Assessing and Managing Soil Quality for Urban Agriculture Dissertation

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Assessing and Managing Soil Quality for Urban Agriculture

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Joshua Beniston, M.S.

Graduate Program in Environment and Natural Resources

The Ohio State University

2013

Dissertation Committee:

Rattan Lal, Advisor

Nicholas Basta

Kristin Mercer

Martin Shipitalo

Joshua W. Beniston

2013

ABSTRACT

The formerly industrial cities of the North Central region have become a rapidly expanding frontier for urban agriculture (UA) in the US. As populations in these cities have declined, a legacy of vacant land and properties has been left behind. UA has emerged as an important means of utilizing vacant land and is capable of producing numerous societal benefits, including: improved nutrition, increased food security, and income generating opportunities. Many UA sites are located in “urban food deserts,” or areas where citizens do not have access to nutritious foods in the quantities required by dietary recommendations. Participation in UA has been linked to increased consumption of fruits and vegetables at both the household and neighborhood levels. UA has also demonstrated the potential to generate economic revenue in impoverished areas in the region.

Urban soils, however, are highly variable and subject to high levels of anthropogenic degradation (Lehman and Stahr 2007). An understanding of local soil properties is a basic starting point for sustainable agriculture, yet very little information exists about the characteristics of urban soils or options for their management in the

North Central region, and little research has been conducted on crop production in urban ii areas in the U.S. as a whole. Even the published soil surveys lack specific information about urban soils and tend to group them together into generic urban land complexes.

Through this dissertation project I have worked address this unique knowledge gap by generating important background data and management recommendations for urban soils.

I wrote the first chapter of this dissertation with the objective of compiling and synthesizing information available about a number of topics that are key to this project, including: the extant of vacant land and UA in the region around Ohio, management of

UA systems, soil quality (SQ) assessment and soil properties and ecology in urban areas.

Post-industrial cities in the region contain large areas of vacant land. The city of

Cleveland, OH currently has more than 1,500 hectares (ha) of vacant land in the city, while Youngstown, OH contains more than 20,000 vacant city parcels. UA, including community gardens and market gardens, is increasing rapidly in the region as a beneficial use for these vacant lands. Extension personnel in Cleveland and Detroit, MI estimate that 35 and 20 market-based urban farms have come online in the past five years in those two cities, respectively. Soils in many vacant lots, however, have undergone heavy disturbance often associated with construction and demolition activities. As such these soils may be degraded. Key soil properties that must be addressed for UA include compaction, organic matter content, moisture characteristics and lead (Pb) contamination.

Soil testing and the application of amendments such as compost produced from urban greenwastes are key soil management practices for UA.

The second chapter of this dissertation presents an experiment that I designed, managed and analyzed with the objectives of evaluating soil properties in an urban soil following the demolition of vacant houses, as well as documenting the ability of

iii amendments produced from organic waste materials to improve SQ and support vegetable crop production under these conditions. The experimental site is located in a series of adjacent vacant urban lots in Youngstown OH where vacant houses were recently demolished and removed. The experiment utilized a split-plot design to test differences in soil properties and crop growth. Main plots received the following treatments: 1) control, 2) leaf compost, 3) leaf compost + hardwood biochar, 4) leaf compost + intensive cover cropping. Sub plots compared compared in ground cultivation with cultivation in 20cm raised beds. The experiment was conducted during the 2011 and

2012 growing seasons and a number of soil physical, chemical and biological properties were measured at the initiation and conclusion of the experiment. A SQ index was developed using multivariate analyses and scoring functions from the Soil Management

Asssessment Framework (SMAF). Significant soil compaction was observed at the site following demolition activities with bulk density values of 1.79 Mg m3 in ground and

1.55 Mg m3 in raised beds. Organic matter amendments resulted in significant improvement to a number of soil physical, chemical and biological properties. 2012 crop yields ranged from 0.35-3.6 kg m2 for tomatoes and 0.16-2.7 kg m2 for sweet potatoes.

SQ index values ranged rom 0.60 in control plots to 0.85 in compost+biochar. All observations suggest that amendments produced from urban green wastes can improve

SQ for UA.

The third chapter comprises a field study of SQ at nine urban market gardens in the cities of Cleveland and Youngstown, OH that I conducted during Autumn 2012. Soil samples were collected from these sites where producers are growing vegetables for market on urban soils. The samples were evaluated for soil physical, chemical and

iv biological properties and SQIs were generated for each site using SMAF. Linear discriminant analysis indicated that sites on soils with loam texture had distinct soil properties from those sites with sandy soil textures. Factor analysis indicated that several soil properties were important to explaining variability in UA sites included total C, CEC,

Mehlich 3 P, Mehlich 3 Pb, bulk density, aggregate mean weight diameter and available water capacity. SQI values for the urban market garden sites ranged from 0.75-0.91, which is a comparable to those reported for rural agro-ecosystems. All observations in this study indicate that UA is not constrained by soil properties at these sites and that management for UA can result in high quality soils.

The fourth chapter contains a synthesis of observations taken from the research presented in Chapters 2 and 3, as well as other studies on urban soils and UA published since Chapter 1 was written, and presents data on the potential of UA to address food security in American cities, soil conditions in vacant urban lots, recommended management practices and researchable priorities for the topic of UA.

DEDICATION

This dissertation is dedicated to the many people that have worked hard to transform derelict urban spaces into beautiful, living, and productive gardens that

are a source of inspiration and pride for their communities.

ACKNOWLEDGEMENTS

I could not have completed this extensive project without the assistance of many people who helped me along the way. With sincere gratitude, I thank you all. I was able to pursue my PhD at The Ohio State University because of opportunities provided to me by my advisor Prof. Rattan Lal. Thank you, Dr. Lal for everything that you have done on my behalf. Your work ethic, dedication and humor are an inspiration. I also wish to thank my committee members: Dr Nick Basta, Dr Kristin Mercer, and Dr Martin

Shipitalo. Dr Basta gave me early encouragement to pursue research on urban soils and I learned a tremendous amount about soil contamination from him. Dr Mercer’s continued openness to discussion, thoughtful critiques and sharp eye for details have helped me to improve this project at every stage in its development. Dr Shipitalo has advised me throughout my time at Ohio State and has consistently tried to help me to become a better soil scientist and writer. Dr Joe Kovach also served on my committee through my candidacy. Dr Kovach’s work was an inspiration to me on the ability of research experiments to be both novel science and intensely practical and he gave me very useful advice on setting up my own experiment. vii This project would not have been possible without the help of many people involved in making urban agriculture a reality in Ohio. I thank my brother Ian Beniston,

Liberty Merrill and the staff of the Youngstown Neighborhood Development Corporation for support and collaboration on my field experiment. I applaud YNDC on their continued ability to create positive change in their community. I thank Morgan Taggart at

Ohio State Extension in Cuyahoga County and Erin Laffay of Erie’s Edge Farm for their valuable support of my field study. I thank the urban gardeners who discussed their work with me and allowed me to sample their sites, including: Urban Growth Farm, Bay

Branch Farm, Needham Gardens, Iron Roots Farm, Refugee Response Farm, Sandra

Albro and the Cleveland Botanical Garden’s Green Corps Program. I thank everyone with Green Triangle for the opportunity to collaborate on teaching classes in NE Ohio.

I thank the institutions that provided funding and material support for my research and education. CERES Trust and Youngstown Neighborhood Development provided the funding for my urban soil experiment. The OARDC SEEDs program provided funding for my field study. USDA’s National Institute of Food and Agriculture funded me as a doctoral fellow for the past year, which gave me the time and focus to complete this project. The School of Environment and Natural Resources and the Carbon Management and Sequestration Center employed me as a teaching and research associate which made it possible for me to pursue my degree.

Many staff members and visiting scholars in CMASC and SENR have helped me to complete my work and develop as a researcher. Sandy Jones and Basant Rimal helped me whenever I asked. I thank Theresa Colson, Jennifer Donovan, and Amy Schmidt for their administrative and personal support throughout this process. Dr Greg Hitzhusen was

viii a supportive and inspiring teaching mentor. Dr Jennifer Dungait was a strong research mentor who encouraged and challenged me. Dr Juca Sa’s work and enthusiasm have been an inspiration. Dr Gudrun Gisladottir provided me with a great opportunity of studying soil in Iceland.

I thank my fellow students that I shared time and many experiences with while at

Ohio State. I thank Ryan Hottle for being a supportive friend and colleague. I thank Ji

Young Jung, my senior student, who taught me many things that helped me succeed in graduate school. I thank Claire Sutton and Jessica Schroeder for the hard work they contributed to my field and lab work. I thank Nick Stanich, Chris Eastman, Samy Sekar, and Olga Vilmundardottir for their friendship and collaboration.

Lastly, I thank my family who have supported me throughout my life and through the process of working on my PhD. My parents Martha and Bill gave me a good home and anything that I have needed throughout my life. I thank my brother Ian and sister in law Krista, and my sister Abby, nephew Ben and brother in law Colin for their support during the past couple years of working in NE Ohio. I thank my sister in law Nicole and my father in law Guy for their encouragement and support during my program. I love you all and thank you for everything you have done for me. I thank my wife and best friend

Kat and my son Wyatt for their love, support, and patience and the joy that they bring to our home. I love you both more than anything else.

VITA February 10, 1978...... Born-Youngstown, OH U.S.A. 1996-2001...... B.S. in Plant Biology, Ohio University, Athens OH 2001-2002...... Americorps VISTA, Rural Action Sustainable Forestry Project, Rutland OH 2002-2004...... Staff and Manager, Belize Agroforestry Research Center San Pedro Columbia, Belize, C.A. 2004-2007...... Horticulturist/Landscape Designer, Habitats Ecological Design and Landscaping 2007-2009...... M.S. in Environment and Natural Resources, specialization in Soil Science, The Ohio State University, Columbus OH 2009-Present...... Graduate Research and Teaching Associate, School of Environment and Natural Resources, The Ohio State University, Columbus OH

PUBLICATIONS

Beniston J, Lal R (2012) Improving soil quality for urban agriculture in the North Central U.S. In Lal R, Augustin B (eds.) Carbon sequestration in urban ecosystems. Springer, Dodrecht, Holland pp 279-314.

Culman S, Snapp SS, Schipanski ME, Freeman MA, Beniston J, Drinkwater L, et al. (2012) Permanganate oxidizable carbon reflects a processed soil fraction that is sensitive to management. Soil Science Society of America Journal 76: 494-504.

x FIELD OF STUDY

Major Field: Environment and Natural Resources

Specialization: Ecosystem Science

TABLE OF CONTENTS

ABSTRACT ...... ii DEDICATION ...... vi ACKNOWLEDGEMENTS ...... vii VITA ...... x TABLE OF CONTENTS ...... xii LIST OF TABLES ...... xvi LIST OF FIGURES ...... xix LIST OF PHOTOS ...... xxi

CHAPTER 1 IMPROVING SOIL QUALITY FOR URBAN AGRICULTURE IN THE NORTH CENTRAL U.S...... 1 1.1 Abstract: ...... 1 1.2 Introduction ...... 2 1.3 Shrinking Cities ...... 3 1.4 The Potential for an Urban Agriculture in the Midwest U.S...... 5 1.5 Urban Agriculture and Food Security ...... 6 1.6 Production Potential of Urban Agriculture ...... 8 1.7 Agroecology for Urban Agriculture ...... 10 1.8 The Soil Quality Framework ...... 12 1.9 Urban Soil Ecology ...... 16 1.10 Soil Compaction ...... 18 1.10.1 Soil Management to Mitigate Compaction ...... 19 1.11 Soil Organic Carbon Content ...... 21 1.11.1 Reduced Tillage ...... 21 1.11.2 Organic Matter Inputs ...... 22 1.12 Soil Moisture Characteristics ...... 25 1.12.1 Poorly Drained Soils ...... 25 1.12.2 Soils with Water Deficit ...... 26 xii 1.13 Soil Lead (Pb) Contamination ...... 27 1.13.1 Testing Soils for Pb Contamination ...... 28 1.13.2 Management for Soils with Pb Contamination ...... 29 1.14 Biological Functioning in Urban Soils ...... 31 1.14.1 Improving the Biological Properties of Urban Soils ...... 33 1.15 Conclusion ...... 35 1.16 References ...... 40

CHAPTER 2 ASSESSING AND MANAGING SOIL QUALITY FOR URBAN AGRICULTURE IN A DEGRADED VACANT LOT SOIL ...... 65 2.1 Abstract ...... 65 2.2 Introduction ...... 66 2.3 Materials and Methods ...... 69 2.3.1 Site description and experimental design ...... 69 2.3.2 Management practices and crop measurements ...... 70 2.3.3 Crop yield measurements ...... 72 2.3.4 Soil sampling ...... 72 2.3.5 Soil physical analyses ...... 73 2.3.6 Soil chemical analyses ...... 73 2.3.7 Soil biological analyses ...... 74 2.3.8 Data Analysis ...... 74 2.3.9 Soil quality index ...... 75 2.4 Results and Discussion ...... 76 2.4.1 Baseline soil physical properties ...... 76 2.4.2 Baseline soil chemical properties ...... 77 2.4.3 Baseline soil biological properties ...... 77 2.4.4 Soil physical properties following two years of management ...... 78 2.4.5 Soil chemical properties following two years of management ...... 79 2.4.6 Soil biological properties following two years of management ...... 80 2.4.7 Soil C pools ...... 80 2.4.8 Crop yields ...... 81 2.4.9 Correlation between soil properties and crop yields ...... 83 2.4.10 Soil quality index ...... 85 2.4.11 Implications for urban agriculture ...... 87 2.5 Conclusions ...... 89 2.6 References ...... 90

CHAPTER 3 SOIL QUALITY EVALUATION OF URBAN MARKET GARDENS ...... 108 3.1 Abstract ...... 108 3.2 Introduction ...... 109 3.3 Materials and Methods ...... 112 3.3.1 Site descriptions ...... 112 3.3.2 Soil sampling ...... 112

xiii 3.3.3 Soil physical analyses ...... 113 3.3.4 Soil chemical analyses ...... 113 3.3.5 Soil biological analyses ...... 114 3.3.6 Data Analysis ...... 114 3.3.7 Soil Quality Index ...... 115 3.4 Results and Discussion ...... 117 3.4.1 Soil physical properties ...... 117 3.4.2 Chemical properties ...... 117 3.4.3 Biological or biochemical properties ...... 119 3.4.5 Grouping sites according to soil conditions ...... 120 3.4.6 Soil quality index for sites with Loam textures ...... 121 3.4.7 Soil quality index for sites with Sandy textures ...... 122 3.4.8 Soil quality index using all measured indicators ...... 123 3.5 Conclusion ...... 126 3.6 References ...... 128

CHAPTER 4 UTILIZING URBAN SOILS FOR CROP PRODUCTION ...... 145 4.1 Abstract ...... 145 4.2 Introduction ...... 146 4.3 Can urban agriculture address food security in developed countries? ...... 148 4.4 Ecosystem services from UA systems ...... 152 4.5 Soil conditions in vacant urban lots ...... 154 4.6 Critical limits of agronomic soil properties ...... 156 4.7 Soil Testing ...... 157 4.8 Testing urban soils for Pb ...... 157 4.9 The application of organic amendments to urban soils ...... 158 4.10 Utilizing cover crops for UA ...... 161 4.11 Conclusions ...... 162 4.12 References ...... 164

CHAPTER 5 SYNTHESIS AND FUTURE RESEARCH DIRECTIONS ...... 175 5.1 Synthesis ...... 175 5.2 Future Research Directions ...... 179

REFERENCES ...... 182

APPENDIX A: SUPPLEMENTARY DATA FOR CHAPTER 3 ...... 204

APPENDIX B: EFFECTS OF MAIZE RESIDUE REMOVAL AND TILLAGE ON SOIL EROSION, CARBON AND MACRONUTRIENT DYNAMICS ...... 214 B.1 Abstract ...... 214 B.2 Introduction ...... 215 B.3 Materials and Methods ...... 219 xiv B.3.1 Study site ...... 219 B.3.2 Experimental design ...... 219 B.3.3 Soil and vegetation sampling and analyses ...... 220 B.3.4 Rainfall simulation ...... 221 B.3.5 Runoff collection and analyses ...... 222 B.3.6 Runoff analyses ...... 222 B.3.7 Sediment analyses ...... 223 B.3.8 Data Analysis ...... 223 B.4 Results ...... 224 B.4.1 Effects of tillage on soil properties ...... 224 B.4.2 Effects of residue removal on soil properties ...... 224 B.4.3 Effects of tillage on run-off and nutrient export ...... 225 B.4.4 Effects of residue removal on run-off and nutrient export ...... 226 B.4.5 Effect of tillage on bulk soil and eroded sediment δ13C and δ15N ...... 227 B.4.6 Effect of residue removal on bulk soil and eroded sediment δ13C and δ15N . 227 B.4.7 Effect of tillage on sources of C in the soil and eroded sediments ...... 228 B.4.8 Effect of residue removal on sources of C in the soil and eroded sediments 229 B.5 Discussion ...... 229 B.5.1 The effect of tillage ...... 229 B.5.2 Effects of residue removal ...... 232 B.5.3 Relationships between macronutrients during erosion by water ...... 233 B.6 Conclusions ...... 234

LIST OF TABLES

Table 1.1 Expansion of vacant land and urban agriculture in shrinking cities...... 50

Table 1.2 Measured urban agriculture yields...... 52

Table 1.3 Altered environmental factors affecting crop productivity in urban areas

……………………………………………………………………………………..53

Table 1.4 Critical ecological functions supported by urban soils and methods useful in their assessment...... 54

Table 1.5 Potential soil-based constraints found in urban soils...... 55

Table 1.6 Recommended small scale intensive management strategies for soil-based constraints to production in urban agriculture...... 56

Table 1.7 High and low bulk density values observed in ecological studies of urban soils...... 58

Table 1.8 Recommended management practices (RMPs) for mitigating Pb contamination risk in urban agricultural soils...... 59

Table 2.1 Summary statistics of baseline soil properties from soil samples from collected in June 2011...... 96

xvi Table 2.2 Measured soil properties from an agriculture experiment in a vacant urban lot soil from final sampling in September 2012...... 98

Table 2.3 Model selection of covariables for explaining variation in relative crop yields...... 102

Table 2.4 Factor loadings of soil properties at an agriculture experiment in a vacant urban lot soil from, a factor analysis, using a quartimax rotation and five factors

...... 103

Table 2.5 Soil property and soil quality index (SQI) scores from Soil Management

Assessment Framework (SMAF)...... 104

Table 3.1 Locations and soil descriptions for urban market gardens in northeast Ohio

...... 133

Table 3.2 Summary statistics of measured soil properties at urban market gardens in northeast Ohio...... 135

Table 3.3 Factor loadings of soil properties for the sites with loam soil texture classes

…………………………………………………………………………………….138

Table 3.4 Measured values and soil quality scores from the soil management assessment framework (SMAF) for overall soil quality and for individual soil properties at sites with loam textures in northeast Ohio...... 139

Table 3.5 Factor loadings of soil properties for the sites with sandy soil texture classes

...... 140

Table 3.6 Measured values and soil quality scores from the soil management assessment framework (SMAF) for overall soil quality and for individual soil properties at sites with sandy textures in northeast Ohio...... 141

xvii Table 3.7 Measured values and soil quality scores from the soil management assessment framework (SMAF) for all measured soil quality indicators and overall soil quality for urban market garden sites in northeast Ohio……………………………...….…..142

Table 4.1 Potential benefits of urban agriculture...... 169

Table 4.2 Potential for crop production from urban agriculture to meet consumption demands through cultivation of vacant land in cities in the U.S...... 170

Table 4.3 Threshold levels of key soil properties impacting site suitability for urban agriculture...... 171

Table 4.4 Selected soil surface properties from a survey of vacant lots in Cleveland, OH

...... 172

Table 4.5 Researchable priorities for enhanced management of urban agriculture systems...... 173

Table A.1 Summary statistics for soil physical properties for individual sites…...205

Table A.2 Summary statistics for soil chemical properties for individual sites…..208

Table A.3 Summary statistics for soil biochemical properties for individual sites.211

Table B.1 Effect of tillage and residue removal treatments on soil physical and chemical properties……………………………………………………………………..…....243

Table B.2 Effect of tillage and residue removal treatments on run-off and concentrations of sediment and macronutrients in runoff……………………………………...... 244

Table B.3 Effect of tillage and residue removal treatments on the mass of sediment, sediment bound C and macronutrients, and dissolved C and macronutrients in run-off.

………………………………………………………………………………..……245

xviii

LIST OF FIGURES

Figure 1.1 The effect of previous land use on soil properties…………………….60

Figure 1.2 Conceptual model for utilizing organic waste to improve soil quality in urban environments...... 61

Figure 1.3 Conceptual model for assessment and management of vacant lot soils for

UA...... 62

Figure 2.1 Soil C pools calculated on equivalent soil mass at an agricultural experiment in a vacant urban lot soil...... 105

Figure 2.2 Measured 2012 yields for (a.) tomato and (b.) sweet potato crops grown in an agricultural experiment in a vacant urban lot soil...... 106

Figure 3.1 Histograms of results of linear discriminant analysis demonstrating the separation of groups of sites according to classing variable of (a.) “Soil Texture” and (b.)

“Parent Material”...... 144

Figure 4.1 Area of land under peri-urban agriculture in metropolitan Vancouver, B.C

……………………………………………………………………………………174

xix Figure B.1 Natural abundance δ13C and δ15N stable istotope signatures from topsoil and eroded sediment samples……………………………………………………...….246

Figure B.2 Contribution of C4 and C3 sources of C to carbon contents of topsoil and eroded sediment samples…………………………………………………………247

LIST OF PHOTOS

Photo 1.1 The transformation of vacant properties into UA spaces...... 63

Photo 1.2 Permanent raised beds are used for vegetable production at the urban farm in

Braddock, PA...... 64

Photo 2.1 The layout of the experimental garden…………………………...……107

xxi

CHAPTER 1

IMPROVING SOIL QUALITY FOR URBAN AGRICULTURE IN THE NORTH

CENTRAL U.S.

1.1 Abstract: An abundance of vacant land exists in the formerly industrial cities of the north central

U.S., which have seen tremendous declines in their populations over the past 50 years.

Many cities are looking to utilize this land for functional greenspace to improve the overall quality of life. In Ohio, Cleveland and Youngstown contain >1,500 and >2,500 ha of vacant land, respectively, while larger Detroit, MI is estimated to contain between

2,000 - 10,000 ha. Urban agriculture (UA) has emerged as a land use that can provide food production, economic benefits and enhanced ecosystem services on vacant urban parcels. Early data suggest that urban specialty crop cultivation can be quite productive, yielding 2 -7 kg m-2, depending on crop and conditions. Given the inherent variability and impact of heavy disturbance common in urban soils, an objective soil quality assessment is necessary to optimize their use for crop production and functional greenspace. Soils in many vacant lots have undergone heavy disturbances, which can lead to severe degradation of their physical, chemical and biological properties. Key constraints to agricultural productivity faced in urban soils include compaction, low levels of organic

1 matter, altered soil moisture characteristics, and lead contamination. Numerous low cost, small-scale intensive methods of improving urban soils exist including the utilization of organic wastes and by-products. The huge quantities of organic materials produced in urban areas have the potential to be processed into high quality soil amendments. Vacant lots in the North Central US can become a valuable asset for community development and improved food security if their soil quality is adequately addressed.

1.2 Introduction Amidst the tremendous increases in the interest and application of urban agriculture (UA) in U.S, this review attempts to relate studies on UA from other parts of the world and ecological surveys of soils in urban areas to the context of UA in North

America, and specifically the shrinking cities of the North Central and Eastern U.S.

While there is a wide body of literature on UA from around the world, little research to date has been conducted on UA in the U.S. Similarly, while ecological studies of soils in urban areas are increasing, few have assessed the agronomic potential of urban soils. The principal objective of this review is to describe the management of soil quality for crop production in urban areas of the U.S. Additional objectives are to: 1) describe the increase in urban agriculture in the North Central U.S within the context of vacant urban properties, 2) assess the potential of UA to contribute to food production and food security in the U.S., 3) review background information on urban soils in the U.S., 4) describe the importance and utility of soil quality assessment in managing urban soils for crop production, 5) identify specific soil-related constraints to crop production that may be common in urban areas, and 6) suggest small scale intensive management strategies for improving soil quality in urban agro-ecosystems.

2 1.3 Shrinking Cities “What it (the Home Gardening association) has accomplished cannot be computed in dollars and cents. By beautifying vacant lots and yards in nearly every section of the city, it has greatly increased realty values beside adding beauty to the city.

But what is more important, it has made the health of the city better. It has gotten people out of doors to cultivate flower and vegetable gardens who before never ventured into a garden. They live and feel better. The value of the work of the association in this way can hardly be overstated” (Cleveland Plain Dealer 1907). These words were written just over a century ago about the work of a community gardening organization in Cleveland,

Ohio, and yet the observations expressed here are completely relevant to changes that are unfolding across the U.S. today. Urban gardens and agriculture are going through a great resurgence and generating tremendous interest. During the past few years, UA has become an important topic to a number of disciplines including city planning, community development, horticulture, and soil science. Communities across the country are attempting to utilize UA projects as a means of improving the health and sustainability of cities and as a means of utilizing vacant urban land, nowhere are these trends more visible than in the so called “shrinking cities” of the Eastern and North Central U.S.

While populations are currently increasing in many urban areas, the collapse of industrial economies and the migration of residential and commercial land uses to suburban areas have left many cities in the North Central U.S. with shrinking populations. Nowhere is this trend more visible than in the “rustbelt” cities, such as

Detroit, Cleveland, and Youngstown (Table 15.1). These cities share the historical pattern of being designed for peak populations during the regional industrial boom of the

3 early and mid twentieth century. Many of the industries, such as steel and manufacturing, that employed the region’s population either reduced their work force, or in many cases, relocated their operations internationally during the 1970s-90s. Since that time, populations in these cities have declined steadily and large areas of vacant land and vacant properties have been left behind.

The population decline figures of industrial cities in the north central US are staggering. In Ohio, Cleveland has lost 57% of its 1950 population and the smaller city of Youngstown has witnessed a 61% decline from its 1950 population (Table 1.1).

Population decline over just the past 30 years (since 1980) has also been significant with

Cleveland’s population shrinking by 32% between 1980 and 2007, and Youngstown’s population shrinking by 44% during the same period (Mallach and Brachman 2010).

The infrastructure and footprint of these cities were designed according to peak population numbers, so large population declines have led to large increases in vacant properties. The current figures on vacant land within city limits in these cities and others in the region are just as striking as the population figures. The city of Cleveland currently has at least 1500 ha of vacant land within city limits, while Youngstown, OH has over 2,800 ha of vacant land (CUDC 2008). Depending on the estimate, Detroit, MI has an area between 2,000- 10,000 ha of vacant land within the city (Table 1.1).

An abundance of vacant properties can lead to negative patterns of reduced property values and increased crime in affected areas, so many cities have undertaken initiatives to demolish or deconstruct vacant buildings. A recent report on the subject of shrinking cities in Ohio by the Greater Ohio organization and the Brookings Institute has suggested that given the oversupply of housing and current weak markets, shrinking

4 cities in Ohio and throughout the region will need to develop methods of utilizing vacant land that do not rely on traditional redevelopment models (Mallach and Brachman 2010).

The opportunities that vacant lots provide in cities are primarily opportunities to create functional greenspaces.

Cities are now attempting to utilize vacant lots for the social and ecological services that greenspaces can provide. An extensive, multidisciplinary literature review on the benefits of urban greenspaces suggests that well planned greenspaces can improve ecological services and the physical and psychological health of people in cities (Tzoulas et al. 2007). Pocket parks, wildflower meadows, storm-water infiltration basins, community gardens and urban farms are all models that have been utilized and proposed in vacant lots (CUDC 2008). The proximity of these vacant lots to large residential neighborhoods has placed them at the center of the upsurge in UA in the north central

U.S. As communities look to find solutions to the problem of vacant land, community gardens and urban farms have emerged as a land use with the potential to provide numerous societal benefits (Smit et al. 1996) (Photo 1.1).

1.4 The Potential for an Urban Agriculture in the Midwest U.S. UA is undergoing a massive resurgence in the north central U.S., as a win-win solution to the issue of vacant land. Extension personnel in Cleveland, OH estimate that approximately 75 new urban gardens have come online in the past 5 years (Table 1.1)

(M. Taggart, personal communication 2010), while larger Detroit now has over 700 urban gardens and has become a showcase of UA being implemented at a significant scale. Chicago, Milwaukee, and Pittsburgh are among the other cities in the region where

UA is expanding rapidly on vacant land. UA has allowed communities to put vacant

5 land, and the citizens who live in its proximity, to work on the task of producing food for those communities. Agriculture in cities can bring a host of societal benefits. The United

Nations Development Program’s review of UA around the globe has suggested that it increases nutrition, enhances food security, and creates employment opportunities (Smit et al. 1996).

1.5 Urban Agriculture and Food Security While little research exists on UA efforts in the US and more developed countries, a large number of studies have documented social and economic benefits of UA in

Africa, Latin America, and Asia (Pearson et al. 2010, De Bon et al 2010). A recent review suggested that UA is done primarily for recreation in developed economies, while its primary objective is enhancing food security in developing economies (Pearson et al.

2010). Similarly, a common sentiment in this body of research is that UA offers a greater benefit to populations in the global south, as people in the northern cities of the U.S. tend, on the whole, to spend a small percentage of their total income on food, live in cities with better food distribution networks, and have higher quality foods available to them, than people in the cities of the global south (Redwood 2010, Smit et al. 1996). While it is true that conditions of food access are different in the U.S., compared with the global south (Smit et al. 1996), there is strong evidence that UA has a significant role to play in the goal of improving food security in the cities of the U.S.

In a report focused on improving the quality of life in Ohio’s cities, researchers from the Brookings Institute and the Greater Ohio organization stated that in Ohio the opportunity exists to implement UA at a scale where it can be a major resource for both community food security and economic development (Mallach and Brachman 2010).

6 These opportunities stem largely from the fact that vacant urban land tends to be centralized among the most economically disenfranchised urban areas (Mallach and

Brachman 2010, CUDC 2008). These areas often face serious challenges with regard to food security and access to fresh food: a phenomenon termed “urban food deserts.” Food deserts are defined as areas where nutritious foods, such as fruits and vegetables, are simply not available to the average citizen in the quantity needed to make up a healthy diet (Wrigley 2002). Research has demonstrated that low-income, non-white urban areas in the U.S. tend to have higher densities of fast food restaurants (Block et al. 2004), higher densities of liquor stores (LaVeist et al. 2000), and lower densities of grocery stores (Chung and Meyers 1999), compared with more affluent areas. A walk through a low-income neighborhood in any American city is likely to validate these observations, as it is far easier to purchase alcohol, tobacco, and sugar-rich candies than it is to purchase fresh vegetables or healthy groceries in the corner stores of major cities.

The current global economic depression and concurrent rise in food prices appear to be exacerbating food insecurity in urban areas. The most recent USDA survey of food security in the U.S., with data from 2008, indicated that 14.7% of U.S. households experienced some food insecurity and 5.7% of all households had very low food security, during 2008 (Nord et al. 2009). These levels of domestic food insecurity are the highest recorded by the USDA, since they began food security surveys in 1995. There is also evidence that globally the urban poor are the group experiencing the greatest food insecurity impacts from the global financial crisis and increasing food prices, as these populations are highly dependent on the cash economy and purchased foods to meet their dietary needs (Ruel et al. 2010). UA, while far from being a complete solution to this

7 problem, may at least offer urban populations a reliable, affordable food source and an increased access to nutrient rich foods (Reul et al. 2010, Zezza and Tasciotti 2010).

World War II era “Victory Gardens” offer an American historical precedent for implementing UA during times of economic crisis and are still cited among the most celebrated examples of gardening and civic activism.

UA is improving access to nutritious foods in the inner cities of the U.S. Data from Cleveland, OH demonstrate that many urban gardens are located in parts of the city that meet a food access criteria to be considered food deserts (CUDC 2008). A survey of community gardeners in Flint, MI observed that adults who participated in community gardens were 3.5 times more likely to consume 5 daily servings of fruits and vegetables than neighbors who did not participate in community gardens (Alaimo et al. 2008). A survey of UA data from 15 developing nations also demonstrated consistent correlations between participation in UA and adequate dietary nutrition (Zezza and Tasciotti 2010).

By virtue of their smaller, more intensive scale, urban gardens and farms commonly focus on producing high nutrition vegetable and fruit crops (often termed specialy crops) (De Bon et al. 2010). Examination of UA operations in Africa and Asia found that they were primarily producing nutritious vegetable crops, such as leafy greens

(De Bon et al. 2010). The perishable nature of these crops, and the scale of urban markets have made specialty crop production a good niche for urban producers globally.

