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Potential Benefits of Cover Crop Based Systems for Sustainable Production of Vegetables

HIDE
 Title Page
 Dedication
 Acknowledgement
 List of Tables
 List of Figures
 Abstract
 Introduction
 Cover crop: Biomass and nitrogen...
 Growth, N accumulation, and yield...
 Cost, energy, and emergy analysis...
 Conclusion
 Appendices
 References
 Biographical sketch
 

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POTENTIAL BENEFITS OF COVER CROP BASED SYSTEMS FOR SUSTAINABLE PRODUCTION OF VEGETABLES By LAURA MATILDE VILA SEGURA A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Laura Matilde Avila Segura

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A mis queridsimos padres, en honor a sus enseanzas, sacrificios, cario y apoyo incondicional.

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iv ACKNOWLEDGMENTS This research would have not been possi ble without the suppor t of a diverse group of committed people. I would like to thank my advisor, Dr. J ohannes Scholberg, for providing me with an opportunity to come to the University of Fl orida and for his help and guidance during the past three years. I also want to thank Andy Scheffler, Kari Reno, Huazhi Liu, Alicia Lusiardo, Hannah Snyder, Susan Sorell, Jos Linares, Corey Cherr, among others, for their assistance with field and laboratory work ; but particularly for their friendship, which permitted me to learn by doing. Special thanks go to Green Cay Farms, Nancy Roe and UF-IFAS Plant Science Research and Education Unit in Citra. Thanks go to Meghan Brenna n and Dr. Ramon Littell, for their great help with statistical analysis. I greatly value the help of Dr. Robert McSorley for his assistance with data presentation and manuscript corrections and Dr. Clyde Kiker for his help with economics and for encouraging me to look at systems from different scales. Last but not least, I want to acknow ledge Wesley Ingwersen, not only for supporting and helping me through this research, but for walking with me the challenging path of professional definition. This research was funded by a grant from the Sustainable Agriculture Research and Education program of the United States Depa rtment of Agriculture (grant number LS02140, A System Approach for Improved Integration of Green Manure in Commercial Vegetable Production Systems).

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES.............................................................................................................ix LIST OF FIGURES........................................................................................................xvii ABSTRACT...................................................................................................................xviii CHAPTER 1 INTRODUCTION........................................................................................................1 Conceptual Approach...................................................................................................1 Rationale................................................................................................................2 Management..........................................................................................................5 Knowledge Gaps...................................................................................................7 Experimental Design and Measurements.....................................................................8 Experimental Unit.................................................................................................8 Measurements.......................................................................................................9 On Farm Experiment...........................................................................................10 Measurements......................................................................................................11 Hypotheses..................................................................................................................11 Objectives...................................................................................................................11 General Objective................................................................................................11 Specific Objectives..............................................................................................12 2 COVER CROP: BIOMASS AND NITROGEN ACCUMULATION.......................18 Introduction and Literature Review............................................................................18 Materials and Methods...............................................................................................22 Set-up and Design................................................................................................22 Timeline of Operations........................................................................................23 2003-04.........................................................................................................23 2004-05.........................................................................................................24 Sampling Procedures...........................................................................................24 2003-04.........................................................................................................24 2004-05.........................................................................................................25 Sample Processing...............................................................................................25

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viStatistical Analysis..............................................................................................26 Results........................................................................................................................ .27 Summer Cover Crops (SCC)...............................................................................27 Sunn hemp 2003...........................................................................................27 Cowpea 2004................................................................................................28 Pearl millet...................................................................................................28 Sesbania........................................................................................................29 Species comparison......................................................................................29 Winter Cover Crops (WCC)................................................................................30 Winter rye 2004............................................................................................30 Hairy vetch 2004..........................................................................................31 Overall winter cover crop system performance 2004..................................32 Winter rye 2005............................................................................................33 Hairy vetch 2005..........................................................................................34 Overall winter cover crop system performance 2005..................................34 Species Comparison.....................................................................................35 Discussion...................................................................................................................36 Summer Cover Crop Systems.............................................................................36 Sunn hemp 2003...........................................................................................36 Cowpea.........................................................................................................38 Pearl millet...................................................................................................40 Sesbania........................................................................................................41 Overall summer cover crop growth dynamics.............................................43 Winter Cover Crop Systems................................................................................44 Winter rye.....................................................................................................44 Hairy vetch...................................................................................................46 Conclusion..................................................................................................................50 3 GROWTH, N ACCUMULATION, AND YIELD OF VEGETABLE CROPS AS AFFECTED BY CROP RESIDUE S AND N-FERTILIZER RATE.........................65 Introduction.................................................................................................................65 Materials and Methods...............................................................................................69 Set-Up and Design...............................................................................................69 Timeline of Operations........................................................................................71 2004..............................................................................................................71 2004-05.........................................................................................................72 Sampling Procedures...........................................................................................73 2004..............................................................................................................73 2004-05.........................................................................................................73 Sample Processing...............................................................................................74 Nitrogen Applied to Crops..................................................................................75 Statistical Analysis..............................................................................................75 Results........................................................................................................................ .76 Sweet Corn (Spring 2004)...................................................................................76 Sweet corn growth........................................................................................77 Sweet corn yield...........................................................................................78

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viiBroccoli (Fall 2004)............................................................................................79 Broccoli growth............................................................................................80 Broccoli yield...............................................................................................81 Watermelon (Spring 2005)..................................................................................82 Watermelon growth......................................................................................82 Watermelon yields........................................................................................83 Discussion...................................................................................................................85 Sweet Corn Growth.............................................................................................85 Sweet Corn Yields...............................................................................................85 Broccoli Growth..................................................................................................89 Broccoli Yields....................................................................................................91 Watermelon Growth............................................................................................92 Watermelon Yield...............................................................................................94 Conclusion..................................................................................................................96 4 COST, ENERGY, AND EMERGY ANALYSIS OF COVER CROP-BASED PRODUCTION SYSTEMS......................................................................................114 Introduction...............................................................................................................114 Florida Farming System Characteristics...........................................................117 Economics and Energy Dynamics of Cover Crops...........................................118 Methodology.............................................................................................................120 Farm Description...............................................................................................120 Experimental Set-up..........................................................................................121 Measurements....................................................................................................122 Cost-Effectiveness Analysis..............................................................................123 Energy Analysis.................................................................................................124 Operational expenses..................................................................................124 Inputs..........................................................................................................125 Emergy Analysis...............................................................................................126 Sunn Hemp Replacement Scenarios..................................................................127 Results.......................................................................................................................128 CostEffectiveness Analysis.............................................................................128 Energy Analysis.................................................................................................132 Emergy Analysis...............................................................................................134 Discussion.................................................................................................................135 Cost-Effectiveness Analysis..............................................................................135 Energy Analysis.................................................................................................139 Emergy analysis.................................................................................................141 General discussion.............................................................................................143 Conclusions...............................................................................................................146 5 CONCLUSION.........................................................................................................162 APPENDIX

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viii A EFFECT OF INTERACTIONS IN COVER CROPS DRY MATTER ACCUMULATION, N CONCENTRAT ION AND N ACCUMULATION...........168 B CARBON AND NITROGEN CONCENTRAT ION IN DIFFERENT PLANT PARTS OF SUMMER AND WINTER COVER CROPS.......................................177 C WEATHER DATA FOR RE SEARCH STATION..................................................179 D NITROGEN DYNAMICS AND INTE RACTIONS FOR SWEET CORN, BROCCOLI AND WATERMELON.......................................................................182 E COST EFFECTIVENESS ANALYSIS................................................................196 F ENERGY AND EMERGYANALYSIS...................................................................204 REFERENCES................................................................................................................243 BIOGRAPHICAL SKETCH...........................................................................................268

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ix LIST OF TABLES Table page 1-1. Cover crop research focus over time in Florida and Georgia, a small sample.........13 1-2. Outline of crop rotation a nd experimental treatments..............................................17 2-1. Outline of crop rotations and experimental treatments during the research period (03-05)...........................................................................................................52 2-2. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect, along with ST*Np interaction effect on dry weight, N concentration, and N accumulation of sunn hemp ( Crotalaria juncea) ...............53 2-3. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect, along with ST*Np interaction effect on dry weight, N concentration, and N accumulation of cowpea ( Vigna unguiculata )...................54 2-4. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect, along with ST*Np interaction effect on dry weight, N concentration, and N accumulation of pearl millet ( Pennisetum glaucum ).........55 2-5. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect, along with ST*Np interaction effect on dry weight, N concentration, and N accumulation of sesbania ( Sesbania sesban ).....................56 2-6. Total dry weight accumulation and dry matter allocation to different plant parts for summer/fall cover crops.....................................................................................57 2-7. Total Nitrogen (N) accumulation and N allocation to different plant parts for summer/fall cover crops...........................................................................................57 2-8. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) and residue [R ES = residue of sunnhemp (SH ) or fallow vegetation (F) ] main effect on rye ( Secale cereale ), during summer/fall 04...........58 2-9. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) and residue [RES = residue of sunn hemp (SH ) or fallow vegetation (F) ] main effect on hairy vetch ( Vicia villosa ), during summer/fall 04..59

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x 2-10. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding sweet corn crop (Np and residue [RES = residue of sunn hemp (SH ) or fallow vegetation (F) ] main effect on hairy vetch and rye, during summer/fall 04.............60 2-11. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect and ST*N p interaction effect on dry weight, N concentration, and N accumulation of rye ( Secale cereale ), winter 04/05..............61 2-12. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect and ST*N-p interaction effect on dry weight, N concentration, and N accumulation of hairy vetch ( Vicia villosa ), winter 04/05.....62 2-13. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect and ST*N p interaction effect on dry weight, N concentration, and N accumulation of hair y vetch + rye, during winter 04/05........63 2-14.Total dry weight accumul ation and dry matter allocation different plant parts for winter cover crops....................................................................................................64 2-15. Total Nitrogen (N) and N allocation to different plant parts for winter cover crops, studied during 04 and 05...............................................................................64 3-1. Outline of crop rotations and experi mental treatments used during 03-05..............98 3-2. Effects of sampling time (ST), kg ha-1of N fertilizer applied to sweet corn (Nrate) and cropping system (CS) main e ffect; along with ST*N-rate, ST*CS, Nrate*CS interactions on sweet ( Zea mays ) corn shoots, during the spring of 04.....99 3-3. Effect of kg ha-1of N fertilizer applied to sweet corn (N-rate) and cropping system (CS) interaction (N-rate*CS) on shoot dry weight, N concentration, N accumulation and SPAD readings of sweet corn ( Zea mays ), spring 04...............100 3-4. Pair-wise contrast comparison by trea tment for dry weight, N concentration and N accumulation in sweet corn ( Zea mays ) shoots, during the spring of 04...........101 3-5. Effects of kg ha-1of N fertilizer applied to sweet corn (N-rate) and cropping system (CS), along with CS*N-rate intera ction on total, marketable, fancy and culls yield of sweet corn ( Zea mays ), during the spring of 04...............................103 3-6. Pair-wise comparison of selected treatm ents for total, marketable and culls yield, total N applied to sweet corn (N applie d), nitrogen use efficiency (NUE), and un-utilized applied nitrogen (UAN), during the spring of 04................................104 3-7. Regression equation for total and ma rketable yields of sweet corn for a conventional sweet corn trea tment (FF) amended with 5 different levels of N fertilization, during the spring of 04.......................................................................104

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xi 3-8. Effects of sampling time (ST), kg ha-1of N fertilizer applied to broccoli (N-rate) and summer cover crop residue (RES), along with ST*RES and N-rate*RES interaction effect on broccoli, during the winter of 04/05......................................105 3-9. Pair-wise contrast comparison by treatm ent for dry weights, N concentration and N accumulation along sampling times (in w eeks after transplanting [WAT]) in broccoli ( Brassica oleracea ), during the winter of 04/05......................................106 3-10. Effects of kg ha-1of N fertilizer applied to broccoli (N-rate) and cover crop residue (RES), along with RES*N-rate in teraction effect on yields of winter broccoli yields, during the 04/05 ...........................................................................108 3-11. Pair wise comparison between cowpea and pearl millet based systems amended with different N-fertilizer rates for fresh marketable, process marketable, total marketable, culls marketable, and cu lls process categories of broccoli.................109 3-12. Effects of kg ha-1of N fertilizer applied to watermelon (N-rate) and cropping system (CS), along with CS*N-rate inte raction on dry matter accumulation, N concentration and N accumulation of watermelon during the spring of 05...........110 3-13. Effect of kg ha-1of N fertilizer applied to wa termelon (N-rate) and cropping system (CS) interaction (N-rate*CS) on shoot dry weight, and N accumulation of watermelon ( Citrullus lanatus ) for last sampling date......................................111 3-14. Effects of kg ha-1of N fertilizer applied to watermelon (N-rate) and cropping system (CS), along with CS*N-rate intera ction on total, marketable, and non marketable (culls) yield of wate rmelon during the spring of 05............................112 3-15. Pair-wise contrast comparison by tr eatment for fresh marketable, total marketable and non marketable (culls) of watermelon during the winter of 04....113 3-16. Regression equation for total and marketable yields of watermelon for a conventional treatment (FF), with 5 levels of N fertilization, during the spring of 05............................................................................................................................113 4-1. Overview of cropping sequence and e xperimental treatments at Boynton Beach (02-05)....................................................................................................................148 4-2. Summary of yields for tomato, peppe r and sweet corn as affected by summer cover crop (sunn hemp) and N-fert ilizer rate (04 and 05)......................................148 4-3. Average cost of grow ing sunn hemp (03 and 04)..................................................148 4-4. Average summer weed control production expenses (03 and 04)..........................149 4-6. Energy analysis summary for toma to production per ha at Boynton Beach, Florida, for 12 different (hypothetic al) production scenarios (03-04)...................151

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xii 4-7. Energy distribution among different production components for tomato production per ha at Boynton Beach, Flor ida, for 12 different (hypothetical) scenarios.................................................................................................................152 4-8. Energy distribution from the energy an alysis for pepper production per ha at Boynton Beach, Florida, for12 different (hypothetical) scenarios.........................153 4-9. Energy distribution from the energy an alysis for pepper production per ha at Boynton Beach, Florida, for 12 diffe rent (hypothetical) scenarios........................154 4-10. Energy analysis summary for sweet corn production per ha at Boynton Beach, Florida, for 12 different (hypothetical) scenarios...................................................155 4-12. Energy distribution from the energy analysis for crop production per ha at Boynton Beach, Florida, for four di fferent (hypothetical) scenarios.....................156 4-13. Emergy analysis main indicators from energy analysis for tomato production per ha at Boynton Beach, Florida, for twel ve different (hypothe tical) scenarios.........157 4-14. Emergy analysis main indicators from energy analysis for pepper production per ha in Boynton Beach, Florida, for 12 different (hypothetical) scenarios...............159 A-1. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) interacti on (ST*Np) on dry weight, N concentration, and N accumulation of sunn hemp ( Crotalaria juncea ), during summer/fall 03...168 A-2. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) interacti on (ST*Np) on dry weight, N concentration, and N accumulation of cowpea ( Vigna unguiculata ), during summer/fall 04.......169 A-3. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) interacti on (ST*Np) on dry weight, N concentration, and N accumulation of pearl millet ( Pennisetum glaucum ), summer/fall 04.........170 A-4. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) interacti on (ST*Np) on dry weight, N concentration, and N accumulation of sesbania ( Sesbania sesban ), during summer/fall of 04.....171 A-5. Effect of sampling time (ST) and residue [RES = residue of sunnhemp (SH ) or fallow vegetation (F) ] interaction (ST*RES) on dry weight, N concentration, and N accumulation of rye ( Secale cereale ), during the winter of 03/04.....................172 A-6. Effect of sampling time (ST) and residue [ RES = residue of sunnhemp (SH) or fallow vegetation (F) ] interaction (ST*RES) on dry weight, N concentration, and N accumulation of hairy vetch ( Vicia villosa ), during the winter of 2003/04.......172

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xiii A-7. Effect of sampling time (ST) and residue [ RES = residue of sunnhemp (SH) or fallow vegetation (F) ] interaction (ST*RES) on dry weight, N concentration, and N accumulation of hairy vetch +rye during the winter of 2003/04.......................173 A-8. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) interac tion (ST*Np) effect on dry weight, N concentration and N accumulation in rye ( Secale cereale ), winter 04/05..............174 A-9. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) interac tion (ST*Np) effect on dry weight, N concentration and N accumul ation in hairy vetch ( Vicia villosa ), winter 04/05....175 A-10. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) interac tion (ST*Np) effect on dry weight, N concentration and N accumulation in rye+hairy, winter 04/05..............................176 B-1. Carbon (C) to Nitrogen (N) relation (C :N ratio) for different plant parts in summer cover crops...............................................................................................177 B-2. Carbon (C) to Nitrogen (N ) relation (C:N ratio) for diffe rent plant parts in winter cover crops.............................................................................................................178 C-1. Average temperature (at 60 cm hei ght), minimum and maximum temperature (MinT and MaxT at 60 cm height), and av erage of solar radi ation (AVGsolrd at 2 m height) for twelve months during 2003...........................................................179 C-2. Average temperature (at 60 cm hei ght), minimum and maximum temperature (MinT and MaxT at 60 cm height), and av erage of solar radi ation (AVGsolrd at 2 m height) for twelve months during 2004...........................................................179 C-3. Average temperature (at 60 cm hei ght), minimum and maximum temperature (MinT and MaxT at 60 cm height), and av erage of solar radi ation (AVGsolrd at 2 m height) for twelve months during 2005...........................................................180 C-4. Average rainfall for twelve months during 2003...................................................180 C-5. Average rainfall for twelve months during 2004...................................................181 C-6. Average rainfall for twelve months during 2004...................................................181 D-1. Nitrogen applied to sweet corn ( Zea mays var. Saturn Yellow) in form of NH4NO3 fertilizer and summer and winter cover crops residue and weeds, during the spring of 2004 (kg ha-1).........................................................................182 D-2. Effect of sampling time (ST or WAE) and kg ha-1of N fertilizer applied to sweet corn (N-rate) interaction effect (ST* N-rate) on dry weight, N concentration, N accumulation in shoots and SPAD readings (chlorophyll readings) of sweet corn leaves ( Zea mays ), during the spring of 2004........................................................183

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xiv D-3. Sampling time (ST) and cropping syst em (CS) interaction effects (ST*CS) on dry weight, N concentration, N accumu lation in shoots and SPAD readings (chlorophyll readings) of sweet corn leaves ( Zea mays ), during the spring of 2004........................................................................................................................184 D-4. Effect of kg ha-1of N fertilizer applied to watermelon (N-rate) and cropping system (CS) interaction (N-rate*CS) on dry weight, N concentration and N accumulation of sweet corn ( Zea mays ), during the spring of 2004......................185 D-5. Effects of sampling time (ST) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (ST*N -rate) on marketable, fancy, non marketable (culls) and total yield of sweet corn ( Zea mays ), during the spring of 2004.....................186 D-6. Equations for critical points of SPAD for sweet corn, SPAD, and NO3 for watermelon, and critical N concentration (g N kg-1) in broccoli leaves................187 D-7. N applied to broccoli ( Brassica oleracea var. Pac Man) in form of fertilizer (NH4 NO3), cover crops residue and weed s, during the winter of 2004/05...........187 D. Effect of sampling time (ST) and kg ha-1of N fertilizer applied to broccoli (Nrate) interaction (ST*N-rate) effect on dry weight, N concentration and N accumulation in broccoli ( Brassica oleraceae ), during the winter of 2004/05......188 D-9. Effect of sampling time (ST) and residue [ RES = residue of cowpea (CP) or residue of pearl millet (P) ] interaction (ST*RES)effect on dry weight, N concentration and N accumulation in broccoli ( Brassica oleraceae )....................189 D-10. Effect of kg ha-1of N fertilizer applied to broccoli (N-rate) and residue residue [ RES = residue of cowpea (CP) or residue of pearl millet (P) ] interaction (Nrate*RES) on dry weight, and N accumulation in broccoli....................................190 D-11. N applied to watermelon ( Citrullus lanatus var. Mardi Gras) in form of fertilizer (NH4NO3), cover crops residue and weed s, during the spring of 2005...191 D-12. Effect of sampling time (ST) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (ST* N-rate) on dry weight, N concentration, and N accumulation in watermelon shoots and fruits ( Citrullus lanatus ), spring 2005...192 D-13. Effect of sampling time and cropping sy stem interaction (ST*CS) effect on dry weight, N concentration and N accumulation in watermelon shoots and fruits( Citrullus lanatus ), during the spring of 2005...............................................193 D. Effect of cropping system (CS) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (CS*N-rate) effect on dry weight, N concentration and N accumulation in watermelon shoots and fruits ( Citrullus lanatus ), during the spring of 2005.........................................................................................................194

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xv D. Effect of cropping system (CS) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (CS*N-rate) on dry weight, N concentration and N accumulation in watermelon total tissues ( Citrullus lanatus ), spring 05...............195 D-16. Effect of cropping system (CS) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (C S*N-rate) on weeds dry weight accumulation, N concentration and accumulation, during the spring of 05......................................195 E-1. Generic expenses for tomato or pepper and sweet corn production systems (2003-2004)............................................................................................................196 E-2. Tomato crop producti on expenses (2003-2004).....................................................197 E-3. Bell pepper crop production e xpenses average years 2003-2004..........................198 E-4. Sweet corn crop production e xpenses average years 2003-2004...........................199 E-5. Sensitivity analysis for the effect of product price on revenues from specific pepper treatments based on aver age pepper yield (2004 and 2005).......................199 E-6. Sensitivity analysis for the effect of product price on revenues from specific tomato treatments, based on average tomato yield (2004 and 2005).....................200 E-7. Sensitivity analysis for the effect of product price on revenues from specific sweet corn treatments, based on averag e sweet corn yield (2004 and 2005).........200 E-8. Budget analysis for the different ma nagement scenarios without synthetic N fertilizer..................................................................................................................201 E-9. Budget analysis for the different management scenarios with 112 kg N ha-1 N fertilizer..................................................................................................................202 F-1. Energy analysis for the different mana gement scenarios for tomato production...205 F-2. Energy analysis for the different mana gement scenarios for pepper production...207 F-3. Energy analysis for the different management scenarios for sweet corn production...............................................................................................................209 F-4. Energy coefficients calculated of gather from literature for the energy analysis...211 F-5 Emergy memory or calculations (Not al l the calculations are applicable to the different scenarios or crops)...................................................................................212 F-7. Emergy analysis for tomato pr oduction scenario Fallow 0 N-rate.........................219 F-8. Emergy analysis for tomato produc tion scenario Compost 0 N-rate.....................220 F-9. Emergy analysis for tomato producti on scenario Broiler litter 0 N-rate................221

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xvi F-10. Emergy analysis for tomato produc tion scenario Cover Crop 0 N-rate.................222 F-11. Emergy analysis for tomato pr oduction scenario Fallow 112 N-rate.....................223 F-12. Emergy analysis for tomato pr oduction scenario Compost 112 N-rate.................224 F-13. Emergy analysis for tomato producti on scenario Broiler litter112 N-rate.............225 F-14. Emergy analysis for tomato produc tion scenario Cover Crop 112 N-rate.............226 F-15. Emergy analysis for tomato pr oduction scenario Fallow 224 N-rate.....................227 F-16. Emergy analysis for tomato produc tion scenario Compost 224 N-rate.................228 F-17. Emergy analysis for tomato producti on scenario Broiler litter 224 N-rate............229 F-18. Emergy analysis for tomato produc tion scenario Cover Crop 224 N-rate.............230 F-19. Emergy analysis for pepper pro duction scenario Fallow 0 N-rate.........................231 F-20. Emergy analysis for pepper produc tion scenario Compost 0 N-rate......................232 F-21. Emergy analysis for pepper producti on scenario Broiler litter 0 N-rate................233 F-22. Emergy analysis for pepper produc tion scenario Cover Crop 0 N-rate.................234 F-23. Emergy analysis for pepper pro duction scenario Fallow 112 N-rate.....................235 F-24. Emergy analysis for pepper produc tion scenario Compost 112 N-rate..................236 F-25. Emergy analysis for pepper producti on scenario Broiler litter 112 N-rate............237 F-26. Emergy analysis for pepper produc tion scenario Cover Crop 112 N-rate.............238 F-27. Emergy analysis for pepper pro duction scenario Fallow 224 N-rate.....................239 F-28. Emergy analysis for pepper produc tion scenario Compost 224 N-rate..................240 F-29. Emergy analysis for pepper producti on scenario Broiler litter 224 N-rate............241 F-30. Emergy analysis for pepper produc tion scenario Cover Crop 224 N-rate.............242

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xvii LIST OF FIGURES Figure page 3-1. Calculated N accumulation for different N-rates for cropping systems (CS) as a function of weeks after em ergence (WAE) for A) sweet corn amended with 0 kg N ha-1; B) sweet corn amended with 67 kg N ha-1; C) sweet corn amended with 133 kg N ha-1; D) sweet corn amended with 200 kg N ha-1; and E) sweet corn amended with 267 kg N ha-1..................................................................................102 3-2. Nitrogen accumulation in different cropping systems (RES) as a function of days after emergence (DAP) for A) broccoli amended with 0 kg N ha-1; B) broccoli amended with 131 kg N ha-1; C) broccoli amended with 196 kg N ha-1..107 4-1. Overview of inter-rel ation between processes and economic scales using an Object-Oriented programming appro ach outlining how cover crop best management practices at a micro s cale interact with meso scales.........................161

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xviii Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science POTENTIAL BENEFITS OF COVE R CROP-BASED SYSTEMS FOR SUSTAINABLE PRODUCTION OF VEGETABLES By Laura Matilde Avila Segura August, 2006 Chair: Johannes Scholberg Major Department: Agronomy Although cover crops (CC) historically were an integral part of cropping systems, there is limited information on how to best integrate CC in current production operations, especially in transitional environments. More over, on-farm cost-effec tiveness analysis are needed for evaluating the benefits from CC in vegetable production systems. At Citra, Florida, we conducted a 2-year field study to evaluate yield response of spring sweet corn ( Zea mays var. Saturn Yellow) to a summer CC (sunn hemp [ Crotalaria juncea ]) and/or winter CC (hairy vetch [ Vicia villosa ] and rye [ Secale cereale ]) during 2003/04. We also evaluated the respon se of watermelon ( Citrullus lanatus var. Mardigrass) in a crop rotation with summer CC (pearl millet [ Pennisetum glaucum var. Tifleaf], cowpea [ Vigna unguiculata var. Zipper Cream] or sesbania [ Sesbania sesban ], followed by either winterplanted broccoli ( Brassica oleracea var Pac Man) or a winter CC (hairy vetch and winter rye mix) during 2004/05. We conducted a farm -based cost, energy, and emergy analysis for tomato ( Lycopersicon esculentum ), pepper ( Capsicum annum ), and sweet corn

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xix production systems at a Community Supporte d Agriculture farm, located in Boynton Beach, Florida, during 2003/04. At Citra, sweet corn plante d in CC residues received 0, 67, or 133 kg N ha-1, whereas sweet corn non-CC (contro l) systems received 0, 67, 133, 200, or 267 kg N ha-1. Broccoli was considered am ended with 0, 131, or 196 kg N ha-1 The CC-based watermelon systems received either 0, 84, or 168 kg inorganic N ha-1, while non-CC systems received ei ther 0, 84, 126, 168, or 210 kg N ha-1. Sunn hemp accumulated 7.2 Mg ha-1 and 111 kg N ha-1, but continuous cultivat ion resulted in build up of soil-borne-disease. Pearl millet pe rformed well in low fertility and high precipitation environment, accumulating 9.4 Mg DM ha-1 and 75 kg N ha-1. Cowpea, on the other hand, tended to be sensitive to hi gh humidity, and early senescence reduced biomass yield (2.9 Mg ha-1 and 54 kg N ha-1). Sesbania stands were affected by nematodes causing this crop to perform ve ry poorly. The winter CC mix produced 7.7 Mg ha-1 and 139 kg N ha-1 and 12.3 Mg ha-1 and 264 kg N ha-1 during 2004 and 2005, respectively. A double cropping syst em fertilized with 133 kg N ha-1 produced comparable yields to fallow sw eet corn fertilized at 200 kg N ha-1 (15.8 vs. 17.3 Mg ha-1, respectively). Pearl millet enhanced broccoli biomass accumulation while yields were not affected by summer cover crop at high N-fert ilizer rates. In contrast, non-fertilized cowpea-based systems had greater and earlier broccoli yields compared to pearl milletbased systems. Watermelon initial growth a nd fruit development was hampered by cold and wet conditions and continuous growth of hairy vetch afte r mowing. The cost, energy, and emergy analysis concluded that when CC enhanced yields, they provide higher gross returns compared to conventional manageme nt, further reducing the dependency on fossil fuel-derived inputs, and helping achieve farm sustainability

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1 CHAPTER 1 INTRODUCTION Conceptual Approach This chapter will outline the scope of work of the research pr ogram underlying this thesis and provides a conceptual framework of subsequent chapters of this thesis, along with a brief discussion of how these chapters are interrelated. The first chapter also includes initial hypotheses and a brief overv iew of experiments, treatments, and measurements. This thesis aims to look at pr ocesses at different scales and a number of system components as well. Chapter 2 is ma inly physiologically oriented and looks at processes at a plant level. It outlines biomass and nitrogen accumulation patterns of cover crops. Chapter 3 discusses the interactive effect s of cover crops and nitrogen (N) fertilizer application rates on the growth and yield of subsequent vegetable crops in North Central Florida (NCF), such as sweet corn in spring 2004, broccoli in winter 2004/2005, and watermelon in Spring 2005. Chapter 4 presen ts a much broader fr amework and assesses the potential benefits of cover crops on a farm scale fo r a commercial vegetable operation in South East Florida (SEF). For this loca tion the effects of cover crop on sweet corn, tomato and peppers yield, production cost a nd profitability were examined. Energy and emergy analysis of the cover cropping system is also included using farm records and values obtained from the literature. In the last section of this chapter a meso-scale theoretical evaluation framework is presente d which allows for improved assessment of the importance of cover croppi ng systems in the context of sustainable small farming

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2 operations. Chapter 5 synthesizes information from previous chapters and also provides suggestions for future program activities. Rationale Complex biological systems such as agroeco system require a systems approach in order to fully appreciate their structure a nd function. The underlying assumption is that agroecosystems are complex and interrela tionships among environmental conditions, management, and biological processes are im portant in determining outcomes such as yield, pest pressure, and environmental imp acts (Drinkwater, 2002), wh ich might serve as indicators of the agroecosystem sustainabil ity. It is also important to evaluate the economic component of managed agroecosy stems (Ante and Capalbo, 2002). Recent environmental guidelines and regulations can only be integrated into agricultural management practices when farmers can also sustain long term profitability of their operations (Baggs et al., 2000). Florida possesses a large and stable agri cultural economic base. According to the Florida Department of Agriculture and C onsumer Services (2003) Florida has 44,000 commercial farms, occupying 4.13 million hect ares. Most of these operations use conventional production practices. Floridian agriculture occurs mainly on very sandy (>98% sand) soils, with low soil organic matter content and low inherent soil fertility. This implies that frequent application of synthetic nitrog en fertilizers is required fo r optimal production (Hochmuth, 2000). Excessive N fertilizer application when combined with high intensity rainfall events and poor water and nut rient holding capacity of so ils may result in N leaching below the active root zone (Prakash et al,. 1999). For example, groundwater nitrate (NO3N) values in excess of the maximum contaminant limit (MCL) of 10 mg NO3-N L-1 are

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3 commonly found in citrus production areas of central Florida (M attos et al., 2003). Florida appears to be follow ing a general global trend, as mentioned by Nair and Graetz (2004), and it is projected that by 2020 the co ntribution to crop nutrient requirements from the soil will be as low as 21%, compar ed to 9% from organic amendments and 70% from an inorganic fertilizer. These project ions show the need for more sustainable practices. In Florida for example, The Offi ce of Agricultural Water Policy (OAWP) of the Florida Department of Agriculture a nd Consumer Services (FDACS) has been developing Best Management Practices (BMP s), addressing both wate r quality and water conservation on a site-specific, region al, and watershed basis (OAWP, 2005). The dependence of conventional agriculture on inorganic fertilizers and thereby in fossil fuels, may constrain pr oduction in the near future. Today, as in th e early 1970s, fossil fuels supply is uncerta in (Hlsbergen et al., 2001). For example in 1997 the total energy inputs necessary to cultivate one hect are of maize in the Unites States was about 10 million kCal, or 1000 liters of oil (Pimentel et al., 1998). Reduced fuel availability and sharply increasing fuel prices may favor replacement of chemical fertilizers by manures and other organic amendments in agriculture Use of leguminous cover crops, that via symbiotic N fixation use solar energy to gene rate on-site N in a sustainable fashion, can be seen as a strategy for d ecreasing energy invested per out put of crop. Similarly nontillage systems can also greatly reduce machinery use, and thereby energy expenditures in cultivation (Conservation Technol ogy Information Center, 2002). Among other alternatives to fo ssil fuels, yard compost or biosolids appear to be more widespread, but it has been documen ted that compost or chicken manure, may contain small amounts of heavy metals a nd also may result in hyper-accumulation of

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4 phosphorous. In this regard, no tillage syst em combined with cover crop rotations represents a more sustainable manageme nt strategy to secure production while minimizing externalities. An improved understanding of soil-plant-en vironment interactions is necessary when selecting cover crops (Cobo et al., 2002 ). Cover crops enhance soil quality by attracting beneficial insects (Bugg et al.,1991), improving peds aggregation (Gregory et al., 2005), infiltration capacity, increasing or ganic matter (Salinas-Garcia et al., 1997), stimulating microbial activity during first weeks after in corporation (Lundquist et al., 1999), and reducing nematode populations (Abawi and Widmer, 2000). The latter characteristic is especially important in sandy soils where nematode s tend to proliferate easily (Griffin, 1996). However, it is very difficult to quantify economic benefits from cover crops use in vegetable production system s, since their benefits are cumulative over time and soil quality improvement might not be evident in the short term. Another difficulty in economic evaluation of cove r crops is the appropriate allocation of establishment cost of the cover crop (Kl onsky, 2003). From the strict economic stand point, the use of cover crops is only cost effective if production input requirements decrease and/or results in a si gnificant increase in crop yields Therefore yield decrease in cover cropping systems may redu ce profits, due to th e cover crop high establishment cost (Baldwin and Creamer, 1999). For all these reas ons, the real benefits of cover crops may be masked when just focusing on short-term economic results, and this underlines the importance of also comparing the energeti c cost of cover cropping systems versus conventional cropping systems. It is also desi rable to also assess the economic value of all the environmental long term se rvices provided by cover crops.

