer time period will be positively associated with AFAE adaptations

Table 4.30 Hypothesis in relationship to Multinomial Logit Regression of Years Respondents Expect Their Enterprise to Persist

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Chapter Four Conclusions

The purpose of this chapter was to examine the relationship between the years commercial farmer expect to continue farming and how long they expect their enterprises

to persist by household characteristics, household values and farm structure variables.

Significant differences were observed among the groups, concluding remarks and

implications will be elaborated on in chapter 7. To further understand the similarities and

differences within the commercial farm population chapter 5 presents a quantitative

analysis comparing commercial farmers (AFAE and Non-AFAE) and Rural Residential

farmers.

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CHAPTER 5

COMPARISON OF HOUSEHOLD CHARACTERISITCS, HOUSEHOLD VALUES AND FARM STRUCTURE AMONG AFAE, NON-AFAE AND RURAL RESIDENTIAL FARM TYPES

In this chapter, I seek to answer my second research question which asks what are the factors are associated with AFAE adaptations, and I compare commercial farmers

(AFAE and Non-AFAE respondents) with Rural Residential respondents.

COMPARISON OF MEANS - ONE WAY ANOVA AND CHI SQUARE TESTS

Initial hypothesis testing is conducted through a comparison of means test examining the relationship between the dependent and independent variables.

AFAE and Rural Residential farmers. For the most part, only relationships deemed statistically significant will be discussed and elaborated.

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Household Characteristics

In terms of household characteristics, significant differences were observed

between respondents grouped according to farm type. Differences were observed in the

case of the farmer’s age, availability of farm successor, sex, years of ownership,

education, family contributions to labor and operator off farm work (Table 5.1). Non-

AFAE farmers were the oldest group of farmers with a mean age of 60.5 years. AFAE

farmers were younger (mean 57.1), while Rural Residential farmers were the youngest

group with a mean age of 56.3. On average Rural Residential farmers reported the

highest education levels (mean 3.5) followed by AFAE farmers (mean 3.3) while the

Non-AFAE farmers reported significantly lower education levels (mean 2.9). The Non-

AFAE farmers were most likely to bemale (87.9%) followed by the AFAE farmers

(80.7%). The Rural Residential farmers exhibited the most gender diversity as 70.7% of respondents were male.

The health of the key operator was least likely to be a problem for Rural

Residential farmers (mean 4.1), and was identified as a more modest problem for AFAE farmers (mean 3.6) and Non-AFAE farmers (mean 3.5). Likewise Rural Residential farmers were least likely to indicate the availability of a successor was a problem (mean

4.4) followed by Non-AFAE farmers (mean 4.0). AFAE farmers reported the availability

of a successor was a modest problem for the future of their enterprise (mean 3.7).

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F-test or Non- Rural Pearson Post Hoc AFAE AFAE Residential χ2 Tests Mean Mean Mean N 161 132 133 Household Characteristics Age 57.1 60.5 56.3 4.7 * NA > A, RR Health of Key Operator 3.6 3.5 4.1 6.7 * RR > A, NA Availability of Farm RR > NA Successor 3.7 4.0 4.4 15.1 ** >A Optimism for Future of Your Farm 4.7 4.4 4.5 1.0 Years you've owned NA > A > farmland 28.4 34.5 20.8 16.1 ** RR

Male (%) 80.7% 87.9% 70.7% 12.3 * Education 3.3 3.0 3.5 8.3 * A, RR > NA

Family Labor Contributions 22.0 ** Family contributes less then half of the labor to the farm (%) 30.4% 20.5% 8.3% Family Contributes more then 1/2 of the labor to the farm (%) 69.6% 79.5% 91.7% Off-Farm Income 59.9 ** No off farm income (%) 46.6% 46.2% 14.3% Operator No Off Farm, Spouse Off farm Income, (%) 21.1% 22.7% 16.5% Operator Off farm income, Spouse No Off Farm Income (%) 10.6% 13.6% 19.5% Spouse and Operator Off farm Income (%) 21.7% 17.4% 49.6% *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 5.1 Comparison of Household Characteristics

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Examining family contributions to labor, Rural Residential farmers were almost exclusively reliant on family, 91.7% indicated more then half of the labor on the farm is from the family. Over three quarters of Non-AFAE farmers (79.5%) are primarily reliant on family labor, while only 69.6% of AFAE farmers reported family members contributing at least half their labor to the farm. AFAE operations place heavy emphasis on marketing in addition to production which can increase their labor requirements. The finding that one third of AFAEs are relying on non-family labor fits with the additional assistance these operations require.

Reliance on off-farm work varied greatly between the groups. Commercial farmers were more likely to report no off-farm income. Just under half of AFAEs

(46.6%) and Non-AFAEs (46.2%) reported no off-farm income, while only 14.3% of

Rural Residential farmers did as well. Both types of Commercial farmers were also more likely to report a spouse working off the farm while the operator had no-off farm employment. Within the AFAE respondents, 21.1% reported the operator had no off- farm job while the spouse did work off-farm as did 22.7% of Non-AFAEs. Only 16.5% of Rural Residential farmers reported this pattern. Rural Residenital farmers were slightly more likely to report the primary operator as having an off-farm job and no other off-farm income sources (19.5%) compared to 13.6% of Non-AFAE farmers and 10.6% of AFAEs. Additionally, Rural Residenital farmers were the most likely to report both the operator and spouse working off the farm (49.6%) compared to 21.7% of AFAEs and

17.4% of Non-AFAEs.

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No significant differences among optimism levels for the future of the farm were

observed between the different groups.

Household Characteristic Findings and Hypotheses

Hypothesis R.2.1.1 and R.2.1.2 state an expectation that household characteristics

will vary across the farm types. The first hypothesis examining age was supported; the

post hoc tests did find Commodity farmers are statistically older then the AFAE and

Rural Residential farmers. This analysis also found support for the second hypothesis.

On average Rural Residential farmers did report the highest education levels. However,

post-hoc tests reveal that both Rural Residential and AFAE respondents had statistically

higher education levels then Non-AFAEs.

Hypothesis R2.1.3 states AFAE farmers will report fewer problems associated

with operator health, availability of a successor, and be more optimistic for the future of

their farm compared to Non-AFAE and Rural Residential farmers. Findings for this

hypothesis were mixed. AFAE respondents were the most optimistic towards the future

of their farm, however this relationship was not statistically significant. AFAEs report

operator health and availability of successor are more of a problem than the other groups.

Rural Residential respondents report these factors have the smallest impact on their

enterprise. Since the Rural Residential farmers do not rely on their farm as a primary

income source operator health and availability of a successor have less of an impact on their operations compared to commercial farms. Although the literature posits AFAEs are

a more resilient form of agriculture at the RUI, these results suggest the persistence of

AFAEs may be impacted by the availability of a successor, raising questions about the

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ability to replicate and reproduce the next generation.

Household Values

Significant differences were observed among the groups in relation to the level of importance they placed on a number of the instrumental and substantive values measured in this research (Table 5.2).

Instrumental Values

AFAE respondents placed the most value overall on instrumental values (15.0) followed by Non-AFAE (mean 14.7) while Rural Residential respondents placed the least emphasis on instrumental values. Within the instrumental factors, AFAE farmers placed the most emphasis on minimizing debt (mean 4.1) as did Non-AFAE respondents (mean

4.1). In contrast to the commercial farmers, Rural Residential farmers placed the least emphasis on minimizing debt (mean 3.5). The commercial farmers were also most likely to report maximizing farm income and ensuring household income as important motivations guiding land use decision making compared to the Rural Residential respondents. Ensuring household income is adequate was most important to AFAE respondents (mean 4.0) and Non-AFAE farmers (mean 4.0). Household income was not as important of a factor influencing Rural Residential land use decision making (mean

3.2). Following a similar pattern, maximizing net farm income was reported to be most important to AFAE farmers (mean 4.2) and Non-AFAE respondents (mean 4.2).

Maximizing net farm income was least important to Rural Residential farmers (mean

2.8). Staying ahead of the competition was most important to AFAE farmers (mean 2.7) followed by Non-AFAE (mean 2.5) respondents. Staying ahead of the competition was of

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little importance to Rural Residential farmers (mean 1.9).

Maximizing the sale value of farmland was most important to Non-AFAE respondents (mean 3.2). It was less important for AFAE respondents (mean 2.9) and

Rural Residential farmers (mean 2.9).

Commercial farmers reported a greater percentage of household income stemming

from the farm operation compared to the Rural Residential respondents. These findings

are expected given the classification system I used to distinguish Commercial from Rural

Residential Respondents. However, these findings also support the hypothesis that commercial farmers (both AFAE and Non-AFAE) are more likely to report economic

instrumental factors influencing enterprise development decision making compared to

Rural Residential respondents. In addition, AFAE respondents were more likely to report

staying ahead of the competition as an important factor influencing business development strategies, these findings are supported by other studies that have found direct marketers

wary of local competitors (Che et al. 2005).

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Non- Rural Post Hoc AFAE AFAE Residential F-test Tests Mean Mean Mean N 161 132 133 Factor Instrumental Values 15.0 14.8 11.4 42.0 ** A, NA > RR Importance of minimizing debt 4.1 4.1 3.5 10.7 ** A, NA > RR Importance of ensuring household income is adequate 4.0 4.0 3.2 20.9 ** A, NA > RR Importance of maximizing net farm income 4.2 4.2 2.8 73.5 ** A, NA > RR Importance of maximizing sale value of farmland 2.9 3.2 2.9 2.6 Importance of staying ahead of the competition 2.7 2.5 1.9 15.2 ** A, NA > RR *F-test significant at the .05 level **F-test significant at the .01 level

Table 5.2 Household Instrumental Values

Substantive Values

Non-AFAE respondents placed the most value overall on substantive values

(mean 16.6) closely followed by AFAE farmers (mean 16.5. Rural Residential farmers placed considerably less value on substantive values (mean 15.7). The desire to be one’s own boss was most important to AFAE farmers (mean 4.2) and Non-AFAE farmers

(mean 4.2). Being one’s own boss was of more moderate importance to Rural Residential farmers (mean 3.6).

No significant differences were identified in regards to the substantive values concerning: the desire to keep livening in a rural area; the desire to keep the farm in the family; or the desire to spend more time with family.

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Non- Rural Post Hoc AFAE AFAE Residential F-test Tests Mean Mean Mean N 161 132 133 Factor Substantive Values 16.5 16.6 15.7 3.0 * A, NA > RR Desire to keep living in a rural area 4.3 4.4 4.4 0.8 Desire to keep this farm in the family 4.0 4.0 3.9 0.6 Desire to spend more time with family 4.1 4.1 3.8 1.9 Desire to be my own boss 4.2 4.2 3.6 13.3 ** A, NA > RR *F-test significant at the .05 level **F-test significant at the .01 level

Table 5.3 Household Substantive Values

Stewardship Values

AFAE respondents placed the most value overall on stewardship values (mean

17.2) followed closely by Non-AFAE respondents (mean 17.0). Rural Residential farmers placed considerably less value on stewardship values (mean 15.8). Maintaining and improving the quality of soil was most important to AFAE farmers (mean 4.3) and

Non-AFAE farmers (mean 4.3). Maintaining and improving the quality of soil was of more moderate importance to the Rural Residential farmers (mean 3.8). Minimizing nutrient and chemical runoff was most important for Non-AFAE farmers (mean 4.3) and

AFAE farmers (mean 4.3). Rural Residential farmers reported more moderate importance on minimizing nutrient and chemical runoff (mean 3.7).

No significant differences were identified in regards to the stewardship values concerning: the importance of being a good steward of the land or protecting the scenic quality of the land.

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Non- Rural Post Hoc AFAE AFAE Residential F-test Tests Mean Mean Mean N 161 132 133 Factor Stewardship Values 17.2 17.0 15.8 6.9 * A, NA > RR Importance of maintaining and improving quality of my soil 4.4 4.3 3.8 13.5 ** A, NA > RR Importance of being a good steward of the land 4.4 4.4 4.2 2.3 Importance of minimizing nutrient & chemical runoff 4.3 4.3 3.7 12.5 ** A, NA > RR Importance of protecting scenic quality of the property 4.1 4.0 4.1 0.7 Desire to stay on good terms with neighbors 4.0 4.1 3.8 3.1 * NA > RR *F-test significant at the .05 level **F-test significant at the .01 level

Table 5.4 Household Stewardship Values

Household Values Findings and Hypotheses

Hypothesis R.2.1.4 states an expectation that AFAEs will place greater importance on substantive, instrumental and stewardship values compared to Non-AFAE and Rural Residential Farmers. Findings for this hypothesis were mixed. AFAEs did place greater importance on instrumental and stewardship values compared to the other groups. However, Non-AFAEs placed greater importance on substantive values compared to the other groups.

Future Plans for Land Use

Significant differences were observed among the groups in relation to their future plans for land use, more specifically if they anticipated a relative would take over the farm (Table 5.3). Non-AFAEs reported the greatest percentage of respondents who

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indicated a relative would take over the farm when they retired (31.8%), less than a quarter of AFAE respondents (20.5%) did as well. Only 13.5% of Rural Residential farmers indicated a relative would take over their operation when they retired.

Among the variables examining future land use plans significant differences were not observed between the groups regarding: the likeliness to sell land for development in the next five years; the importance of selling farmland to afford retirement; and the likeliness of selling the farm to a developer upon retirement.

F-test or Post Non- Rural Pearson Hoc AFAE AFAE Residential χ2 Tests Mean Mean Mean N 161 132 133 Factor Likeliness to Sell Land for Development in Next 5 Years 5.3 5.7 5.1 1.7

Future Retirement Plans Importance of selling farmland in order to afford retirement 2.2 2.1 2.0 1.6 Relative is a Successor (%) 20.5% 31.8% 13.5% 13.2 * Will sell to developer (%) 16.8% 16.7% 10.5% 2.8 *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 5.5 Future Land Use Plans

Future Plans for Land Use and Hypotheses

Hypothesis R.2.1.5 states Non-AFAEs will be more likely to sell land for

development in the future. The analysis found mixed support for this hypothesis. Non-

AFAEs were most likely to sell land for development in next five years, however, AFAEs

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were most likely to indicate selling farmland was of greater importance to their ability to

afford retirement (though the relationships were in the positive direction they were not

significant). This analysis did not support hypothesis R2.1.3 which stated AFAEs would most likely identify a relative as a successor. In this analysis Non-AFAEs were the most

likely group to identify their successor is a relative.

FARM STRUCTURE

Structural Variables

In terms of farm structure, significant differences were observed between

respondents grouped according to farm type. Differences were observed in the case of:

acres operated; acres owned; acres rented; farm receipts; soil quality; production systems;

sales from sources, planned changes to the enterprise in the last five years and the next

five years; marketing strategies; past and future farm enterprise trajectory; the number of

years respondents expect to continue farming; and how long they expect their enterprise

to continue were significantly different among the groups.

Farm scale can be measured by both acres and farm receipts. Non-AFAE farmers

operated the largest acreage, however AFAE farmers had the highest average farm

receipts (Table 5.6). Non-AFAE respondents operated physically larger farms, with a

mean of 513.0 acres, followed by AFAE farmers whose farmers averaged 345.6 acres.

Rural Residential Farmers had the smallest acreage, operating a mean of 55.9 acres. Non-

AFAE farmers also owned (mean 331.1) and rented (mean 181.8) the largest number of

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acres. AFAE operations reported more modest acreage, on average owning 187.1 acres and renting 106.1 acres. The smallest acreage was reported by Rural Residential farmers who on average owned 47.5 acres and rented 7.2 acres. AFAE farmers reported the highest average farm receipts with a mean 4.6 representing farm receipts between

$50,000 to $249,000. Non-AFAE farmers reported similar but slightly smaller average farm receipts mean 4.4, also representing farm receipts between $50,000 to $249,000. In stark comparison Rural Residential farmers reported a mean farm income of 1.1, meaning the vast majority were making less than $10,000 in gross farm receipts. Both Non-AFAE and AFAE farmers rated their soil quality as average to better then average (mean 3.3 and

3.3 respectively), Rural Residential farmers reported their soil quality was closer to average (mean 3.0).

F-test or Non- Rural Pearson Post Hoc AFAE AFAE Residential χ2 Tests Mean Mean Mean N 161 132 133 NA > A > Acres Operated 345.6 513.0 55.9 18.1 ** RR NA > A > Acres Owned 187.1 331.1 47.5 20.4 ** RR NA > A > Acres Rented 106.1 181.8 7.2 11.4 ** RR Soil Quality 3.3 3.3 3.0 7.5 * NA, A > RR Total Farm Receipts in 2006 4.6 4.4 1.1 219.1 ** A, NA > RR Total Household Income in 2006? 5.4 5.2 5.5 0.8 Farm Debts are 40% or Below Assets (%) 92.5% 93.9% 91.0% 0.8 Farm Debts are 40% or Above Assets (%) 7.5% 6.1% 9.0% *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 5.6 Farm Structure Variables 273

Farm Structure and Hypotheses

Hypothesis R.2.1.6 states Non-AFAEs will operate the largest acreage, carry more debt and farm on better soil compared to AFAE and Rural Residential. Findings for this hypothesis were mixed. Non-AFAEs farm the largest acreage and better soil quality, however Rural Residential respondents carry the most debt.

Hypothesis R.2.1.7 anticipates AFAEs will report the largest farm income. This analysis found AFAEs report slightly higher average farm incomes compared to the other groups. However, post-hoc tests indicate commercial farmers (AFAE and Non-AFAE) report significantly higher incomes then Rural Residential farmers..

Crop Production and Sales Outlets

Almost half (41.6%) of all AFAE farms produced high value crops. Less than a quarter, 17.3% of Rural Residential and 12.9% of Non-AFAE farms did as well. Over half of Non-AFAE farms (59.1%) reported having livestock. Less than half of Rural

Residential farms raised livestock (42.1%) as did one third (33.5%) of AFAE farms. Just over a quarter (28.0%) of Non-AFAE respondents were dairy farmers, compared to

11.8% of AFAE and less than 1% (0.8%) of Rural Residential farmers.

On average AFAE farms reported a greater percentage of receipts from crop sales

(49.4%) compared to 37.8% of Non-AFAE farms and 31.7% of Rural Residential farms.