1.6 Production Potential of Urban Agriculture UA produces significant quantities of food in the many cities of the world where it is a widespread practice. A United Nations Development Programme report on UA estimated that upwards of 15-20% of the world’s food supply may be produced by UA

8 (Smit et al. 1996). Mougeot (2005) provided an excellent synthesis of the available data on food production through UA which demonstrated that UA provides a significant dietary contribution in many of the world’s cities, including: a number of African cities that produce 90-100% of their leafy vegetables; 60% of food consumption in 25% of poor households in Harare, Zimbabwe; 47% of produce in urban Bulgaria; and 500,000

Mg of produce in urban Poland. In Cuba, UA accounted for around 60% of all vegetable production in the nation during 2001(Premat 2005).

Recent production estimates for American cities suggest that UA can make measurable contributions to specialty crop markets in the U.S. A recent study estimated that Detroit, MI could produce 31% of the vegetables and 17% of the fruits currently consumed by city residents on between approximately 100 - 350 ha, given either relatively “high” or “low” yields (from Jeavons 1995) from bio-intensive agriculture

(Colasanti and Hamm 2010). Researchers from the University of Pennsylvania conducted an extensive survey of 226 community gardens in Philadelphia during the

2008 growing season (Vitiello and Nairn 2009). They estimated that over 900,000 kg of vegetables, worth over $4,000,000, were produced in these gardens and distributed throughout the community through informal networks.

The potential of UA to contribute to food systems in the U.S. is just beginning to be understood. A major gap in current research is data on measured yields of specialty crops in urban areas. Table 1.2 is a compilation of available data on UA production statistics for North America. One significant message that these data convey is that the

UA production numbers are significantly higher than the average commercial yields of vegetables (Zandstra and Price 1988). This is likely due to the small-scale nature of UA

9 operations, as many researchers have noted that small scale systems can be more productive than commercial scale agriculture because small scale systems tend to be more intensive in nature (Pretty 1997). Kovach’s (2010) data show the promise of well tended fruit crops to be highly productive in urban spaces. His research on intensive polyculture production has also demonstrated that fruit crops can generate higher economic returns for urban scale producers than vegetables. These early figures and estimates suggest that this is merely the beginning of understanding the potential impact that UA can have on the North American food system.

1.7 Agroecology for Urban Agriculture While the social and economic benefits of UA have been extensively researched, little research exists on the agronomy and ecological management of UA systems

(Eriksen-Hamel and Danso 2010). While horticulture and crop production are very similar in nature in both urban and rural areas, there are a number of unique ecological influences in urban areas that affect crop production. Urban landscapes have unique biogeochemical conditions and processes compared with more natural ecosystems and agricultural landscapes (Kaye et al. 2006). Many basic ecosystem components and processes are profoundly altered within cities, including climatic conditions, water infiltration, nutrient cycling, resource inputs, and vegetative cover and composition

(Pickett et al. 2001, Kaye et al. 2006). All of these processes have profound effects on agricultural systems, so it follows that agronomic conditions in urban areas are also altered.

Eriksen-Hamel and Danso (2010) identified thematic topics where more research is needed to better understand the effects of urban biophysical conditions on UA and

10 management responses to those altered conditions. They identified both altered ecological conditions affecting crop growth in urban areas (Table 1.3) and limiting factors that may constrain crop production in UA. Their list of potential production constraints includes water availability, nutrient supply, soil degradation, pest pressure and soil pollution. These are all issues which can be addressed, at least to some degree, through the sustainable management of urban soils.

In cities, as in all environments, the understanding and management of soils is a basic starting point for sustainable agriculture. The remainder of this review explores the unique ecology of urban soils, the agronomic assessment of these soils, soil-related constraints to agricultural productivity that are common in urban soils and intensive management strategies aimed at alleviating these constraints. UA represents a frontier for soil and agronomic science. Developing research and management programs focused on the agro-ecological management of urban soils can allow for the more effective utilization of these areas for crop production and improve UA’s ability to contribute to food security.

Intrinsic variability, and the fact that urban soils exist on a gradient between strong and poor overall quality suggests that urban agriculturalists must take an analytical, site-based approach when determining agronomic management options for these soils. The overall quality and health of vacant lot soils is strongly determined by their previous land use. Soils on lots that have been under perennial vegetation for long periods of time are likely to be of strong overall quality (Grewal et al. 2010). These soils may be readily suited for horticultural production. Conversely, soils on lots that have recently undergone building demolition are likely to have been heavily degraded by that

11 process and require significant targeted management to improve their overall condition

(US EPA 2011). The variability in urban soils, coupled with the high number of soil properties that may be affected by the urban environment, make comprehensive soil quality evaluation an excellent management choice for these soils.

1.8 The Soil Quality Framework The soil quality framework is a useful tool for assessing site-specific soil conditions and developing adaptive management strategies. The concept of soil quality

(or soil health) is generally recognized as “the ability of a soil to function within ecosystem boundaries to sustain biological productivity, maintain environmental quality and promote plant, animal and human health”(Doran and Parkin 1994). Healthy soil function promotes the robust functioning of the wider ecosystem. Many essential ecological services are provided by soils, including: hydrological cycling, supporting plant growth, the cycling and storage of plant nutrients, the decomposition of organic matter, and the moderation of biogeochemical cycles (Daily et al. 1997). Deriving these functions from soils may be especially critical in urban areas (Table 1.4), where areas of soil and vegetation must provide ecosystem services within a much larger landscape of impervious surfaces. The terms soil quality and soil health are often used interchangeably. Soil quality is the term that is generally thought of as representing quantitative research on the subject, while soil health represents a farmer’s assessment of these properties in the field. Soil health may be a useful term to educators as the connection between healthy soils, healthy crops, and healthy people is concrete and easily explained.

12 Soil quality is typically evaluated by field and laboratory analyses of a suite of soil physical, chemical, and biological properties. Specific properties are chosen based on their value as ‘indicators’ of the effects of management on the soil. There is no standard for choosing soil indicators. Ideally, indicators are chosen based on the unique ecological and management conditions of a given soil system or the goals of a research project. Values measured for individual properties are then typically scored against measured and reported distributions from similar ecological conditions (Andrews et al.

2004; Gugino et al. 2009) and tabulated into an index, which gives a score of overall soil health (Karlen and Stott 1994). Thus a completed soil quality report for a site includes scores for individual soil properties and and the overall score of the site’s soil quality.

The Cornell Soil Health team has developed a unique approach to evaluating and managing soil quality (Gugino et al. 2009, Schindlebeck et al. 2008). Their soil quality lab presents results in a report card fashion that provides scores (0-100) for the individual indicators and an overall soil health score. Overall scores of 0-30 are considered low, while above 70 are in the optimum range, and those between 30-70 are intermediate. Soil properties receiving low scores (<30) are identified as constraints for that specific soil and these properties receive focus in the development of management plans to improve soil health. The Cornell team has developed the soil quality evaluation as a test that is available to producers and the public for a fee. The completed analysis results in a soil quality scorecard and management recommendations specific to the individual soil’s condition.

The process of identifying soil-related constraints to agricultural productivity and targeting them with adaptive management plans is an excellent framework for pursuing

13 improved soil quality. A full soil quality evaluation offers far more information about the overall condition of a soil than standard soil nutrient testing. Standard nutrient testing generally only provides information about a soil’s chemical condition and overlooks the critical physical and biological makeup of a soil. A soil that appears to be in good condition from a standard soil test may have severe physical and biological constraints such as degraded structure (Schindlebeck et al. 2008). Furthermore, many soil chemical constraints (nutrient deficiencies, non-optimal pH, etc.) are easily alleviated through fertilizer application while physical and biological constraints require longer-term changes in management practices (Magdoff and Van Es 2010).

Soil quality assessment is a useful tool in differentiating the condition of soils and sustainability of management in a wide variety of scenarios, including: soils receiving different nutrient inputs (Glover et al. 2000), managed by different tillage regimes

(Wander and Bollero 1999), receiving different levels of crop residues (Moebius-Clune et al. 2008), cultivated under diverse commodity crop rotations (Karlen et al. 2006) and amended/restored mineland soils (Shukla et al. 2004). Thus, results are promising for utilizing soil quality assessment to differentiate between the condition of urban soils.

Restoration ecologists working in cities have suggested that soil quality evaluation offers an excellent index for assessing the success of restoration projects in degraded urban sites (Heneghan et al. 2009). They suggest that a focused assessment and site-specific management is necessary to improve soil quality in uniquely urban soils

(Heneghan et al. 2009, Pavao-Zuckerman 2008). Sites with high levels of degradation require strong interventions to be restored to ecological health (Pavao-Zuckerman 2008).

As in agricultural soils, targeted management and manipulation of individual physical,

14 chemical or biological properties acting as constraints, such as soil compaction, may aid in the restoration of overall quality in degraded urban soils by improving ecosystem processes that limit the whole system (Heneghan et al. 2009).

In order to best assess the quality of UA soils, it will be necessary to generate more datasets from North American cities focused on agronomic conditions specific to these soils. These data can then generate soil scoring functions that will provide a more accurate tool for assessing soil quality in urban ares. A key gap in the scientific understanding that must be addressed in moving forward in UA is to identify specific soil-related constraints to production common in urban soils. In the absence of agricultural studies on urban soils in the U.S., this article reviews existing ecological and horticultural studies of soils in cities and identifies soil properties which are regularly subject to degradation in urban areas. This chapter begins with a basic overview of unique qualities of urban soils and then present potential soil constraints as well as management strategies which have shown promise for alleviating these in an attempt to further the discussion of managing urban soil quality for crop production.

Urban soils, particularly vacant lot soils, may be characterized by a range of agronomic constraints (Table 1.5). The most significant agricultural constraints that may occur regularly in degraded urban soils include the physical constraints of low levels of organic matter (OM), compaction and poor structure, and the chemical constraint of contamination. Constraints of degraded urban soils are reviewed and management options for each class of constraints will be presented. The vast majority of UA in the

U.S is currently located in small parcels of land (<.5ha), with many urban gardens being just single urban lots (approx. .05ha). Small parcels lead to a small-scale form of

15 agriculture, which requires that urban producers adopt highly intensive methods to achieve successful levels of productivity. Thus, this chapter focuses on methods of soil management that are both small scale and intensive in nature (Table 1.6). These management practices are characterized by requiring few external energy inputs, having low costs, and utilizing locally available resources.

1.9 Urban Soil Ecology An urban ecosystem can be thought of as a “biophysical social complex” (Pickett et al. 2008). Within this complex, social and economic activities alter and drive many biophysical processes. Urban ecosystems also tend to be defined by heterogeneity, as high levels of spatial heterogeneity have been identified for nearly all aspects of the urban ecosystem. Anthropogenic influence and high levels of spatial variability also characterize soils in urban areas (De Kimpe and Morel 2000). Urban soils can be defined at the most basic level as those existing within the boundaries of a city. Lehmann and

Stahr (2007) offered a useful distinction in defining urban soils by differentiating between natural and anthropogenic urban soils. The latter are soils that have been heavily affected by human activities, such as housing, industrial production, and disposal activities.

Early references on urban soils tended to apply a generalized view that soils in cities are heavily disturbed and characterized by undesirable properties that made using them for horticultural purposes difficult (Craul 1992). More recent ecological surveys of urban areas suggest that urban soils are highly heterogeneous and occur on a continuum between soils that are identical to the native soil types of the region and highly disturbed anthropogenic soils (Pickett et al. 2008, Pouyat et al. 2003). Spatial variability in urban

16 soils is often dictated by the dramatic societal influence on urban land. Land use has emerged as a principal predictor for variation in soil properties in urban areas. Studies comparing urban soils under different land uses have observed differences in a whole suite of soil properties, including: soil physical and chemical properties (Pouyat et al.

2007), compaction (Gregory et al. 2006), soil organic C (SOC) content (Pouyat et al.

2006, Pouyat et al. 2003), microbial biomass C and N (Lorenz and Kandeler 2005,

Lorenz and Kandeler 2006), and lead (Pb) contamination (Chaney et al. 1984, Wagner and Langley-Turnbaugh 2008) (Fig 1.1).

Heterogeneity is also often seen vertically in the profiles of disturbed, anthropogenic urban soils where horizons are not always found in parallel to the soil surface (DeKimpe and Morel 2000). This profile perturbation is the result of past land use practices of dumping fill and refuse and grading topography. This situation can result in unexpected profile distributions of soil properties. Lorenz and Kandeler (2005) documented a number of soils in their survey of urban areas in Stuttgart, Germany with deposits of high concentrations of SOC deep in the soil profile (>1m). Another study of

Pb contamination in Portland, Maine, U.S.A. reported high concentrations of Pb in subsoils of some residential areas (Wagner and Langley-Turnbaugh 2008).

Pickett and Cadenasso (2009) argued that urban influence on soils is so great that

Jenny’s (1941) model of soil formation must be adjusted significantly to explain pedogenesis in urban areas. They suggest that disturbance, altered resources, and spatial heterogeneity of social and ecological processes make up an additional level of soil forming processes that often supercede the traditional soil forming factors. In the majority of studies on soils in cities, disturbance and land use history have an overarching

17 influence on soil properties. Given their critical influence on urban soils, it is necessary to consider land use history in siting and managing urban farms and ideal sites are ones that have not been subjected to recent heavy disturbances.

1.10 Soil Compaction Soil compaction is a common condition in many urban soils (Meuser 2010; Craul

1999), and can be a serious constraint to plant growth and productivity. Soil compaction leads to significant reductions in soil pore space, plant available water capacity, rooting depth, soil biological activity and crop yield (Lal and Shukla 2004). Compaction is generally associated with the heavy traffic and disturbances, processes that occur regularly in the urban environment. Research has documented soil compaction in urban and peri-urban areas due to building construction (Pit et al. 1999), construction vehicle traffic (Gregory et al. 2006), and even for high rates of pedestrian traffic (Millward et al.

2011). Depending on the texture of a given soil, bulk density (BD) values ranging from

1.4 -1.7 g cm-3 are thought to have a negative effect on plant root growth, while BD values ranging from 1.5 - 1.8 g cm-3 are restrictive to root growth (NRCS 2000). Table

15.7 features mean BD values for different land uses reported in studies of soil physical properties in urban areas. A number of soils in those studies demonstrated BD values consistent with serious compaction. The values in the table also represent the variability encountered in urban soil BD, with more disturbed areas having greater BD values.

Soil compaction is a major ecological and horticultural constraint that leads to reduced functioning in affected soils. Compacted urban soils have consistently demonstrated significantly reduced water infiltration rates when compared with adjacent un-compacted soils (Millward et al. 2011; Gregory et al. 2006; Pit et al. 1999). The

18 widespread nature of compaction and the associated reductions in water infiltration in urban areas may lead to reduced soil moisture and lower rates of groundwater recharge

(Meuser 2010).

1.10.1 Soil Management to Mitigate Compaction While soil compaction can be a serious problem, there are several strategies for managing soils to alleviate compaction. Practices designed to alleviate compaction for crop and plant growth include subsoiling and cover cropping, and raised bed cultivation can allow the improved cultivation of crops in a less dense medium above the compacted layer.

Sub-soiling or deep tillage practices have been recommended in the past for alleviating the serious levels of compaction often found in degraded urban soils (Craul

1992). Sub-soiling is an agricultural operation where a chisel plow is used to “rip” or aerate the deeper subsoil layers. It is widely used by farmers to alleviate compaction in agricultural soils. While sub-soiling offers an effective method of reducing soil compaction, the specialized agricultural equipment involved may not lend itself to the smaller lots used in UA. The cost of sub-soiling may also limit its utility in restoring degraded urban soils (Heneghan et al. 2008). Thus, subsoiling is a feasible solution perhaps only for larger-scale urban farming scenarios.

Intensive cover cropping has the potential to be an excellent strategy for urban producers working with compacted soils. Research during the past two decades has documented the ability of a number of deep-rooted cover crops to reduce soil compaction. Species that serve this function exist for most planting times of the year.

Oilseed or daikon radish (Raphanus sativus) may be particularly well suited for small

19 vegetable operations. It is planted in the late summer/early autumn, winter kills, and has been observed reducing soil compaction in a soybean (Glycine max) rotation in Maryland

(Williams and Weil 2004). Sorghum/sudangrass (Sorghum bicolor x S. bicolor var. sudanese) is a highly productive summer fallow crop which produces tremendous root biomass when it is mowed (Clark 2007). An evaluation of the ability of cover crops to alleviate soil compaction on vegetable farms in upstate New York suggested that sorghum/sudagrass is exceptional in its ability to relieve soil compaction (Wolfe 1997).

Other cover crops of interest for compacted urban soils include yellow sweet clover

(Melilotus officinalis) and annual ryegrass (Lolium multiflorum) which, while not tap rooted, can improve soil structure with its extensive root biomass as an overwintering cover crop.

A related soil physical constraint in urban areas is the presence of asphalt. In some neighborhoods, former parking lots, or large asphalt covered lots are the only large areas available for gardening. Removing asphalt is often not economical for UA, so increasingly gardeners in these areas are exploring methods for creating soil on top of the asphalt (Kroll 2007). Large quantities of compost, wood chips, and other forms of OM are used to create raised beds over the asphalt. Cleveland, Ohio is home to several community gardens and at least two urban farms that grow produce in “soil” created over asphalt (Kroll 2007). Accounts of asphalt gardens are increasing in urban areas across the north central region, yet it is a subject that has not been researched. Questions remain regarding the possibility of contamination from the asphalt and regarding the overall quality of the soil substrate that is created. Given that these soils have been created largely from OM, research is needed on the nutrient content of both the substrate and the

20 resulting crops. Supplemental micronutrient fertilizers are likely necessary to optimize nutrient content.

1.11 Soil Organic Carbon Content In general, increasing the SOC, or soil organic matter (SOM), content of degraded soils improves their physical condition and soil health. Indeed addition of OM is the primary factor controlling the improvement of soil quality. Most sustainable soil management strategies involve improving the quantity, quality and diversity of OM inputs in a cropping system (Magdoff and Van Es 2010). Increases in a soil’s SOC pool are widely known to enhance and improve a wide range of properties and processes in the soil ecosystem, including: increasing invertebrates and microbes, improving soil structure, increasing soil water and nutrient reserves, and improving water quality by sorbing and filtering pollutants (Lal 2007). In degraded agricultural soils, an increase of

1 Mg of SOC has been shown to improve the yield of a number of staple crops (Lal 2006;

Lal 2010a, b). The fact that vacant lot soils are often physically degraded implies that they stand to gain significant agronomic benefits from increases in SOC.

1.11.1 Reduced Tillage The SOC is increased through a variety of soil management practices. SOC regenerative management practices that may be useful for urban producers include: reducing tillage, utilizing crop rotations, growing cover crops, using mulch, and applying compost/manure/biosolids (Lal et al. 1999). Excessive tillage is widely known to reduce

SOC, and to degrade soil physical quality. This is type of soil degradation can occur with the excessive use of rototillers, which are widely employed in small-scale vegetable production. Urban producers, and other small-scale farmers, are advised to minimize

21 tillage operations whenever possible, in the interest of building up soil structure and the

SOC pool. For those producers operating at a small, garden scale of production, a broad fork may be a useful implement for aerating and working amendments and cover crops into the soil. A broad fork is a large digging style garden fork that is increasingly available through garden equipment suppliers. The broad fork aerates the soil and distributes amendments into the profile without breaking apart the soil structure or inverting the soil surface. Permanent raised beds are another viable option for small- scale producers (Photo 1.2). Raised beds can offer the seedbed and increased soil temperature that tillage provides, but without the degradation of soil structure. They can also be highly productive for small producers, as evidenced in the Cuban

“organoponicos” (Table 1.2) (Campionini et al 2002).

1.11.2 Organic Matter Inputs Cities are places of abundant organic waste materials. Yard and green waste, food waste, organic industrial byproducts, and municipal biosolids are just a few of the many classes of materials which can be used for composting and mulching in an effort to increase the SOC concentration of urban soils (Fig. 1.2). Urban environments offer numerous opportunities to collect both C and N-rich plant materials to combine in the making of stable and nutrient-rich composts. Cogger (2005) reviewed a number of studies on applying green by-product compost to agricultural soils. These studies all showed improvement in a number of soil physical properties, after repeated compost application. These data were used to generate recommendations for disturbed urban landscaping soils: to apply 5 to 8 cm of compost for plantings in disturbed soils, or up to

25% by volume in extreme examples (Cogger 2005). A recent meta-analysis on the

22 effect of OM applications on urban landscape and tree plantings indicated that soil physical properties and plant growth consistently increased with OM additions to soil

(Scharenbroch 2009). In the previously mentioned study of water infiltration in urban soils degraded by construction, heavy compost application increased water infiltration rates between 1.5 and 10 times compared to unamended soils (Pit et al. 1999).

A number of researchers have investigated the use of municipal solid waste

(MSW), which is a compost made up of both green waste and food waste from urban areas. These materials are composted in many municipalities in an effort to minimize materials being sent to the landfill. Hargreaves et al. (2008) reported that the physical, and biological benefits to the soil were numerous with MSW application, but cautioned that the composition and quality of MSW compost materials must be routinely evaluated and that the incorporation of sewage sludge in MSW compost can lead to elevated heavy metal content. In Europe, Diacono and Montemurro (2010) suggested that long term application of composts, including MSW compost, to cropped soils had many benefits including improvement to a number of soil properties and dramatic crop yield increases, compared with unamended controls. They reported no evidence of heavy metal contamination from MSW application. Thus, cities and institutions interested in supporting UA systems must begin to explore options for dealing with organic byproducts locally through large scale composting programs. These programs can divert material from landfills and improve soil quality by turning waste into a valuable resource

(Fig 1.2).

In addition to producing compost from organic wastes in urban areas, the production of biochar from urban organic waste products is a process that deserves

23 further investigation for improving degraded soils. Biochar is a term used to describe charcoal produced with the goal of creating a soil amendment, energy, or both. Biochar is created through combusting organic materials in the absence of oxygen: a process known as pyrolysis. Pyrolisis provides a number of additional environmental benefits in comparison with composting or natural decomposition. The process produces energy, reduces greenhouse gas (GHG) emissions from decomposition, and leaves behind a greater quantity of stable C in the soil than natural decomposition (Lehman 2007).

Numerous urban green wastes, such a MSW and landscape trimmings, can be used to produce biochar (Lehman et al. 2006). Pyrolysis technologies range from simple devices that produce only biochar to expensive, electricity generating pyrolizers.

One of the most significant benefits of biochar is its ability to improve soil quality and crop productivity in degraded soils through increasing their SOC pool (Kimetu et al.

2008; Glaser et al. 2002). While the much of the research on biochar’s benefits in soils has been carried out in nutrient-poor tropical soils, results of the early work on biochar in temperate environments suggests that biochar application provides marked improvement to soils in these environments as well (Atkinson et al. 2010). Biochar application has demonstrated improvement to a number of soil physical properties, including increased

SOC pool, improved soil structure, increased plant available water capacity, decreased

BD, and increased cation exchange capacity(Glaser et al. 2002; Lehman et al. 2006;

Liang et al. 2006; Atkinson et al. 2010).

Biochar application may prove to be a highly effective practice for improving soils for UA in the U.S. for a number of reasons. First, the degraded nature of many vacant lot soils may be greatly enhanced by the addition of stable C, as suggested by the

24 research on degraded tropical soils (Kimetu et al. 2008). Second, cities contain abundant organic wastes (Fig 1.2). Early research suggests that pyrolisis may be useful for transforming any number of C based materials into biochar (Atkinson et al. 2010). It is rather easy to foresee scenarios where urban organic residues such as food waste, organic industrial byproducts, and landscape byproducts are used to create biochar, and possibly energy, through pyrolisis. The current high costs of pyrolysis technologies and industrially produced biochar suggest that developing methods to produce high quality biochars using low-tech pyrolysis of green waste may be the most appropriate strategy for UA contexts.

1.12 Soil Moisture Characteristics 1.12.1 Poorly Drained Soils Urban soils can be subject to poor drainage for a number ecological reasons.

Constructed physical obstructions such as concrete curbing and uneven site grading may lead to inadequate drainage outlets for a given soil, causing waterlogged conditions

(Craul 1999). Drainage may also be inhibited by soil compaction as well as the incorporation of buried impervious materials in disturbed soil profiles. Hydric soils are also common in many cities. These soils formed naturally under wet conditions in wetland or coastal areas. In Cleveland, city planners have mapped the extensive hydric soils, as these soils are less desirable for UA applications (CUDC 2008). The waterlogged conditions in poorly drained soils reduce the growth of most crops.

Management options for poorly drained soils include both surface and subsurface drainage. An evaluation of site-specific conditions is necessary to determine the appropriate drainage options. Surface drainage consists of simple earthworks such as

25 diversion swales that are designed to direct water away from poorly drained areas (Craul

1999). Perforated plastic drainpipe for subsurface drainage is a commonly available landscaping material and most commercial landscapers have experience installing these drains. These simple drainpipes can often be an excellent solution to poorly drained urban soils, but they require at least a slight slope to fully move water from affected areas. Raised bed gardens may also be a solution to utilizing poorly drained soils, though their performance is also likely to be enhanced through the inclusion of other drainage measures on the site.

1.12.2 Soils with Water Deficit The opposite extreme of a dry moisture regime may also be an issue in urban soils. The profoundly altered hydrology and water tables in urban areas can lead to anthropogenically driven “hydraulic drought” in soils of urban riparian areas (Groffman et al. 2003). These altered environmental conditions, along with the widespread problem of decreased water infiltration in disturbed urban soils, suggest that elevated water deficit for plant growth is a condition likely to be encountered in urban soils.

A variety of options exist for increasing plant available water in UA soils. The addition of OM to the soil in the form of compost, mulch and other amendments can both improve infiltration and increase the water holding capacity of UA soils. Identifying appropriate water sources for irrigation is also a necessity for UA. City water departments can generally install irrigation hydrants for UA projects. These installations may have prohibitive costs in some situations. The harvesting of rainwater for sustainable irrigation has tremendous potential for application in urban areas. Rain barrels that capture water from rooftop gutters have become a common feature in many

26 gardens, but larger tanks and cisterns may be required for market scale UA. In addition to tanks and barrels, water harvesting earthworks can be an effective means of irrigating urban soils (Lancaster 2007). Level swales, infiltration basins, and unique urban applications such as curb cuts with small diversion ditches can all be utilized to capture substantial quantities of rainwater in cities. Lancaster (2006) offers an excellent guide to the principles of designing rainwater catchment systems for multiple scales of users. His book also features case studies of rainwater irrigation of small scale agriculture, including a very inspiring look at the use of rainwater to irrigate edible landscapes and home gardens in the arid urban area of Tuscon, AZ.

1.13 Soil Lead (Pb) Contamination When asked about soil-related concerns, most people working in UA in the north central U.S., including producers, extension educators, and researchers, mention contamination, and specifically Pb contamination as their primary concern regarding soil quality. Soils in many urban residential areas have been contaminated with Pb from Pb- based paints, exhaust from leaded gasoline and from past industrial manufacturing.

Though Pb is no longer used in either paint or gasoline, it was a common ingredient in white paints in the early twentieth century and a standard gasoline additive during much of the latter half of the century. This led to many millions of tons of Pb ending up in

American cities in paint and automobile exhaust (Mielke and Laidlaw 2011; Mielke

1999). Thus, large quantities of this Pb were deposited in soils, which caused widespread

Pb contamination of soils in urban areas. There are trends of increasing soil Pb in soils closer to busy roadways in many cities (Fillipelli et al. 2005).

27 With Pb no longer being used as an industrial ingredient but having a long half life in soils, the ingestion of soil and airborne soil particulates have emerged as principle

Pb ingestion vectors and primary risk pathways for Pb poisoning (Fillipelli and Laidlaw

2010; Laidlaw and Fillipelli 2008; Mielke and Reagan 1998). Pb-contaminated soil can be ingested in a variety of ways, including: direct ingestion of soil by children, incidental soil ingestion by adults working in gardens or greenspaces, and ingestion or inhalation of soil-based dust particles on both indoor and outdoor surfaces. Additionally, Pb can be taken up by garden vegetables grown in contaminated soils, at levels capable of causing health concerns (Finster et al. 2004). Thus, even though Pb contamination may not constrain crop growth, public health concerns about soil Pb contamination are high for

UA.

1.13.1 Testing Soils for Pb Contamination Given the potential health risks from Pb exposure, all urban soils should be tested for Pb content before establishing gardens, playgrounds, or UA. Pb contamination is generally most intense in the soil surface (0-5cm). Thus, surface samples are often used for Pb estimation, but sampling to the depth of rooting (0-40cm) has also been suggested for garden soils (Clark et al. 2008). High levels of spatial variability of soil Pb concentration have been observed in urban soils (Wagner and Langely-Turnbaugh 2008;

Chaney et al. 1984) and in urban gardens (Clark et al. 2006). So, whenever possible, numerous individual or composite samples should be analyzed from a given site, in an effort to capture any variability that may be present.

Currently in the U.S., commercial and university soil testing laboratories use a number of analytical methods to determine total soil Pb content. US EPA methods

28 3050B (acid digestion) and 3051A (microwave assisted acid digestion) are the protocols used by the US EPA and have been considered the benchmark method for total Pb analysis (Scheckel et al. 2009). Costs of the EPA methods are higher than other extraction procedures though, so these are not always used. Recent comparisons between

EPA method 3050B and the more widespread and cheaper Mehlich III soil extraction have indicated strong correlations in results, suggesting that this test which is commonly used for nutrient analysis may also be a robust proxy for the EPA methods (Witzling et al. 2010; Minca et al. 2013). The USEPA has established a limit of 400ppm total Pb content for bare soil in residential areas (US EPA 2001), and this standard has been recommended as a screening level for risk in urban garden soils (Minca et al. 2013).

Proper analysis of Pb levels in urban soils is a key step in utilizing them for UA, so that an accurate estimate of risk can be attained to guide management strategies.

Many sites may have Pb levels that do not require extensive remediation. A study at The

Ohio State University analyzed 65 vacant lot soils from Cleveland and observed a mean total Pb concentration of 224 ppm (Minca and Basta 2013). Robust testing is, however, necessary to ensure that sites have risk levels appropriate for UA.

1.13.2 Management for Soils with Pb Contamination A number of management practices are useful in dealing with the effects of Pb contamination in garden soils (Table 1.8). None of these practices is a complete solution to the problem, but all can provide some measure of risk abatement in UA settings.

Testing soil for Pb is a must in urban gardens. Site history is a major factor in Pb levels, so testing is especially crucial in sites close to roadways and those which have been utilized previously for buildings and industrial uses. Instituting remediation management

29 in soils with >400ppm Pb makes good sense. UA should be about improving health in cities, not increasing risks.

Providing physical exclusion barriers between garden soils and contaminated soils is a common strategy. In affected sites, applying a heavy mulch or cover to soil areas in pathways and uncultivated areas is an excellent way to reduce total risk from Pb (Clark et al. 2008). In cultivated areas, importing soil and OM and constructing raised beds above contaminated soils (sometimes with an underlaying barrier) are often recommended and utilized strategies (Hynes 2001; Witzling et al. 2011). Witzling et al.’s (2011) survey of urban gardens in Chicago indicated that sites involving raised beds to deal with Pb concerns received additional benefits to their overall soil quality. A major caveat exists with raised beds as a strategy to prevent risks from soil Pb; studies have documented the recontamination of surface soils in raised beds by fine soil particles from adjacent contaminated areas (Clark et al. 2008). This trend suggests that raised beds in contaminated areas should be re-tested for soil Pb every few years to ensure that Pb levels remain low.

In addition to exclusion strategies, practices that result in the chemical immobilization of Pb compounds are another tool for urban soils. Lime is commonly applied to soils with heavy metal contamination, as adjusting the pH to neutral or slightly alkaline reduces the biological availability and plant uptake of Pb and other heavy metals

(Shaylor et al. 2009; Rosen 2002; Basta et al. 2001). Compost and OM are also commonly added to soils to immobilize contaminants. Composted municipal biosolids have demonstrated significant reductions in the bio-availability of Pb in both urban soils

(Brown and Chaney 2003) and former smelter site soils (Basta et al. 2001). Though it is

30 rarely mentioned in popular and extension literature on Pb contamination, the application of phosphorous (P) rich fertilizers significantly reduces the bioavailability of Pb present in the soil (Hettiarachchi and Pierzynski 2004). Rock phosphate (Basta and Gradwohl

1998), a common organic soil amendment, and phosphoric acid (Ryan et al. 2004) can significantly reduce the bioavailability of Pb compounds in highly contaminated soils.

Phosphate immobilization leads to the formation of highly stable compounds (Scheckel and Ryan 2004), but caution must be taken to ensure that water soluble phosphate fertilizers are not applied in quantities that can pollute waterways (Kilgour et al. 2008).

While much of the literature on Pb remediation deals with studies that have looked at mitigating Pb at the site scale, the recent studies on airborne re-suspension of contaminated soil particles (Laidlaw and Fillipelli 2008; Fillipelli et al. 2005) suggest that in many urban areas Pb contamination of soils is so pervasive that it must be viewed as a landscape level ecological process. Small-scale remedial measures in backyards and community gardens may only provide a temporary solution, as recontamination may occur until the issue is addressed at the landscape or neighborhood level (Clark et al.