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5 Management Environmental characteristics, as well as soil type, are crucial factors when managing cover crops. In North Central Florid a, fall cover crops have to withstand high temperatures in the months of July, August and September, wh ile frost may occur starting the beginning of November. Th e summer cover crop that was used in this study for the first three years is commonly referred to as sunn hemp ( Crotalaria juncea ). This fabacea is native to India and Pakistan, and cul tivated in Southeast Asia for fiber and as live mulch (Li et al., 2006). Its positive attributes include potential nematicidal action (McSorley, 1999), erosion and weed cont rol, and high biomass and nitrogen accumulation capacity (Li et al., 2000). However, sunn hemp is sensitive to Verticillium spp. (Cherr, 2003) and frost (Mansoer, 1997). It has been shown that under Florida conditions, sunn hemp can cause NO3-N leaching, compared to non legumes, when incorporated into the soil (Wang et al., 2003) therefore a non tillage system may be more appropriate. In California, summer cover crops often require supplemental irrigation during its establishment (Van Horn, 2003) to attain maximum growth. In Illinois, no-till corn following ryegrass as a cover crop for three years, yielded 5.2 Mg ha-1 under water limited conditions, compared to 4.1 Mg ha-1 for no-till without a c over crop on a fragipan soil. Corresponding results for a silt loam soil were 8.7 Mg ha-1 versus 1.4 3.5 Mg ha-1 (Collins, 2003). Poor summer cover crop development means a reduction in the biomass added to the soil, and thereby a decrease in soil organic matter accumulation. In this case a vigorously growing winter cover crop may be required to sustain soil organic matter.

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6 According to Weinert et al., (2002) ove r-wintering non-leguminous crops prevent N movement through the soil. This supports the hypothesis that in order to avoid N leaching and to enhance soil nutrient retention. Therefore, in excessively drained soils and warm climates it is better to plant a non-leguminous crop or cover crop after a leguminous cover crop (Kuo and Jellu m, 2002). Leguminous cover crops can substantially reduce N fertilizer requiremen ts. However, poor synchronization between cover crop residue mineralization and subse quent peak N demand of a commercial crop may either reduce N availability and/or th e risk of excessive N leaching and thereby hamper efficient N utilization (Sperow, 1995; Weinert et al,. 2000; L ogsdon et al., 2002). In the Central Corn Belt of the United States of America (M issouri, Illinois, Indiana, and Ohio) hairy vetch ( Vicia villosa ) is a commonly used legume, while winter rye ( Secale cereale ) is a preferred non-leguminous cover crop. Hairy vetch is mainly used as a nitrogen source for cash crops. Rye is utili zed as a catch crop for residual nitrates and due to its vigorous growth in the fall and its winter hardiness, it also provides an excellent soil cover that can both prevent soil erosio n and suppress weeds. Maize growing after hairy vetch had a higher yield than when following rye (Bollero et al., 1994). Mixtures of cover crops app ear to be more suitable for improving soil N retention and crop N availability. Winter rye performs well when mixed with hairy vetch, rye can tolerate a wide variety of soil types and climatic condi tions (Creamer and Baldwing, 1999). For example, a mix of hairy vetch and rye can create an optimal C:N ratio, which decreases the risk of N leaching and at the same time may enhance N supply for a subsequent crop (Ruffo and Bollero, 2003), es pecially when overhead irrigation is provided. When alive, non-legume cover crops, such as rye, may be more effective in

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7 reducing residual NO3 and potential leaching from the soil early in the season, compared to legumes, such as hairy vetch and crimson clover (Sainju et al., 1998). Another management consideration is th e method used for suppressing the cover crop, for example spraying with herbicide and mowing enhances inorganic N availability in the short-term while simultaneously re ducing carbon and N inputs (Snapp and Borden, 2005). Knowledge Gaps Several studies have been carried out in northern states pertai ning to cover crop physiology, ecology, mineralization and weed suppression in non-tillage systems (Carrera et al. 2005?; Rosecr ance et al., 2000). Florida re search has contributed to generating knowledge about cover crops rotatio ns for tomato, peppers, field corn and or nematodes suppression; but there is no or litt le information on temporal dynamic of cover crops growth, and their effect on soil orga nic matter build-up an d/or soil nitrogen dynamics (Table 1-1). Few studies have look ed at intercropping of green manures or brassicae and cucurbitae behavior under non-ti llage system, or timing for planting and elimination of the cover crops (Table 1.1). Although information on non-tillage systems nitrogen dynamics and carbon accumulation for Ge orgia is readily available (Kuo et al., 1997, Sainju et al., 2002; Sainju et al., 2003; Sa inju et al., 2005), these results may not be pertinent to directly applicable to North Central Florida systems because both soil and climatic condition differ between these two regions. Analyses looking at energy expenditure s and economics for non-tillage cover cropped systems have been developed for Minnesota, Maryland, California, Wisconsin and Tennessee (Gregory et al ., 2005; Lu et al., 2003; A ndraski and Bundy, 2005; Wyland

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8 et al., 1996; Ogbuchiekwe et al. 2004; Stute 1995 ; Roberts et al. 1998). However, there is a critical need for similar information for Florida cover crop systems. The experiments presented in this thesis aimed to enhance our understanding of improved use of cover cropping in vegetable pr oduction systems in North Central Florida and provide information on C and N dynamics as well as energetic and economic considerations for on-farm cover crop use at South Florida. Experimental Design and Measurements Experimental Unit Studies were conducted at the Plant Scie nce Research and Education Unit (PSREU) near Citra, Marion County, FL. The prevailing soil type of the research area were a Candler fine sand (Typic Qu arzipsamments, hyperthermic, uncoated; 98% sand in the upper 15 cm) and Lake fine sand (Typic Quar zipsamments, hyperthermic, coated; 97% sand in the upper 15 cm) (Carlisle et al.,1988). This study provides a conti nutation and also complements two previous years of research in cover cropping systems and aime d to evaluate if im proved integration of cover crops can increase soil organic matte r and reduce inorganic N-fertilizer demand of subsequent vegetable crops. During the su mmer of 2003, sunn hemp was planted for a third consecutive year. Since its continuous cultivation resulted in a build-up of Verticillium spp., alternative summer cover cr ops species including sesbania ( Sesbania sesban ) and cowpea ( Vigna unguiculata ) were evaluated during summer 2004. In order to avoid anticipated N loses during the fall seas on (as shown by Cherr, 2004), pearl millet ( Pennisetum glaucum) was also included as a summer cover crop because it was assumed that its higher C:N ratio w ould reduce mineralization and enhance N retention. Winter cover crops used included a mix of hairy vetch and winter rye during 2003 and 2004. But

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9 during the latter year, broccoli was also plante d in the late fall following previos summer cover crops (cowpea and pearl millet). In this case, it was expected that broccoli would directly benefit from N mineralized from summer cover crops residues. During the spring commercial vegetable crops were grown including sweet corn ( Zea mays ) during the spring of 2004 and watermelon ( Citrullus lanatus ) during the spring of 2005. Both are crops with high nitrogen demands. In addition to cover crops and vegetable treatments a non-planted (complete control) plot was also included which was managed as a controlled weed fallow via 3-monthly applic ation of herbicides. All leguminous seeds were inoculated with proper rhizobium, before planting. Fertilizer ra tes differed for sweet corn, broccoli and watermelon, and were based on University of Florida Institute of Food and Agricultural Sciences (IFAS) fertilizer recommendations. Treatments were replicated four times and arranged in a complete randomized block design and total plot number equaled si xty plots. The dimensions of each plot were 7.62 m x 9.14 m (69.7 m2). Total area of the plots and alleyways was approximately one hectare (or 2.5 acres). An outline and more detailed description of experimental treatments is presented in Table 1-2. Measurements Cover crop biomass sampling: sunn hemp (2003) and hairy vetch/ rye (2003/04) Plots were sampled every 3 weeks using a sample area of 0.23 m2. Fresh and dry weight of leaves, stems, roots and flowers along with leaf area, leaf number, plant height, and plant density were determined from a representative subsample. For sesbania, pearl millet, cowpea (2004) and hairy vetch/rye (2004) total fresh and dry weight of shoots, roots and reproductive organs (flowers and/or pods) were determined. Dried tissue was ground a nd analyzed for N concentration. Vegetable crop sampling: sweet corn ( 2004), broccoli (2004), and watermelon (2005) were sampled every 3 weeks. Fres h and dry weigh of shoots, roots and reproductive (flower, ear, fruit or head) orga ns were determined for representative areas of 0.23 m2, 0.31 m2, and 1.86 m2, respectively. Total and marketable yield was determined for net harvested plots at the end of the growing season using a

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10 harvesting area of 19 m2of 12 m2, 70 m2 for sweet corn, broccoli, and water melon, respectively. Diagnostic tissue sampling: for broccoli tota l leaf N concentration was determined at 11 weeks. For watermel on, chlorophyll r eadings and NO3 -N concentration in the petioles of 6 representative leaves were determined, along with the N concentration of diagnostic leaves. Weeds: total weed above ground biomass and N concentration was determined at the end of season (Data not included in this thesis, to be published as a separate paper which will also outline the effects of cover crop treatments on changes in nematode numbers over time). Nematodes: nematode counts were determined for composite samples collected from 5 different points at end of season of each crop for all plots (Data not discussed in this thesis, to be published as a separate paper which will also include weed data). Soil: the soil pH (2:1 water extract) was measured at end of season for the 0-15 and 15-25 cm for cover crops treatments, and for the 0-7.5 and 7.5-15 cm soil depth for sweet corn (2004) and watermelon (2005). Ni trate leaching from selected sweet corn plots was measured using suction lysimeters placed at 0.3 and 1.2 m; soil coring (0.3 m increments to a soil depth of 1.2 m); and resin traps (0.9 m depth). Nitrate leaching from selected waterm elon plots was determined by soil coring. Soil particulate organic matter (POM) wa s determined during the spring of 2004. Total soil C and N concentrations were determined during the spring of 2004 and 2005 (Data not discussed in this thesis and will be included in a separate publication). On Farm Experiment This part of the program was carried out on a Community Supported farm located in Boynton Beach Florida and th is operation managed by Dr. Nancy Roe. This farm is not certified organic, but sust ainable practices drive the pr oduction process. Moreover the product is sold under the modality of Commu nity Supported Agriculture (CSA), where costumers have a subscription and pay for their produce in advance, resulting in unique economic characteristics. Cr op rotations included sunn he mp as a summer cover crop during 2003 and 2004; tomatoes and peppers as fall vegetable crops during 2003 and

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11 2004, and sweet corn grown during the spring of 2003, 2004, and 2005. Sampling procedures were similar as those de scribed for the studies at PSREU. Measurements Summer cover crops: fresh and dry shoot weights were determined at the end of the growing season. Sweet corn: fresh and dry weights of stover and ears were determined at the end of the season, along with chlorophyll re adings of diagnostic leaves. For tomato and pepper: fresh and dry weight s of fruits and stover were measured at the end of growing season Weeds: dry weights were determined at the end of the 2003 growing season. Economic and energetic parameters: producti on cost data were gathered by Nancy Roe during 2003/2004. Hypotheses Including leguminous cover crops duri ng the summer and/or fall season will provide additional nitrogen (N) via symbiotic N fixation and improved soil N retention and their use will reduce supplem ental synthetic nitrogen requirements. Nonleguminous winter cover crop will sequest er N that is being mineralized from summer cover crop residues. A fall vegetable crop dire ctly following a summer c over crop will make more efficient use of mineralized N, becau se during the fall growing season in nontillage systems cover crop biomass decomposes slower and is not as lost as readily. Use of cover crops will reduce farm depe ndence on external resources and overall farm energy consumption. Appropriate use of cover crops can e nhance the sustainability of existing agroecosystem. Objectives General Objective Determine if a combination of cover crops will reduce supplemental nitrogen fertilizer requirements and improve soil a nd/or environmental quality of vegetable production systems, in North Ce ntral and South East Florida

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12 Specific Objectives Evaluate the performance of selected cove r crops in terms of their potential to accumulate biomass and/or nitrogen in No rth Central Florida and South Florida (Chapter 2). Determine if the use of cover crops will result in maximum sweet corn, broccoli and water melon yields, while reducing crop N-fertilizer requirements (Chapter 3). Evaluate the economic feasib ility of the cover crop base d systems for a Community Supported Agriculture farm in South Florida (Chapter 4). Perform an energy balance and emergy an alysis to determine the ecological sustainability of the cover crop based ve getable crop producti on systems (Chapter 4). Measure the potential environmental bene fits of cover crops due to reduced N leaching and increased carbon sequestration and soil quality (Not included in thesis). Synthesize research findings and outline the pertinence and potential use of cover crops in southeaster U.S.A with special refe rence to future research needs, suitable management practices, and farm adoption (Chapter 5)

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13Table 1-1. Cover crop research focus over time in Florida and Georgia, a small sample. Cover crops studied Main crop Focus of the study Location of experiments Source Legumes Corn Intercropping and double cropping of corn with green manures. North Florida Smith and Prine (1982) Summer cover crops Cabbage, field corn Effects of fallowing, summer cover crops, and fenamiphos on nematode populations and yields. Florida Rhoades (1984) Cowpea Preharvest infestation of weevil and population trends. Florida Hagstrum (1985) Rye Soybean Population dynamics soil-borne fungi in multi-cropped field, under reduced tillage. Florida Ploetz et al., (1985) Sudangrass hybrid Potato Effects of planting date and mowing interval of the summer cover crop on the abundance of wireworms, and subsequent damage to tubers in the following crop cycle. Southern Florida Jansson and Lecrone (1991) Soybean, velvet bean, cowpea, 'Asgrow Chaparral' sorghum Densities of plant-parasitic nematodes on crops grown for forage. North Central Florida seven sites McSorley and Gallaher (1992) Hairy indigo Response of hairy indigo to water deficits in a greenhouse experiment North Central Florida Gainesville Winzer et al., (1992) Soybean, corn, cowpea, velvet bean, sorghum Change in nematodes population densities from winter to summer cover crops. Dry matter yields and levels of Ca, Mg, K, P, N, Cu, Fe, Mn, and Zn in leaves of summer cover crops. North Florida McSorley and Gallaher (1993) Cowpea Testing cowpea varieties for nematode resistance. Florida sandy soils Gallaher and McSorley (1993)

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14Table 1-1. Continued. Cover crops studied Main crop Focus of the study Location of experiments Source Rye Nematode population changes during winter. North Florida McSorley (1994) Lupine rye hairy vetch, crimson clover Corn Effects of management (winter cover crop and tillage) on nematode densities for an associated corn crop. North Florida (five sites) McSorley and Gallaher (1994) Cowpea Research on precision seeding and row spacing. Florida-Forth Pierce Kahn (1995) Castor, velvet bean, cowpea, American jointvetch, sorgum-sudangrass, rye Cotton, okra, soybean, eggplant, corn, sesame Cover cropping system and its effect on parasitic nematodes. Florida McSorley and Dickson (1995) Hairy vetch, crimson clover, wheat Abundance of thrips during winter and early spring. North and Central Florida Toapanta et al., (1996) Rye Soybean Densities of nematode in six trophic groups, in rows and between rows of soybean. Nematodes population density after cover crop. Florida McSorley and Frederick (1996) Browntop miller, 'Iron Clay' cowpea, marigold Tomato, pepper Production systems (including cover crops) for managing plant-parasitic nematodes in a double-crop system. Southwest Florida McSorley et al., (1999) Sorgum sudangrass, cabbage and potato Parasitic nematodes niche distribution. Florida Perez et al., (2000) Sorghum-sundangrass, velvet bean Potato Nematodes population densities and crop yields from different potato cropping systems with summer cover crops. Florida Hastings Crow et al., (2001) Hairy vetch Climatic conditions influence in the proliferation of thrips in its host hairy vetch. North Florida Toapanta et al (2001)

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15Table 1-1. Continued. Cover crops studied Main crop Focus of the study Location of experiments Source Rye, hairy vetch and crimson clover Tomato, eggplant and field corn Management practices (non tillage, chisel plowing, moldboard plowing and cover crops) and their influence on soil C and N, and yield. Georgia Greenville and Forth Valleyfine sandy loam Sainju et al., (2002b)NIR? Wheat, rye, oat, lupine, hairy vetch, crimson clover Invertebrate community. North Central Florida Tremelling et al., (2003) Hairy vetch, rye, hairy vetch/rye mixture Tomato Cover crops and nitrogen fertilization effects on soil aggregation, C and N pools. Georgia Greenville and Forth Valleyfine sandy loam Sainju et al., (2003) Sunn hemp, velvet bean, and cowpea Tomato Evaluate the effects of three legume cover crops on populations of nematodes in the successive crop. FloridaHomestead Wang et al., (2003) Sunn hemp, Iron Claycowpea Pepper Impact of alternative crop production practices, among them cover crops, on soil quality and yields. Florida Chellemi and Rosskopf (2004) Cowpea Basil, Chinese cabbage Field experiments were conducted to evaluate three non-chemical alternatives to methyl bromide, for the management of plant-parasitic nematodes. Florida Wang et al., (2004) Hairy vetch, rye, hairy vetch/rye mixture Cotton and sorghum Influence of tillage, cover crops and fertilization on soil carbon. Central GeorgiaDothan sandy loam Sainju et al., (2005) Rye Peanut Conservation tillage systems and intercropping effect on yield. Florida Alachua Tubbs and Gallaher (2005)

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16 Table 1-1 Continued. Cover crops studied Main crop Focus of the study Location of experiments Source Sunn hemp Squash Use of sunn hemp hay as organic N fertilizer compared to synthetic fertilizer, and its effects on nematode communities. Gainesville, Florida Wang et al. (2006)

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17 Table 1-2. Outline of crop rotation and experimental treatments. YEAR 1 YEAR 2 Trt. Fall 2003 Winter 2003 Spring 2004 N ratio (kg ha-1) Fall 2004 Winter 2004 N rate (kg ha-1) Spring 2005 N rate (kg ha-1) 1 S H+R SC 0 CP B 0 W 0 2 S H+R SC 67 CP B 131 W 84 3 S H+R SC 133 CP B 196 W 168 4 S F SC 0 PM B 0 W 0 5 S F SC 67 PM B 131 W 84 6 S F SC 133 PM B 196 W 168 7 F H+R SC 0 SB H+R 0 W 0 8 F H+R SC 67 SB H+R 0 W 84 9 F H+R SC 133 SB H+R 0 W 168 10 F F SC 0 F F 0 W 0 11 F F SC 67 F F 0 W 84 12 F F SC 133 F F 0 W 126 13 F F SC 200 F F 0 W 168 14 F F SC 267 F F 0 W 210 15 F F F None F F 0 F None

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18 CHAPTER 2 COVER CROP: BIOMASS AND NITROGEN ACCUMULATION Introduction and Literature Review Cover crops (CC) have been used exte nsively throughout the world and they may provide a myriad of services including ni trogen (N) fixation and improved nutrient recycling/retention (Ibewiro et al., 2000; Weinert et al., 2002), nematode control (McSorley 1999, Wang et al., 2002), erosion prevention (Sainju et al., 2005), insect trapping and/or pest inhibition (Bottenberg et al., 1997; Hooks et al., 1998), allelopathic weed suppression (Caamal-Maldonado et al., 2001; Hartwig and Ammon, 2002), water conservation (Schonbeck et al., 1993) while th ey also may enhance soil organic matter and beneficial soil organism activity (Rol dn et al., 2003). Successf ul CC systems require that CC complement commercial crops in space and/or time (Derksen et al., 2002; Carrera et al., 2005). Synchronization between CC nutrient releas e and commercial crop nutrient demand are the base for designing CC-based syst ems (Thnnissen et al., 2000; Fortuna et al., 2003). Based on a greenhouse study, researchers concluded that the benefits obtained by rapeseed ( Brassica napus ) and wheat ( Triticum aestivum ) depended on precedent type of leguminous crop and their N fixation capacity (Mayer et al., 2003). On sandy loam soils wheat and canola recovered 8 to 12% of the residual N at maturity. However, on loamy sand soils in a semi-arid region in Mali use of cowpea as a CC increased sorghum ( Sorghum spp ) and pearl millet ( Pennisetum glaucum ) stover and grain yields by 25

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19 and18%, while corresponding increases for sesbania ( Sesbania sesban ) ranged from 32 to 45% (Kouyat et al., 2000). Nutrients release to subsequent crops will be also affected by how the CC is terminated (killed) and on residue placement. Reduced tillage and surface application of residues will decrease mineralization rates. In a seven-year study on a silt loam at Pennsylvania, tilling legume cover cropped pl ots with chisel-disc and moldboard plow, enhanced initial N mineralizi ng (Drinkwater et al., 2000). Ho wever, results for reduced tillage systems may be inconsistent. For exampl e, yields of crops such as peanuts have shown improved or comparable yields compar ed to conventional ti llage systems (Tubss, 2005). However, no-tillage tomato ( Lycopersicum esculentum ) and eggplant ( Solanum melongena ) systems on the similar soils did not increase yields (S ainju et al., 2002). Soil fertility issues may also interfere w ith CC performance. When nutrients (N, P, K) are readily available, CC tend to allocate more resources to aboveground biomass than to roots formation. In the absence of fertiliz er application, root N content of tropical leguminous CC was relatively stable whil e shoot N content increased by 30% when supplemental fertilizer was applied (T ian and Kang, 1998). Nitrogen accumulation by hairy vetch ranged from 45 to 224 kg N ha-1(Sustainable Agriculture Network, 2001). Although symbiotic fixation can co ntribute a substantial frac tion of this N, excessive residual soil N levels reduce the efficiency of N fixation (Hartwig and Ammon, 2002). Studies in Denmark showed that N biomass accumulation and yield of legumes such as Pisum sativum decreased with N fertilizer rate (Ghaley et al., 2005). Low inherent soil organic matter (SOM) in sandy soils prevailing in Central Florida requires integration of suitable CC species into existing production systems in

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20 order to maintain SOM. Use of cereal CC ma y be the most effective in enhancing SOM (Snapp et al., 2005). High temperatures and radiation levels during the summer/fall fallow season in Florida, combined with ad equate rain result in high biomass production potential. Although gramineous C4 crops are c onsidered to prolific biomass producers in high radiation environments (Fageria et al., 1997), in certain case legumes may actually exceed gramineous growth performance. Sunn hemp ( Crotalaria juncea ) for example, accumulated 5.9 Mg ha-1 of DM and 126 kg N ha-1on a sandy loam soils in Alabama in a 9-12 week period and 59-63% of this N was released during winter (Reeves et al, 1996). In Homestead Florida, sunn hemp produced between 12.2 Mg ha-1 of dry biomass and accumulated 351 kg N ha-1 (Li et al., 2006). Residue lignin content and soil environm ental conditions may also affect CC mineralization. Therefore carbon to nitroge n (C:N) ratio alone may not provide an accurate predictor of subsequent residue N re lease rates (Ruffo a nd Bollero, 2003). In the southeastern U.S., N from legu mes terminated right before corn cultivation exhibited C:N ratios around 10 to 20 (Ranells and Wagge r, 1997). Mixing gramineous crops with legumes increases the C:N ratio, thereby reduc ing initial mineralization rates. Use of a mix of non-legumes and legumes cover crops such as rye ( Secale cereale ) and hairy vetch ( Vicia villosa ) on sandy soils with poor nutrient re tention capacities thus can reduce both N leaching during rainy fallow periods and fix additional N for subsequent commercial crops (Kuo and Sainju 1998, Ruffo and Bollero, 2004, Sainju et al., 2005). Moreover, residual N from leguminous CC can enhance N accumulation and crop growth of subsequent gramineous crops (Glasene r et al., 2002). However, on poor sandy soils intercropping cowpea with high biomass accumulator, such as pearl millet, led to a

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21 decrease in overall biomass production (Z egada-Lizarazu and Iijima, 2005), and intercropping it with sesbania did not provide extra biomass accumulation benefits compared to pure sesbania stands (Toomsan et al., 2000). Both overly low and high C:N ratio associated with mono-cropped leguminous and gramineous CC systems may require the addition of supplementary inorganic N to make up for N losses due to leaching and/or immobilization (Creamer and Baldwin, 2000). Although several studies have outlined end-of-season DM and N content for different CC-based systems, most of these st udies do not address temporal time trends, nor do they address how cover crop residue affects crop N requirements of subsequent cover cropping systems, nor N losses from summer cover crop residues during winter fallows. Suitable cover crops for Florida vegetable production systems include sunn hemp, a native from India, which has a high capac ity for both C and N sequestration (Cherr, 2004). Cowpea ( Vigna unguiculata ) is a prospective cover crop due to its symbiotic N fixation and capacity to generate economic re turns (Toomsan et al., 2000). Pearl millet, is widely used in Africa (Maman et al., 1999; Bationo and Ntare, 2000; Buerkert et al., 2000) but it is also adapted to Coastal sandy soil s of the South East U.S. (Menezes et al., 1999), which could help retain and recycle residual soil nutr ients, build up soil OM via the accumulation of large amounts of recal citrant biomass (Kennedy et al., 2002). Sesbania, is widely cultivated in tropical Af rica (Kwesiga et al., 1999; Phiri et al., 2003; Mudhara et al., 2003), and it is a prolific biomass producer (Sthl et al., 2005), and due to its symbiotic N fixation capacity has the poten tial to also increase both soil C and N pools and thus further enhance SOM.

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22 The specific objective for this componen t of the study was to evaluate the performance of selected single summer CC or winter CC mixes in terms of their potential to accumulate biomass and/or (to partly) m eet nitrogen requirements of subsequent vegetable crops in North Central Florida. The hypotheses of the study were 1) thr ough the use of leguminous cover crops during the summer and/or fall season, N ca n be fixed and therefore supplemental synthetic nitrogen applications to a spring crop can be redu ced; 2) a winter non-legume cover crop will recover N that is being mine ralized from the summer cover crop residues and may also provide a more stable N source for spring vegetable crop. Materials and Methods Set-up and Design Research was conducted at the Plant Scie nce Research and Education Unit near Citra, Florida (University of Florida, Gainesville). The dominant soil types at this site were a Candler fine sand (Typic Quarzips amments, hyperthermic, uncoated) and Lake fine sand (Typic Quarzipsamments, hypertherm ic, coated). Both soil types contained more than 95% sand in the upper 1-2 m of the soil profile (Carlisle et al., 1988). The study included selected cropping systems consisting of a combination summer and/or winter cover crops residues ame nded with different N fertilizer rates and these combinations were compared with conventional (without CC residues) production systems. Summer CC included sunn hemp ( 2003), cowpea, pearl millet and sesbania (2004) and during winter a hairy vetch / rye mix was planted (2004 and 2005). By following summer CC with a mix of legume and gramine ous CC we aimed to improve the C:N ratio of the CC residue and the N retention from N released by sunn hemp, cowpea and pearl millet, while also facilitating additional N fixation.

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23 The crops succeeding winter cover crops during spring were sweet corn (2004) and watermelon (2005). Sweet corn has a high demand for inorganic N (>200 kg N ha-1) and served as a as a biological indicator of overall residue N availability and also provided a common com ponent for the different cropping systems outlined in Table 2-1. Each cropping system was amended with 3 inorganic N fertil izer rates (0, 0.33, 0.67 times IFAS N recommendation for sweet corn (Olson and Simonne, 2005); and 0, 0.5, and 1.0 times N recommendation for waterm elon and broccoli (Olson and Simonne, 2004). For systems that did not include a CC, two additional N rates (1.00, and 1.33 vs 0.75 and 1.25 times IFAS recommendation) were included for sweet corn and watermelon, respectively. An overview of expe rimental treatments is provided in Table 2-1. All treatments were arranged in a ra ndomized complete block design with four replicated blocks. Timeline of Operations 2003-04 During the last week of July 2003, s unn hemp (SH) was planted following herbicide application and mowing of the fiel d. Seed was inoculated with cowpea-type rhizobium and planted at 30 mm depth usin g an in-row spacing of 0.03 m and betweenrow spacing of 0.76 m. The crop was terminat ed on 23 October with an application of ammonium sulfate 50% (1.2 L ha-1), Mirage Plus (Glyphosate 41.0%) at a rate of 1.2 L ha-1(Loveland Products, INC., Greeley, CO), a nd Remedy (Triclopyr 61.6%) at a rate of 1.2 Lha-1 (Dow AgroSciences, Indianapolis, IN). Hairy vetch was inoculated with hairy-vetch type rhizobium and the winter CC mix was planted at a rate of 56 kg ha-1 rye and 22 kg ha-1 hairy vetch on 13 November of 2004, with a rip-strip planter using a row spaci ng of 0.19 m and plan ting depth of 13 mm.

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24 Hairy vetch and rye emerged December 7th of 2003 and all plots were mowed and sprayed on April 2nd of 2004 with Pendimethalin (BASF, Florham Park, NJ) and Atrazine (Syngentha, Basel, Switzerland) and on April 6th of 2004 with Amm onium sulfate 50% (applied at a rate of 1.2 L ha-1), Mirage Plus (Glyphosate 41.0%) at a rate of 2.4 L ha-1 (Loveland Products, INC., Greeley, CO) and on. 2004-05 Cowpea (variety Zipper Cream) and sesban ia were inoculated at recommended rates prior to planting. Pear l millet (PM), cowpea (CP), and sesbania (SB) were planted on July 8th 2004 with a rip-strip planter at the sp acing of 0.38 m using a plant depth of 13, 19 and 38 mm, respectively. Corresponding seed rates were 34, 56, and 28 kg ha-1, respectively. Plants emerged on July 15th and grew until October 10th of 2004. After mowing, they were sprayed with Ammoni um sulfate 50% (at a rate of 2.3 L ha-1), Mirage Plus (Glyphosate 41.0% at a rate of 9.4 L ha-1, Loveland Products, IN C., Greeley, CO) on 14 October and with Ammonium su lfate 50% (at a rate of 1.2 L ha-1) and GLY-4 Plus (Glyphosate 41.0% at a rate of 4.7 L ha-1 Albaugh Inc., Valdos ta, GA) on 20 October of 2004. Hairy vetch was inoculated with rhizobium and mixed with rye and planted with a zero-till grain-drill at a s eed rate of 56 and 22 kg ha-1 on October 28th, of 2004 and plots were strip tilled on March 22nd 2004, no herbicides were applied, before intercropping the watermelon seedlings. Sampling Procedures 2003-04 All 24 plots planted with sun hemp were sampled at 3-wk intervals and sampling dates were expressed in weeks after emergence (WAE). At each sampling, a representative 0.6 m long row section was cl ipped at the soil level (sampling area 0.46

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25 m2). Total fresh weight was determined and a representative sub-sample was used for growth analysis. The root system for this sample was excavated carefully and plant material was stored in coolers during transpor tation and refrigerated until further analysis. Hairy vetch was sampled from all 24 plots, except for the 8 and 11 WAE samplings when only 8 plots were harvested. In this case, a representative 0.6-meter-long row section was clipped at the soil le vel (sampling area 0.12 m2). Total sample leaf number and area and leaf, stem, root, and reproductive (flowers) fresh weights were ta ken for each sample, except at WAE 8 and 11. 2004-05 Summer CC (CP, PM and SB) were samp led from all 24 plots every three weeks until WAE 11. At each sampling, a representati ve 0.6-m-long row section was clipped at the soil level (sampling area 0.23 m2) and roots were excavated with a shovel. Because of the viny nature of the hairy vetch, overl apping between rows occurred and a sampling frame of 0.31 x 0.76 m (0.23 m2) was used for the sampling of winter CC in 2004/05 to ensure a more representative sample. Samplings were repeated at 3-wk intervals for a representative plot section. Sample Processing Plants were separated into leaves, stems, roots, and reproduc tive tissues (flowers and pods, when present). Roots were carefully rinsed to remove soil and debris above a 1-mm sieve. Leaf area was determined with an LI-3000 (Li-cor; Lincoln, NE) using a representative sub-sample. Dry weights were recorded for sub-samples and roots after oven drying at 65 oC for at least 72 hours. For all sampling dates, except the last one, plants parts were recombined and then ground in a Wiley mill to pass through a 2 mm screen. For end-of-season samplings plant or gans were processed separately. Grindings

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26 were then subjected to a wet-acid Kjeldahl digestion, diluted, filtered, and analyzed for total Kjeldahl nitrogen at the UF-IFAS Soils Laboratory and at the Agronomy Physiology Laboratory (University of Florida, Gaines ville, FL) using EPA Method 351.2 (Jones and Case, 1991). Final growth samples for selected treatments were also analyzed for total C and N. Roots, stems and leaves tissue-ma terial were re-ground in a Willey mill and passed trough a 1-m screen, weig hed in an analytical balan ce, and then analyzed for C and N using a Carlo Erba CN analyzer (Carlo Erba Reagenti, Milan, Italy). Statistical Analysis Growth data was recorded on datasheets, and organized, and conve rted to a hectare basis using EXCEL (Microsoft, Corporation, Lo s Angeles, CA). Sta tistical analysis was performed with SAS (Statistical Analysis Systems, Cary, NC ). Since sampling dates were correlated over time (covariance), the Pro c Mixed procedure of SAS was used to analyze results with sampling da te (ST) being the main fixed effect in the model. Since all summer CC were planted in sweet corn residue, it was hypothesized that the different N-fertilizer ra tes previously applied to sw eet corn (Np) may potentially affect the growth of the subsequent summer CC and therefore Np was included in the model along with a ST*N p interaction term. During the 2004 and 2005, winter rye was intercropped with hairy vetch and system components were analyzed both separa te and in conjunction with each other. In 2004, winter CC followed either sunn hemp or a summer fallow. This approach allowed us to evaluate both the effects from sunn he mp residue (RES) and Np and in this case both the effect of residue (R ES) and Np were included in the model Random variation was attributed to replicates (blocks) and sampling time (ST) and this was a common component for the statistical model used for both winter a nd summer CC. During 2005,

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27 winter CC always followed sesbania, and ther efore only ST and Np were included as model effects. Mean separation was performed by the Tu keys T-statistic (p < 0.05). Response variables tested included dry matter accumulation (Mg ha-1), tissue N concentration (g N kg-1), and crop N accumulation (kg N ha-1) for all sampling dates. For general comparisons among cover crops within the same season, another model was employed that included block (Rep) and species (CP vs SH vs PM or HV04-05 vs R04-05) as main effects. In this case the random and repeated statements were dropped from the model, since only end-of-season values were used. Results Summer Cover Crops (SCC) Sunn hemp 2003 Root dry weight of sunn hemp incr eased quadratically over time while corresponding responses for shoots and total bi omass were cubic (Table 2-2). Root and shoot DM tended to level off after WA E 11 and maximum observed total DM was 7.07.2 Mg ha-1. Maximum dry matter (DM) and N accu mulation rates were 161 and 3.2 kg ha-1 d-1 at WAE 8 and DM allocation to ro ots was relatively low (~10%). The N application rate to the preceding sweet co rn crop (Np) did not affect crop DM accumulation. Root, shoot and total N tissue concentra tions decreased quadratically over time (Table 2-2). Overall crop tissue N concentra tion decreased from 37 (WAE 2) to 16 g N kg-1 (WAE 17) and roots had a 25% lower N concentration compared to shoots. The ST*Np interaction was significant for both shoot and total N tissue c oncentration during WAE 5 and values for the Np=133 treatment were highest. In all other cases tissue N