Rural Residential farms reported the largest percentage of sales from livestock (37.9%) closely followed by Non-AFAE (36.3%) and AFAE farms (26.5%). However, AFAE

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farms had the greatest percentage of receipts from direct sales (44.3%) followed by Rural

Residential farms (35.4%). Non-AFAE reported an average of 16.7% of farm receipts from direct sales (this high number may reflect direct sales of hay, grain and other items to other farmers).

Post Non- Rural Pearson Hoc AFAE AFAE Residential χ2 Tests % % % N 161 132 133 High value crops (veg, tobacco, nursery, green, fruit, nut, * orchard, etc) 41.6% 12.9% 17.3% 38.1 * Raises corn and/or soy and majority of income from these 3.7% 3.8% 1.5% 1.6 Raises livestock and majority of * income from livestock 33.5% 59.1% 42.1% 19.5 * Raises dairy and majority of * income from livestock 11.8% 28.0% 0.8% 43.1 *

Percentage of 2006 farm receipts from crops sales 49.4% 37.8% 31.7% 6.5 * Percentage of 2006 farm receipts from livestock sales 26.5% 36.3% 37.9% 3.5 * Percentage of 2006 farm receipts * from direct sales 44.3% 16.7% 35.4% 16.5 * *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 5.7 Production Type and Sales Outlets

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Crop Production and Sales Outlets and Hypotheses

Hypothesis R.2.1.8 and R.2.1.9 anticipate crop production and sales outlets will be associated with specific farm types. This analysis found support for both hypothesis.

Non-AFAE farmers are more likely to be associated with dairy and grain operations and

commodity sales. AFAE and Rural Residential farmers are more likely to be associated with high value crop production and direct marketing initiatives.

Past and Future Changes Planned for the Enterprise

Examining changes made to the enterprise over the last five years, AFAE farms were most likely to have increased alternative marketing strategies (mean 6.5) compared to Non-AFAE (mean 6.2) and Rural Residential farmers (mean 6.1) (Table 5.6).

Likewise AFAEs were most likely to have increased the sales of products directly to consumers (mean 3.4) and value adding activities (mean 3.2). Non-AFAE and Rural

Residential respondents made only minor increases in the sales of products directly to consumers (mean 3.1 and 3.1 respectively); and value added processing of farm products

(mean 3.0 and mean 3.1 respectively).

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F-test or Non- Rural Pearson Post Hoc AFAE AFAE Residential χ2 Tests Mean Mean or % or % Mean or % N 161 132 133 Past Changes to Enterprise (Last Five Years) Past Capital Investment 7.2 7.0 6.8 2.0 A > NA, Past Alternative Marketing 6.5 6.2 6.1 8.2 ** R Last 5 years changes to number of distinct commodities produced 3.2 3.1 3.1 1.7 Last 5 years sales of products directly A > RR, to consumers 3.4 3.1 3.1 11.7 ** NA Last 5 years value added processing A > RR, of farm products 3.2 3.0 3.1 5.1 * NA

Future Planned Changes To Enterprise (Next Five Years) Factor Future Capital Investment 6.8 6.6 6.7 1.4 A > RR > Factor Future Alternative Market 10.1 8.9 9.4 26.7 ** NA In next 5years changes to number of A > RR, commodities produced 3.3 2.9 3.1 8.6 ** NA In next 5 years sales of product A > RR directly to consumers 3.5 2.9 3.2 29.4 ** >NA In next 5 years value-added A > RR, processing of farm products 3.3 2.9 3.1 15.3 ** NA *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level

Table 5.8 Past and Future Planned Changes to the Enterprise

Examining future changes planned for the enterprise over the next five years,

AFAE respondents are most likely to increase alternative marketing strategies (mean

10.1) followed by Rural Residential farms (mean 9.4) (Table 5.6). Non-AFAE indicated a

more modest increase in alternative marketing strategies (mean 8.9). Likewise AFAEs

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plan to increase the number of commodities they produce (mean 3.3). Rural Residential farms plan a more modest increase of the number of commodities they produce (mean

3.1). Non-AFAEs actually report a slight decline in the number of commodities they produce (mean 2.9). AFAEs also report they plan to increase direct sales to customers

(mean 3.5) as do Rural Residential respondents (mean 3.2). Non-AFAE farmers indicate

they plan to decrease direct sales (mean 2.9). Additionally, AFAE respondents report

they plan to increase value added activities on the farm (mean 3.3). Rural Residential

respondents also report plans to slightly increase value adding activities (mean 3.1). Non-

AFAE report they plan to moderately decrease any value adding activities (mean 2.9).

These results suggest that AFAEs -- enterprises oriented towards direct and niche

marketing -- are exhibiting behaviors and activities indicating continued growth and

expansion of their enterprises, reinforcing their status as alternative farming systems. The

increased alternative marketing activities Rural Residential farmers report reinforces the

findings of other studies that argue the production and direct marketing capabilities of

hobby farms (especially at the RUI) should not be discounted. Non-AFAEs

demonstrated no change in status (and in some cases a decline) in their alternative

marketing activities, suggesting these farmers intend to keep their operations oriented

towards commodity markets.

Among the variables examining past and future planned changes significant

differences were not observed between the groups regarding: past and future capital

investments; and changes made to the number of distinct commodities produced over the

last five years.

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Past and Future Enterprise Trajectory

Examining past business trajectory in relationship to changes in land and sales,

the majority of AFAE farms (59.0%) were on a growth trajectory while a third (30.4%)

were in decline and 10.6% were stable (Table 5.9). In contrast just under half, or 45.5%

of Non-AFAE farms were on a growth trajectory, 40.9% indicated a decline, and 13.6%

appeared stable. The Rural Residential farms were the most likely to be in decline,

nearly two thirds or 69.9% of respondents were in decline, only 21.8% were on a growth

trajectory, and 8.3% were stable.

In regards to future five year trajectory, almost three quarters of AFAE farms

(72.0%) indicate they are on a growth trajectory, only 19.9% are in decline and 8.1%

expect to remain stable. Over half (53.8%) of Non-AFAE farms indicate a growth

trajectory while almost a third (29.5%) will be in a decline mode, while 16.7% indicate

their operations will remain stable (an increase from the past five year trajectory). Rural

Residential farms can be characterized by a binomial split between growth and decline.

Just under half (43.6%) indicate growth and (45.1%) indicate decline, while a small

minority 11.3% will remain stable.

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Rural Residen Pearson AFAE Non-AFAE tial χ2 % % % N 161 132 133

Past Five Year Trajectory 51.1 ** Decline 30.4% 40.9% 69.9% Stable 10.6% 13.6% 8.3% Growth 59.0% 45.5% 21.8%

Future Five Year Trajectory 30.5 ** Decline 19.9% 29.5% 45.1% Stable 8.1% 16.7%11.3% Growth 72.0% 53.8% 43.6% *Pearson Chi-square significant at .05 level **Pearson Chi-square significant at .01 level

Table 5.9 Past and Future Business Trajectory

Farm Persistence

Examining the number of years an individual expects to continue farming, the majority of AFAE respondents (39.1%) expect to farm indefinitely, 21.1% indicate they only plan to farm for an additional 12-30 years, 20.5% respondents report they plan to farm for another 1-10 years and 19.3% are unsure how long they will continue farming

(Table 5.10). The Non-AFAE farmers are more evenly split between those who are unsure how long they will continue to farm (36.4%) and those expecting to farm indefinitely (31.8%). Among the remaining Non-AFAE respondents, 20.5% expect to continue farming for another 1-10 years and 11.4% expect to continue on for an additional 12-30 years. The majority of Rural residential respondents expect to continue farming indefinitely (44.4%), while 33.1% are Unsure of how long they will farm. The

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remaining Rural Residential respondents are evenly split between those who expect to continue farming for an additional 1-10 years (11.3%) and those who plan to continue farming for another 12-30 years (11.3%).

Rural Residen Pearson AFAE Non-AFAE tial χ2 % % % N 161 132 133 Years Individual Expects to Continue Farming 22.1 * 1-10 years 20.5% 20.5% 11.3% 12-30 years 21.1% 11.4% 11.3% Indefinitely 39.1% 31.8% 44.4% Unsure 19.3% 36.4% 33.1%

Years Respondent Expects Enterprise to Continue 1-30 years 22.4% 19.7% 20.3% 1.9 Indefinitely 43.5% 39.4% 39.1% Unsure 34.2% 40.9% 40.6% *Pearson Chi-square significant at .05 level **Pearson Chi-square significant at .01 level

Table 5.10 Years Individual Expects to Continue Farming and Years Respondent Expects Enterprise to Persist.

No significant differences were observed between the groups regarding the length of time respondents expected their enterprise to persist.

Business Trajectory and Farm Persistence and Hypotheses

Hypothesis R.2.1.10 states AFAEs are most likely to be on a growth trajectory

and are more likely to anticipate their enterprises lasting indefinitely. This hypothesis was

partially supported; AFAEs are most likely to be on a growth trajectory. The majority of

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AFAE respondents do expect their enterprise to last indefinitely compared to the other

groups, however this relationship was not statistically significant.

CONTROLS

In terms of the control variables, significant differences were observed between

respondents grouped according to farm type. Differences were observed in the case of:

cost of health insurance; optimism for agriculture in the county; development pressure;

global competition and farm economics; local government support for land use policy;

labor conditions; weather; availability of local infrastructure; neighbor effects; non-farm

group support; sample frame; and county of origin (Table 5.11 and Table 5.12).

The cost of health insurance was reported as the biggest problem for AFAE (mean

3.9) and Non-AFAE (mean 3.9) farmers. Health insurance was significantly less of a

problem for Rural Residential farmers (mean 2.9). These results most likely reflect the fact that the majority of Rural Residential farmers have off-farm jobs that also provide health insurance benefits.

Non-AFAE farmers were the most optimistic for the future of agriculture in their

county (mean 9.5). AFAE farmers reported more modest optimism levels (mean 8.8) as

did Rural Residential respondents (mean 8.1). Local government support for land use policy was rated strongest by Non-AFAE farmers (mean 5.8). AFAE respondents reported a more modest support for local land use policy by local governments (mean

5.3) as did Rural Residential respondents (mean 5.2).

Development pressure was perceived to be the biggest problem for the farm by

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Non-AFAE respondents (mean 9.5) followed by AFAE farmers (mean 9.4). Rural

Residential respondents did not perceive development pressure to be as great of a problem for their farm enterprise (mean 7.9). Both AFAE farmers and Non-AFAE farmers reported neighbor influences as minor to moderate problems for their business

(mean 5.5 and 5.5 respectively). Rural Residential farmers reported neighbor influences as less of a problem (mean 4.0). Non-AFAE farmers reported global competition and farm economics to be a more severe problem for their business (mean 19.1). AFAE farmers reported global competition and farm economics to be a more moderate problem

(mean 18.1). In comparison, Rural Residential did not perceive global competition and farm economics as a problem for their business (mean 12.7).

Labor conditions were reported as a modest to severe problem for AFAE farms

(mean 6.1) and Non-AFAE farms (mean 6.1). Labor conditions were a non-existent to

minor problem for Rural Residential farmers (mean 4.3) Weather was reported as a moderate problem for Non-AFAE (mean 5.2) and AFAE farms (mean 5.0), and was a more minor problem for Rural Residential farms (mean 4.3). The availability of local infrastructure were reported as a modest to severe problem for Non-AFAE (mean 6.3) and AFAE farms (mean 6.3). Rural Residential farmers did report weather was a more moderate problem for their enterprise (mean 5.0).

These results most likely reflect the different contexts commercial AFAE and

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are Rural Residential farmers. Additionally, as indicated earlier, Rural Residential farmers rely almost exclusively on family labor and are therefore buffered from problems associated with access to farm labor. Non-AFAE farms engaged in (low margin) commodity production operate extensive operations at a scale that makes them most vulnerable to development pressure, neighbor effects, global competition and farm economics. The AFAEs are equally as vulnerable to these factors, but are somewhat buffered from their impacts by virtue of their smaller scale and greater orientation to local

markets as opposed to global ones.

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F-test or Non- Rural Pearson Post Hoc AFAE AFAE Residential χ2 Tests Mean Mean Mean N 161 132 133 A, NA > Cost of health insurance 3.9 3.9 2.9 23.8 ** RR Factor Community Support 10.6 10.5 10.9 1.1 Factor County Optimism 8.8 9.5 8.1 3.5 * NA > RR A, NA > Development Pressure 9.4 9.5 7.9 8.2 ** RR Effectiveness of Land Use Policy 9.5 10.5 10.3 2.5 Factor Global Competition and Farm A, NA > Economics 18.1 19.1 12.7 45.0 ** RR Factor Local Government Support NA >A, for Land Use Policy 5.3 5.8 5.2 3.3 * RR Factor Labor A, NA > Conditions 6.1 6.1 4.3 25.2 ** RR A, NA > Weather 5.0 5.2 4.3 4.9 * RR A, NA > Local Infrastructure 6.3 6.3 5.0 7.5 * RR A, NA > Neighbor Influences 5.5 5.5 4.0 17.7 ** RR Non-Farm Group Support 16.3 16.8 16.9 0.7 *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level Table 5.11 Control Variables

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Sample Frame and Survey County Origin

AFAE farms were evenly split between the two sample frames, 50.9% originated from the random landowner sample frame and 49.1% were from the more purposeful commercial farm sample frame (Table 5.9). The majority of Non-AFAE farms were from the landowner random sample frame (64.40%), while 35.6% came from the commercial farm sample. The vast majority, 92.5% of Rural Residential farms were from the landowner random sample and only 7.50% were from the commercial farm sample.

Post Non- Rural Pearson Hoc AFAE AFAE Residential χ2 Tests % % % N 161 132 133 Dummy Sample Frame 59.0 ** Landowner Random Sample 50.9% 64.4% 92.5% Commercial Farm Sample 49.1% 35.6% 7.5%

Survey County 74.9 ** Cache 11.8% 22.7% 12.8% Frederick 20.5% 18.2% 12.0% Kent 31.7% 17.4% 2.3% Shelby 13.0% 11.4% 19.5% Spencer 5.0% 12.1% 20.3% Forsyth 3.1% 3.0% 9.0% Hall 3.7% 2.3% 7.5% Yamhill 11.2% 12.9% 16.5% *F-test significant at the .05 level; Pearson Chi-square significant at .05 level **F-test significant at the .01 level; Pearson Chi-square significant at .01 level Table 5.12 Dummy Sample Frame and Survey County Origin

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Examining the county of origin, the majority of AFAE farms were from Kent

(31.7%) followed by Frederick (20.5%), a smaller percentage were from Shelby (13.0%),

Cache (11.8%) and Yamhill County (11.2%) (Table 5.10). Less then 5% of AFAE survey respondents were from Spencer (5.0%), Hall (3.7%) or Forsyth County (3.1%).

The majority of Non-AFAE farms were found in Cache County (22.7%) followed

by Frederick (18.2%) and Kent (17.4%), while a smaller percentage of respondents were

from Yamhill (12.9%), Spencer (12.1%) and Shelby County (11.4%). Following a similar

pattern as the AFAEs, less then 5% of Non-AFAEs were from Forsyth (3.0%) and Hall

County (2.3%).

Rural Residential farm respondents were concentrated in Spencer (20.3%), Shelby

(19.5%) and Yamhill County (16.5%) A smaller percentage of Rural Residential

respondents were from Cache (12.8%), Forsyth (9.0%), Hall (7.5%) and Kent County

(2.3%).

Discussion and Conclusion

The purpose of this analysis was to compare household characteristics, household

values and farm structure variables across commercial farmers (AFAE and Non-AFAE

respondents) and Rural Residential respondents. Table 5.13 summarizes the relationships

observed in regards to research question number two. Significant differences were

observed among the groups, concluding remarks and implications will be elaborated on

in chapter 7. To further understand the similarities and differences across these three

groups, chapter 6 presents a qualitative analysis of 35 families that highlights the more

subtle nuances influencing enterprise adaptation and persistence.

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Hypothesis Conclusion R.2.1.1: Commodity farmers will be older compared to Supported AFAE and Rural Residential farmers R.2.1.2: Rural Residential farmers will have higher Supported education levels and rely on family labor to a greater extent then AFAE and Non-AFAE farmers. R2.1.3: AFAE farmers will report fewer problems Mixed Support – AFAE’s are the most associated with operator health, availability of a optimistic*. AFAE’s report operator health and successor, and be more optimistic for the future of their availability of successor are more of a problem farm compared to Non-AFAE and Rural Residential then the other groups. Rural Residential farmers. respondents report these factors have the smallest impact on their enterprise. Non- AFAE’s are most likely to identify their successor is a relative. R.2.1.4: AFAE will place greater importance on Mixed Support – AFAE placed greater substantive, instrumental and stewardship values importance on instrumental and stewardship compared to Non-AFAE and Rural Residential values compared to the other groups. Non- Farmers. AFAE’s placed greater importance on substantive values compared to the other groups. R.2.1.5: Non-AFAE’s will be more likely to sell land Mixed Support – Non-AFAE’s were most likely for development in the future. to sell land for development in next 5 years, however, AFAE’s were most likely to indicate selling farmland was of greater importance to their ability to afford retirement* R.2.1.6: Non-AFAE’s will operate the largest acreage, Mixed Support – Non-AFAE’s farm the largest carry more debt and farm on better soil compared to acreage and better soil quality. Rural Residential AFAE and Rural Residential carry the most debt R.2.1.7: AFAE’s will report the largest farm income Supported R.2.1.8: Non-AFAE farmers are more likely to be Supported associated with dairy and grain operations and commodity sales R.2.1.9: AFAE and Rural Residential farmers are more Supported likely to be associated with high value crop production and direct marketing initiatives. R.2.1.10: AFAE are most likely to be on a growth Supported trajectory and anticipate their enterprises lasting indefinitely *Relationship is present but not significant

Table 5.13 Hypothesis in relationship to Comparison of Means Testing of AFAE, Non-AFAE, and Rural Residential Farm Type

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CHAPTER 6

THE INFLUENCE OF HOUSEHOLD LEVEL DECISION MAKING FACTORS ON FARM ENTERPRISE ADAPTATION AND PERSISTENCE AT THE RUI: A QUALITATIVE EXPLORATION

The analysis related to the third research question is reported in this chapter. The

first section describes the study sites and methods used to collect the qualitative data. In

the second section, I describe the quantitative and qualitative results exploring household

level decision making factors, comparing and contrasting similarities and differences

across the groups. The final section examines the specific role succession (the presence

or absence of an heir) has on enterprise adaptation and persistence among case study respondents.