2008). Additionally, these results suggest that Pb contaminated soils should continue to be tested for recontamination after mitigation strategies have been put in place.

Urban soils are also subject to contamination from several other trace elements and numerous organic compounds. Meuser (2010) provides and excellent scientific overview of contamination issues affecting urban soils globally.

1.14 Biological Functioning in Urban Soils The soil habitat is highly modified in urban areas, with distinct alterations in abiotic communities, plant and arthropod communities, biogeochemical cycling, and land

31 use patterns (Byrne 2007). Given that biological properties and assays are often correlated with robust soil quality, understanding the dynamics of urban soil ecology is critical to understanding quality and function in urban soils.

Disturbances associated with urbanization and urban areas are a fundamental driver of the biological functioning of urban soils. Soil studies conducted in residential landscapes (Scharenbroch et al. 2005) and urban community gardens (Grewal et al. 2010) have documented reduced biological activity following the disturbances of landscape construction and of converting an empty lot into a garden, respectively. Following a few decades of stable conditions, sites in these surveys demonstrated increased microbial biomass C, greater potentially mineralizable N, and a stable microbial metabolic rate in older residential landscapes (>50yrs) (Scharenbroch et al. 2005) and more complex nematode foodwebs in older community garden sites (Grewal et al. 2010). Surveys of soils under different urban land uses in Stuttgart, Germany observed a similar pattern where more disturbed land uses often had lower levels of soil microbial biomass C (Fig

1.1) and N and reduced enzyme activity (Lorenz and Kandeler 2006; Lorenze and

Kandeler 2005). Decreases in biological activity following disturbance in urban soils may in some instances be associated with compaction, as indicated by higher bulk density values at more disturbed sites (Scharenbroch et al. 2005). Nontheless, soil microbial biomass and activity have demonstrated recovery on a decadal timescale following disturbance.

A number of studies comparing urban soils to more natural areas suggest that soil biological processes and metabolism are altered in urban soils. A study on litter decomposition in forest soils on an urban to rural gradient in New York observed that

32 both decomposition and N mineralization rates are higher in urban areas (Pouyat et al.

1997). A similar study in North Carolina documented increased N mineralization but decreased decomposition in urban soils (Pavao-Zuckerman and Coleman 2005). A comparison of soil C and N dynamics comparing shortgrass prairie and urban lawns in

Colorado observed greatly increased respiration in urban areas, which the authors attributed to irrigation and nutrient application at those sites (Kaye et al. 2005). These observed increases in soil metabolic rates in urban areas may be related to urban alterations in climate (Table 1.3 ) and in the case of decomposition rate, have also been associated with increased earthworm populations (Pouyat et al. 1997).

Urbanization and urban land uses may also affect the distribution and abundance of soil invertebrates. In the New York area, earthworm abundance can be more than 10 times greater in urban forests than in rural forest stands (Steinberg et al. 1997).

Earthworms have also been greatly reduced in highly contaminated urban sites (Hartley et al. 2008). Further, total nematode biomass and predatory nematode functional groups, along with microbial biomass, have also demonstrated declines in urban soils, compared to rural areas (Pavao-Zuckerman and Coleman 2005, 2007).

1.14.1 Improving the Biological Properties of Urban Soils Soil biological alterations observed in urban areas such as increased N mineralization and increased numbers of earthworms may be beneficial to UA, as these processes improve conditions for crop growth. In urban soils with reduced biological activity, a range of options for increasing biological activity are available.

Management strategies aimed at improving biological properties in urban soils are closely related to those mentioned for improving soil physical properties in that a key

33 consideration is adding OM to the soil, as additions of OM stimulate microbial biomass and the soil food web. The previously mentioned management strategies of compost and biochar additions, and cover cropping increase soil biological activity and health in the soil through increasing the input of OM and C-rich biomass. Additional strategies and treatments may complement OM inputs in improving the biological condition of urban soils including: vermicompost application, microbial inoculation, and perennial plant based systems.

Biological management of soils has been central to the rise of organic urban agriculture in Cuba. Cubans have made extensive use of recycling urban green and food waste into composts and have implemented targeted microbial inoculation strategies in their efforts to improve urban soils for agriculture (Altierri et al. 1999; Treto et al. 2002).

Cuban land managers have had particular success in increasing crop yields and soil health through applying bacteria based bio-fertilizers, myccorhizal inoculation, vermicompost, and N-fixing cover crops (Treto et al. 2002). They have also initiated extensive composting efforts in their urban areas. Urban farms and organoponicos are active in composting animal wastes, plant residues and green waste, organic industrial wastes, and organic household wastes using traditional composting methods, vermicomposting and biodigesters (Altierri et al. 1999). These amendments are then applied to soils for urban crop production. The majority of these strategies can be implemented with fairly low cost in most urban areas.

Vermicomposting is the process of creating high quality composts by feeding food wastes, green wastes and other OM to worms, primarily the tropical species

Einsenia foetida. Vermicomposting has been implemented widely as a low cost and

34 effective means of transforming wastes into soil amendments and an extensive body of research literature exists on the subject (see Arancon and Edwards 2003). Extensive research on the effects of vermicompost application on agricultural crops and soils has been carried out at the Ohio State University, with many positive results. Researchers at

OSU have observed increased soil biological activity (Arancon et al. 2006) and improved vegetable crop growth (Arancon et al. 2005), as well as suppression of insect herbivores

(Yardim et al. 2006), after applying vermicompost to crops. Vermicomposting operations can range in scale from farm-scale processing units and windrows to small bins, or worm boxes, which can be kept in small urban spaces such as kitchens, basements, or garages. E. foetida worms and information on how to manage them are widely available. The process is relatively simple and widely adapatable. Given the measurable benefits of vermicompost application, it appears to be an excellent technique for improving plant growth in urban soils.

1.15 Conclusion Vacant lot soils are becoming an abundant natural resource in the eastern and north central U.S. Access to open land has the potential to provide social and ecological benefits in these areas (Tzoulas et al. 2007). Urban farms and community gardens can bring beauty, community engagement, improved ecosystem services, increased access to nutritious foods, and modest economic benefits to city neighborhoods. These benefits make UA an ideal land use for vacant urban lots.

There is increasing public and institutional interest in re-localizing, or regionalizing the American food system. Increased awareness of the energy costs of food production, growing concerns about the health and safety of the industrial food system,

35 and the foreboding evidence of a changing climate and reduced energy availability in the

21st century have all contributed to a great surge in interest in local food systems and small scale agriculture in the U.S. Despite growing affluence, many Americans are simply not consuming enough high nutrition foods, a problem that appears to be heightened in the depressed urban areas where vacant lots are found (Wrigley 2002,

CUDC 2008). These factors and the available data on the increasing number of urban gardens and farms (Table 1.1) suggest that the conditions are right for UA to become a viable form of food production in the U.S. Additionally, UA’s documented successes in producing significant quantities of food (Mougeout 2005) and improving access to nutritious foods (Zezza and Tasciotti 2010) in the developing world, suggest that UA has great potential to perform well under similar conditions in the U.S.

Among the challenges of scaling UA up to impact food security in the U.S. is a general lack of data on the subject in the North American context. Researchers, educators, and producers can benefit from credible data on production potential, economic potential and food security impacts of UA in North American cities. The current information on these subjects is sparse and generally lacking in peer review.

Given the unique social and biophysical conditions of the urban environment (Pickett et al. 2008, Kaye et al. 2006) there is also a need for long-term agricultural research in urban environments. Intensive production methods, management for degraded soils, low- cost management strategies, well suited crop varieties, season extension, identification of pests and pathogens, and integrated pest management (IPM) are all research areas with strong potential to benefit urban producers. Given the current increases in interest and

36 participation in UA, it should become a key agricultural research priority in the North

Central US, as well as other regions in the developed world where UA is expanding.

Despite its significance, the research information about the management of urban soils is scanty and fragmentary. While the data base has improved on conditions and processes in urban soils during the past two decades, relatively few studies have examined the effects of management-induced changes in urban soils. Given the high levels of heterogeneity found in urban soils, the best management strategies for these soils must begin with a comprehensive, site-based evaluation of their overall condition and quality (Fig 1.3). A key factor in determining the extent of adaptive management is the previous land use that the soil has been under (Pouyat et al. 2007). Urban soils that have been under stable, ecological land uses, such as perennial vegetation, may exhibit healthy soil functioning (Grewal et al. 2010). Soils that have undergone recent heavy disturbance are likely to demonstrate degraded physical and biological properties

(Scharenbroch et al. 2005). Understanding the constraints present in a given soil allows land managers to plan targeted strategies for improving those soils.

In many urban and industrial neighborhoods, the contamination of soils by Pb and other industrial compounds is likely to continue to be a major concern. In areas where contamination is a major concern, analyzing soils for heavy metals content is a priority, and a starting point for soil quality analysis and determining the suitability of parcels for agriculture usage. There is still much to be learned about contaminated urban soils.

Analytical methods, biological availability, remediation methods, and larger-scale approaches to remediation are all areas that continue to need more research.

37 Given the small number of studies that have been conducted on the biology of urban soils and the unique biophysical conditions found in urban areas, urban soils may be a promising area for biological research. Biologically degraded soils in cities may present opportunities for continuing to develop scientific understanding of managing and regenerating soil biota. The recent experiences of Cuba suggest that biological management methods may be an effective means of increasing soil quality and productivity for UA (Treto et al. 2002).

A key component of managing urban soils for food production is the development of strategies and systems for capturing the large quantities of organic by-products produced in cities and transforming them into high quality soil amendments. Municipal and institutional composting programs, vermicomposting of kitchen wastes, the refinement of low cost methods of biochar production and continued education about the importance of these practices can all contribute to the knowledge to turn wastes into resources. These amendments can be a cornerstone in small scale, intensive management regimes for improving urban soils.

The current demographic trend of increasingly urban global populations suggest that urban soil management is a subject that is likely to increase in importance. Healthy soils are a necessity for ecosystem services in cities, as in all ecosystems. The unique ecological conditions in urban areas, coupled with tremendous potential for education and extension in population centers, make urban soils an exciting frontier in soil science research. Urban soils are likely to be a focus where soil scientists can extend the ecological understanding of soils as well as contribute to improved ecological and social

38 conditions. By assessing and improving the quality of soils in cities, urban soils can be utilized as a resource to improve food security and environmental quality.

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Table 1.1. Expansion of vacant land and urban agriculture in shrinking cities. Detroit Cleveland Youngstown Population 1950 1,849,568 2 914,808 1 168,330 1 Census Population 1980 1,203,339 2 573,822 1 115,511 1 Census Current Population 837,711 2 395,310 1 65,056 1 (2007 ACS Survey) % Change in Population (1950- -55% - 57% - 61% 2007) Number of Vacant >60,000 8 15,000 buildings 3 23,000 4 Parcels Area of Vacant Land >1,900 5 - >1,500 ha 3 2,838 ha 4 10,000 6 ha 9 Urban Farms and >300 - 7 4 * 75 15 Community Gardens 1,500 10

Market/Production- 8 7 4 based Urban Farms * 20 25-35 2

1 Population data for Cleveland and Youngstown from Mallach and Brachman (2010)

2 Population data for Detroit from U.S. Census (www.census.gov)

3 Vacant land data for Cleveland from CUDC (2008)

4 Vacant land and urban agriculture data for Youngstown from the Youngstown Neighborhood Development Corporation (pers comm 2010)

Table footnotes continued on next page.

Table 1.1 notes continued.

5 Estimate of amount of vacant land in public land banks in Detroit from Colasanti and Hamm (2010)

6 Estimate of total area of vacant land in Detroit from Gallagher (2008)

7 Urban agriculture data for Cleveland from The Ohio State Extension, Cuyahoga County, Urban Agriculture Program (pers comm 2010)

8 Urban agriculture data for Detroit from Michigan State Extension, Wayne County, Urban Horticulture Program (pers comm 2011)

9 Wayne County Extension estimates that there are currently more than 300 community gardens in Detroit

10 Wayne County Extension estimates that there are a total of >1,500 home, school and community gardens in the city

* Denotes urban gardens and farms founded during the past 5 years.

Table 1.2 Measured urban agriculture yields. Source Location Date Agricultural Crop Yield/Area System (kg m-2) Camden, 2.5 Vitiello et Community NJ 2009 Mixed Veg al 2010 Gardens USA Vitiello 6.8 Philadelphi Community and Nairn 2008 Mixed Veg a, PA USA Gardens 2009 Strawberry 4.7 Raspberry 6.0

Wooster, 2006 8.8 Kovach OH Urban Polyculture Tomato 2010 -2009 Experiment USA Edamame 1.4 Soy 4.3 Blueberry

Companio Mixed Veg 24 ni et al. Cuba 1999 “Organoponicos” 2002 Beans (snap) 0.9 Commercial Cucumber 2.2 Zandstra Agriculture Tomato 3.4 Michigan Averag and Price Good” Yields for USA ed Mixed 1.3 1988 Vegetable Crops Cooking Greens

Table 1.3. Altered environmental factors affecting crop productivity in urban areas (adapted from Eriksen-Hamel and Danso (2010).

Environmental Alteration in Urban Areas Condition

Temperature Generally increased

Air Quality Increased levels of ozone (O3), nitrous oxides (NOx), sulfur

dioxide (SO2) and suspended particulate matter (SPM)

Solar Radiation Reduced by air pollution

Climate Urban heat islands and altered hydrologic cycles

Table 1.4. Critical ecological functions supported by urban soils and methods useful in their assessment. Ecological Function Methods of Assessment

Stormwater Infiltration • Infiltration measurement • Saturated hydraulic conductivity

Sorption of Pollutants • Contaminant bioavailability assays (including heavy metals)

Sorption and Transformation of • Regular analysis of nutrients of Excess Nutrients interest in critical areas

Soil C Sequestration • Measurement of SOC pool

Habitat for Micro-organisms • Microbial biomass and activity assays • Invertebrate assays and Invertebrates • Enzyme assays

Foundation for Plant Growth • Soil quality assessment

Table 1.5. Potential soil-based constraints found in urban soils. Constraint References

Soil Physical Properties Compaction Gregory et al. 2006; Scharenbroch et al. 2005; Pit et al. 1999; Jim 1998b Decreased Water Infiltration Gregory et al. 2006. Pit et al. 1999 Soil Chemical Properties Lead (Heavy Metal) Contamination Witzling 2010; Clark 2006; Finster et al. 2004; Minca et al. 2013

Alkaline pH Jim 1998b Soil Biological Properties Low Organic Matter Craul 1992 Decreased Microbial Activity Scharenbroch et al. 2005

Table 1.6. Recommended small scale intensive management strategies for soil-based constraints to production in urban agriculture. Constraint Management References High Compaction

- Cover Cropping Wolf 1997; Clark 2007; Williams and Weil 2004 - Subsoiling Craul 1992 - Add Organic Matter Pit et al. 1999 - Raised Beds Poor Drainage - Surface Draining Craul 1999; Lancaster 2007 Earthworks - Subsurface Drainpipe Craul 1999 Available Water Capacity - Compost Application Pit et al. 1999 - Rainwater Catchment Lancaster 2006 - Water Harvesting Lancaster 2007 Earthworks Low Organic Matter - Compost Application Cogger 2005 - Biochar Application Hargreaves et al. 2008 - Cover Cropping Clark 2007 - Reduced Tillage

Table continued on next page.

Table 1.6 continued.

Constraint Management References Lead Contamination

- Soil Testing Witzling et al. 2011; Finster et al. 2004

- Raised Beds Hynes et al. 2001; Witzling et al. 2011 - Mulching/Covering Bare Clark et al. 2008 Soil in Adjacent Areas - Apply Phosphate Ryan et al. 2004 Fertilizers Low Nutrient Content

- Soil Testing - Apply Rock Fertilizers - (Lime, Rock Phosphate, Green Sand, Kelp,etc.) - N-fixing Cover Crops Clark 2007

Table 1.7. High and low bulk density values observed in ecological studies of urban soils. Reference (Location) Bulk Density (g cm- Land Use 3) Millward et al. 2011 1.4 Non-naturalized park area (Toronto ON, 1.1 Naturalized park area Canada) Pouyat et al. 2007 1.3 Commercial and industrial land uses (Baltimore MD, 1.1 Forested areas USA) Scharenbroch et al. 1.7 New (<10yrs) residential 2005 landscape (Moscow ID, USA) 1.4 Old (>50yrs) residential landscape Stahr et al. 2003 1.7 Public park (Stuttgart, Germany) 0.9 2 Sites: Public Park and Garden Area

Jim 1998a 2.1 Heavily used urban park (Hong Kong, China) 1.2 Bulk density values are from soil surface samples BD values greater than 1.5 g cm-3 may decrease root growth in urban soils (NRCS 2000)

Table 1.8. Recommended management practices (RMPs) for mitigating Pb contamination risk in urban agricultural soils. RMP Comments

Soil Test • Necessary in all urban garden sites • EPA Methods 3050B (Acid digestion) and 31A (Microwave digestion) are considered standard

• EPA limit of 400 ppm Pb for bare soil in residential areas.

Cover Bare Soil at Garden • Reduces direct ingestion and dust exposure Sites

Amend Soil pH to Neutral • Reduces bioavailability of heavy metals

Raised Bed Cultivation • Reduces risk of soil ingestion and crop Pb uptake

• Increased cost and resources

• Subject to recontamination by surrounding environment

• Continued soil testing may be necessary Application of Soil • Compost, composted biosolids, and phosphate Amendments fertilizers. • Reduces bioavailability of Pb compounds

• Potential for water pollution with water soluble forms

Figure 1.1 The effect of previous land use on soil properties. (a) Microbial biomass C in surface soils of urban land uses in Stuttgart, Germany (Adapted from Lorenz and Kandeler 2006) . Urban land uses include: railway area (R), apartment building (A),high density urban areas (H2 and H3), public parks (P1 , P2 , and P3), garden-no vegetation (G1) and garden-vegetable (G2). (b) Effect of previous land uses on soil Pb in urban gardens in Chicago (Adapted from Witzling et al. 2011). Previous land uses include driveway (D), vacant house lot (VHL), park entryway (PE), schoolyard (SY), and park turf area (PT).

Figure 1.2 Conceptual model for utilizing organic wastes to improve soil quality in urban areas

Figure 1.3 Conceptual model for assessment and management of vacant lot soils for UA.

Photo 1.1 The transformation of vacant properties into UA spaces. Photos A and B show a vacant house (A) and a vacant urban lot where a house was recently demonlished (B), on the South side of Youngstown, OH. The “Lots of Green” program of the Youngstown Neighborhood Development Corporation works to transform vacant properties into greenspaces including community gardens (C). Raised beds have been created in the gardens with strawbales and imported organic matter (D) and pathways have been mulched heavily with cardboard and wood chips. Photos by Youngstown Neighborhood Development Corporation.

Photo 1.2. Permanent raised beds are used for vegetable production at the urban farm in Braddock, PA. Photo by Krista Beniston

CHAPTER 2

ASSESSING AND MANAGING SOIL QUALITY FOR URBAN AGRICULTURE

IN A DEGRADED VACANT LOT SOIL

2.1 Abstract Following the decline of industrial manufacturing, many U.S. cities have experienced severe population declines that produced large areas of vacant land. Urban agriculture

(UA) has emerged as a desirable land use for these spaces, but degraded soils are common. Therefore, we to measured soil and plant responses to amendments and management in urban lots where vacant houses had recently been demolished in

Youngstown, OH U.S.A. (41°04’49” N, 80°40’35” W). Our split plot experiment manipulated organic matter (OM) amendments produced from yard wastes and the use of raised beds to grow vegetable crops. Significant compaction was observed following demolition activities with bulk density values of 1.55-1.79 Mg m-3. OM amendments resulted in significant improvement to a number of soil physical, chemical and biological properties. 2012 crop yields were improved by both OM amendments and the use of raised beds and ranged from 0.35-3.6 kg m-2 for tomatoes (Solanum lycopersicum var.

“Bellstar”) and 0.16-2.7 kg m-2 for sweet potatoes (Ipomoea batatas var. “Beauregard”).

A soil quality (SQ) index, developed using factor analysis and scoring functions from the

65 soil management assessment framework (SMAF), produced values ranging from 0.60-

0.85. These values are comparable to those reported for rural agricultural soils. All results indicate that UA can be productive in vacant urban land and that amendments produced from urban yard wastes can improve SQ and crop yields for UA.

2.2 Introduction More than 50% of the world’s people live in cities and populations are expected to become increasingly urban during the coming decades (UNDP 2011). Contrary to this global demographic trend, populations of the cities in the North Central U.S. have declined severely over the past 50 years primarily due to large reductions in industrial manufacturing in the region (Dewar and Thomas 2013). These shrinking populations have resulted in an abundance of vacant land and properties. By 2010, the cities of

Youngstown and Cleveland, Ohio and Detroit, Michigan all had lost around 60% of their peak (mid-20th century) populations and now contain around 2,800, 1,500, and 6,500 ha of vacant land, respectively (reviewed in Beniston and Lal 2012). Vacant urban properties have been linked with increased crime and decreased safety (Kraut 1999;

Branas et al. 2011), while “greening” of urban lots has positively impacted surrounding property values (Heckert and Mennis 2012) and reduced crime (Branas et al. 2011). Thus, many urban communities are attempting to re-purpose vacant land as functional greenspace with the goal of achieving social and ecological benefits (CUDC 2008).

At the same time, the economic recession and the promotion of locally produced foods have renewed interest in gardening in the U.S. (Schupp and Sharp 2012) and participation in urban agriculture (UA) has increased (Blaine et al. 2010). Sparse data on crop yields in urban areas demonstrate that UA can produce robust yields of vegetable

66 and fruit crops (reviewed in Beniston and Lal 2012). While vacant urban land often occurs in areas without regular access to fresh food (CUDC 2008), UA has been linked to increased access and consumption of fruits and vegetables (Alaimo et al. 2008; Zezza and

Tasciotti 2010). Estimates also suggest that if a large percentage of existing vacant land were utilized for UA, cities such as Detroit (Colasanti and Hamm 2010) and Cleveland

(Grewal and Grewal 2011) could produce a large portion of the specialty crops that their populations consume. Agricultural producers in urban areas, however, face a unique set of ecological limitations to crop production and further research is needed to improve agronomic management and productivity for UA (Eriksen-Hamel and Danso 2010;

Mallach and Brachman 2010).

Urban soils, in particular, may pose a significant challenge for crop production.

These soils are often highly modified by disturbance (Pouyat et al. 2010) and occur on a continuum from soils that reflect the native, regional soils to highly altered anthropogenic soils (Pouyat et al. 2003; Lehmann and Stahr 2007; Pickett et al. 2008). Severe soil degradation is common in urban areas and can make horticultural activities difficult

(Craul 1992; DeKimpe and Morel 2000; Lehman and Stahr 2007). Construction activities and the demolition of vacant structures can lead to physical soil degradation, compaction, and reduced hydrologic function(Gregory et al. 2006; USEPA 2011). Meanwhile, huge quantities of organic waste products are available in cities for potential processing as amendments to improve degraded soils (Brown et al. 2012). Application of large quantities of compost has consistently improved soil physical properties in urban soils under ornamental landscapes and trees suggesting potential benefits for UA (Cogger et al.

2005; Scharenbroch et al. 2009).

67 Soil quality (SQ) evaluation utilizes the identification, measurement and scoring of soil indicator properties to characterize the overall ecological function of soils, as well as the effects of soil management practices (Doran and Parkin 1994; Andrews et al.

2004). A generalized process for evaluating SQ includes selecting soil properties as indicators, measuring those properties and calculating a score or quantitative index for both the individual properties and the whole soil (Andrews et al. 2004; Schindelbeck et al. 2008). Multivariate statistical tests, such as factor analysis, have been used to select a subset of soil properties that explain a large proportion of the variation in a larger data set as SQ indicators (Shukla et al. 2006). For calculating an index, the Soil Management

Assessment Framework (SMAF) provides researchers with scoring functions or equations that calculate a score between 0.0 and 1.0 for the measured values of several key soil properties (Andrews et al. 2004). These scores can then be averaged for a SQ index. Together these methods provide a process for evaluating the ecological function of

UA soils and for identifying individual soil properties that are important to the soil’s overall condition. Studies of urban vegetable garden soils, however, have generally focused on specific soil properties, and lead contamination (Clark et al. 2008, Witzling et al. 2011).

The study reported here aimed to assess SQ for vegetable crop production in an urban lot where vacant buildings had recently been demolished. The specific objectives included: (1) assess soil properties in an urban soil following the demolition of vacant houses; (2) investigate the ability of amendments produced from organic waste materials to improve SQ and support vegetable crop production with and without raised beds in a recently disturbed vacant lot soil over the course of two growing seasons; and (3)

68 determine which soil properties served as the most effective indicators of crop growth under these conditions. UA in vacant lot soils is a rapidly expanding horticultural activity and this study provides a unique opportunity to evaluate agronomic properties at a site following demolition. To the best of our knowledge, this study is among the first conducted in an urban environment to report crop yields, explore soil properties driving those yields under different management practices, or to use a quantitative evaluation of

SQ for a garden soil.

2.3 Materials and Methods 2.3.1 Site description and experimental design The experimental site was on two contiguous urban lots in the city of

Youngstown, OH (at approximately 41°04’49” N, 80°40’35” W). Two vacant houses were demolished on the site during winter 2010/2011 and the majority of the resulting material and debris were removed from the site, as per local ordinances. The site was then graded with heavy machinery to achieve a consistently level surface, using only soil from the site.

The field experiment was performed with a split-plot design that facilitated the measurement of soil and plant responses to amendments and management practices. The treatments were established in the Spring 2011. The main plot treatments were combinations of organic amendments and vegetable crops (see below). Amendments included: (1) un-amended control (CNT); (2) 15 kg m-2 compost (CMP), (3) 15 kg m-2 compost and 2 kg m-2 biochar (CMP+B); and (4) 15 kg m-2 compost and managed under an intensive cover cropping (CMP+ICC) regime. This quantity of compost application resulted in a layer of compost 10 cm deep, prior to incorporation. The compost was

69 produced exclusively from leaves and grass clippings by a local landscaping company.

The nutrient concentrations of the compost were such that macronutrients were applied at the following rates (all per m2): 3.6 kg C; 0.26 kg N; 0.004 kg P; 0.021 kg K; 0.09 kg Ca;

0.02 kg Mg; and 0.004 kg S. The biochar was produced from oak feedstocks at a temperature of approximately 400˚C. It had a total C content of 786 g C kg-1 and a pH of

9.5. A detailed chemical and physical description of this biochar is provided in Hottle

(2013). All main plots were split with subplots that either had: (1) plants grown directly in the ground, or (2) 20-cm raised beds with wooden sides that were filled with an additional 10 cm of soil from the site (Photo 2.1a). Each main plot was approximately

1.52 x 6.10 m, with the long side following a north/south axis. The main plots were arranged in a randomized complete block design with six replications for a total of 24 plots (Photo 2.1b).

2.3.2 Management practices and crop measurements All plots were rototilled in June 2011 and amendments were incorporated to a depth of 10 cm. Supplemental irrigation was provided uniformly to all plots as needed throughout the duration of the experiment by placing two drip tape lines through each bed and a fence was erected around the site during summer 2011. Weed control was achieved through removal by hand or with hand tools throughout the growing season.

Tomato (Solanum lycopersicum var. “Bellstar”) and Swiss chard (Beta vulgaris subsp. cicla var. “Bright Lights”) crops were planted as transplants following tillage in

June 2011. Swiss chard was harvested in early August and tomatoes were harvested in early September 2011. All plots were planted to an annual ryegrass (Lolium multiflorum var. “Bruiser”) in October 2011. The ryegrass was mowed in early May 2012 and

70 incorporated as all plots were rototilled. In 2012, tomatoes (same variety as above) were planted in mid May and harvested in early September. Sweet potatoes (Ipomoea batatas var. “Beauregard”) were planted in early June 2012 and harvested in early October.

These crops were chosen in an effort to explore the effects of soil management on both a fruiting and root crop. In 2012, crops were assigned to main plots such that tomatoes were only grown in plots that did not have tomatoes during 2011. Each crop type in 2012 had three complete blocks that were interspersed together such that each of the three blocks contained the full suite of treatment and crop type combinations. Heavy herbivory from deer (Odocoileus virginianus) occurred in some plots before the installation of the fence and destroyed a portion of the 2011 crop. As a result, replication was not sufficient for robust statistical analysis, so crop yields from 2012 are the focus of this report.

The CMP+ICC plots were planted with a succession of cover crops during the entire first year of the experiment and thus did not have tomatoes or chard. In June 2011 the CMP+ICC plots were planted to sorghum sudangrass (Sorghum bicolor X S. bicolor var. sudanese var ”BMR”). The sorghum sudangrass was planted in six 20 cm wide rows in the plots and was cut with a gas-powered hedge trimmer to approximately 5 cm height twice during the growing season. All of the clipped biomass was then placed on the surface of the plot. In late August 2011, tillage radish (Raphanus sativus var. “Tillage”) was seeded in between the rows of sorghum sudangrass. The sorghum sudangrass was killed by autumn frosts and the radishes grew to maturity in Dec 2011. Annual ryegrass was planted in October 2011 (as in all other plots) by broadcasting over the radishes.

Aboveground biomass samples of the sorghum sudangrass were taken at the time of each

71 cutting and in October 2011 and samples of annual ryegrass were taken prior to mowing in May 2012. The biomass was dried for 48 h at 60 °C and the dry mass was recorded.

2.3.3 Crop yield measurements At the time of vegetable crop harvest, all biomass was collected from the plots.

Crops were sorted by hand and those which were damaged or unripe were measured separately from those considered market quality. Total aboveground vegetative biomass was also recorded. All crop yields reported in this study reflect the yield of market quality produce. Relative yield was calculated on a plot basis as the yield of marketable produce divided by the mean yield value for that crop type across all treatments. This allowed us to combine data from the tow crops for the 2011 data and provided a standardize response variable for regression analysis.

2.3.4 Soil sampling Baseline soil samples were collected in late May 2011, after construction of the raised beds but just prior to the application of the amendments and planting. Intact core samples were collected from each sub-plot at 0 -10 cm depth. Bulk soil samples were collected by taking five samples per sub-plot and mixing them for 0-10 cm depth. Soil aggregate samples were separated from field moist soil by passing it through an 8-mm sieve and capturing the aggregates retained on a 4.75-mm sieve. All sub-plots were sampled again in September 2012 at the conclusion of the experiment, including bulk soil, aggregates and intact cores. Intact cores were collected from 0-10 and 10-20 cm at the final sampling date. An additional set of samples was collected from all plots for microbial biomass C (MBC) analysis during May 2012.

72 2.3.5 Soil physical analyses Bulk density values were obtained using the core method (Grossman and Reinsch

2002). Gravimetric water content was determined by drying a subsample from the cores

at 105°C for 48 h. The dry bulk density (BD) was then calculated using the water content value.

Soil aggregate stability was measured with a wet sieving process (Nimmo and

Perkins 2002) using a laboratory apparatus first described by Yoder (1936). The resulting data were then used to calculate the percent of soil in water stable macro- aggregates (>0.25 mm) (%WSA) and aggregate mean weight diameter (MWD).

Available water capacity (AWC) of the soil was estimated by measuring water retention at matric potentials of the field capacity (-33 kPa) and permanent wilting point

(-1500kPa) using a ceramic pressure plate apparatus (Soil Moisture Equipment Corp.,

Santa Barbara, CA, U.S.A.) (Dane and Hopmans 2002). Intact core samples were used to measure the water retention at FC, while sieved soil (<2.0 mm) was used for the PWP measurement. Water contents were measured gravimetrically and converted to volumetric water content (θ) by multiplying with the measured BD for each sample. The AWC was calculated as the difference in volumetric water content at -33 and -1500 kPa.

2.3.6 Soil chemical analyses Soil pH was measured with a 1:1 soil to water solution. Cation exchange capacity

(CEC) and the percentage of cation base saturation were measured using 1M ammonium acetate extraction of exchangeable cations (Helmke and Sparks 1996). The total concentration of major and trace elements, including heavy metals, was analyzed using

EPA Method 3150a, a microwave-assisted digestion of soil in a solution of HCl and

73 HNO3 followed by inductively coupled plasma (ICP) emission spectrometry (Hossner

1996). Plant available, or extractable (Ext.), nutrients were estimated using a Mehlich-3 extraction process (Mehlich 1984) with ICP analysis of the extract. Total C and N were measured by the dry combustion (900°C) method (Vario Max, Elementar

Analysensysteme, Hanau, Germany). Soil C pools were calculated using an equivalent mass method, to account for differences in BD among treatments (Ellert and Bettany

1995).