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28 concentration was not affected by Np (T able A-1). Nitrogen accumulation mimicked biomass accumulation patterns and crop N increased quadratically from 10 kg ha-1 at WAE 2 to 111 kg ha-1 at WAE 14 (Table 2-2). By WA E 14, shoots accounted for 93% of total crop N. Cowpea 2004 Root, shoot and total DM increased quadr atically with time, while previously applied fertilizer did not affect cowpea grow th (Table 2-3). Maximum total DM occurred at WAE 8 (4.7 Mg ha-1). End-of-season shoot and root N concentration were 54 to 60% lower compared to initial values and roots had a 30% lower N concentration compared to shoots. Calculated daily N accumulation rate s reached maximum values of 4 kg N ha-1 d-1 at 5 WAE resulting in overal l N accumulation of 94 kg N ha-1, with 95% of this amount being allocated to above-ground biomass. For the purpose of consistency, the ST*Np interaction effects are outlined in Table A-2, although none of these interaction terms were significant. Pearl millet Root, shoot, and total DM of pearl millet (P M) increased linearly with time (Table 2-4). Total DM accumulation rate was 9.4 Mg ha-1 at WAE 11 and the maximum calculated rate calculated of DM accumulation was 204 kg ha-1 d-1 at WAE 8. Preceding N application (Np) rates did not affect plant growth nor tissue N concentration. Towards the end of the growing season, root and s hoot N concentration decreased by 61 and 70%, while overall crop N concentration decr eased over time from 26 to 8 g N kg-1. Total crop N accumulation showed a lin ear increase. Maximum N accumulation was attained between WAE 8 and 11, follo wing the biomass accumulation trend. At WAE 11 total N accumulation was 75 kg N ha-1, with 93% coming fr om shoots (Table 2-

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29 4). The interaction of ST*Np was only si gnificant for roots N accumulation and at WAE 8, with plants growi ng in residual 67 kg ha-1 N-fertilizer accumulating more N compared to non-fertilized treatment (Table A-3). Sesbania Shoot, root and total DM accumulation follo wed a cubic trend (Table 2-5). Total dry weight accumulation peak ed at WAE 5 (1.1 Mg ha-1). Roots accounted for 29% of total DM at WAE, while Np did not affect overall crop DM accumulation or allocation. Nitrogen concentration in the plant decr eased over time from 32.2 to 8.0 g N kg-1 exhibited a cubic trend. Shoot N concentration showed a 74% decline which is greater than any of the other systems. The ST*N p interaction was si gnificant for root N concentration but means for Np treatments we re similar for each sampling date (Table A4). Total N accumulated by the crop foll owed a cubic increase. Maximum N accumulation was reached at WAE 5 for both roots and biomass, while 82% of the N was accumulated in the above-ground parts. Root, shoot and total N accumulation was greatest for the Np=133 treatment (Table 2-5). Species comparison In order to compare the grow th characteristics and overall performance of the three legumes (SH, SB, CP) and one gramineous (PM) summer CC species under local conditions, DM and N content and allocation was compared among these species at WAE 11 (Table 2-6 and 2-7). Pearl millet had the highest (9.4 Mg ha-1) biomass production followed by sunn hemp, while the productivity of cowpea was in termediate, and sesbania performed very poorly (0.7 Mg ha-1). Overall DM allocation to roots and stems was highest for SB and lowest for PM, while SH and PM had the hi ghest DM allocation to leaves. Overall N

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30 accumulation was as follows: SH > CP ~ PM >> SB. But it should be noted that earlyseason N accumulation for cowpea was comparable or higher than that for sunn hemp (Tables 2-2 and 2-3). Overall N root content was similar for all crops except for sesbania (Table 2-7). Both SH and PM appeared to a llocate less N to stems and more to leaves compared to other crops. But this may be re lated to the poor perfor mance of sesbania and the early onset of leaf sensescence for cowpea. Nitrogen allocation to reproductive growth was similar for both cowpea and pear l millet and relatively low for sunnhemp and sesbania. Winter Cover Crops (WCC) Winter rye 2004 Root and shoot DM accumulation for wi nter rye during 2004 (R04) increased quadratically over time (Table 2-8). Maximum DM accumulation was 5.3 Mg ha-1 at WAE 17 and the maximum observed dail y DM accumulation rate was 85 kg ha-1 d-1 (WAE 14). Dry matter allocation to roots d ecreased from 20% (WAE 2) to 6% (WAE 17). Although Np had no effect on dry matte r accumulation, the ST*RES interaction was significant for total dry weight (Table 2-8). Root, shoot, and total DM content of winter rye were greater in plots follo wing SH compared to fallow. However, for shoot and total DM content, the ST*RES interaction was significant and benefits from sunn hemp residue become more evident toward the end of the growing season (Table A-5). At the end of the season DM accumulation of rye doubled with SH residue (6.8 for fallow versus 3.7 Mg ha-1 for SH). Nitrogen concentration in below-ground tis sue showed a quadratic response and decreased from 16.9 to 7.7 g N kg-1 over time, whereas shoots and total tissue followed a cubic trend diminishi ng from 32 to 12 g N kg-1 (Table 2-8). Residue treatment affected N

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31 concentration differentially ove r time. By the end of the se ason, overall N concentration in SH-amended plots was lower compared to fallow plots and corresponding overall N concentrations were 9.2 versus 14.2 g kg-1 (Table A-5). Root and shoot N content showed a cubic increase over time where as shoot DM increased linearly (Table 2-8). Total N content was greatest at WAE 17, but N accumulation rates were highest at WAE 11. S hoots accounted for 96% of overall crop N accumulation. While shoot and total biomass N accumulation across time was greatest in sunnhemp plots. Although the ST*RES inter action effect was significant, SH-based systems had either similar or higher root N accumulation rates than fallow plots (Table A-5). Hairy vetch 2004 Root, shoot and total dry weight accu mulation for hairy vetch increased quadratically reaching a maximum value of 2.5 Mg ha-1 at WAE 17 (Table 2-9). Roots accounted for 50% of total biomass at WAE 2, while the root fracti on was reduced to 7% at WAE 17. Maximum observed DM accumulation rates were 54 kg ha-1 d-1, occurring at the end of the growing season. The ST*Np interaction term had a significan t effect on root weight and at WAE 14 fallow plots had higher (p<0.05) r oot dry weights (Table A-6). However, at the end of the season, root and total DM accumulation was similar for SH-amended and fallow treatments. Root and overall N concentrations exhi bited a cubic trend, while shoot values decreased quadratically with time (Table 29). Compared to other crops, hairy vetch retained relatively high and/or consta nt N tissue concentrations and overall N concentration ranged from 30 to 41 g N kg-1, while root tissue maintained fairly high N

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32 concentrations until the end of the growing season (26 g N kg-1 for hairy vetch vs 8 g N kg-1 for rye). The ST*Np interaction term had a significant effect on root N concentration and at WAE 14 fallow plots had lower root N concentrations (Table A-6). Root, shoot and total N concentration incr eased quadratically with time attaining values of up to 80 kg N ha-1 at WAE 17, of which 97% came from above-ground biomass (Table 2-9). Overall, N accumulation rate attained a maximum value of 1.5 kg N ha-1 d-1 at WAE 17. Similarly to root weights, the ST*Np interacti on terms was significant and at WAE root N accumulation was greater in fallow plots (Table A-6). Overall winter cover crop system performance 2004 Since winter rye and hairy vetch were gr own as an intercropped system, overall system performance characteristics are al so presented for the combined system components. Root dry weight followed a linear trend, where as biomass and total accumulation increased quadratically. Dry ma tter reached its maximum at 14 WAE with 7.7 Mg ha-1, while daily DM accumulation rates also reached maximum values of 133 kg ha-1 d-1 at WAE 14. Roots accounted for 27 and 7 % of total biomass at WAE 2 and 17, respectively (Table 2-10). The ST*RES inte raction effect for ro ots dry weight was significant, with difference be tween residue types being most articulated early in the season (WAE 5). The winter CC mix grow ing on SH residue accumulated 0.34 vs 0.14 Mg ha-1 for systems following a summer fallow, but this difference dissipated during subsequent samplings (Table A-7). Root and total N concentration followed a cubic trend, while shoot N concentration decreased quadratically ove r time (Table 2-10). Overall tissue N concentration decreased by 33% throughout the growing season to final N concentration of 18.8 g N kg-1. Root, shoot and overall N concentra tions showed a significant ST*RES

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33 interaction effect. At WAE 5, s hoot N concentrations were greater for SH-based systems, whereas at WAE 14 fallow treatments had much greater root and total tissue N concentrations (Table A-7). Root N accumulation followed a linear trend whereas shoot and total N accumulation increased quadratically. Maximum total accumulation was attained at WAE 17 and by that time the total biomass contained 139 kg N ha-1, with 95% being allocated to above-ground plant parts. Similar to root growth, interaction effects of ST*RES were significant for root N accumulation and at WAE 14, fallow based systems had higher overall root N accumulation rates (Table A-7). However, final root N accumulation values were not affected by residue treatments. Winter rye 2005 During 2005, winter rye was always preceded by sesbania. As a result, only sampling time (ST) and N application rate ap plied to the previous corn crop (Np) are included in the statistical analysis (Table 2-11). Root dry weight accumulation increased quadratically with time, while shoot and total DM accumulation followed a cubic and linear trend, respectively. Maximum DM was 2.8 Mg ha-1 at WAE 17, while growth rates attained maximum values of 44 kg ha-1 d-1 at WAE 11 Proportional changes in DM accumulation were similar for roots and shoots and at the end-of growing season, shoots and roots accounted for 84% of the total crop dry weight biomass. Root, shoot, and total N concentrations showed cubic decreases over time, with total N concentrations decreased from from 38.4 to 7.2 g N kg-1. Total shoot content was affected by Np and was greatest for the Np=133 treatment (Table 2-11). Total N accumulation followed a linear trend over time. Overall N accumulation rates were greatest at WA E 11 and subsequent N accumulation values was not

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34 significant. At the end of the growin g season, overall N content was 20 kg ha-1 and shoots contributed 83% of the overall N accumulation (Table 2-11). Hairy vetch 2005 Hairy vetch root, shoot, and total dry ma tter accumulation followed a cubic trend across sampling dates (Table 2-12). Total DM accumulation was 9.4 Mg ha-1 in the 15 weeks between 2 to 17 WAE. In contrast with rye, maximum DM accumulation rates were greatest (240 kg ha-1 d-1) towards the end of th e growing season. Due to unseasonably cool weather, growth even con tinued after final mowing. Roots represented 10% of the biomass by the end of the season. There was no significant Np effect on root growth but the ST*Np interaction was significa nt for total DM, and was the greatest for Np= 67 treatment on DM for both shoot and total biomass (Table A-9). Decreases in N concentrations over time were linear, cubic, and quadratic for root, shoot, and overall tissue N, respectively. However, in comparison with winter rye, end-of the season N concentrations remained rela tively high for all the tissues and overall N concentration between WAE 2 and 17 d ecreased by only 42% (Table 2-12). Nitrogen accumulation in roots and shoot s over time followed quadratic and cubic trends, respectively. Overall N accumulation was 235 kg N ha-1 by the end of the season. Overall winter cover crop system performance 2005 In order to assess overall winter CC syst em performance, both species were also analyzed together. Root, shoot, and total DM accumulation exhibited a cubic increases over time, reaching their highest points at WAE 17 with 12.3 Mg ha-1 of total DM and 10.5% of total biomass was allocated to root s (Table 2-13). Due to increased growth vigor of vetch toward the end of the gr owing season, overall DM accumulation rates attained maximum values of 262 kg ha-1 d-1 at WAE 17.

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35 Total N concentration for a ll shoots and total dry weight followed a cubic trend. Overall shoot N tissue concentratio n decreased from 45 to 23 g N kg-1 between WAE 2 and 17. Due to the higher fraction of vetch in the 2005 crop mix, N concentration in roots was similar to overall biomass N concentration. Crop N accumulation increased quadratically and maximum to tal N accumulation was 264 kg N ha-1, with 90% being accrued above-ground. Overall N accumulation ra tes attained a maximum value of 5.7 kg N ha-1 d-1 at WAE 17 (Table 2-13). Species Comparison Total biomass accumulation of winter rye was 5.4 and 3.0 Mg ha-1 during 2004 and 2005, respectively (Table 2-14). While corre sponding values for vetch were 2.5 and 9.6 Mg ha-1 (Table 2-14). During 2004, ry e roots represented 6% of the biomass, while in 2005 there was an increase to 16% Hairy vetch had an intermediate, yet more constant root DM allocation percentage. For rye, DM allocation to other tissues also differed between years and values decreased from 23% in 2004 to 10% in 2005. Stems and leaves, on the other hand, accounted for 56-58% and 68% of the final biomass. It should be noted that leaves accounted for most of the senescent tissue so the overall leaf fraction for rye would be on the order of 15 to 26%. Rye roots accumulated 4-18% of N, wh ile stems accounted for 37 to 52% and leaves (including senescent tissue) accounted for14 to 21% of crop N. During 2005, N allocation to roots and reproducti ve structures was increased, while stems and total leaf N allocation was being reduced (Table 2-15). Hairy vetch partitioned more DM toward building stems than to other plant parts (Table 2-14). However leaves accounted for 47-58% of the N allocation. Nitrogen contained in leaves could f unction as a readily available N source to the succeeding crop.

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36 During the second year, hairy vetch had not allocated any assi milates towards the construction of reproductive structur es at final sampling (WAE 17). The C:N ratios of different tissue material s for different cover crops are presented in Table B-1. The significance of C:N ratios is that they may provide better insight into the likelihood of mineralization rates for each tissue. Species with a C:N ratio of >25 may increase the potential risk of (initial) N immobilization. Gramineous crops had higher C:N ratios compared to leguminous crops, while for plant tissue types C:N ratios ranked as follows: stems> roots > leaves, indicating th at leaves and roots propensity to faster mineralization than stems (C:N = 61). Discussion Summer Cover Crop Systems Sunn hemp 2003 Total biomass and N accumulation of sunn hemp (SH) in 2003 (Table 2-2) were lower compared to 12.3 Mg ha-1 produced during the 2003 cropping season (Cherr, 2004). In Homestead Florida, SH also pe rformed better and accumulated 12.2 Mg ha-1 and provided up to 351 kg N ha-1 (Li et al., 2006). Reduced DM accumulation during 2003 was related to cultivation of sunn hemp for three consecutive years in the same plots resulting in an accumulation of Verticillium sp ., a soil-borne disease. This hampered biomass production since up to 70% of the pl ants presented disease symptoms by 14 WAE. Continuous use of sunn hemp as a summe r CC appeared to have resulted in fungus population surpassing the infestation threshold (Aba wi and Widmer, 2000). Other researchers have also shown that population densities of Pythium spp and Rhizoctonia solani were greater following legum es and those levels decrease d in mixtures of legumegrass or crucifers, compared to legumes (Sum ner et al., 1995). Soil -borne diseases may

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37 thus pose challenges for continuous cover cr opping, and may require appropriate changes in cover crop rotation. However, even duri ng the third year, biomass accumulation by SH was still acceptable compared to results repor ted for Southeastern U.S. In Alabama sandy loam soils for example, in a 9-12 week pe riod, respective DM and N content rates were 5.9 Mg ha-1 and 126 kg N ha-1 (Reeves et al., 1996). Corresponding values for North Carolina were 7.6 Mg ha-1 and 144 kg N ha-1 (Balkcom and Reeves, 2005). Although sunn hemp is a leguminous crop, it can also utilize residual soil N (Mendonca and Schiavinato, 2005). In the cu rrent study, residual N from a previous sweet corn planting affected shoot and tota l N concentration of sunn hemp at WAE 5, when dry matter accumulation was the highest fo r Np 133 treatment but not for any of the other sampling dates (Table A-1). The increa se in N concentration may have resulted from mineralization of sweet corn stover resu lting in increased N availability but this effect was not consistent through the growth cycle Nitrogen concentration in roots was lower th an in shoots since leaves contain large amounts of N rich compounds. As a result, leav es make up an appreciable fraction of the above-ground nitrogen. Since growth virtually peaked at WAE 8 while N concentration slightly decreased, total N accu mulation stabilized after this time (Table 2-2). This may have consequences for the management of SH as a summer cover crop. Over time, a greater proportion of SH dry matter is part itioned to stems (Cherr, 2004), and the high C:N ratio associated with stems (Table B-1) results in more recalcitrant crop residue that can be fairly effective in suppressing weeds. If N accumulation is the main objective, SH should be mowed in WAE 8, whereas a more prolonged growth cycle may contribute to increasing soil organic matter. However in other systems, including mulched production

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38 beds, large stems may interfere with cu ltural production pract ices, including bed formation and can also damage plastic mulch. Cowpea Cowpea (CP) was used as a double purpose cover crop, which c ould also provide extra income to farmers, during the summer-fa ll season in north central Florida. In this experiment, CP did not appear to benefit fr om residual N from sweet corn (Table 2-3). The apparently poor utilization of residual soil N may be attributed to the following issues: 1) residual N may already been le ached prior to the establishment of an adequately deep cowpea root system; 2) sl ow and/or incomplete N mineralization from sweet corn stover; 3) reduction in N fixati on in plots with higher residual soil N levels. Soil N may provide up to 80% of CPs a boveground needs during its first 42 days of growth (Awonaike et al., 1991), supporting the idea that effects of Np would be most obvious during initial growth. A study in Okla homa also showed that residual N did not alter cowpea rooting patterns at pod setti ng stage (Kanh and Schroeder, 1999). In chickpeas grown in silt clay soils in Syria, at physiological maturity 60% of accumulated nitrogen had been derived from N fixation, 35% from the soil and 5% from fertilizer (Kurdali, 1996). Other studies also have shown that the efficiency of N fixation decreases with an increase in residual soil N levels (G haley et al., 2005). Base d on this, residual N may not affect overall growth and/or N accumulation by cowpea. While shoot DM reached a maximum of 4.3 Mg ha-1 at WAE 8, roots continued growing for three more weeks (Table 2-3). This can be explained by the heavy rainfall events experienced in experimental area, due to hurricane Frances, in September 2004 (Table C-5). During this time leaves, stems and reproductive structures became damaged and combined with wet conditions, this may ha ve enhanced fungal growth and early crop

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39 senescence. Similar findings were reporte d by Creamer (1999) when cowpea biomass accumulation reached only 4.0 Mg ha-1, after enduring two hurricanes. Maximum DM accumulation (4.3 Mg ha-1 at WAE 8) by cowpea were similar to values reported by Schroeder et al., (1998), but 59% below t hose for SH (at WAE 14) reported by Cherr (2004). Overall DM accumulation was below the 6.9 Mg ha-1 reported by Harrison et al., 2004 for Iron Clay CP. This variety has a long er growing season, is less compact in its growth habit, and thus appears to be a more prolific biomass producer (Linares et al., 2005). The decrease in overall shoot N concentration from 43.3 to 18.9 g N kg-1 (Table 2-3) may be related to a dilu tion of nutrients in DM associ ated with rapid growth, an increase in stem fraction of cover crops ove r time (Cherr, 2004), a nd the N translocation from other tissues to pods (Douglas, 1993) However for greenhouse grown mungbean ( Vigna radiata L. Wilczek), blackgram ( Vigna mungo L. Hepper), cowpea ( Vigna unguiculata L. Walp.), and peanut ( Arachis hypogaea L.), N translocation was only significant for mungbean (Senaratne and Ra tnasinghe, 1993). Cowpea stems accounted for the highest DM fraction, but due their re latively low C:N ratio, the N from stems should be readily availa ble to succeeding crops. However, combined with lower overall DM production capacity of this crop, it may not be as effective as sunn hemp in sustaining soil organic matter and nitrogen. Average dry pod yields were similar or sl ightly below those a study in Thailand on sandy soils (Toomsan et al., 2000). Low produc tivity levels may have been related to unfavorable production conditions as mentioned previously.

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40 Maximum total N accumulation (94 kg N ha-1) occurred when N concentrations in both roots and biomass were high and total biomass accumulation was the second highest from all the sampling dates (Table 2-3). Report ed values in the literature ranged from 68 kg N ha-1 (John et al., 1992) to 261 kg N ha-1 (Piha and Munns, 1987). Although CP accumulated 20 kg N ha-1 less N, actual N accumulation rates were 25% greater for CP compared to SH. It appears that cowpea may be more suitable as a short-term (< 6 wk) summer cover crop if grown as an N source or green manure In addition seed cost of cowpea may be also lower ($210 ha-1 vs $408 ha-1 for sunnhemp), while cowpea may also provide a marketable edible seed. Pearl millet During 2003, pearl millet (PM) was the most prolific biomass producer and surpassed SH DM accumulation by 1.6 Mg ha-1 (Tables 2-2 and 2-4). Observed linear growth patterns are indicative of continuous and rather constant root and shoot growth throughout the entire season and similar result s were reported by Brck et al. (2003). Biomass dry matter reached 8.8 Mg ha-1. It was expected that PM would recover mineralized N and would benefit from Np, as was shown for other gramineous crops (Sainju et al., 1998, Paponov et al.,1999). However, Np di d not have a significant effect on any of the studied response variables. In other studies, when PM was planted as a grain crop, N fertilization did not affect stover we ight (Maman et al ., 1999), nor did it dramatically increase shoot N concentrati on (Kennedy et al., 2002). In South Carolina, PM yielded up to 6.7 Mg ha-1 of DM, even after two hurricanes (Creamer and Baldwin, 1999). PM thus appears to be a rather r obust crop. In loamy sand Indian soils, DM accumulation of rainfed PM without fertilization reached 0.8 Mg ha-1when growing after fallow and 1.1 Mg ha-1 when following PM residue (Aggarwal et al., 1997).

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41 The pronounced decrease in tissue N concentr ations after initial growth (Table 2-4) may be related to the low SOM and very low in herent soil fertility and nutrient retention capacity of Florida sandy soils Other researchers reported a more gradual decline (Payne et al., 1995) unless lack of read ily available soil N induced a more drastic drop in tissue N concentration (Kennedy et al., 2002). The decline in shoot N concentration may also be partly caused by N remobilization be fore flowering (Diouf et al., 2004). Despite high DM accumulation, PM only accrued 75 kg N ha-1 which was 39 kg N ha-1 less than SH, but it coul d be argued that symbiotic N fixation in SH may have accounted for this difference. Maximum N and biomass accumulation were better synchronized and both occurred at WAE 11 and PM thus may be better suited as a medium term (> 11 wk) summer cover crop. This has implications for winter crops, because use of a summer CC with a longer growth cycle will reduce potential N losses (Weinert et al., 2002). The C:N ratios for PM were relatively high (Table B-1), which may be related to it being a C4 gramine ous crop (Loomis and Connor, 1992). High C:N ratios can be beneficial in sandy soils, becau se nutrients and specially N are released more slowly, decreasing potential N leachi ng risk (Kuo et al., 2002) However, use of more recalcitrant residues can result in a rela tively large fraction of the labile N pool tied up in microorganism biomass, thus compro mising N availability for a succeeding crop (Creamer and Baldwin, 2000). Sesbania Sesbania (SB) was severely affect ed by root-knot nematode (Meloidogyne incognita) infestation (data not shown) which hampered its initial growth, nodulation, and overall N accumulation. Sesbania is very susceptible to the root-knot nematode ( Meloidogyne javanica ) which greatly affect s its growth (Desaeger and Rao, 2001). As a

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42 result, leaves showed N deficiency sympto ms and crop growth declined after WAE 5 (Table 2-5). Overall biomass and N accumulatio n by SB was thus only a fraction of that for other summer CC crops. Li et al., (no date ) reported similar results at Homestead, Florida, on a calcareous soil. Initial shoot N concentrations, when most of the N is obtained from seed and soil N storage pools, was 33.9 g N kg-1 (Table 2-5) and values were similar to those reported by Mafongoya and Dzowela (1999). However, in th e absence of successful nodulation, shoot N levels rapidly dropped to values that are indicative of N deficien cy (Zhiznevskaya et al., 1997). Since up to 70-90% of N accumulated by SB is produced via symbiotic N fixation, this underlines the criti cal role of root health to optimize the performance of leguminous cover crops (Sthl et al., 2002). In cidence of nematodes in the current study would have reduced assimilate availability for nodule development, thereby hampering N fixation. As a consequence, in th e absence of external soil N, leaf tissue N concentrations dropped, thus greatly reducing photosynthesis and overa ll shoot growth. Although some residual N might have been captured by the root systems, presence of nematodes may also have reduced overall root growth and e ffective root depth (A raya and Caswellchen, 1994). In the absence of effec tive nodulation, the crop appear ed to be greatly limited for N. As a result, crop N accumulation was greatly affected by Np. Similar to PM, stems had the highest C:N ratios (Table B-1), and stems represented half of the DM accumulation. Since overall biomass and N accumu lation of sesbania was rather poor and this CC is also very susceptibility to a commonly occurring root knot nematode, it may not be the most suitable summer CC for vegetable cropping systems in Florida.

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43 Overall summer cover crop growth dynamics Overall biomass accumulation could be ra nked as follows: PM > SH > CP >> SB. Overall N accumulation patterns were: SH > CP > PM >> SB (Table 2-6). Cowpea and pearl millet had a precocious growth; both accumulated 123 kg-1 d-1 ha-1 by 5 WAE. In contrast, DM production of SH and PM peaked at 161 and 204 kg-1 d-1 ha-1 later in the season (WAE 8). The major drop in DM and N concentration after 8 WAE provides a justification for mowing both crops at that time. For legumes and gramineous CC, stem DM allocation was the greatest. Allocation towards roots was similar for PM, SH, CP, except for SB, as discussed above. Under Florida conditions when N leaching can be ap preciable, it is may be desirable when dry matter is partitioned towards more recalcitrant above-ground tissues, presuming a slower C mineralization thereby poten tially increasing particulate organic matter (POM). However, presence of adequate N in crop re sidues may also be important since steady state soil OM levels may also be affected by overall system N inputs (Jenkinson et al., 1985; Raum et al., 1998). Alternatively, it coul d be argued that a crop residue with adequately high (>30) C:N ratio may functi on as sponge inmobilizing labile N from fertilizer materials, thus functioning as an on-site slow-release nutrient source (Janzen et al., 1992; Thompson et al., 2002). Overall N allo cation to leaves was the highest for SH and PM. Since leaves have low C:N ratio, this N pool is more prone to rapid mineralization compared to other plant struct ures. Therefore, N from leaves is more likely to be lost via leaching from the system if a winter crop is not planted directly after the extermination of the summer CC. Both SH and PM allocated similar (24 vs 30%) percentage of dry matter to leaves, but N concentration was lower, while C:N ratio was higher in PM leaves compared to SH. (Table 2-7).

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44 Cover crop phenology is an important issu e for North Florida conditions, since it would be desirable to plant a cover crop that w ill fix or scavenge residual N from July to October, until winter cover cr ops and/or commercial fall cr ops can be planted. In this study, the species that could meet this goal with appear to be sunn hemp and pearl millet. Although sesbania may potentially recover residual N, it is rather susc eptible to parasitic nematodes. Cowpea also provides the potential benefit of symbiotic N fixation during dry summers while also providing direct economic yield returns. Use of a late maturity variety with a bush type growth habit woul d be more suitable for CC-based systems compared to zipper cream. Both sunn hemp and sesbania present the potential for building up soil-borne diseas es, and therefore it is critical to im plement a sound crop rotation. Winter Cover Crop Systems Winter rye Winter rye is one of the most commonly used winter CC in temperate regions of the U.S. As expected, the residual N from fertilizer applied to a previous corn crop had no effect on any variable measured, but cropping system treatments did (Table 2-8). The quadratic increase in DM and N accumulation was relate d to a gradual decrease in crop growth and N uptake as the crop matured. Presence of SH residue almost doubled DM and N accumulation by winter rye and overall DM a nd N accumulation for this system was 6.4 Mg ha-1 and 65 kg N ha-1, which was higher than the 1.0 Mg ha-1 and 27 kg N ha-1 reported by Garwood et al. (1999). In another study, rye was repo rted to recover up to 30 kg N ha-1 from residual inorganic fertilizer (Cli ne and Silvernail, 2001). According to Cherr (2004), 64 % of the N from SH was lo st within two weeks after crop senescence, while the remaining fraction is relatively stable up to 28 weeks after death. The N release

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45 from this residue thus appear ed to benefit rye DM accumulati on. In contrast with this, rye N concentration in SH-based systems were lower which may be related to N dilution in the dry matter associated with enhanced ry e growth for SH-based systems (Table 2-8). Similar results were reported for other cove r crops (Derksen et al., 2002). Rye plants growing in SH-based systems also may have been more precocious and the decline in tissue N concentration associated with cr op maturation was reached faster a similar phenomenon has been described for DM accumulation (Paponov et al., 1999). During 2005, winter rye was planted exclus ively in sesbania-based systems and residue effects were not tested. In order to attain higher N accumulation rates, a more vigorous rye variety was used (Florida 401), a nd the rye to vetch ratio, was also reversed (30% rye and 70% vetch). As a result, overall rye DM content was lower than during the previous year, while for vetc h the reverse was true. Duri ng 2005, the root system was much vigorous compared to previous winter season, and despite a lower plant density, root biomass was greater whic h may be related to genoty pic difference and increased competition between species. Total biomass and N content in 2005 were 46 and 65% lower, respectively (Table 2-8 and 2-10). Th e lower biomass accumulation was related to a two-fold reduction in plant densities and also to the fact that N accumulation associated with the sesbania crop was only 5 to 12 kg N ha-1 (Table 2-5). As a result, rye did not benefit much from residual crop residues and DM and N content results appear to be similar to those reported for the fa llow treatment in 2004 (Appendix A-5). The disproportionably large reduction in N accumu lation provides further indication that despite luxurious growth of hairy vetch, soil N availability appeared to be the limiting factor for the growth of winter rye.

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46 Since N supply from sesbania was limited, the ST*Np interaction effect became more apparent and total tissue N concentr ation was highest for the Np 133 treatment (Table A-4). This carryover effect was unexp ected, principally because the heavy rains of September 2005 during Hurricane season may have displaced most of the residual soil N below the surface soil (Table C-5). However, since SB had very poor growth, weeds may have tied up residual N or N mineralizing from sweet corn, and released it after herbicide application. Alternatively, due to the more vigorous root growth in 2005, rye may have been able to make more efficient use of NO3 located at deeper soil layers as was proposed by Thorup-Kristensen ( 2001). But from a practical pe rspective, the increase in the N concentration associated with Np wa s relatively small and overall N content was not affected. Overall, most (56-59%) of the rye DM was partitioned to stems (Table 2-14). Stems are recalcitrant and also provide a good control against weeds when left as a surface residue. The relatively high root DM accumulation in 2005, was related to a Florida 401 having a much greater root allocation for both DM (16% vs 6%) and N (18% vs 4%) compared to rye accumulation during 2004. Hairy vetch In Florida, hairy vetch ha s a short growing season comp ared to other regions of the United States. Guldan et al., (1996) reported dry weig ht accumulations of 1.5 to 2.8 Mg ha-1 after 17 weeks of growth in sandy loam soils in New Mexico. During 2004, root growth of hairy vetch was initially enhanced in SH-based systems, but over time this trend was reversed (Table A-6). This may be related to increased competition with winter rye (which was favored by the SH-residue). By the end of the season, N accumulation in roots of hairy vetch growing after fallow was s lightly higher than in plants growing on

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47 SH residues; this effect might be the resu lt of biological N fixation inhibition by residual NO3N from SH (Ledgard and Steele,, 1992; Mengel, 1994). Usually exogenous N does not inhibit legume growth, but N coming from biological fixation decreases its efficiency (Sanginga, 1996). In 2005, this effect was ap parent for residual N from previously planted sweet corn (Table A-9). The tremendous increase in DM during 2005 (Table 2-12) was related to higher seed rates (70 vs 30 kg ha-1) combined with unseasonably co ol (Table C-1, C-2 and C-3) and relatively wet spring (Table C-4 and C-5), which extended the rapid growth phase of hairy vetch thereby greatly enhancing overall growth. This vigorous re-growth after final mowing was not expected, since studies have shown that hairy vetch does not vigorously re-grow after mowing, even when temperatur es range from 5 to 10 C (Branster and Netland, 1999). It has been shown that hairy ve tch performs best when soil temperature is about 10 C (Zachariassen and Power, 1991), and air temperature is about 20 C (Teasdale et al., 2004). Hairy vetch can resist frost better than many other template adapted legumes (Branster et al., 2002), which is why its growth is enhanced during spring time (Teasdale et al., 2004 ). It may also be that co ntinuous cultivation of hairy vetch may have resulted in a gradual build of soil rhizobial inoculum and better initial growth because vetch growth continued to improve each year (Cherr, 2004). Nitrogen fixation during the second year (T able 2-12) was also high compared to values reported in the literatu re for northern southeastern states (Abdul-Baki et al., 1996; Cline and Silvernail, 2001) but similar to va lues reported for Georgi a (Sainju and Singh, 2001). Hairy vetch nodulation and N fixation could also have been benefited from rains

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48 during its establishment, sinc e nodulation is susceptible to water stress (Hungria and Vargas, 2000). Overall winter cover crop growth dynamics The 59% and 70% increases in overall DM and N accumulation during 2005 (Table 2-13) show that increased seed rate s for vetch did greatly enhance the overall system performance. Compared to legumi nous summer CC, it appears that this mixed winter cover crop system is very successful in recovering resi dual soil N and fixing additional N. An explanation for the success of intercropping is that the non-legume component more effectively utilizes residual soil N, forcing the legume to fix additional N (Hardarson and Atkins, 2003). The system components also appear to complement each other very well. The erect structure of rye allowed the vetch to more rapidly expand its canopy volume and rye and thereby intercep t more light and main tained higher growth rates (Odhiambo and Bomke, 2001). Rye also had higher initial DM accumulation rates whereas vetch had the greatest DM accumulati on rates toward the end of the growing season. It thus appears that the different canopy, shoot, and root growth characteristics of these species allow a mixed system to be more efficient in water, nutrient, and radiation utilization (Karpenstein-Man chan and Stuelpnagel, 2000). During the second year of the rotation, st em and leaf N concentrations in rye appeared to decrease compared to the previ ous year. This may be related to hairy vetch competing for light, water, and nutrients Although, vetch accumulated substantial amounts of N (80 and 243 kg N ha-1), most of this N was tied up in shoot growth and due to favorable growth, less then 5% of this N pool was available for uptake by rye.