Case Study Sites and Methods

In order to gain a more comprehensive understanding of the nuances involved in farm adaptation and succession at the RUI, face-to-face semi-structured interviews with

35 farm families were conducted. The qualitative data for this research was collected from a more restricted geographical context compared to the Land Owner survey population.

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Interviews were primarily conducted in the Columbus Metro Region in Ohio and the Grand Rapids Metro Region in Michigan, two areas of the Midwest with rapidly

urbanizing populations with considerable growth at the RUI. The Columbus Metro region

in this study includes a six county area (Franklin, Delaware, Licking, Madison,

Pickaway, and Union Counties). These counties are agriculturally diverse with farmers

raising corn and soybeans, dairy, livestock and fruit and vegetable crops. The Grand

Rapids Metro region is a one county area (Kent County) although some respondents were

located on the border of Kent and Ottawa Counties. Parts of Kent and Ottawa Counties

are part of the Fruit Ridge, a unique micro-climate in Western Michigan where the

majority of Michigan’s tree fruit crop is grown. Growers in this region also produce

vegetable crops, dairy, and a small amount of corn and soybeans. Some farmer interviews

conducted in the landowner case study counties were also included in the qualitative

analysis (Table 6.2).

Grant funding from North Central SARE Graduate Student Research Program and

an OSU-OARDC Graduate Student Research Grant provided the funds to conduct the

interviews in Central Ohio and Western Michigan.

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Columbus Metro Grand Rapdis Metro Census Region Midwest Midwest Population 1990 1,273,958 500,631 Population 2000 1,458,307 574,335 County Acres 1,966,582 547,952 Square Miles 2000 3,073 856 Land in Farms 1997 (acres) 1,255,043 197,951 Land in Farms 2002 (acres) 1,258,371 173,381 Number of Farms 1997 5,116 1,343 Number of Farms 2002 5,370 1,212 Total Farm Sales 1997 458,955,000 129,142,000 Total Farm Sales 2002 395,801,000 149,670,000

Table 6.1 Farm and Population Demographics in Columbus and Grand Rapids Metro Regions.

Farm families interviewed represented a diversity of AFAE and commodity farms. Parallel to the methodology used in other succession studies (Salamon 1992;

Taylor et al 1998) data gathered from these interviews was used to generate profiles of how internal family dynamics influence farm succession and adaptation by identifying how entry into farming occurred, the working relationships between generations, household values and goals, the decision-making process involved in choosing an heir(s), gender roles, how management responsibilities and land ownership are transferred, and the role of local context (local economy, land prices, development pressures, etc.) on land use decision making. When possible interviews were conducted with as many farm family members as possible, including the primary farm operator their spouse and dependents over 18 years old. Participants were asked to fill out a short survey at the end of each interview.

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Participants were identified through a snowball sampling methodology (Berg

2004) accomplished through initial contact with the North American Direct Marketing

Association, and State Extension and State Departments of Agriculture. All interviews were taped and transcribed by professional transcriptionists. The interviews were coded and analyzed with NVivo, a computer program that can assist in identifying and mapping patterns in qualitative data (Richards 1999; Bazeley and Richards 2000; Gibbs 2002).

Description of Qualitative Sample

The qualitative data was derived from in person interviews with 53 individual participants representing 35 farm families, primarily in Western Michigan (45.7% of farm families) and Central Ohio (48.6% of farm families), with additional interviews from Kentucky (2.9%), and Georgia (2.9%) (Table 6.2). The focus of this analysis is on the farm and farm family, and characterization of the farm or family is based on the one or more persons interviewed in each farm family household.

Individual Region Respondents Farm Families N Percent N Percent Grand Rapids Metro 22 41.5% 16 45.7% Columbus Metro 28 52.9% 17 48.6% Louisville Metro 11.9% 12.9% Atlanta Metro 23.8% 12.9% Total 53 100.0% 35 100.0%

Table 6.2 Qualitative Analysis - Respondent Region of Origin

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Upon examination of the characteristics of the families interviewed, five distinct groups of farm families were identified (Table 6.3). This categorization was based on an

initial analysis that examined: the types of crops produced; marketing streams

(Commodity, AFAE, Mixed); family contributions to farm labor; hired labor; off-farm work; acres (owned and rented); individual family member roles on the farm and in the household; individual and household goals and values; succession plans; organizational membership; and plans for enterprise growth, stability or retraction.

The five groups can be characterized as: 1) “Commodity”, farm families selling into commodity markets (n =11); 2) “First-generation AFAEs” (n =8); 3) “Multi- generation AFAEs” (n=6); 4) “Mixed” farms selling both into commodity and AFAE markets (n=8); and 5) “Non-farming heirs” who are renting out their farmland but not actively farming the land themselves (n = 2). The identifying names “Commodity” and

“AFAE” do not refer to specific management practices (i.e. organic or sustainable) only to marketing strategy.9 A closer analysis of the family history of the AFAE farms

brought to light the existence of two distinct sets of AFAE farm families based on the

length of time their families had been farming, those who were First-generation and those

who were Multi-generation AFAEs. The First-generation AFAEs are distinguished by their beginning farmer status, while Multi-generation AFAEs reported at least two generations engaged in direct marketing activities. The Mixed type farmers sold through both commodity and direct marketing relationships.

9 To illustrate the definition of “Commodity” vs. “AFAE” in this study a grass based organic dairy farm that sold all of its milk into a bulk wholesale market (e.g. Organic Valley) would be considered a commodity farm. The key attribute differentiating commodity from AFAE is direct marketing. 293

First Multi- Non- Generation Generation farming Commodity AFAE AFAE Mixed Heir N 11 8 6 8 2 Grand Rapids Metro 54.5% 50.0% 33.3% 37.5% 50.0% Columbus Metro 45.5% 50.0% 66.7% 37.5% 50.0% Louisville Metro 0.0% 0.0% 0.0% 12.5% 0.0% Atlanta Metro 0.0% 0.0% 0.0% 12.5% 0.0%

Table 6.3 Qualitative Analysis – Respondent Type and Region of Origin

The geographic distribution of respondent types is outlined in Table 6.3. The

majority of Commodity farmers were based in the Grand Rapids Metro region (54.5%)

and 45.5% were located in the Columbus Metro Region. First-generation AFAEs were

equally split between Grand Rapids Metro Region (50.0%), and 50.0% were in the

Columbus Metro Region. Two thirds (66.7%) of Multi-generation AFAE farms were

located in the Columbus Metro Region and a third (33.3%) were located in the Grand

Rapids Metro area. Among the Mixed farms 37.5% were located in the Grand Rapids

Metro area, 37.5% were from the Columbus Metro Area, 12.5% were from Atlanta Metro and 12.5% were from the Louisville Metro area. Half of the non-farming heir farms were

from the Columbus Metro region (50.0%) and 50.0% were from the Grand Rapids Metro

region. The focus of the qualitative data analysis is focused exclusively on the first four

types.

Demographic Data of Respondents

The demographic comparisons between groups are reported in Table 6.4 and

Table 6.5. The primary difference between groups was the number of acres operated.

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Mixed farmers operated the largest farms with an average of 533.8 acres followed by

Commodity farms who operated an average of 478.9 acres. Multi-generation AFAE farms were slightly smaller, operating an average of 229.6 acres while First-generation

AFAEs operated the smallest sized farms with an average of 30.8 acres.

Commodity First-Gen AFAE Multi-Gen AFAE Mixed Mean Mean Mean Mean N 11 8 6 8 Age 51.0 39.7 63.3 46.6 Education 4.1 4.6 4.2 4.9 Acres Operated (mean) 478.9 30.8 229.6 533.8 Acres Operated (median) 400.0 15.5 40.0 550.0 Table 6.4 Farm Family Demographics

Table 6.5 presents the demographic statistics (age, education and farm acres) of

the individual respondents interviewed.

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Age Education Acres Commodity (mean) 51.0 4.1 478.9 RMMI13. 38 4 450 RWM88 70 5 300 RMM18 32 3 300 RMMI5 45 4 400 RMMI101 46 4 999 RMOH53 52 4 731 RMOH109 47 3 999 RWOH72 31 5 700 RMOH70 65 1 287 RMMI11 43 4 200 RMOH58 58 3 1400

First-Generation AFAE (mean) 39.7 4.6 30.8 RWMI12. 39 6 15 RWMI6 51 3 16 RWMI19 29 5 7 RMMI2 999 5 85 RWOH66. 999 4 999 RMOH44 58 6 999 RMOH67 61 6 94 RWOH76 68 5 406

Multi-Generation AFAE (mean) 63.3 4.2 229.6 RMOH74 49 5 999 RMOH107 48 4 15 RMOH106 82 3 15 RMOH60 64 5 628 RMMI15 78 3 40 RMOH89 59 5 450

Mixed (Mean) 46.6 4.9 533.8 RWKY104 49 5 999 RMMI1 45 4 400 RMOH69 62 5 770 RMOH71 37 6 265 RMMI17 50 5 500 RMGA102 33 6 999 RMMI20 56 4 600 RMMI105 41 4 999 Table 6.5 Individual Farm Family Demographics 296

Influence of Household Dynamics and Farm Structure on Enterprise Adaptation

The following section describes the influence household dynamics and farm

structure have on enterprise adaptation among First-generation AFAEs, Multi-generation

AFAEs, Commodity and Mixed type farmers. I compare and contrast similarities and differences across the groups. The final section examines the specific role succession

(the presence or absence of an heir) has on enterprise adaptation and persistence among case study respondents.

First-generation AFAE

Household Goals and Values

The farming household members of First-generation AFAEs were generally young beginning farmers who did not come from farm families and consciously chose to pursue farming. A dominant theme running throughout this group was a questioning of

American consumer values, and an embrace of environmentalism (concerns over peak oil and climate change), community, the health and ecological benefits of small scale locally based agriculture. As one respondent stated:

“Yeah. I think being environmentalists, I think that was one of the top five. One of the top five factors for going into farming was sort of looking at the really big picture - - I mean we had spiritual needs that were not being met in our culture and we had, you know, personal needs…And we want to create and build community. And it's very hard to do those things with any nine to five job. You have to do it outside of your job…We were like why don't we just give all that and just have what we care most about be the job. …And so, you know, we've always thought of this farm as like Little House on the Prairie.”

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A core theme throughout this group was an emphasis on spirituality (not

necessarily based in religion) often rooted in the environment as key reason for choosing

to farm. AFAEs stressed their love of farming with the need to maintain a standard of

living while ensuring health insurance, retirement benefits, and a college fund for their

children. Among some of these families at least one member of the household had a full time or seasonal off farm job in order to support the farm, as illustrated by a respondent:

“You have to have some other way to support it, sustain it [the farm], otherwise you couldn’t do it. We are at a point now where, yeah we could probably make a living off of it, but when you sit back and think about paying your own health insurance and trying to invest in a retirement, it’s not there.”

However, the quality of life these First-generation AFAEs tended to emphasize was not the conventional American focus on consumer goods and monetary values. Their focus on the non-monetary benefits of farming, including community development, health benefits and spiritual needs, allowed them to reconcile the low levels of income garnered from the farm. Some respondents were actively creating alternative forms of sustainable community through their farm. For example, one farmer revealed

“We have chiropractors that are members of the farm that we barter with. …And those people are flat out like if something happens to you, I will take care of you. So I know that I can lean on those people…-- we don't have healthcare for the kids. My pediatrician goes to our church and is a member of the farm…. I call her on the phone all the time. I show her my kids at church. I don't go in for visits, you know. If you're not going to have healthcare, you really have to rely on your community in a totally different way. The only reason healthcare is important now is because it can bankrupt you.”

First generation AFAE household members interviewed tended to be younger than household members of other types of farms, and many families were in the initial development phase. The children of First-generation AFAEs were generally too young to

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influence succession planning and enterprise redevelopment. Many expressed hope that

they would be creating a place their children might want to come back to, but emphasized that their children would not be forced to feel obligated to or burdened by the farm, they would be encouraged to go to college and explore the world before deciding to come back to the farm.

Many members of the First-generation AFAE households emphasized substantive values as guiding principles for their land use decision making, however they simultaneously recognized the need to ensure a livelihood that would support their household and pay for the farm. They often exhibited values contrary to mainstream

American culture but were obviously still embedded in that culture as they discussed the importance of college education and their a for their children to experience the world and choose a path “that made them happy”, putting no pressure on them to return to the farm.

In fact, few parents with older children seemed to actively create opportunities for their children on the farm, and instead emphasized off-farm sports and cultural activities.

Farm Structure and Business Development

Although many of First-generation AFAE farmers were farming primarily due to substantive motivations, they demonstrated an inordinate amount of market savvy. Some

First-generation AFAEs briefly entered commodity production only to exit very quickly, recognizing the majority of money was in niche marketing or agritainment rather then production agriculture. As one respondent described:

“We ran the orchard for five years. Like I say when we got into it was at a time when China was taking over our market, flooding our market with the apple juice concentrate. So that’s when there was really not much money to be made being an apple farmer and my wife and I kind of got into horses… Finally one day I 299

looked at it and said hey I think I’m missing the boat here... Money, I think that the money is not as—Being a farmer, the money is in being the direct payments portion of it. So one of our neighbors whose a large orchard farmer, we talked with him and he said he’d take the orchard over and continue to run the orchard. And then we would focus our efforts towards the entertainment portion of farming. So really our goal is now is to be an educational entertainment farm.”

Recognizing that more money can be made in agri-tainment then in production

agriculture is one strategy for fulfilling the desire to farm while simultaneously keeping

land out of development. However, the shifting focus from food production to entertainment raises larger questions about local food system development goals and the ability to ensure a new generation of farmers who will produce edible crops for local and regional markets.

Many First-generation AFAEs household members recognized the advantage of their

RUI location and saw their proximity to large populations as a strategic advantage to their operations. Many also recognized the influence zoning and food safety regulations had on their ability to run a profitable farm enterprise and ensure their lifestyle goals could be met. One respondent described:

“Well, that's one of the plusses is that this area -- we're seven miles from Grand Rapids…a triangle of 1.2 million people…Our land stayed zoned agriculture.. That's something that you're just not going to necessarily have…we're in the middle of the suburbs, zoning is so important, you know, as to what you can and can't do. So that was like this big plus for us.”

The difficulty of balancing the desire to meet both substantive lifestyle goals and

instrumental needs to pay taxes and ensure livelihood strategies intersects with the point

in the lifecycle the family is at. One farm couple who sold raw goat milk felt very

restricted in their ability to develop their enterprise because of the regulations limiting the

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sale of raw milk. As an alternative they contemplated producing cheese or starting a bed and breakfast, however, their off-farm work and family commitments made it a struggle to commit to the time required for these options demonstrating the influence lifecycle effects can have on enterprise development.

“I keep bugging him about a bed and breakfast… agritourism. I mean, we haven’t even looked at that yet, but it is really an option. People are kind of fascinated. They come here and they can’t believe it, how something like this existed. And some people want to have a little bit of the farm experience and so forth, but with our schedules now I don’t think we can handle that. You know, if the state would just come in and say, Okay you can’t do goat no more. Well we have all these acres and all of these goats, what do you do with it? You got to pay the taxes on it.”

Although many of the First-generation AFAEs are dedicated to sustainable agriculture rooted in ecology they had to negotiate the reality of accumulating the capital necessary to implement those tools as they balance household needs. One First-generation vegetable grower stated:

“I mean I read about farms where they're like they're using bio fields and all that kind of stuff. We couldn't afford to do that because it's like we had to build up a business immediately. And when you try to build up a farm that can maintain your family…”

Structure of Agriculture

First-generation AFAE household members tended to be highly critical of large scale industrial agriculture. The majority could quote authors promoting small scale sustainable agriculture such as Wendell Berry and Gene Logsdon. Their critique of large scale industrial agriculture centered on the negative environmental externalities associated with this type of farming. There was little to no awareness of factors affecting

the larger structure of agriculture including the farm crisis or the treadmill of

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technology many large scale farmers find themselves on. Many First-generation AFAEs

were critical of national farm policy, particularly in its disregard for small farms.

Capitalizing on the entrepreneurial spirit and interests in community development

a number of the First-generation AFAE farmers were actively working with local

organizations to promote local foods and local agriculture. Many were able to draw on

experiences and skills they had developed in other professions before they began

farming. While many were dedicated to efforts such as “developing the [fruit] ridge as a

tourist destination” they were simultaneously frustrated by the lack of participation by other farmers, especially those that had been in the area for multiple generations. Their frustration extended into the unfamiliarity many farmers had with advertising and marketing and their unwillingness to pay for the types of promotional campaigns they believed would make a critical difference to the area.

The First-generation AFAEs relied on multiple labor sources ranging from the volunteer intern model to paid wage labor. Since so many farms had very little acreage, migrant labor was not employed, and since so many of these operations were customer oriented there was a focus on hiring individuals skilled in working with the public. Some

First-generation AFAEs re-emphasized labor along gender lines, seeing females as better suited to marketing and males for manual labor, however, other First-generation AFAEs displayed none of these biases. One U-pick respondent stated:

“I am hiring those who help me during the summer to be customer oriented. Guide them to the rows that are ready to be picked, help them with the children which they love to do. I have hired women because I believe women will be much more receptive and patient in handling customers.”

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These First-generation AFAEs are highly motivated by substantive values but are still embedded in larger American cultural values (e.g. the desire to send children to college) that highlight the instrumental values guiding their enterprise development plans and influence land use decision making.

Multi-generation AFAEs

Household Goals and Values

The Multi-generation AFAE enterprises have been direct marketing for at least two generations and exhibit a strong business orientation that influences their land use decision making. Unlike the other groups there was little mention of stewardship or environmental concerns as factors in land use decision making.

This group of farmers valued farming as a lifestyle and livelihood, and were highly influenced by the need to achieve instrumental goals in order to meet substantive values. The Multi-generation household members interviewed were a somewhat older group of farmers, many had children who were old enough to return to the farm.