2.3.7 Soil biological analyses MBC was determined in sieved (6.75 mm) field moist soil (Vance et al. 1987). Soil samples (10 g) were fumigated with chloroform (50 ml) for 24 hours then extracted with

80 ml of 0.5 M K2(SO4). The extract was filtered through Whatman no. 42 filter paper.

Control samples were extracted using the same process, but were not fumigated. The samples were frozen prior to analysis. Carbon content of the samples was then analyzed in a Shimadzu TOC-V Analyzer (Columbia, Maryland, USA), using the non-purgeable organic C method.

2.3.8 Data Analysis Treatment effects were tested on all soil and crop data by analysis of variance (ANOVA) in PROC MIXED in SAS v9.2 statistical software. PROC MIXED uses a restricted maximum likelihood approach to fit the model to data. The model tested main plot OM amendments, sub-plot raised beds, and the OM amendments by raised bed interaction as fixed effects, while block and block by OM amendments were treated as random effects.

Due to the split-plot design, the error due to the interaction between block and OM amendments was used as the denominator in the F-test of the effect of amendments. For

74 crop data, each crop type was analyzed separately for the 2012 season. Tukey’s honest significant difference (HSD) was used to perform mean separations. No transformations were required to improve the normality of the residuals.

A forward-selection, stepwise multiple regression of soil properties against relative crop yields was conducted using the “all possible models” function in JMP v10 with the goal of identifying a small number of specific soil properties that explained a large proportion of the variation in crop yields.

2.3.9 Soil quality index A soil quality index (SQI) was calculated by developing a minimum data set from soil data from the final sampling date of September 2012 using the following process adapted from Andrews and Carroll (2001). The first step in reducing the dataset was to include only those soil properties where a significant treatment effect was detected (p<0.05), for either organic matter (OM) amendments or raised beds. An exploratory principal components analysis (PCA) was then conducted on the soil properties using the princomp function in R software (R Core Development Team 2013), with a correlation matrix input. PCA was used to determine the number of principal components needed to explain

>85% of the cumulative variation in the dataset, here this required five principal components. A factor analysis was then conducted on the dataset using the factanal function in R, with a quartimax rotation from GPARotation package. The factor analysis was conducted on five factors, in an attempt to approximate the cumulative variation explained in the PCA, with the goal of exploring the contribution of individual soil properties to explaining variation in the dataset. Soil properties with factor loading scores

>0.70 were considered for inclusion in the SQI minimum dataset. At this point, soil

75 properties were included in the SQI if they met two criteria: (1) they had a loading value within 0.1 absolute value of the highest loading under individual factors in the factor analysis, and (2) they had an existing scoring curve in the SMAF spreadsheet (see below).

Scores were calculated for the individual soil properties in the SQI using SMAF

(Andrews et al. 2004). SMAF is both a framework for developing SQIs and a spreadsheet that contains scoring algorithms for approximately twelve soil properties. The scoring curves provide a score between 0.0 and 1.0 for measured soil property values based on previously described relationships to key soil functions such as crop production, hydrologic cycling and environmental buffering (Andrews et al. 2004; Stott et al. 2010).

The algorithms/curves are adjusted according to site-specific factors related to soil taxonomy, agricultural management, and climate. The overall SQIs reported for the treatments in this study represent the average of the scores given to the individual soil properties in SMAF. Scores for both individual soil properties and overall SQ were calculated for each observation and then analyzed using the ANOVA model described above (2.8).

2.4 Results and Discussion 2.4.1 Baseline soil physical properties Soil at the site had BD values of 1.79 Mg m3 in the in ground plots and 1.55 Mg m-3 in the raised beds (Table 2.1), demonstrating a high level of soil compaction. BD values in this range are considered to negatively affect or restrict the growth of roots

(USDA NRCS 2000). Compaction can result from heavy machine traffic and grading

(Gregory et al. 2006). The raised beds were filled with soil from the site and the high BD

76 values measured in them may have been due to the rapid settling of this already compacted soil with poor structure. Compaction leads to reduced levels of water infiltration in urban soils (Millward et al. 2011; Gregory et al. 2006; Pit et al. 1999) and is thought to inhibit many soil mediated ecosystem services (Lal and Shukla 2004). Our baseline sampling and initial site analysis suggested that compaction was a principal soil- based constraint to crop growth at the site.

2.4.2 Baseline soil chemical properties Soil at the site had a slightly alkaline pH (Table 2.1), which may be due to mixing of alkaline subsoil layers into the surface from the disturbances at the site, as well as the weathering of construction materials rich in calcium carbonate, such as mortar and

-1 cement (Howard and Olszewska 2011). CEC analysis indicated a charge of 10 cmolc kg , which is a low to moderate exchange level.

Baseline soil samples were also analyzed for trace element concentrations, primarily lead (Pb), which occurred at a mean concentration of 95 mg kg-1 (Table 2.1).

Soil testing for Pb is widely recommended for UA (Witzling et al. 2011; Minca and Basta

2013), as Pb represents a significant public health risk (Fillipelli and Laidlaw 2010). The

Pb concentration observed at the site is significantly lower than the U.S. EPA’s screening level of 400 mg kg-1 and thus was not considered a significant risk to agriculture at the site.

2.4.3 Baseline soil biological properties Soil at the site had a mean total C concentration of 12.8 g kg-1 soil and a MBC concentration of 20.8 mg kg-1 soil. These are both low levels and are largely due to the removal or mixing/burial of existing topsoil during the demolition activities at the site.

77 The MBC was likely also affected by the severe compaction at the site which reduces pore space for microbial habitat. Low soil C (Craul 1992) and MBC (Scharenbroch et al.

2005) levels have been documented previously following construction activities in urban areas and both have been shown to increase over time in residential landscapes

(Scharenbroch et al. 2005).

2.4.4 Soil physical properties following two years of management The application of the main plot OM amendments drove significant changes in all physical properties except AWC in the soil surface (0-10 cm) (Table 2.2). Many of the measured properties demonstrated a trend where all three of the OM amendments performed better than the control, but there was not further statistical separation among the treatments. Differences among the OM amendments were observed, however, for aggregate MWD. The highest MWD values (1.43 mm) were observed in the CMP+ICC plots. The sorghum-sudangrass grown in these plots is known to produce large amounts of root biomass (Clark 2007). This dense root system likely increased macro-aggregate formation through mechanical forcing by growing roots, deposition of particulate OM, and by supporting rhizosphere microbial communities that are known to be important to aggregate formation (Jastrow et al. 1998).

BD at the 10-20 cm depth was not affected by OM amendments and remained at a relatively high values of 1.49 to 1.52 Mg m-3. This result suggests that physical improvements from adding OM amendments to a highly compacted soil are limited to the soil surface, over the short time scale (2 yrs) reflected in this study. Improving soil structure and mitigating compaction below the surface layer is a major challenge for

78 degraded sites such as this one, and is a topic that would benefit from further experimentation and long-term field trials.

Raised beds did not impact BD or MWD, while in ground plots had higher

%WSA, AWC and total porosity. We expected that raised beds would improve soil physical properties and these observations are counter to that assertion. The likely reason for this is that the initial bulk density in the in ground plots (Table 2.1) was so high that incorporation of amendments, even with heavy tillage, was difficult. So a larger proportion of the OM amendments remained higher in the profile in the in ground plots giving them a higher overall concentration of OM. This situation resulted in observations of higher values for in ground plots across numerous soil physical, chemical, and biological properties in this study.

An interaction between OM amendment and raised bed was detected for both

AWC and total porosity (data not shown). These differences were largely driven by trends of larger increases in AWC and porosity in ground plots receiving OM amendments than in un-amended raised bed plots, suggesting that the presence of OM was important for facilitating these two soil properties.

2.4.5 Soil chemical properties following two years of management OM amendments resulted in 4 times higher total soil N (TSN) and 2-5 times higher extractable concentrations of all nutrients measured in the study (Table 2.2). Like the soil physical measurements (3.4), OM amendments were not statistically distinct from one another. These data support the proposition that applying amendments produced from urban green waste products can result in increases in plant available nutrient pools. OM amendments lowered the soil pH significantly from 7.9 in CNT plots to 7.6-7.7 in

79 amended plots. These amended pHs are still fairly alkaline for optimum nutrient availability. In general, TSN and extractable nutrient concentrations were higher in the in ground plots than in the raised beds, again driven by better mixing of amendments in the raised beds. Bed type did not affect pH.

2.4.6 Soil biological properties following two years of management OM amendments resulted in 5 times larger concentrations of total C in the soil surface layer, raising soil C concentrations to approximately 6% by mass in amended plots (Table 2.3). This concentration will likely get progressively lower for several years before reaching an equilibrium level, as the amendments continue to decompose. Brown et al. (2012) observed continual decomposition in compost amendments added to urban soils, with 24% of the mass of the initial mass of C applied remaining as soil C after 15 years. The additional C supplied by the additions of biochar and cover crops did not result in measurable differences in total soil C compared with plots only receiving compost.

MBC was 20 times greater in May 2012 and 5-8 times greater in September 2012 in amended vs. control plots, likely due to the large C substrate available for decomposition, as well as increases in soil aggregation and water retention (Wardle

1992). The very low levels of MBC observed in the control plots suggest that the demolition disturbance and resulting compaction greatly impair soil microbial activity.

2.4.7 Soil C pools Both OM amendments and raised beds had a significant effect on total soil C pools at the site (Figure 2.1). Plots receiving OM amendments had 4-5 times as much soil C as unamended CNT plots. As observed with the C concentrations (3.6), the data suggest that

80 the CMP+B and CMP+ICC plots did not contain more C than the CMP plots despite receiving significant additional C inputs in the form of the biochar and cover crop biomass.

Raised beds contained 40% more soil C than in ground plots, an interesting observation in light of the higher soil C concentrations observed in the ground plots. This trend suggests that the improved incorporation of the amendments in the raised beds may have facilitated increased preservation of C, at least during the two year time frame reported here, probably due to decreased decomposition below the soil surface.

The application of 150 Mg ha-1 of compost contained the equivalent of approximately 38 Mg ha-1 C (2.1). After two growing seasons, comparison of C pools in amended plots vs. the CNT plots suggests that around 80% of that initial C application remains in CMP plots, though as mentioned above it is expected that the compost C will continue to decompose for several years before reaching an equilibrium level (Brown et al. 2012). Thus, applying amendments produced from urban yard wastes is an effective practice for rapidly increasing urban soil C pools.

2.4.8 Crop yields Organic matter treatments demonstrated a strong effect on tomato yield with amended plots producing more than unamended ones in 2012 (Figure 2.2 a). The three OM amendments did not differ according to the mean separation tests, but the trend in the data suggest that CMP+ICC plots produced slightly higher tomato yields. Using raised beds also increased tomato yields (2.7 kg m-2), as compared with in ground plots (2.1 kg m-2) (Figure 2.2 a). There was not a significant interaction between OM amendments and bed type (data not shown). Yields produced in this experiment averaged only 6-59% of

81 the average on-farm yield of 6.2 kg m-2 for processing tomatoes in Ohio between 2006-

2009, according to figures from the U.S.D.A.’s National Agricultural Statistics Service

(http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1210) and were in the range of “low” yields for commercial processing tomatoes according to a regional reference (Zandstra and Price 1988). Tomatoes were planted on 25 cm centers in this experiment and it is likely that increasing the planting density would have raised per plot yields.

Both the OM and raised bed treatments demonstrated strong positive effects on sweet potato yield (Figure 2.2 b). As with tomatoes, 2012 sweet potato yields with OM amendments produced more than unamended plots and the CMP+ICC plots tended to yield slightly more. Raised beds (2.2 kg m-2) also increased yields compared to in ground production (1.1 kg m-2). The interaction between OM amendments and bed type did not produce a significant effect (data not shown). Observed yields from plots amended with

OM in this study were slightly higher or similar to the average yields for commercial sweet potatoes in North Carolina (2.0 kg m-2) and New Jersey (1.25 kg m-2) during 2007-

2010(http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=14

92) and were in the range of “excellent” yields regionally (Zandstra and Price 1988).

The increases observed here in vegetable crop production in soils amended with

OM are consistent with many previous reports. Compost application has consistently increased crop yields and improved soil properties (Diacono and Montemurro 2010).

Similarly, biochar has increased yields in highly degraded soils in the tropics (Kimetu et al. 2008). There may be multiple reasons for the trend that CMP+ICC plots produced the largest vegetable yields. Both sorghum-sudangrass (Wolfe 1997) and tillage radish

82 (Williams and Weil 2004) have been linked to increased crop growth through improvements to soil properties, including reduced BD and penetration resistance.

Second, the CMP and CMP+B plots both exported significant quantities of nutrients offsite in the form of the harvested vegetable crops during year one of the experiment

(2011), while all cover crop biomass grown in the CMP+ICC plots was returned to those plots following harvest. The extremely low yields in the CNT plots were likely influenced by the high temperatures and drought of summer 2012, as grain crops in the

Midwestern U.S. performed best in soils with higher organic matter and associated moisture holding capacity during the drought(Al-Kaisi et al. 2013).

During 2011, the relative yield of all crops was strongly improved by the OM amendments. Relative yield was 0.54, 1.21, and 1.25 for CNT, CMP, and CMP+B plots, respectively. Raised beds did not result in a significant effect on relative yields in 2011, which were 0.94 for in ground plots and 1.06 for plots with raised beds. Sorghum- sudangrass produced the equivalent of 10.5 and 9.4 Mg ha-1 of dry matter in the in ground and raised bed CMP+ICC plots in summer 2011, demonstrating an excellent ability to produce biomass on the previously degraded site.

2.4.9 Correlation between soil properties and crop yields A basic framework for determining the suitability of sites for vegetable crop production is to evaluate the nutrient levels reported in a soil test and compare them against recommended levels for crop production. Gaus et al. (1993) reported desirable soil test levels for tomato production included the following: 62-75 mg kg-2 Ext. P; 162-212 mg kg-2 Ext. K; 150-225 mg kg-2 Ext. Mg; 1,500-2,250 mg kg-2 Ext. Ca; organic matter

>2.5%; and pH between 6.4 and 6.8. Judging our results from the final sampling date

83 against these recommendations, we observe a few notable discrepancies. The control plots contained only 24.6 mg kg-2 Ext. P and 55 mg kg-2 Ext. K, indicating a considerable deficiency of these key macronutrients (Table 2.2). OM amendments, however, raised the levels of Ext. P, Ext. K and all other nutrients well above the desired levels for tomato production, suggesting that vegetable nutrient requirements can be provided for UA with

OM inputs. The one soil property that was not corrected by the OM amendments was pH, which was significantly more alkaline than the desired level across all plots (ranging from 7.6-7.9). Thus, alkaline soils represent a challenge for maximizing nutrient availability for UA.

Stepwise regression analysis was conducted to explore the effects of the measured values of individual soil properties (September 2012) on 2012 crop yields, using the relative crop yield of each plot as the response variable. This analysis was performed twice, once with data from all plots in the experiment and once with data from only the plots that received OM amendments (Table 2.3). The analysis of all plots corroborated the soil test comparison with the recommended nutrient sufficiency levels for tomato production, as Mehlich Ext. K emerged as a robust single variable predictor of crop yields explaining 64% of the variation in the data . BD (10-20 cm), AWC, Mg, and S were also identified as important predictors of relative yield, but did not have a large impact on the model’s ability to explain yields. This analysis suggests that the 4-fold increase in Ext. K in the plots receiving OM amendments was a key factor that increased tomato and sweet potato yields. In the analysis of only the OM amended plots, AWC was selected as the most robust single predictor variable, indicating that differences in plant available water impacted crop yields in the amended plots. Ext. K, BD (10-20 cm), Ext.

84 Mg, and Ext. S were the other soil properties identified in this analysis. The AWC only model, however, explained 34% of the variation in the data, approximately half as much as Ext. K did in the first analysis. Together the two analyses convey that the impact of K additions from OM amendment produced a larger response in crop yields than any observed differences that occurring among the amended plots. This suggests that K and macronutrient management are important for UA vegetable production in previously degraded sites.

2.4.10 Soil quality index Factor analysis and the process of taking the most highly weighted soil properties for individual factors (Table 2.4) resulted in a minimum data set that included:

Ext. P, Total C, AWC and pH (Table 2.5). Factor 1 included several other soil chemical properties with high weights, including Ext. Ca, Ext. Mg, Ext. S, but all were found to be highly correlated (data not shown), and Ext. P was selected because SMAF contains a scoring curve for it. This result, however, highlights the importance of macro-nutrients to

SQ at the site.

The overall SQI scores calculated with SMAF indicate that all OM amendments resulted in a significant increase in SQ compared with the unamended control, while the

SQI for raised beds was not significantly different than for in ground plots (Table 5). The

SQI scores observed in this study (ranging from 0.60-0.85) are slightly lower than the ranges reported (approx. 0.75-0.95) in recent studies that used SMAF to evaluate soils in annual croplands in the midwestern U.S. (Jokela et al. 2011; Stott et al. 2011; Stott et al.

2013). The SQI scores for amended plots indicate OM amendments improved soil

85 function in the surface layer (0-10 cm) to a level comparable with rural agricultural soils.

Our CNT plots were clearly degraded compared to agricultural soils.

SQI scores calculated with SMAF indicated large differences between the CNT plots and those treated with OM amendments, but like the ANOVA and regression analyses, the SQI scores did not indicate a high degree of separation among the various

OM amendments. Among the individual soil properties included in the SQI, AWC and

Total C both had low values for CNT plots and higher values for amended plots, illustrating previously discussed benefits of the amendments to plant growth and soil function. Ext. P, however, was slightly lower in the amended plots than in the CNT, which scored very high despite not being measured at levels considered desirable for tomato cultivation. This is likely an indication that the concentrations of extractable P in the amended plots were sufficient to contribute to pollution by runoff (Andrews et al.

2004). Thus care must be taken to minimize runoff from soils amended with large quantities of compost to prevent surface water pollution.

If compared with the crop yield data (Figure 2.2), however, the scores, and particularly that of the control (0.60) appear to overestimate the condition of the soil. The control plots produced almost no measurable crop yields in 2012, suggesting that their level of soil function is likely not at 60% of potential. There could be a number of reasons for this. First, as a framework, SMAF works to incorporate a number of ecological outcomes, such as environmental buffering, into scores, rather than solely focusing on crop production as an endpoint. Second, the disturbed conditions of the study site are highly distinct from the agricultural conditions from which the SMAF was created. Third the extremely low crop yields in the CNT plots suggest that with suboptimal SQ, soil

86 function is greatly impaired by severe environmental stressors such as the high heat and drought conditions of summer 2012 (Al-Kaisi et al. 2013).

Two important patterns in the data did not emerge in the SQI. First, regression analysis indicated that Ext. K was the single most important soil variable for explaining crop yields, yet factor analysis did not result in its inclusion in the SQI, despite being included in four of the five factors. Secondly, 2012 yields were higher for both crops in raised beds than in ground cultivation, yet the SQI does not indicate that these methods are different. This suggests that while useful, processes of determining soil indicators through multivariate analysis may omit indicators that are key to soil function or crop performance at a given site. The SQI values do, however, suggest that soil quality can improve rapidly in degraded urban soils with the addition of large quantities of compost, which is in line with earlier observations of improvements in soil physical properties in urban landscape soils following the addition of large quantities of compost (Cogger et al.

2005).

2.4.11 Implications for urban agriculture The results presented here suggest that applying large amounts of OM amendments, produced from yard wastes, is a viable strategy for improving SQ and facilitating crop production in physically degraded urban soils. The quantity of compost applied in this study (15 kg m-2 or 150 Mg ha-1) is quite large and testing the effect of smaller, but still significant, compost application rates (50-100 Mg ha-1) on crop production in similar soils is a logical progression from these data. These comparatively smaller quantities have proven useful for improving soil properties in ornamental landscapes (reviewed in

Cogger et al. 2005). It is worth noting that the quantity of compost necessary to amend

87 this 0.1 ha research site was purchased for approximately $225, indicating that this rate of compost application is financially feasible for many UA sites. The addition of 2 kg m-2 of biochar to amended plots did not produce any compelling results in this study, as biochar application has in other degraded soils (Kimetu et al. 2008). This is likely because the large initial compost application masked any effect produced by the biochar.

The trends of increased crop yields, improvement to soil physical properties and large quantities of biomass produced by the sorghum-sudangrass indicate that cover cropping, and sorghum-sudangrass in particular, is an excellent management option for urban producers with sufficient space to incorporate them in their management practices.

Raised beds also provided significant increases in crop yields and improvement to some soil properties. Thus, raised beds are another management strategy that may improve UA outcomes.

These data also suggest that soil macro-nutrients, such as K, P, Ca, and Mg, and S are both key to crop growth, and occur at variable concentrations in vacant urban lots.

This trend suggests that conducting multiple, discreet soil tests and developing nutrient management plans may be key practices for maximizing crop yields in similar soils.

Producers utilizing organic management may need to supplement OM amendments with mineral fertilizers such as lime, gypsum, green sand, and elemental sulfur to optimize soil nutrient content (Magdoff and VanEs 2010). The slightly alkaline pH observed in this study, as well as in other reports on urban soils (reviewed in Beniston and Lal 2012), is not ideal for the nutrient availability required by vegetable crops, such as tomatoes (Gaus et al. 1993). Adding OM is a widely recommended strategy for slightly lowering pH and elemental S is often used to condition soil for acid loving crops. Other authors, however

88 have suggested that reducing the pH of soils with high calcium carbonate contents, a common condition in vacant lots (Howard and Olszewska 2011), may prove extremely difficult as carbonates are likely to neutralize any acidity (Magdoff and VanEs 2010).

Additionally, excess nutrient levels should be monitored in intensively managed UA systems, as the levels of Ext. P observed in the amended plots were given a slight penalty by the SMAF.

2.5 Conclusions This study documented soil properties and crop growth, as well as their response to experimental treatments of OM in a vacant urban lot soil. The results indicate that demolition of vacant houses and regrading of the site impaired soil function by resulting in high BD values and low Total C and MBC. Demolition did not, at this site, result in

Pb contamination in the soil surface. The data also provide evidence that the application of large quantities (15 kg m-2) of compost produced from urban yard waste can improve numerous soil properties, result in measurable differences in SQ, and facilitate favorable yields of vegetable crops within two years of application. Both the evaluation of soil nutrient status and the creation of a SQI indicated that OM amendments improved soil conditions for crop production at the site. Analyses of the measured soil properties from the study suggested AWC, pH, Total C, and extractable P as robust indicators of overall

SQ, and illustrate that extractable K is a key nutrient for crop production in these systems. Taken together these observations provide unique experimental evidence that vacant urban land in shrinking industrial cities does hold significant potential for UA and that the transformation of biomass wastes into soil amendments can provide a key input for such systems.

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Table 2.1. Summary statistics of baseline soil properties from soil samples (n=48) from an agriculture experiment in a vacant urban lot soil collected in June 2011 directly after the demolition of vacant houses and re-grading of the site.

Soil Property Mean Standard Range Coefficient Error of of Variation the Mean (CV)

Physical Properties

% Clay 16.8 0.17 13.4-19.6 0.07

% Sand 36.8 0.42 32.3-49.5 0.08

Bulk Density 0-10 cm depth (g cm3) In Ground (n=24)† 1.79 0.03 1.53-1.99 0.07 Raised Beds (n=24) 1.55 0.02 1.37-1.71 0.06 AWC 0-10 cm depth (cm) In Ground 1.1 0.14 0.0-2.6 0.64 Raised Beds 1.0 0.10 0.0-1.8 0.50 Chemical Properties Total N (g kg-1 soil) 0.8 0.02 0.5-1.3 0.19 pH 7.5 0.03 6.4-7.9 0.03

-1 CEC (cmolc kg ) 10.4 0.35 7.4-22.5 0.23 Chemical Properties - Extractable Elements (mg kg-1) P 22.6 1.30 11.9-50.8 0.40 K 47.0 2.41 33.9-126.2 0.36 Ca 1810 107.24 1120-5070 0.41 Mg 138 3.87 100-240 0.19 S 158 17.75 64.7-700 0.78 Al 331 9.31 156-494 0.19

Table continued on next page. 96

Table 2.1 continued

Soil Property Mean Standard Range Coefficient Error of of Variation the Mean (CV)

Chemical Properties - Trace Elements (mg kg-1) Pb 95.0 5.05 Pb 95.0 As 11.8 0.22 As 11.8 Cd 0.5 0.03 Cd 0.5 Biological Properties Total C (g kg-1 soil) 12.8 0.05 8.2-24.3 0.03 Microbial Biomass C (mg C kg-1 20.8 3.45 0.9-109 1.15 soil) † Values reported for bulk density and available water capacity (AWC) for both in ground and raised bed sub-plots because soil physical properties were markedly different between those two treatments.

Table 2.2. Measured soil properties from an agriculture experiment in a vacant urban lot soil from final sampling in September 2012.

Organic Matter Amendments† Raised Beds OM X Raised Bed Interactio n

Soil F CNT CMP CMP+ CMP+IC F value In Raised F value ¶ Property value‡ B C § Groun Bed d

Physical Properties

Bulk Density 0- 23.6 1.46 a †† 1.05 b 0.98 b 0.98 b 0.67 ns 1.10 1.14 0.73 ns 10 cm *** # (Mg m-3)

Bulk Density 0.04 ns 1.51 1.50 1.49 1.52 2.4 ns 1.46 1.55 1.99 ns 10-20 cm (Mg m-3)

%WSA 7.0 ** 0.30 a 0.49 b 0.48 b 0.49 b 10.7 ** 0.49 a 0.40 b 0.18 ns

MWD 4.4 * 0.60 a 1.11 ab 0.93 ab 1.43 b 0.3 ns 1.05 0.99 0.37 ns (mm)

AWC 0-10 2.7 ns 0.9 1.4 1.7 1.4 19.8 ** 1.6 a 1.0 b 3.98 * cm (cm)

Total 14.3 Porosity 0.37 a 0.53 b 0.55 b 0.53 b 14.0 ** 0.54 a 0.45 b 3.87 * *** (m3 m-3)

Table continued on next page.

Table 2.2 continued.

Organic Matter Amendments† Raised Beds OM X Raised Bed Interactio n

Soil F CNT CMP CMP+ CMP+IC F value In Raised F value ¶ Property value‡ B C § Groun Bed d

Biochemical Properties

Total C 25.6 (g C kg 12.1 a 61.9 b 59.7 b 57.7 b 16.7 ** 55.9 a 39.8 1.60 ns *** soil-1)

MBC (mg C kg-1 10.0 ** 7.8 a 223 b 229 b 265 b 5.8 * 213 a 150 b 1.63 ns soil) May 2012 MBC (mg C kg-1 11.4 53. 6 a 266 b 195 b 284 b 6.84 * 233 a 166 b 0.56 ns soil) *** Sept. 2012

Chemical

Properties pH 19.6 7.9 a 7.6 b 7.7 b 7.6 b 7.0 * 7.7 a 7.7 b 0.46 ns ***

Total N 28.4 (g N kg 0.9 a 4.7 b 4.2 b 4.4 b 15.4 ** 4.1 a 3.0 b 1.54 ns *** soil-1)

Table continued on next page.

Table 2.2 continued.

Organic Matter Amendments† Raised Beds OM X Raised Bed Interactio n

Soil F CNT CMP CMP+ CMP+IC F value In Raised F value ¶ Property value‡ B C § Groun Bed d

Extractable Elements (mg kg-1)

85 85.3 48.1 .3 P 24.6 a 95.6 b 96.9 b 100.8 b 91.2 a P 24.6 a *** *** ** *

46 46.2 .2 K 55 a 211 b 226 b 216 b 11.5 ** 157 a K 55 a *** ** * 38 38.6 32.0 .6 Ca 2244 a 4035 b 4157 b 4094 b 3983 a Ca 2244 a *** *** ** * 33 33.0 .0 Mg 182 a 558 b 553 b 610 b 13.4 ** 514 a Mg 182 a *** ** * 31 31.0 25.9 .0 S 84.9 a 153 b 160 b 156 b 151 a S 84.9 a *** *** ** * 46 46.9 .9 Al 455 a 293 b 311 b 282 b 13.8 ** 313 a Al 455 a *** ** *

Table continued on next page

100

Table 2.2 continued.

Organic Matter Amendments† Raised Beds OM X Raised Bed Interactio n

Soil F CNT CMP CMP+ CMP+IC F value In Raised F value ¶ Property value‡ B C § Groun Bed d

Extractable Elements (mg kg-1)

13.5 Fe 120 a 147 b 154 b 154 b 19.2 ** 151 a 137 b 0.48 ns ***

Zn 1.0 ns 16.3 19.9 28.8 19.6 8.3 ** 28.8 a 13.5 b 0.75 ns

† Main plot organic matter amendment treatments include: un-amended controls (CNT), amended with compost (CMP), amended with compost + biochar (CMP+B), and compost + intensive cover cropping (CMP+ICC). Values are means from ANOVA.

‡ F-test of organic matter amendments had degrees of freedom of 3 in numerator and 15 in denominator.

§ F-test of raised beds had degrees of freedom of 1 in numerator and 20 in denominator.

¶ F-test of OM and raised beds interaction had degrees of freedom of 3 in numerator and 20 in denominator.

# * = p<0.05, ** = p<0.01, *** = p<0.0001, and ns = not significant

†† Lower case letters indicate mean groupings according to Tukey’s honest significant difference (HSD).

101

Table 2.3. Model selection of predictor variables for explaining variation in relative crop yields.

All Plots Only plots with OM amendments

Predictor Equation R2 † AIC Predictor Equation R2 AIC variables ‡ variables

Ext. K RY = -0.15 + RY = 1.84 - 0.64 56.3 AWC 0.34 46.3 0.0065(K) 0.39(AWC)

Ext. K, RY = 0.90 + RY = 1.06 - AWC, Ext. BD20 0.0066(K) - 0.67 54.8 0.31(AWC) + 0.43 44.4 K 0.0781(BD20) 0.0031(K)

Ext. K, RY = -0.052 + RY = 2.19 - AWC, Ext. 0.0043(K) - AWC, Ext. 0.27(AWC) + 0.70 52.3 0.50 42.9 Mg 0.23(AWC) + K, BD20 0.0039(K) - 0.0012(Mg) 0.90(BD20) Ext. K, RY = 2.16 + RY = 3.56 - BD20, Ext. 0.0059(K) - BD20, Ext. 1.66(BD20) + Mg, Ext. S 1.07(BD20) + 0.72 51.7 K, Ext. Mg, 0.0055(K) + 0.58 40.3 0.0032 - 0.015(S) Ext. S 0.0031(Mg) - 0.017(S) † R2 is the coefficient of determination. A higher value indicates improved fit of the model.

‡ AIC is Akaike information criterion. A smaller value indicates improved fit of the model with a smaller number of predictor variables.

102

Table 2.4. Factor loadings of soil properties at an agriculture experiment in a vacant urban lot soil from, a factor analysis, using a quartimax rotation and five factors.

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5

Bulk Density -0.799 -0.191 -0.197 %WSA 0.688 0.432 MWD 0.483 0.124 0.533

Total Porosity 0.729 0.618

AWC 0.370 -0.143 0.806

Total C 0.643 0.748 -0.104

Total N 0.660 0.730 -0.107 0.106 pH -0.563 -0.243 -0.685 Ext. P 0.962 Ext. K 0.805 0.316 -0.221 0.105 Ext. Ca 0.954 -0.122 -0.177 0.130 Ext. Mg 0.986 -0.120 Ext. S 0.969 0.196 Ext. Al -0.822 -0.110 -0.199 Ext. Fe 0.814 -0.214

Ext. Zn 0.278 -0.244 -0.112 0.642

MBC - Oct. 0.730 0.126 0.163

MBC - May 0.658 0.107 0.142

103

Table 2.5. Soil property and soil quality index (SQI) scores from Soil Management Assessment Framework (SMAF).

Treatm F value‡ F value § CMP+I In Raised ent CNT † CMP CMP+B CC Ground Bed

AWC 6.6 ** ¶ 0.30 a # 0.55 b 0.62 b 0.51 b 18.1 ** 0.60 a 0.40 b

Ext. P 4.2 * 0.99 a 0.91 ab 0.92 ab 0.86 b 20.4 *** 0.86 a 0.98 b pH 18.9 *** 0.82 a 0.88 b 0.86 b 0.88 b 7.1 * 0.86 a 0.87 b Total C 183.7 0.32 a 1.0 b 1.0 b 1.0 b 2.0 ns 0.81 0.85 ***

Overall SQI 41.5 *** 0.60 a 0.83 b 0.85 b 0.81 b 0.36 ns 0.78 0.77 Score

‡ F-test of organic matter amendments had degrees of freedom of 3 in numerator and 15 in denominator.

§ F-test of raised beds had degrees of freedom of 1 in numerator and 20 in denominator.

¶ * = p<0.05, ** = p<0.01, *** = p<0.0001, and ns = not significant.

# Lower case letters indicate mean groupings for properties according to Tukey’s honest significant difference (HSD) where a significant treatment effect (p<0.05) was detected.