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49 Although root exudates and root sloughing may result in hi gh C losses and release of N (Grayston et al.,1997), rye did not appear to benefit from this potential N source. The steep increase in DM accumulation rate of hairy vetch toward the end of the growing season during 2005, is rather unique and may be related to unseasonly cool weather combined with a relatively wet spri ng (C1, C-2 and C-3). Regardless of this, the mixed rye/vetch system appears to be a su itable winter CC system for north central Florida since it clearly out performs mono-cropped leguminous CC systems (Cherr, 2004). In Kentucky, DM accumulation rates for a similar system were 3.8 Mg ha-1 with no residual N and almost 5.8 Mg ha-1 in presence of residual N. This study also showed that rye was more dependent on residual N than hairy vetch, as shown by Cline and Silvernail (2001). In Denmark, DM accumula tion for a 64:36 rye:hairy vetch mix was 4.7 Mg ha-1, while in Georgia a 68:32 ratio rye: hairy vetch mix, yielded 6.6 Mg ha-1 (Sainju et al., 2005). Higher potential production fo r our system may be related to warmer winters and higher radiation levels. Using gramineous and legumes mixes enha nces the balance between C pool build up, and N retention in the soil (Kuo and Sainju, 1998). In the case of the hairy vetch and rye mix overall C:N ratios were similar fo r both years and based on the low values it appears that overall mineralizat ion of hairy vetch residue woul d be very fast (Table B-2). Moreover, CC mixes may benefit from summer cover cropping, by either scavenging residual N or from cover crop st over that preserves soil moisture. In temperate zones, only 9-29% of the N adde d through cover cropping is recovered by the following crop, while in other regions use of ap propriate crop rotations is considered to be more sustainable than intercropping (D akora and Keya, 1997). Cover crops mixes thus

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50 could enable farmers and cover crop user s to accomplish the goal of fixing N and accumulating biomass; however this should be studied more extensively under Florida conditions. Conclusion Summer cover crops may provi de a number of services and benefits and may fit into different proiduction systems depe nding on their growth cycle and tissue composition characteristics. Sunn hemp and pearl millet are suitable cover crops for summer-fall cultivation in north central Florida, due to th eir prolonged growth cycle and prolific biomass accumulation. Sunn hemp, acc umulated high amounts of C and N, but should be followed by either a commercial fall vegetable crop or a suitable winter cover crop system, to ensure that the N is not lost via leaching after plan t senescence. The C:N ratio of pearl millet is relatively high due to the recalcitrant stem fraction, thus holding promise for enhancing soil organic matter build up and also could act as a slow-release source of nutrients. Due to its short gr owing cycle and high initial N and DM accumulation rates, commercial cowpea, such as zipper cream may be most suitable to take advantage of short summer fallow periods. Use of late maturing varieties, such as iron clay may be more de sirable in order to achiev e satisfactory N accumulation. Although sesbania has good potential for Nrecovery (Ruffo and Bollero, 2004), it appeared to be overly susceptible to plantparasitic nematodes, especially root-knot nematodes. Although sunn hemp was shown to be the most prolific biomass producer among summer cover crops (use different re ference), continuous cultivation may not be desirable due to the potential for build up of soil-borne diseases, such as Verticillium sp

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51 Use of winter cover crop mixes appeared to greatly enhanced the performance of these cropping systems. As documented in the lite rature, during the first year of our trials rye scavenged N from residual sunn hemp. Howe ver, N benefits appeared to be greatest when hairy vetch was the predominant species, and based on our resu lts it appears that most of the N will only become available after the senescence of hairy vetch. However, more detailed information is required pert aining to the quality and degradation of structural compounds, such as lignin, an d how these processe s are affected by environmental conditions and cultural practices in order to improve our understanding of subsequent N release patterns. It is also important to keep in mind that environmental conditions may vary on temporal and spatial scales, in fluencing the performance of cover crop-based systems. As a result, long-term field studies with larger production units that are replicated both in space and time may be required to fully unde rstand the more subtle system dynamics.

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52 Table 2-1. Outline of crop rotations and experimental treatments during the research period (2003-2005). Trt. = Treatment, S = sunn hemp, F = fallo w, H+R = hairy vetch/rye mix, SC = sweet corn, CP= cowpea, PM = pearl millet, SB= sesbania, B= broccoli, W = watermelon. Year 1 Year 2 Trt. Fall 2003 Winter 2003 Spring 2004 N rate (kg ha-1) Fall 2004 Winter 2004 N rate (kg ha-1) Spring 2005 N rate (kg ha-1) 1 S H+R SC 0 CP B 0 W 0 2 S H+R SC 67 CP B 131 W 84 3 S H+R SC 133 CP B 196 W 168 4 S F SC 0 PM B 0 W 0 5 S F SC 67 PM B 131 W 84 6 S F SC 133 PM B 196 W 168 7 F H+R SC 0 SB H+R 0 W 0 8 F H+R SC 67 SB H+R 0 W 84 9 F H+R SC 133 SB H+R 0 W 168 10 F F SC 0 F F 0 W 0 11 F F SC 67 F F 0 W 84 12 F F SC 133 F F 0 W 126 13 F F SC 200 F F 0 W 168 14 F F SC 267 F F 0 W 210 15 F F F None F F 0 F None

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53Table 2-2. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect, along with ST*Np interaction effect on dry weight, N concentration, and N accumulation of sunn hemp ( Crotalaria juncea) during summer/fall 2003. RootsShootTotalRootsShootTotalRootsShootTotal ST WAE2 0.04c0.24d0.27d21.1a40.3d37.3a0.9b9.3c10.1c WAE 50.31b1.54c1.84c9.9b27.8c24.9b3.0b42b45.1b WAE 80.75a4.48b5.23b9.6b23.5b21.5c7.5a106a113a WAE 110.78a6.18a6.95a8.5bc16.3a15.5d6.5a99a106a WAE 140.72a6.43a7.15a11.8c16.1a15.7d8.3a103a111a Significance L***Q***L***Q**C**L***Q***C***L***Q***L***Q***L***Q***L***Q*L***Q***L***Q***Np 00.483.393.8212.224.722.94.963.4 b 68.3 b 670.554.234.7712.224.522.85.681.7 a87.2 a 1330.533.754.2512.225.223.25.270.6 ab75.9 ab SignificanceNSNSNSNSNSNSNS** ST*Np NSNSNSNS**NSNSNS ---------------------Mg ha-1-----------------------------------g kg-1 -------------------------------------kg ha-1 -----------------Fixed Effects Dry WeightN concentrationN accumulation Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote higher to lower ranking.

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54Table 2-3. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect, along with ST*Np interaction effect on dry weight, N concentration, and N accumulation of cowpea ( Vigna unguiculata ) during summer/fall 2004. Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking. Root s Shoot s Tota l Root s ShootsTota l Root s Shoot s Tota l ST Week 20.04c0.20c0.23c33.2a43.3a42.1a1.2b10.1c11.3c Week 5 0.23bc2.71b2.93b24.4a31.6b30.8b5.2a90.3a95.7a Week 80.33ab4.34a4.67a14.5b20.8c20.3c4.7a88.9a93.6a Week 110.38a2.56b2.94b13.2c18.9c18.1d8.0a48.5b53.5b Significance L ***Q* L ***Q*** L ***Q*** L ***Q* L ***Q*** C L ***Q*** C L ***Q*** C L **Q*** L *Q*** N p 00.262.542.7921.028.827.8 4. 3 61. 7 65.8 6 7 0.222.322.5 5 21. 5 28.928.2 3.8 55. 5 59. 3 13 3 0.2 5 2.492.7421. 5 28.227. 5 4.1 61.265.6 Significanc e N S N S N S N S N S N S N S N S N S ST*N p N S N S N S N S N S N S N S N S N S Fixed Effects Dry Weight N concentration N accumulation ---------------------Mg ha-1---------------------------------g kg-1 -------------------------------------kg ha-1 ------------------

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55Table 2-4. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect, along with ST*Np interaction effect on dry weight, N c oncentration, and N accumulation of pearl millet ( Pennisetum glaucum ) during 2004. Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking.

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56Table 2-5. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect, along with ST*Np interaction effect on dry weight, N concentration, and N accumulation of sesbania ( Sesbania sesban ), during summer/fall 2004. Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking. RootsShootsTotalRootsShootsTotalRootsShootsTotal ST Week 20.02c0.07c0.08c25.5a33.8a32.2a0.4b2.3b2.7 b Week 50.17a0.93a1.09a15.1b12.2b12.7b2.6a11.4a13.9a Week 80.12ab0.61b0.72b12.7b9.2c9.8c1.7ab5.8b7.5a Week 110.10b0.59b0.69b7.8c8.0c8.0c0.9b5.3b6.2b Significance L*Q***C*L*Q**C*L*Q*C**L***Q**C*L***Q***C***L***Q*** C***Q***C*Q*C*Q**C*Np 00.070.350.4214.515.915.60.8b4.0b4.8b 670.090.460.5515.516.015.81.3ab5.2ab6.4ab 1330.150.830.9715.815.615.62.1a9.5a11.6a SignificanceNSNSNSNSNSNS *** ST*Np NSNSNS*NSNSNSNSNS ---------------------Mg ha-1---------------------------------g kg-1 -------------------------------------kg ha-1 -----------------N accumulation Dry Weight Fixed Effect N concentration

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57Table 2-6.Total dry weight accumulation and dry matter alloca tion to different plant parts for summer/fall cover crops. RootStemLeafReproductive -Mg ha-1 -Sunn hemp7.16 b10 bc61 bc24 a6 b Cowpea3.37 c11 ab70 ab5 b14 a Pearl Millet9.44 a7 c51 c31 a11 a Sesbania0.70 d14 a79 a1 b5 b SpecieTotal Biomass Biomass Allocation -------------------------%-------------------------Means followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p< 0.05), letters a, b, c denote a higher to lower ranking. Table 2-7. Total Nitrogen (N) accumulati on and N allocation to different plan t parts for summer/fall cover crops. RootStemLeafReproductive -kg N ha-1 -Sunn hemp111 a7 b38 b42 a14 c Cowpea53.5 b7 b56 a10 b27 a Pearl Millet74.9 b6 b30 b41 a24 ab Sesbania6.3 c16 a69 a2 b13 bc Nitrogen allocation -------------------------%-------------------------Total N Accumulation Specie Means followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p< 0.05), letters a, b, c denote a higher to lower ranking.

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58Table 2-8. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding swee t corn crop (Np) and residue [RES = residue of sunnhemp (SH ) or fallow vegetation (F) ] main effect, along with ST*Np, ST*RES, Np*RES interactions effects on dry weight, N concentrat ion, and N accumulation of rye ( Secale cereale ), during summer/fall 2004. RootsShootsTotalRootsShootsTotalRootsShootsTotal ST WAE 20.02 c0.07 c0.09 c17.1 a39.6 a33.8 a0.4 b3.5 c3.9 c WAE 50.20 b0.45 c0.65 c 8.8 b20.0 b17.0 b1.8 ab8.5 c10.2 c WAE 80.23 b1.04 c1.27 c8.8 b17.5 c15.8 b2.0 a18.5 bc20.4 bc WAE 110.28ab2.45 b 2.73 b7.4 bc18.0 bc16.7 b2.1 a31.1 b33.2 b WAE 140.32 a4.19 a4.52 a5.9 c13.4 d12.8 c1.9 a54.3 a56.2 a WAE 170.33 a4.94 a5.27 a7.7 b12.0 d11.7 c2.6 a55.9 a58.4 a Significance L*Q*L*L*L***Q***C***L***Q***C***L***Q***C***Q*C**L***L***Np 00.212.062.288.920.118.11.629.230.8 670.252.212.459.420.718.41.829.331.1 1330.232.302.549.619.417.21.927.329.3 Significance NSNSNSNSNSNSNSNSNS RES SH0.29 a2.90 a3.20 a9.619.4 b17.42.4 a35.7 a38.0 a F0.17 b1.49 b1.65 b9.020.8 a18.41.2 b21.6 b22.8 b Significance *********NS*NS***** ST*NpNSNSNS NSNSNSNSNSNS ST*RESNS******* *NSNS RES*NpNSNSNS NSNSNSNSNSNS ----------g kg-1 --------------------kg ha-1 ----------N accumulation ----------Mg ha-1 ----------Dry WeightN concentration Fixed Effect Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking. SH= summer sunn hemp cover crop residue, F= summer fallow residue.

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59Table 2-9. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding swee t corn crop (Np) and residue [RES = residue of sunn hemp (SH ) or fallow vegetation (F) ] main effect, along with ST*Np, ST*Res, Np*RES interactions effects on dry weight, N concentration, and N accumulation of hairy vetch ( Vicia villosa ), during summer/fall 2004. RootsShootsTotalRootsShootsTotalRootsShootsTotal ST WAE 20.01 b0.01 c0.02 c17.1 e--0.2 c-WAE 50.03 b0.05 c0.08 c20.6 d38.3 a30.4 b1.0 bc1.7 c2.7 c WAE 80.02 b0.09 c0.11 c31.9 a40.0 a38.2 a1.3 b8.2 c9.9 c WAE 110.03 b0.34 c0.36 c23.6 c 41.0 a40.8 a0.8 bc15.1 c19.6 c WAE 140.16 a1.19 b1.35 b26.8 b37.0 ab35.4 a4.2 a45.3 b49.1 b WAE 170.17 a2.34 a2.49 a25.6 bc32.8 b32.1 ab4.5 a77.4 a80.1 a Significance L *** Q *** L *** Q *** L *** Q *** L ** Q *** C ** L *** Q ** L ** Q *** C L ** Q L *** Q *** L ** Q Np 00.070.630.6924.7 a39.236.42.027.232.2 670.070.670.7523.1 b35.734.02.130.532.0 1330.070.700.7725.0 a38.635.82.030.833.6 S i gn ifi cance NSNSNS*nsNSNSNSNS RES SH0.05 b0.630.6824.036.3 b 35.11.5 b27.329.6 F0.09 a0.710.8024.539.3 a35.72.5 a31.734.9 Significance *NSNSNS*NS**NSNS ST*Np NSNSNSNSNSNSNSNSNS ST*RES ***NSNS*NSNS***NSNS RES*Np NSNSNSNSNSNSNSNSNS ----------Mg ha-1 --------------------g kg-1 --------------------kg ha-1 ----------N accumulation Fixed Effect Dry WeightN concentration Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking. SH= summer sunn hemp cover crop residue, F= summer fallow residue.

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60Table 2-10. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding sw eet corn crop (Np and residue [RES = residue of sunn hemp (SH ) or fallow vegetation (F) ] main effect, along with ST*Np, ST*RES, Np*RES interactions effects on dry weight, N concentration, and N accumula tion of hairy vetch and rye, during summer/fall 2004. RootsShootsTotalRootsShootsTotalRootsShootsTotal ST WAE 20.03 c0.08 d0.11 d17.1 a34.7 a 28.1 a0.6 c4.6 d4.9 d WAE 50.24 b0.49 d0.73 d10.6 c22.1 b18.3 b2.8 b10.2 d13.0 d WAE 80.25 b1.13 d1.38 d11.4 c20.0 b18.0 b3.3 b27.7 cd30.4 cd WAE 110.31b 2.80 c3.09 c 9.6 c22.0 b22.0 b3.1 b46.0 c53.7 c WAE 140.48 a5.39 b5.87 b12.9 bc19.4 b18.7 b6.2 a99.6 b106 b WAE 170.50 a7.20 a7.70 a 14.4 b19.2 b18.8 b7.1 a132 a139 a Significance L**L***Q***L***Q***Q***C*L***Q***C***L***Q***C***L*L***Q***L***Q**Np 00.282.672.9512.523.521.33.653.455.8 670.322.883.2012.423.221.03.955.559.5 1330.312.993.2913.122.120.14.052.158.2 Significance NSNSNSNSNSNSNSNSNS RES SH0.35 a3.51 a3.85 a11.9 b21.719.7 b4.058.862.0 F0.26 b2.19 b2.44 b13.5 a24.221.8 a3.747.952.7 Significance *********NSNSNS ST*Np NSNSNSNSNSNSNSNSNS ST*RES *NSNS********NSNS RES*Np NSNSNSNSNSNSNSNSNS Fixed Effect ----------Mg ha-1 --------------------g kg-1 --------------------kg ha-1 ----------N accumulation Dry WeightN concentration Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking. SH= summer sunn hemp cover crop residue, F= summer fallow residue.

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61Table 2-11. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sw eet corn crop (Np) main effect and ST*Np interaction effect on dry weight, N concentration, and N accumulation of rye ( Secale cereale ), during winter 2004/05. RootsShootsTotalRootsShootsTotalRootsShootsTotal ----------Mg ha-1 --------------------g kg-1 --------------------kg ha-1 ----------ST WAE 20.02 c0.10 c0.12c18.8 a43.0 a38.4 a0.4 c4.4 b6.0 b WAE 50.06c0.30 c0.36 c9.5 b25.3 b22.7 b0.5 c7.6 b8.1 b WAE 80.11 c0.62 c0.73 c7.8 b12.2 c11.5 c0.9 c7.5 b8.4 b WAE 110.22 b1.44 b1.67 b8.7 b9.8 d 9.6 d 1.9 b14.1 a16.2 a WAE 140.22 b2.14 a2.36 a9.3 b6.6 e6.8 e1.9 b14.3 a16.2 a WAE 170.45 a2.38 a2.83 a8.1 b7.0 e7.2 e3.5 a16.7 a20.2 a Effect L***Q*L***C*L***L**Q**C*L***Q***C***L***Q***C***L***Q*L***L***Np 00.161.041.2010.117.315.6 b1.39.711.4 670.181.151.349.317.015.9 ab1.410.211.7 1330.191.301.5011.717.716.6 a1.912.414.5 SignificanceNSNSNSNSNS*NSNSNS ST*NpNSNSNSNSNSNSNSNSNS Fixed Effect Dry WeightN concentr ationN accumulation Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, res pectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking.

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62Table 2-12. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sw eet corn crop (Np) main effect and ST*N-p interaction effect on dry weight, N c oncentration, and N accumulation of hairy vetch ( Vicia villosa ), winter during 2004/05. RootsShootsTotalRootsShootsTotalRootsShootsTotal ----------Mg ha-1 --------------------g kg-1 --------------------kg ha-1 ----------ST WAE 20.01 c0.04 e0.04 e46.6 a50.6 a49.5 a0.35 c2.3 d2.8 c WAE 50.04 bc0.24 e0.28 e46.1 a45.9 a45.9 a2.03 c11.1 d13.1 c WAE 80.12 bc1.42 d1.53 d38.4 b25.4 b27.7 b4.77 c37.0 d44.3 c WAE 110.27 b2.68 c3.11 c31.9 c32.3 b33.2 b7.98 bc82.2 c103 b WAE 140.36 b4.03 b4.40 b33.6 bc31.8 b31.9 b12.1 b127 b139 b WAE 170.93 a8.52 a9.44 a24.6 d27.2 b26.5 b20.9 a221 a243 a SignificanceL***Q***C**L***Q***C**L***Q***C***L***L***Q**C*L***Q*L***Q**L***Q***L***Q***Np 00.322.63 b3.03 ab35.2 b34.635.38.378.389.7 670.283.31 a3.58 a37.9 a34.335.08.987.597.6 1330.262.54 b2.80 b37.5 a37.637.37.878.885.5 SignificanceNS***NSNSNSNSNS ST*NpNS*****NSNSNSNSNSNS Dry Weight Fixed Effect N concentrationN accumulation Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking

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63Table 2-13. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sw eet corn crop (Np) main effect and ST*Np interaction effect on dry weight N concentration, and N accumulation of hairy vetch + rye, during winter 2004/05. RootsShootsTotalRootsShootsTotalRootsShootsTotal ST WAE 20.03 d0.14 e0.17 e25.7 a44.8 a41.4 a0.8 c6.5 d6.8 d WAE 50.10 c0.54 e0.65 e25.0 a34.3 b33.3 b2.6 c18.6 d21.4 cd WAE 80.22 c2.04 d2.27 d24.1 a21.0 c22.1 c5.6 c44.5 d53.3 c WAE 110.47 bc4.11 c4.68 c22.7 a25.0 c24.3 c10.4 bc104 c115 b WAE 140.59 b6.18 b6.77 b24.1 a23.0 c23.1 c14.0 b142 b156 b WAE 171.38 a10.9 a12.3 a19.0 a22.8 c22.2 c25.4 a238 a264 a SignificanceL***Q***C*L***Q***L***Q***C*L***L***Q***C*L***Q***C*L***Q*L***Q***L***Q***Np 00.473.66 b4.1623.828.928.59.786.7100 670.464.46 a4.9123.027.226.910.297.5109 1330.463.84 b4.3223.529.427.810.091.398.0 SignificanceNS*NSNSNSNSNSNSNS ST*NpNS****NSNSNSNSNSNS ----------Mg ha-1 --------------------g kg-1 --------------------kg ha-1 ----------Fixed Effect Dry WeightN concentrationN accumulation Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ). Means followed by identical lower case letters in the same column are not significantly dif ferent according to Tukeys test (p<0.05), letters a, b, c denote a higher to lower ranking.

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64Table 2-14.Total dry weight accumulati on and dry matter allocation different plant parts for winter cover crops. RootStemLeafReproductive Sensc. Tissue ---Mg ha-1--Rye, 20045.35 b6 c58 a3 c9 b24 a Rye, 20052.96 c16 a56 a5 c13 a10 b Hairy vetch, 20042.50 c8 bc57 a30 b2 c3 c Hairy vetch, 20059.58 a7 b38 b 55 a0 c0 c Total Biomass Biomass Allocation -----------------------------------%---------------------------------Specie Means followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p< 0.05), letters a, b, c denote a higher to lower ranking. Table 2-15. Total Nitrogen (N) and N allo cation to different plant parts for wint er cover crops, studied during 2004 and 2005. RootStemLeafReproductive Sensc. Tissue -kg N ha-1 -Rye, 200458.8 bc4 c53 a6 c24 b15 a Rye, 200520.45 c18 a37 bc8 c31 a6 b Hairy vetch, 200480.2 b7 bc41 b47 b3 c2 c Hairy vetch, 2005246 a10 b31 c59 a0 c0 c Specie -----------------------------------%---------------------------------Nitrogen Allocation Total N Accumulation Means followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p< 0.05), letters a, b, c denote a higher to lower ranking

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65 CHAPTER 3 GROWTH, N ACCUMULATION, AND YIELD OF VEGETABLE CROPS AS AFFECTED BY CROP RESI DUES AND N-FERTILIZER RATE Introduction A continuous expanding global population forces agriculture to meet the worlds calorie intake at the expense of natural resource depletion (Ehrlich et al., 1993; Matson et al., 1997). Agricultural soil fertil ity was traditionally replenished by the use of crop residues and legume rotation and via use of integrat ed farming systems (Bohlool et al., 1992; Howarth et al., 2002; Tonnito et al., 2006). Cu rrently, higher crop yields per unit area in developed countries are typical ly achieved through use of hi gh yielding varieties, which usually demand large doses of nitrogen (N) fertiliz er (Novotny, 1999). Conventional production systems, depend greatly on extern al inputs and agro chemicals and thus compromise the long-term sustainability of agriculture. Attaining su stainability requires revisiting traditional agricultural practices in a so called second green revolution (Giampietro, 1997; Welch and Graham, 1999; Alti eri, 2004). This proces s entails reviewing agro-ecosystems production capabilities (Robertson and Swinton, 2005), enhancing agricultural biodiversity (Altier i 2000), and using sound crop ro tations (Caporali and Onnis, 1992; Gregory et al., 2005). Many current vegetable production systems are characterized by an intense use of pesticides as well as chemical fertilizers (Rice et al., 2001). However, market demand for organic produce and more environmentally sound production practices is expected to increase farmers interest in using cover cr ops (Cline and Silvernail 2001). In Florida, spring vegetable crops such as sweet corn ( Zea mays ) and watermelon ( Citrullus lanatus )

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66 are very important for the local agricultural economy. For example, Florida provided about 32% of the U.S. watermelons and 81% of spring grown sweet corn in 2000 (Sargent, 2000; Stevens et al., 2003). Florida sweet corn production maximum recommended N fertilizer dose is 224 kg N ha-1 (Olson and Simonne, 2005). Corres ponding values for broccoli ( Brassica oleracea ) and watermelon are 196 and 168 kg N ha-1 (Olsen et al, 2005). Some authors suggest that meeting crop N demands requires a combination of both external N fertilizer and symbiotic N-fixation (Cambell et al., 1995; Bockman, 1997). Although legumes use symbiotic N fixation, it is necessary to place symbiotic N fixation in perspective, since legumes can be both sources and/or sinks of N depending on re sidual soil N status (Is se et al., 1999). Even though N from legumes is more stable in the so il (Crews and Peoples 2005), this N source still contributes, along with fer tilizers and manure, to increases in N incorporated into the biosphere (Goulding et al., 1998; Mosier et al., 2001). On a silty clay soil in Colorado, maximum sweet corn yield was obtained at a total (residual + fertilizer) of 258-265 kg N ha-1 (Halvorson et al. 2005). In a study carried out on a silt loam soil in Maine, alfalfa ( Medicago sativa ), winter rye ( Secale cereale ) and hairy vetch ( Vicia villosa Roth) replaced 50 to 156 kg N ha-1 of synthetic fertili zer, thus providing almost all N required by a subsequent sweet co rn crop (Griffin et al., 2000). In the coastal plain area in Maryland, economic N-fertilizer rates for sweet corn following vetch were 30 to 76 kg N ha-1, compared to 65 to 193 kg N ha-1 for rye and vetch mixture, 161 to 247 kg N ha-1 for rye and 201 kg N ha-1 in a fallow system (Clark, 1997). Andraski and Bundy (2005) found that on a Wisconsin loamy sand soil, corn yields were significantly higher following non-leguminous cover crops compared to winter fallow.

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67 In Virginia, watermelon had higher fruit yiel ds when it followed hairy vetch (49.8 Mg ha-1) compared to hairy vetch/rye (45.5 Mg ha-1) or crimson clover ( Trifolium incarnatum ) and rye (43.9 Mg ha-1). In order to avoid excessive N fluxes th rough the soil, quality of cover crop (CC) biomass and timing of extermination should be linked with the nu tritional needs of subsequent commercial crops as affected by environmental conditions A Danish research group reported that residue composition is perhaps even more influential than temperature (Magid et al., 2001), while N mineralization is also less affected by temperature changes than C mineralization (Magid et al., 2004). Because crop N demand of Brassicae is high (Kage et al., 2003), they are considered to be very effective in scavenging residual nitrogen (Dabney et al., 2001). Since they are well-adapted to low te mperatures, they could be successfully used following a summer legume rotation in subtropical and temperate environments. Brassicae-derived residues in turn mineralize faster compared to gramineous residues due to their higher N concentration and lower C:N ratio (Garwood et al., 1999). For this reason, it is preferable to follow a Br assicae directly with another crop to ensure optimal N retention. Central Florida sandy soils are very prone to nitrogen leaching (Alva, 1992; Perrin et al., 1998). Although there is no sp ecific information in the lit erature about N leaching from sweet corn production systems in Florida, it has been documented that N uptake from fertilizer is typically on the order of 50% (Bundy and Andras ki, 2005), and that N leaching potential is high (Isse, 1999). In Wisconsin, 71 % of the applied N eventually reached the groundwater (Kraft and Stites, 2003). In the Fl orida production environment, a more stable source of N could be provided by using a mix of gramineous and leguminous cover crops. Use of cover crops can also play an impor tant role in intercepting nitrates (NO3 -) from

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68 residual N fertilizer or crop re sidues (Kristensen and Thor oup-Kristensen, 2004). McDonald et al., (2005) reported that the presence of weeds and/or wint er rye significantly decreased nitrate leaching on the sandy loam compared w ith a bare fallow. Fo r catch crops to be effective they should be prolific biomass pr oducers and should have rapidly growing deep root systems (Thoroup-Kristensen, 2001). Winter cauliflower and broccoli ( Brassica oleracea var. botrytis), are examples of doublepurposed crops that can be used as bot h catch crop and cash crop. It was reported that broccoli yields were higher when broccoli was planted into cowpea ( Vigna unguicuolata ) residue compared to bare soil system s (Harrison et al., 2004). In a study carried out in Virginia, yields of non-tillage broccoli planted in mulches of foxtail millet ( Setaria sp .) and/or soybean ( Glycine max ) residue were equal or higher compared to clean cultivation controls (Abdul-Bak i et al., 1997). In an organic farm study in New England, notillage vs conventional tillage did not affect broccoli and cabbage ( Brassica oleracea var. capitata) performance on a sandy loam soil (Schonbeck et al., 1993). Additional studies are needed for identifying productive, yet environmentally sound, cropping systems suitable for North Central Florid a. There is also a need to enhance our understanding of how soil N and C cycles are affected by co ver crops, and in what manner plant nutrition and weed control can be enhanced via improved in tegration of cover crops in vegetable minimum-tillage systems. Cropping system components that were of special interest included graminaeous summer cover crops, leguminous and graminaeous winter cover crop mixes and brassicae crops suitab le for double cropping following summer cover crops. As part of a larger study to promote th e improved use of cove r crops in vegetable cropping systems in Florida, this chapter aims to enhance our understanding of the

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69 interaction between cover crop performance and inorganic Nfertilizer requirements of commercial vegetables in Florida. The specific objective for th is research component was to determine if the use of cover crops will result in maximum sweet corn, broccoli, and watermelon growth and yields, while reduci ng supplemental N-fertilizer requirements. Our hypotheses were that: 1) A fall/winte r vegetable crop following a summer cover crop will utilize most of the mineralized N efficiently, because dur ing the fall growing season in minimum-tillage systems cover crop residues decompose slower; 2) Cropping systems and N fertilizer rate will affect the growth, N accumulation, yield, and quality of sweet corn, broccoli and watermelon; 3) Use of cover crops will reduce farm dependence on external inorganic N-fertilizer inputs; and 4) Appropriate use of cover crops can enhance the sustainability of existing agroecosystems. Materials and Methods Set-Up and Design The research was conducted at the University of Florida, Plant Science Research and Education Unit near Citra, Florida. The dominant soil types at this site were a Candler fine sand (Typic Quarzipsamments, hyperthermic uncoated) and a Lake fine sand (Typic Quarzipsamments, hyperthermic, uncoated). Both soil types contained more than 95% sand in the upper 1-2 m of the soil profile (Carlisle et al., 1998). The experiment consisted of 14 treatments and a complete control, arranged in a factorial randomized complete block design. E ach treatment was replicated four times and each replicate was considered a block. Treatme nts were the combination of two factors: cropping system and N fertilizer rate. There were four levels of cropping systems, which denoted the presence or absence (fallow) of summer and winter cover crops residues. There were several levels of fertilizer rates. Du ring spring 2004 all treatments were planted with

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70 sweet corn (Zea mays var. Saturn Yellow), because this crop has a high demand for inorganic N (224 kg N ha-1) and served as biological indicator of overall residue N availability. Cropping system s for sweet corn were: 1. A summer cover crop of sunn hemp ( Crotalaria juncea ) in 2003 + a winter cover crop mix of hairy vetch ( Vicia villosa ) and rye ( Secale cereale ) during 2003/04. This system is referred to as SW, or double CC system with S for summer cover crop and W for winter cover crop mix. 2. Summer cover crop (sunn hemp) during 2003 + winter fallow 2003/04. With SF refering to Summer cover crop and F in 2nd position to winter fallow. 3. Fallow during summer 2003+ winter cover crop mix (hairy and rye mix). This systems is denoted as FW, with F for summer fallow and W for winter cover crop mix. 4. Fallow + Fallow, denoted as FF, F for summer fallow and the second F for winter fallow. The following year, after the completion of 2003/04 summer sunn hemp, hairy vetch + rye mix, and spring sweet corn cycle, four different cropping systems were established. Instead of one summer cover crop, three different summer cover crops were planted. During the winter 2004/05, the hairy and rye mi xed was planted ag ain, but broccoli ( Brassica oleracea var Pac Man) was also tested, replacing some of the winter fallow treatments. Broccoli was used because it s potential as high value cash crop and moderate biomass accumulation. During spring 2005 watermelon ( Citrullus lanatus var. Mardigrass) was included as nematode and weed sensitiv e high value crop instead of sweet corn. Watermelon was preceeded by: 1. Summer pearl millet ( Pennisetum glaucum var. Tifleaf ) in 2004 + winter broccoli 2004/05.This system was called PM+B, PM for pearl millet and B for broccoli. 2. Summer cowpea ( Vigna unguiculata var. Zipper Cream ) in 2004 + winter broccoli during winter 2004/05. This system was cal led CP+B, CP for cowpea and B for broccoli.

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71 3. Summer sesbania ( Sesbania sesban ) in 2004 + a winter cover crop mix of hairy vetch and rye during 2004/05. The system was called SW, S for sesbania and W for the winter CC crops). 4. Fallow during the summer 2004 + Fallow during the winter 2004/05. The systems was denoted as FF or double fallow. Same as previous the system during the previous year. Sweet corn planted in CC residues received 0, 67 or 133 kg N ha-1(N0, N67, and N133) whereas sweet corn growing in double fallow received 0, 67 133, 200 or 267 kg N ha-1 (N0, N67, N133, N200, and N267). Broccoli was considered a co mmercial crop and was therefore amended with 0, 131, or 196 kg N ha-1 fertilizer (N0, N131, and N196). The CC-based watermelon systems received either 0, 84, or 168 kg inorganic N ha-1 (N0, N84, and N168) while double fallow plots received either 0, 84, 126, 168, or 210 kg N ha-1 (N0, N84, N126, N168, and N210). Timeline of Operations 2004 Sweet corn was planted on 14 April 2004, following summer sunn hemp and winter hairy vetch and rye mi x (22 and 56 kg seed ha-1, respectively). Planting was done by a ripstrip planter, with in-row spacing of 0.18 m and between-row sp acing of 0.76m (73,100 plants ha-1) and seeds were planted 30 mm deep. Sweet corn emerged on 21 April, 2004. For each N fertilizer rate (N-rate) 20, 40, and 40% of the total doses were applied to sweet corn at 1, 3, and 7 wk after emergence (WAE ), respectively. Fertilizer was applied as NH4NO3 for all cropping systems. Plant biomass was determined on WAE 2, 4, 6, and 9 while final harvest occurred at 10 WAE. After final harvest, sweet corn was mowed and Glyphosate 41% (Roundup Ultra, Monsanto Co mpany, D.C., at a rate of 1.2 L ha-1) was applied to all plots on July 6th of 2004.