Engaged in the active process of succession, the interests of children returning to the farm had clear effects on enterprise redevelopment. Parents were visibly attempting to make opportunities for the next generation either by expanding the business, most often vertically (via intensification or adding on complimentary businesses for additional family members) and in some cases horizontally (through land expansion). The strategy of expanding both vertically and horizontally enabled the Multi-generation AFAEs to

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create farm enterprises that extended beyond production based agriculture into a wider conceptualization of farming. Their increased market orientation led many to actively adopt and embrace the term agribusiness. For example, when asked if they consider themselves farmers one respondent answered:

“I don’t, but I mean I think it's -- I think if you ask every person around the table, you'd get a different answer…Yeah, I mean she’s in retail garden centers. Eric’s in landscaping. And we're in production. So yeah. I mean, half and half probably….No, I don’t consider myself a farmer…I think agri-business retail and agri-business would be a fair assumption… I’d say we had a good blend of everything between commercial and agriculture.”

Structure of Agriculture

As a whole this group was not overly vocal about the direct effects the structure of agriculture or the farm crisis had on their farming operations, nor did they emphasize spirituality or religion as a primary force motivating their land use and business decisions. However, the longevity of their enterprises through the generations has sensitized them to fluctuations in the market and the difficulty of competing with national and global markets and the impacts of consolidation in the fresh fruit and vegetable distribution and marketing networks. One respondent explained:

“We’ve changed our focus to be more on the nursery as of late than we have the fruit and vegetable because it’s a lot of California competition and a lot more local competition in the fruit and vegetable world.”

Farm Structure and Business Development

Many of these Multi-generation AFAEs considered themselves to be in agribusiness at a very local level, conscious of local, national and global competition. Their strong business orientation reinforced their self-image as savvy producers, marketers,

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distributors and managers whose skills set reflected a wider professional status than the simple identifier of ‘farmer.’

This strong business orientation enabled many of the Multi-generation AFAEs to ensure their enterprises stayed highly flexible and adaptable in order to take advantage of

new niches in the market. One respondent explained their willingness to remain open and

flexible to new farm enterprises:

“Our future is to do this as long as we can, and planning different and innovative ways. You know I’ve seen stuff about growing lavender…and…bread…just doing different stuff with whatever crop you have, the peaches,…or we can branch out into alternative crops such as basil or herbs.”

This group was more likely to operate larger acreages then the First-generation

AFAEs and therefore tended to employ both migrant and local labor. Migrant labor was

employed to pick field crops while a local English speaking labor force was employed in

marketing and sales. Some respondents reported employing 50 to 75 workers during the

summer including field and market labor.

The Multi-generation AFAEs place a great deal of emphasis on the need to be an

effective manager, especially when supervising labor. To ensure they utilized their labor

force to their maximum potential there was a strategic effort made to stagger crops to

increase and extend inventory supplies but also to maximize available labor resources.

Their strong business orientation also made some Multi-generation AFAEs critical and

doubtful of their own children’s ability to carry on the farm. Some initiated alternative

and creative strategies for recruiting and incorporating valued workers into their

ownership structure (through stocks and taxes) if they lacked confidence in the next

generation.

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“Well, these two guys were employees; and I knew there were going to be years when things went bad…If they are owners then they will understand exactly why there are times when you really have to tighten your belt big time. You just take the big pay cuts. You personally cut back and sacrifice at home and maybe take a job off the farm for a year, but to keep them interested they have to see it and feel it and take it just exactly like we do. And I wanted to keep them here. I did not want—because I knew some day I was going to get old…You need somebody that is really competent to do all of that. We’d be smart to just make them owners and give them the opportunity.”

The above quote illustrates that although Multi-generation AFAEs are highly motivated by instrumental goals, they will not automatically sell the land for development in order to attain their lifestyle and economic goals. Rather the above case illustrates the lengths some farmers will go to ensure their farm stays a farm especially when they are unsure if the next generation will be able to take it over the farm.

Multi-generation AFAEs carry their strong business orientation into the way they manage their household needs. To buffer household needs from fluctuations in the growing season these families actively invest in stocks and bonds and diversified their assets in order to ensure a standard of living equal to much of mainstream America.

Many discussed using their investments as a way to pay for family vacations, home remodeling projects, in addition to health insurance and their children’s college education. They were also resourceful in utilizing family labor to increase their savings, as one respondent explained:

“We were able to pay our own children without having to pay taxes and so we would pay a nice salary and put it away for their college fund. And therefore they were able to pay for their entire college by the money that we were able to put away….And the stocks at the time did very well.”

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Additionally, this group was not as wary of debt and saw borrowing as an important tool useful for expanding their enterprise.

“I told my dad, I said, I can’t do this unless you’re willing to make additional changes. They had that farm market... I said controlled atmospheres and storages to store apples… and they needed to upgrade the packing line…but my dad was afraid of borrowing money… I said, don’t be afraid to borrow money. The ability to borrow is just another asset that you have and you’re not using it.”

Nor was the group wary of investing in technology and tools to increase their business.

One respondent explained, “We then had the first controlled atmosphere apple storage in the State of Ohio, which enables us to get a lot of extra money for late season apples.

And we are to sell apples an extra four months of the year.”

The Multi-generation had a mixed attitude toward growth and development pressure they faced at the RUI. The advantage of direct marketing was evident, especially the “very high margin you-pick business. Because you capture the retail dollar but none of the cost of retailing are involved. No packaging, no transportation, no $25,000 fork lifts, no $5,000 a month electric bill, no big packing line, no truck drivers, no pallets.”

However, one’s exact location at the RUI also influences enterprise development strategies. Some farmers at the RUI but still somewhat distant from the growth center appreciate the lack of neighbor complaints from the relatively low growth pressure immediately proximate to their farms but must travel to reach their core customer base.

As one respondent explained:

“Agriculturally, yeah, agriculture and production is helped because of less competition with, you know, homes coming in or factories and stuff. Now at the retail end, which we do both, production and retail, that’s where the trouble starts. For production it’s probably only helped when we don’t have to compete with as much traffic on the road or concerns. For customers we have to go out further. Go where the growth is… We go to farmer’s markets and, you know, where there’s I guess a higher income, greater demographic, easier to sell our

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product for I guess a little better market.”

Multi-generation AFAE’s designed creative solutions to extend their business while also

keeping their option to sell land open. For example, rather then invest in a permanent

roadside stand one family sets up a ‘tent stand’ each season which not only avoids the

hassle of building permits and regulations but also avoids investing in permanent

structures. The family kept away from making large capital investments in buildings and

infrastructure given the uncertainty of land prices and the uncertainty that their children

would take over the farm. One family member explained:

“But we quit investing in anything permanent. No more new anything. We will drop back here and watch what happens. What we’re seeing is that we believe that a small part of the farm will be permanently sustainable. Permanent is a long time. We may be able to compete with those shopping centers, but that’s pretty doubtful, isn’t it?”

Even among the most successful enterprises contingency plans were made to protect against future uncertainties, in many cases the larger risk was the next generation’s interest in farming rather than land prices.

Commodity Farmers

Household Values and Goals

Commodity farmers exhibited similar values and goals as the Multi-generation

AFAEs did. Motivation for land use decisions were often oriented towards instrumental objectives in order to achieve substantive lifestyle goals. This group tended to see farming as both a livelihood and a lifestyle, and with the exception of one respondent, did

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not refer to any spiritual or community development stimuli influencing their decision making. When asked why they farm, a typical respondent would state: “I still see it as a livelihood. And it’s a way of life too, don’t get me wrong. But it’s what we plan on doing for a living.”

By and large this group saw themselves as stewards of the land, implementing no- till and stream conservation, integrated pest management, and fertility management programs in their operations. However, stewardship was distinguished from environmentalism, which many construed as being at odds with their definition of good

farming practices.

None of the commodity farmers were new entrants to agriculture and were thus

able to discuss how their own succession and their children’s plans to come back to the

farm influenced enterprise development. Opportunities for the next generation were

created largely through horizontal growth strategies, though some vertical ones were also

implemented.10 The majority of Commodity farmers planned to fit new family members

into the existing enterprise structure rather than by starting new complementary

businesses. Respondents commented on two general growth strategies: 1) increasing their

acreage through rental or purchase (which was increasingly difficult at the RUI); and 2)

shifting into higher value commodity crops or adding on season extension structures. One

family described how they had expanded their orchard: “Outside of the farm, we have

packing facilities. We have our own storage facilities as far as controlled atmosphere

10 The study period overlapped with a time in which a great deal of discussion was being generated over several proposed ethanol plants in Central Ohio. The bulk of the commodity producers operated grain farms located in Central Ohio and many openly discussed the potential benefits a nearby ethanol plant would have for their farm.

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goes. Cold storage. Diversified. We're in about three different counties that we grow food in.”

Structure of Agriculture

The Commodity farmers were cognizant of how the larger structure of agriculture, particularly international trade policies, global competition, and changes in technology create the need to get big or get out while simultaneously shrinking profit margins.

Commodity farmers tended to have a global perspective when it came to their operation,

one respondent stated: “It’s a world market. It is. It is not local anymore. What happens

in Washington State affects us a lot. What happens in Europe affects us. We ship apples

to Central America, Europe.”

However, in order to ensure their survival and remain competitive this group of farmers willingly participated in the dominant agricultural system, investing heavily in

technology and playing commodity markets. When asked how their operation had

changed one respondent explained: “Efficiencies of orchards is probably the biggest change in our deal. Machinery has changed quite a bit. We started with 60 acres [20 years ago] and now we have 340. Machinery has done that plus opportunities and the need to be large enough to spread cost.”

Additionally, grain Commodity farmers discussed how advances in technology enabled them to have an off-farm job while pushing their operations to grow in acreage.

However, these advances were not necessarily discussed favorably, many respondents lamented the inevitability of needing to get bigger, as one respondent stated:

“And now with the advent of the larger machinery and no till and ground up beans and corn, you know, [even at] 1,200 acres a guy can farm part-time. He

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can have a full-time job and farm 1,200 acres on the side. So I mean, you know, what’s the limit as to how big farms will get, I don’t know.”

An example of Commodity farmers actively investing in new technology could be found among apple growers in Kent County. Commodity farmers along the fruit ridge compete with Washington State (in addition to overseas competitor) in the fresh apple market. While Washington apples tend to be displayed on open trays in the grocery store,

Michigan apple have tried to maintain a competitive edge in the bagged apple market segment. To ensure their survival growers must enter the bagged market segment and invest in capital intensive technology, as one fruit ridge respondent explained: “It is a very expensive machine to put apples in the mesh bag with the closures and stuff. It is a huge investment.” However, many feel forced to make the transition.

The interviews revealed many Commodity farmers were fairly pessimistic about the ultimate fate of their farm compared to the other groups. When asked if their son would inherit their farm, one respondent replied:

“Or at least what’s left of it if we don’t sell soon. Well, we’re seriously considering selling. Why would you continue to scrape along when you’ve got a net worth of several million dollars and don’t have enough money to buy groceries without borrowing money.”

Commodity farmers were also the most likely to comment on the nuisance complaints non-farming neighbors raised about their operations. While many understand and shared the sentiment of unpleasant odors and noise, this was understood as a cost of doing business for them and ensuring their livelihood. Many voiced frustration over the lack of understanding their neighbors (and general public) had for farming and their ignorance of the system that allows them to have such a low cost food supply. One respondent

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articulated:

“I want to make $40,000 a year, I’ve got to feed 4,000 head of hogs. If I’ve got a couple brothers and we want to make that kind of money, you know, we’re looking at 12,000 head of hogs a year…Where are we going to have these operations? If people want to keep milk down around $2 a gallon or cheap as it is, you know, you're going to have to have -- somebody's going to have to raise it somewhere. It’s kind of like land fills, you know, you got to have it but they don’t want it next door.”

Farm Structure and Business Development

When asked if they had ever considered adapting AFAE strategies, Commodity

farmers frequently replied they had thought about the possibility but quickly dismissed

the idea. Many had no desire to interact with the public or spend the extra time it takes to

develop a market. Respondents frequently commented they would rather sell their

product though a broker, others preferred and recognized the competitive advantage they

could maintain through specialization rather then being a production generalist. One such

respondent explained:

“I’ve direct marketed before. Pick-your-own strawberries, of course, I hated strawberries. Pick-your-own cherries, stuff like that. Not a market as a market is. I take stuff to markets; that’s another job. I’ve already got a job. My intention was to farm not sell. I don’t sell my fruit anymore. But we’re being specialized. We aren’t growing peppers. We aren’t growing sweet corn. We are going to grow apples. We are going to do everything right that we possibly can do with them because that is the only way we can survive. You can’t be half- assed at 100 things, you’ve got to be good at one thing.”

The reliance on such large acreage led many Commodity farmers to recognize how vulnerable their operations were to fissure within the family. Some were actively planning and structuring their operation to ensure should there be divides in the family

the enterprise would still be protected against land fragmentation. One respondent

elaborated:

“And then my grandparents when they got it, they had to buy out the other six

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kids. So they only really owned one seventh...This farm was set up and so forth that we split off each section of ground so each person has their own section….if we ever got mad at one another and wanted to tell them to go take a flying leap, why there wasn’t going to be any problem with who owns what where.”

Commodity farmers were the most likely to discuss selling land for development compared to the other groups. One respondent commented, “I mean we’ve got probably three miles of road front property, so you know it’s awfully hard to turn that development price down. We don’t want to. We don’t plan to, but that is our savings account.” The need for larger swatches of acreage increases the vulnerability of Commodity farmers at the RUI as they balance household needs with the uncertain returns from farming. Some farmers entertained selling some land for development in order to alleviate economic concerns and be free to enjoy the farming lifestyle. One family explained:

“It might be such a thing that ground gets valuable enough that you can sell a little of it for enough money that you have no debt load and so on and all of a sudden it would be a lot easier that things would actually pick up and thrive more, and you might look differently about the whole thing…Then you could do it as a lifestyle instead of having to rely on it for your total income.”

Commodity farms were also the most likely to rely on migrant labor (particularly the orchardists) and were therefore more sensitive and aware of the immigration debates and also more critical of national labor and farm policies compared to the First- generation and Multi-generation AFAEs. The interest in labor and immigration issues tended to be commodity specific. Labor intensive commodity crops (fruit, vegetable, nursery and greenhouse) producers were most aware of the issues; their labor needs would fluctuate from 80 workers during the height of the season and go down to eight in the winter. Dairy and livestock farmers were able to find more local sources of labor, and grain farmers had minimal need for farm labor.

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Commodity farmers were also open to the influence lifecycle effects have on the farm enterprise and their larger community engagement. Older family members shifted into upkeep and small management roles that required extra time and detail to attention the primary operator was unable to give. Children also influence the degree to which family members are involved with on and off-farm activities, as one respondent noted:

“Once you get to 35 plus you do get a little bit more involved. Your kids aren’t as needy.”

Overall the Commodity farmers interviewed for this research were oriented towards instrumental values as a means to obtain substantive lifestyle goals. This group of farmers placed the most emphasis on growth and expansion. While many Commodity farmers enjoyed the lifestyle benefits of farming they were business oriented viewing farming primarily as a livelihood, making them most similar to the Multi-generation farm respondents. Commodity farmers were also cognizant of the effects of farm policy and global markets had on their operations but choose to structure their operations to compete in the world market rather then implement direct marketing strategies. The Commodity farmers interviewed in this study also operated some of the largest acreage (Table 6.4), at a certain point their scale and past capital investments in buildings and equipment favor commodity production over direct marketing.

Mixed Farms

Household Goals and Values

The Mixed type enterprises displayed the greatest interplay between instrumental

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and substantive rationalities. Engaged in both commodity and AFAE markets this group

of farmers was extremely active in finding new alternatives and engaging in multiple

strategies (both horizontal and vertical) to keep their families on the farm and increase the

number of people the farm can support. Mixed type household members took advantage of life cycle differences within the family to fill production, marketing and childcare/household needs. The motivations for searching out new production and marketing systems were highly heterogeneous, some respondents were similar to the

First-generation AFAEs and tended to de-emphasize American consumer culture, choosing to fill their lives with spirituality and religion. However, very few cited stewardship or the environment as primary influences on land use decision making. Many of the Mixed type were highly influenced by the 1980s farm crisis and the losing game commodity markets represented for their families.

When asked why their family got out of tobacco farming, a long established cash crop in Kentucky, one family member explained:

“Well we didn’t have time to do it, and… we all wanted to do something that was good for people… tobacco has been really good to this state because it has enabled all of these people to stay on the farm…You do have to hire a lot of seasonal help with tobacco… we were hiring Mexicans to do that. And with the chickens we can make more money and the kids can do it. They can do it all. They don’t always want to do it all, but they have the ability to do it all plus it is something that is our product, you know, it’s kind of fun to have your own product. We go down to Whole Foods and do those little demos, and it is fun for the kids, especially on the eggs, because Rebecca those people come by and they say ‘oh this is Rebecca. I buy your eggs.’ And it’s really a lot of fun. With the tobacco you would never have that kind of communication and connection with people.”

Similarly, a dairy farm family member explained their transition into direct marketing,

“We had gotten up to 190 cows at one time, and we decided that we weren’t going to be

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able to handle getting enough cows for three or four families and go down that road.”

These substantive values are highly important to the quality of life the farming

operation affords their family. This group tended to be interested in more then just the

economic benefits of the farm for a livelihood but more importantly, what kind of

lifestyle it could provide. However, even with the transition into higher profit direct

marketing venues, commodity markets were still an important outlet for these enterprises.

Among the majority of farm families interviewed (regardless of production type), only 5-

20% of their inventory was going through AFAE direct marketing streams; the remainder

was sold into bulk commodity markets. However many realized that selling exclusively

into commodity markets was a “losing game,” especially for medium and small farms.

One respondent stated: “We realized the wholesale market is just a losing game. Prices

are just ridiculously low. Relying on selling your fruit to someone else and they

determine what they are going to pay you, and unless you are a real big grower it is very

hard to keep going at it.”

Farm Structure and Business Development

The Mixed enterprises also were quick to take advantage of skills children and

spouses had developed off the farm. One respondent described the contributions his wife

was making to their family operation, “She's a graphic designer so she can take my mom

and dad's ideas and put them into a design…if you think about it, our generation, we're

able to utilize the internet. We're able to utilize different resources then my mom and

dad's generation.”