104

Figure 2.1. Soil C pools calculated on equivalent soil mass at an agricultural experiment in a vacant urban lot soil. Least squared means are presented for main plot treatments of un-amended controls (CNT), amended with compost (CMP), amended with compost + biochar (CMP+B), and compost + intensive cover cropping (CMP+ICC), as well as sub-plots where crops were grown either in ground (GR) or in raised beds (RB). Error bars are standard errors and lower case letters indicate mean groupings according to Tukey’s HSD test.

105

Figure 2.2. Measured 2012 yields for (a.) tomato and (b.) sweet potato crops grown in an agricultural experiment in a vacant urban lot soil. Least squared means are presented for main plot treatments of un-amended controls (CNT), amended with compost (CMP), amended with compost + biochar (CMP+B), and compost + intensive cover cropping (CMP+ICC), as well as sub-plots where crops were grown either in ground (GR) or in raised beds (RB). Error bars are standard errors and lower case letters indicate mean groupings according to Tukey’s HSD test.

106

Photo 2.1. The layout of the experimental garden: (a.) the plots in July 2011 with control (CNT) in the foreground, compost amended (CMP) plot in the middle, and compost plus intensive cover cropping (CMP+ICC) with sorghum/sudangrass in the background; and (b.) and overview of the garden in September 2011, sorghum sudangrass is still growing following the vegetable harvest.

107

CHAPTER 3

SOIL QUALITY EVALUATION OF URBAN MARKET GARDENS

3.1 Abstract In the U.S., urban agriculture (UA) has emerged as a desirable use for vacant land in post-industrial cities with declining populations. Urban market gardens, or farms, represent the largest scale of UA and have the potential to impact local economies and food security. This study was conducted with the central objective of characterizing soil quality (SQ) at nine urban market gardens in the cities of

Cleveland and Youngstown, OH. Soil samples were collected during October 2012 and a suite of soil physical, chemical, and biological properties were measured. SQ scores were calculated for individual soil properties and soil quality indices (SQIs) were generated for each site using the soil management assessment (SMAF). SQI values for the urban market garden sites ranged from 0.75-0.91, which is a comparable to those reported for rural agro-ecosystems. SQI values were higher on average at sites on soils with loam texture classes (0.86) compared to sites with sandy soils (0.81). Factor analysis indicated that several soil properties were important to explaining variability in UA sites included total C, CEC, Mehlich 3 P, Mehlich 3 Pb, bulk density, aggregate mean weight diameter and available water capacity. Several sites contained high concentrations of total soil C, which averaged 56.9 g kg-1 across all sites, likely due to repeated applications of compost. All observations indicate that 108 UA is not constrained by soil properties at these sites and that management for UA can result in high quality soils.

3.2 Introduction Urban areas have profoundly altered biogeochemical processes and fundamentally distinct soil types in comparison to rural areas (Kaye et al. 2006; Lehmann and Stahr

2007). Urban land use alters ecological processes such as climatic conditions, water infiltration, nutrient cycling, and plant communities (Pickett et al. 2001; Kaye et al.

2006; Pickett et al. 2008). Anthropogenic disturbances are a key driver in the development of urban soils and these disturbances often result in the degradation of soils and soil-mediated processes (Groffman et al. 2003; Lehmann and Stahr 2007;

Pickett and Cadenasso 2009). Despite their small land area, heavily disturbed soils, and numerous interacting constraints to plant growth found in cities, it is estimated that upwards of 20% of the world’s food may be produced in or around urban areas

(UNDP 1996; Eriksen-Hamel and Danso 2010).

In the U.S., urban agriculture (UA) has emerged as a desirable use for vacant land and properties in post-industrial cities with declining populations (reviewed in

Beniston and Lal 2012). UA occurs in three distinct forms: home gardens, community gardens, and urban farms or market gardens, which tend to be the largest scale of UA

(Mok et al. 2013). Detroit, MI and Cleveland and Youngstown, OH have all seen increasing numbers of urban market gardens during the past five years (reviewed in

Beniston and Lal 2012). The implementation of UA at production scales has the potential to impact both community food security and economic development in shrinking cities (Mallach and Brachman 2010). Grewal and Grewal (2012) and

109 Colasanti and Hamm (2010) estimated that between 33 and 50% of the fruits and vegetables consumed within the cities of Cleveland and Detroit could be produced in the cities if large areas of existing vacant land were utilized for UA. In a case study on local food production in northeast Ohio Masi et al. (2010) concluded that if 25% of the food consumed in a 16-county region were produced locally it could generate up to $4 billion in new economic activity. They further estimated that between $2-3 million worth of vegetable crops were already being grown within Cleveland in 2009 on only 2% of the cities vacant land.

The unique ecological conditions found in urban areas, however, present distinct challenges to crop production. Potential constraints to crop production encountered in cities include: water availability, nutrient supply, soil degradation, pest pressure and soil contamination (Eriksen-Hamel and Danso 2010). Most of these constraints are mediated, at least in part, by the soil. Additionally, topsoil is often removed and the remaining soil compacted at sites where construction or demolition activities have occurred (Gregory et al. 2006; Shuster et al. 2011; Chapter 2 this dissertaiton). Given the high level of ecological heterogeneity in urban environments, a key step in determining appropriate site-specific management plans includes robust evaluation of the soil resources and constraints present at a site (Schindelbeck et al.

2008). Thus, improving the assessment and management of urban soil could accelerate the success of UA.

Soil quality (SQ) assessment evaluates the ecological function of soil by measuring soil physical, chemical and biological properties and developing an index to score measured values (Karlen and Stott 1994; Andrews et al. 2004; Gugino et al.

110 2008). Individual soil properties are chosen for inclusion in the minimum data set necessary to characterize SQ based on their importance to soil-mediated processes and site-specific management goals (Doran and Parkin 1996; Andrews et al. 2004).

SQ indicators may also be selected based on their ability to explain variation within and among study sites. Datasets comprised of numerous soil properties can also distinguish between distinct classes or groups of sites or soils through discriminant analysis (Pouyat et al. 2007). Multivariate statistical analyses, including principal components analysis and factor analysis, have been employed successfully to determine subsets of measured soil values that explain a large proportion of the variation in the total dataset(Wander and Bollero 1999; Andrews and Carroll 2001;

Shukla et al. 2006). To date, however, we lack a rigorous evaluation of SQ indicators for UA soils.

This study evaluated soils from nine urban market gardens in the cities of northeast Ohio with the central research objective of characterizing soil quality at urban farming sites. Specific objectives included: (1) Determining whether geologic parent materials identified in the soil survey or soil texture measurements provided means to discriminate UA sites into groups of closely related soil types; (2)

Developing a minimum data set of soil properties important to differentiating soil quality amongst UA sites; and (3) Identifying specific soil properties receiving consistently low SQ scores across sites that may act as potential constraints to agricultural productivity. With the rapid increase of UA production, knowledge of their agronomic soil conditions can assist in managing soils for optimal productivity.

111 3.3 Materials and Methods 3.3.1 Site descriptions All samples were collected from sites where vegetable crops were being grown in urban lots for the purpose of sale in local markets. Potential research sites were identified through contact information for urban market producers provided by The

Ohio State University Extension, Cuyahoga County Extension in Cleveland, OH and

The Youngstown Neighborhood Development Corporation in Youngstown, OH.

Participating producers were all asked a standard set of questions regarding: the location of their site; their knowledge of the site’s history; and their soil management practices. A total of nine sites were included and detailed site information is provided in Table 1. All sites were managed by organic methods and produced vegetable crops either exclusively or with small fruit crops. Soil classification information, including series names, were obtained from the Web Soil Survey

(websoilsurvey.nrcs.usda.gov).

3.3.2 Soil sampling Soil samples were collected during October 2012 from areas of each site that shared similar landscape positions and that growers indicated had been under uniform management. Five samples were collected in an X design at each site from the 0-15 and 15-30 cm depths. Samples consisted of intact cores and bulk soil. Soil aggregate samples were obtained by breaking field moist clods over 8.0 and 4.75 mm nested sieves and separating the fraction retained on the 4.75 mm sieve. Bulk soil samples were passed through a 2-mm sieve prior to analysis.

112 3.3.3 Soil physical analyses Depth of topsoil was measured in the field using changes in structure and Munsell color as primary indicators. Soil texture was measured by the pipet method (Gee and

Or 2002). Bulk density values were obtained using the core method (Grossman and

Reinsch 2002). Gravimetric water content was determined by drying a subsample from the cores at 105°C for 48 h. The dry bulk density (BD) was then calculated using the water content value.

Soil aggregate stability was measured with a wet sieving process (Nimmo and

Perkins 2002) using a laboratory apparatus first described by Yoder (1936) and the resulting data were used to compute the percent of water stable macro-aggregates

(>0.25 mm) (%WSA) and the aggregate mean weight diameter (MWD).

Available water capacity (AWC) of the soil was estimated by measuring the volume of water retained at field capacity (-33 kPa) and permanent wilting point (-

1500kPa) using a ceramic pressure plate apparatus (Soil Moisture Equipment Corp.,

Santa Barbara, CA, U.S.A.) (Dane and Hopmans 2002). Intact core samples were used to measure the water retention at field capacity, while sieved soil (<2.0 mm) was used to measure the permanent wilting point. Gravimetric water contents were measured and converted to volumetric water content (θ) by multiplying with the measured BD for each sample. AWC was calculated as the difference in volumetric water content at -33 and -1500 kPa.

3.3.4 Soil chemical analyses Soil pH was measured with a 1:1 soil to water solution. Cation exchange capacity was measured using 1M Ammonium acetate extraction of exchangeable

113 cations (Helmke and Sparks 1996). Plant available nutrients and lead (Pb) were estimated using a Mehlich 3 extraction process (Mehlich 1984) with ICP analysis of the extract. Total C and N were measured by the dry combustion method (900°C)

(Vario Max, Elementar Analysensysteme, Hanau, Germany).

3.3.5 Soil biological analyses Microbial biomass carbon (MBC) was determined in sieved (6.75 mm) field moist soil (Vance et al, 1987). Soil samples (10 g) were fumigated with chloroform (50 ml)

for 24 hours then extracted with of 80 ml 0.5 M K2(SO4). The extract was filtered through Whatman no. 42 filter paper. Control samples were extracted using the same process, but were not fumigated. The samples were frozen prior to analysis. C content of the samples was then analyzed in a Shimadzu TOC-V Analyzer (Columbia,

Maryland, USA), using the non-purgeable organic C (NPOC) method.

3.3.6 Data Analysis Due to the considerable range in soil types in the study sites, we assessed that soil quality analysis would more accurately reflect soil conditions if the data set were split into smaller, more consistent groups before constructing the soil quality indices.

Linear discriminant analysis was conducted using the MASS package (Venables and

Ripley 2002) in R statistical software to compare separation of the dataset into two groups (R Core Development Team 2013). Linear discriminant analysis uses a multivariate dataset to generate a linear function that provides separation between two pre-ordained classes. The linear function contains a coefficient for each of the measured variables in the dataset and the resulting equation is then applied to generate a unique value for each observation.

114 The variables that were tested as classing groups in the discriminant analysis were “Parent Material” as indicated for each site by the soil survey, and “Soil Texture

Class” as measured. The dataset used to conduct the linear discriminant analysis was comprised of the measured soil property values for each observation.

3.3.7 Soil Quality Index In our process for creating a soil quality index (SQI) for groups of similar sites, adapted from Andrews and Carroll (2001), an exploratory principal component analysis (PCA) was first conducted on each group of samples using the princomp function in R. We then used the PCA results were used to determine the number of principal components required to explain approximately 85% of the cumulative variation in each sample group, and this number was then used to determine the number of factors necessary for factor analysis in that group. The goal of the factor analysis for this study was to explore the contribution of individual soil properties to explaining variation in the total data set. Factor analysis was conducted on a correlation matrix derived from the soil properties dataset, in R, using a “quartimax” rotation from the GPArotation package (Bernaards and Jennrich 2005). The quartimax rotation minimizes the number of factors required to explain variation for individual variables such that variables receive high loading values for only one or two factors. Soil properties displaying factor loadings with an absolute value greater than 0.7 were noted, since a factor loading that high means that half or more of the variance for that property is explained by that particular factor. Those properties that had factor loadings greater than 0.7 and also had existing scoring curves in the soil

115 management assessment framework (SMAF) (Andrews et al. 2004) spreadsheet were included in a minimum data set for the scored SQI.

SMAF is both a process for developing SQIs and a spreadsheet that contains scoring curves or algorithms that provide scores between 0.0 and 1.0 for measured values of approximately 12 soil properties. The shape and performance of each scoring curve are based upon previously described quantitative relationships between individual soil properties and key soil functions such as crop production and environmental buffering. Soil properties scored with SMAF in this study included:

BD (0-15 cm), BD (15-30 cm), %WSA, AWC, pH, Ext. P, Ext. K, Total C, and

MBC. Indicators were scored according to the mean values for each site and a number of factors were included to modify the scoring algorithms according to site- specific soil conditions. Two organic matter and three soil texture classes were used based on the mapped soil series and texture data (Table 1). Two slope factors of 0-2 and 2-5% were entered based on site observations. The climate and weathering factors were the same for all sites. Crop type was selected as “Tomato” to approximate needs for vegetable crops. All samples were collected in fall which was entered as the season factor and Mehlich 3 was distinguished for the Ext. P scores.

The SQI was calculated by taking the average of the scores for each of the individual soil properties. Property scores and SQIs were calculated for each individual observation and averaged for each site. A paired t-test of SQI values between sites with different soil texture classes was conducted using the matched pairs analysis in

JMP v10.

116 3.4 Results and Discussion 3.4.1 Soil physical properties Overall, soil textures in the study were dominated by sand content which ranged between 21.2-88.8% in 0-15 sampling depth (Table 2), which was driven by location. The majority of the Cleveland sites were in close proximity to Lake Erie on lake and outwash parent materials (Table 1). The two sites in Youngstown, OH contained higher proportions of silt and exhibited Loam and Silt Loam textures.

Depth of topsoil was quite variable across sites, ranging from 3-45 cm (Table

2), likely due to varied land use histories among sites. On some of the sites, buildings had been demolished, while other sites had been under lawn or vegetation for at least a few decades. The majority of sites contained low bulk density values with means of

0.9 and 1.2 Mg m-3 for the 0-15 and 15-30 cm depths, respectively (Table 2).

Previous studies have shown that some urban soils, particularly those under long-term vegetation, have low bulk density values and may, on average, have a better physical condition than some rural agricultural soils (Edmondson et al. 2011). This is in contrast to observations of high levels of soil compaction observed in some urban soils, particularly following construction and demolition activities (Gregory et al.

2006; Chapter 2 this dissertation).

3.4.2 Chemical properties pH ranged from slightly acid (6.4) to somewhat alkaline (8.0) but overall was close to neutral with a mean value of 7.4 in the 0-15 cm depth (Table 2). CEC measurements followed a similar pattern with low minimum (7.8), high maximum

(27.6) and moderately high (18.5) mean values, suggesting that overall these soils have reasonably high levels of inherent fertility. 117 Soil test P levels were fairly high across sites with a mean value of 151.2 and a range of 59.3-367.9 mg kg-1 soil in the surface (0-15 cm) depth (Table 2). Using the

Mehlich 3 method, P concentrations of 50-100 mg kg-1 are considered optimal for meeting nutrient demand of crop plants, while those above 150 mg kg-1 are thought to increase the risk of P leaching and non-point pollution (Sims et al. 2002). Thus, urban growers with high soil test P levels should consider limiting further application of P rich amendments, particularly on larger sites capable of producing significant runoff.

Various animal manures and green waste composts are known to continue significant quantities of P

Mehlich extractable Pb levels averaged 41.3 mg kg-1 and ranged from 5.5-

224.1 mg kg-1 in the soil surface (0-15 cm). A robust comparison of Mehlich 3 extractable Pb concentrations with concentrations of total Pb obtained with the EPA

3051a acid digestion method, conducted on soils from similar urban sites, suggested that the concentration of total Pb is approximately double the concentration obtained with Mehlich 3 (Minca and Basta 2013). Given that consideration, the majority of the soils in this study fall well below the screening level of 400 mg Pb kg-1 soil at which

Pb becomes a concern for playgrounds and gardens. Interestingly, extractable Pb demonstrated high variability among samples, with a CV value twice as large as any other measured soil property. This highlights the spatial variability of soil Pb levels and suggests that multiple, discrete samples should be taken to accurately assess Pb content of UA sites.

The following soil property levels have been reported as desirable soil test levels for tomato production: 62-75 mg kg-2 Ext. P; 162-212 mg kg-2 Ext. K; 150-225

118 mg kg-2 Ext. Mg; 1,500-2,250 mg kg-2 Ext. Ca; organic matter >2.5%; and pH between 6.4 and 6.8 (Gaus et al. 1993). All of the sites in the study had soil test levels at or above the recommended level for Ext. P, Ext K, Ext Mg and Ext Ca, indicating that these nutrients are not limiting for crop production at these sites. Eight of the nine sites in the study had pH levels above 7, suggesting these soils are slightly alkaline for vegetable production. These same eight sites also had Ext. Ca levels above 3,000 mg kg-1, suggesting that calcium carbonate content is a factor in the high observed pH values.

3.4.3 Biological or biochemical properties Total C levels were consistently high across all sites with mean concentrations of 56.9 and 41.7 mg C kg-1 soil in the 0-15 and 15-30 cm soil depths, respectively.

This is most likely because producers at all sites indicated that they had followed organic management practices and had applied significant quantities of compost to their soils during their tenures. Research studies in Washington state have demonstrated that repeated compost applications can significantly increase soil C levels in urban soils and that those increases persist for several years (Brown et al.

2012). Similarly, MBC levels were generally moderate to high across all sites indicating robust soil microbial activity, which is also likely due to the application of compost to the sites (Diacono and Montemurro 2010). Considerable increases were observed for both total soil C and MBC in a study that applied a large quantity of compost to a recently degraded urban soil (Chapter 2 this dissertation). The cover cropping and reduced tillage employed by several of the sites in this study may also have contributed to high observed levels of MBC.

119 3.4.5 Grouping sites according to soil conditions Linear discriminant analysis using soil property data suggested that grouping sites by soil texture classes (loam vs sandy) better separated the sites into two distinct sub-groups than did grouping sites by geologic parent material (outwash vs till) obtained from the web soil survey.

Figure 1 displays histograms of the values obtained from the linear discriminant functions for each observation. Figure 1a displays a larger degree of multivariate separation between the sites with a loam type texture and sites with sandy texture than the same comparison of sites mapped according to outwash and glacial till parent materials (Fig 1b). The fact that texture provided greater separation of site groups than parent material groups from the soil survey, may be because some sites have a history of material being imported as fill and do not match the mapped soil type for the surrounding area. The trend also suggests that soil texture is a strong driver of dynamic soil properties in these urban study sites and that soil texture may be a key measurement for understanding the management of UA sites.

While soil texture was chosen as a stronger discriminating variable, the linear discriminant analysis also suggested that there was fairly strong separation of sites based on their parent material classifications. This observation is consistent with an earlier survey of urban soils that reported differences in soil properties based on geologic parent materials (Pouyat et al. 2007), and suggests that soil survey information can be a useful management tool for UA in the U.S.

120 3.4.6 Soil quality index for sites with Loam textures The exploratory analysis with PCA indicated that six principal components were necessary to explain >85% of the variation in the dataset from the loamy sites

(data not shown). The subsequent factor analysis was conducted on six factors to explore the contribution of individual soil properties to explaining variation. Factor analysis indicated that pH, CEC, Ext. Al, Ext. Ca, MWD, Ext. P, Ext. Pb, Total C,

Total N, and BD 30 all had factor loadings with an absolute value greater than 0.70 and thus contributed significantly to explaining the total variation among these sites

(Table 3). Scoring curves are available in SMAF for pH, Ext P, Total C, and BD, and each represented the variable with the highest loading for an individual factor,so these properties were then included in a minimum data set for scoring.

Overall SQI scores generated with SMAF from the minimum data set of soil properties for loam sites were high, ranging from 0.79-0.94 (Table 4). Sites 3 and 5 had slightly lower scores, of 0.79 and 0.80 respectively. This appears to be due to the presence of high concentrations of Ext. P at these sites. Managers at both of these sites indicated that they applied compost to their soil annually and it is possible that regular input of nutrients is responsible for the high levels of P. The only other instance of a lower soil property score among the loam sites was that of bulk density at 15-30 cm for site 9. This site was established on vacant lots where building demolition had recently occurred, so the observation of high sub-surface bulk density is likely due to that recent disturbance (US EPA 2011; Chapter 2 this dissertation). pH and Total C both scored high (>0.70) across all sites. Thus, the presence of soil P

121 in excess of crop requirements and subsoil compaction are two key considerations for

UA in soils with loam textures.

The factor loadings presented above indicate that the variability in soil conditions observed at these UA sites with loam soil texture classes was driven by a number of soil physical, chemical and biochemical properties. The high loading value observed for aggregate MWD diameter illustrates that soil structure was variable among these sites. Reduced tillage, cover cropping, and providing organic matter inputs are all strategies for improving soil structure and differences in management practices across sites are a likely cause of variation for this soil property (Bronick and

Lal 2004). The factor loadings also indicate that there is significant variation in soil chemical properties and nutrient status of these sites (CEC, Ext Ca., Ext Al, Ext. Pb), beyond those described for P. This observation supports previous assertions that soil testing and nutrient management are key to improving SQ for UA (Eriksen-Hamel and Danso 2010; Chapters 1 and 2 this dissertation). The degree to which the observed differences in soil chemical properties were driven by either previous land use history or current management practices at these sites is not clear from this study

3.4.7 Soil quality index for sites with Sandy textures Exploratory analysis with PCA indicated that five principal components were required to explain >85% of the variation in the dataset for the sites with sandy textures, so factor analysis was conducted on the soil properties dataset for these sites using five factors. Total C, Total N, CEC, MWD, Ext. P, Ext. Al, Ext. Pb, BD 15 and

Ext. K all had factor loadings above 0.7 in the factor analysis (Table 5). Total C,

BD15, and Ext. K were all included in the minimum data set for scoring with SMAF

122 (Table 6). %WSA was also added to the minimum data set because it had the highest loading (0.632) in factor 2 of property included in SMAF.

SQI scores from the minimum data set derived for sandy sites were very high, ranging from 0.95-1.O, which is comparatively higher than the scores for the loam sites (Table 8). All sites and soil properties displayed scores higher than 0.90.These observations suggest that with proper management sites on sandy parent materials have excellent potential for UA. The inclusion of %WSA in the minimum dataset indicates that aggregation is particularly critical to soil function in sites with high sand content. Ext. K, also included as an indicator, has been observed to be an effective predictor of crop yields in an UA experiment on a degraded soil (Chapter 2, this dissertation). The concentrations of extractable K measured in this study are significantly higher than those measured in the experimental study, indicating that K is likely not a constraint to crop production at these market garden sites. As with the sites with loam soil texture, a wide range of soil properties demonstrated importance in the factor analysis.

3.4.8 Soil quality index using all measured indicators In addition to the SQI derived for each group of sites by creating a minimum data set through multivariate analysis, a SQI of all soil properties measured in this study that can be scored with SMAF scoring curves is given in Table 7. The inclusion of all indicators resulted in slightly lower SQI scores for most sites (0.75-0.91). A matched pairs t-test indicated that SQI values were higher in sites with loam soil, where SQI averaged 0.86, than in sites with sandy soils, which averaged 0.81. This is in contrast to the SQIs generated by selected soil indicators by multivariate analysis

123 which resulted in higher average scores for the sandy sites. This suggests that although the process of determining SQ indicators through multivariate analysis generated useful information about soil properties at the study sites, it did not capture the full extent of variation present in soil properties. Thus, our results suggest that a larger dataset of soil indicator properties produces a more accurate picture of SQ.

AWC and Ext. P exhibited a much larger range in scores than any of the other indicators, which were uniformly high. The variability in Ext. P scores is again driven by sites with Ext. P in excess of crop demand. The two sites that have been utilized for UA for the longest time (sites 6 and 7) (Table 7) contained the highest concentrations of Ext. P, which suggests that the high concentrations of Ext. P observed in this study may be due to the repeated application of P rich materials, such as compost, animal manures, or phosphate fertilizers. There does not appear to be a clear pattern driving the variability in the AWC results. Most sites received scores for

AWC between 0.5-0.7. AWC is a function of the structure of the soil, which affects the field capacity soil water content, and soil texture, which determines the soil’s permanent wilting point (Lal and Shukla 2004). The high scores given to the BD and

%WSA measurements in this study suggest that these sites have robust soil structure.

The scores for the AWC imply that it does not change as rapidly as other soil physical indicators. Thus producers in urban areas will need to provide adequate irrigation to meet crop needs in excess of plant available soil water .

The high scores observed for the majority of SQ indicators measured in this study suggest that there are not any consistent soil-based constraints to productivity in

UA systems. This may be due in part to the high levels of Total C observed at these

124 sites. Soil C is widely regarded as a keystone indicator of SQ and increases in total C levels are known to produce concurrent increases in crop yields in degraded soils (Lal

2006; Lal 2007). Two studies of soil conditions in unmanaged vacant lots in

Cleveland observed average total soil C concentrations of 20 and 37 mg C kg-1 soil

(Shuster et al. 2011; Knight et al. 2013). Those C concentrations are on average 35 and 65% of the average soil C concentrations observed at the UA sites in this study, suggesting that organic management for UA may increase soil C content considerably. The strategy of applying large quantities of compost and organic matter to soils for UA is clearly resulting in high levels of soil function within landscapes that are subject to widespread disturbance and soil degradation.

Recently, several studies have used the process of measuring dynamic soil properties and scoring them with SMAF to create SQIs in a variety of agricultural production systems in the U.S., including: soils under corn (Zea mays L.)/soybean

(Glycine max (L.) Merr) rotations in Iowa (Stott et al. 2011); soils under perennial grasslands and integrated crop/livestock systems in North Dakota (Liebig et al. 2011), and soils under annual cropping and long-term forages in Texas (Stott et al. 2013).

These investigators used 6-11 soil properties as indicators and calculated their SQIs with SMAF just as the SQIs reported herein and thus represent a robust comparison.

Overall SQI scores ranged from 0.63-0.94 in Iowa, 0.91-0.93in North Dakota, and

0.75-0.94 in t Texas soils. These ranges are nearly identical to those measured at urban market gardens in this study (0.75-0.91), suggesting that SQ at the UA sites is comparable to SQ measured in these rural agricultural areas. The high SQI scores

125 calculated for the UA sites in this study suggest that these soils are not degraded and have a high capacity for crop production.

Urban soils with high levels of C and low bulk densities have been reported in other urban areas (Pouyat et al. 2003; Edmondson et al. 2011). Thus, the observations of high SQ at UA sites reported in this study may not be unique to cities in Ohio, but may be possible across the developed world. Disturbance and land use history are known drivers of soil conditions in urban areas, so selecting sites that have not been subjected to recent, heavy disturbance may make it easier to manage soils for UA (Pouyat et al. 2010; Beniston and Lal 2012).

SQI values are explicitly linked to soil-mediated ecosystem services such as nutrient and C cycling, infiltration and filtering of water, and harboring biodiversity

(Andrews et al. 2004). The high SQI values and soil C concentrations observed in this study imply that UA is a means of deriving multiple functions from urban land. This is particularly valuable in dense urban areas where ecosystem services, such as those listed above, must be provided by a small percentage of the total land surface (Kaye et al. 2006).

3.5 Conclusion The study we conducted of working market garden sites in the cities of

Cleveland and Youngstown, OH, U.S.A indicated that urban market gardens have very good overall SQ that is comparable to well managed agro-ecosystems in rural landscapes. This suggests that UA sites support robust ecosystem services and offer a multifunctional use of urban land. Soil texture proved to be an effective classing variable for splitting the sites into two smaller subsets of similar sites for comparison.

126 Soil properties observed to be important to explaining variability in UA sites included

Total C, CEC, Ext. P, Ext. Pb, BD, MWD and AWC. SQIs comprised of all measured indicators produced a better comparison of sites than SQIs consisting of only a few indicators selected through multivariate analyses and sites on soils with loam textures had higher overall SQI values than sites on sandy soils. All soil properties measured had moderate to high values, so no consistent soil-based constraints to agronomic production were observed. Taken together these observations suggest that agronomic soil properties are not a major limitation to UA and that UA activities can result in high quality soils.

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132

Table 3.1. Locations and soil descriptions for urban market gardens in northeast Ohio, U.S.A.

Sit Locat City Size Years Map Soil Series Parent % % % Te e ion (ha) of ped Description Material San Cl Sil xtu Crop Soil d ay t re Produ Seri Cla ction es ss

1 41.477 Cleveland 0.1 4 Urba Mixed, mesic Glacial 68.8 5.9 25. San N; 0 n Aquic Lake/Out 3 dy 81.719 Elnor Udipsamment wash Loa W a Sediment m

2 41.487 Cleveland 0.0 2 Urba Coarse-loamy, Glacial 85.8 3.7 10. Loa N; 8 n mixed active, Lake/Out 5 my 81.717 Osht mesic Typic wash San W emo Hapludalf Sediment d

3 41.487 Cleveland 0.8 3 Urba Mixed, mesic Glacial 38.0 14.6 47. Loa N; 0 n Aquic Lake/Out 4 m 81.704 Elnor Udipsamment wash W a Sediment

4 41.477 Cleveland 0.0 4 Urba Fine, illitic, Glacial 47.3 15.9 36. Loa N; 5 n mesic Aeric Till 8 m 81.775 Mah Epiaqualf W onin g

5 41.429 Cleveland 0.1 2 Urba Fine, illitic, Glacial 45.3 13.4 41. Loa N; 0 n mesic Aeric Till 2 m 81.769 Mah Epiaqualf W onin g

6 41.472 Cleveland 0.0 12 Urba Fine, illitic, Glacial 63.8 10.3 25. San N; 8 n mesic Aeric Till 9 dy 81.699 Mah Epiaqualf Loa W onin m g

Table continued on the next page. 133

Table 3.1. continued

Sit Locat City Size Years Map Soil Series Parent % % % Te e ion (ha) of ped Description Material San Cl Sil xtu Crop Soil d ay t re Produ Seri Cla ction es ss

7 41.506 Cleveland 0.2 16 Urba Glacial 65.8 6.3 27. San N; 8 n Lake/Out 9 dy 81.644 Land wash Loa W Sediment m

8 41.071 Youngsto 0.6 2 Urba Fine-loamy, Glacial 26.2 16.5 57. Silt N; wn 0 n mixed, active, Till 3 Loa 80.678 Ritt mesic Aquic m W man Fragiudalf

9 41.080 Youngsto 0.0 2 Urba Fine-loamy, Glacial 36.8 16.8 46. Loa N; wn 5 n mixed, active, Till 4 m 80.677 Ritt mesic Aquic W man Fragiudalf

134

Table 3.2. Summary statistics of measured soil properties at urban market gardens in northeast Ohio, U.S.A.

Soil Property Depth Mean Standar Median Range Coefficien (cm) 1 d Error t of of the Variation Mean (CV)

Physical Properties

% Clay 0-15 11.6 0.73 13.1 3.3-18.8 0.42

15-30 13.1 0.95 13.1 2.7-24.5 0.49

% Sand 0-15 52.6 2.80 49.5 21.2-88.8 0.36

15-30 50.0 3.41 47.6 21.7-90.9 0.46

% Silt 0-15 36.0 2.15 37.1 7.6-62.8 0.40

15-30 36.9 2.59 38.5 6.1-61.3 0.47

Depth of Topsoil (cm) 22.2 1.62 20 3.0-45.0 0.49

Bulk Density 0-15 0.9 0.02 0.9 0.7-1.3 0.17 (Mg m3)

15-30 1.2 0.02 1.2 0.8-1.6 0.14

%WSA 0-15 0.5 0.02 0.5 0.2-0.7 0.25

MWD (mm) 0-15 1.4 0.09 1.3 0.5-2.7 0.44

AWC (cm) 0-15 2.1 0.16 2.5 0.0-4.1 0.50

15-30 2.0 0.15 2.1 0.0-4.0 0.52 Table continued on the next page.

135

Table 3.2 continued.

Soil Property Depth Mean Standar Median Range Coefficien (cm) 1 d Error t of of the Variation Mean (CV)

Chemical Properties - Nutrients

Total N 0-15 4.1 0.23 4.2 1.1-9.0 0.38 (g kg-1 soil)

15-30 3.0 0.19 3.1 1.0-5.6 0.42

pH 0-15 7.4 0.06 7.5 6.4-8.0 0.05

15-30 7.4 0.07 7.4 6.2-8.2 0.06

CEC (cmol) 0-15 18.5 0.82 19.3 7.8-27.6 0.3

15-30 15.2 0.88 15.3 4.6-29.4 0.39

Extractable P (mg kg-1) 0-15 151.2 12.02 124.5 59.3-367.9 0.53

15-30 120.7 12.49 79.9 29.6-301.8 0.69

Extractable K 125.7- 0-15 327.3 21.39 315.8 0.4 (mg kg-1) 714.8

15-30 236.7 14.07 242.1 90.9-548.6 0.40

Extractable Ca 0-15 3530 142.36 3840 1430-5060 0.27 (mg kg-1)

15-30 2910 159.51 2990 1070-5170 0.37

Extractable Mg 127.1- 0-15 366.0 18.96 383.8 0.35 (mg kg-1) 593.0 Table continued on the next page.