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72 2004-05 A total of 24 out of 56 cropped plots were planted with broccoli on November 1st 2004, following either summer pearl millet or co wpea (Refer to Chapter 2). Plant spacing was 0.3 m x 1.0 m (3,333 plants ha-1). The remaining plots were planted with a mix of hairy vetch and rye (56 and 22 kg seed ha-1, respectively) which were planted on October 28th of 2004 and exterminated on March 22nd of 2005. Gaps were replanted 1 and 2 wk after initial planting. For each N fertilizer level, 25.0, 37.5 and 37.5% of total N doses were applied to broccoli at 1, 6 and 9 wk after the initial transplanting (WAT). Biomass samplings was determined at 3, 6, 9, 13, 16 and 19 WAT and pl ots were harvested at 6, 8 and 11 WAT Broccoli plots were sprayed on March 23rd of 2005 with Glyphosate Isopropylamine Salt 41% (Roundup Original, Monsanto Co mpany, D.C.) at a rate of 5.0 L ha-1. Hairy vetch-rye plots were strip-tilled on March 22nd of 2005 but no herbicides were applied before planting watermelon seedlings. Holes for the watermelon transplants were placed into the tilled strips. Watermelon was planted on April 4th of 2005, at a plant spacing of 1.52 m x 1.22 m (5,405 plants ha-1). Gaps were replanted within a week after initial transplanting. Nitrogen fertilizer was split into three doses (25, 37.5, and 37.5%) applied at 1, 4, and 9 WAT. Biomass samplings were collected at 3, 6, 9, and 12 WAT and watermelon fruits were harvests at 11 and 13 WAT. Watermelon plan ts were mowed and sprayed with Dicamba dimethylamine (Banvel, Micro Flow, Memphis, TN) at a rate of 1.22 L ha-1, Ammonium Sulfate 50%, and Glyphosate 53.6% (Durango, Do w AgroScience, Indianapolis, IL) at a rate of 3.7 L ha-1 on July 12th of 2005, with Dicamba dime thylamine and Glyphosate 53.6% (at a rate of 2.4 L ha-1) on July 21st of 2005, and with Para quat dichloride 43.8%

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73 (Gramoxone Max, Syngenta Crop Protectionat, Greensboro, NC, a rate of 3.7 L ha-1 on July 29th of 2005 prior to planting subs equent summer cover crops. Sampling Procedures 2004 Sweet corn plant counts were determined 1 wk after emergence. Biomass samples were obtained outside the inner area (4.6 x 4.6 m) used for yield sampling but away from plot edges (same as for the other two crops), using a representative 0.91 m of row length (0.69 m2). In order to minimize disturbance, the root systems of one representative plant was carefully excavated to assess root weights while all other plants were clipped at ground level. Clipped plants were weighed and kept refrigerated until furt her processing in the Agronomy Physiology Laboratory in Gainesville, FL. Final bi omass samplings were taken the day before harvesting ears (WAE=10). Ears were harvested at maturity from the inner plot area (21.2 m2) and ears were graded using USDA st andards (United States Department of Agriculture, 1997) while representative subsamples were kept fo r further growth and tissue analysis. 2004-05 A row length of 0.61 m (0.61 m2) of broccoli was samp led using the procedure outlined above. On 19 January 2005, diagnostic leaf samples were collected and analyzed for leaf N concentration. Broccoli plots were harvested on January 14th and 16th (WAT=10 and 12) and February 11th (WAT=14) and yield was determin ed for the inner plot (6.1 m x 3.0 m = 18.3 m2). Broccoli crowns were graded in the field according to USDA standards (United States Standards for Grades of B unched Italian Sprouting Broccoli, 1997, USDA) and a representative harvest sub-sample wa s used for growth and tissue analysis.

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74 One representative plant from the outer watermelon rows was selected at each sampling and samples were processed using procedures outlined above. Due to the viny nature of the crop delimitation of a net fruit-harvest-area was not feasible and the entire experimental unit (plot), approximate 69.7 m2 was harvested instead. On 2 and 25 June 2005, chlorophyll and petiole sap n itrates readings of diagnostic leaves were determined using a Minolta SPAD-502 and Horiba Cardy NO3-meter (Spectrum Technologies; Plainfield, IL). Mature fruits were picked on 23 June (WAT =11) and 5 July 2004 (WAT=13) and fruits were graded using standard procedures (United States Standards for Grades of Watermelons, 1997, USDA). Representative fr uit samples were used for dry matter determination and N analysis. Sample Processing Weed and/or organic debris were removed at the lab befo re recording tissue fresh weight. Samples were separated in shoots, r oots and reproductive parts (inflorescence or crowns in the case of broccoli, fruits in th e case of watermelon, and ears for sweet corn were included in the shoot tissue. If s hoot samples weight exceeded approx. 1000 g, a representative sub-samples was used for DW determination. Roots were rinsed with tap water and blotted before recordi ng fresh weights. In the case of watermelon, fruits were cut into small pieces and processed to a slu rry using a blender (brand, model, location manufacturer). Approximately, 100 g of th e sub-sample liquid was decanted into a graduated beaker; fresh weight was r ecorded and then set to dry at 50 C for more than 96 hours. Shoot and root tissues of all other tissues were dried for a minimum of 72 hr at 65 oC before recording dry weights. Dried tissue material was ground in a Wiley mill to pass

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75 through a 1-mm screen, and a thoroughly mixed 10 g portion of each grinding was subsequently stored. Ground samples were dige sted using a modifica tion of a procedure developed by Gallaher et al. (1975) and dilu ted samples were then analyzed for total Kjeldahl N (TKN) at the UF Analytical Re search Laboratory (Uni versity of Florida, Gainesville, FL) using EPA Method 351.2 (Jones and Case 1991). Nitrogen Applied to Crops Nitrogen applied (NA) to corn and wa termelon was calculated as follows: NAx = Chemical-Nx + Residue-Nx; where Chemical-Nx = N applied as NH4NO3 to corn in plot x and Residue-Nx ~ 0.2 total N content summer CC (based on N decomposition curves by Cherr, 2004) + N content winter CC at last sa mpling + winter weeds, prior to planting. Nitrogen-uptake efficiency (NUE) was calculated as: NUEx = (Total N Contentx Total N Content FF0) / NACx; where Total N Contentx = TKN present in total spring crop biomass in plot x and Total N Content FF0= average TKN present in total crop biomass of FF0 treatment. Unaccounted applied N (UAN) was calculated as: UANx = NACx Total N Contentx. Statistical Analysis Growth data were recorded in datasheets, organized, and standardized to a per hectare basis using EXCEL (Microsoft Corporation, Lo s Angeles, CA). SAS software (Statistical Analysis Systems, Cary, NC ) was used for statistical analysis. Since plant growth was correlated over time (covariance), the Proc Mixed procedure in SAS was utilized. Response variables tested included dry matter (DM) accumulation (Mg ha-1), tissue N concentration (g N kg-1), crop N accumulation (kg N ha-1), NUE, NAP, UAN, and yield (kg ha-1) The main fixed effects used in the mo del were sampling time (ST), N-rate and cropping system (CS). Additional interactions effects included in the model were ST*N-

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76 rate, ST*CS, and N-rate*CS. Linear, quadratic and cubic trends were tested for sampling time and N-rate, whenever this was appropriate. Random variation was attributed to plots (r eplicates*block) and replicates (blocks). Mean separation was performed by the Tukeys Tstatistic (p < 0.05). Yield models did not include the time component, but included all the other parameters, and in this case the Proc GLM function in SAS was used for the analysis of variance. For the st atistical analysis of broccoli data, the term CS was substituted by RES the residue material of the preceding summer cover crop. To test the selected hypothe ses, pair-wise comparisons were performed for different yield categories, dry matte r accumulation, N accumulation, SPAD readings, NUE, and UAN for pertinent treatments. Yield response of sweet corn and watermel on systems that did not include cover crops for different fertilizer rates was assessed to test for significant trends were fitted with appropriate regression equations using a regr ession function (Proc Reg) in SAS and both significance level and model fit ( r2) are briefly discussed in the results section. Linear plateau yield response functions were develope d for chlorophyll readings for sweet corn and watermelon and for leaf N concentration in broccoli using Proc Nlin of SAS. Results Sweet Corn (Spring 2004) During the spring 2004 season, average N derived from residues and weeds was greatest for SW (181 kg N ha-1) and FW (141 kg N ha-1), intermediate for SF (55 kg N ha-1) and lowest for FF (18 kg N ha-1; Table D-1). The sum of N derived from residues and weeds across systems were 100, 170, and 228 kg N ha-1 for the N0, N67, and N133 treatments, respectively.

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77 Sweet corn growth Sweet corn shoot dry matter (DM) content, N concentra tion, and total N accumulation increased cubically over time, while N concentr ation was lowest at the end of the season (Table 3-2). Maximum DM and N accumulati on occurred at 6 WAE corresponding to daily DM and N accumulation rates of 266 and 3.2 kg ha-1 d-1, respectively. Overall DM and SPAD values increased quadratically with N rate, while N concentration and N content showed a linear response. Overall growth and N accumulation was highest for the SW system, while the FF and SF treatments ha d the lowest N concentrations and SPAD readings. The ST*N-rate interaction term was sign ificant for all res ponse variables with differences between N rates typically beco ming more evident over time (Table D-2). Towards the end of the growing season, DM c ontent, N concentration, and N accumulation were 54, 30, and 65% higher for the first N-fertilizer increment (N0 N67). Corresponding values for the second Nfertilizer increment (N67 N133) were 22, 26, and 43%. Based on the ST*CS interaction term, it appear s that effects of CS systems generally became more pronounced over time (Table D-3). The SW system had significantly greater DM content and numerically higher N concen trations thus resulting in augmented N accumulation (28% higher than FF) by the end-of-season (Table D-3). The N-rate*CS interaction effect was significant for all parameters except for N concentration (Table 3-2). Analysis of the end-of-season N-rate*C S interaction term showed that for double cropping system either N67 or N133 performed best, while the FF system was typically inferior to CC-based systems at lower (N0 and N67) N rates (Table 33). Pair wise comparisons allowed more detailed evaluation of DM and N content differences across cropping system s and N rates that were of sp ecial relevance (Table 3-4).

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78 This analysis showed that DM and N content was the same for FF200 and FF267. By the end of the season, DW content for SW133 and SF133 were also similar DW compared to FF200 and FF267. Overall N concentration in shoots was the same for SW133, WF133, FF200 and FF267. Shoot DM content, N concentration and N content was lower for CC-based systems amended with 67 kg N ha-1 compared to treatments receiving higher N rates. Overall daily N uptake varied in different systems was affected by N-rate. Maximum daily N uptake was reached by SW133 with 8 kg N d-1 ha-1. For all treatments and all N-rates N daily uptake dropped off after 6 WAE. Daily N uptake for SW133 was higher than FF200. Sweet corn yield A non-linear model fitted to SPAD values fo r diagnostic leaf tissue testing showed that the critical chlo rophyll content for maximum yield was 56.8 4.5 (Table D-6). Overall yield and dry weight increased linearly with N rate (Table 3-5). Overall yi elds and DM content tended to be highest for the double CC (SW) system and the summer fallow-winter CC (FW) system, intermediate for the summer CC-winter fallow (SF) system, and lowest for the summer + winter fallow (FF) system. However, the interacti on between the CS and N-rate affected all yield categ ories (Table 3-6). Although in a ll cases there was a significant response to each N-rate increment, differences among cropping systems (CS) became less pronounced as N rate incr eased (Table D-4). Use of pair-wise contrast also allowed comparisons between CC-based systems with FF treatments receiving highest (200-267 kg N ha-1) N rates (Table 3-6). Despite the fact that SW133 treatment produced the highest total a nd marketable yield among the cover crop treatments, its yield was only 8% highe r. Similarly, the productivity of SW133 was comparable to FF200, but still 18 % lower than FF267 yields while the FF267 treatment had 10% higher yields than FF200.

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79 When comparing the FS133 system against treatment FF133, benefits from CC residues ranged between 8-17% and 3-10 %, for total and marketable yields, respectively. The double cropping system SW67 produced 41 and 34% lower yield than FF200 and FF267. Low marketable yields were also obtained with treatment FW67 (46% and 52% less than FF200 and FF267, accordingly). Even lower yields were obtained with treatment SF67 (60 and 64% lower yield than FF200 and FF267 respectively). Based on calculated N use efficiency (NUE) va lues at harvest, it appears that among the selected treatments, the most effective N use was achieved by treatment FF133 (NUE=0.77). For the FF systems, NUE decreased with N rate, while for the CC-based systems, the reverse appears to be true. Among the CC-based systems, SF133 appeared to be the most efficient (NUE=0.48). Overall NUE for other CC-based systems was comparable to treatment FF267. In general, cover cropping systems including a hairy vetch and rye mix tended had NUE as low as those for the FF267 treatment and relativel y high corresponding un-utilized applied nitrogen (UAN) valu es. The N response model developed for conventional (FF) system showed that a major ity of the variability in yield difference was related to fertilizer a pplications (Table 3-7). Cubic fit of the models was good for total and marketable yield prediction ( r2= 0.97). Broccoli (Fall 2004) Broccoli received 80 kg N ha-1 vs 63 kg N ha-1 from cowpea (CP) and pearl millet (PM) residues (Table D-7). When adding up both N fertilizer and N derived from cover crop residues, broccoli received 189 and 172 kg N ha-1 from CP and PM, respectively. The total amount of N coming from cover crops (ave raged across systems) and N-fertilizer was 72, 133, and 265 kg N ha-1 for the N0, N131, and N196 treatments, respectively.

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80 Broccoli growth Root and crown DM content followed a c ubic trend, while shoot growth increased quadratically over time (Table 3-8). Both root s and shoots reached maximum DM content at 16 WAT (2.9 Mg ha-1). The 21% decline in crown N c oncentration between WAE 9 and 19 was relatively low compared to the 54-57% decrease in roots and shoots concentration. Maximum shoot N accumulation occurred at WAT 9. Crown and shoot DM content increased qua dratically with N-rates, while roots showed a linear response. Nitrogen content incr eased cubically for roots, while shoot and crown N concentrations showed a linear res ponse to N rate. Shoot N accumulation leveled off prior to the first harvest (WAT 9), but DM and N content of root s increased up to WAT 16. Shoot and crown N concentration increased linearly with N rate, while for root tissue this increase was quadratically. Shoot N accumulation was highest for the N196 treatment, while N content of roots and crowns were similar for N131 and N196 treatments. In general CP-based systems had 23, 23, a nd 27% greater root, shoot, and crown DM content compared to PM-based systems. Althoug h N concentrations where similar, plants in CP-based systems also had 18-21% higher N content. Overall, growth showed differential responses to N-rate over time as indicated by significant ST*N-rate interacti on effects (Table 3-8). By the end of the season, shoot DM content was lowest for N0 and corresponding reductions in comparison with N131 were 58, 66, and 60% less for roots, shoots and crowns, respectively (Table D7) starting at WAT 9. In general, DM content was similar for both N131 and N196 treatments (Table D-8). Towards the end of the growing season, tissue N concen trations in shoots showed an incremental increase up to 196 kg N ha-1, while root and crown N concentration were not affected by N-

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81 rate. Overall differences between N-rate treatments were greatest at WAT 6 and 9, coinciding with previous N-fertilizer applications. The significant ST*RES interaction effect on shoot DM content (Table 3-8) was related to CP-based systems having 43% greate r shoots DM content at the end of the season (Table D-9). When comparing shoot DM accumulation for specific systems across N-rates via pair wise comparisons, it was observed that non-fe rtilized treatments for CP-based systems accumulated 45% more shoots than PM-based systems (Table 3-9). At the same time, CP196 and PM196 had similar shoot DM content, while CP131 produced as much biomass as CP196. By the end of the season, the only treatment that had a different performance was CP196, with a 33% higher DM content than PM. Tende ncies in N content at final sampling were not representative of overall dynamics across the growing season. Most of the N benefits from residual cover crops occurred during in itial growth (WAT 3) in lower N-rates (N0 and N131). Throughout the season, N concentrations for CP at N0 and N131 were lower than CP196 (Table 3-9). Weekly N uptake rates based on crop N accumu lation showed two distinct peaks in N uptake rates (Figure 3-2). The first peakoccurr ing at WAT= 6 and a subsequent one toward the end of the growing season (WAT=16) were associated with th e bolting of the crop. Overall maximum N uptake rates were on the order 3 kg N ha-1 d-1, which is low compared to sweet corn. Broccoli yield Broccoli yield was separated into fresh mark et and process market and further divided into marketable and non-marketable (culls). Based on diagnostic leaf N concentration, it

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82 was concluded that maximum yield occurred at leaf tissue concentr ations of 45.5 7.6 g N kg-1 (Table D-6). Fresh and process marketable and total yi eld exhibited a quadratic response to Nfertilizer rate (Table 3-10). Yields of non-fertilized broccoli were very low (1222 kg ha-1). Processing yield and total yield did not in crease for N rates in excess of 131 kg N ha-1. However, fresh marketable yield, was 16% higher for N196 than N131. Residue did not affect fresh yield, but processing yield wa s lower for PM-based systems. Pair-wise comparison across production system s and N rates showed that total yield for CP196 was produced 5519 kg ha-1 more than PM196. However, most of that amount was accounted by process non marketable or culls (c rowns with a diameter greater than 6 cm). The N benefits from mineralized cowpea were more evident for the N0 treatment (Table 311). Watermelon (Spring 2005) During the spring of 2005, N content of cowp ea and broccoli (CP+B) and pearl millet (PM+B) residues and weeds were on the order of 41-45 N ha-1 which was much lower compared to the 293 kg N ha-1 accumulated in the SB+W (double CC) system (Table D-11). Watermelon growth Accumulation of DM and N increased li nearly over time (Table 3-12). Although N concentration decrease d linearly with time, total N content was greatest at WAT 12. Nitrogen N-rate had a quadratic effect on DM and N content of shoots, while corresponding trends for fruits were linear, similar to broccoli. Shoot DM and N content leveled off at N84, while fruit DM and N content res ponded to extra fertilizer. Nitrogen fertilizer rate did not affect the concentrati on of N in fruits; however it did influence shoot

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83 concentration. In addition, N concentration in shoots differed among systems, with SB+W having the highest N tissue concentration. The ST*N-rate interaction effect was signifi cant for total DM and N content (Table 312). Overall values were similar during the first 6 wks, whereas beginning at WAT 9, differences between N0 and N84 were more pronounced (Table D-12). The ST*RES interaction effect was significan t for shoot N concentration and also for shoot and total N content (Table 3-12). Di fferences among cropping systems were only significant towards the final sampling, with shoo t N concentration and total N content being highest for the CP+B system and lowest for the FF system (Table D-13). Fruit DM content by the final harvesting for the PM+B system was 39, 46 and 22% greater compared to the CP+B, PM+W, and FF cropping systems, respectively (Table D-13). The CS*N rate interaction was significant for fruit and total DM and N content (Table 3-12). Overall, differences among croppi ng systems were clear at the N0 level with the CP+B and SB+W outperforming the FF treatme nt, while no differences between systems occured for fertilized treatments (Table D14 and D-15). At the end of the season, the CS*N-rate interaction reaffirmed the trend that benefits from CC systems were evident only for un-amended watermelon plants (Table 3-13) Both DM and N content were lowest for N-fertilized treatments and similar for the N84 and N168 treatment. Watermelon yields The critical SPAD and petiole nitrate concentr ations of diagnostic leaves required for attaining maximum marketable yield were 33.7 2.3 SPAD units and 214 58 mg NO3-N L-1 (Table D-6). Marketable and total watermelon yield increased linearly with N rate, while cull weights increased quadratically (Table 3-14). Marketable yield for the N168 were 31% and

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84 36% greater compared to the N84 treatment, respectively. The PM+B system produced 48% more marketable yield than compared to the SB+S system. N-rate Culls never represented more than 30% of total yield for any given N-rate. The N-rate*CS interaction effect was signifi cant for total and marketable yield (Table 3-14). Differences among cropping syst ems were most pronounced for N0 and N168 treatments (Table D-15). The unfertilized SB +W cropping system produced highest total and marketable yields compared to other unfertilized treatments. At intermediate N rates differences among cropping systems were not significant, whereas for the N168 treatments, CP+B had greatest total a nd marketable yields. Specific contrasts across the most pertinen t cropping systems showed that treatment PM+B168 produced similar marketable yields as the complete fallow FF210 (Table 3-15) Treatments SB+W84 and SB+W168 had the lowest productivity. Benefits from pearl millet residues were most articulated for N168 treatments, resulting in 47% higher marketable yield compared to the CP+B system. Benefits fr om the residual summer cover crop were not evident for any of the N84 treatments. Although, despite the lack of obvious yield differences among this group, the CP+B84 did have the highest observed NU whereas, the SB+W systems had the greatest amount of unutilized applied N Regression equation relate to tal yield (y) to N-rate ha-1 (N) was: y= 406.1 +102.9 N+ 0.102N2-0.00076N3 With r2 values of 0.69-0.74 it appears that a rela tively large fraction of the overall variability in yield could not be accounted for by N rate (Table 3-16).

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85 Discussion Sweet Corn Growth The double cropping system signi ficantly enhanced sweet co rn growth and stover dry weight and N accumulation throughout the season. As shown by Kuo and Jellum (2002) total available soil N can explain up to 70% of th e variability of corn N uptake. It has been reported that crops following a leguminous CC uptake almost 20% of the total biomass N from the mineralized legume residue (Kra mer et al., 2002). The quadratic SPAD response indicated that early in the season N was being partitioned mostly toward plant structures rather than leaf chlorophy ll (Argenta et al., 2004). This is supported by the dry matter accumulation trend. The slightly higher shoot N accumulation for FF200 compared to FF267, may be due to greater N allocation to ears with the FF267 treatment. Stover N content attained a maximum value of 129 kg N ha-1 by the end of the season for both SW and FF for N133 treatments. This demonstrates that part of the cover crop N is still tied up in residues, and could help enhance productivity of the system in the medium term. Sweet Corn Yields Overall SPAD readings across N-rates showed a clear crop response to supplemental N applications. SPAD values required for at taining maximum yields (56.8 4.5) were relatively high compared to values reported by Gr iffin et al. (2000), but were in accordance with those reported by Ar genta et al. (2004). Summer cover crops did not benefit sweet co rn yield directly, but they did enhance the growth of the gramineous component (winter rye) of the winter CC mix (Refer to Chapter 2). Due to low inherent soil fertility of Florida sandy soils, sweet corn growth and yields greatly benefited from supplemental N fertilizer, and for non-CC systems, yields continued to increase up to N rates of 200-267 kg N ha-1.

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86 Although the N content in residues and weeds accrued by double CC system nearly equaled the Nfertilizer amount recommen ded for Floridas swee t corn production, productivity of CC-based systems for the N0 treatments was only a fraction of potential yields. The winter mix had a C:N ratio of 21 (Chapt er 2). Perhaps the mineralization of the residue was poorly synchronized with the peak of corn N demand, thus potentially reducing the efficiency of N utilization from crop residues as was evident from low NUE for CCbased systems. Despite that CC extermination and sweet corn planting occurred within two weeks, sweet corn germination did not occu r until one week afterward, while peak N demand only occurred at 6 WAE, thus reducing the potential benefits from the winter mix residue. Early crop establishmen t of sweet corn was hampered by presence of crop residues on the soil surface which interfer ed with planting and resulted in uneven germination/initial crop establishment, as has also been reported by Dyck a nd Liebman (1994). Although the more stable CC-Derived resi dual N could have been tied up in rye residue, which has a higher C:N ratio, stud ies have shown that N from rye shoots incorporated to the soil can be found in the exte rior layers of soil aggr egates as s oon as 17 days after application (Kavdi r and Smucker, 2005). Weather c ould also have triggered the loss of the early mine ralized N, since precipitation r ecords showed th at during May 2004 rainfall was twice the amount of the previ ous year (Refer to previous Chapter 2). Un-fertilized double CCbased system (S W0) produced greater yields than FF0 which is in agreement with results fr om other studies (Carrera et al., 2005). Tonnito et al., (2006), calculated that the average grai n yield (based on 228 studies) for systems in which legumes replaced N fertilizer was 6.4 Mg ha-1. This amount falls beyond the results obtained in this

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87 study from the use of SW or FW combined with N0, which may be related to the low inherent fertility of Florida sandy soils. In the current study, at a rate of 133 kg N ha-1, CC-based systems had yields comparable to those obtained for non-CC systems amended with 200 kg N ha-1. Similar results were reported by Karpenstein-Mach an and Stuelpnagel (2000). However, Nfertilizer has potential drawbacks for residual N uptake; it has been re ported to reduce hairy vetch -N recovery by sweet corn, despite its fa st mineralization (Hadas et al., 2002). This compromises the objective of replacing N fer tilizer by using legume residues and may also increase potential N leaching. In our case, yield time trends provide evidence of some medium term yield benefits from CC-based systems which is in agreement with reports in the literature (Carter et al., 2003). Marketable yield during 2004 was numerically superior compared to data published by Cherr (2004) for previous years, clearly s howing that overall productivity of sweet corn increased over time. From the second to the third year, marketable yields for CC-based systems fertilized with 133 kg N ha-1 increased by 6 to19 %, and productivity of cover cropbased systems fertilized with only 67 kg N ha-1 increased by 19 to 45%. Although this could be attributed to differences in sweet corn vari eties from one year to the other, it could be argued that long-term use of cover crops may enhance so il ecology and quality, thus providing a synergistic cumulative benefit ove r time. Therefore, CC systems may provide long-term benefits beyond N uptake from re sidues; Bundy and Andraski (2005) found that in sandy soils, sweet corn yields following a ro tation of either potato or rye were augmented by the effect of rotation, rather than by the residual crop N. Based on yields (16548 kg ha-1) and NUE results for treatment FF133, supplementation of CC-based systems with 133 kg N ha-1, was require for attaining acceptable sweet corn

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88 yields, as previously reported by Cherr (2004). Yields for FF133 were on the order of statewide average yields (17373 kg ha-1, in year 2005; National Agriculture Statistics Service, 2006). Although overall yield differences across N rates between the SW and SF CC systems were on the order of 11-25%, thes e differences diminished at hi gher N rates and yields were similar across cropping systems for N133 treatments. Pair-wise comparisons showed that treatments SW133, FW133 and FF133 were statistically similar to FF200 and FF267. This shows that cover crops provide some benefits at low/intermediate fertilizer levels, and that low input CC production systems can still provide short-term benefits. It could be argued that the effect from the double cropping system is more pronounced if total yields are compared (almos t 10% higher yields). When comparing total yields of the SW133 and FF133, it was observed that cover crop-based treatments had 17% higher yields compared to conventional systems, but this production benefit reduced to only 10% for marketable yield. However, other auth ors have shown that sweet corn preceding a mix of hairy vetch and rye produced 30% more yields than bare fallow (Carrera et al., 2005). Discrepancies between these findings may be related to the effect of CC on crop establishment. Since germination was often less uniform in CC-based systems, especially if excessive accumulation of surface residue interfer ed with planting, plots at times had to be replanted. This often delayed growth and combined with irregul ar stands, impacted uniformity and delayed ear maturation and also reduced potential yields. Harvest index decreased with N rate and values for FF 133, FF200, and FF267, were 0.44, 0.39, and 0.31 compared to 0.39 for the SW133 (data not shown). These results are in the range reported in th e literature for amended corn (Wilts et al., 2004), and it appears that the performance of SW133 was similar to that of the FF200 system and that there is an inverse

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89 relationship between available N and yield efficiency. The extra 181 kg N ha-1 coming from the legume-grass mix and weeds for the SW133 treatment produced only a 11% yield increment compared to the FF133 system, which translates to a yield efficiency of 9 kg ear weight per kg N being a pplied. In contrast, FF200 produced 46 kg extra ears per kg of N while the (SW67) produced 54 kg extra ears per kg of N compared to the FF67 system. However, it should be noted that CC-based systems accumulated more biomass than the fallow system (430 kg ha-1 more than FF267), and that this would further enhance soil N build up (Seo et al., 2006). Broccoli Growth Although root, crown and shoot DM conten t was greater in CP-based systems compared to PM-systems, overall marketable fresh yield for these systems were similar across N rates, which may be related to differen tial responses to N rate. This could indicate that early mineralized N was accumulated in stor age tissue that did not contributed to yield formation. Therefore, the lack of a clear yiel d response was not due to low N content by the end of the season (at least in fertilized treatm ents) because N concentrations in leaves were sufficiently high based on values reported in th e literature (A lcntar et al., 2002). Moreover, any disadvantage of pearl millet relative to co wpea may have been counteracted by the pearl millet residue acting as moisture preserving mu lch during the winter time (Aggarwal, et al. 1997), therefore enhancing water content in in florescences and enabling broccoli to reach same yields as cowpea-based systems at higher N rates. Based on the assumption that soil N was more scarce in the pearl millet treatments, and that broccoli relocated N from leaves to inflorescences under these conditions (Bowen et al., 1999), it makes se nse that broccoli DM at the end of the season was lower following pearl millet, but that fresh market yields were still comparable.

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90 A similar effect was observed for N fertilizer rates, which also enhanced shoot DM accumulation (Everaarts and de Willigen, 1999; Vgen et al., 2004), but did not increase yields proportionally. Commercially acceptable yields were achieved with N-rates below the recommended doses, due probably to the extra N provided by crop residues. The overall shoot DM and N c ontent increased linearly unt il crown formation, then increased quadratically after first harvest be fore leveling off at WAE 16. Bowen et al. (1999) found that for summer-planted brocco li, DM and N accumulation rates were higher before flowering occurred and lower afterwards. From the statistical analysis it does not appear that N-rate affected quality (crown size), contrary to results show ed by Thompson et al. (2002) in southern Arizona. However, from the pair-wise comparison across selected treatments, it seems that CP-based systems produced larger quantities of smaller or excessi vely large crowns (culls) compared to PMbased systems. This may be attributed to a delay in harvesting, s uggesting that cowpea residue enhanced crop development with crow ns maturing earlier compared to PM-based systems. Toivonen et al. (1994) also showed that moderate N rates (125 and 250 kg ha-1) produced optimal crown size distributions. Relatively low N requirements at early growth stages are in agreem ent with reports by Feller and Fink (2005), and this may also expl ain why readily available residue N did not have an appreciable impact on broccoli yield. The increase in tissue N concentration and N rate may be related to increased soil N levels enhancing plant uptake and in general, there is a close correlation between N application and pe tiole nitrate concentrations (Hartz et al., 1994). Cover crops, especially cowpea, may release N more gradually over time compared to inorganic N-fertilizer, but this did not appear to significantly affect tissue N concentration.

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91 In the current study, N accumulation by broccoli wa s relatively low (Zebarth et al., 1995; Thompson et al., 2002) while diag nostic tissue N concentration va lues were consistent with values reported in the literature (Olson and Simonne, 2005). However, based on the lack of yield respons e to N fertilizer, it is possible that N mineralized from CC residues provided ad equate amounts to meet crop N demand. Combined with the residual N, the N133 treatment would have provided over 200 kg N ha-1. Since the N recommendation for broccoli is 196 kg N ha-1 (Olson et al., 2005), it is possible that crop N demand was met, which explains the lack of yield response to N rates in excess of 131 kg N ha-1 for CC-based systems. Higher N rate s thus may only have increased N leaching from the system or added to soil N pool. Broccoli Yields Broccoli had to be replante d twice, which could have hampered early benefit to broccoli from CC-mineralized N. Available residual soil N following pearl millet was low, while residual N for cowpea-based systems was hi gher and this N also appeared to be more readily available. Broccoli was expected to benefit initially from the cowpea (C:N = 11.6) while pearl millet (C:N = 57.4) would provide a more steady flux of nutrients toward the end of the growing season and perhaps non-N re lated benefits (Chapter 2). As a result, overall yield performance across N-rates was sim ilar for both systems. This might be due to the relatively low cowpea DM accumulation (2.6 Mg ha-1) compared to 8.8 Mg ha-1 accumulated by pearl millet. The lack of a fallow control further increased the difficulty in assessing benefits from cover crops. Other brassica systems have shown negative effects from CC residues when compared to conventi onal clean soil cultivat ion (Mwaja et al., 1996).

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92 Pair-wise yield comparison showed that tota l yield was higher for CP over PM at any N-rate. However, trends were not consistent across different marketable categories. Other studies in sandy loam soils have shown that yiel d of broccoli planted into millet mulch were lower compared to soybean mulch or clean cultivation systems (Abdul-Baki et al., 1997). As shown by other studies, the suppleme ntal N provided by cowpea at low N-rates was not enough to support maximum marketable yields. Results obtained by Schroeder et al. (1998) indicated that cowpea had a negative effect on plant establishment, however plant density counts do not support this for our st udy (data not shown). Harrison et al. (2004) found that broccoli planted in cowpea residues and amended with 168 kg N ha-1 produced 7.6 Mg ha-1 of yield, compared to 4.6 Mg ha-1 for non-CC systems. Overall, yields achieved in this study were in accordance with state wide yields of 12,200 kg ha-1 of marketable broccoli (ACES, 2006). Watermelon Growth Watermelon biomass, responded to N fertili zer quadratically, meaning that growth in cropping systems leveled off at N-rates lower than the recommended fertilizer rate (168 kg N ha-1). This effect was not the result of additional N from cover crops, since overall response was similar across cropping systems. Th e reduced rate of grow th at the beginning of the season for medium and hi gh rate fertilizer treatments may be related to unfavorable growth conditions for watermelon and/or incr eased weed growth at higher N rates. The significant interaction effect of cr opping system on N tissue concentrations of watermelon was related to plants in the CP+B system having higher shoot N concentrations, probably related to N minerali zed from cowpea residue. Moreove r this interaction effect could have been triggered by watermelon plants growing into hairy vetch re sidue, which

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93 exhibited relatively high shoot N concentratio ns at the end of the season due to onset of mineralization of the life mulch. The relatively low overall tissue N conten t was related to the poor growth of watermelon, the low N accumulation by brassicas, and because N was still tied up into the living tissues of the winter cover crop mix. Moreover weed s grew aggressively, outcompeting watermelon plants for N uptake. Weeds accounted for more than 50% of the N accumulated by the system during that season (T able D-16). In the absence of living mulch, total weed DM content was on the order of 7.3 Mg ha-1. Although it was expected that PM would provide a better contro l against weed germination, weed growth was greatly enhanced by N fertilizer app lication and by the lack of a de nse canopy of the watermelon, in contrast with a crop like sweet corn that is more effective in shading out weeds toward the end of the growing season. Often-mentioned benefits from intercroppi ng are both weed and disease suppression (Weston, 1996; Altieri, 2000). For this study, severa l critical factors counteracted each other in the SB+W system. Although mowing instead of herbicide killing of the residue reduced N losses prior to crop emergence and reduced w eed growth, continuous growth of the hairy vetch after mowing reduced light, water, and nut rient availability to watermelon, delaying initial growth. Watermelon plant development in the three other systems was not vigorous either, probably as a result of transplant mortality and unfavorable growth conditions associated with a gradual build -up of weed species associated with continuous use of zero tillage. The unseasonably cold weather resulting in poor initial growth of water melons (Rivero et al., 2001; Korkmaz and Dufault, 2001). As a result, weeds gained a competitive edge thus further reducing the growth of water melon.