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Similar to the First-generation AFAEs many Mixed type enterprises were designing the enterprises to include an educational component. Respondents enjoyed reporting the satisfaction they received from educating their customer base about agriculture. The outreach work done by some farmers was described by one respondent:

“They [our customers] really don’t understand much of it [farming] and it is just fascinating for them. It is something we enjoy because we like to teach….Well they get so excited. You know, they don’t know what a farm is….and because most of the people now come from the development when they see land like this is just so unusual for them and they just love it…they also say please, just stay in business.”

The Mixed type household members interviewed in this study continuously emphasized the importance of wanting their operation to be “a real family farm” and there desire to ensure opportunities for all family members. A minority of Mixed farmers emphasized spirituality and religion as a strong motivation guiding their farm enterprise development plans. Similar too many of the First-generation AFAE this group of Mixed farmers were concerned about larger community development and social justice issues.

One family described the underlying structure of their farm businesses as:

“It’s not our land, its God’s land, and what we are doing is for his glory and to point people towards him. We’re not trying to just make a bunch of money, we’re trying to support our families in the context of pointing people towards the Lord…In spite of what you might see in the price of our cheese, we are trying to keep it down so that more of the ordinary people can buy it.”

However, at the same time these substantive values are emphasized, American cultural values come into play such as the pressure to provide health insurance and ensure the opportunity for children to attend college. The difficulty of guaranteeing a livelihood from agriculture that could ensure these instrumental goals was often a point of

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frustration. A Mixed type respondent stated:

“My experience in agriculture is that I could not honestly say to my kids come get in this game. They are going to have to want to get in it themselves. But it is in my opinion it is a nice way to make a living if you can make a living, but I’m not sure I can honestly tell them that I want them in the game because it’s a tough game. I think a person needs to look at his family and say, I’ve got two kids or I’ve got kids and I want to send them to Ohio State or I want to send them to college. Or I want to do some things for them. I think the question in that aspect of it and provide for them and provide a good living I don’t think the opportunity has not been there monetarily. I’m speaking in terms of monetary.”

The desire to live the American dream extends beyond aspirations for college

education and extends into the desire to reap American baby boomer retirement

expectations. A number of the Mixed respondents expressed skepticism that their

children would work as hard as they had, or that they could afford to buy the farm from

their parents and thereby ensure the older generations’ retirement. One respondent

described their frustration:

“I’m disappointed in the amount of work that we put into the operation and things like that that we are probably not going to see a return out of. I’m disappointed in the sense that my retirement and my wife’s well being is tied up in a lot of machinery that we thought possibly we would be able to sell to my son who wanted to continue to farm and he would buy that out and provide our retirement but that’s not going to happen. Because I don’t think he is going to witness a great enough income to buy that machinery. Then this generation is either hunting for ways to give it to them, but is to say machinery wise I used to consider we’d have $300,000 or $250,000 worth of machinery asset here that the next generation might possibly buy. Well quite frankly I don’t see how in the world they are ever going to come up with enough cash to buy it. Now they can buy parts of it and things like that, but the parents tend to help the next generation. Well they are helping the next generation at the expense of themselves. I’m not selfish. I mean, I’m not saying that. They are cutting, you know, it’s your retirement and it’s what you worked for and there in again your 401K kind of went out the window.”

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The Structure of Agriculture

Among the Mixed type respondents, the decision to adopt direct marketing

strategies was embedded in their awareness of the difficulties associated with access to

land and capital and the need to restructure the farm away from pure commodity

production in order to ensure its survival. One respondent stated:

“The person that is going to get into farming today, if they can’t find a nitch or a way in, it’s very difficult. If they don’t have land, if they don’t have an uncle or a family relationship of some kind to get them in the door, they are not going to be able to get in that door very easy.”

Another respondent noted:

“This farm deal, it’s nothing like it used to be. Either you are real big or you are real small, and used to be we had all medium-sized guys but not very many big guys and not very many small guys. What I see is we got the real big operators and we got the real small operators. And medium-size operators are kind of going by the wayside.”

The Mixed type respondents were extremely critical of the structure of

agriculture, farm policy, international trade agreements and the Extension and Land

Grant system for creating a low margin system and larger crisis in agriculture. One

respondent commented, “The colleges are just to blame as anybody. They just keep

promoting the death out of that high production, lots of cows. They are just sending these

farms one after another to their death.” Another respondent also noted:

“Now I can remember when I was over at Ohio State going to college and started over there, and that’s all I heard in the late ‘60s; the first part, Gear up, we’re going to feed the world. Well I want to tell you, I’m sorry, the income isn’t there and it never has been on a year-in-year-out basis. Yes, I’m disappointed in that.”

The influence of the farm crisis and risks associated with overextension in the

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1980s were still fresh in many Mixed type respondents’ minds. One respondent described their experience:

“It was 1981, and land values were fairly high. That is right before we had that big crash. And we all borrowed from FHA to buy the farm and I think our interest rate was like 17% or something. It was crazy. We financed it 100% because we were right out of college and didn’t have any money. From there we just hung on, because I think land values after that dipped like 40% around here. So it was kind of tough and we were just trying to start out and we didn’t know what we were doing. And that’s probably one of the worst things about going to a university; they don’t really teach you how to make money. There is no emphasis on that at all. So we figured out that we couldn’t make money doing it the way that a university would teach you how to do stuff. You spend too much.”

Similar to Commodity enterprises, Mixed type enterprises though wary of taking

on too much debt, were not afraid to embrace technologies they believed would

contribute and enhance the viability of their businesses whether it be genetically modified

corn or controlled atmosphere storage to assist with season extension. Furthermore,

Mixed type operations were similar to Commodity farms in their sensitivity to

fluctuations in the farm labor market. Many recognized that a large part of the labor issues they faced were tied to cheap food policies. Some suggested alternative policies:

“I mean if they shut these imports from coming into this country that means we will get more money for our product which means we can pay more to train people or just pay more period to hire local help. But when I bring this up, I get shot right down. The local people won’t work. Well, maybe they won’t and maybe they will if you pay them enough. Maybe they will work.”

While the majority of Mixed type farms rely on migrant labor for field work and local labor for marketing, some respondents have strategically chosen to by-pass the migrant labor issue by relying more on family labor and local seasonal labor. One respondent described their reliance on family, migrant and local labor:

“My dad hires two full-time men and then we have some migrants that come in

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and pick the crops. In the market my mom has a full-time lady that helps. And we hire a lot of high school kids. And people that are retired will come and help my mom during seasonal time like June, July, sometime in October … a lot of part- time, seasonal, retired ladies.”

As noted in the literature review, the resilience of the family farm has been linked to the willingness of the family to engage in self exploiting behaviors to ensure the persistence of the enterprise. Expanding out from family based production systems into new marketing strategies that require hired labor poses unique challenges for farm families as they search for employees who will dedicate themselves to the farm as a family member would. One respondent explained:

“One of our biggest things that we really need is somebody that has a pleasant voice on the phone [because] that's our opportunity to sell the farm. That is our biggest challenge. Getting somebody to take ownership. That's the biggest thing. The family can sell it. I mean when one of us get on the phone, I mean we can roll. But it's getting a non-family person to sell the farm like we do. And that's difficult because they're not part of the business. They take a paycheck. We need them to take ownership as well.”

The critique of farm policy extends into the global and international competition and free market policies that have influenced their own farm profitability and contributed to the erosion of their farming community. The Mixed type farmers were most astute at tracing the impact of specific policies on their farm operations compared to the other groups. One respondent explained:

“Actually it all started with Ronald Reagan…I mean he opened up the borders. We felt it right away with the Chinese apple juice coming in. He was the free market guy…He filled the economy at the expense of certain groups of people. The farmers are that one group of people and we keep losing our voice because there is less and less of us all the time. There are less and less farmers every year, so down in Washington our political voice—we still have a voice down there, but it is less and less all the time because our numbers are down. It is easier to let the farmers complain than the general public because we are only about 1 or 2% of the population.”

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As the above quotes illustrate many of the Mixed type respondents felt politically disempowered and felt they needed to take control of their farms and marketing outlets in order to ensure their survival. This group of farmers embraced an entrepreneurial vision of agriculture where their farms could capture a greater share of the consumer dollar and be less reliant on government subsidies and sheltered from fluctuations in the global market. At their core AFAE strategies are essentially neoliberal economic prescriptions.

However, despite the critiques charged against entrepreneurship rooted in neoliberal

ideology (Guthman 2008) these farmers were exercising agency and embracing

entrepreneurship as a form of protest against the dominant commodity system.

Many Mixed respondents, in the search of alternatives and intent on carving a

new path for their farm, rejected government subsidies and farm programs seeing them as

distortions that negatively effected the viability of farms as they look to new

entrepreneurial market solutions to save their own enterprises. One family described their

philosophy as:

“Price for the product. But that should be a genuine price based on demand and not government subsidies and such…and subsidies actually hurt a lot of farms. They are mostly available to large farms. Two, they change it so that it is not a real price anymore and they really screwed up the market.”

Orienting their operations towards more local markets, these farmers have also

found these supposedly safe havens can be just as uneven as global markets. One orchard

producing award winning cider, expanded sales from their road side stand out through

local retailers, however, they found competition among local retailers had intensified

(especially with the influence of Wal-Mart) depressing prices for their product, and so

they retracted away from the local market. As they explained:

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“We’ve kind of switched gears on our product. We used to be exclusive with stores, and what happened is there is another mill in the area that undercuts us quite a bit on price…our quality we felt was quite a bit better, but they would always like the other mill’s price…and what happened when Wal-Mart came onto the scene. Ten to fifteen years ago, it really turned. Then it really got really cost conscious. So we just got out of the store deal pretty much. We shifted mainly to farm markets. They want a higher quality product, and they will pay extra money to get it. Where stores they just want a high quality product at a very cheap price, so we just stay away from the stores now.”

As the above experience demonstrates even as farmers attempt to pull themselves out of the traps commodity agriculture represents, capital adapts and finds new ways to penetrate into the family farm. While entrepreneurship should allow for the disposal of wealth that satisfies consumption needs while re-investing in local people and institutions, larger free-market policies influence local retailers and local markets ultimately affecting the structure and availability of local markets accessible to local farmers.

These Mixed type respondents demonstrated the greatest interplay between substantive and instrumental rationalities and were most cognizant of the effects global markets and farm policy have on the fate of their operations and way of life. The Mixed type respondents help illuminate how family farms continue to evolve in a capitalist economic system as they seek new strategies (rooted in free-market principles) to ensure their viability.

Commonalities Across the Groups

At the RUI all members of these various farm types were sensitive to issues of land access and land price, however, beyond these initial challenges respondents across all four groups raised common concerns regarding the difficulty of providing health

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insurance, regulations (e.g. food safety and building codes), and the availability of labor

as issues selecting against their long term ability to grow and persist.

The order in which respondents identified problems varied based on commodity

type and farm business structure. For example, a medium scale Mixed respondent identified migrant farm labor issues as a primary concern, they stated: “Number one would be the land issue. We can’t get it, it’s too expensive. The second one would be the legal [migrant labor] issue.” While a Multi-generation AFAE identified health insurance as a critical issue for their operation:

“Two things are working as hard against the configuration of the business. One is rising land value and the other is rising healthcare costs that have completely gone out of control. Healthcare costs, the only way we can have a managed healthcare cost is to fire everybody that is old and hire only young people.”

Some of the obstacles and issues identified by respondents were not necessarily

related to growth and development pressure per se but were related to larger regulatory

issues including the National Animal Identification System (NAIS) and regulations

banning the sale of raw milk. Regarding the movement to mandate NAIS, one dairy

farmer stated: “The National Animal Identification System is going to drive many small

farmers out… The issue is a lot easier for large farms to comply with than smaller farms

but that doesn’t make them any safer. The smaller farms tend to get less help per

animal.” Other respondents complained about zoning and code regulations limiting their abilities to build additional structures on the farm that could generate additional income streams from the farm.

Respondents clearly articulated that issues related to labor, health insurance and

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the regulatory environment they were forced to operate in were selecting against small and medium farms. Though not always easy, these farmers had been able to access land in order to ensure their farms remained viable respondents identified the need for policy

tools that would enable the continued growth of their enterprises. Some policy tools such

as health insurance coverage and labor legislation were focused at the national level, others such as raw milk were directed towards the state level, while issues such as building and zoning codes could be addressed at the local level. The degree to which these issues and concerns are in fact addressed and dealt with will affect the long term persistence of all types of agriculture at the RUI.

Summary Comparison

A summary comparison of the five different farm types identified: First-generation

AFAEs, Multi-generation AFAEs, Commodity, Mixed farms and Non-farming heirs is presented in Table 6.6.

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Commodity First-generation AFAE Multi-generation AFAE Mixed Values Oriented to Strong substantive values, Oriented to instrumental values Greatest Interplay between instrumental values in are motivated by spiritual in order to obtain substantive Instrumental and Substantive order to obtain and environmental lifestyle goals rationalities substantive lifestyle concerns goals Environment Stewardship Environment (peak oil, Little mention of stewardship or Some discussion of stewardship distinguished from climate change) are environment and environment – mixed environmentalism dominant concerns emphasis Spirituality/ Little discussion of Strong sense of Little discussion of religion or Mixed- minority are influenced Religion religion or spirituality spirituality, spirituality as a primary focus of by spirituality and religion, as a primary focus of willingness to trade family life or motivations majority make no mention of family life or material for non-material these factors motivations (spiritual and community) benefits 326 Embeddedness Highly embedded in Greater rejection of Highly embedded in American Mixed – some reject of in American American consumer consumer culture but consumer culture and values consumer culture but stress Culture culture and values stress importance college importance college education, education, health health insurance and achieving a insurance and achieving a reasonable income; others highly reasonable income embedded in American consumer culture

Continued

Table 6.6 Summary Comparison of Household Values and Influence of Farm Structure on Enterprise Adaptation Strategies

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Table 3.6 Continued

Multi-generation Commodity First-generation AFAE AFAE Mixed Business Emphasis on Growth Market savvy and Emphasis on Growth Emphasis on vertical and Attitudes (primarily oriented towards direct (vertical and horizontal) horizontal growth to create horizontal and some marketing and niche and stress running the opportunities for next generation. vertical) marketing. Use skills enterprise as a business Take advantage of life cycle obtained outside of differences within the family to agriculture to enhance fill production, marketing and their businesses childcare/household needs. Succession Next generation fits into Not much emphasis on Making opportunities Actively implementing new existing structure. succession, majority for next generation strategies to keep their families on have very young either in existing the farm and increase the number

327 children structure or adding on of people the farm can support new enterprises. Structure of Moderate focus on the Critical of industrial Moderate focus on the Most cognizant and vocal about Agriculture structure of agriculture. agriculture, from structure of agriculture the structure of agriculture and the Invest heavily in environmental way it effects their own operations technology and focus on perspective. Do not larger farming community the market discuss how structure affects farmers and farm families. Farm Crisis Some focus and mention Not influenced by farm Modestly influenced by Majority influenced by the Farm of farm crisis but not crisis farm crisis Crisis, overexpansion, and debt foremost in minds Market Global competition Not influenced by global Recognize potential Purposefully choose to increase Competition impinges on commodity market competition threats from regional, niche production and localize in markets shrinking national and global order to avoid global competition margins competition

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Influence of Succession On Enterprise Adaptation

As noted earlier, succession plays a critical role in enterprise adaptation and persistence, an analysis of the qualitative data reveals succession was a key variable influencing the ways farms manage risk and expand their enterprises at the RUI.

Succession plans may be known or unknown. When succession plans were known, I

found when an heir was present it had a significant effect on enterprise adaptation, catalyzing an active mix of adjustments depending on the type of commodities,

marketing interests and household goals these farmers and their families are interested in

pursuing. When no heir was available the farm follows a path of decline and disinvestment. Based on the general findings from my interviews with 35 farm families at the RUI and their succession planning and farm management decisions, the following schematic (Figure 6.1) summarizes a general pattern that was observed and models the influence succession has on enterprise adaptation and persistence. In this study when an heir was identified four types of adaptations were observed: the Expanders, the

Intensifiers, the Stackers and the Entrepreneurial Stackers. The following section describes the patterns generated by the presence or absence of an heir.

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Figure 6.1 Schematic Diagram of Influence Farm Succession on Enterprise Adaptation and Persistence

No Heir Identified

The farms that could not identify an heir fell into two groups: 1) those that opted to put their land into preservation through some sort of land trust; and 2) farms that were clearly in a state of decline and disinvestment, making no improvements to existing infrastructure and entering a state of winding down. Two farms with no heir were actively enrolling their land into some form of land trust or land protection agreement.

Farms exhibiting decline and disinvestment characteristics refrained from purchasing new equipment, investing in capital improvements or expanding their enterprise in any form. One of these farms was actively selling and auctioning off land.

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Heir Identified

Among the farms with an heir, four different farm organization patterns were

observed: the expanders; the intensifiers; the stackers; and the entrepreneurial stackers.

The Expanders

The first pattern of “expansion” observed occurred most frequently among

Commodity farmers. These farmers chose a strategy of expansion, whereby they increased their acreage (through rent or purchase) and thereby increased the volume they could produce and sell into bulk commodity markets. This strategy was particularly

prevalent among commodity apple farmers in Kent County. A series of inclement

weather events and increasing international competition led to a bifurcation of survival

strategies. One set of orchard growers decreased their acreage to pursue direct marketing

initiatives, while the Expanders actively purchase orchards being sold at auction and

renting any available land. These Expanders are also investing in new packing, storage

and processing equipment and technology oriented to the commodity fresh market.

The Intensifiers

Since land is a very limited input at the RUI, some farms (predominantly Multi-

generation AFAEs) were going through a process of intensification. These Intensifiers

were increasing production of higher value crops (such as nursery crops) in order to

support more family members on the same piece of land. This group was actively

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investing in new equipment and buildings. A third generation AFAE farm established in

1958 illustrates this intensification process. In order to continue supporting an expanding family solely from the farm, this family began a process of intensification by simultaneously growing vertically and horizontally by increasing both production of higher value crops (i.e., shifting from vegetables to fruit and nursery crops) and volume.

As one family member described, “We’re going higher end in products that maybe other people don’t have the patience to do but we can get more of a profit -- not only are we going different places, but we’re growing different things.” By investing in new equipment and buildings and expanding out from an on-farm roadside market they now travel to farmers markets around the region.