136

Table 3.2 continued.

Soil Property Depth Mean Standar Median Range Coefficien (cm) 1 d Error t of of the Variation Mean (CV)

Extractable Pb (mg kg-1) 0-15 41.3 7.35 21.0 5.5-224.1 1.19

15-30 50.9 8.88 23.4 4.8-229.9 1.17

Extractable Al 0-15 262.3 30.77 244.6 17.9-665.7 0.79 (mg kg-1)

15-30 364.8 30.41 340.8 30.1-747.6 0.56

Extractable Fe 0-15 190.3 8.75 178.0 93.8-364.9 0.31 (mg kg-1)

15-30 212.2 11.51 215.8 82.9-373.1 0.36

Extractable Zn 0-15 52.1 2.37 46.7 31.7-104.5 0.31 (mg kg-1)

15-30 50 3.92 40.8 18.6-134.9 0.53

Biological

Properties

Total C (g kg-1 0-15 56.9 3.26 61.2 17.5-105.6 0.38 soil)

15-30 41.7 2.98 40.5 13.2-96.3 0.48

Microbial Biomass C 0-15 236.4 19.84 213.1 0-514.0 0.56 (g C kg-1 soil) 1 Each depth increment summarizes 45 individual samples.

137

Table 3.3. Factor loadings of soil properties for the sites with loam soil texture classes from the factor analysis, using a quartimax rotation and six factors.

Soil Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Property

BD 15 -0.283 -0.525 0.396 0.431

BD 30 0.144 -0.292 0.211 -0.259 0.882

AWC 0.148 0.214 -0.149

%WSA -0.343 0.191 -0.300 0.431 -0.233

MWD 0.890 0.118 -0.165

Total C 0.502 0.154 -0.124 0.802 -0.246

Total N 0.344 0.256 0.861

MBC 0.403 0.260 0.464 0.133 -0.144 0.271

pH 0.898 -0.105 -0.250

CEC 0.954 0.173 0.142

Ext. P 0.156 0.706 0.402 0.257 0.296

Ext. K 0.368 0.442 0.235 0.598

Ext. Al -0.955 -0.142

Ext. Ca 0.974

Ext. Fe 0.144 0.608 -0.426 0.213 -0.134 -0.127

Ext. Pb 0.977 -0.142 0.140

138

Table 3.4. Measured values and soil quality scores from the soil management assessment framework (SMAF) for overall soil quality and for individual soil properties identified as key indicators for soil quality at urban market garden sites with loam textures in northeast Ohio.

Site pH Ext. P Total C BD 30 SQI (mg kg -1) (% (Mg m-3) (Total concentration) Score)

3 7.4 (0.92) 151 (0.26) 6.49 (1.0) 1.06 (0.99) 0.79 4 7.7 (0.87) 91.5 (1.0) 7.03 (1.0) 1.19 (0.99) 0.97 5 7.5 (0.90) 149 (0.3) 6.15 (1.0) 1.24 (0.99) 0.80 8 6.6 (1.0) 77.3 (1.0) 2.27 (0.78) 1.14 (0.99) 0.94 9 7.6 (0.87) 99.5 (0.94) 5.11 (1.0) 1.50 (0.58) 0.85

Values in soil property columns are measured means and values in parentheses are the corresponding scores from SMAF.

139

Table 3.5. Factor loadings of soil properties for the sites with sandy soil texture classes from the factor analysis, using a quartimax rotation and five factors.

Soil Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Property

BD 15 -0.420 -0.903 BD 30 -0.110 0.576 0.456 0.254 0.109

AWC 0.169 0.345 0.656 %WSA 0.462 0.632 -0.120 0.110 0.138 MWD 0.782 0.207 0.313 -0.221 0.313

Total C 0.979

Total N 0.973 0.152 0.270 -0.139 MBC 0.554 -0.345 0.347 -0.296 -0.207 pH -0.546 -0.275 -0.627 0.215

CEC 0.918 -0.187

Ext. P 0.797 0.408 0.193

Ext. K 0.284 0.119 0.947

Ext. Al -0.146 0.806 0.155

Ext. Ca 0.686 -0.428 -0.138

Ext. Fe 0.711 -0.258 -0.142 0.635

Ext. Pb -0.353 0.770 -0.202

140

Table 3.6. Measured values and soil quality scores from the soil management assessent framework (SMAF) for overall soil quality and for individual soil properties identified as key indicators for soil quality at urban market garden sites with sandy textures in northeast Ohio.

Site Total C BD 15 Ext. K %WSA SQI (%) (Mg m-3) (mg kg -1) (Total Score)

1 6.36 (1.0) 0.89 (0.99) 231 (0.94) 49.8 (0.99) 0.98 2 3.21 (1.0) 0.99 (0.99) 193 (0.90) 29.5 (0.92) 0.95 6 8.23 (1.0) 0.88 (0.99) 199 (0.91) 40.2 (0.93) 0.96 7 7.02 (1.0) 0.87 (0.99) 425 (1.0) 36.5 (1.0) 1.0 Values in soil property columns are measured means and values in parentheses are the corresponding scores from SMAF.

141

Table 3.7. Measured values and soil quality scores from the soil management assessment framework (SMAF) for all measured soil quality indicators and overall soil quality for urban market garden sites in northeast Ohio.

Site Total MBC % Bulk Bulk AWC pH Ext. P Ext. K Total C (%) (mg WSA Densit Densit (g g-1 (mg (mg kg- Score kg-1 y 0-15 y 15- soil) kg-1 1 soil) soil) cm 30 cm soil) (Mg (Mg m-3) m-3)

Sites with loam texture classes

6.49 † 283 150 59.1 0.84 1.06 0.16 7.4 399 (1.08) (26.05 (11.81 (2.43) (0.02) (0.09) (0.03) (0.04) (35.58) § ) ) 0.87 3 (0.03) 1.0 ‡ 1.0 1.0 0.99 0.99 0.64 0.92 0.29 1.0 (0.0) (0.0) (0.0) (0.0) (0.0) (0.10) (0.01) (0.16) (0.0)

202 7.03 55.3 0.80 1.19 0.18 7.7 91.5 327 (49.35 (0.27) (4.07) (0.04) (0.06) (0.03) (0.05) (8.55) (59.54) ) 0.91 4 (0.02) 1.0 0.71 1.0 0.99 0.99 0.71 0.86 0.97 0.97 (0.0) (0.14) (0.01) (0.0) (0.0) (0.08) (0.01) (0.03) (0.02)

6.15 360 53.5 0.81 1.24 0.13 7.5 149 413 (1.0) (1.0) (1.0) (0.99) (0.99) (0.52) (0.90) (0.30) (1.0) 0.86 5 (0.03) 1.0 0.98 1.0 0.99 0.96 0.52 0.90 0.39 1.0 (0.0) (0.01) (0.0) (0.0) (0.03) (0.09) (0.01) (0.24) (0.0)

2.37 116 68.1 1.09 1.19 0.09 6.7 78.2 350 (0.17) (9.46) (3.76) (0.06) (0.07) (0.01) (0.12) (5.40) (27.31) 0.75 8 (0.02) 0.78 0.31 1.0 0.97 0.88 0.28 0.99 0.99 1.0 (0.06) (0.05) (0.0) (0.03) (0.07) (0.05) (0.01) (0.01) (0.0)

319 5.11 53.8 0.98 1.50 0.20 7.7 99.5 392 (41.86 (0.75) (5.04) (0.07) (0.08) (0.02) (0.03) (6.0) (32.29) ) 0.88 9 (0.01) 1.0 0.95 0.98 0.99 0.55 0.75 0.87 0.92 1.0 (0.0) (0.03) (0.01) (0.0) (0.09) (0.06) (0.01) (0.05) (0.0) Table continued on next page.

142

Table 3.7 continued

Site Total MBC % Bulk Bulk AWC pH Ext. P Ext. K Total C (%) (mg WSA Densit Densit (g g-1 (mg (mg kg- Score kg-1 y 0-15 y 15- soil) kg-1 1 soil) soil) cm 30 cm soil) (Mg (Mg m-3) m-3)

Sites with sandy texture classes

124 208 6.36 49.8 0.88 1.28 0.06 7.3 231 (14.71 (36.21 (0.40) (4.63) (0.06) (0.09) (0.0) (0.08) (15.93) ) ) 0.82 1 (0.03) 1.0 0.82 1.0 0.99 0.90 0.51 0.94 0.24 0.94 (0.0) (0.11) (0.0) (0.0) (0.09) (0.01) (0.01) (0.17) (0.02)

160 103 3.21 29.5 0.99 1.22 0.06 7.8 194 (84.08 (17.76 (0.45) (4.59) (0.08) (0.02) (0.01) (0.12) (47.16) ) ) 0.81 2 (0.03) 0.97 0.44 0.89 0.99 0.99 0.53 0.84 0.80 0.84 (0.03) (0.23) (0.03) (0.0) (0.0) (0.04) (0.02) (0.09) (0.04)

236 227 8.23 40.3 0.88 1.01 0.06 7.6 199 (18.12 (27.85 (0.47) (1.01) (0.01) (0.04) (0.03) (0.04) (14.27) ) ) 0.78 6 (0.01) 1.0 0.93 0.93 0.99 0.99 0.30 0.89 0.15 0.90 (0.0) (0.02) (0.01) (0.0) (0.0) (0.11) (0.01) (0.15) (0.02)

347 279 424 7.02 36.5 0.87 1.09 0.12 7.2 (66.54 (34.97 (119.20 (0.67) (9.24) (0.08) (0.01) (0.01) (0.13) ) ) ) 0.83 7 (0.01) 1.0 0.96 0.90 0.99 0.99 0.74 0.94 0.0 0.96 (0.0) (0.04) (0.10) (0.0) (0.0) (0.04) (0.02) (0.0) (0.03) † Values in upper row for each site are means of measured values for soil properties.

‡ Values in lower row for each site are means of scores from SMAF for each soil property.

§ All values in parentheses are standard errors.

143

Figure 3.1 Histograms of predicted values from the linear discriminant analysis demonstrating the separation of groups of sites according to classing variable of (a.) “Soil Texture” and (b.) “Parent Material”.

144

CHAPTER 4

UTILIZING URBAN SOILS FOR CROP PRODUCTION

4.1 Abstract Urban agriculture (UA), or the production of food within cities, is a longstanding practice globally. Estimates have suggested that upwards of 20% of world’s food may be produced through UA and recent appraisals indicate that more than 250 million households participate in UA in developing nations. UA is also expanding rapidly in more developed nations, driven in part by the economic recession, the promotion of the benefits of locally produced foods, an d large areas of vacant land in post- industrial cities. Recent reports suggest that post industrial cities could produce more than 20% of the vegetables consumed in them with UA and relevant models of this scale of peri-urban agriculture already exist in Australia. UA outcomes will be dictated by the condition and management of soils. Ecological surveys of vacant urban lots are increasing in the U.S. and early results demonstrate reasonable suitability of most sites for UA. The transformation of organic wastes produced in urban areas into soil amendments is a key practice for improving soils for UA.

Critical soil property thresholds established for other agricultural systems are useful to assessing soils for UA. This report synthesizes information on all of these topics

145 and points to researchable priorities for maximizing the benefits derived from UA in the U.S.

4.2 Introduction More than 50% of people on Earth now live in cities, a significant demographic milestone that occurred during the past decade (UNDP 2011). Urban areas are thought to occupy around 338 Mha globally and 24 Mha, or 2.6%, of land area of the U.S. (reviewed in Lal 2012). Urban areas are highly altered landscapes where numerous ecosystem processes have been altered by anthropogenic disturbances (Kaye et al. 2006). A key ecological characteristic of cities is that the quantities of materials and energy that are imported and consumed are disproportionately large compared to the their land area, as are the exported waste products (Kaye et al. 2006). This principle is perhaps most profoundly illustrated by the fact that cities make up approximately 3% of the global land surface, but are responsible for an estimated 70% of all anthropogenic C emissions (Grimm et al.

2008; UN HABITAT 2011). At the landscape scale, unique ecological features of cities include: a high percentage of the landscape covered by impervious surfaces, altered climatic and hydrologic conditions, and novel assemblages of plants and animals (Grimm et al. 2008; Pickett et al. 2008). Despite these disturbed conditions, urban landscapes, particularly those with un-covered soils, retain their ability to provided ecosystem services critical to social and ecological well being (Pouyat et al.

2010; Setala et al. 2013).

Urban agriculture (UA), or the production of food within cities, is a longstanding practice globally (Hamilton et al. 2013, Pearson et al. 2010). Estimates

146 have suggested that upwards of 20% of world’s food may be produced through UA

(UNDP 1996) and recent appraisals indicate that more than 250 million households participate in UA in developing nations (Hamilton et al. 2013). UA is also expanding rapidly in more developed nations, driven in part by the economic recession as well as the promotion of the benefits of locally produced foods (Schupp and Sharp 2012;

Mok et al. 2013). Many northern cities that have lost significant proportions of their mid 20th century populations as industrial manufacturing declined are attempting to utilize UA as a beneficial use of vacant urban land and properties (Beniston and Lal

2012; Grewal and Grewal 2012). UA in developed countries is often classed according to the scale and intention of production with scale increasing from home gardens, community gardens or allotments, and urban farms or market gardens (Mok et al. 2012). Urban market farming is a relatively recent concept in the U.S., but urban and peri-urban farms already produce a large percentage of fruits and vegetables sold in many Australian cities (Mok et al. 2012).

Given the proximity of large markets, food insecure populations, and availability of resources in cities, conditions are favorable for UA to have an increasing impact on urban life (Mallach and Brachman 2010, Grewal and Grewal

2012). The unique biophysical conditions encountered in cities may also provide significant challenges to crop production. Agronomic constraints identified in urban areas include water availability, nutrient supply, soil degradation, contamination of soils, and the potential for increased pest pressure (Eriksen-Hamel and Danso 2010).

As in rural areas, many of these processes are affected and driven by the management of soils. The variability, disturbance, and opportunity characteristic of urban systems

147 is mirrored in their soils. The classification, description and management of urban soils has been a fertile and expanding area for soil research as the desire to manage urban systems for ecosystems services has emerged (DeKimpe and Morel 2000;

Lehman and Stahr 2007; Pouyat et al. 2010; Shuster et al. 2011). Soil management is the foundation of any system of sustainable agriculture and the goal of this report is to review the management of urban soils for crop production by utilizing emerging research from ecosystem, soil, and agronomic science conducted within cities. The specific objectives of this review are: (1) Document recent advances in understanding the potential of UA; (2) View recent descriptions of urban soils in light of their potential for agronomic management; and (3) Identify and describe best management practices for utilizing urban soils for food production. This report will focus primarily on research and perspectives relevant to developed countries, but will point to more global perspectives when relevant.

4.3 Can urban agriculture address food security in developed countries? UA has often been described as a land use with many potential societal and ecological benefits (Table 1). The majority of the studies that have documented these benefits have to date been conducted in developing countries (Smit et al. 1996; DeBon et al.

2010). The current interest and proliferation of UA in developed countries has generated a strong need for reports on the measurable impacts of UA in these cultural settings, as a means to inform both producer practices, as well as policy and planning decisions on the topic.

Observations have indicated that participation in UA impacts both dietary consumption of fruits and vegetables at the household and individual level (Alaimo et

148 al. 2008; Blaine et al. 2010). The small scale of many UA projects (<0.5 ha) in the

U.S. has lead many, however, to question the potential of UA to produce quantities of food sufficient to impact food security at the city level. Recent surveys have examined the production potential of UA for the American cities of Cleveland, OH and Detroit, MI (Table 2). The authors estimate that by utilizing a large percentage of existing vacant lots for UA (62.5-80% of lots), these cities could produce a significant proportion of the fruits and vegetables consumed by their populations. A similar study from Oakland, CA, a city with far less vacant land than Detroit or Cleveland, estimated that around 5% of recommended vegetable consumption for the city could be satisfied through UA (Table 2). These estimates lead logically to at least two questions about their feasibility: (1) Is it practical to implement UA at this level of scale?; and (2) Will the actual crop yields from urban soils be sufficient to support these levels of production?

Relevant evidence exists to support the assertion that it is possible to implement UA at the scale necessary (approx. 1,000 ha) to impact food security for a major city. While there are currently no examples of cities where this level of production is occurring within the city on vacant lots, there are cities with large areas of urban and peri-urban agriculture. Data from Condon et al. (2010) illustrate that in metropolitan Vancouver, BC there are more than 6,000 ha of land in horticultural land uses, including more than 4,000 ha under fruit and vegetable production (Figure

1). Much of this land is, however, in peri-urban areas of the city. A study of market gardens in Cleveland and Youngstown OH observed that seven of the nine sites included in the study were >0.30 ha in size (Chapter 3, this dissertation). Open lots in

149 cities often comprise no more than 3-4 contiguous city parcels, as exemplified by the market gardens in Ohio, so producers will likely need to aggregate multiple separate production sites at the neighborhood scale to access the areas of land required for significant crop production. This will require logistical creativity, but the practice is already being implemented for UA. The Franklinton Gardens organization in

Columbus, OH produces crops on several non-contiguous sites across their neighborhood in an effort to expand their production space and capacity

(franklintongardens.org).

Another important factor in determining the production potential of UA is to obtain data on the actual yields of crops grown in urban soils. Until recently, data on measured crop yields had not been collected in northern cities. A handful of recent studies have documented crop yields in a variety of contexts in American cities and early results are promising. A study on growing crops in a vacant urban lot soil amended with compost recorded yields of 2.8-3.6 kg m2 for tomatoes and 1.7-2.7 kg m2 for sweet potatoes in amended plots. The tomato yields are considered low while the sweet potato yields are in the range of excellent for commercial production in the region (Zandstra and Price 1988). A study reporting tomato yields in both community and market gardens in Cleveland, OH observed average yields between 3-6 kg m2, but indicated that yields were highly variable ranging from 1.5-15.7 kg m2 across sites

(Reeves et al. 2013). That study did not observe a difference in mean yields between community and market gardens. Surveys of community gardens conducted in

Philadelphia, PA in 2008 and Camden, NJ in 2009 measured mean yields of 6.8 and

2.5 kg m2 for all vegetable crops, respectively (Vitiello and Nairn 2009; Vitiello et al.

150 2010). The authors suggested that those yields were highly dependent on climatic conditions, as the higher yields were observed in their region during the more favorable weather conditions of summer 2008. Collectively, these observations indicate crop yields from UA are highly dependent on site specific biophysical conditions, such as soil quality and climate. Thus, if sites with strong soil quality are selected (Chapter 3 this dissertation) or intensive management is implemented on degraded sites (Chapter 2 this dissertation) robust yields are possible. Season extension, or the practice of growing vegetable crops through the winter in “high tunnel” greenhouses is a practice that is expanding widely among small scale vegetable producers in the U.S. The extended season and improved microclimate that high tunnels offer could improve both the annual and seasonal yields of UA producers, and understanding production potential of UA systems incorporating high tunnels is a useful topic of inquiry.

Finally, Australia also provides working examples for American cities seeking to produce food through UA. A recent review article compiled data that indicate that in eight major cities in Australia numerous fruit and vegetable crops are produced to an extant that more than half of the crop consumed in the city is produced locally

(Mok et al. 2013). For two cities, Darwin and Canberra, more than half of all vegetable crops consumed are produced within the metropolitan area. Mougeot et al.

(2005) also synthesized extensive data on the significant contributions of UA to fruit and vegetable consumption in cities globally. Clearly evidence that UA has the potential to be a major producer of fruit and vegetable crops in the cities of the U.S. is increasing. Understanding the management practices and policy measures necessary

151 to scale UA up to address food consumption at the city level in the U.S., as well as identifying barriers to the scaling up of UA is a researchable priority.

4.4 Ecosystem services from UA systems The need and potential for urban soils to provide ecosystem services is a topic that has been written about extensively (Pouyat et al. 2010; Setala et al. 2013).

Recently studies have begun to gather data on the ability of urban soils and UA systems to provide for specific ecosystem processes. Shuster et al. (2011) proposed that the large areas of vacant land in Cleveland could be utilized to assist in the infiltration of large volumes of storm water runoff. They developed a framework for assessing soil suitability for this type of “green infrastructure,” but cautioned that its potential would be limited in soils with high compaction or coarse fragment contents.

Some UA sites are large enough to potentially impact storm water infiltration at a neighborhood scale and the measurement of infiltration rates and capacity for UA sites is a notable research need.

Soil organic C (SOC) pools vary widely in urban areas and can be large or small depending on land use history and site management practices (Lorenz and Lal

2009). A growing number of studies suggest that managing soils for UA has the potential to increase SOC pools. Soils at 9 UA sites in Cleveland and Youngstown,

OH had C concentrations 50% higher than those reported for vacant lots in Cleveland

(Chapter 3 dissertation). The increased soil C concentration was likely due to repeated compost applications at the UA sites. Heavy compost application in a UA experimental garden raised the surface (0-10 cm) soil C pool by 4-5 times and after two years the soil had retained 80% of the C content of the applied compost (Ch2 this

152 study). In Tacoma, WA an average of 24% of the C content of compost applied to urban soils was still retained in the soil after 15 yrs (Brown et al. 2012). In England,

Kulak et al (2013) estimated that peri-urban vegetable production could reduce CO2

-1 -1 emissions by 34 t CO2 e ha a through reductions in greenhouse gas emissions from energy use for storage and transportation in conventional vegetable production.

Collectively, these data indicate that UA has the potential to be a C sink. Though the areas of land and magnitude of the sink are small in the scope of biogeochemical cycles, increases in SOC pools will ultimately lead to other ecological benefits such as improved hydrologic cycling, environmental buffering, and invertebrate habitat in urban soils (Lal 2007).

In Cleveland, OH, Gardiner et al (2013) found that the establishment of new

UA sites resulted in a small decline in predatory insect diversity, but overall did not reduce the pest biocontrol services derived from predatory insects. The authors suggested that UA systems should include plants that flower in spring and early summer to encourage habitat for beneficial predators. Similarly, a study of soil food webs in vacant lots and UA sites in Ohio suggested that UA sites reduce the use of tillage as much as possible during establishment in an effort to maintain beneficial soil nematodes and the biocontrol and nutrient cycling services those organisms provide (Grewal et al. 2010). UA sites represent significant green space in many urban areas, thus management practices that encourage insect habitat and biodiversity may ultimately result in ancillary benefits to both the UA sites and surrounding areas.

153 4.5 Soil conditions in vacant urban lots In many cities in the eastern U.S. tremendous population reductions have resulted in large quantities of vacant urban lots and large tracts of vacant urban land

(Dewar and Thomas 2013). These vacant properties have provided the catalyst and resource base for the burgeoning growth of UA in many cities (Beniston and Lal

2012). Rising awareness of the need to repurpose vacant land has generated research interest in vacant lot soils on the premises of understanding their potential for UA

(chapter 2 this dissertation; Knight et al. 2013), suitability for usage for stormwater infiltration (Shuster et al. 2011), pedological conditions and trajectories (Howard and

Olszewska 2011) and the level of Pb contamination (Minca and Basta 2013).

A number of salient findings have emerged from these studies. An experiment initiated immediately following the demolition of vacant buildings observed that the demolition process resulted in high levels of soil compaction and low biological activity (Chapter 2 this dissertation). The negative effects of demolition on soils may persist in unmanaged vacant lots as Shuster et al.’s (2011) study suggested that the management potential of some vacant sites was limited by the presence of large amounts of debris and high levels of soil compaction. These conditions were often the result of demolitions that occurred >20 years prior to the study. In Detroit, construction debris from past land uses was observed as a driver of resulting soil chemical conditions with the formation of new iron oxides from metal material such as nails and increases in Ca-carbonate levels from the breakdown of cement materials

(Howard and Olszewska 2011).

154 Minca and Basta’s (2013) survey of soil Pb levels in vacant lots in Cleveland reported that only a small percentage of the lots had Pb concentrations that represented significant health risk and precluded their use for UA. Knight et al.

(2013) observed that soil conditions in vacant lot soils were sufficient to support growth of lettuce (Lactuca sativa) and that plant health was connected to soil C levels at their sites. The vacant lot agricultural experiment demonstrated that crop production was drastically improved in the degraded lot soil through compost application (Chapter 2 this dissertation).

These studies all provide evidence that it is possible to utilize vacant urban lots for commercial scale fruit and vegetable production. SQ and crop production outcomes will be limited initially by the land use history and disturbance legacy of the sites (Pouyat et al. 2010). Grewal et al (2010) reported that vacant lots under vegetation had well developed nematode food webs, suggesting that they could be readily converted to UA. Significant management interventions will be necessary to overcome soil-based constraints resulting from past construction and demolition activities, including: the removal of topsoil, compaction, and the presence of large quantities of debris and coarse fragments. In some instances, degraded soil conditions will occur to an extant that UA of site management will be limited by the labor, resources and capital necessary to improve the site (Shuster et al. 2011), while at other sites rehabilitation may be possible with limited financial inputs (Chapter 2 this dissertation).

155 4.6 Critical limits of agronomic soil properties A critical limit is the range of measured values for a specific soil property that must be maintained for ecosystem functioning (Arshad and Martin 2002). When measured values for soil properties go below this range, agricultural production declines rapidly, as do other ecological processes such as hydrologic functioning and environmental buffering (Lal 1994). Table 3 lists critical limits and ideal conditions for a number of agronomic properties important to UA. In sites with a history of heavy disturbance and degraded soil conditions it can be particularly important to understand critical soil limits so that they may be addressed immediately with targeted management practices. This is likely to be particularly important during the establishment of UA systems. A study of 9 existing urban farms in Ohio did not document many sties with soil properties below threshold levels (Chapt 3 this dissertation). Studies of the suitability of vacant lots for stormwater infiltration

(Shuster et al. 2011) and utilizing vacant urban lots for UA following demolition

(Chapter 2 this dissertation) encountered numerous soil properties below threshold levels, including: bulk density, coarse fragment content, effective rooting depth, pH, and K content. Table 4 shows mean values for measured soil properties in vacant lots from a large study in Cleveland (Shuster et al. 2011). On average these vacant lot soils do not exceed soil critical limits, though many individual sites had one or more properties occurring at levels that exceeded the threshold for severe impacts. It is encouraging, however, that many of these critical limits were corrected in <2 years at the UA experimental site through the addition of large quantities (15 kg m2) of compost and cover cropping (Chapter 2 this dissertation). Intensive management

156 practices for overcoming critical soil limitations for UA are discussed in detail in

Beniston and Lal (2012) and US EPA (2012).

4.7 Soil Testing Soil nutrient testing is a fundamental practice in many production agriculture systems. In UA, the need for soil testing is compounded by the high level of environmental heterogeneity, as well as the potential for contamination. Recent reports on agronomic conditions in urban soils suggest that (1) soil nutrient and trace element concentrations are highly variable both within and across sites, and (2) macro-nutrient concentrations are key indicators of both soil quality and the potential for crop production in urban soils (Chapters 2&3 this dissertation). Previous studies have also indicated that nutrients and contaminants vary strongly according to previous land use in urban soil (Lorenz and Kandeler 2005; Witzling et al. 2011).

Thus conducting representative soil testing is a key practice for optimizing production outcomes in urban soils. The existing studies mentioned above all point toward the need for multiple, discreet soil samples to be collected for analysis from UA sites, in an effort to capture some of the variability present. In sites where multiple previous land uses have occurred in unison, such as an urban lot that has had buildings removed but also has areas of intact yard from the previous property, it is advisable to collect samples from each area separately (Shuster et al. 2011).

4.8 Testing urban soils for Pb Lead (Pb) is a primary contaminant of concern for UA and urban soils, as soils are now the main reservoir of Pb deposited in cities during its previous era of widespread usage in paint and gasoline (Fillipelli and Laidlaw et al. 2010). The U.S.

157 Environmental Protection Agency (EPA) has provided some guidance on the risks associated with soil Pb concentrations and has suggested that soils exhibiting total Pb concentrations above 400 mg kg-1 warrant further mitigation if they are to be used for playgrounds or public greenspaces. These standards are based on the usage of laboratory methods of measuring total soil metal content through digestion with strong acid (EPA Methods 3050b, 3051a). These procedures, are however, somewhat costly and not in regular practice in many soil labs. Recent research with great practical implications on this topic has indicated that total Pb content can be accurately predicted based on the amount of Pb extracted by the standard Mehlich 3 soil test, which is cheaper and already in wide use for soil nutrient testing (Minca and

Basta 2013). Soils exceeding the 400 mg kg-1 screening level can then be tested with the EPA methods. Thus there is a need, a potential market, and existing analytical infrastructure for more soil laboratories to provide soil Pb analysis with the Mehlich 3 method. Soil Pb is know to vary spatially, often concentrating in hotspots, so the use of multiple samples from a single site is recommended for Pb testing as well (Chaney et al. 1984). Management options for soils with moderate Pb levels are reviewed in

Chapter 1 of this dissertation.

4.9 The application of organic amendments to urban soils Though constraints to crop production can be common in urban soils, cities also contain enormous quantities of organic wastes, many of which have the potential to be transformed into amendments, such as compost or biochar. The urban ares of the

U.S. generate approximately 56.6 Tg yr-1 of organic wastes in the form of biosolids

(6.7 Tg yr-1), yard waste (26.8 Tg yr-1) and food waste (24.1 Tg yr-1) (reviewed in

158 Brown et al. 2012). Brown et al. (2012) estimated that approximately 33% of these materials are currently recycled for land application, with only 2% of food wastes currently being used for composting. These data indicate that there is great potential to increase the production of amendments from these materials. These wastes represent a tremendous potential resource for managers of urban soils.

Numerous benefits have been observed from the application of organic amendments to urban soils. Measurable improvements in soil quality, as well as 8-10 fold increases in vegetable crop production were reported 2 growing seasons after the application of large quantities (15 kg m2) of yard waste compost to a physically degraded vacant lot soil (Chapter 2, this dissertation). Application of green waste compost has also demonstrated consistent improvement in soil properties in physically degraded urban soils being utilized for ornamental landscapes and tree plantings (Cogger et al. 2005; Scharenbroch 2009).

In the U.S. cities with modern wastewater treatment infrastructure, solid human wastes, or biosolids, are often removed and separated during water treatment.

These biosolids can then be combined with other organic materials such as wood chips, composted, and applied as a soil amendment. Many cities now market and sell these composted materials for use in urban landscapes and gardens. Human wastes are rich in C, as well as macronutrients including N and P. This type of anthropogenic nutrient cycling may become increasingly important for all scales of agriculture as growing populations, rising energy costs, and depletion of finite resources such as mineral P all challenge our ability to address food security in the coming decades

(Hottle et al. In Prep). Biosolids have also demonstrated utility for mitigating soil

159 contamination, as biosolid application resulted in measurable reductions in the bioavailability of Pb in contaminated urban soils (Brown and Chaney 2003).

Many urban areas in the developing world lack even basic sewage infrastructure for dealing with human wastes, so treatment of these materials is even more pressing to avoid public health risks. The utilization of human waste for UA is a more controversial topic in these settings, as in many cities farmers still engage in the traditional practice of applying untreated solid human wastes and untreated sewage sludge directly to soils where crops are produced (Hamilton et al. 2013). Urban farmers in Ghana, where these practices are widespread and widely studied, have observed increased crop yields from human waste application at a fraction of the cost required for fertilizers, but the nature of these materials has also lead to increased exposure to pathogens in areas where they are used (Cofie et al. 2005). Compelling models do exist, however, for the safe transformation of human wastes into soil amendments in developing world cities: a process that has the potential to improve both public health and agricultural outcomes.

The Sustainable Organic Integrated Livelihoods (SOIL) organization in Haiti has pioneered a framework for safely collecting and utilizing human wastes generated in the urban areas of the developing world (www.oursoil.org). SOIL’s work focuses on providing dry composting toilets in poor neighborhoods in Haiti’s cities. They employ a team of workers who collect these wastes and take them to a central composting facility on the outskirts of the city. There they subject the materials to a thermophilic composting process that transforms them to compost and minimizes health risk and odor associated with using them as amendments. The compost is then

160 checked for pathogens with laboratory assays and made available for use in local farm fields. SOIL was recently chosen as the winner of the United Nations

Convention to Combat Desertification’s 2012 Land for Life Award that recognizes initiatives aimed at restoring health and productivity to soils globally

(http://www.unccd.int/en/programmes/Event-and- campaigns/LandForLife/Pages/Winners-2012.aspx). In light of the many challenges facing global food security, particularly as it relates to food insecure populations in the developing world, the examination of methods by which human wastes can be utilized safely as soil amendments is a researchable priority for UA.