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94 Given the high fertilizer rates applied to the crop and the discussed reduced growth, NUE was poor for all treatments and UAN exce ssive. The most efficient treatment was CP+B84, with a NUE of 0.37. However yields for this treatment were sub-optimal and it appears that NUE may be inve rsely related with yields. Watermelon Yield The linear increase in watermelon yields up to the 1.25 times standard N-fertilizer recommendation may have been related to the strong competition of weeds which accumulated up to 95 kg N ha-1. The N taken by weeds might have been resulted in reduced N availability for the crop and thus increase d fertilizer requirements for maximum yields. Maximum yields (28 Mg ha-1) were still below statewide yields of 35 Mg ha-1 (National Agricultural Statistics Service, 2006). Differential cropping system responses to N-rate were particularly evident for the SB+W system, and the lack of response to higher N-rate doses was probably related to 2-wk de lay in crop development due to continuous growth of the vetch and N content of the winter CC residue s that may have functi oned as a slow-release fertilizer. In contrast, the PM+B system outperformed all other systems at the highest N-rate, which could have been due to the pearl mille t acting as an sponge retaining N fertilizer and releasing it slowly, as it wa s hypothesized in the previous chapter. In other studies, broccoli has provided benefits equivalent to 60% of the corn yield produced with full dose N fertilizer (Castellanos et al., 2001). As a consequence of poor transplant establ ishment, and from fierce competition for nutrients and light from hairy vetch watermel on plants in SB+W system did not reach full development until late during the season, and yiel d was significatively lower than the other

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95 CC (since all plots were harvested at the same time). Similar results were found by Lotz et al. (1997) when cabbage was interc ropped into mowed clover strips. Unseasonably cool weather during year 2005 extended the life cycle for hairy vetch, allowing it to re-sprout in the mowed stri p and reduce watermelon light interception. Watermelon plants, on the other hand, did not co mpete very effectively with vetch due to their prostrate growth habit, and possibly the cool weathe r conditions further hampered their effectiveness to gain a competitive edge. Fruits production has also been hindered in strawberries ( Fragaria ananassa ) when intercropped with white clover mulch (Neuweiler et al., 2003) in temperate climates. Vanek et al., (2005) found that intercropping pumpkin on already established stan ds of lana vetch ( Vicia glabrescens ) and winter rye reduced pumpkin yield, while negative effects were not observed when the cover crops were planted after pumpkin ( Cucurbita pepo ) establishment. Control of the hairy vetch after establishment of the watermelon was only po ssible trough mechanical mowing of the hairy vetch, due to sensitivity of wate rmelon to herbicide application. However, use of live mulches is encourag ed as a way of reducing plastic mulch disposal problems (Roe et al., 1994). Some st udies have shown that straw mulches reduced soil temperatures, therefore hampering biomass accumulation (Johnson et al., 2004), and that plastic mulches enhanced watermelon gr owth and early harvest by increasing soil temperatures (Romic et al., 2003). Temperatures below 20 C could also have induced watermelon seedlings into part ial chlorophyll deficiency (Pr ovvidenti, 1994), which could have hampered the ability of th e plant to translocate assimilate s and nutrients towards fruits. Despite the low watermelon yields for the SB+W system, in this system vetch served as a living mulch, and in comparison with other systems it reduced weed growth by 44-52%. However, as discussed above, impr oved weed control in these treatments

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96 associated with the thick swath of vetch/ry e residue also coincided with reduced growth of watermelon during the first part of the growing season. Pair-wise comparisons between systems and N rates, showed that despite the fact that PM+B168 (at the recommended N dose) did not suppress weed growth, it did outperform the FF systems and also resulted in the highest marketab le yields (27,569 kg N ha-1), even higher than FF210. Nonetheless, yields were still 26% below results obtained by NeSmith (1993) in Georgia, with plant densities of 2066 plants ha-1 in conventional systems. Conclusion Timing of cover crop extermination is cruc ial in cover cropping systems management. In this experiment, both broccoli and sweet co rn were affected by the timing between cover crop extermination and crop planting. Additiona lly, for non-tillage systems, this timing might negatively affected plant establishmen t resulting in irregular germination of a subsequent commercial crop and in creased transplant mortality. Due to its low DM and N content, broccoli does not appear to be a promising CC in terms of its potential to increa se soil organic matter, yet it performed very well as a winter crop, reaching yields reported in the literatur e, as well as enhancing watermelon yields when following a pearl millet summer cover cr op. Cover crops which are able to grow through the summer-fall are a good option for suppl ementing nutrients to winter vegetables. Pearl millet appears to be a good cover crop to precede fresh market broccoli since it forms a nice mulch that suppressed weed growth and its use reduced broccoli crowns to a size suitable for fresh market. However, use of cowpea actually enhanced broccoli yield, which could translate into a earlier crop.

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97 Mixed CC systems have a great potential fo r weed reduction, as well as crop biomass enhancement and N accumulation, and therefore Nfertilizer reduction. Benefits of cover crops in this study were always clearer for non -fertilized treatments. However, due to the low inherent fertility of Florida soils, ade quate amounts of supplemental N fertilizer are required in order to achieve acceptable crop yields. Watermelon and the hairy vetch and rye cover crop mix, were very sensitive to temperature changes, and did not perform well unde r intercropping. It is critical to use wide enough clean strips to avoid initial competition between groundcovers and watermelon. Use of rye as the dominant species in the mix, may have reduced the risk of excessive re-growth of vetch, resulting in a more persistent groundcover. Additional research is needed to enhance short-term benefits from cover cr opping systems, including improved integration and synchronization of cr opping system components.

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98 Table 3-1. Outline of crop rotations and e xperimental treatments used during 2003-2005. Trt = Treatment, S = sunn hemp, F = fallow, W = hairy vetch/rye mix, SC = sweet corn, CP= cowpea, PM = pearl millet, SB= sesban ia, B= broccoli, WT = watermelon. YEAR 1 YEAR 2 Trt. Fall 2003 Winter 2003 Spring 2004 N-rate (kg ha-1) Fall 2004 Winter 2004 N rate (kg ha-1) Spring 2005 N-rate (kg ha-1 ) 1 S W SC 0 CP B 0 WT 0 2 S W SC 67 CP B 131 WT 84 3 S W SC 133 CP B 196 WT 168 4 S F SC 0 PM B 0 WT 0 5 S F SC 67 PM B 131 WT 84 6 S F SC 133 PM B 196 WT 168 7 F W SC 0 SB W 0 WT 0 8 F W SC 67 SB W 0 WT 84 9 F W SC 133 SB W 0 WT 168 10 F F SC 0 F F 0 WT 0 11 F F SC 67 F F 0 WT 84 12 F F SC 133 F F 0 WT 126 13 F F SC 200 F F 0 WT 168 14 F F SC 267 F F 0 WT 210 15 F F F None F F 0 F None

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99 Table 3-2. Effects of sampling time (ST), kg ha-1of N fertilizer applied to sweet corn (Nrate) and cropping system (CS) main e ffect; along with ST*N-rate, ST*CS, Nrate*CS interactions on dry weight, N concentration, a nd N accumulation of sweet ( Zea mays ) corn shoots, during the spring of 2004. Fixed Effects DM content N concentration N content SPAD ----Mg ha-1-------g N kg-1------kg N ha-1--ST WAE 2 0.06 c 51.8 a 3.4 d 30.9 d WAE 4 0.84 b 35.4 b 30.9 c 34.0 c WAE 6 4.83 a 15.8 c 79.0 a 39.6 b WAE 9 4.64 a 13.4 d 68.1 b 45.2 a Significance L***Q***C*** L***Q***C*** L***Q***C*** L***Q* N-rate 0 1.50 c 22.4 c 20.0 c 30.1 c 67 2.79 b 29.3 b 45.1 b 39.3 b 133 3.49 a 35.3 a 71.6 a 42.9 a Significance L***Q** L*** L*** L***Q** CS SW 2.84 a 30.7 a 52.7 a 38.9 a SF 2.73 a 27.4 c 42.5 b 36.6 b FW 2.40 b 29.8 ab 43.7 b 38.8 a FF 2.41 b 28.2 bc 41.9 b 35.5 b Significance ** ** *** ST*N-rate *** *** *** *** ST*CS NS * N-rate*CS NS ** DM content = dry matter content. Sampling time in weeks after emergence (WAE). SW = sunn hemp used as a summer cover crop followed by hairy vetch + rye winter cover crop mix, SF=sunn hemp as a summer cover crop combined with a winter fallow, FW= summer fallow combined with hairy vetch + rye winter cover crop mix, FF = summer and winter fallow. NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear (L ), quadratic (Q), or cubic ( C ) for each effect (ST, N-rate, or CS). Means followed by identical lower case letters in the same column are not significan tly different according to Tukeys test (p<0.05), le tters a, b, c denote higher to lower ranking.

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100Table 3-3. Effect of kg ha-1of N fertilizer applied to sweet co rn (N-rate) and cropping system (CS) interaction (N-rate*CS) on shoot dry weight, N concentration, N accumulati on and SPAD readings of sweet corn ( Zea mays ), for the last sampling date (WAE 9), during the spring of 2004. Means followed by identical upper case letter in the same row, or identical lower case letters in the same column are not sign ificantly different according to Tukeys test (p<0.05), lette rs a, b, c denote higher to lower ranking. SW = sunn hemp used as a summer cover crop followed by hairy vetch + rye winter cover crop mix, SF=sunn hemp used as a summer cover crop combined with winter fallow, FW= summer fallow combined with hairy ve tch + rye winter cover crop mixed, FF = summer and winter fallow. Cropping systems (CS) N-rate SW SF FW FF SW SF FW FF DM content N accumulation ----------------------------Mg ha-1---------------------------------------------kg N ha-1------------------------0 2.87 Ab 2.54 ABb 2.45 ABb 1.54 Bc 33 Ac 21 ABc 25 ABc 12 Bc 67 5.53 Aa 5.40 Aa 4.86 Aab 4.46 Ab 83 Ab 59 Ab 68 Ab 54 Ab 133 6.98 Aa 6.52 Aa 5.77 Aa 6.79 Aa 129 Aa 105 Aa 102 Aa 129Aa N concentration SPAD ----------------------g N kg-1-----------------0 11.3 Ab 8.4 Bc 9.8 ABc 7.7 Bc 38.7 Ab 31.3 Ac 34.5 Ac 29.3 Ac 67 15.0 Aab 11.2 ABb 14.2 ABb 12.0 Bb 50.6 Aa 45.3 Ab 49.5 Ab 45.3 Ab 133 18.6 Aa 15.7 Aa 18.2 Aa 19.0 Aa 54.5 Aa 52.0 Aa 55.9 Aa 57.9 Aa

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101Table 3-4. Pair-wise contrast comparison by treatment for dry weight, N concentration and N accumulation in sweet corn ( Zea mays ) shoots, during the spring of 2004. Dry weight N concentration N accumulation Treatment 2 WAE 4 WAE 6 WAE 8 WAE 2 WAE 4 WAE 6 WAE 8 WAE 2 WAE 4 WAE 6 WAE 8 WAE -----------------Mg ha-1------------------------------g N kg-1----------------------------kg N ha-1----------FF200 0.07 1.07 6.12 7.18 61.4 45.0 23.8 19.4 4.30 48.1 145 139 FF267 0.06 1.37 7.19 6.50 58.8 41.4 20.3 18.7 3.50 57.3 149 122 FF133 0.06 1.09 6.68 6.79* 57.40 44.7 18.3* 19.0 3.10* 48.60 119 129 SW67 0.07 0.89 5.38 5.53* 52.4* 40.9 16.8* 15.0* 3.50 35.9 86.5* 83.1* SF67 0.06 0.97 5.65 5.40* 54.0* 31.4* 14.6* 10.9* 3.20 31.0* 82.7* 58.7* FW67 0.06 0.73* 4.96* 4.86* 51.8* 40.4 15.5* 14.2* 2.90* 29.0* 77* 68.0* SW133 0.05 0.97 6.95 6.98* 60.1 48.5 24.1 18.6 2.60* 46.6 165 129 SF133 0.08 1.08 6.18 6.52 59.0 41.3 15.1* 15.7* 4.30 44.8 93.5* 102* FW133 0.07 1.04 5.58 5.77 59.3 46.1 20.7 18.2 3.90 47.8 115* 105* Treatments are FF200 = summer and winter fallow and 200 kg N ha-1; FF267 = summer and winter fallow and 267 kg N ha-1; FF133 = summer and winter fallow and 133 kg N ha-1; SW67 = sunn hemp used as a summer cover crop followed by hairy vetch + rye winter cover crop mix and 67 kg N ha-1; SF67 = sunn hemp used as summer cover crop combined with winter fallow and 67 kg N ha-1; FW67 = summer fallow combined with hairy vetch + rye winter cover crop mix and 67 kg N ha-1; SW133 = sunn hemp used as a summer cover crop followed by hairy vetch + rye winter cover crop mixed and 133 kg N ha-1; SF133 = sunn hemp used as summer cover crop combined w ith winter fallow and 133 kg N ha-1; FW133 = summer fallow combined with hairy vetch + rye winter cover crop mix and 133 kg N ha-1. Denoted statistically significan t difference from treatment FF200. Denotes statistically significant difference from treatment FF267.

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102 WAE 0246810 N (kg N d-1 ha-1) 0 2 4 6 8 SW SF FW FF WAE 0246810 N (kg N d-1 ha-1) 0 2 4 6 8 SW SF FW FF WAE 0246810 N (kg N d-1 ha-1) 0 2 4 6 8 SW SF FW FF WAE 0246810 N (kg N d-1ha-1) 0 2 4 6 8 FF WAE 0246810 N (kg N d-1ha-1) 0 2 4 6 8 FF A E B C D Fig. 3-1. Calculated N accumulation for differe nt N-rates for cropping systems (CS) as a function of weeks after em ergence (WAE) for A) sweet corn amended with 0 kg N ha-1; B) sweet corn amended with 67 kg N ha-1; C) sweet corn amended with 133 kg N ha-1; D) sweet corn amended with 200 kg N ha-1; and E) sweet corn amended with 267 kg N ha-1.

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103 Table 3-5. Effects of kg ha-1of N fertilizer applied to swee t corn (N-rate) and cropping system (CS), along with CS*N-rate interacti on on total, marketable, fancy and culls yield of sweet corn ( Zea mays ), during the spring of 2004. Yield Fixed Effects Marketable Fancy Culls Total DM content ---------------------------------------kg ha-1-----------------------------------kg ha-1---N-rate 0 1375 c 297 c 1111 a 2485 c 784 c 67 8227 b 5086 b 1096 a 9322 b 2326 b 133 15106 a 12584a 1442 a 16548 a 3862 a Significance L *** L ***Q* NS L *** L *** CS SW 9798 a 7265 a 1594 a 11393 a 2797 a SF 7839 b 5892 ab 719 b 8559 b 2152 bc FW 8792 a 6244 a 1553 a 10346 a 2568 ab FF 6512 c 4557 b 998 ab 7511 b 1779 c Significance *** *** ** *** *** N-rate*CS ** ** * NS SW = sunn hemp used as summer cover crop followed by hairy vetch + rye winter cover crop mix, SF= sunn hemp used as a summer cover crop combined with winter fallow, FW= summer fallow combined with hairy vetch + rye winter cover crop mix, FF = summer and winter fallow. NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectivel y, and linear (L), quadratic (Q), or cubic ( C ) for each effect (N-rate or CS). Means followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), letters a, b, c de note higher to lower ranking.

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104 Table 3-6. Pair-wise comparison of selected trea tments for total, marketable and culls yield, total N applied to sweet corn (N applied) nitrogen use efficiency (NUE), and unutilized applied nitrogen ( UAN), during the spring of 2004. Yield Treatment Total MarketableCulls N Applied NUE UAN --------------------------------------------Mg ha-1----------------------------------------------FF200 18289 17316 973 215 0.59 76 FF267 20794* 19377* 1488 288* 0.38* 166* FF133 15111* 14234* 877 151* 0.77* 23* SW67 12282* 11371* 912 250 0.29* 167* SF67 7728* 7001* 727 125* 0.38* 66 FW67 10432* 9352* 1080 218 0.27* 150* SW133 18128 15848 2280* 312* 0.41* 183* SF133 16394* 15723* 672 190 0.48 87 FW133 16556 14618* 1939* 257 0.38* 152* Treatments are FF200 = summer and winter fallow and 200 kg N ha-1; FF267 = summer and winter fallow and 267 kg N ha-1; FF133 = summer and winter fallow and 133 kg N ha-1; SW67 = sunn hemp used as a summer cover crop followed by hairy vetch + rye winter cover crop mix and 67 kg N ha-1; SF67 = sunn hemp used as a summer cover crop combined with winter fallow and 67 kg N ha-1; FW67 = fallow summer combined with hairy vetch + rye winter cover crop mix and 67 kg N ha-1; SW133 = sunn hemp used as a summer cover crop followed by hairy vetch + rye winter cover crop mix and 133 kg N ha-1; SF133 = sunn hemp summer cover crop combined with winter fallow and 133 kg N ha-1; FW133 = summer fallow combined with hairy vetch + rye winter cover crop mix and 133 kg N ha-1. *Denotes statistically significant different from treatment FF200, Denotes statistically significant different from treatment FF267. Table 3-7. Regression equation for total and marketable yields of sweet corn for a conventional sweet corn trea tment (FF) amended with 5 different levels of N fertilization, during the spring of 2004. Yield a B c d r2 Total 406.1 102.9* 0.102 -0.00076 0.967 Marketable -94.9 73.1* 0.375 -0.00142* 0.969 a =intercept; b c d =regression coefficients for the equation of the form y = a + bN + cN2 + dN3

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105Table 3-8 Effects of sampling time (ST), kg ha-1of N fertilizer applied to broccoli (N-rate) and summer cover crop residue (RES), along with ST*RES and N-rate *RES interaction effect on roots, s hoots and crowns, dry matter content, N concentration and N content in r oots, shoots and crowns of brocco li, during the winter of 2004/05. Sampling time in weeks after transplant (WA T). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, re spectively, and linear (L), quadratic (Q), or cubic ( C ) for each effect (ST, N-rate, or CS).Means followed by identical lower case letters in the same co lumn are not significantly different according to Tukeys test (p<0.05), letters a, b, c de note higher to lower ranking. CP = cowpea used as summer cover crop, PM = pearl millet used as summer cover cropDM content N concentration N content Fixed Effects Roots ShootsCrownsRoots Shoots CrownsRoots Shoots Crowns ----Mg ha-1-------g N kg-1-------kg N ha-1---ST WAT 3 0.00e 0.05 e 23.4 a 51.2 e 0.11 d 2.9c WAT 6 0.04 c 0.47 d 20.4 b 43.7 d 0.94 c 22.6 b WAT 9 0.11 c 1.09 c 0.20 b 15.7 c 30.6 c 45.9 ab2.04 bc 38.3 a 12.6 a WAT 13 0.23 b 1.40 b 0.15 b 11.4 d 20.8 b 50.9 a 2.56 b 32.7 a 8.0 a WAT 16 0.53 a 2.40 a 0.06 c 7.3 e 14.1 a 33.3 c 4.12 a 37.5 a 3.3 b WAT 19 0.48 a 2.28 a 0.22 a 6.7 f 14.4 a 36.4 bc3.37 a 37.9 a 8.5 a L***C*** L***Q*** Q***C*** L***C*** L***Q***C***L***Q* L***Q** L*** Q***C** L*Q***C** N-rate 0 0.12 b 0.50 c 0.06 b 9.7 c 20.9 c 24.1 c 0.74 b 9.9 c 3.9 b 131 0.30 a 1.59 b 0.19 a 12.8 b 30.5 b 46.6 b 2.58 a 31.0 b 8.5 a 196 0.27 a 1.79 a 0.22 a 17.1 a 36.8 a 52.2 a 3.41 a 43.8 a 11.0 a L***Q** L***Q*** L*** L***Q* L *** L*** L*** L*** L*** RES CP 0.26 a 1.45 a 0.18 a 13.3 29.5 42.5 2.56 a 32.4 a 8.7 a PM 0.20 b 1.11 b 0.13 b 13.6 29.6 40.3 1.99 b 24.0 b 7.2 b Significan. *** NS NS NS *** ST*N-rate *** * ** NS NS ** ST*RES NS NS NS NS NS NS NS NS N*RES NS NS NS NS NS NS NS NS NS

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106Table 3-9. Pair-wise contrast comparison by treatment for dry we ights, N concentration and N accumulation along sampling times (in weeks after transplanting [WAT]) in broccoli ( Brassica oleracea ), during the winter of 2004/05. DW N concentration N accumulation Treatm. 2 WAT 4 WAT 6 WAT 8 WAT 10 WAT 12 WAT 2 WAT 4 WAT 6 WAT 8 WAT 10 WAT 12 WAT 2 WAT 4 WAT 6 WAT 8 WAT 10 WAT 12 WAT ------------------------Mg ha-1-------------------------------------------g N kg-1-----------------------------------------kg N ha-1----------------CP196 0.08 0.91 1.91 2.11 3.29 3.63 61.2 54.2 41.6 27.9 19.4 20.1 5 48.8 77.6 59.2 64.6 73.2 PM196 0.05 0.55* 1.33* 2.07 3.08 2.47* 59.9 53.4 36.9 26.0 18.4 13.4* 3.1 29.4* 49.8* 52.2 57.4 41.8* CP0 0.03* 0.21* 0.52* 0.46* 1.48* 1.31* 13.5* 30.9* 18.6 14.7* 9.80* 13.0* 1.2* 6.5* 9.2* 5.6* 14.5* 17.5* CP133 0.07 0.55* 1.69 1.8 2.97 3.81 56.7 43.9* 29.7* 20.8* 12.7* 15.3 4.1 23.8* 50.4* 35.7 37.7* 58.9 PM0 0.01* 0.04* 0.12* 0.4* 0.81* 0.67* 24.5* 31.5* 22.3* 14.6* 9.4* 8.6* 0.2* 2.4* 2.7* 5.7* 7.9* 6.0* PM133 0.06 0.56 1.24* 1.64 2.87 1.92* 60.6 47.3* 34.0* 21.0* 14.7* 14.5 3.6 25.8* 40.5* 35.5* 41.8* 28.7* Treatments are CP196 = cowpea used as summer cover crop and 196 kg N ha-1; PM196 = pearl millet summer cover crop and 196 N ha-1; CP0 = cowpea used as summer cover crop and 0 kg N ha-1; CP133 = cowpea used as summer cover crop and 133 kg N ha-1;PM0 = pearl millet used as summer cover crop and 0 kg N ha-1; PM133 = pearl millet used as summer cover crop and 133 kg N ha-1. *Denotes statistically different from treatment CP196, denotes statistically different from treatment PM196, denotes statistically different from treatment CP0 Denotes statistically different from CP133.

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107 WAT 05101520 N (g N d-1 kg-1) 0 1 2 3 4 CP PM WAT 05101520 N (g N d-1kg-1) 0 1 2 3 4 CP PM WAT 05101520 N (g N d-1 kg-1) 0 1 2 3 4 CP PM C B A Fig. 3-2. Nitrogen accumulation in different cro pping systems (RES) as a function of days after emergence (DAP) for A) broccoli amended with 0 kg N ha-1; B) broccoli amended with 131 kg N ha-1; C) broccoli amended with 196 kg N ha-1.

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108 Table 3-10. Effects of kg ha-1of N fertilizer applied to broc coli (N-rate) and cover crop residue (RES), along with RES*N-rate in teraction effect on fresh marketable, process marketable, total marketable, and non-marketable (culls) yields of winter broccoli yields, during the 2004/05 growing season. Yields Marketable Non marketable (culls) Fixed Effects Fresh Process Fresh Process Total ----------------kg ha-1---------------------------kg ha-1----------kg ha-1-N-rate 0 221 c 0.00 b 985 16 b 1222 b 133 6206 b 1832 a 7223 2496 a 17756 a 196 7398 a 1288 a 9713 1963 ab 20362 a Significance L***Q* L*Q* L*** L* L ***Q* RES CP 4175 1686 a 6451 2331 14643 PM 5042 394 b 5496 652 11584 Significance NS NS NS N*RES NS NS NS NS Nitrogen fertilizer in kg N ha-1 (N-rate). CP = cowpea used as summer cover crop, PM = pearl millet used as summer cover crop. NS,*, **,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear (L), quadratic (Q), or cubic (C) for each effect (N-rate or CS). Means followed by identical lower case letters in the sa me column are not significantly di fferent according to Tukeys test (p<0.05), letters a, b, c deno te higher to lower ranking.

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109 Table 3-11. Pair wise comparison between co wpea and pearl millet based systems amended with different N-fertilizer rates for fresh marketable, process marketable, total marketable, culls marketable, and culls pr ocess categories of broccoli, during the winter of 2004/05. Yield Marketable Culls Treatments Fresh Process Fresh Process Total -----------------------------kg ha-1------------------------------CP196 7111 2299 10470 3244 23124 PM196 7686 280* 8956* 683 17600* CP0 442* 0* 1594* 23 2059* CP131 4972 2758 7289 3728 18746 PM0 0* 0* 376* 8* 384* PM131 7440 905* 7158 1264* 16767 Treatments are CP196 = cowpea used as summer cover crop and 196 kg N ha-1; PM196 = pearl millet used as summer cover crop and 196 N ha-1; CP0 = cowpea used as summer cover crop 0 kg N ha-1; CP133 = cowpea used as summer cover crop and 133 kg N ha-1; PM0 = pearl millet used as summer cover crop and 0 kg N ha-1; PM133 = pearl millet used as summer cover crops and 133 kg N ha-1. Denotes statistically different from treatment CP196, denotes statistically different from treatment PM196, denotes statistically different from treatment CP0 denotes statistically different from CP133.

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110Table 3-12. Effects of kg ha-1of N fertilizer applied to watermelon (N-rate) a nd cropping system (CS) along with CS*N-rate interaction on dry matter accumulation, N concentration and N accumulation of watermelon shoots, fruits and total tissues, during the spring of 2005. Sampling time in weeks after transplant (WAT). CP +B =cowpea used as a summer cover crop followed by winter broccoli, PM+B= pearl millet used as a summer cover crop followed by winter broccoli, SB+W= sesbania used as a summer cover crop followed by hairy vetch + rye winter cover crop mix, FF = summer and winter fallow. NS,*, **,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and line ar (L), quadratic (Q), or cubic ( C ) for each effect (ST, N-rate, or CS). Means followed by id entical lower case letters in the same column are not significan tly different according to Tukeys test (p<0.05), lette rs a, b, c denote higher to lower ranking. Dry matter N concentration N Accumulation Fixed Effects Shoot Fruits Total Shoot Fruits Shoot Fruit Total -------------Mg ha-1---------------------g N kg-1---------------kg N ha-1-----ST WAT 3 0.00 c 0.00 b 0.01 c 38.0 a 0.00 b 0.2 b 0.0 b 0.2 d WAT 6 0.09 c 0.00 b 0.09 c 31.4 b 0.00 b 2.9 b 0.0 b 2.9 c WAT 9 0.49 b 0.00 b 0.49 b 23.9 c 0.00 b 11.5 a 0.0 b 11.5 b WAT 12 0.74 a 0.29 a 1.04 a 18.3 d 25.6 a 14.4 a 7.9 a 22.2 a Significance L*** L ***Q***C*** L*** L*** L ***Q***C*** L *** L ***Q***C*** L*** N-rate 0 0.15 b 0.01 c 0.17 b 22.9 b 6.31 3.4 0.2 c 3.6 b 84 0.47 a 0.07 b 0.55 a 28.9 a 6.25 9.6 1.9 b 11.5 a 168 0.36 a 0.14 a 0.50 a 31.7 a 6.68 9.1 3.7 a 12.9 a Significance L*Q* L*** L***Q*** L*** NS L *Q* L*** L ***Q** CS CP+B 0.33 0.06 0.39 27.6 ab 7.00 8.1 1.7 9.9 PM+B 0.30 0.10 0.40 24.6 b 5.93 5.8 2.7 8.5 SB+W 0.42 0.05 0.48 29.7 a 6.70 9.4 1.5 10.9 FF 0.27 0.08 0.35 29.0 ab 6.03 6.0 2 8.0 Significance NS NS NS NS NS NS NS ST*N-rate NS *** *** *** NS NS *** *** ST*Res NS NS * * N*Res NS ** NS NS NS *

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111Table 3-13. Effect of kg ha-1of N fertilizer applied to watermelon (N-rate) and cropping system (CS) interaction (N-rate*CS) on shoot dry weight, and N accumulation of watermelon ( Citrullus lanatus ), for the last sampling date (WAE 7), during the spring of 2005. Means followed by identical upper case letter in the same row, or identical lower case letters in the same column are not signi ficantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking. CP+B = cowpea used as summer cover crop followed by winter broccoli, PM+B= pearl millet used as summer cover crop followed by winter broccoli, SB+W= sesbania used as summer cover crop followed by hairy vetch + rye winter cover crop mix, FF = summer and winter fallow. Cropping system (CS) CP+B PM+B SB+W FF CP+B PM+B SB+W FF N-rate Dry matter N accumulation ----------------------Mg ha-1------------------------------------------kg N ha-1-----------------0 0.07ABb 0.11ABb 0.44Aa 0.05Bb 1.2 Abb 1.8ABb 10.5Aa 0.9Bb 84 0.68Aa 0.61Aa 0.55Aa 0.37Aa 17.7Aa 11.9Aa 9.8Aa 6.7Aa 168 0.42Aa 0.49Aa 0.46Aa 0.63Aa 10.7Aa 11.6Aa 12.4Aa 16.8Aa

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112 Table 3-14. Effects of kg ha-1of N fertilizer applied to wate rmelon (N-rate) and cropping system (CS), along with CS*N-rate intera ction on total, marketable, and non marketable (culls) yield of wa termelon during the spring of 2005. Yield Fixed Effects Marketable Non marketable (Culls) Total --------------------------kg ha-1---------------------N-rate 0 899 c 361 b 1260 c 84 11475 b 2442 a 13917 b 168 18051 a 2229 a 20280 a Significance L* L **Q* L** CS CP+B 9024 ab 1199 10224 PM+B 13684 a 1483 15468 SB+W 7166 b 2489 9655 FF 10693 ab 1537 12230 Significance NS NS N*Res NS CP+B = cowpea used as a summer cover crop followed by winter broccoli, PM+B= pearl millet used as summer cover crop followed by winter broccoli, SB+W= sesbania used as summer cover crop followed by hairy vetch + rye winter cover crop mix. FF = summer and winter fallow. NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectivel y, and linear (L), quadratic (Q) for each effect (N-rate or CS).. Means followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), a, b , c denote higher to lower ranking.

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113 Table 3-15. Pair-wise contrast comparison by treatment for fresh marketable, total marketable and non marketable (culls) of watermelon during the winter of 2004. Yield Treatment** Marketable Non Marketable (Culls) Total N Applied NUE UAN ----------------kg ha-1-----------------CP+B168 14594 1666 16259 224 0.09 202 PM+B168 27569 2519 30086 214 0.11 188 SB+W168 9249 1893 11141 472 0.04 452 FF168 20684 2800 23483 180 0.18 148 FF210 26468 3036 29503 232 0.18 190 CP+B84 12250 879 13428129 0.37 80 PM+B84 13489 1746 15233 132 0.19 105 SB+W84 8740 3793 12532 386 0.08 358 **Treatments are CP+B168 = cowpea used as summer cover crop followed by winter broccoli and 168 kg N ha1; PM+B168 = pearl millet used as a summer cover crop followed by winter broccoli and 168 kg N ha-1; SB+W168 = sesbania used as summer cover crop followed by hairy vetch + rye winter cover crop mix and 168 kg N ha-1; FF168 = summer and winter fallow and 168kg N ha-1; FF210 = summer and winter fallow and 210 kg N ha-1; CP+B84 = cowpea used as a summer cover crop followed by winter broccoli and 84 kg N ha-1; PM+B84 = pearl millet used as a summer cover crop followed by winter broccoli and 84 kg N ha-1; SB+W84 = sesbania used as a summer cover crop followed by hairy vetch +rye winter cover crop mix and 84 kg N ha-1. Denotes statistical difference from treatment CP+B168, Denotes statistical diffe rence from treatment M+B168. Denotes statistical difference from treatment SB+W168 Denotes statistical difference from treatment FF168. Denotes statistical difference from treatment FF210. Table 3-16. Regression equation for total and marketable yields of watermelon for a conventional treatment (FF), with 5 levels of N fertilization, during the spring of 2005. Yields a b c d r2 Total -2.3 198.7 -0.616 0.0016 0.740 Marketable 27.3 173.7 -0.674 0.0021 0.690 a =intercept; b c d =regression coefficients for the equation of the form y = a + bN + cN2 + dN3.

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114 CHAPTER 4 COST, ENERGY, AND EMERGY ANALYSIS OF COVER CROP-BASED PRODUCTION SYSTEMS Introduction Currently, environmentallyand socially-c onscious citizens are participating more and more in the market decision dynamics of agricultural commodities (Conner, 2004). For example, during the last decade the organi c agriculture acreage in United States has doubled, while consumption of organic pr oduce increased annually by 20%. Organic products receive premium prices compared to conventional managed products, but bigger than the premium prices is their market e xpansion (Oberholtzer et al., 2005), which has pushed organic agriculture to be one of the fastest growing components of U.S. agriculture. As conventional agricultural practi ces impact and/or deplete natural resources due to increased use of fertilizers and pes ticides along with a loss of genetic diversity (Matson et al., 1997), the grow ing markets for sustainable produced goods might be a consequence of better informed citizens (Hinrichs, 2000). Successful alternative production sy stems are exemplified by Community Supported Agriculture (CSA) farms. Introduced to the United States from Europe in the mid-1980s, CSAs provide satisfaction to consum ers interested in environmental, health, and social issues, while assuring farmers a stable market for their crops (DeMuth, 1993). Locally grown food, such as that provi ded by CSAs, is an attractive option for those interested in the b ackground of food products (Mardensen and Smith, 2005). Applying Sagoffs political conceptual model, it could also be ar gued that support to

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115 agricultural models such as CSAs exemplifies a principle-oriented citizen decision rather than a utilitarian-o riented consumer decision (S agoff, 1998). By supporting an environmentally friendly agricultural scheme, citizens transcend the satisfaction of individual needs (which could be solved by buying a similar organic product at a retailer) to contribute to local food sufficiency and societal well-being (Wordern, 2004). In south Florida, the detrimental impacts of agriculture on water bodies is widely recognized (Anderson and Rosendahl, 1998), a nd different policies are in place to promote best management practices (BMP s), which will allevi ate nutrient runoff (Anderson and Flaig, 1995). While most of the water quality attention has been focused on phosphorous loading (Perry, 2004), nitrog en management in agricultural lands remains a pressing issue. Some studies have already begun subjecting organic amendments used in organic vegetable pr oduction systems to the same scrutiny as conventional systems (Jaber et al., 2005). In addition to more stringent environmenta l regulations, it is expected that fossil fuel depletion and pending energy crisis w ill provide further incentives for reduced energy use in agriculture. Petroleum demand will only increase; as developing countries become more industrialized and energy demand s in developed continue to expand. High petroleum costs and limited oil availabilit y, in addition to unsustainable consumption patterns from several sectors, might lead to further increases in fertilizer prices (Obreza et al., 2006). As a result, operational cost in fa rms could rise, increasing the fertilizer cost per output of crop. In the case of an energy crisis, consumers could also potentially be affected by increased food transportation-relate d costs, since the US will become a net

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116 importer of horticultural products during the next 10 years (Economic Research Service, 2005 b ). Based on the issues raised in the previous section, it may be conc luded that for food production systems to become more resilient th ey should be less dependent on external inputs and energy and more geared towards local markets. Theoretically, CSA act as face-to-face markets, which are seen as a cent ral component of local food systems (Hinrichs, 2000). By shipping products in a short radius, CS A could also greatly decrease energy demand associated with food pr oduction, transportation, and distribution. Since N is the most limiting nutrient for plant growth (use different reference), most of the agricultural operations depend greatly on external synthetic N-fertilizer, which production requires large quantities of fossil fuels. Despite the sharp rise in petroleum prices, the price in the U.S. per kg of N fertilizer ($0.76 kg-1 N) according to has not been drastically altered (Economic Rese arch Service, 2005). Th is is the result of subsidies and lack of internalization of environmental cost into the fertilizer price (Socolow, 1999). Appropriate use of cover crops could redu ce farm energy use since they function as fertilizer and pesticide replacements (Hartwig and Am mon, 2002; Roldn et al., 2003; Ruffo and Bollero, 2004). However, since economic systems value goods in terms of monetary units instead of energetic units, low prices for N fertilizers decrease the likelihood of cover crops as a fertilizer replacem ent, despite all the indirect benefits that cover crops could provide. Therefore the us e of cover crops should also be evaluated from energetic and emergetic standpoints. En ergy analysis is the objective analysis of the physical quantities of energy involved in a process according to Beardsworth (1975).