The family also makes room for new interests and talents of the next generation by adding on new enterprises, however all of these different streams are seen as part of the same business. One family member explained: “I just did a calculation and I think the way our company is divided now, it’s now about a third, a third and a third… like landscaping is a third, retail garden center is a third, the wholesale produce and the nursery is about a third. So it’s a rather diversified business.” Another family member explained part of the reason they needed to intensify was because: “in 2007 we’ll probably have half the customers we had in 2001. So we have to go to more high-end products.”

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The Stackers

A second pattern emerged among Multi-Generation AFAEs and the Mixed type

farms, where families were stacking their talents within the same business. Among these

Stackers, some family members were in charge of field crops, others were in charge of fruit and vegetable crops, while yet others turned the harvest into value added products

(jams, pie, etc) and were in charge of direct marketing these goods. Many of these families began to stack enterprises starting in the 1980s when falling commodity prices encouraged them to look for alternative farm enterprises to support the family. They have also taken advantage of skills developed off the farm particularly those related to

marketing and education.

An example of this strategy is a second generation family farm that was a corn

and bean operation until the mid 1980s. Recognizing the falling commodity markets they

planted berries, and when there was an overabundance of fresh market product, the

matriarch began processing the fruit into jams, jellies and pie fillings. Today in addition

to the corn and beans the operation has grown into a full fledged fruit patch with an on

farm market and a full bakery, with school tours, pumpkin festivals, corn maze, petting

zoo, and a harvest cruise in for the adults. All of the children are employed full time off

the farm, but are still involved in the farm operation and help out part time basis. One son

runs the grain farming operation, while the other children and daughter in-law are

involved with marketing and the farm market. This case was similar to others where the

division of labor is often split across generations, with older family members responsible

for production and younger members in charge of marketing. This observation raises a

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question about the long term trajectory of production activities among such enterprises

when parents eventually retire.

The Entrepreneurial Stackers

The fourth and final pattern observed were those families able to use a set amount

of land to stack complimentary farm enterprises, and build off each other’s production

systems and provide for independent yet complimentary income streams. This strategy of

Entrepreneurial Stacking (most often observed in grass based animal production systems)

allowed for more family members to be a part of the farm enterprise without the need to

acquire more land. The term “Entrepreneurial Stackers” is adapted from concepts described by Joel Salatin (2001) where farmers are encouraged to adopt an integrated

closed loop pasture based agricultural system allowing complimentary enterprises to exist

on the same land base. In a similar vein, the term Entrepreneurial Stacking refers to

families stacking complimentary businesses as a greater number of individuals are able to

generate additional income streams off the same land base and utilize common resources.

A case typifying the Entrepreneurial Stacker system is a fifth generation farm that

until the mid 1990s was a confinement dairy operation, barely able to support one family.

Making the conscious decision to adopt a holistic grazing system, the operation became

certified organic and sold their milk into the bulk commodity fluid organic milk market

and diversified their product line. Individual family members have added additional

enterprises including: grass based meats (beef, pork, lamb, pastured poultry); pastured

eggs; and artisan cheese production. To capture a greater share of the consumer dollar

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the family built an on-farm retail store and sold their products through local retailers

throughout the region. The producers are primarily the male members of the family, and

while the female members occasionally help with farm chores the bulk of their time is spent in retail and marketing activities. This strategy allows the farm to support four

families full time.

Summary

Traditionally, when farmers wanted to expand or bring children into an operation

they were able to purchase more land. At the RUI land is scarce and expensive, making

the inheritance process more complex and uncertain. This research found that very few

farmers were choosing a strategy of pure land expansion. The majority of farms were intensifying through an already established commodity mix (growing higher value crops), or expanding by stacking enterprises (of varying size and intensity) to allow more family

members to earn a living from the farm and accommodate different phases of the

lifecycle. Figure 6.2 models the two strategies of intensification of growth available at the

RUI.

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Figure 6.2 Model of Expansion, Intensification and Entrepreneurial Stacking at the RUI

Conclusions and Discussion Related to Qualitative Analysis

The qualitative interviews revealed a diversity of farm types and motivations for farming at the RUI. The presence or absence of an heir was also found to influence enterprise development adaptations. Farm families without an heir were clearly on a path of decline and disinvestment, while those with one were engaged in processes of: expansion; intensification; stacking or entrepreneurial stacking.

Evidence of similar AFAE adaptations on the landscape are rooted in different motivations. In North America entrepreneurial farming strategies have been promoted as a mechanism to simultaneously increase on-farm profitability, maintain

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small and medium sized family farmers and contribute to local food system development.

While three quarters of the qualitative sample were engaged in AFAE production systems, a closer examination reveals the motivations catalyzing these adaptations are highly varied and complex. In particular the diverging influence instrumental and substantive rationalities have among the different groups (First-generation AFAEs, Multi- generation AFAEs, Commodity and Mixed farms) shape enterprise development strategies. The way in which individual farm families have chosen to develop their enterprises has also been influenced by larger macro-structural changes in agriculture including the farm crisis, international trade policies and government farm policy.

Development Pressure at the RUI does not necessarily have the biggest impact on enterprise persistence and adaptation. Household dynamics including the presence or absence of an heir, family values and motivations for land use, size and type of enterprise affect the diversity and persistence of agriculture at the RUI. All farm types were vulnerable to pressures stemming from access to health insurance, labor and regulations. The strongest indicators of persistence and agency were exhibited by respondents (across all farm types) who substituted non-material substantive values

(rooted in community, spirituality and environment) for material instrumental values

(American consumer culture values). The influence of this philosophy was summarized by one respondent:

"We were making a living for three families with less than 200 acres and doing a good job. It isn't the high cost of living that gets you down. It's the cost of living high. It isn't that the life is only pleasant if you are able to consume. The happiest life is a simple life. Those are some philosophies that we are losing and have lost. And when you lose those philosophies, it makes it near impossible to pass a generation on, the farm on from one generation to the next and the people be content with having what they need.”

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These findings suggest the heterogeneity of household goals and values and motivations for land use contributes to the resilience and persistence of agriculture at the RUI.

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CHAPTER 7

DISCUSSION AND CONCLUSIONS

The purpose of this dissertation was to understand how household dynamics

including succession moderate farm enterprise persistence and adaptation at the RUI. By

examining the relationships between household factors and farm structure relative to

external political, economic and land based pressures, this research fills a gap in the

literature focused on agricultural change at the RUI.

In recent years entrepreneurial agriculture with an emphasis on direct marketing

and value adding has been promoted as a strategy for preserving agriculture, especially at

the RUI. I was therefore also interested in understanding how opportunities and

constraints presented at the RUI in combination with household dynamics influence the

decision to adapt entrepreneurial farming strategies.

To understand how household factors and farm structure affect different groups of

farmers I compared variables influencing enterprise persistence and adaptation within the

Commercial farm population (chapter 4) and across different types of Commercial

farmers (AFAE and Non-AFAE) and Rural Residential farmers (chapter 5). To dissect

these issues further I analyzed qualitative interview data from 35 families to understand how farm families negotiate enterprise adaptation in areas experiencing increasing

growth and development pressure (chapter 6).

In the process of this research the following three research questions were explored:

1. How are household dynamics, household values and farm structure variables

associated with commercial farm persistence at the RUI?

2. How do household level decision making factors including values and land use

motivations influence enterprise adaptation and persistence at the RUI?

3. How do household level decision making factors vary among distinct farm types

(First-generation AFAEs; Multi-generation AFAEs; Commodity, and Mixed type

farms) at the RUI?

The following section reviews and synthesizes the research questions and findings, and

then embeds them in larger policy debates.

CHAPTERS 4-6 – A SYNTHESIS

This section reviews and synthesizes the findings presented in chapters 4, 5, and 6.

Commercial Farm Persistence at the RUI

Chapter 4 examined the association between household and farm structure factors

and farm persistence within the commercial farmer population. Examining these

variables with One-Way ANOVA, Chi-Square statistics and a multinomial logistic

regression models this analysis found lifecycle and household decision making factors

and to a lesser extent farm structure variables had a stronger association with how long a

respondent expects to continue to farm and how long they expect their enterprise to

persist compared to the variables controlling for growth and development pressure at the

RUI. The results also demonstrate how specific factors (e.g. lifecycle, succession, and

farm structure) can be more salient for specific groups of farmers.

Years Individual Expects to Continue Farming

The analysis highlighted the role of lifecycle in determining length of persistence

for the years an individual expects to continue farming.

Respondents who only expect to continue farming for 1-10 Years are the oldest group of farmers and exhibit disinvesting behaviors often associated with the impermanance syndrome. This group has the most trouble identifying a successor, and were the most likely to emphasize the importance of selling farmland for development in order to afford retirement. Commonly, when one generation prepares to take over the farm, they will often buy the land or business from the parents or enter some sort of financial agreement that provides monetary funds towards the elder generation’s retirement (Jonovic and Messick 1986). Without such arrangements farmers must look to other resources for retirement funds, including cashing in the development value of their land.

Respondents who are Unsure of how long they will continue farming were older, had less education and were most pessimistic about the future of their farm.

While the availability of a successor was not rated as a very serious problem, this group

is highly likely to sell land for development. The pressures this group are under may be

partially due to the structure of their farms rather than lifecycle effects. This group was the most likely to be engaged in livestock and dairy farming which in general have a

more difficult time persisting at the RUI due to potential neighbor conflict (Sharp et al.

2002). The Unsure group was also least likely to engage in AFAE strategies or to make future investments in alternative marketing and production techniques over the next five

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years. It is more difficult to transition livestock and dairy farms into alternative food and

agricultural enterprises (Barbieri et al. 2008) compared to other types of production

systems. Therefore type of farm enterprise may influence the uncertainty certain groups

of farmers feel towards their farming careers.

Those respondents who plan to farm Indefinitely are younger, have the

highest education levels, are the most optimistic about the future of their farms, and

are least likely to indicate the availability of a successor as a problem. Compared to

other categories this group had a more moderate farm income and were the most likely to

have off farm income (either part time or full time). The indication one plans to farm

indefinitely may be a reflection of the point in the life cycle these farmers are at given

their younger age. However, this group placed the greatest importance on substantive

values and was more likely to have off-farm income, suggesting these farmers were more likely to seek solutions that allowed their families to stay on the land for substantive reasons rather then purely economic ones and sought strategies that allowed them to achieve a livelihood along with a lifestyle.

Respondents that expect to continue farming another 12-30 years were one of the most dynamic groups exhibiting characteristics that suggest these farm families were actively engaging in a process of enterprise redevelopment as they prepared to bring on the next generation (Bennett 1982). This group placed the most emphasis on instrumental values, had the largest farm receipts and carried the most debt while simultaneously indicating their business is on a future growth trajectory. Almost a quarter had identified a relative as a successor, and were less likely to indicate selling farmland

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was important for their ability to afford retirement. Unlike the 1-10 Year group who expressed their expectation to sell their farmland for development, the terminal end date

identified by the 12-30 Year group is a reflection of passing the enterprise from one

generation to the next and not the demise of the farm business itself.

Years Expect Enterprise to Persist

Lifecycle effects and household values were significant variables associated with

the years responds expected their enterprise to persist.

Respondents who expect their enterprise to continue for 1-30 Years exhibited a number of disinvestment characteristics. As a group these respondents are the oldest group of farmers, they are most likely to indicate the health of the key operator and the

availability of a farm successor as a problem and were most likely to emphasize the need

to sell farmland for retirement. They were also the least likely to indicate a successor is a

relative compared to the other groups. The combination of age and availability of a

successor appeared to foster a sense of impermanence, leading this group of respondents

to anticipate a terminal end date for their enterprise and an anticipated exit from

agriculture that leads to land being developed for non-farm uses. These results are similar

to the findings reported by Gasson et al. (1988) and Lobley Potter (2004).

Enterprises in the Indefinitely category are most likely to persist and exhibit

enterprise growth characteristics. Those respondents anticipating their enterprise will

continue Indefinitely tended to be in an earlier stage of their lifecycle compared to those

who expected their enterprise to persist for another 1-30 Years or those who were

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Unsure. This Indefinitely group included the youngest group of farmers who had the

highest education levels, and were the most optimistic about the future of their farms.

The health of key operator and availability of farm successor were least likely to be a problem compared to the other groups. This group also reported stronger stewardship and substantive values and tended to de-emphasize instrumental values. Initially, one might assume the early life cycle stage characterizing these respondents explains their relatively non-existent need to sell land for retirement and the availability of an heir.

However, this group was the most likely to identify a relative as a successor upon retirement. Compared to the other groups respondents in the Indefinitely category had made and were planning to increase capital investments, alternative marketing strategies, direct sales to customers, and intended to chart a growth trajectory. Additionally, the

slightly higher debt levels can be associated with enterprise start up and also with enterprise expansion.

Respondents indicating they were Unsure how long their enterprise will continue were often sandwiched in between those who expect their enterprises to persist for 1-30 Years and the Indefinitely group. Lifecycle effects, household dynamics and farm structure may be influencing this groups’ Unsure status. Respondents in the Unsure category included the oldest group of farmers, who had the lowest levels of education. This group reported the health of key operator and the availability of a successor as less of a problem than the 1-30 Year group but more of a problem compared to the Indefinitely group. They were also the most pessimistic about the future of their

farm and place moderate importance instrumental, substantive and stewardship values.

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The Unsure group is fairly likely to sell land for development in the next five years, and indicates selling farmland is somewhat important for the ability to afford retirement.

They are more likely then the Indefinitely group, but considerably less likely then the 1-

30 Year group to sell their land to a developer when they retire. The Unsure group also exhibit moderate investments in their enterprises though many are on a growth trajectory.

Additionally, the Unsure group was most sensitive to problems associated with global competition and farm economics suggesting that commodity markets may have a great deal of influence over the confidence these respondents feel in the ability of their enterprise to persist.

The multivariate analysis also brought forward the influence perceived effectiveness of local land use policy has on enterprise persistence. The more effective policy is perceived to be the more likely respondents expect their enterprise to persist

Indefinitely then for 1-30 Years. The significant effect perceived effectiveness of land use policy has on the number of years an individual expects to continue farming demonstrates how farm families take a variety of factors operating at various levels into account (Bryant and Johnston 1992; Smithers and Johnson 2004; Smithers et al. 2004) when making decisions about the future of their enterprise. These results suggest that the perceived effectiveness of local land use policy is associated with respondents anticipating their enterprises will remain on the landscape for a longer time horizon.

However, land use policy alone is not a simple elixir for ensuring farms will remain on the landscape. Rather this analysis demonstrates it is the complex interplay between internal household dynamics, farm structure and land use policy that influences the

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persistence of farms on the landscape.

Adaptation and Persistence Strategies – A Comparison of AFAE, Non-AFAE and Rural Residential Farms

Chapter 5 presented a more in-depth analysis of the influence household and farm structure factors have on AFAE adaptations by comparing respondents within the

Commercial farmer population (AFAE and Non-AFAE) with Rural Residential farmers.

Examining these variables with a One-Way ANOVA and Chi-Square statistics this analysis found two distinct patterns, variables associated with the differences between

Commercial farmers as whole (both AFAE and Non-AFAE) and Rural Residential farmers, and variables associated with differences clearly demarcating the three groups.

These two patterns are discussed below.

Differences Between the Commercial Farmers and Rural Residential Farmers

Household level factors including operator health and availability of a successor are more of a problem for Commercial farmers (both AFAE and Non-

AFAE) than they are for Rural Residential farmers. Likewise hypothesis R2.1.4 was supported as commercial farmers (both AFAE and Non-AFAE) are more likely to report economic instrumental, substantive, and stewardship factors are more important factors influencing enterprise development decisions then did Rural Residential respondents. In regards to farm structure variables, Commercial farmers operated more acres and were more likely to rely on farm income for household income than were Rural Residential farmers.

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The differences in values, attitudes and adaptation strategies observed among the AFAE, Non-AFAE and Rural Residential farmers reflects the varying

contexts they are embedded in. Significant differences were observed between the

Commercial farmers and Rural Residential farmers in regards to cost of health insurance, optimism for agriculture in the county, development pressure, global competition and farm economics, local government support for land use policy, labor conditions, weather, availability of local infrastructure, neighbor effects, non-farm group support, sample frame and county of origin.

These results most likely reflect the different contexts Commercial AFAE and

Non-AFAE farmers are embedded in compared to Rural Residential respondents. Relying on the farm as their primary livelihood, AFAE and Non-AFAE respondents are more sensitive to fluctuations in weather, labor, and the availability of local infrastructure than are Rural Residential farmers. Additionally, as indicated earlier Rural Residential farmers rely almost exclusively on family labor and off-farm income and are therefore buffered from problems associated with access to farm labor and health insurance. Non-AFAE farms engaged in (low margin) commodity production operate extensive operations at a scale that makes them most vulnerable to development pressure, neighbor effects, global competition and farm economics. The AFAEs are equally as vulnerable to these factors, but are somewhat buffered from their impacts by virtue of their smaller scale and greater orientation to local markets as opposed to global ones.

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Differences Between the AFAE, Non-AFAE and Rural Residential Farmers

The AFAE, Non-AFAE, and Rural Residential groups differed across a number of lifecycle and household level factors. AFAE respondents reported operator health and the availability of successor as more of a problem compared to Non-AFAEs and

Rural Residential farmers. Rural Residential respondents reported these factors represented a very small problem for their enterprise. Non-AFAEs were most likely to sell land for development in next five years, however, AFAEs were most likely to indicate selling farmland was of greater importance to their ability to afford retirement

(though the relationships were in the positive direction they were not significant).

Although the literature posits AFAEs are a more resilient form of agriculture at the RUI, these results suggest the long term persistence of AFAEs may be impacted by the availability of a successor, raising questions about the ability of these enterprises to replicate and reproduce the next generation. Non-AFAEs were the most likely group to identify their successor is a relative. Furthermore this analysis did not find support for hypothesis R2.1.3 which stated AFAEs would most likely identify a

relative as a successor. However, at least in the short term, AFAEs were most likely to be

on a growth trajectory compared to the other groups.