4.10 Utilizing cover crops for UA Cover crops can provide a number of benefits to all scales of annual agriculture, including: biomass production and soil C inputs, biological N fixation, reducing soil compaction, improved nutrient retention and cycling, and pest suppression. There are numerous cover crop choices to address these management goals available for the climatic zones of the norther U.S. (Snapp et al. 2005). Summer cover cropping of vegetable production beds with sorghum-sudangrass (Sorghum bicolor X S. bicolor var. sudanese) produced the equivalent of 10 Mg ha-1 dry aboveground biomass and resulted in the highest crop yields the following growing season in a UA experimental garden (Chapter 2 this dissertation). Warm season cover cropping with sorghum sudangrass can be an effectve practice for producing biomass and improving soil properties for urban producers who are able to take some space out of production during the summer.

161 A pattern that emerged from the analysis of soils from urban market gardens in Cleveland was that soil P levels appeared to increase continually with time under

UA, likely from the continual application of compost as described above (Chapter 3 this dissertation). The high C and P levels observed in these soils suggest that these sites may be able to satisfy their crop nutrient needs through growing over-wintering or early spring legumes such as hairy vetch (Vicia villosa). Winter grown hairy vetch can supply 50-120 kg N ha-1 while maintaining soil cover during the fallow season

(Snapp et al. 2005). The dynamics of managing legumes to meet crop N demands in

UA systems with high soil C and P is a topic that will benefit from further experimentation.

4.11 Conclusions UA is currently a vibrant area of both civic activity as well as scientific research in the U.S. Table 5 lists priority areas for further research to aid in improving effectiveness of management for UA, and includes many topics listed in the text above. UA displays great potential to provide both fruit and vegetable crops, as well as ecosystem services at both neighborhood and city scales. Soil assessment and management at UA sites are key steps in realizing these potential benefits. Urban soils are highly variable and in instances when they have undergone previous heavy disturbance and exceeded critical thresholds for function, targeted management will be necessary to improve their condition. American cities generate enormous quantities of organic waste materials and the transformation of these wastes into soil amendments is a key practice for managing soils for UA. Early studies suggest that this practice is already widespread, and that many more waste materials are available

162 to be utilized as inputs. All evidence suggests that the increasing popularity of UA will continue in the U.S. and that improving the processes of establishing and managing UA sites can maximize the potential benefits derived from UA.

163 4.12 References

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Arshad MA, Martin S (2002) Identifying critical limits for soil quality indicators in agro-ecosystems. Agriculture, Ecosystems, and Environment 88:153-160.

Blaine TW, Grewal PS, Dawes A, Snider D (2010) Profiling community gardeners. Journal of Extension 48(6).

Brown S, Chaney R (2003) Biosolids compost reduces lead bioavailability in urban soils. Biocycle 44:20–24.

Brown S, Miltner E, Cogger C (2012) Carbon sequestration potential in urban soils. In Lal R, Augustin B (eds.) Carbon sequestration in urban ecosystems. Springer, Dodrecht, Holland pp 173-196.

Cofie OO, Kranjac-Berisavljevic G, Dreschel P (2005) The use of human waste for peri-urban agriculture in Northern Ghana. Renewable Agriculture and Food Systems 20:73-80.

Condon PM, Mullinix K, Fallick A, Harcourt M (2010) Agriculture on the edge: Strategies to abate urban encroachment onto agricultural lands by promoting viable human-scale agriculture as an integral element of urbanization. International Journal of Agricultural Sustainability 8:104-115.

Colasanti KJA, Hamm MW (2010) Assessing the local food supply capacity of Detroit, Michigan. J Agric Food Sys Comm Develop 1: 41-58.

Cogger CG (2005) Potential compost benefi ts for restoration of soils disturbed by urban development. Compos Sci Util 13:243–251.

De Bon H, Parrot L, Moustier P (2010) Sustainable urban agriculture in developing countries: A review. Agron Sustain Dev 30: 21-32.

De Kimpe CR, Morel J (2000) Urban soil management: a growing concern. Soil Sci 165:31–40.

Eriksen-Hamel N, Danso G (2010) Agronomic considerations for urban agriculture in southern cities. Int J Agric Sustain 8:88–93. 164

Fillipelli GM, Laidlaw MA (2010) The elephant in the playground: confronting lead contaminated soils as an important source of lead burdens to urban populations. Perspect Biol Med 53:31–45.

Gardiner MM, Prajzner SP, Burkman CE, Albro S, Grewal PS (2013) Vacant land conversion to community gardens: Influences on generalist arthropod predators and biocontrol services in urban greenspaces. Urban Ecosystems In Press.

Grewal SS, Grewal PS (2012) Can cities become self-reliant in food? Cities 29:1-11.

Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319:756-760.

Hamilton AJ, Burry K, Mok HF, Barker SF, Grove JR, Williamson VG (2013) Give peas a chance? Urban agriculture in developing countries. A review. Agronomy for Sustainable Development In Press.

Howard JL, Olszewska D (2011) Pedogenesis, geochemical forms of heavy metals, and artifact weathering in an urban soil chronosequence, Detroit, Michigan. Environmental Pollution 159: 754-761.

Kaye JP, Groffman PM, Grimm NB, Baker LA, Pouyat RV (2006) A distinct urban biogeochemistry? Trends Ecol Evol 21:192–199.

Knight A, Cheng Z, Grewal SS, Islam KR, Kleinhenz MD, Grewal PS (2013) Soil health as a predictor of lettuce productivity and quality: A case study of urban vacant lots. Urban Ecosystems In Press.

Kulak M, Graves A, Chatterton J (2013) Reducing greenhouse gas emissions with urban agriculture: A Life Cycle Assessment perspective. Landscape and Urban Planning 111:68-78.

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Lal R (2007) Soil science and the carbon civilization. Soil Sci Soc Am J 71: 1095-

165 1108.

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Lorenz K, Kandeler E (2005) Biochemical characterization of urban soil profiles from Stuttgart, Germany. Soil Biol Biochem 37: 1373-1385.

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Mougeot LJA (2005) Introduction. In: Mougeot LAJ (ed.) Agropolis: The social, political and environmental dimensions of urban agriculture. Earthscan, London, U.K.

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Pouyat RV, Szlavecz K, Yesilonis ID, Groffman PM, Schwarz K (2010) Chemical,

166 physical and biological characteristics of urban soils. In Aitkenhead J and Volder A (eds.) Urban Ecosystem Ecology. Agronomy Monograph 55. Soil Science Society of America. Madison, WI USA.

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168

Table 4.1. Potential benefits of urban agriculture. List adapted from Condon et al. (2010)

Ecological

Enhances natural environment and ecosystem services Preserves open urban land Economic

Contributes significantly to local and regional economies

Creates new jobs Direct marketing provides producers with improved return on crops

Social

Enhances food security and increases availability of healthy foods Fosters community

169

Table 4.2. Potential for crop production from urban agriculture to meet consumption demands through cultivation of vacant land in cities in the U.S.

Food Production City Land Area Reference Potential †

1,240 ha 22 -48% fresh Grewal and Cleveland, OH (80% of vacant lots in produce Grewal, 2011 city) 25% Poultry 100% Honey Detroit, MI 406.4 ha for current and 31% Vegetables Colasanti and 1364 ha for 17% Fruit Hamm, 2010 recommended consumption (18.5 - 62% of vacant land area) Oakland, CA 202.3 ha 21.8 % of current McClintock (60% of vacant urban or 4.6% of et al. 2013 land) recommended vegetable consumption

170

Table 4.3. Threshold levels of key soil properties impacting site suitability for urban agriculture (adapted from Lal 1994; US EPA 2012). Soil property Good Condition - Threshold for Threshold No Limitations Moderate Impacts for Severe Impacts

Sand content (%) * >75 >90

Clay content (%) * >50 >65

Coarse fragments in surface (% by <10 20-40 >60 volume)

Rooting Depth (cm) >100 <50 <25

-3 Bulk density - clay soil (Mg m ) <1.2 >1.4 >1.5

Bulk density - loam soil (Mg m-3) <1.3 >1.5 >1.6

-1 Water infiltration rate (cm hr ) >5 <1 <0.5

Soil acidity (pH) <6 <4 6-7 Soil alkalinity (pH) >7.5 >8.5

Cation exchange capcity (meq/100g) * <5 <3

Potassium † (mg kg-1 on Mehlich 3 soil test) 150 90 70

Phosphorous >40 <30 <20 (mg kg-1 on Mehlich 3 soil test)

Organic C Content (%) >5 <1 <0.5

Soluble salts (mg kg-1) 600 1,000

† Potassium and Phosphorous soil test levels from Iowa State University Extension guide “A General Guide for Crop Nutrient and Limestone Recommendations in Iowa,” (http://www.extension.iastate.edu/Publications/PM1688.pdf).

171

Table 4.4. Selected soil surface properties from a survey of vacant lots in Cleveland, OH. Data includes a comparison between areas of the that contain a more native type soil and those that contain imported fill material. Data from Shuster et al. (2011).

Soil Property Native Soil Fill

Sand (%) 58.4 50.5 Clay (%) 11.4 14.1 Coarse Rock Fragments 23 24 (% by volume) Total C (%) 4.3 3.1 pH 7.2 7.8 P (mg kg-1 soil) 139 50 K (mg kg-1 soil) 153 141

172

Table 4.5. Researchable priorities for enhanced management of urban agriculture (UA) systems. Identify management changes and policy provisions necessary to scale UA up to address food consumption at the city level. Identify barriers and constraints to the scaling up of UA. Assess the production potential of UA systems incorporating high tunnel greenhouse production. Quantify contribution of UA systems to specific ecosystem services (ie. storm water infiltration, C sequestration, etc.) Conduct detailed hydrologic assessment of UA soils. Mitigation of soil compaction below the soil surface. Identify optimum rates of compost application in degraded soils. Evaluate the ability of amendments to supply plant available macro-nutrients. Evaluate the ability of cover crops to provide crop N requirements. Determine methods for increasing plant available water capacity in rooting zone.

173

Figure 4.1 Area of land under peri-urban agriculture in metropolitan Vancouver, B.C. (data from Condon et al. 2010).

174

CHAPTER 5

SYNTHESIS AND FUTURE RESEARCH DIRECTIONS

5.1 Synthesis The health and condition of soils determine the condition of the ecosystems, human communities and societies supported by those soils. The assessment and description of soil quality (SQ), or soil’s dynamic ecological function, is among the most practical and well developed means of assessing the sustainability of land management practices on natural resources (Doran and Parkin 1994; Lal 1994). The unifying research themes of this dissertation are: (1) attempting to quantify the

“ecological and agronomic function” of soils; and (2) using the framework of ecological function to assess anthropogenic and management derived impacts on soil function. As a researcher, I entered my doctoral program inspired by ongoing efforts to assess soil function or SQ using approaches that led to practical results and management directives (Andrews et al. 2004; Gugino et al. 2008). My goal for my dissertation research was to train myself to utilize these methods, so that I as a professional would be able to evaluate the sustainability of agricultural and land management practices. As my Ph.D. program got underway, I also became inspired by the growth of urban agriculture (UA) as a solution to the growing issues of vacant

175 land and food insecurity in the cities of the region surrounding my home state of Ohio

(as described in Chapter 1 of this dissertation). While the application of UA was spreading rapidly in the region, little research existed on the soil conditions and particularly their management in urban areas, despite the fact that soils would impact the success of UA implementation and production. Thus, I determined that there was a need for a robust application of SQ evaluation within the context of UA in North

American, specifically the cities of Ohio. This evaluation is the unique contribution of this dissertation to the research literature and community.

Soil function is particularly critical in urban areas, as relatively small land areas are required to provide extensive ecological services to large human populations

(DeKimpe and Morel 2000). The prevailing management strategy for dealing with vacant urban properties in the U.S. has been to demolish vacant structures and regrade the soil before establishing greenspaces or the next land use (USEPA 2011).

The demolition disturbance often results in the degradation of soil physical properties, thereby impairing the function of those soils (USEPA 2011; USEPA 2012; Shuster et al. 2011). Data collected in the studies presented herein support the proposition that urban soils that have been under long term stable land uses (such as perennial vegetation) and have not undergone recent heavy disturbances such as demolition generally display higher levels of SQ and are more suitable for UA (Chapters 2, 3, and 4, this dissertation).

SQ assessment was carried out for the studies presented here by measuring soil physical, chemical and biological properties of the soil samples and then using a quantitative framework called the Soil Management Assessment Framework (SMAF)

176 to provide scores for the measurements (Andrews et al. 2004). SMAF provides scores for soil properties based on previously described relationships between those properties and key functions such as crop yields, hydrologic function, and environmental buffering. These scores provide a means of evaluating the function of soils at a given site and a means for comparing soil function across sites. Results of our assessment of both a UA experimental site as well as working urban market gardens indicate that soils managed for UA display levels of SQ that are similar to those measured and reported for rural agricultural soils (Chapters 2 and 3 this dissertation). While these measurements are useful for evaluating management and comparison, they remain a proxy for the actual measurement of ecosystem services provided by soils.

In the study presented in Appendix 2 of this dissertation, soil function was assessed by making direct measurements of ecosystem function in agricultural soils under different tillage and crop residue removal treatments. A rainfall simulator allowed for the measurement of runoff, water infiltration, soil erosion and carbon (C) and macronutrient fluxes during a high intensity rainfall event. While this approach is likely more labor intensive than SQ indices, it provides real-time observation of soil function. The salient conclusions of this study were that soil management practices produce marked differences in the functioning and resilience of agricultural soils.

These effects can also be very acute, as in the observation that a single tillage event effectively erased the resistance to erosion produced by 40 years of conservation agriculture (Appendix B this dissertation). Based on the observations in this study,

177 rainfall simulation provides a robust tool for evaluating the hydrologic functioning of soils, as well as management induced impacts on those services.

A consistent theme that emerged in all three of the research studies presented herein was that the level and management of C in soils had a great impact on SQ and function in all studies. Following the demolition of vacant structures, the application of C rich compost produced from urban yard wastes provided immediate improvement to numerous soil properties and facilitated robust yields of vegetable crops (Chapter 2 this dissertation). Consistently high levels of SQ were observed across the nine market garden sites evaluated in the field study (Chapter 3 this dissertation). The managers of these sites all indicated that they applied compost or organic matter amendments on an annual basis and we observed that these soils contained extremely high concentrations and pools of soil C, likely as a result of those amendments (Chapter 3 this dissertation). Taken together these studies suggest that the process of producing C rich amendments from urban yard wastes and applying those amendments to soils can result in large soil C pools and high levels of

SQ. In the rainfall simulation study, the management combination of the retention of all all crop residues and long term no-till management, both practices aimed at sustaining and increasing soil C pools, resulted in a near absence of runoff, erosion, and nutrient fluxes during a high intensity rain storm (Appendix B this dissertation).

Crop residue removal and long-term tillage both resulted in reduced soil C pools and increased levels of erosion and nutrient fluxes. Thus, the management of soil C is likely a keystone approach to managing SQ and its associated ecosystem services (Lal

2007).

178 5.2 Future Research Directions The results and limitations of the studies presented in this dissertation suggest a number of future research directions.

1) While useful in determining management impacts and facilitating comparisons

among sites, existing SQ indices such as SMAF will benefit greatly from

continued use and refinement by the soil research and management communities.

Specific needs include developing regionalized scoring functions based on soil

conditions and properties in specific ecological regions. The development of the

Cornell Soil Health Test to assess soils of New York state is an excellent example

of how this process can be applied to other regions globally (Gugino et al. 2008).

The collection, development, and calibration of regional datasets and SQ indices

is a priority area for soil management research.

2) Similarly, SQ evaluation of urban soils will benefit from the identification and

interpretation of SQ indicators that are particularly important in urban areas, such

as soil lead (Pb) concentration.

3) SQ indices and measurement will also benefit from direct comparisons between

SQ values generated through indices such as SMAF and the direct measurement

of ecological functions such as those measured in rainfall studies.

4) It may also be possible to predict SQ, as well as the productivity and resilience of

soils, remotely, in areas currently lacking extensive soil information through the

use of models that incorporate existing data on landscape attributes and inherent

soil properties. These models will benefit from calibration with laboratory

measurements and direct use of SQ indices.

179 5) Research on UA will benefit from further experimentation to determine unique

agronomic needs and conditions in urban areas.

6) Similarly research is needed to improve the ability of UA programs to be

increasingly accessible and affect food security outcomes among urban

populations.

7) Finally, there remains a strong need to study the methods, assessment and

knowledge transfer associated with managing soil C. One particular area

highlighted by this dissertation is that the transformation of organic waste

materials in the U.S. into soil amendments provides one method for rapidly

increasing soil C and SQ levels. More than half of all organic wastes continue to

be sent to landfills in the U.S. and there is a pressing need and opportunity to

utilize these materials in the improvement of soils. In the U.S. organic wastes are

present on a scale that makes the application of compost and amendments feasible

not only in small urban plots, but also as a means of raising soil C levels in the

wider American agricultural landscape. There remains much work to be done in

expanding the application of these practices as a means of improving soil

function.

“Upon this handful of soil our survival depends. Husband it and it will grow our food, our fuel and our shelter and surround us with beauty. Abuse it and the soil will collapse and die taking humanity with it.” from the Hindu Vedas, 1500 B.C.

180 5.3 References

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US EPA (2012) Evaluation of urban soils: Suitability for green infrastructure or urban agriculture. EPA Publication No. 905R1103. http://water.epa.gov/infrastructure/ greeninfrastructure/upload/Evaluation-of-Urban-Soils.pdf

U.S. Environmental Protection Agency. (2011) Improving demolition practices. Land revitalization fact sheet EPA-F-11-005.

181

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203

APPENDIX A: SUPPLEMENTARY DATA FOR CHAPTER 3

204

Table A.1 Summary statistics for soil physical properties for individual study sites.

Site Statistic %Sand %Clay Depth of Bulk AWC %WSA MWD Topsoil Density (cm) (Mg m3)

Mean 68.00 5.9 25.0 0.88 2.11 0.50 1.17

Std Error 2.18 0.39 3.38 0.06 0.12 0.05 0.09

1 Range 63.8- 0.72- 1.78- 0.39- 4.8-7.1 13-30 0.17 75.4 1.00 2.39 0.63

CV 0.98- 0.07 0.15 0.30 0.16 0.13 0.21 1.45

Mean 85.8 3.7 12.2 0.99 1.49 0.30 0.53

Std Error 1.24 0.14 4.42 0.08 0.45 0.05 0.03 2 Range 81.4- 0.71- 0.23- 0.46- 3.3-4.1 3.0-28.0 0.0-2.58 88.8 1.13 0.48 0.65

CV 0.03 0.08 0.81 0.08 0.45 0.35 0.14

Mean 38.0 14.6 13.8 0.84 2.93 0.59 2.34

Std Error 3.08 0.44 0.58 0.02 0.19 0.02 0.27 3 Range 31.3- 13.3- 12.0- 0.77- 2.54- 0.53- 1.28- 46.1 15.6 15.0 0.90 3.48 0.68 2.74

CV 0.18 0.07 0.09 0.15 0.15 0.09 0.26

Mean 47.3 15.9 16.6 0.80 2.52 0.55 1.56

Std Error 3.21 1.21 1.12 0.04 0.50 0.04 0.26 4 Range 40.3- 12.8- 13.0- 0.71- 0.92- 0.43- 0.76- 57.1 18.8 20.0 0.87 3.52 0.69 2.15

CV 0.15 0.17 0.15 0.10 0.44 0.16 0.37 Table continued on next page.

205

Table A.1 continued

Site Statistic %Sand %Clay Depth of Bulk AWC %WSA MWD Topsoil Density (cm) (Mg m3)

Mean 45.3 13.5 18.0 0.81 3.39 0.53 2.10

Std Error 3.06 13.5 1.82 0.01 0.26 0.01 0.08

5 Range 33.8- 12.3- 12.0- 0.78- 2.84- 0.51- 1.88- 51.9 15.9 23.0 0.83 4.13 0.55 2.30

CV 0.15 0.10 0.23 0.03 0.17 0.03 0.08

Mean 63.8 10.3 28.6 0.88 1.64 0.40 1.28

Std Error 0.95 0.44 1.86 0.01 0.13 0.01 0.09 6 Range 62.3- 24.0- 0.86- 1.38- 0.38- 0.96- 9.1-11.7 67.4 35.0 0.92 1.99 0.44 1.49

CV 0.03 0.10 0.15 0.03 0.17 0.06 0.15

Mean 65.8 6.3 40.6 0.87 2.37 0.46 1.32

Std Error 2.66 0.94 1.21 0.08 0.13 0.02 0.12 7 Range 56.4- 38.0- 0.67- 1.87- 0.41- 0.92- 3.6-8.7 70.0 45.0 1.09 2.57 0.51 1.50

CV 0.09 0.33 0.07 0.20 0.12 0.08 0.20

Mean 26.2 16.5 33.8 1.12 2.36 0.66 0.99

Std Error 2.05 0.30 1.50 0.06 0.13 0.05 0.08 8 Range 21.2- 15.6- 28.0- 0.95- 1.90- 0.57- 0.86- 30.3 17.1 36.0 1.30 2.60 0.74 1.12

CV 0.17 0.04 0.10 0.12 0.12 0.18 0.18 Table continued on next page.

206

Table A.1 continued

Site Statistic %Sand %Clay Depth of Bulk AWC %WSA MWD Topsoil Density (cm) (Mg m3)

Mean 39.8 15.8 11.0 0.97 3.03 0.57 1.16

Std Error 2.66 0.77 1.26 0.09 0.17 0.05 0.17

9 Range 34.2- 13.5- 0.71- 2.64- 0.48- 0.55- 8.0-14.0 49.5 17.8 1.23 3.47 0.71 1.48

CV 0.15 0.11 0.26 0.20 0.13 0.19 0.32

207

Table A.2 Summary statistics for soil chemical properties for individual study sites.

Site Statist pH CEC Total Ext. Ext. Ext. Ext. Ext. Ext. Ext. Ext. Ext. ic (cmol Soil N P (mg K Al Ca Fe Mg Mn Pb Zn +/kg) (g kg-1 kg-1 (mg (mg (mg (mg (mg (mg (mg (mg soil) soil) kg-1 kg-1 kg-1 kg-1 kg-1 kg-1 kg-1 kg-1 soil) soil) soil) soil) soil) soil) soil) soil)

Mean 7.30 14.3 4.16 203 231 528 3004 144 310 36.5 161 69.6

Std 0.0 0.98 0.19 36.2 15.9 52.6 283 8.37 22.7 5.27 23.8 11.2 Error 8

1 Range 7.0 2482 25.7 43.6 10.9- 3.52- 114- 193- 366- 117- 240- 76.7 - - - - 16.4 4.53 310 288 646 167 382 -224 7.5 3835 55.0 104

CV 0.0 0.15 0.10 0.39 0.15 0.22 0.21 0.13 0.16 0.32 0.33 0.36 3

Mean 7.8 11.9 2.14 103 194 252 3091 105 262 33.2 68.6 45.4

Std 0.1 1.45 0.32 17.8 47.2 49.3 179 4.27 43.3 3.08 10.6 2.79 Error 2

2 Range 7.3 2643 23.1 40.0 7.8- 1.14- 59.3- 126- 70.8- 93.8- 164- 44.9 - - - - 16.8 2.95 141 379 345 119 389 -106 8.0 3457 40.8 55.6

CV 0.0 0.27 0.34 0.39 0.54 0.44 0.13 0.09 0.37 0.21 0.34 0.14 3

Mean 7.4 20.6 5.66 150 399 180 3666 255 474 48.9 11.5 44.7

Std 0.0 1.36 0.89 11.8 35.6 57.2 140 8.85 40.3 4.95 0.89 1.58 Error 4

3 Range 7.3 3253 33.4 8.32 40.0 17.4- 3.65- 110- 274- 26.2- 236- 378------24.2 8.98 175 482 292.4 287 576 7.5 4071 61.3 13.6 49.2

CV 0.0 0.15 0.35 0.18 0.20 0.71 0.09 0.08 0.19 0.23 0.17 0.08 1 Table continued on next page. 208

Table A.2 continued

Mean 7.7 24.9 4.36 91.5 327 49.5 4410 216 497 69.0 8.82 41.9

Std 0.0 0.72 0.13 8.55 59.5 1.65 57.3 7.84 15.3 2.30 1.01 4.2 Error 5

4 Range 7.5 4253 62.6 7.23 33.8 22.9- 3.98- 75.5- 193- 45.1- 194- 464------27.3 4.71 123 514 53.4 242 552 7.8 4587 75.7 12.8 57.7

CV 0.0 0.06 0.07 0.21 0.41 0.07 0.03 0.08 0.07 0.07 0.26 0.22 2

Mean 7.5 20.9 4.40 149 413 235 3957 188 417 34.8 30 52.9

Std 0.0 0.67 0.17 28.3 72.1 44.4 133 14.0 22.5 2.73 10.3 8.87 Error 9

5 Range 7.2 3623 27.6 11.3 36.5 19.3- 4.0- 73.9- 277- 61.5- 149- 356------23.2 4.93 232 692 311 217 468 7.6 4428 42.3 58.2 80.4

CV 0.0 0.07 0.09 0.43 0.39 0.42 0.07 0.17 0.12 0.18 0.77 0.38 3

Mean 7.6 19.9 5.53 227 199 154 4271 294 367 45.3 15.0 62.6

Std 0.0 1.16 0.30 27.9 14.3 38.1 200 19.5 29.4 1.54 5.71 3.29 Error 4

6 Range 7.5 3933 41.4 5.50 53.3 17.9- 4.78- 133- 173- 46.8- 245- 310------24.3 6.58 304 244 245 365 456 7.6 5055 49.4 37.3 70.0

CV 0.0 0.13 0.12 0.27 0.16 0.55 0.10 0.15 0.18 0.08 0.85 0.12 1 Table continued on next page 209

Table A.2 continued

Mean 7.2 18.6 5.17 279 425 259 3605 196 365 34.5 40.7 68.9

Std 0.1 0.79 0.58 35.0 119 44.0 212 15.9 31.4 2.52 5.52 4.64 Error 3

7 Range 7.0 3085 28.5 20.3 56.8 15.6- 3.51- 191- 156- 182- 151- 240------20.0 7.03 368 715 418 244 407 7.7 4374 41.2 49.8 83.2

CV 0.0 0.09 0.25 0.28 0.63 0.38 0.13 0.18 0.19 0.16 0.30 0.15 4

Mean 6.6 10.9 2.00 77.3 341 608 1610 167 144 79.5 20 39.2

Std 0.0 0.15 0.09 6.51 31.6 16.4 50.4 6.24 8.76 5.81 3.81 2.57 Error 9

8 Range 6.4 1429 57.5 11.4 34.7 9.61- 1.74- 60.3- 248- 572- 144- 127------13.6 2.25 100 423 666 180 176 6.8 1734 92.0 31.7 48.8

CV 0.0 0.15 0.10 0.19 0.21 0.06 0.07 0.08 0.14 0.16 0.43 0.15 3

Mean 7.7 24.3 3.38 102 364 58.0 4424 157 462 120 28.6 48.5

Std 0.0 1.37 0.24 6.87 20.2 18.8 247 7.35 38.6 3.11 5.32 4.37 Error 3

9 Range 7.5 3853 19.3 36.3 19.3- 2.82- 85.9- 326- 18.0- 133- 381- 112- - - - - 27.6 4.21 125 416 114 178 593 128 7.7 5000 49.0 61.9

CV 0.0 0.13 0.16 0.15 0.12 0.72 0.12 0.10 0.19 0.06 0.42 0.20 1

210

Table A.3 continued.

Site Statistic Total C MBC (g kg-1 soil) (g C kg soil)

Mean 63.70 124

Std Error 4.04 14.7 1 Range 48.4-72.5 69.1-154

CV 0.14 0.27

Mean 32.1 160

Std Error 4.55 84.1 2 Range 17.5-42.5 0.0-427

CV 0.32 1.17

Mean 64.9 283

Std Error 10.8 26.1 3 Range 41.9-106 219-373

CV 0.37 0.21

Mean 70.3 202

Std Error 2.71 49.4 4 Range 63.2-76.1 80.9-383

CV 0.09 0.55 Table continued on next page.

211

Table A.3 continued.

Site Statistic Total C MBC (g kg-1 soil) (g C kg soil)

Mean 61.5 360

Std Error 1.97 48.6 5 Range 54.3-65.1 228-514

CV 0.07 0.30

Mean 82.3 236

Std Error 4.73 18.1 6 Range 69.4-98.1 209-303

CV 0.13 0.17

Mean 70.2 347

Std Error 6.68 66.5 7 Range 51.1-90.4 111-474

CV 0.21 0.43

Mean 22.7 112

Std Error 1.65 10.4 8 Range 18.5-26.8 90.0-150

CV 0.16 0.21 Table continued on next page.

212

Table A.3 Summary statistics for soil biochemical properties for individual study sites.

Site Statistic Total C MBC (g kg-1 soil) (g C kg soil)

Mean 44.0 293

Std Error 3.19 39.8 9 Range 37.4-55.0 192-377

CV 0.16 0.30

213

APPENDIX B: EFFECTS OF MAIZE RESIDUE REMOVAL AND TILLAGE

ON SOIL EROSION, CARBON AND MACRONUTRIENT DYNAMICS

B.1 Abstract Erosion by water is a principal process of soil degradation in agricultural lands and influences the storage and fluxes of C, N and P in soil surface layers. No-till (NT) crop management significantly reduces erosion on susceptible landscapes. The selective removal of crop residues for bio-energy production has been suggested as a secondary crop, but the effect of this practice on the conservation benefits of NT has not been quantified. Therefore, this study was initiated in spring 2012 to examine the effects of tillage and crop residue removal practices on erosion and associated macronutrient fluxes on erodible soils subjected to a high intensity simulated rain storm in Coshocton, OH, U.S.A. A field rainfall simulator was utilized to apply rainfall at an intensity of 7 cm hr-1 for 30 minutes. The largest runoff fluxes were generated by complete crop reside removal (NT0) (22.1mm), while soil loss was greatest under long-term conventional tillage (CT) (2.7 Mg ha-1). Tillage of the no-till

(TNT) soil produced the largest sediment-bound fluxes of C (30.5 kg ha-1) and N (2.9 kg ha-1), while sediment-bound P fluxes were largest in the CT soils (700 g ha-1).

Natural abundance δ13C and δ15N values were distinct between eroded sediments and the source topsoils and suggested enhanced loss of older (>28 yrs) C residues in CT 214 plots. All observations suggest NT management provides greater resilience to soils than CT during high intensity rainfall events and that crop residue removal at levels of 50% or greater compromises many of NT’s hydrologic benefits on sloping agricultural lands.

B.2 Introduction The biogeochemistry of eroded macronutrients is a vibrant area of research, focused on the quantification of the potential of eroded sediment to as act a net sink or source of greenhouse gases and wider environmental pollution (see recent review by Quinton et al., 2010). Soil erosion by water is a critical pathway for macronutrient losses and represents major economic and environmental costs in sloping agricultural landscapes

(Dodds et al., 2008; Pretty et al., 2003). Recent increases in the frequency and intensity of rainfall in temperate regions (Groisman et al., 2005) are coupled with predictions that they will continue to increase in regularity due to climate change

(IPCC, 2007). Therefore, accelerated erosion of soil and associated macronutrients by water in temperate environments is likely to become increasingly important. The majority of soil losses from water erosion occur during isolated, high intensity storms

(Shipitalo and Edwards, 1998). Chronic losses of dissolved and particulate phosphorus (P) from soil regularly occur in storm runoff (Withers and Hodgkinson,

2009), and selective transport of the light fraction of organic matter and slaking of aggregates by water increases the availability of fresh and previously stabilised nitrogen (N) and carbon (C) for mineralisation during erosion (Gregorich et al., 1998;

Lal, 2003).

215 Soil tillage is a major factor contributing to an increased risk of soil erosion and nutrient losses by water (Lundekvam and Skøien, 1998), which is exacerbated by increased cultivation of bioenergy row crops such as maize (Zea mays L.) in temperate environments with sloping topographies (Jaafar & Walling, 2010). Losses of N, P, and C associated with soil tillage may become manifest as diffuse pollution in the immediate and wider environment at significant social, economic and environmental cost (Dungait et al., 2012).

Erosional exports of N from agricultural land are similar in magnitude to fertilizer application and crop removal combined (112 Tg N yr-1; Delgado et al., 2002;

Quinton et al. 2010). Losses of P are greatest during extreme or prolonged storms (<

30 kg P ha-1; Dungait et al., 2012), but more frequent low intensity events (< 6 kg ha-

1; Ulén et al., 2007) account for the majority of P lost by erosion (Quinton et al.,

2001). Although P losses are small in agricultural terms, they can be significant for eutrophication of surface waters (Johnston and Dawson, 2005), and are equivalent in magnitude to crop uptake (14 Tg yr-1) and fertilizer P addition to agricultural land (ca.