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117 In the case of cover crops, energy analysis quantifies fossil fuels required for the production of seeds and their cultivation. But ag riculture as an open sy stem uses a mix of solar and fossil fuels energy (Fluck and Baird, 1980). Therefore it is important not only to understand how much energy was used for pr oducing the cover crops or the intrinsic energy contained in its resi due, but also how much ener gy was invested. Odum (1986, 1988) and Scienceman (1987) define this invest ment as emergy which is the available energy to generate a service or product and its value is expressed in emjoule. Economic, energy, and emergy evaluations will facilitate improved assessment of the short-, medium-, and long-term effects of modifying agricultura l practices, such as increased use of cover crops. Ea ch method of analysis is s ubject to distortions, since subsidies distort prices, while emergy and en ergy evaluations have empirical limitations, although the three methodologies are useful. Cropping system s have a fast turn-over cycle, and thus may be best captured by using a short-term economic approach. Additionally, a complete systems approach may allow for a better understanding of how BMPs affect the sustainability of farm opera tions at a macro-scale, but it is beyond the cope of this study to explore th is topic at the current time. Florida Farming System Characteristics According to the Department of Agricu lture Florida has 44,000 commercial farms utilizing 4.13 million hectares, providing the st ate with a large and stable economic base (Florida Agricultural Statis tics, 2004). Sweet corn, tomato and pepper are especially important for Floridas agricultural economy. Florida tomatoes account for one third of US production, while Florida is the largest pr oducer of pepper, tomato, and sweet corn during early spring and la te fall (NASS, 2006).

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118 Some initiatives have been taken to reduce use of inorganic fertilizer and to address environmental concerns associated with vege table production in Fl orida. For example, studies have shown that vegetables can be produced organically by utilizing compost. Farm gate cost of these organic amendments range from $300-$1544 ha-1 while application cost are on the order of $59-$494 ha-1. Reported yield increases were up to 650 and 1671 kg ha-1 for tomato and pepper, respecti vely (Rahmani et al., 1999). In Alabama, use of broiler litter (18 Mg ha-1) increased tomato fruit yields by 20%, while it also enhanced early yield and fruit size (D iver et al., 1999). Th e organic acreage in Florida is gradually increasing, and in 2001 th ere were 4860 ha of certified organic farm land (Florida Organic Growers, 2002). Farming techniques such as cover crop utilization are encouraged for in organic farming. Economics and Energy Dynamics of Cover Crops Economic evaluations of cover cropping sy stems are frequently reported in the literature, while energy and emergy evaluati ons of cover crops are relatively rare. In some studies where the use of cover crops wa s profitable, decreases in costs related to reduced inputs use was accompanied by yield increases. Whenever yield decreased in cover cropping systems, the reduction in profit margins was partly related to establishment costs of cover cr ops (Baldwin and Creamer, 1999). It is very difficult to eval uate cover crop contribution in the short run, since effects on soil fertility may extend across production seasons and benefits may accrue over time. Kelly et al., (1996), in Maryland used the Eros ion Productivity Impact Calculator (EPIC) model to analyze a no-till corn-double croppe d wheat soybean rotation; a crown vetch living mulch corn-winter wheat-soybean rota tion; a cover crop corn -full season soybean rotation; and a manure-bas ed corn-wheat-forage rotation. Simulating yield and

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119 environmental impacts over a 30-year period showed that no-till rotation provided the greatest net returns, followed by the conventional rotation. The net returns for the two cover crop rotations were the lowest because only two rather than th ree crops contribute to the net returns. Pannell and Falconer (1988) reported that it was very difficult to separate the benefits from synthetic nitrogen and legum es when both were simultaneously used. Fertilizer and biological N are interdependent and to better assess the benefits of N the opportunity cost of alternative farm activi ties must also be c onsidered. The economic returns from N depend on the va lue of the extra grai n, crop stubble or pa sture it produces. The yield response to N will also depend on the timing of applications while the level of N fertilizer application on previous non-legume crops may affect the level of N fixation by legumes. Using a simpler approach, Brennan and Evans (2001) assessed whether or not obtaining N through legume-based systems was less expensive than through N-fertilizer in New South Wales. They found that in the long run it was more profitable to incorporate legumes in the cropping system, because fertilization costs were reduced. Studies in northern U.S. demonstrated that sweet corn following winter hairy vetch in non-tillage systems was more profitable than fallow corn production (Hanson et al., 1993; Roberts et al., 1998). Other studies have shown that the cost of cover crop cultivation is significantly lower than the use of herbicides for weed control during winter (Wyland et al., 1996). Although there are several energy anal yses for different production systems reported in the literature (Flu ck, 1992; McLaughing et al., 20 00; Hlsbergen et al., 2001;

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120 Bender, 2003; Lal et al., 2003) there are no specific papers related to cover cropping systems. Eventhough Brandt-Williams (2002) presented emergetic analysis for Florida agriculture, this work only in cluded conventional systems. The objectives of this chapter were to : 1) Analyze the economic feasibility of integrating cover crops into a CSA farm in southeast Florida, by performing a cost effectiveness analysis; and 2) to propose a th eoretical framework for future analysis of this type of best management practices for alternative agriculture operations. Four hypotheses were developed: 1) Su mmer cover crops represent an energyefficient N source because of their photosynthe tic capacity; 2) Sunn hemp used for weed control during summer in southeast Florida pr ovides a cost-effective and energy-efficient alternative for weed control compared to use of herbicides; 3) Use of sunn hemp as a cover crop in Southeast Florida provides a viable strategy for summer weed control and nitrogen supplementation to a main crop (tom ato, sweet corn or pepper) compared to fallow, chicken broiler litter, or com post; and 4) Cover crop systems entail less embodied energy than conventional, broiler li tter, or compost-based production systems. Methodology Farm Description The CSA farm studied, Green Cay Farm, was established in the 1960s and is located in Boynton Beach, Flor ida. It produces about 49 di fferent crops and herbs from October to May, while during th e summer (May through September), land is left fallow. Currently agricultura l land in Palm Bach County is being encroached upon by the urban sprawl. Under the pressure of future development, th e owners of Green Cay Farm sold most of their land to Palm Beach County fo r the creation of a wetla nd park as part of the Comprehensive Everglades Restoration Pl an. Because of the reduced size of the land

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121 left in agricultural production, only a sma ll alternative intensely managed production system could offset the high marginal cost s of growing vegetables. Based on economies of scale, conventional farm s may require specialization and large opera tion size to minimize overhead/operational costs. Howeve r, the CSA system marketing niche may offer an exception to this trend. Green Cay fa rm utilizes customer subscription services where buyers place special value on a diversif ied package of locally grown commodities. Shareholders pay per basket of produce in a dvance, which confers the farm with a steady cash flow to cover operational expenses. Produc e is sold in boxes and baskets. Box sizes might vary (small and large), as well as the delivery or pick up poi nt. Prices range from $17.5 to $35 per week. Moreover, there is a seasonal subscription which entails a weekly pick up ($507 to $1015) or bi -weekly pick up ($245 to $490) Nancy Roe, of Farming Systems Research, Inc., was the on-site resear cher manager of this farm operation. She works with Green Cay to introduce these new systems of growing and selling vegetables to the area. Experimental Set-up Soils in the farm are of the type Mya kka sand (sandy, siliceous, hyperthermic Aeric Alaquods). Cover cropping system experi ments were conducted on a 0.57-ha area between the summer of 2002 and the winter 2005. The rotation used included sunn hemp ( Crotalaria juncea ) during the summer, tomato ( Lycopersicon esculentum ) or pepper ( Capsicum annum ) during the winter, and sweet corn (Zea mays var. Summer Sweet) during the spring. There were two production blocks. Each pr oduction block consisted of 24 plots (6 treatments in a randomized comp lete block design with a factorial, which were replicated four times) and the overall production cycle for peppers and tomatoes

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122 took 13-14 wks. Treatments were composed of two factors, cropp ing system (summer fallow vs. sunn hemp cover crop) and N fertilizer rates (N-rate). Sunn hemp, which served as a cover crop and an alternative to summer weed fallow, was planted at a rate of 45 kg ha-1 and mowed after 7 wks. At the end of the summer, the soils were prepared by repeated tillage prior to bed formation. Crops were watered with drip irrigation system and be ds were covered with white biodegradable plastic mulch. During the winter time, either tomato or pepper were grown, which in turn was followed by sweet corn in the spring time Tomatoes were planted 0.51 m apart in a single row, while peppers were planted in twin rows at 0.46 m apart and 0.25 m between plants. Sweet corn was also planted in double rows 0.46 m apart and 0.25 m between plants. Center to center distance between beds was 1.83 m. Nitrogen fertilization was applied as weekly fertigation with ammoni um nitrate following Institute of Food and Agricultural Sciences of the University of Florida recommendati ons (Olson and Simone, 2005) Measurements End-of-the season biomass and yield meas urements were collected for both sunn hemp and commercial crops between 2002-2005. However this chapter only includes yield information. Two surveys were conducte d for collecting information pertaining to labor and operational costs from the expe rimental area duri ng 2003 and 2004. Data was corroborated through electroni c communication. Data for la bor, machinery, pesticides and fertilizer use were gathered during surv eys and this information was compiled using the Excel program (Microsoft Corporation, Los Angeles, CA ), and resulting Excel spreadsheets were used for the calcula tion of a simple budget analysis (Food and Resource Economics Department, Un iversity of Florida, 2006).

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123 Due to changes in the experimental desi gn, only averages of yield data for sweet corn from 2004 and 2005 were used. Similarl y, for pepper and tomato only average yields from winter season 2003/04 and 2004/05 were used fo r yield calculations. Only above-ground biomass data for season 2002/03 wa s available, therefore an average for dry matter stover of tomato and pepper for that year was used for all treatments. Cost-Effectiveness Analysis Total operational costs were calculated as the sum of inputs, labor, machinery rental, and fuel expenses for a specific production cycle. Shared costs, from field cleaning of the previous crop and weed control (thro ugh herbicides) or cover crops required for the production of the following crop, were partitioned across crops. The partitioning was based on N mineralization equations (outlined in the next section). All information was standardized to a per hectare cycle basis ($ ha-1 cycle-1) using standard metric units. Given the nature of the CSA operation, e quipment was shared between cover crops and other farm crop tasks, and fixed costs were not included in this analysis. Since the harvest was outsourced, this cost was also not included. The pack aging options varied widely, from boxes to units of produce in a basket, rendering the partitioning of this cost per kg of produce inaccurate. As a result, it wa s decided to also exclude post harvest cost from our calculations. Gross revenues were calculated based on actual yield data (averaged across 2003 and 2004). A sensitivity analysis for prices wa s made based on vegetable market prices for year 2004 (,Economic Research Servi ce, 2005; Food and Resource Economics Department, University of Florida, 2006) Prices were usua lly obtained as $ lb-1, $ crate-1 or $ box-1, and values were standardized to a per kg basis. To better understand the interactive effect of nitrogen fertilizer a nd use and cover crops in these vegetables

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124 systems, 12 different scenarios (consisting of 3 N rates and 4 s ubsets of N sources scenarios) were established for pepper, tomato, and sweet corn: Scenario 0 kg N ha-1: a) Summer fallow and herbicid e weed control; b) use of sunn hemp as a summer cover crop; c) bro iler litter applicatio n during the summer, instead of summer cover crop; d) compost (made of w ood chips and horse manure) application during the summer, instead of summer cover crop Same correction Scenario 112 kg N ha-1: a) summer fallow and herbicide weed control; b) summer sunn hemp cover crop; c) broiler litter app lication in summer, instead of cover crop; d) compost (made of wood chips and horse manure), instead of cover crop Same Scenario 224 kg N ha-1: a) summer fallow and herbicide weed control; b)summer sunn hemp cover crop; c) broiler litter application in summer, instead of cover crop; d) compost (made of wood ch ips and horse manure), instead of cover crop Energy Analysis Using the values calculated for the budget in the cost analysis, energetic expenses from labor and machinery were calculate d using the methodology outlined by Pimentel (1979). The outputs of the system included yi eld (vegetable crops) or dry matter (DM) biomass in the case of sunn hemp. For sunn hemp energetic stover value was calculated based on 1.61% N (from Chapter 2; Table 2-2) 6.25 (%N to % protein conversion factor) x % protein kg DM sunn hemp ha-1 = x g protein ha-1 J g-1 protein = J ha-1. Description of the methodology for en ergy calculation was based on the methodology outlined by Sartori et al. (2005). Operational expenses Human labor: Values were calculate d based on hourly biochemical energy metabolized by a human of 523 J h-1 (or 1.26+E7 J d-1), assuming an average body weight of 65 kg (Food and Agricultu re Organization and World Health Organization, 1974).

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125 Indirect use of machinery: Pimentels coefficients for machinery manufacturing energy depreciation by labor pe rformed per hectare were used. Coefficients were 89.8 MJ for light weight machinery work such as mowing or pesticide applications, and 1027 MJ for heavy machinery work, such as disking, beds conformation and planting. Direct use of machinery: this value is the result of multiplying the number of machinery hours invested in certain task (f rom farm records), by a factor of fuel consumption (L ha-1 hr -1), assuming full PTO rate of 100 horse power (hp). In addition to fuel (diesel), 10% lubricant o il was included per L of diesel used. Diesel energetic content was estimated as 35.7 MJ L-1(Conway, 2005). Coefficients used for machinery work were 0.9 L ha-1 hr-1 for spraying, 12.0 L ha-1 hr-1 for planting, scratching and dragging, 3.3 L ha-1 hr-1 for mowing and 6.1 L ha-1hour-1 (Downs and Hansen, 1994), and 17.6 L ha-1 hr-1 for amendment distribution in the field (Lazarus and Selley, 2005). Inputs The amount of inputs (kg ha-1 cycle-1) were multiplied by their energy, which consider intrinsic energy value, and also fuels required for their production (Pimentel, 1980): Pesticide use was calculated based on the average application rate for all the products applied on the farm (fungicides, in secticides and herbicides), multiplied by the number of applications per production cycle. Pesticides were classified in miscible oils, wettable powder and granules, and for each of them a different coefficient was used (Pimentel, 1980). Th e kg of active ingred ient (a.i.) were calculated based on the applied dose per hectare (a.i. kg ha-1), and the concentration of the active ingredient in the pro duct. Used active ingredient (kg ha-1) was multiplied by the sum of energy in a.i. (J kg-1), energy in formulation (J kg-1), energy in packaging (J kg-1) and energy for transportation (J kg-1). An average energy of 32.6 MJ per pesticide application ha-1 was then used as conversion factor. Fertilizer application: The quantity of N and K2O (kg ha-1) were calculated from lb acre-1 applications. Coefficients for energy cost of producing the fertilizer and intrinsic energy of NH4NO3 and K2O were then used. Plastic mulch: The energy contained in plastics was multiplied by the kg of plastic used per ha-1 (for a hectare only 50% of the area was considered to be covered by the plastic mulch). Since plastics were u tilized for both pepper or tomato and sweet corn, the energetic cost was partitioned towards both crops. Sin ce the plastic used in the experiment was biodegradable, the en ergetic cost per kg of PCL was used in the analysis (8.50 *107 J Gross and Karla, 2002 ), w ith an assumed approximately density of 1145 kg m3 was used and this translated in a plastic use of 68 kg ha-1.

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126 Energy efficiency was calculated as the sum of the crop yield energy (in Joules, J) and crop residue energy (in Joules, J ), and the cover crop residue energy when applicable, divided by the energy inputs. Emergy Analysis The standard procedure for static emergy analysis utilizes standardized emergy Excel spreadsheet, according to the Folio # 4 for emergy evaluation of Florida agriculture (Brandt-Willliams, 2002). Calculations and fo rmulas used in the emergy evaluation for each scenario for tomato and pepper cr ops appear are outlined in Table F-5. Cost-effectiveness budgets and energy analysis were used as a basis for the emergy analysis. Energy of labor, fuels, machinery depreciation, and genera lly weight of inputs (g ha-1 cycle-1) were multiplied by a transformity factor (sej unit-1) which was obtained from the literature (Odum, 1996; Brandt-Williams, 2001). The transformity factor is the quotient of the emergy divided by its intrinsic energy content (Odum 1976, 1988) and its un its are emjoules per joule. Some transformities are also reported as emjoules per g of material. Transformity is basical ly a conversion factor calculated from the energy from nature, fossil fuels, labor, and othe r sources (expressed as em$) which were required to produce a unit of such material. Since transformities for agricultural by-produc ts have not been calculated, and the only transformity used in other analysis was that for soil organic matter, new transformities were calculated for broiler l itter and horse manure compost (because these soil organic amendments are utilized on the fa rm). Several assumptions had to be made for the transformities calculations; those assumptions are outlined in Appendix G-1. Due to the inefficient feed metabolism of livestock (40% for chicken and 60% for horses), energetic cost of manure was relativ ely high. Moreover, since manure is not the

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127 primary purpose of livestock production, it was inadequate to partition the energy expenses directly by excrement weight. Ther efore the weight of the excrement (assuming 80 % dry matter percentage (Parnes, 1990) wa s multiplied by the transformity of the primary product for livestock feeding. In the cas e of broiler litter, the value for corn was used. For horse manure, values were base d on a 40% legume pa sture (using soybean transformity) and a 60% grass pasture (bahia grass transformity) using values from the literature (Brandt-Williams, 2002). Only 10% of the remaining energy expenses for livestock, such as labor and services based on data presented by Brandt-Williams (2002) for hens and beef, were included as i nput to the manure This methodology was suggested by Brown (2006; personal communi cation). Additionally, for horse manure the energy expenses from weight of wood chips wa s multiplied by its transformity coefficient based on values from the literature (Odum 1996). For composting process activities quantities of labor and fossil fuels were mu ltiplied by appropriate transformities (Odum, 1996). Sunn Hemp Replacement Scenarios Two soil fertility enrichment alternatives, broiler litter and compost, were also included to evaluate potential benefits of the cover crops. Each alternative was tested for N replacement in lieu of N mineralized by sun hemp (annual biomass addition of 3824 kg contributing up to 76 kg N ha-1). Carbon was not used as the replacement nutrient since quantities necessary for replacing the cove r crop were below the typical compost and broiler litter recommended application rates for vegetable production. Moreover, evaluating C dynamics is beyond the scope of this thesis.

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128 A first order mineralization equation was used for calculating mineralized sunn hemp, with N mineralization (Nmin) calculated as follows: Nmin= No (1-e-kt) where No is the total N content of the residue, k is a decay constant, and t is time in weeks since reside application. Nitr ogen concentration in sunn hem p, broiler litter and compost was assumed to be 2% (Li et al., 2005; Balkcom and Reeves, 2005). Mineralization coefficients in the literature were found, but a constant k f 0.058 for sunn hemp (Rao and Li,, 2003) was used for the calculation. After determining how much sunn hemp mi neralizedN had been released during three years to tomato or pepper and sweet corn, it was calculated that for each year, tomato and pepper uptake acc ounted for roughly 87% of mineralized nitrogen, while sweet corn was assumed to benefit from th e remaining portion (13 %). Therefore for the cost-effectiveness portion of the energy a nd emergy analysis, share expenses were multiplied by the appropriate fractions. In order to match the total N mineralized from sunn hemp during three years, a Michaelis-Menten equation of the form: y = a*t/(b+t), where a= maximum mineralization rate (in kg N ha-1 week -1) and b=time/2 upon reaching maximum mineralization rate (in week s). based on mineralization kmin values for chicken manure reported by Obreza and Ozores-Hampton ( 2000). Using a curve-fi t program yielded values of 0.76 and 27 for a and b, respectively with r2=0.999 (Curve Expert Version1.37, Daniel Hyam s, Starkville, MS). Results CostEffectiveness Analysis Average yield of un-amended treatments (T able 4-1) was consistently higher for sunn hemp summer (SH)-based systems compar ed to the conventiona l (fallow) system.

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129 Yield benefits from N mineralized SH0 were on the order of 30% 30%, 24% for tomato, peppers and sweet corn, respectively (Table 4-2). Yield for all crops increased and reached their maximum values with the high est N-rate, even with the additional N from SH. For tomato yield, benefits from the 112 to 224 N-rate were on the order of 16%, for both fallow and SHbased treatments; for peppe r the extra N enhanced fruit yields by 22% for fallow treatments compared to only 1% for SH treatments. Sweet corn had between 14 to 20% higher yields in SH-based systems. The production costs associated with sunn he mp were comparable to the additional cost of herbicide application in the weed fallow (Tables 4-3, and 4-4). For SH-based systems, seed cost ($265 ha-1) represented the highest expe nse whereas herbicide input ($300 ha-1) was the highest cost for the summer fallow system. Table 4-5 summarizes the results from the budget analyses (Tables E-1, E-2, E-3 and E-4) for the operational expenses of tomato, pepper, and sweet corn for the 12 different management scenarios ou tlined in the methodology section. Gross returns were based on average price per kg of produce for 2003/2004 growing season (Food and Resource Economics, 2005). It should be noted that price changes may greatly affect calculation for the scenarios presented in this chapter. For more details about how prices could affect th e different scenarios see Tables E-5, E-6 and E-7. Average prices used for kg of tomato, pepper and sweet corn were $0.85, $0.85, and $0.55 per kg, respectively. The operational cost for tomato was the hi ghest of all three crops (ranging from $9,559 for the un-amended SH scenario to $10,406 for the compost scenario amended with 224 kg N ha-1). Total operational cost increased by only 1-2% for the intermediate

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130 and highest N rate, which underlines the fact that fertilizer only accounts for a small segment of total production cost. For summer fallow production system scenarios, the gross return per ha was augmented with increm ental fertilizer application rates. However, gross return was even higher for organic am endment based alternative scenarios (gross return was based on the yield of SH treatme nts, therefore gross returns for the three alternatives were assumed to be equally high). Returns before fixed cost showed that for the 0 N-rate, the alternative scenarios almost doubled returns (fallow scenario was 44, 42 and 43%, lower than sunn hemp, compost, and broiler litter scenar ios, respectively). Advantages from alternative systems over th e fallow system decreased with increasing N-rate. Due to the returns, the benefit-cost ratio before fixed costs and harvest cost was the highest for the SH scenario at all N-rate s. Broiler litter, com post, and cover crops scenarios had the lowest marg inal returns per extra kg of N fertilizer from 0 to 112 Nrate, meaning that most of the contribution fr om the organic amendments occurred at low fertilizer doses. For tomato, major operational costs items included pesticide applications and stakes (both about 25-26%), while la bor represented about 22%. Cover crop production cost, cost of compost and its application, and broiler litter associated costs, represented 4, 7, and 5% of tota l production cost for tomato, respectively (Table E-2, E8). For pepper, the broiler litter scenario had the lowest operational cost (14 to 21% lower than other scenarios). As for tomat o, the total operational cost increased with additional fertilizer application rates. For all scenarios, the gross return increased with additional fertilizer level, but gross return s were even higher for organic amendmentbased (alternative) systems, especially at the 0 N-rate. Benefit/cost ratios before fixed costs were highest with sunn hemp and br oiler litter for orga nic amendment-based

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131 systems. The benefit/cost ratio did not incr ease beyond supplemental N-fertilizer of 112 kg N ha-1. The marginal returns per extra kilogram of fertilizer were very low for the alternative scenarios, showing that most of the benefits fo r pepper were coming from the alternative amendment rather than from supplemental inorganic fertilizer. Highest operational costs included seed cost and tr ansplant production, while labor represented about 36% of total operational costs. C over crop establishment cost, purchase and application cost of compost and costs associ ated to broile r litter used accounted for 4, 8, and 6%, respectively (Table E-3, E-8). Sweet corn operational cost was the lo west among the studied crop production systems. For all scenarios, the gross return in creased with additional fertilizer level, but gross returns were even higher for the altern ative scenarios, and specially those at 0 Nrate (20 % higher than the summer fallow 0 N-rate). Returns before fixed cost were negative for all scenarios. The SH scenario was the best option followed by broiler litter, and compost scenarios. Benefit cost ratio before fixed costs and harvest cost was highest for sunn hemp based-systems followed by broile r litter and compost scenarios and values increased with extra N-fertilizer applications. However the marginal returns indicate that the economic system was less efficient when higher fertilizer rate s were used. Highest operational costs included pesticide, machinery, and labor (32, 25, and 27%, respectively). For alternative scenarios, cove r crop cost represented 1%, compost 4% and broiler litter only 3 % of the total operational costs (Table E-3, E8). Overall it appears that with increasing N -rate there were positive increases for all scenarios, but they were not as great and clear for the alternative systems as for the conventional fallow. Moreover, cover crop sc enario operational costs were higher than

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132 cost for a conventional fallow, however returns before fixed costs and benefit cost ratios were higher for cover crop-based systems. Com post and broiler litter alternatives entailed significantly higher cost th an cover crop scenarios. Energy Analysis Total energy input for production of to mato was on the order of 2.0 to 2.4 1011 Joules (Table 4-6). Tomato production with SH at 0 N-rate allowed for a significant reduction in the total energy budget. Total energy input figures for the different scenarios did not increased dramatically with increasing N-fertilizer rate s. However, tomato energy yield did increase with increa sing fertilizer rate. Efficiency calculations show that sunn hemp based systems were more efficient in utilizing energy inputs compared to other scenarios, at all N-rate leve ls (Table 4-6). A more detail ed energy budget is presented in Table F-1. Most of the energy for tomato produc tion was accounted for by inputs derived from fossil fuels such as fertilizer and plas tics, except for the com post and broiler litter scenario where these organic amendments accounted for about 56-66 and 93-95% of the energy in the system (Table 4-7). Human la bor was an insignifican t part of the energy required by the different production systems. Pepper production systems required less energy compared to tomatoes (Table 48). This was particularly true for the cover cr ops scenario at all N -rates. Yield energy was high, comparable to tomatoes yield energy. The efficiency across scenarios varied greatly, for the sunn hemp scenario there was more energy contained in output than total input energy. The lowest efficiency occurred with the broiler litter scenario, as was the case for tomato production as well.

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133 The energy distribution budgets varied greatly (Table 4-9). In the case of the SH scenario, a large portion of the energy was rela ted to fossil fuel input s while energy cost associated with machinery, labor and fuels was insignificant compared to fossil fuelderived inputs. Compost and broiler litter am endments constituted a significant portion of the energy budget, including SH and values we re relatively high compared to those for tomato (Table 4-9) Sweet corn had the lowest total energy cost across scenarios. However, total energy was on the same order of magnitude across s cenarios and crops. Yield energy from sweet corn was comparable to the energy containe d in other crops, despite significant lower crop yields per hectare. Overall efficiency fo r this system was high for all scenarios and N-rates (Table 4-10), but part icularly high for cover crop-based systems. Fallow, compost and cover crop scenarios had a large percentage of ener gy coming from fossil fuel derived inputs, while fuels also accounted for an appreciable fraction of the total energy budget, with the exception of the broiler litter scenario (Table 4-11). Broiler litter accounted for the largest energy fraction for that scenario; but even in this case, overall energy use was lower than for th e other two cropping systems. Some trends were identified regarding the use of supplemental inorganic Nfertilizer. If N-rates increas ed, the percentage of energy for fossil fuel derived inputs increased too, while the other input categories decreased, and this trend held for all crops. A traditional way of looking at the significance of the us e of cover crops, compost and broiler litter is to convert their ener getic value [sum of their intrinsic energy, machinery, labor and weed control (if applicable) ] into a volume of diesel fuel (L), which has an intrinsic energy value of 3.57 x 107 J L-1 (Fluck, 1992). Energy contained in broiler

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134 litter amendment is equivalent to the high est diesel consumption, while sunn hemp amendment was equivalent to the lowest fuel consumption from the four scenarios (Table 4-12). Emergy Analysis For unfertilized tomato and peppers, mo re of the emergy was accounted by human labor. However, for the other N-rates, la bor, fertilizer, and organic amendments accounted for most of the non-renewable ener gy inputs. The total emergy and em$ value for all crop scenarios increased with increa sing fertilizer rate. Across all N-rates the transformity and empower density of tomato cultivation was lower for fallow and sunn hemp scenarios. Meanwhile transformities for broiler litter and compost scenarios were at least one order of magnitude higher. The e nvironmental loading ratio (ELR) Comment you probably should have outlined what this is, how its calculated, and how its interpretation in Materials and Methods (so define this term at some point ) across Nrates followed an increasing order starting by cover crop
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135 scenarios were at least an or der of magnitude lower than fo r compost and broiler litter. The ELR value increased proportionately to N-rate increase. More over, the cover crop scenario had the lowest environmental loading ratio, compared to fallow, compost, and broiler litter. Environmental loading ratio fo r the broiler litter scenario was extremely high. Emergy yield ratio (EYR) for fallow a nd cover crop scenarios decreased with increasing N-fertilizer. The highest EYR was obtained by the cover cropping system, however all systems were very similar. C over crop-based systems were 30 and 95% more sustainable than fallow and compost scenario s, accordingly (Table 4-14). More detailed information about the distribution of emergy co sts for the 24 scenarios (12 for tomato and 12 for pepper) is provided in appendix F. Discussion Cost-Effectiveness Analysis Cover crop cost was comparable to the co st of herbicides application during the summer. Since costs were similar, the adva ntage of using cover crops over herbicides may be related to multiple services that cover crops can provide, including the provision of weed control while assuring a more stable source of on-farm genera ted N fertilizer for subsequent winter vegetable crop. More than reducing produc tion expenses (the amendments and cover crop costs did not acco unt for more than 8% of total production expenses), cover crop scenarios were successf ul due to cover crop related increase in potential yield, as suggested by Baldwin and Creamer (1999). Other authors also reported that the cost of producing the cover crop we re minor compared to total production cost (Wyland et al., 1996).

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136 Due to the fact that sunn hemp is an annual crop, most of its production cost is associated with seed (Sullivan, 2003), which is expensive compared to other cover crops, especially compared to either perennial or annual species that re-seed themselves. The superior performance of cover crops scenarios at all N-rates producing higher gross returns than compost and broiler litter may be related to the transportation and application cost of these organic amendmen ts. For cost analysis, it was assumed that yield was not affected by the source of the organic amendment. Despite the lower price per kg of compost and broiler litter ($0.06 and $0.07 compared to $0.76 per kg of fertilizer, for example), due to their low N content, organic-amendment-based systems required relatively high application rates and tr ansportation cost may be cost-prohibitive. A positive aspect of compost and broiler litter is that they are relatively easy to manage compared to the cover crops. However, there ar e also potential risks associated with their use. Broiler litter may contai n traces of hormones, metals (from feeding) and excessive use may result in nutrient imbalance and/ or hyper accumulation of phosphorus, which may hamper long-term use. Compost can ha ve inconsistent quality and variable percentages of nutrients; more over temperatures during the composting process should be monitored for eradic ation of pathogens. In this study, the cost of cover-crop-derived N was about $5.39 kg -1 (assuming that 76 kg N ha-1 mineralized from SH residue, divide d by the cover crops cultivation cost from Table 4-3). Therefore from an econom ic standpoint the argument of completely replacing the N-fertilizer by using cover crop N appears to be unrealistic. However, if benefits from reduce herbicide use is take into account, cover crops based systems provide a cost savings of $76 ha-1. In addition, the yield benefits for tomato and pepper in

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137 sunn-hemp-based systems for the 0 and 122 N-rate s (Table 4-2) appear ed to be favorable for the tomato and peppers, respectively comp ared to state-wide yields which were 33,434 and 31,383 kg ha-1 (Economic Research Service, 2005?6). Therefore, N requirements of cover crop systems may be 112 kg N ha-1 lower compared to summer fallow systems, which translat es to a net benefit of $86 ha-1. It should also be stated that in the current cost-effectivene ss analysis, externalities costs associated with the use of pesticide or soil erosion were not quantified, however these aspects are critical when evaluating the cost-effectiveness of best management practices For example, Pimentel et al, (1998) suggested that extern ality costs related to loss of N fertilizer and pesticides environmental damage reach about $300 per hectare under intensive maize production. Both, cover crop and its replacement scenario s may address some of these issues, and reduce environmental impacts and enhance so il quality. Regarding fertilizer nutrients loss, it will depend on the quality (C:N, ligni n content) of the amendment and the timing between cover crop extermination and winter cash crop planting. Produce prices play an important role in dete rmining the profitability of this type of farming systems. Prices used for this analysis were static, and the price selected was the average of prices paid to farmers. The marginal gross returns show how the extra kilogram of N fertilizer aff ected the economic optimum for each crop. Tomato yield was strongly enhanced by cover crops mineralizing N, therefore at low N-rates there were low returns per extra kg of fertilizer-N. For pepper yield, enhancement by cover crops was less accentuated at lower N-rates, therefore at lower N-fertili zer rates, returns per extra kg of fertilizer were higher.

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138 Operational costs for Florida tomato and pepper were $12,235 ha-1 and $11,282 kg ha-1, and $4025 ha-1, respectively (Food and Resource Economics, 2005). Production costs for this CSA were consistently lower than those reported in extensive commercial operations. This might be due to the double cropping system in place, where the structures from one crop, such as beds, plastic much and irrigation lines were used for the following crop. Although not included in the current calcul ations, harvest and marketing related costs for tomato, pepper and sweet corn south Florida were $21,069, $18,894, and $5,590, respectively (Food and Resource Ec onomics, 2005). Using these values, scenarios for tomato systems could have positive net returns unde r conventional 224 Nrate and net returns for the alternative systems could be reached at 112 and 224 N-rates. If prices for pepper were to increase, it is possi ble that the fallow syst em would be able to offset these costs. While sweet corn is a crop with much lo wer production costs, its profit margin was well-below tomatoes and peppers, and theref ore it could greatly benefit from extra N from cover crops. Several studies have shown that sweet corn planted right after cover crops performed economically better than conventional corn (H anson et al., 1993; Roberts et al., 1998). However, our sweet corn data did not show a signi ficant yield increase in the sunn hemp treatments, cont rary to the response from tomatoes and peppers. This might be due to the fact that peppers and tomatoes appeared first in the rotation, thus benefiting the most from the fa st-mineralizing N. Therefore the used costs allocation appears to be appropriate. The br oiler litter scenario appeared to provide greater revenues for sweet corn, due to the lower cost from broiler litter.