These results suggest the ways in which AFAEs structure their businesses is

influenced both by the need to generate income while achieving conservation and

environmental goals. AFAE respondents placed slightly more importance on

instrumental and stewardship values compared to Non-AFAE. While Non-AFAEs are

oriented towards the global market and international competition, AFAEs are embedded

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within local markets and are attuned to more local sources of competition.

Non-AFAE farmers were the most optimistic for the future of agriculture in their

county and perceived local government to exhibit strong support for land use policy

while AFAE and Rural Residential farmers reported more moderate attitudes.

In regards to farm structure Non-AFAE farmers are more likely to be operating dairy and grain farms. AFAE and Rural Residential farmers were more likely to be engaged in high value crop production and direct marketing initiatives.

Non-AFAE farmers operated the largest acreage; however AFAE farmers had the highest

average farm receipts. These results support other studies that have found the

intensification of production and sales characterizing AFAEs allows these smaller scale

farms to persist at the RUI.

Bryant and Johnston (1992) argue there is a greater diversity of farm types at the

RUI representing the more varied reasons individuals have for farming (whether based in lifestyle or livelihood priorities). They also note hobby farmers in particular may be more likely to invest in more expensive equipment and new technologies given inclination toward experimentation and their motivation to farm for non-economic goals. This same pattern is seen within this data set and help to explain why Rural Residential respondents carry the most debt, reflecting their interest in farming as a lifestyle.

The Influence of Household Values and Land Use Motivations on Enterprise Adaptation and Persistence – A Qualitative Analysis

The degree to which the analysis presented in chapters 4 and 5 could delve into household dynamics was in part limited by the structure of the landowner survey. Relying on a qualitative data methodology Chapter 6 delves deeper into the influence life cycle

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effects, household goals and values, farm structure and the pressures of farming at the

RUI have on enterprise adaptation and persistence. More specifically these interviews

brought forward the different types of AFAE farmers on the landscape and the influence

succession (the presence or absence of an heir) has on the enterprise.

The Influence of Life Cycle Effects, Household Values, and Goals

First-generation AFAE farmers entered into agriculture as a conscious

lifestyle and career choice, and were highly motivated by a strong set of substantive

values generally rooted in spiritual and environmental concerns. Many actively

critiqued industrial agriculture from an environmental perspective. Their strong sense of spirituality allowed this group of farmers to trade material (income) for non-material

(spiritual and community) benefits. Material concerns were secondary as this group

demonstrated a marked rejection of consumer culture. However, their embeddedness in

American culture was still evident by the emphasis they placed on college education,

health insurance and achieving a reasonable income.

The First-generation AFAEs did not place much emphasis on succession, which

most likely reflects their lifecycle stage, as many were young and had very young

children. Furthermore, this group could not be characterized as ‘back to the land homesteaders,’ in fact these farmers were highly attuned to local marketing opportunities

and maintaining a niche advantage by focusing on direct marketing and value adding

activities. Many used the skills they had learned in earlier careers or experiences outside

of agriculture to enhance their businesses. This orientation to the local market and direct

marketing buffered many from global market competition. They were not influenced by

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the farm crisis as other groups were due to their relatively recent entry into agriculture.

However, some First-generation AFAEs were actively dividing production from

entertainment and education activities, taking on more managerial roles as opposed to

being both producer and manager. The First-generation AFAEs have many similarities to

the group of respondents who expected to continue farming Indefinitely. Both groups

were younger, in earlier stages of their lifecycle, were more educated and optimistic

about the future of their farms, tended to emphasize substantive goals, and reported a

higher incidence of off-farm work and more moderate farm-income. These similarities

reinforce the importance of substantive values as motivating factors for keeping land in

agriculture among these groups of farmers.

Multi-generation AFAEs were more oriented towards instrumental values in

order to obtain substantive lifestyle goals compared to the First-generation AFAEs.

This group of farmers made little mention of stewardship ethics, the environment,

religion or spirituality among the factors they weigh when making land use and business

decisions. The Multi-generation AFAE’s were highly embedded in American culture and

values, and their business goals reflected this orientation. This group emphasized a

business strategy rooted to ensure growth (vertical and horizontal) with income

generation and profit as critical goals. Succession was a key concern for this group,

families were actively making opportunities for the next generation by expanding the

existing business or by adding on new enterprises. This group was only moderately

influenced by the farm crisis and while they recognized the potential threats from

regional, national and global competition (particularly in the fresh fruit and vegetable

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markets) they placed only moderate emphasis on the structure of agriculture, attuning

themselves to more local market conditions.

Commodity farmers were most sensitive to global competition and

fluctuating commodity markets, this group tended to be oriented towards

instrumental values in order to obtain substantive lifestyle goals. Stewardship was

often distinguished from environmentalism, and there was little discussion of religion or

spirituality as a primary focus of family life or motivation for land use decision making.

The Commodity farmers tended to be highly embedded in American consumer culture

and values. They emphasized growth (primarily horizontal and some vertical), and sought to establish the next generation in the farm business by fitting them into the existing structure. This group was most likely to invest heavily in technology and focus on the market. More moderate thought was given to the structure of agriculture and on

the farm crisis. These farmers were pre-occupied with the effects global competition has

on impinging commodity markets and shrinking margins. The large scale these farmers operate at combined with an exhibited lack of interest in engaging with the public make

AFAE adaptations unlikely.

The Mixed type respondents were the most complex group, demonstrating the

greatest interplay between instrumental and substantive rationalities to accomplish

farm reproduction goals. While stewardship values and the environment resonated as

important influences for some, others made no mention of these factors. Additionally,

only a minority of Mixed type respondents were influenced by spirituality and religion.

This group was also mixed in the degree to which they rejected American mainstream

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culture. Some questioned consumer values, while others embraced them. The majority stressed the importance of college education, health insurance and achieving a reasonable income. We see the strongest emphasis on vertical and horizontal growth being actively pursued by this group of farmers with the intention of creating opportunities for the next

generation. These farmers actively implemented new strategies to keep their families on

the farm and increase the number of people the farm can support. As these enterprises

grow and transition into AFAEs many take advantage of life cycle differences within the

family to fill production, marketing and childcare/household needs. This group was by far

the most cognizant and vocal about the structure of agriculture and the ways in which

these larger structures impact their own operations and the larger farming community.

The majority of Mixed type farmers were influenced by the Farm Crisis and the risks

associated with overexpansion and debt. This group purposefully choose to increase

niche production and localize in order to avoid global competition. These farmers also

tended to operate more medium sized farms which may afford them some flexibility in

transitioning into AFAE strategies not available to very large Commodity producers.

The Role of Succession in Enterprise Adaptation and Persistence

Succession was also found to play a critical role in enterprise adaptation

strategies. When no heir could be identified farms either fell into a state of decline and

disinvestment or opted to put their land into some form of preservation. In this study

when an heir could be identified, families engaged in four distinct types of adaptation

strategies: the expanders; the intensifiers; the stackers; and the entrepreneurial stackers.

At the RUI land is scarce and expensive, making the inheritance process more complex

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and uncertain. This research found that with the exception of Commodity growers, very few farmers were choosing a strategy of pure land expansion. The majority of farms were intensifying through an already established commodity mix (growing higher value crops), or expanding by stacking enterprises (of varying size and intensity) to allow more

family members to earn a living from the farm and accommodate different phases of the

lifecycle.

Lifecycle effects moderated the roles of individual family members in the Multi- generation, Mixed type farms, Stacking and Entrepreneurial Stacking groups. Among some families, as individual family members aged they had transitioned their roles on the farm from producers into marketers. However, among some families the older members continued to retain control of production responsibilities while the younger generation ramped up the farms marketing functions. This latter pattern raises questions about the long term production function of these enterprise types. Will the younger ‘marketing’ generation eventually transition into a producer role, or will they maintain a manager status, employing farm managers and field labor to raise their crops? Future research should include long term panel studies to track changes in production and management decisions. The ultimate way in which these AFAEs evolve informs larger questions within the sociology of agriculture that seek to understand the dynamic relationship between capital, production, labor and management and the family farm.

Limitations of Study and Future Research

A limitation of this research was the type of data available. The data for this

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analysis was from a cross sectional survey. Long term panel data would provide greater information about the evolution of the farm enterprise as roles, values and goals change over the course of the life cycle. Long term panel data would allow for more detailed comparisons across groups, and be able to track the ability of specific types of enterprises to persist at the RUI. This analysis was also limited by the land owner survey design; I was unable to classify farmers as First-generation, Multi-generation, Mixed or as

Commodity in a way that would have been comparable to the qualitative data. A future panel study should include a measure of the percentage of sales from AFAE activities, this variable would be useful for understanding the degree to which AFAE strategies contribute to farm and household income and how that contribution may change over time given enterprise adaptations.

Future research should include an examination of gender and the role women play in farm succession and enterprise adaptation. Farm families are dynamic entities: they negotiate the social relationships of production and work within the context of the farm wives’, husbands’ and children’s life cycles, family cycles and farm cycles (Colman and

Elbert 1984). These social conditions make it necessary to understand how AFAEs shifting emphasis on hospitality, entertainment, and aesthetics (which in some cases is seen as equal and even more important then field work and crop management) alters the traditional relationships among the sexes. These conditions raise new questions regarding how the gendered division of labor on AFAEs impacts the farm succession process and enterprise adaptation strategies.

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CONCLUSIONS

Social Sustainability: The Connection Between the Diversity of Household Goals and Values and Farm Persistence at the RUI

The heterogeneity of household goals and values and motivations for land use

identified in this research contributes to the resilience and persistence of agriculture at the

RUI. Reinhart and Barlett (1989) argued the cyclical process of development,

maintenance and redevelopment identified by Bennet (1982) increases the ability of the

family farm to survive and persist on the landscape by ensuring at no one point in time is

it likely that the majority of firms will try a new technology or adopt radical changes in

production methods and inputs. In a similar vein, the diversity of household goals and

values create subtle but important structural differences in the ways farms at the RUI are

structured, thereby at any one point in time protecting different groups of farmers from

down swings in the market, fluctuating land use pressures and changing household

conditions.

The rich diversity of farm types at the RUI is exemplified by the three different

types of AFAEs identified. First-generation AFAE, Multi-generation AFAE and Mixed

type farms demonstrate that while farmers across the RUI landscape appear to be

adapting and implementing AFAE strategies their reasons for doing so are embedded in

widely varying motivations. Additionally, variations in household goals and values were

found within the Commercial farm population and among AFAE, Non-AFAE and Rural

Residential farmers. The importance of substantive and instrumental rationalities in

guiding enterprise adaptation and persistence at the RUI is an extension of Bartlett’s

(1993) and Mooney’s (1982b) works examining the moderating influences of household

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goals and values on the farm enterprise. The farm crisis occurred during a period of

American history when consumer culture was on the rise. Bartlett (1993) found farm families who subjugated material needs and wants, and who resisted risky growth, expansion and debt incurring strategies were better able to persist through the farm crisis.

In a parallel to Bartlett’s work, this study found certain groups of farmers emphasize and find fulfillment in the community, and spiritual and environmental benefits in lieu of monetary rewards, thereby enabling their farms to persist despite challenges.

Some of these groups, the First-Generation AFAE’s and Mixed type respondents in particular, are actively questioning American consumer culture and values. However, even while they critique the larger culture in which they are embedded in, they are still a part of it. These groups actively express their desire to send their children to college, give their children the freedom to pursue whatever paths make them most happy, and discuss the need to ensure health insurance and a reasonable standard of living that

includes vacations and material possessions. These factors differentiate them from the

1960s and 1970s back to the land homesteading movement which was an outright rejection of mainstream American values. Rather many of the AFAE farm families participating in this study were embracing the shift in American culture towards entrepreneurship and were imposing small business models on their farms, injecting their operations with market savvy and sophistication to ensure their persistence and a livelihood for their families. Simultaneously, similar to the farmers Salamon (1992) studied, these families relied on a complex set of kin and family relationships as they adapt their operations to achieve farm reproduction and persistence goals.

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When fields and communities include a wide range of crops (Smith et al. 2008), farm sizes and types (Goldschmidt 1978), they become more resilient and better able to adapt to changing conditions. Likewise, the varying importance of instrumental and substantive values (embedded in monetary goals, community, the environment and spirituality) in guiding land use decision making add another layer of resiliency at the

RUI, buffering farm families from the potentially adverse affects of increased population and development pressure.

The Tension between Entrepreneurship, Free-Market Policies and Farm Persistence at the RUI

This research was also able to shed light on the critiques Guthman (2008) and

Allen (1999) levy against entrepreneurialism as a neoliberal policy applied to farmland protection and local food system development policies. The qualitative interviews demonstrated how farm families are implementing a broad range of entrepreneurial farming strategies as a means for achieving both instrumental and substantive goals. For many farm families, particularly the Mixed type, these strategies are a form of agency, by which they attempt to extract themselves from the dominant commodity paradigm in order to exercise some control over the fate of their farms. The ways in which farm families choose to adopt, adapt and implement specific management and production strategies as they balance instrumental and substantive goals informs to the uneven nature by which capital continues to evolve and penetrate the family farm. This analysis reinforces Beamish’s (2007) argument for a return to Weber’s work on interpretation and

Joseph Schumpeter’s analysis of entrepreneurship as a way to conceptualize new forms

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of agency embedded in social relationships. In this study entrepreneurship was seen as a form of agency by farm families, thereby reinforcing the complexity and nuances that operate when we seek to understand economic actions within the social structure.

The question as to the long term persistence of these enterprises and the degree to

which capital is able to transform these family farms remains to be answered. Even as

these farms attempt to find space in a local food system, they are challenged by larger

neo-liberal free-market policies that influence local retailers and local markets, ultimately

affecting the structure and availability of local markets accessible to local farmers.

However, these farmers may also create new spaces of resistance if they lobby local and

state decision makers to create policies that address their needs and ensure their ability to

compete and thrive at the local level. While farm families are embedded in a larger

American culture embracing entrepreneurship, Guthman (2008) and Allen (1999) are

correct in their cautionary tale, alerting us to the fact that the success of these ventures

will remain in a precarious and fragile state if policies that address planning, zoning,

building codes and food safety regulations are not pressed for and implemented.

A New Policy Agenda for Maintaining Family Farms at the RUI

Farmland preservation policy at the RUI has been primarily focused on land use

regulations, however the results presented in this dissertation demonstrate the policy

focus needs to expand and evolve in order to more adequately deal with social issues

related to: succession; ability to afford retirement; regulations (including food safety,

zoning, and building codes); access to health insurance; availability of labor; farm

economics and global competition.

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At the RUI all respondents are sensitive to land access and land price, however, beyond these initial challenges, the issues mentioned above affect the long term ability of farm enterprises to grow and persist. At the local level, township and county officials can address regulatory issues affecting a farm family’s ability to expand (e.g. allowing additional structures to be built and modified). Local officials can also work with the

State health departments and departments of agriculture to design and implement food safety regulations that remain affordable to small and medium farms and simultaneously ensure a safe food supply. Access to health care is an issue that needs to be addressed at the local, state and federal level.

In designing and implementing new types of farm protection policy at the RUI it is important to remain sensitive to the internal needs of the farm family. Research examining farm succession has continuously documented that in the absence of an heir farms may either go into some form of land protection (often costly) or be sold for farm or non-farm development. Creating policies that increase farm profitability help to ensure the availability of an heir (as children are more likely to return to the farm if they can ensure a livelihood). Variables influencing household decision making include a wide range of factors operating at multiple levels and cannot be decoupled from farmland protection policy.

This analysis brought forward the complex interplay between life cycle effects, household goals and values, and land use motivations influencing enterprise adaptation and persistence strategies at the RUI. The Commercial farmer data presented in chapter 4 brought forward the different factors influencing the number of years an individual

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expects to continue farming versus the number of years a respondent expects their enterprise to persist. Coupled together these two sets of analysis can inform policies intended to protect and foster commercial agriculture at the RUI.

The terminal end dates respondents identified in regards to the number of years they expect to continue farming was for the most part related to lifecycle effects.

Respondents able to anticipate a terminal end date and are in a mode of decline and are

without an heir should be targeted by farmland protection programs assisting in farm

transfer.

Overall (and particularly in regards to enterprise persistence) respondents tended

to aggregate in the Indefinitely and the Unsure group. Those who plan to farm

Indefinitely are at an earlier phase in their lifecycle and business cycle. They may have

greater need for assistance programs related to farm financing (capital investments and

start up costs) and business planning. They may also be more receptive and benefit from

programs incorporating the environment. The Indefinitely respondents placed great

importance on both substantive and stewardship values suggesting they are most open to

building value based markets and production systems that will also provide a livelihood

for their families.

The Unsure respondents appeared to be embedded in commodity production

systems heavily influenced by local economics and global market competition. This

group indicated the availability of an heir; however, to ensure their children remain on

the farm increasing farm profitability will be key. The Unsure group may benefit from

programs (at the national and local level) that aim to help with adjustments in production,

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marketing and/or business organization that will ensure their viability.

Chapter 5 examined the differences among three groups of farmers at the RUI:

AFAE, Non-AFAE and Rural Residential farmers. The results of this analysis highlight

the heterogeneous nature of motivations different groups of farmers at the RUI exhibit.

This diversity helps to explain why a one size fits all land use policy framework at the

RUI will not ensure farmland protection. Policies need to account for the varying reasons

why and ways in which different groups of farmers utilize their farms. The primary

difference between the Commercial (AFAE and Non-AFAE) and Rural Residential

farmers is the reliance on the farm for a livelihood. The Commercial farmers have a much

more complex relationship with the land (compared to the Rural Residential farmers) as

they seek to meet instrumental, substantive and stewardship goals. Policy makers and agricultural professionals may be better able to realize community land use objectives

(e.g. protect working agricultural landscapes, maintain healthy agro-ecosystem functions)

by tailoring policies, programs and land use campaigns to the needs and interests of these

different Commercial and Rural Residential farm groups.

In conclusion, expanding the definition of farmland protection policy at the RUI

can also influence community economic development and prosperity and achieve larger

sustainability goals. Diversity is a key principle in sustainability, whether it is

environmental, economic or social sustainability. Over-centralized systems whether they

be environmental (e.g. mono-cropping) or economic (e.g. banking and financial systems)

are inherently at risk to shocks in the system. In such a system when one individual or

institution is affected all are affected, limiting the ability to react and respond at the local

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level. Social systems are also subject to the same risks. This research demonstrated how the diversity of household goals and values, farm types (and sizes) and land use motivations encourages household agency thereby promoting and ensuring the persistence of agriculture at the RUI. In an applied setting these results can help guide community land use and economic development policies. Land protection and economic development policies at the RUI should be co-designed to foster diversity in order to build flexibility and resilience into these agricultural, economic and social systems.