18 Tg yr-1; Quinton et al., 2010). Although the burial of eroded sediments may constitute a small C sink (Harden et al., 2008), substantial quantities of eroded soil

organic carbon (SOC) may be mineralized thereby increasing atmospheric CO2 concentrations (Jacinthe et al. 2004; Dungait et al., 2013). In addition, the eroded

SOC (0.08 Pg C yr-1; Quinton et al., 2010) can cause water quality problems in receiving waterways, while loss of SOC can reduce soil quality and crop yields in eroded landscape positions (Lal and Pimental, 2008).

216 In contrast to conventional tillage (CT), continuous no-till (NT) management encourages soil stabilization and water conservation (Cruse and Herndl, 2009). The implementation of NT strategies, however, usually requires herbicide and pesticide treatments to reduce weed and pest loads, which have their own environmental and economic costs. Soil loss from water erosion can be reduced by an order of magnitude in erodible agricultural sites under NT compared with CT in similar landscapes, at both the plot (Rhoton et al., 2002) and watershed scales (Harrold and

Edwards, 1974; Shipitalo and Edwards, 1998). This is because NT reduces physical disruption of the soil surface horizons and can also lead to increases in SOC by decreasing the rate of physicochemical and biological decomposition of crop residues.

Removal of crop residues as a secondary crop for bioenergy feedstocks from

NT systems, however, can lead to rapid degradation of surface properties such as soil aggregate stability and water infiltration rate (Bradford and Huang, 1994; Blanco-

Canqui et al., 2006; Blanco-Canqui and Lal, 2009). This in turn is likely to increase the risk of water erosion and macronutrient loss, and may reduce the benefits of NT, but these effects have not yet been quantified. Moreover, the specific effects on co- variant erosional fluxes of C, N, and P macronutrients after crop residue removal in

NT agriculture have not been investigated, but are necessary to define the sustainability of using crop residues for bioenergy production.

A number of techniques have been used to assess the effect of crop management practices on macronutrient fluxes in surface runoff. Traditionally, runoff from agricultural watersheds has been measured and sampled to assess N (Palis et al.,

217 1990; Soileau et al., 1994; Teixeira and Misra, 2005), P (Owens et al., 1984; Soileau et al., 1994; Quinton et al., 2001; Verbree et al., 2010) and C (Jacinthe et al., 2004;

Owens et al., 2002; Owens and Shipitalo, 2011; Quinton et al., 2006) losses. All have shown increases in export of macronutrients with increasing rainfall intensity, with the amplitude of the response affected by tillage management. The source of macronutrients in eroded sediment can be further investigated using their natural abundance stable isotope signature. In particular, the 13C:12C ratio of SOC can be used

to distinguish C4 crop (e.g. maize) C from C3 crop or native vegetation (Puget et al.,

2005; Dungait et al., 2010). This approach has been used at the field-scale to quantify the fraction of maize C input in eroded sediments (Dungait et al., 2013) and trapped sediment in conservation buffers (Jacinthe et al., 2009), and to investigate the effect of vegetation change on erosion dynamics (Turnbull et al., 2008; Puttock et al., 2012).

Stable isotopes of N (15N) have also used to discriminate sources of eroded soils (Fox and Papancoalou, 2008) or in tandem with 13C determinations (i.e. dual stable 13C/15N isotope analysis; Bellanger et al., 2004; Fox and Papanicolaou, 2007; Dungait et al.,

2013). Phosphorus isotopes other than 31P are not stable and thus are not useful in investigating P dynamics, although the use of 18O isotopes in phosphate holds promise for tracking P in soils (Frossard et al., 2011).

Maize crop residues (corn stover) are estimated to be the source for ~40% of the total agricultural biomass supply currently available for biofuel production in the

U.S. (Perlack et al. 2005). The environmental impact of using this material will likely be dependent on factors including the rate of the rate of removal and the topography of the source landscape. Therefore our objective was to determine the effect of maize

218 residue removal and tillage on runoff losses of sediment and macronutrients from a

NT cropping system on sloping land as a result of high intensity rainfall. Simulated rainfall was applied to long-term NT field plots that had been subjected to various levels of residue removal for 8 years and compared to long-term CT managed maize plots on the same soil type and slope. We quantified the gross sediment loss and runoff characteristics and associated losses of soil C, N, and P, and used dual 13C/15N stable isotope analysis of the eroded sediments to partition the source of eroded SOM and N

B.3 Materials and Methods B.3.1 Study site The study was conducted at the North Appalachian Experimental Watershed

(NAEW), a USDA Agricultural Research Service (ARS) station near Coshocton,

Ohio, United States (40⎦24ʹ N; 81⎦48ʹ W) in April 2012. The mean annual precipitation for the area is 950 mm and the long-term mean annual temperature is

10.3 ⎦C. All plots investigated were within a single map unit of Rayne silt loam, a well-drained, fine loamy, mixed, active, mesic Typic Hapludult composed of approximately 20% sand 65 % silt and 15 % clay (Kelley et al., 1975).

B.3.2 Experimental design The residue removal plots were established in May 2004 as a completely randomized experiment (n = 3) within NAEW watershed 188 (WS 188) that has been planted to long-term, NT continuous maize since 1970. Each residue removal plot was 3 by 3 m and contained four rows of maize spaced 0.75 m apart. Residue removal was accomplished on a per plot basis by raking all residues into a pile and removing the

219 prescribed amount. An adjacent watershed (WS 128) of conventionally ploughed

(mouldboard; depth 20 cm) continuous maize established in 1984 was used as a control. These watersheds have been managed identically since 1984, except for tillage, and were planted and fertilized at the same rate and dates. Nitrogen fertilizer

-1 was applied each spring at a rate of 150 kg N ha as NH4NO3 and herbicides were used for weed control. The slope of the experimental plots was 6% (Blanco-Canqui and Lal, 2007). The experimental treatments were: (1) NT100 no-till (NT) with

100% crop residue (10 Mg ha-1); (2) NT50 - NT with 50% crop residue (5 Mg ha-1) removed; (3) NT0 - NT with complete crop residue removal; (4) CT - long-term conventional tillage; and, (5) TNT – tilled NT100 plots. The CT plots were mouldboard ploughed and disked three days before the rainfall simulation. The TNT plots were cultivated to a depth of ~ 20 cm within WS188 with a walk behind rototiller to emulate conventional tillage the day before the rainfall simulations. There was no natural rainfall between the ploughing or rototilling treatments and the commencement of the rainfall simulations.

B.3.3 Soil and vegetation sampling and analyses A composite bulk soil sample (0 - 5 cm depth) from each plot was obtained by taking five subsamples with a trowel. Two intact cores (7 cm dia x 5 cm depth) were collected from the surface of each plot for soil bulk density (ρb) analysis using the core method (Blake and Hartge, 1986). Representative samples of residual maize biomass (root and shoot) were taken from each plot. Vegetation samples were oven dried (40 °C) and visible roots were removed by hand from the soil samples, which were then sieved (2 mm) and oven dried (40 °C). The soil water content was

220 determined gravimetrically. Plant available P was determined using the Mehlich-3 method for soils (Mehlich ,1984) The soil and vegetation were ground to a fine powder and analyzed for total C, total N, and bulk δ13C and δ15N using a Carlo Erba

NA2000 analyser (CE Instruments, Wigan, UK) interfaced to a SerCon 20-22 isotope ratio mass spectrometer (SerCon Ltd, Crewe, UK). Soil samples were acidified (0.1

M HCl) prior to isotopic analysis to remove carbonates and re-dried. The bulk δ13C

15 and δ N values were expressed relative to VPDB and atmospheric N2, respectively.

The instrument error was 0.1 ‰ for C and 0.2 ‰ for N.

B.3.4 Rainfall simulation The rainfall simulation using NAEW well water was conducted in April 2012 prior to the planting of maize or fertilizer application for the 2012 growing season. Runoff plots (4 m2) were installed in the centre of each plot using metal border plates 10 cm above and below ground. The borders were oriented according to the unique slope of each plot. A collection point was established at the down slope corner by excavating a pit adjacent to the collection area and installing a 10 cm diameter PVC pipe to carry runoff from the plot to a collection vessel in the pit. A field rainfall simulator based on the design of Miller (1987) utilizing a single Teejet ½ HH-SS50WSQ nozzle

(Teejet Technologies, Wheaton, Illinois, USA) mounted 3 m above the soil surface was used to apply rainfall at an intensity of 70 mm h-1 for 30 minutes. A storm of this size and duration has a return period of once in ten years in this region

(http://hdsc.nws.noaa.gov/hdsc/pfds). The simulator was calibrated just prior to the experiment according to Humphry et al. (2002) and achieved a coefficient of uniformity of 85%. Rainfall was applied to each plot until runoff reached the

221 collection vessel. The time until runoff was recorded and simulated rainfall was continued for 30 additional minutes and all runoff was collected following the USDA

Cooperative State Research Education and Extension Service Regional Committee,

Southern Extension/Research Activity - Information Exchange Group (SERA -IEG

17, 2000) protocol. The total volume of runoff was recorded by measuring depth in a cylinder of known volume.

B.3.5 Runoff collection and analyses Runoff samples were collected for each plot at the field site by stirring the runoff until the sediments were fully suspended and then submerging 1 L bottles to collect homogenized samples. The samples were stored at 4 °C until time of analysis.

B.3.6 Runoff analyses In the laboratory, the sediments were resuspended with a magnetic stirring rod and total solids (TS) were determined gravimetrically by oven drying (60 °C) a 10 mL aliqout. A 20 mL runoff subsample was passed through a 0.45 μm pore diameter filter, acidified and retained for dissolved P (DP) analysis. Runoff total P (TP) was determined by digesting 50 mL of homogenized runoff water with 0.5 g potassium persulfate and 1 mL of concentrated sulfuric acid in a Mars Xpress microwave at 170

Co for 30 min (Pote and Daniels, 2000), followed by analysis via inductively coupled plasma (ICP) emission spectroscopy. Sediment-bound P was calculated as TP - DP.

The remainder of the sample was left overnight at 4 °C for the suspended sediments to re-settle. The top phase was vacuum filtered (0.45 μm) to achieve a dissolved fraction for analysis. Dissolved organic C (DOC) was analyzed using the non-purgeable organic C (NPOC) method on a Shimadzu TOC-V analyzer

222 (Columbia, Maryland, USA), and dissolved nitrate was measured colorimetrically with an Astoria Pacific auto analyzer (Clackamas, Oregon, USA).

B.3.7 Sediment analyses The remaining runoff containing the solid fraction was oven dried (40 ˚C), acidified with 0.1 M HCl to remove inorganic C, and re-dried and ground to a fine powder. The solid residues were analyzed for total organic C (%), total N (%) and bulk δ13C (‰) and δ15N (‰) as above.

B.3.8 Data Analysis The proportion of C the top soils and eroded sediments derived from the

maize crop (C4) or the historical forest vegetation (C3) was calculated using a two end-member mixing model according to the expression:

13 13 where, δ CC4 is the δ C value (‰) of the top soils or sediments from the maize-

13 13 treated plots; δ CC3 is the δ C value (‰) of a C3 forest plot reference soil that was

13 13 sampled at NAEW in 2002 (δ C = -26.2 ‰; Puget et al., 2005); and, δ Cmaize is the

δ13C value (‰) of the maize.

All statistical tests were undertaken with Gentstat (2011, 14th Edition). The effect of treatment was tested on all parameters using one-way ANOVA, with the five distinct treatments included in each test. Fisher’s LSD (α=0.05) was calculated as a mean separation procedure for parameters where a significant (p<0.05) treatment

223 effect was detected. Pearson’s correlation coefficients (r) were calculated to explore relationships among the measured data.

B.4 Results B.4.1 Effects of tillage on soil properties The CT treatment led to significantly reduced bulk density and water content, irrespective of whether water content was expressed gravimetrically or volumetrically, compared to the NT treatment (Table 1). The top soil SOC, total N and Mehlich-P contents were also significantly less under the CT treatment than the

NT treatment (Table 1). Tillage of previous NT plots led to significant reduction in bulk density compared to both the CT and the NT treatments (Table 1). Tillage of the

NT plots was also accompanied by an increase in water content compared with the

CT treatment, but not different compared with the NT treatment (Table 1).

B.4.2 Effects of residue removal on soil properties Residue removal under the NT treatment had no significant effect on bulk density, but it did lead to significantly less water content in the NT treatment with complete residue removal (NT0) compared with the NT100 and NT50 treatments, which did not differ significantly from each other (Table 1). Similarly, the were no significant differences between the NT100 and NT50 treatments for topsoil SOC or total N, but the SOC and total N contents were significantly less in the NT0 soils (Table 1). There were no significant differences with residue removal for the Mehlich-P content (Table

1).

224 B.4.3 Effects of tillage on run-off and nutrient export The time to run-off was greatest for the CT treatment and although run-off from the tilled NT treatment occurred slightly faster it was not significantly different than the

CT treatment (Table 2). Run-off occurred significantly faster on the NT treatment

(Table 2). This difference between the CT and the NT treatments corresponds to the difference in water content: the NT treatment had the greater volumetric water content before the rainfall simulation compared to the CT treatment, which was significantly drier before the rainfall simulation (Table 1). The total run-off volume collected was the same for the CT and the tilled NT treatments, and both these were significantly greater than the run-off volume collected for the NT treatment (Table 2).

Despite, therefore, the faster run-off and the wetter soil conditions, the total volume collected from the NT treatment was only about a quarter that from the CT treatment indicating that initial run-off was faster but that the capacity for the soil hold water was greater in the NT treatment.

The sediment load of the run-off was significantly greater for the CT treatment than for tilled NT, which was greater than that for the NT treatment (Table

3). Thus, the NT treatment had less run-off and a lower sediment load in the run-off which were compounded to lead to a very small sediment export compared with the tilled NT and the CT (Table 3). The NT treatment also had the smallest organic C export as sediment compared with both the CT and the tilled NT treatments, which did not differ significantly from each other (Table 3). The dissolved organic C exported in solution was small by comparison with the particulate and bound organic

C. The NT treatment produced a higher concentration of dissolved organic C in

225 runoff (Table 2), but there were no significant tillage effects on the quantity of dissolved C exported (Table 3). Total N and total P export in the sediment both followed the same treatment pattern as particulate and bound organic C export, with the NT treatment exporting significantly less N and P than the CT and tilled NT treatments, which did not differ significantly from each other (Table 3). Similarly,

export of dissolved NO3-N and dissolved P were both small by comparison with the

N and P exported in particulate and bound forms and there were no significant treatment effects (Table 3).

B.4.4 Effects of residue removal on run-off and nutrient export Although removal of residues led to faster run-off initially, the difference between the

NT100 and the NT50 and NT0 treatments were not significant (Table 2), however, residue removal did lead to significant increases in the total run-off between the

NT100, the NT50 and the NT0 treatments (Table 2) even though the soil in the NT0 treatment was drier before the start of the rainfall simulation than the NT50 and

NT100 treatments (Table 1). The sediment load from the NT0 treatment was also significant greater than that of the other two treatments, and the combination of greater run-off and greater sediment load in the NT0 treatment led to the total export of particulate and bound organic C being significantly greater from the NT0 treatment compared to the other two residue removal treatments, which did not differ significantly (Table 3). Similarly, the total N and total P exported in the sediment phase were significantly greater for the NT0 treatment than the other two treatments,

which did not differ significantly (Table 3). The amount of NO3-N and total P

226 exported in the dissolved phase were both small by comparison with the sediment phase, and there were not residue removal effects on either property (Table 2).

B.4.5 Effect of tillage on bulk soil and eroded sediment δ13C and δ15N Comparison with the bulk δ13C showed that soil from the CT treatment was significantly more depleted (more negative δ value by approximately 4‰) than either the NT treatment or the tilled NT treatment, while the difference in δ13C for soil from these two treatments differed by less than 1‰ (Fig. 1). The bulk δ15N values for soils from these treatments were very similar, differing by less than 0.4‰. By comparison with the bulk soil, the δ13C for the eroded sediment from the CT treatment indicated less 13C depletion in the eroded sediment by approximately 2‰, whereas the difference in δ13C between the soil and the eroded sediment for the NT and the tilled

NT treatments showed either no significant difference for the tilled NT treatment or slightly greater δ13C depletion in the sediment (by approximately 1‰) for the NT treatment (Fig. 1). The eroded sediment was less depleted in 15N than the top soil for all treatments, but the differences were small (typically less than 0.4‰) and were not significant for the CT treatment (Fig. 1). Thus, overall, where there were significant differences in the isotopic composition between the soil and the eroded sediment, the eroded sediment was less depleted in 13C (observed for the CT treatment) and less depleted in 15N (observed for the NT and tilled NT treatments) (Fig. 1).

B.4.6 Effect of residue removal on bulk soil and eroded sediment δ13C and δ15N The δ13C values of all the soils were in the range expected for soils that had received

13 predominantly C4 maize residues (typically δ C = 12‰) for several decades

13 (compared to C3 plant residues which typically have δ C = 26‰) (Fig. 1). Reducing

227 the amount of maize residue by removal of the aerial plant parts after harvest (but leaving the roots) led to small depletions (less than 1‰) for the NT0 and NT50 treatments compared to the NT100 treatment. There was also greater 15N depletion in the NT0 (by about 0.5‰) compared to the NT50 and NT100 treatments, which did not differ significantly (Fig. 1). The eroded sediment were relatively depleted in 13C compared to the bulk soils for the NT0, NT50 and NT100 treatments by up to -2‰

(Fig. 1). However, the eroded sediments were all less 15N depleted than the corresponding bulk soils, although the difference was not significant for the NT0 treatment (Fig. 1).

B.4.7 Effect of tillage on sources of C in the soil and eroded sediments The δ13C values of the top soils and eroded sediments were used to calculate the

contribution of C3-C (SOC) or C4-C (maize) sources to top soils and eroded sediments

(Figure 2). The majority of the C in both soils (66 – 96%) and sediments (78 – 97%) was derived from the maize crop. There was more maize C in the top soils of the NT than the CT treatment and the proportion of maize C was also greater for the TNT treatment. The effect of tilling the NT reduce the overall C content of the soil and also the amount of maize derived C in the surface 5 cm. The amount of C recovered in the eroded sediment from the NT treatment was small (2.0 kg ha-1; Table 3; Fig. 2) and nearly all of it was maize derived (Fig. 2). By comparison, in the CT treatment the amount of C recovered in the eroded sediment was 25.2 kg ha-1 (Table 3; Fig. 2) and

approximately 20% was derived from the indigenous C3-derived SOC (Fig. 2). The amount of C lost in the eroded sediment from the tilled NT treatment was 30.5 kg ha-1 this is however much less than the difference in total C content between the NT and

228 tilled NT soils (which was approximately 10 Mg ha-1). This difference is soil C content in the TNT plots was likely driven by: (1) significantly lower bulk density values following tillage; and (2) burial of a large fraction of the C beneath 5 cm by the tillage treatment.

B.4.8 Effect of residue removal on sources of C in the soil and eroded sediments The effect of residue removal was to reduce the total soil SOC content particularly between the NT50 and the NT0 treatments (Table 1), and this was clearly reflected in

the smaller amounts of C4-derived SOM with increasing residue removal (Fig. 2).

Residue removal also increased the amount of C in eroded sediment so that even though the total SOC content of the soil was 33% smaller for the NT0 treatment compared with the NT100 treatment (Table 1; Figure 2), the total C recovered in the eroded sediment was 11 times greater for the NT0 than the NT100 treatment (Table 2;

Figure 2). Furthermore, despite removal of surface (C4) residues from the NT0 treatment the amount of C4 in the eroded sediment was greater than for the NT50 and

NT100 treatment indicating the contribution of root-derived C from maize to the eroded sediment in the absence of surface residues (Figure 2).

B.5 Discussion B.5.1 The effect of tillage Soil tillage degrades soil structure, which reduces soil hydrological function (Bronick and Lal, 2005). The comparison of end member treatments, long-term CT versus long-term NT (NT100), allowed the investigation of tillage as a major factor driving sediment loss from a watershed where the sloping topography and soil texture increased the tendency for soil erosion by accelerated water erosion. The CT treatment had significant negative effects on the (i) physical characteristics indicative 229 of soil quality, i.e. bulk density and soil moisture content, and resistance to erosion, i.e. runoff and sediment losses (Table 1; Table 2; Table 3), and (ii) macronutrient concentrations, i.e. C, N and P of the top soils and run off sediments (Tables 1; Table

2; Table 3). Total runoff from the CT plots was four times greater than NT100 plots despite the recent tillage and comparative dryness of the CT plots. The surface of the

CT plots were observed to slake quickly after commencement of the rainfall simulation, which reduced permeability and increased runoff. The increased run off caused significant sediment loss, which was fourteen times greater than from NT100.

As a consequence the associated losses of eroded C, N and P from the CT plots were also large, although the macronutrient concentrations in the top soils were the smallest.

The watersheds investigated in this study were sampled in 2002 to examine whole profile SOC dynamics and those data provide a robust point of comparison for changes in SOC during the decade preceding our current report (Puget et al., 2005).

The top soil SOC pools under CT had declined by approximately an additional 1.1

Mg C ha-1 a-1 since 2002 from 7.4 (Puget et al., 2005) to 6.3 Mg C ha-1; Table 3). The

SOC stocks under NT100 had remained static (17.5 Mg C ha-1) suggesting that the top soils in the long-term NT plots were C saturated, and had been so for at least 10 years.

Continuous NT (NT100) resulted in the largest concentrations and fluxes of dissolved organic C, presumably from the larger quantities of decomposing crop residues on the surface of those plots. The proportion of dissolved to sediment bound

C was also greater in NT100 plots (1:10) compared with CT (1:175) and Tilled NT

230 (1:108). P measurements also indicated a trend of higher proportional losses in the dissolved fraction under NT, though those dissolved fluxes were not statistically distinct. The significant losses of sediment-associated P from the CT plot compared to NT100 confirmed the often reported relationship between regular tillage and P exports from agricultural land to the surrounding environment (Quinton et al. 2001).

The tillage after 42 years of continuous NT (i.e. the TNT treatment) caused an immediate and substantial increase in sediment and macronutrient loss as a result of the simulated rainfall compared to the NT100 plots. Prior to the rainfall simulation, the topsoils of the TNT plots had significantly smaller C, N and P contents than NT100, presumably due to lower bulk density and dilution caused by mixing the top soils and decomposing residues with the lower soil horizons, and not to microbial processes after such short timescales (< 1 day). However, the largest losses of C and N and a greater loss of maize-derived C, i.e. C less than 42-years old, was observed from the TNT plots. The TNT plots also lost more soil than any of the other treatments except the CT. Thus the erosion control benefits of NT were largely negated with a single tillage operation. Moreover, a single tillage operation increased soil loss more than 8 years of complete maize residue removal. This clearly illustrates that the benefits of NT for soil quality rely on the continuous maintenance of this practice. Nevertheless, Power et al. (1986) noted positive legacy effects of NT on soil nutrient availability and crop yield for a dryland soil 10 years after the cessation of

NT management.

Recent reports have suggested that soil C gains under conservation tillage and crop residue retention are limited in their magnitude and duration, as well as

231 insufficient to support the sale of C credits from agricultural land (Lam et al. 2013;

Luo et al. 2010). Our results corroborate that C accrual in the soil surface layer from conservation tillage concluded within 30 yrs. The data reported herein do, however clearly indicate that conservation tillage and crop residue retention provide marked increases in soil’s resilience to high intensity rainstorms, thus providing environmental quality benefits.

B.5.2 Effects of residue removal Previous investigations of soil quality indicators one year after the commencement of the residue removal experiment from the same plots used in this study indicated that residue removal at rates >25% adversely affected soil crusting, SOC concentration, soil moisture and infiltration rates, earthworm activity, crop growth and yield

(Blanco-Canqui et al., 2006, 2007; Lal, 2008). After an additional 7 years, our results show that C and N were significantly reduced in the top soils of the NT plots where all residues had been removed (NT0), but that the NT0 plots still contained ~50% more C, N and P than the CT plots, suggesting at least some of the benefits of NT prevailed despite complete residue removal. These negative effects were not observed in less erodible soils with low slopes in the region (Blanco-Canqui et al. 2006, 2007;

Blanco-Canqui and Lal, 2009) indicating that water erosion was the principal mechanism driving the degradation of soil physical properties and significant losses of macronutrients following complete residue removal. The amount of aboveground crop residue needed to sustain SOC pools in soils of the midwestern U.S. was estimated at 6.7 Mg ha-1 yr-1 of dry matter (Johnson et al., 2010). NT50 soils in this study have received 3-5 Mg ha-1 yr-1 of dry matter since the treatments were

232 established and that level of residue appears to have sustained their SOC pools. In addition, the NT0 soil was still considerably less susceptible to erosion than the CT soil losing two times less soil when subjected to simulated rainfall.

Recent discussions of crop residue removal from agricultural soils have highlighted the marked differences that slope and erodibility play in soil quality and conservation outcomes following crop residue removal from annual croplands (Perlak et al. 2005; Johnson et al. 2010; Edgerton et al. 2010). Using a modeling approach, soil organic matter was the limiting factor for residue harvest on slopes less than

2.0%, while erosion became limiting on slops above 2.5% and residue harvest became unfeasible for sites with slopes greater than 4%, if soil conservation was desired (Edgerton et al. 2010). Our results from land with a much greater (6-10%) slope reinforce that interpretation and suggest that it is likely that crop residue removal (≥50%) and soil conservation are likely not reconcilable on sloping land.

B.5.3 Relationships between macronutrients during erosion by water The correlations between the different macronutrient concentrations (C, N and P) and stable isotope values (C and N only) in the top soils and sediments suggested that eroded SOC and TN were associated with the erosion of organic matter, whilst P fluxes were driven by mineral association (Table 4). The multifarious provenance of the macronutrients in the soils and sediments was indicated by the natural abundance stable isotope values of the top soil versus the eroded sediments. The 15N values of the eroded sediments from all treatments were comparatively 15N-enriched (Figure 1) suggested the partitioning of different N sources between sediment and non-sediment bound moieties. Determining the specific source of the N in this system is challenging

233 without further compound-specific analyses of the different N species (i.e. inorganic

- + NO3 or NH4 , or organic e.g. amino acids). The sources of the N and P in the sediments may have been derived from fertilizers, the existing SOC or from the microbial biomass particularly during drying and rewetting (Blackwell et al., 2009).

There was a strong positive correlation between the total C and N from the top soil and sediment in all treatments, indicating that the organic C and N in both pools was from the same source, but this was not verified in this study.

The provenance of the SOC was easier to deduce because of the difference in

13 the stable C isotope values of the maize crop (C4, i.e. ca. -13‰) and previous non-C4 crops or native vegetation (C3, i.e. ca. -27‰; values from Puget et al., 2005). Analysis of the topsoil and eroded sediments from the tilled plots (CT) suggested an enhanced

loss of old SOC, i.e. residues from C3 land use at least 28 years ago, that had been previously stabilized (Figure 2). Determining the fate of this old C, i.e. mineralization or restabilization, beyond the plot scale is particularly important to determine the

strength of the global erosion flux as a sink or source of atmospheric CO2 (Lal, 2008).

B.6 Conclusions This study provides unique insight into the effects of tillage and residue removal management practices on soil erosion and C, N and P fluxes from erodible agricultural soils during high intensity rain storms. The largest fluxes of sediment, as well as sediment bound macronutrients, generated by water erosion occurred on soils under long term CT. The largest observed losses of C and N occurred in recently tilled NT (TNT) soils, while natural abundance stable 13C isotope values also indicated that soils under CT lost larger quantities of older (>28 yrs) soil C during

234 erosion than other treatments. NT management with residue mulch reduced soil erosion potential, but its benefit was severely impaired by residue removal on this sloping soil. Further, removing 50% of residue did not significantly degrade soil quality but did result in large increases in runoff. All observations suggest NT management provides greater resilience to soils than CT during high intensity rainfall events. The data also suggest that crop residue removal at levels of 50% or greater compromise many of NT’s hydrologic benefits on sloping agricultural lands.

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242

Table B.1. Effect of tillage and residue removal treatments on soil physical and chemical properties (0-5 cm depth). Each value is the mean of three replicates.

Bulk Gravimetri Volumetri Total soil Total soil Mehlich P density c water c water C N (kg ha-1) (g cm-3) content content (Mg ha-1) (Mg ha-1) (g g-1) (cm3 cm-3) Tillage CT 1.18 b 0.09 a 0.11 6.3 a 0.66 a 9.9 NT 1.32 c 0.26 c 0.34 17.5 c 1.74 c 18.5 Tilled NT 1.02 a 0.24 c 0.24 10.9 b 1.13 b 15.2 Residue removal NT100 1.32 c 0.26 c 0.34 17.5 c 1.74 c 18.5 (=NT) NT50 1.42 c 0.28 c 0.40 17.2 c 1.73 c 16.7 NT0 1.41 c 0.15 a 0.21 11.8 b 1.28 b 20.1 Pooled SE 0.04 0.01 1.61 0.13 3.55 p <0.001 <0.001 <0.005 <0.02 <0.37

LSD p < 0.05 0.13 0.05 5.18 0.41 NS

243

Table B.2 . Effect of tillage and residue removal treatments on run-off and concentrations of sediment and macronutrients in runoff. Each value is the mean of three replicates.

Time to Total run- Total Dissolved Nitrate Dissolved P run-off off (mm)† Solids Organic C (mg L-1) (mg L-1) (s) (mg L-1) (mg L-1) Tillage CT 1070 b 12.8 b 20,900 c 2.8 a 0.20 0.03 NT 100 500 a 4.4 a 1500 a 6.5 b 0.62 0.08 (=NT) Tilled NT 870 b 12.8 b 8870 b 3.5 a 0.13 0.04 Residue removal NT100 500 a 4.4 a 1500 a 6.5 b 0.62 0.08 NT50 380 a 13.3 b 1730 a 4.2 a 0.12 0.11 NT0 350 a 22.1 c 5970 ab 3.7 a 0.14 0.05 Pooled SE 81 1.45 1506 0.65 0.16 0.03 p <0.001 <0.001 <0.001 0.02 0.22 0.36

LSD p < 0.05 234 4.66 4860 2.09 NS NS † 35 mm of rainfall was applied to the plots.

244

Table B.3. Effect of tillage and residue removal treatments on the mass of sediment, sediment bound C and macronutrients, and dissolved C and macronutrients in run-off. Each value is the mean of three replicates.

Sediment Soil Loss Organic Organic Total N Nitrate Total-P Total-P load (Mg ha-1) C C lost in lost in lost in lost in lost in (g dm-3) (particula solution sediment solution sediment solution te and (kg ha-1) (kg N ha- (g ha-1) (g ha-1) (g ha-1) bound) 1) lost in sediment (kg ha-1)

Tillage CT 21.0 c 2.7 c 25.2 b 0.36 a 2.67 b 26 700 b 4 NT 100 1.50 a 0.07 a 2.0 a 0.28 a 0.21 a 19 30 a 3 (=NT) Tilled NT 8.87 b 1.2 ab 30.5 b 0.45 a 2.92 b 16 540 ab 5 Residue removal NT100 1.50 a 0.07 a 2.0 a 0.28 a 0.21 a 19 30 a 3 NT50 1.73 a 0.2 a 5.5 a 0.53 a 0.63 a 16 90 a 17 NT0 5.97 b 1.4 b 22.2 b 0.82 b 2.36 b 31 610 b 11 Pooled SE 1.51 0.36 5.4 0.09 0.46 0.008 150 50 p <0.001 <0.001 <0.034 <0.02 <0.001 0.60 <0.009 0.32

LSD p < 0.05 4.86 1.15 17.6 0.30 2.48 NS 480 NS

245

4.6 Top soil (0-5 cm) Eroded sediment 4.4 TNT NT0 NT0 4.2 NT100

) NT50 ‰ ( 4.0

N TNT 15 δ

3.8 CT NT100

NT50 3.6 CT

3.4 -18.0 -17.0 -16.0 -15.0 -14.0 -13.0 -12.0 -11.0

δ13C (‰)

Figure B.1 Natural abundance δ13C and δ15N stable istotope signatures from topsoil and eroded sediment samples NT100, NT50, NT0, TNT and CT treatments plots. Topsoil samples were collected prior to onset of rainfall and sediments were from runoff collected during during a 30 min simulated rainfall applied at an intensity of 7 cm hr-1. Error bars indicate standard error values.

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20000 Top soil (0-5 cm) C3-C (SOC) C4-C (maize) 15000

10000

5000 -1

0 kg C ha 40 Eroded sediment 35 30 25 20 15 10 5 0 NT100 NT50 NT0 TNT CT

Figure B.2 Contribution of C4 and C3 sources of C to carbon contents of topsoil and eroded sediment samples NT100, NT50, NT0, TNT and CT treatments plots. Topsoil samples were collected prior to onset of rainfall and sediments were from runoff collected during during a 30 min simulated rainfall applied at an intensity of 7 cm hr- 1. Error bars indicate standard error values.

247