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APPENDIX A

2007 AGRICULTURAL CHANGE AT THE RUI SURVEY INSTRUMENT

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Agriculture at the Rural-Urban Interface

A National Study of Trends and Adaptive Strategies Funded By the USDA National Research Initiative

Please return your completed questionnaire in the enclosed envelope to:

Institute for Social Science Research on Natural Resources Dept. of Sociology, Social Work and Anthropology 0730 Old Main, Utah State University Logan, UT 84322-0730

If you have any questions, please call us at: (435)-797-0582

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TELL US ABOUT THE FARMLAND YOU OWN IN CACHE COUNTY

A1. Do you currently own at least 5 acres of farmland in Cache County? NO Æ return survey in blank envelope YES Æ continue

Thinking of the farmland you own in Cache County, please answer the following questions.

A2. How long have you owned any farmland in Cache County? _____ years

a. Was any of your land in this county originally owned by your (or your spouse’s) parents? NO YES

b. Have you ever personally operated a farm in this county? NO YES

c. How would you describe your current uses of farmland in this county? (Check all that apply) I currently operate a commercial farm I rent my land to someone else who farms it I live on farmland I own in the county I use this land for recreation I have a second home on this land Other (explain): ______

d. How would you describe the area where most of the land you own is located? (Check the one category that best applies) Mostly commercial farms Mix of commercial and hobby farms Mostly hobby farms A mix of non-farm residences and farms Mostly non-farm residences Mostly open land and forests (non-farming uses)

The next question is meant to identify people who are actively farming in Cache County. Some sections of our questionnaire only apply to active farmers and ranchers. Please answer the question and follow the instructions carefully.

A3. Do you or members of your household currently farm any of the land you own in Cache County? NO Æ IF YOU DO NOT ACTIVELY FARM, PLEASE SKIP TO QUESTION F1 on page 10 YES Æ Continue on the next page

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TELL US ABOUT YOUR FARM The next questions are for people who are currently farming in this county. We are interested in capturing details about current farming operations, as well about any recent or planned farm changes. If you do no operate a farm, please skip to page 10.

B1. How much land in Cache County do you currently operate as part of a farm or ranch? (Include both owned and rented farmland.) Total acres operated ______acres

Of these operated acres, how many are: Owned ______acres Rented ______acres

B2. In 2006, roughly how many acres in your operation were used for the following purposes? Land used mainly for harvesting crops ______acres Land used mainly for grazing livestock ______acres Land that is idled or fallow ______acres Land that is forested ______acres

B3. Overall, how would you compare your farmland soil quality to the average in your county? Much worse than average Worse than average About average Better than average Much better than average

B4. Which agricultural commodities were produced on this land in 2006? (Check ALL that apply.) LIVESTOCK CROPS Milk Corn (either grain or silage) Dairy cattle (breeding stock) Hay or haylage Hogs Soybeans Beef Small grains (oats, barley, etc.) Sheep or Goats Vegetables (fresh or processing) Poultry Tobacco Horses Nursery or Greenhouse crops Other: ______ Fruit, nut, or orchard crops Other: ______ Other: ______ Other: ______ Other: ______

B5. PLEASE CIRCLE the single commodity above that provided the most gross farm income on this land in 2006.

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B1. In 2006, did you sell any of your farm products direct to consumers or other local outlets?

Type of Outlet No Yes Sales to hobby farmers ...... Direct sales to consumers from farm (ex. farmstand, U-Pick, pumpkins) ... Direct sales to consumers at a Farmers’ Market or through CSA ...... Sales to local institutions or businesses (ex. restaurant, school, grocery) .

B2. In 2006, did you make any money from any of the following farm-related enterprises?

Type of Enterprise No Yes Agritainment (ex. mazes, hay rides, petting zoos, farm events) ...... Hunting, fishing, wildlife viewing...... Animal boarding, breeding and training...... Value added processing of farm products ...... Other on-farm business (describe: ______)

B3. In 2006, did you market any of your products as having any of the following attributes? (Check all that apply.) Locally Grown State Product (example: Utah Grown) Fresh-in-Season Family-farm raised Natural Organic

B4. How is your farm or ranch business organized? (Pick the category that best applies.) Sole proprietorship (single family or individual operation) Family partnership Non-family partnership A family corporation A non-family corporation

B5. How much of the total labor on your farming operation is provided by you or your family? (Pick the category that best applies.) All Almost all More than half Less than ½ (most labor done by paid, nonfamily workers) None (all labor done by paid, nonfamily workers)

B6. Do you work at any regular off-farm job (either full-time or part-time)? NO YES – full time YES – part-time

B7. Do any other adult members of your household work at any regular off-farm job? NO YES

B8. Have you hired Hispanic or Latino farm workers in the last 5 years? NO YES

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RECENT CHANGES TO YOUR FARM AND HOUSEHOLD

C1. OVER THE LAST 5 YEARS what changes were made in your farm operation? (For each type of change, check the number on a scale from -2 to +2 that best applies to you. If certain practices do not exist -- such as you did not rent land or raise livestock -- please indicate NA for ‘not applicable.’)

Decreased Remained Increased a Lot the Same a Lot TYPE OF CHANGE -2 -1 0 +1 +2 NA Farmland owned...... { Farmland rented ...... { Land in conservation programs (CRP, WRP) ...... { Livestock sold ...... { Capital investment in farm buildings...... { Investment in farm equipment...... { Value of farm commodities sold (total gross sales) .... { Number of distinct commodities produced ...... { Sales of products directly to consumers...... { On-farm (value-added) processing of farm products .. {

C2. OVER THE LAST 5 YEARS, which of the following changes (if any) have you made to adjust to the increasing urbanization in your area? (For each change, note if it was done or not done.)

Not TYPE OF CHANGE Done Done Changed crop spraying activities (to reduce drift or smells) ...... Changed tillage, planting or harvesting practices (to avoid bothering neighbors) .. Moved equipment during low traffic periods ...... Changed manure storage and/or management ...... Held an open house or tour for nonfarm neighbors ...... Sought out and met nonfarm neighbors...... Shifted to crops or livestock that generate more sales per acre...... Raised new crops or livestock to sell to new urban customers ...... Adjusted marketing strategies to sell to new urban customers ...... Avoided new investments in farm operation ...... Sold land for non-farm development...... Idled or left fallow some farmland...... 368

C1. Over the last 5 years, how much have you changed your farming operation, such as what you grow or how you grow it, due to nonfarm development near the land you farm?

Some Substantial No Changes Changes Changes 0 1 2 3 4

C2. Over the last 5 years what changes were made in your household?

Decreased Remained Increased

a Lot the Same a Lot TYPE OF CHANGE -2 -1 0 +1 +2 NA Participation by household members in off-farm work ... { Proportion of total household income from farming ...... { Household income from nonfarm self-employment...... {

C3. People farm for a variety of different reasons and these motivations affect how they manage their land. On a scale of 0 to 4, please tell us how important each of the following goals and strategies are for you when making decisions about your farm.

Not Somewhat Extremely Important important important DECISION-MAKING GOALS 0 1 2 3 4 Maximize net farm income......

Ensure household income is adequate ......

Maximize sale value of farmland......

Minimize debt ......

Stay ahead of competition, even if it entails risk

Desire to keep living in rural area......

Desire to keep this farm in the family ......

Desire to be my own boss ......

To spend more time with family ......

Being a good steward of the land......

Maintain or improve quality of my soil ......

Minimize nutrient & chemical runoff from farm ..

Protect scenic quality of the property ......

Desire to stay on good terms with neighbors ....

Other (specify): ______

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PLANS FOR THE FUTURE For the following questions, think about what you expect to happen in the next 5 years.

D1. Over the NEXT 5 YEARS, what changes do you expect to make in your farm operation? (For each type of change, on a scale from -2 to +2, check the category that best applies.)

I expect that it will… Decrease a Remain the Increase a Lot Same Lot TYPE OF CHANGE -2 -1 0 +1 +2 Not applicable Farmland owned...... {

Farmland rented ...... {

Land placed in conservation programs (CRP, WRP) .... {

Livestock sold ...... {

Capital investment in farm buildings...... {

Investment in farm equipment...... {

Value of farm commodities sold (total gross sales) ...... {

Number of distinct commodities produced ...... {

Sales of products directly to consumers...... {

On-farm (value-added) processing of farm products ..... {

D2. Overall, on a scale of 1 to 7 (where 1 is ‘very pessimistic’ and 7 is ‘very optimistic’) are you optimistic or pessimistic about the future of your farm?

Very Mixed Very Pessimistic Opinion Optimistic 1 2 3 4 5 6 7

D3. How important is selling your farmland to your ability to afford retirement? Not Important Somewhat important Important Very important

D4. How important is selling some of your farmland to allow you to keep farming? Not Important Somewhat important Important Very important

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D1. How many more years do you expect to continue farming? (Give your best estimate in years. If you plan to farm indefinitely or are not sure, check the appropriate circle to the right.)

______years { indefinitely { not sure

D2. How many more years would you estimate that this farm enterprise will be in business?

______years { indefinitely { not sure

D3. What category best describes your plans for passing on your farm? (Pick one)

I have identified a successor Æ What is their relationship to you? There is a potential successor Æ Child Grandchild Other family member Other nonfamily member

There is not an obvious person; our succession plans are uncertain There is no successor available Not applicable, it is too early to tell

D4. What will probably happen to this farm when you decide to retire or quit farming? (Check the one category that best applies to you.)

It’s too early to tell Don’t know A relative will take over the operation I will keep the land, but will idle it I will keep the land, but rent the farm to another farmer I will sell to another farmer (not a relative) I will sell to a developer Other (please specify): ______

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D1. On a scale of 0 to 4 (where 0 is not a problem and 4 is a severe problem) to what extent do the following local conditions represent a problem for your farm business?

Not a Modest Severe problem Problem Problem Local Condition 0 1 2 3 4 a. Difficulty applying ag chemicals due to nearby houses b. Neighbor concerns about fieldwork...... c. Neighbor concerns about livestock ...... d. Traffic congestion ...... e. Cost of farmland ...... f. New housing development near my farm...... g. Land-use policies in this county...... h. Availability of local farm input suppliers ...... i. Availability of local processors...... j. Availability of local marketing outlets ...... k. Availability of farm labor ...... l. Cost of hiring farm labor ...... m. Health of key operator(s) ...... n. Availability of a farm successor...... o. Availability of nonfarm work to supplement income...... p. Long term weather issues (drought, climate change) .. q. Recent weather events (floods, storms, etc) ......

D2. Using the same scale, to what extent do the following regional, national or international conditions represent a problem for your farm business?

Not a Modest Severe problem Problem Problem Condition 0 1 2 3 4 a. More restrictive federal immigration policies ...... b Cost of health insurance...... c. Current prices for farm products I produce ...... d. Net farm income from this farm ...... e. Cost of farm inputs ...... f. Increased global competition in the farm sector...... g. Mergers among farm input suppliers ...... h. Consolidation in the farm processing sector ......

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FARM ECONOMICS To better understand the dynamics of farm changes in this county, it is helpful for us to have a profile of the economic situation of each farm in the study. We realize that this information is sensitive, and we want to assure you that your answers will be treated as strictly confidential.

E1. Which of the following categories represents the total farm receipts for this farm business in 2006? (Please place a check beside the category that comes closest to your total farm receipts. Include all receipts from the sale of crops, livestock, milk products, government payments and refunds, and income from custom work for other farms.) Less than $10,000 $10,000 to $24,999 $25,000 to $49,999 $50,000 to $99,999 $100,000 to $249,999 $250,000 to $499,999 $500,000 and above

E2. What percentage of your 2006 total gross farm receipts came from the following sources?

% in 2006 % from sale of livestock ...... % % from sale of crops ...... % % from all other sources ...... % Total = 100%

E3. What percent of your 2006 farm sales of crops and livestock came from direct sales to consumers, other farmers, or neighbors? (If you had no direct sales, write “0”)

% from direct sales ...... %

E4. Taking into account the current market value of all of your farm business assets (land, buildings, equipment, etc.) and all of your current farm business short and long-term debts, what is your best estimate of the ratio of debts to assets on your farm? We have no outstanding debts Our farm debts are below 10 % of our assets Our farm debts are between 10 to 40% of our assets Our farm debts are above 40% of our assets

E5. What proportion of your total household income comes from farm sources? All income is from farm sources (no off-farm or non-farm income) More than half of income is from farm sources Household income is evenly split between farm and off-farm sources Less than half is from the farm; most income is from off-farm sources (wages, salaries, pensions, income from non-farm businesses, or dividends and interest) Very little is from the farm; almost all income is from off-farm sources

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LAND USE ISSUES IN CACHE COUNTY We are particularly interested in land use change in this county, and the role of local community groups, leaders, and policies in shaping the future decisions of landowners.

F1. Approximately how many houses are adjacent to the land you own or rent? (for example., they share a property line with land you own or rent or are across a road from this land) ____ Houses Of these adjacent houses, • In how many of them do you know at least one household member? ____ Houses • How many were built in the last 5 years? ____ Houses

F2. On a scale of 1 to 7, do you think population growth and development in Cache County is having a positive or negative impact on the quality of life in the county?

Very Mixed Very Negative Impact Positive 1 2 3 4 5 6 7

F3. On a scale of 1 to 7, do you think population growth and development in Cache County is having a positive or negative impact on farming in the county?

Very Mixed Very Negative Impact Positive 1 2 3 4 5 6 7

F4. On a scale of 1 to 7 (where 1 is ‘very pessimistic’ and 7 is ‘very optimistic’) are you optimistic or pessimistic about the future of agriculture in Cache County?

Very Mixed Very Pessimistic Opinion Optimistic 1 2 3 4 5 6 7

F5. If you wanted to sell some or all of your farmland for residential home development, how easy or difficult would it be for you to get permission from local government?

Very Neither Easy Very Easy Nor Difficult Difficult 1 2 3 4 5 6 7

F6. Overall, when it comes to allowing housing development on farmland, do you think local land use policies are too restrictive, about right, or too permissive in this county?

Much too About Much too Restrictive Right Permissive 1 2 3 4 5 6 7

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F1. There are a range of policies used by local governments to manage land use changes. For each of the following types of policies, on a scale from -2 to +2, please indicate how you and your family have been impacted by the policy. (If a policy does not exist here, check the circle)

Type of Impact Policy Strong No impact Strong does negative or mixed positive not impact impact impact Type of Policy exist -2 -1 0 +1 +2 Comprehensive plan { Comprehensive plan that identifies areas for { agricultural use Zoning ordinance that identifies a district for { agricultural uses Requirements that new housing developments { prove they have sufficient water rights Requirements that new subdivisions build roads { to meet county standards Subdivision regulations restricting ability of { property owners to subdivide their parcels Economic development projects or programs to { support agriculture in the county Right-to-farm laws protecting farmers from { nuisance complaints Programs that tax farmland at its farm use, { sheltering it from higher property taxes

F2. Thinking of the land use policies in this county, on a scale from -2 to +2, what would you say the overall (or net) impact of these policies has been on the following outcomes?

Strong No impact Strong negative or mixed positive impact impact impact Type of Impact -2 -1 0 +1 +2 Keeping land in this county in farming ...... The viability of commercial farms in the county...... Enabling new farms to get started in the county...... Keeping residential development out of agricultural areas .. Protecting the rights of property owners......

F3. In general, how consistently are land use regulations enforced in this county? Very consistently Usually consistently Not very consistently

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F1. In the past five years, have any neighbors complained about agricultural operations on the land you own? NO YES

F2. How supportive of farming in the county are the following institutions or groups?

Not at all Somewhat Very supportive supportive supportive Type of Group or Institution 0 1 2 3 4 County government...... City/Municipal governments ...... Economic development organizations...... Media (such as newspapers) ...... Farm Bureau...... General Public ...... Local environmental organizations ......

F3. Over the next 5 years, how likely are you to do any of the following with your land?

Definitely Definitely won’t Not sure wil TYPE OF CHANGE -2 -1 0 +1 +2 Rent land to a farmer ...... Sell land to a farmer...... Sell housing lots directly to individuals ...... Sell any of my land to a developer...... Develop my own land for housing ......

F4. On a scale from -2 (strongly disagree) to +2 (strongly agree), for each of the following statements please indicate whether you disagree or agree.

Strongly Strongly Disagree Neutral Agree Statement -2 -1 0 +1 +2 Most residents of Cache County agree that farming positively contributes to the quality of life in the county Overall, farmers and nonfarmers in this county get along well More should be done to promote and develop agriculture here Diverse agricultural groups in this county work well together Local government does a good job of allowing public input into land use decisions in this county Land use policies in this county are effective at preserving farming in the county In general, the citizens of this county are very supportive of farming in the county

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Background Questions Finally, we need to ask a few questions about your background. This information, as with all information provided in this survey, will remain strictly confidential.

G1. How old are you? ____ years

G2. Are you male or female? Male Female

G3. How many years have you lived in this county? _____ years or all my life

G4. What is your highest level of formal education attained? Some high schools, but no diploma High school graduate (includes equivalency) Some college (no degree), associate degree, or completed technical school program Bachelors degree Graduate or professional degree

G5. Please indicate your current principal occupation or whether you are retired. (Check the one category that best applies to you.) Farmer Nonfarm wage or salary job Nonfarm self-employment Retired Other (Specify: ______)

G6. Do you belong to any of the following kinds of groups?

No Yes Farm organization (Farm Bureau, Grange, Farmers Union, etc.) ...... Farm commodity group...... A property rights organization ...... A smart growth/land-use related organization...... Any other types of local farm-related association......

G7. What is the approximate range of your total household income in 2006? Less than $15,000 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000 and over

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OTHER COMMENTS:

If you have any other comments that you would like to share with us at this time, please write them here (or on additional paper) and include them in the mailing envelope provided.

We would like to THANK YOU for taking the time to complete this survey. We know that you are busy and appreciate your help. Your responses will be combined with those of others across the country.

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