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139 Each crop had a different partitioning of expenses. For tomatoes, stakes and pesticides made up a high fraction of total ex penses. These costs are related to disease prevention, since tomatoes are very sensitive to bacterial and fungi diseases (Mossler et al., 2006). For peppers, seed cost and transpla nt production accounted for an appreciable fraction of the production cost which may be related to the cost for improved varieties resistant to the multiple diseases that aff ect peppers (Matthews et al., 1999). For sweet corn, costs associated with pesticides and labor were relatively large. Energy Analysis As shown by Sartori et al. (2005)and Oska n et al. (2004), energy from fertilizers and fossil-fuel-derived inputs accounted for a large section of c onventional cultivation energy budgets. In the case of tomato, fertil izers accounted for a considerable amount of energy, across all scenarios. However for th e compost and broiler litter scenarios, energetic content was very high, which reduc ed the relative importa nce of other inputs. These findings are in contrast with reports in the literature where farmyard manure was listed as having the lowest energy input for forage cropping systems (Lal et al., 2003). Calculated energy values were simila r to values listed by Fluck (1992), who reported that energy consumption for tomato and pepper production in Florida was on the order of 3.49 and 3.44 x1011 J ha-1, respectively. However, the distribution of energy cost was different since in Flucks study (1992) labor accounted for 14-20%; whereas in this study, labor energy contributions were insignif icant. Similar to reports by Fluck (1992), energy requirements for tomato production systems were higher compared to pepper systems. In Turkey, Canakci et al. (2005) al so found that tomato production cost was the highest among several di fferent vegetable crops.

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140 Among fossil fuel-derived inputs, plastic mu lch after fertilizer was the item with higher intrinsic energy content. However, fu rther energy reductions may be achieved by replacing plastic mulch by straw mulch. But the potential costs associated with an increase in weed and diseases (increase of he rbicides and pesticides use) may defeat the purpose of eliminating plastic mulch. Since most of the energy in the fallow and SH scenarios came from fossil-fuelderived inputs, it is of interest to compare them in terms of fuel equivalents. The replacement scenarios entail higher energy inputs into the production system, compared to fallow and SH. The energy associated with their use was equivale nt to a high diesel usage (L ha-1). High fuel equivalents were the result of use of herbicides for the chemical control of weeds required during summer. Sweet corn was the only crop with a ne t positive energy output (energy input, minus energy output; Hlsbergen et al., 2001). This is not surprising since sweet corn is a C4 crop which tends to be more efficient biomass producers in high radiation production environments (Loomis and Connor, 1992). Sweet corn energy cost associated with organic soil amendments was lower than fo r either pepper or tomato due to the calculation method (87% of the energy cost was partitioned toward tomato or pepper, the remainder towards sweet corn). A Canadian study, where the objec tive of the energy analysis was to compare the use of manure vs. fertilizer, showed that energy input reductions in manure-amended treatments were due to the elimination of fertilizers (McLaughlin et al., 2000). In our case, give n that the compost and broiler litter N concentrations were low and their energy valu es were one and two orders of magnitude higher than the energetic value of high fertilizer rates use (f or tomatoes and peppers), the

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141 complete replacement of fertilizers would pot entially increase the energy budget instead of reducing it. For sweet corn however, the cost of using compost was even lower than the cost of using synthetic fertilizer, ther efore its use could decr ease the total energy budget, but in this case the use of cover cr op would still provide greater energy savings. For integrated farming systems, values of energy used per ha were on the order of 1010 J ha-1 (Pervanchon et al., 2002). In this analys is all scenarios involving the use of cover crops at 0 N-rate fell in to that order of magnitude. Emergy analysis To perform an emergy analysis it was necessary to define the boundaries of the system. In our case the system was defined as the experimental plot Most of the energy flows were exogenous to the plot level, and to the farm level, sinc e a great portion of the production inputs are outsourced. Ther e is not a wide base of in formation in th e literature regarding emergy analysis in agriculture and the interpretation of emergy indices, however its known that agrofore stry systems are more effici ent than annual cover crop rotations (Lefroy and Ryderber g, 2003), due to their low de pendency on external inputs. Total emergy for the summer fallow and SH-based tomato and pepper production systems at all N fertilizer rates were on the same order of magnitude (1013), and values were comparable to those reported by Brand-Williams (2002). This provides an indication that the base costs information used for this analysis (f allow and cover crop treatments) appears to be realistic. Howeve r, emergy calculations for the compost and broiler litter scenarios were extremely hi gh compared to the other two scenarios. The empower density or energy invested per unit area of farmland for conventional and cover crop scenarios at all fertilizer rates were on the same order of magnitude as the numbers presented by Brand-Williams (2002). The transformities for tomato and pepper

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142 in the fallow and cover crop scenarios were similar to vegetables transformities outlined by Haden (2002) for an integrated farm, who reported that the sust ainability index of vegetable crops was extremel y low (around than 0.01). Sustainability indexes (SI) evaluates how much emergy is produced in relation to potential environmental impacts associated with a specific production system; fo r example an hectare of corn in Italy had a SI in the order of 0.45, while a constructed wetland in Florida had a SI of 2.13 (Uligiati and Brown, 1997). For both tomato and pepper, the total emergy in the fallow scenario was lower than for the cover crop scenario; this was due to the increase of energy related to sunn hemp residue and the extra flow of renewable energy associated with cover crop cultivation According to Uligiati and Brown (2004), prod uction systems with a high percentage of renewable emergy are likely to be more sustainable and also more resilient even under economical stress, compared to those whic h use a high portion of non-renewable emergy. The environmental loading ratios for to mato and peppers were very similar, because both systems featured relatively sim ilar production structures. Despite the fact that transformities for broiler litter and compost were calculated for the replacement scenarios the emergy content in such by-pr oducts was still very high, reducing the accuracy of the emergy evaluati ons for such scenarios. For example the transformity of conventional broiler production is of 4.35 sej g-1poultry (Castellini et al., 2006), while calculated transformities for broiler litter and compost can be on the order of 3.7x 106 sej g-1and 3.27 x 109 sej g-1. Transformity calculations for by-products, therefore should be addressed differently. However, until now there is not a c onsensus among Emergy scholars about how to calculate transform ities for by-products. Since manure is a by-

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143 products and not a co-product (lik e meat and skin from cattle), the energy costs of raising the animal can not be proportionally partitione d towards the kg of meat produced and the kg of manure produced. Manure cannot be treat ed as a co-product, since it has no structure. For example, the structure and concentrated energy of the corn fed to the chicken has been lost through the chicken me tabolic process. Since the energy from the feeding in manure has no structur e anymore, it lost its concentr ation, which is critical for transformities calculations. Moreover, it is difficult to partition the expenses from services towards the chicken manure, in th is case only 10% of t hose expenses were accounted for in the manure. For fallow and cover crops scenarios at 0 N-rate, the highest component in the emergy analysis was human labor. The environmental loading ratios showed that fallow systems were less dependent on purchased and indigenous non-renewable resources and that cover crop systems benefite d from renewable energy flows. This confers resilience to these systems an renders them less vulnerable to changes related to fossil fuel inputs. As a result, increased utilizati on of renewable resources rath er than dependence on nonrenewable inputs, the SI of this two scenarios were highest. General discussion Produce prices dynamics can completely alter cost-effectiveness evaluations. Energy and emergy analysis, on the other hand, are less sensitive to market dynamics and changes in input quantities, since energy values (J) have high intrinsic values that are not prone to short-term fluctuat ion. In the case of the Green Cay Farm, however, it was expected that prices will be re lative stable and that the cost of each basket would generate a greater margin of returns, comp ared to overall wholesale prices.

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144 In order to better understand cost-eff ectiveness dynamics for cover crop-based vegetable production systems, a longer timeperiod should be analyzed and perhaps use of a simulation models would be appropriate to capture the stochast ic nature of shortterm price dynamics and variability in the performance of cover crop-based systems (Lu et al., 2003). Use of simulation models su ch as EPIC (Klonsky, 1994). could facilitate assessment of other system components and pot ential benefits of crop rotations including soil erosion control, N leaching, P run-off, and C sequestration. For example Kelly et al. (1996) simulated 30 years of cr op rotation for winter wheat, wheat straw, soybean and hay concluding that manure amendments treat ments obtained higher gross returns than cover crop systems, opposite to the results obtained for this static analysis. The relatively low price of fertilizer and the very small fraction it represents of the total production costs may not justify th e use of cover crops as a N-fertilizer replacements. Even when the cover crop portion of operational costs is low, on N cost basis their use is at least 7-fold higher than the cost of inorganic fertilizer-N. Therefore most of the advantage of using cover crops at low inputvegetable production systems (less than 112 kg N fertilizer ha-1) may be related to their provision of multiple services including improved weed and erosion contro l and/or there role as a slow-release N source. In general, energy and economic analysis do not follow similar trends. Fertilizer and plastic mulch constituted an important section of th e energy budgets, while their importance for the economic analysis was relatively low. Labor showed a similar pattern, accounting for at least 20% of the operationa l costs of farms while only accounting for less than 2% of the energy budgets.

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145 The energy analysis did not support the statement that sustainable production systems are more labor-intense. For this partic ular system, the level of mechanization was relatively high, rendering the human labor com ponent insignificant for the analysis, as suggested by other authors when removing human labor from the energy budget calculations (Fluck and Baird, 1980; Conw ay, 2005; Sartori, 2005). However, human labor was the first or second most important item in the emergy analysis, despite that human labor transformity is very low (in the order of 106;Odum, 1996). This value was on the same order of magnitude as the cattle transformity, which seems counter intuitive, since humans are animals which generate high order products (such as ideas, innovations, and information flow, among others). Moreover, human life cycles are longer and require much more services and goods than cattle. The energy expenditure per dollar (Table 4-12; Miranowoski, no date), could be used as a sustainability indicator, with sy stems with lower energy per dollar ratios being more sustainable. Such systems would use less energy require and overall energy inputs would also be lower. However, there should be a clear distin ction between the energy source for a given input. For example the energy cost of cover cr ops, broiler litter, and compost came from both indirect energy (fossil fuels, fertiliz ers) and direct energy, which comes from the intrinsic value of the actual material. These in trinsic values contain part of solar energy transformed into chemical energy through photos ynthesis. Solar energy constituted a big portion of the input energy for sunn hemp resi due, sawdust in the case of broiler litter, and bedding and woodchips in the case of co mpost. However, the solar energy invested

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146 in those materials does not pose an environmental burdens. Emergy analysis helps standardize these different sources of energy into a co mmon unit, the solar joule. In order to understand the importance of cover cropping systems at a micro scale, and how there are linked to higher order pro cess occurring at meso-scales, it would be necessary to model the entire system (F igure 4-1) and not on ly the cover crop compartment. At a micro scale, other envi ronmental data would also be required in order to account for externalit ies related to cover cropping sy stems. However, this is beyond the scope of this study. The static s cenario analysis did allow an improved understanding in what manner cover crops can enhance farm sustainability, especially for low input crop production. Conclusions Cover crops could help offset market risks by increasing yi elds, rather than minimizing production cost. However, there ma y also be potential savings associated with their use due to the elimination of herbicide applications used for summer fallow systems and N in low input and/or organic syst ems. With increase of N-fertilizer rate, the benefits from cover crop diminished. In th is case, combination of high inorganic-N application rates and cover crops may hamper efficient N utilization (which could create environmental problems), reflected in reduced marginal returns per kg of applied N ha-1. In cover crop-based systems, N fertilizer optima occurred below maximum N-fertilizer recommended dose (or 224 kg N ha-1). Partitioning of cover cr op cost to different crops should reflect the mineralization rate of N and subsequent N benefits. Energetically, cover crops represent en ergy consumption reduction and increased focus on enhanced ability of production syst ems to sustain/enhance productivity while reducing the dependence of the farm on ex ternal inputs. Corre sponding energy input

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147 savings are more likely to be observed in high-yielding produc tion systems. In this case, potential energy benefits will stem from w eed control and reduced fertilizers use while the output energy (yield and residue) is enhanced by the cover crop mineralized-N. Cost-effectiveness, energetic, and emergetic analysis were consistent in identifying the scenarios with highest production asso ciated-costs. However, cost-effectiveness analysis failed to identify the inputs which had the greatest emergetic value, such as fertilizers that have a low monetary value wh ile their emergetical (and energetical) cost are extremely high. Use of energy and em ergy evaluations resulted in similar conclusions. The main difference between th e two techniques is that energy evaluation lacks considering the energy from free res ources, such as sun, water-geopotential, evapotranspiration. However given the reduced s cale of analysis of this work, it would be necessary to reconsider the need of includi ng this broad environmental aspects into the emergy analysis. In the present case, emergy, energy, and economic analyses were successful in determining that cover crops may provide an ecological sound production option which was partly due to the increase in yields due to cover crop (sunn hemp) and reduced use of inorganic fertilizers for optimum yields and herbicides for weed suppression. Given the limited spatial-temporal scale of analysis, this st udy was not able to address complex questions about how cover crops affect relationships of the farm with components at meso-scales (such as markets), but it was able to provide an insight about how cover crops could enhance sustainable cu ltivation and profits at farm level.

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148 Table 4-1. Overview of cropping sequence a nd experimental treatments at Boynton Beach (2002-2005). 1SH T or P 0 SC 224SH T or P 0 2SH T or P 75 SC 149F T or P 112 SC 100 3SH T or P 75 SC 224SH T or P 112 SC 100 4SH T or P 149 SC 149F T or P 224 SC 100 5SH T or P 149 SC 224SH T or P 224 SC 100 6F T or P 224 SC 224F T or P 0 SC 100 Spring 04 and 05 Spring N-rate Summer 02 Spring N-rate Summer 03 and 04 Winter 03/04 and 04/05 Winter N-rate Treatment Winter 02/03 Winter N-rateSpring 03 SH= Sunn hemp; T= Tomato; P= Pepper; SC= Sweet corn. Table 4-2. Summary of yields for tomato, pe pper and sweet corn as affected by summer cover crop (sunn hemp) and N-fert ilizer rate (2004 and 2005). Treatment Tomato Pepper Sweet corn ----------------kg ha-1-----------F0 25,400 16,300 5,200 F112 36,000 24,700 7,525 F224 43,100 31,800 8,900 SH0 36,500 23,400 6,475 SH112 39,200 31,500 8,725 SH224 47,100 31,700 10,050 F0 =fallow and 0 kg N ha-1 ; F112=fallow and 112 kg N ha-1 ; F224=fallow and 112 kg N ha-1 ; SH0= sunn hemp used as a cover crop and 112 kg N ha-1 ; SH112 = sunn hemp used as summer cover crop and 112 kg N ha-1 ; SH224= sunn hemp used as summer cover crop and 224 kg N ha-1. Table 4-3. Average cost of gr owing sunn hemp (2003 and 2004). Item Unit Quantity (# units) Unit Cost ($) Total ($ ha-1) Input Seeds kg 45 6 265 Equipment Rent Planting h 1.1 15 16 Scratching h 1.2 15 18 Disking h 1.2 15 18 Dragging h 1.2 15 18 Mowing h 1.2 15 18 Subtotal 90 Labor Costs

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149 Planting of cover crop h 1.1 9 10 Scratching h 1.2 9 11 Dragging h 1.2 9 11 Disking h 1.2 9 11 Mowing h 1.2 9 11 Subtotal 54 Total $410 Table 4-4. Average summer weed cont rol production expenses (2003 and 2004). Item Unit Quantity ( # units) Unit Cost ($) Total ($ ha-1) Input Herbicide L 28.1 4.34 299.6 Equipment Rent Spraying h 2.9 15 43 Disking h 3.7 15 55 Mowing h 1.2 15 18 Subtotal 117. Labor Costs Spraying h 2.9 9 26 Disking h 3.7 9 33 Mowing h 1.2 9 11 Subtotal 70 Total 486 Herbicide used was Round Up, with a cost of $4.3 L-1 (farmer records) and an application rate of 9 L ha-1 per application (Roe, 2006; personal communication)

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150Table 4-5. Budget analysis for the different crop management s cenarios, based on average cost and yield during 2003 and 2004. TomatoPepper Sweet Corn TomatoPepper Sweet Corn TomatoPepper Sweet Corn TomatoPepper Sweet Corn 0 25.416.35.236.523.46.536.523.46.536.523.46.5 112 36.024.77.539.231.58.739.231.58.739.231.58.7 224 43.131.88.947.131.710.147.131.710.147.131.710.1 0 95898989431097029102412010406980645101017095694461 112 96759074440497889188420610492989146041025596554555 224 97609160449898749273430810577997746981034197414649 0 21590138552860310251989035613102519890356131025198903561 112 30600209954139333202677547993332026775479933320267754799 224 36635270304895400352694555284003526945552840035269455528 0 120014866-14502132310788-5582061910084-9492085510321-899 112 2092511921-265235321758759322828168841952306517120244 224 26875178703973016117672121929458169688292969417204878 B:C Ratio 0 2.31.50.73.22.20.93.02.00.83.12.10.8 112 3.22.30.93.42.91.13.22.71.03.22.81.1 224 3.83.01.14.12.91.33.82.71.23.92.81.2 0 112 79.763.010.619.760.710.319.760.710.219.760.710.2 224 53.153.15.959.20.85.659.20.85.759.20.85.7 Item Cover CropCompostBroiler litter Fallow Marginal Returns ($ N kg-1) Total Operational Cost ($ ha-1) Total Yield (Mg ha-1) Gross return ($ ha-1) Return before fixed cost ($ ha-1) Gross return = yield (kg ha-1) ($ kg-1) Operational cost ($ ha-1). B:C Ratio = Benefit Cost Ratio = price ($ kg-1) x yield (kg ha-1) / Operating cost not including harvest + fix cost ($ ha-1).

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151 Table 4-6. Energy analysis summary for to mato production per ha at Boynton Beach, Florida, for 12 different (hypothe tical) production scenarios (2003-2004). Item Fallow (J) Cover crop (J) Compost (J) Broiler litter (J) Total Cost 0 N-rate 1.95E+11 9. 83E+10 2.51E+11 1.86E+12 112 N-rate 2.17E+11 1. 20E+11 2.72E+11 1.88E+12 224 N-rate 2.38E+11 1.41E +11 2.94E+11 1.91E+12 Yields 0 N-rate 3.13E+10 4. 50E+10 4.50E+10 4.50E+10 112 N-rate 4.44E+10 4. 83E+10 4.83E+10 4.83E+10 224 N-rate 5.31E+10 5.81E +10 5.81E+10 5.81E+10 Residues Tomato 1.47E+10 1.47E+10 1.47E+10 1.47E+10 Sunn hemp 8.04E+09 Efficiency 0 N-rate 0.24 0.69 0.24 0.03 112 N-rate 0.27 0.59 0.23 0.03 224 N-rate 0.28 0.57 0.25 0.04

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152 Table 4-7. Energy distribution among diffe rent production components for tomato production per ha at Boynton Beach, Flor ida, for 12 different (hypothetical) scenarios. Item Fallow (J) Cover crop (J) Compost (J) Broiler litter (J) % Fossil fuels derived inputs* 0 N-rate 94 83 29 4 112 N-rate 94 86 35 5 224 N-rate 95 88 40 6 % Machinery 0 N-rate 7 15 5 1 112 N-rate 6 12 5 1 224 N-rate 6 11 4 1 % Labor 0 N-rate 0 0 0 0 112 N-rate 0 0 0 0 224 N-rate 0 0 0 0 % Fuels 0 N-rate 1 2 3 0 112 N-rate 1 1 2 0 224 N-rate 1 1 2 0 % Cover crop, manure, or litter 0 N-rate 13 66 95 112 N-rate 11 61 94 224 N-rate 9 56 93 Alternative scenarios do not include fossil fuel derived inputs for cover crop production. All inputs, labor, fuels and machinery expenses are aggregated in th e cover crop manure category. For fallow scenarios it considers the cost of pre-planting weed control. Fo r compost and broiler litter scenarios the pre-planting weed control is included in the energy allocation to % cover crop, manure or litter calculation, at the bottom of the table

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153 Table 4-8. Energy distribution from the ener gy analysis for pepper production per ha at Boynton Beach, Florida, for12 diffe rent (hypothetical) scenarios. Item Fallow (J) Cover crop (J) Compost (J) Broiler litter (J) Total Cost 0 N-rate 1.45E+11 4.86E+10 2.01E+111.81E+12 112 N-rate 1.67E+11 7.00E+10 2.23E+111.83E+12 224 N-rate 1.88E+11 9.14E+10 2.44E+111.86E+12 Yields 0 N-rate 2.72E+10 3.91E+10 3.91E+103.91E+10 112 N-rate 4.12E+10 5.26E+10 5.26E+105.26E+10 224 N-rate 5.31E+10 5.29E+10 5.29E+105.29E+10 Residues Pepper 1.73E+10 1.73E+10 1.73E+101.73E+10 Sunn hemp 8.04E+09 Efficiency 0 N-rate 0.31 1.33 0.28 0.03 112 N-rate 0.35 1.11 0.31 0.04 224 N-rate 0.37 0.86 0.29 0.04

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154 Table 4-9. Energy distribution from the ener gy analysis for pepper production per ha at Boynton Beach, Florida, for 12 different (hypothetical) scenarios. Item Fallow (J) Cover crop (J) Compost (J) Broiler litter (J) % Fossil fuels derived inputs 0 N-rate 92 65 12 1 112 N-rate 93 76 70 2 224 N-rate 94 81 27 4 % Machinery 0 N-rate 1 3 1 0 112 N-rate 1 2 1 0 224 N-rate 1 1 1 0 % Labor 0 N-rate 0 0 0 0 112 N-rate 0 0 0 0 224 N-rate 0 0 0 0 % Fuels 0 N-rate 1 3 1 0 112 N-rate 1 2 1 1 224 N-rate 1 2 1 1 % Cover crop, manure, or litter 0 N-rate 27 82 98 112 N-rate 18 74 97 224 N-rate 14 68 96

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155 Table 4-10. Energy analysis summary for sweet corn production per ha at Boynton Beach, Florida, for 12 different (hypothetical) scenarios. Item Fallow (J) Sunnhemp (J) Compost (J) Broiler litter (J) Total Cost 0 N-rate 6.88E+10 4. 82E+10 8.03E+10 4.15E+11 112 N-rate 9.02E+10 6. 96E+10 1.02E+11 4.37E+11 224 N-rate 1.12E+11 9.11E +10 1.23E+11 4.58E+11 Yields 0 N-rate 4.01E+10 5. 04E+10 5.04E+10 5.04E+10 112 N-rate 4.21E+10 5. 58E+10 5.58E+10 5.58E+10 224 N-rate 5.17E+10 6.78E +10 6.78E+10 6.78E+10 Residues Sweet corn 3.67E+10 3.67E+10 3.67E+10 3.67E+10 Sunnhemp 1.20E+09 Efficiency 0 N-rate 1.12 1.83 1.08 0.21 112 N-rate 0.87 1.35 0.91 0.21 224 N-rate 0.79 1.16 0.85 0.23 Efficiency or overall energy ratio (Smith and McChesne y, 1979; cited by Barber, 2004) is calculated as all the energy outputs of the system (sum of yields and residues) divided by the total energy input requirements.

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156 Table 4-11. Energy distribution fr om the energy analysis for sweet corn production per ha at Boynton Beach, Florida, 12 diffe rent (hypothetical) scenarios. Item Fallow (J) Sunnhemp (J) Compost (J) Broiler litter (J) % Fossil fuels derived inputs 0 N-rate 63 44 25 5 112 N-rate 71 62 41 10 224 N-rate 77 71 51 14 % Machinery 0 N-rate 2 2 1 0 112 N-rate 1 2 1 0 224 N-rate 1 1 1 0 % Labor 0 N-rate 1 2 1 0 112 N-rate 1 1 1 0 224 N-rate 1 1 1 0 % Fuels 0 N-rate 18 26 16 3 112 N-rate 14 18 12 3 224 N-rate 11 14 10 3 % Cover crop, manure, or litter 0 N-rate 5 43 89 112 N-rate 3 34 85 224 N-rate 2 28 81 Table 4-12. Energy distribution from the energy analysis fo r crop production per ha at Boynton Beach, Florida, for four di fferent (hypothetical) scenarios. Tomato or PeperSweet cornTomato or PeperSweet cornTomato or PeperSweet corn Fallow1.10E+112.28E+10307964045294 Cover crop1.29E+102.21E+09361623642 Compost1.65E+113.43E+104632962436212 Broiler litter1.78E+123.69E+11499791035532893268 Scenario Energy expenditure per dollar -------------MJ-1$-1------------Diesel equivalents ------------J ha-1cycle-1-----------------------L ha -1cycle -1--------Energy

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157Table 4-13. Emergy analysis main indicators from energy analysis for tomato production per ha at Boynton Beach, Florida, for tw elve different (hypothetical) scenarios. 0 N-rate 112 N-rate Notes Tomato Units Fallow Compost Broiler Cover crop Fallow Compost Broiler Cover crop Summary Total emergy J 1013 1500 9129 32448 1598 1660 9583 32608 2052 Emdollar value Em $ 15323 91620 324808 15976 16925 96154 326410 21063 18 Total yield, dry weight g 1.68E+06 2.41E+06 2.41E+06 2.41E+06 2.38E+06 2.59E+06 2.59E+06 2.38E+06 19 Total stover residue, dry weight g 3.27E+06 3.27E+06 3.27E+06 3.27E+06 3.27E+06 3.27E+06 3.27E+06 3.27E+06 20 Total yield, energy J 3.13E+10 4.50E+10 4.50E+10 4.50E+10 4.44E+10 4.83E+10 4.83E+10 4.83E+10 21 Tomato stover residue, energy J 9.24E+09 9.24E+09 9.24E+09 9.24E+09 9.24E+09 9.24E+09 9.24E+09 9.24E+09 22 Emergy tomato fruit mass sej/g 8.95E+09 3.79E+10 1.35E+11 6.63E+09 6.99E+09 3.70E+10 1.26E+11 8.64E+09 23 Transf*. tomato fruit sej/J 4.79E+05 2.03E+06 7.21E+06 3.55E+05 3.74E+05 1.98E+06 6.75E+06 4.25E+05 24 Transf.tomato plant DW sej/J 3.70E+05 1.68E+06 5.98E+06 2.95E+05 3.10E+05 1.66E+06 5.66E+06 3.57E+05 25 Empower density sej/cycle/ ha-1 5.69E+16 3.47E+17 1.23E+18 6.06E+16 6.30E+16 3.64E+17 1.24E+18 7.79E+16 Indexes 26 Environmental loading ratio 7 47 168 6 8 49 168 8 27 Emergy yield ratio 1.12 1.02 1.00 1.13 1.11 1.02 1.00 1.10 28 Sustainability index 0.16 0.02 0.01 0.19 0.14 0.02 0.01 0.14 Transformity, abbreviated as Transf. Notes refer to Table F-4, that appendix explains th e calculations involved in each Item designed with a note number.

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158 Table 4-13. Cont. 224 N-rate Notes Tomato Units Fallow Compost Broiler Cover crop Summary Total emergy J 1013 2407 10036 33062 2506 Emdollar value Em $ 24392 100689 330945 25598 18 Total yield, dry weight g 2.10E+06 3.11E+06 3.11E+06 2.84E+06 19 Total stover residue, dry weight g 3.27E+06 3.27E+06 3.27E+06 3.27E+06 20 Total yield, energy J 5.31E+10 5.81E+10 5.81E+10 5.81E+10 21 Tomato stover residue, energy J 9.24E+09 9.24E+09 9.24E+09 9.24E+09 22 Emergy tomato fruit mass sej/g 1.15E+10 3.23E+10 1.06E+11 8.81E+09 23 Transf*. tomato fruit sej/J 4.53E+05 1.73E+06 5.69E+06 4.32E+05 24 Transf. tomato plant DW sej/J 3.86E+05 1.49E+06 4.91E+06 3.72E+05 25 Empower density sej/cycle/ha-1 9.13E+16 3.81E+17 1.25E+18 9.51E+16 Indexes 26 Environmental loading ratio 12 51 171 10 27 Emergy yield ratio 1.07 1.02 1.00 1.08 28 Sustainability index 0.09 0.02 0.01 0.11 Transformity, abbreviated as Transf.

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159Table 4-14. Emergy analysis main indicators from energy analysis for pepper production per ha in Boynton B each, Florida, for 12 different (hypothetical) scenarios 0 N-rate 112 N-rate Notes Tomato Units Fallow Compost Broiler Cover crop Fallow Compost Broiler Cover crop Summary Total emergy J 1013 1233 8863 32176 1708 1687 9316 32630 2161 Em$ value Em $ 12664 88961 322095 17637 17199 93496 326630 22171 18 Total yield, dry weight g 4.89E+05 7.02E+05 7.02E+05 7.02E+05 7.41E+05 9.45E+05 9.45E+05 9.45E+05 19 Total stover residue, dry weight g 2.55E+06 2.55E+06 2.55E+06 2.55E+06 2.55E+06 2.55E+06 2.55E+06 2.55E+06 20 Total yield, energy J 2.72E+10 3.91E+10 3.91E+10 3.91E+10 4.12E+10 5.26E+10 5.26E+10 5.26E+10 21 Pepper stover residue J 1.73E+10 1.73E+10 1.73E+10 1.73E+10 1.73E+10 1.73E+10 1.73E+10 1.73E+10 22 Emergy pepper fruit mass sej/g 2.52E+10 1.26E+11 4.58E+11 2.43E+10 2.28E+10 9.86E+10 3.45E+11 2.29E+10 23 Transf*. pepper fruit sej/J 4.53E+05 2.27E+06 8.23E+06 4.37E+05 4.09E+05 1.77E+06 6.20E+06 4.11E+05 24 Transf. pepper plant DW sej/J 2.77E+05 1.57E+06 5.71E+06 3.03E+05 2.88E+05 1.33E+06 4.67E+06 3.09E+05 25 Empower density sej/cycle/ha-1 4.68E+16 3.36E+17 1.22E+18 6.48E+16 6.40E+16 3.54E+17 1.24E+18 8.20E+16 Indexes 26 Environmental loading ratio 9 69 254 6 13 73 257 7 27 Emergy yield ratio 1.26 1.01 1.00 1.14 1.06 1.01 1.00 1.11 28 Sustainability index 0.14 0.01 0.00 0.20 0.08 0.01 0.00 0.15 Transformity, abbreviated as Transf.

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160Table 4-14. Cont. 224 N-rate Notes Tomato Units Fallow Compost Broiler Cover crop Summary Total emergy J 1013 2140 9770 33083 2615 Emdollar value Em $ 21733 98030 331164 26469 18 Life cycle of pepper g 9.54E+05 9.51E+05 9.51E+05 9.51E+05 19 Energy input g 2.55E+06 2.55E+06 2.55E+06 2.55E+06 20 Transformity J 5.31E+10 5.29E+10 5.29E+10 5.29E+10 21 Pepper stover residue J 1.73E+10 1.73E+10 1.73E+10 1.73E+10 22 Emergy pepper fruit mas sej/g 2.24E+10 1.03E+11 3.48E+11 2.75E+10 23 Transf.* pepper fruit sej/J 4.03E+05 1.85E+06 6.25E+06 4.94E+05 24 Transf. pepper plant DW sej/J 3.04E+05 1.39E+06 4.71E+06 3.72E+05 25 Empower density sej/cycle/ha-1 8.12E+16 3.71E+17 1.26E+18 9.93E+16 Indexes 26 Environmental loading ratio 16 76 261 10 27 Emergy yield ratio 1.05 1.01 1.00 1.09 28 Sustainability index 0.06 0.01 0.00 0.11 Transformity, abbreviated as Transf

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161 Figure 4-1. Overview of inter-r elation between processes and economic scales using an Object-Oriented programming appro ach outlining how cover crop best management practices at a micro scale interact with meso scales

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162 CHAPTER 5 CONCLUSION The natural environment is the produc tion base for agriculture, and while functioning as a resource base, it can also act as a limiting factor. Therefore adaptive management is critical for successful and sustainable agri cultural production. Conventional agricultural pract ices have been counter to this notion, trying to superimpose human goals upon its natural e nvironment. This has led to increased depletion and/or degradation of natural resources, including water pollution. The use of cover crops should be regarded as an element of adaptive management that confers resilience to production systems and could help alleviate problems related to conventional production scheme s and traditional markets. Cover crops enhance soil richness (increasing particulate organic matte r and microbial activit y), thereby reducing soil erosion. Via release of allelopathic and/or nematicidal agents, they may reduce nematicides and herbicides use while provi ding habitat for beneficial insects. By enhancing inherent soil fertil ity, nutrient cycling and rete ntion, they can reduce our dependence on external fertilizer inputs and potential negative environmental impacts. Appropriate use of cover crops thus can provi de farmers with a myriad of services and benefits that can enhance the sustainability and profitability of farming systems (Chapter 1). North Central Florida production environmen ts features transitional weather (Cherr, 2004) and combined with low inhere nt soil fertility, cover crop-based systems may consist of a summer/fall or/and winter c over crops, combined with a cash crop with

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163 high N demands. Southeast Florida has a subtropical climate and the cropping season is extended which allowings for the production of vegetables throughout the fall/winter and early spring while cover crops are grown during summer time when temperatures are to high for growing vegetable crops. The main goal of this study was to evaluate the soil-plant dynamics of cover cropbased systems. During the first year of this study sunn hemp ( Crotalaria juncea ) a summer cover crop was grown in the summer /fall and/or followed by a mix of hairy vetch ( Vicia villosa and rye ( Secale cereale ) grown during the wint er. The premise of using cover crops was that they would reduce N-fertilizer needs of spring planted sweet corn crop and may facilitate the build-up of soil organic matter. Sunn hemp indirectly enhanced sweet corn ( Zea mays ) yields, by doubling the dry matter accumulation of the rye. Results showed that a double cover cropping system (sunn hemp during summer followed by a winter cover crop mix) increased yield and biomas of sweet corn. However, yield of sweet corn followi ng a double cover cropping system were only comparable to conventional fu ll-rate fertilizer (200 kg N ha-1), when sweet corn was supplemented with 133 kg N ha-1. In this case sunn hemp pr ovided N-fertilizer savings on the order of 67 kg N ha-1. Moreover, the lack of a pronou nced yield increase for sunn hemp-based systems was partially the result of the poor growth of sunn hemp. This was related to a gradual build up of a soil-borne disease, which greatly impacted the growth of sunn hemp during the third year of continuou s cultivation of that crop in the same location. Lack of synchronization between nutr ient release patterns from crop residues, combined with low inherent so il fertility and poor soil nut rient retention capacity of

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164 Florida sandy soils, also hampered efficien t use of residues across all cover-crop-based systems. During the second year of the study, different crop rotations were tested in order to better understand the interac tion between legume-grasses and high N demanding crops. The systems tested included pearl millet ( Pennisetum glaucus ) during the summer/fall, followed by broccoli ( Brassica oleracea ) during winter, and watermelon ( Citrullus lanatus ) during the spring. The purpose of this rotation was to build-up organic matter during the summer, include a cash crop that woul d also accumulate ad equate quantities of biomass during the winter, and a then ut ilize high demanding crop afterwards which could benefit from residual soil N. The second rotation tested was cowpea ( Vigna unguiculata ) during the summer/fall, followed by br occoli during winter and watermelon during spring. The idea behind this system was to test a double purpose cover crop (N fixing and yield bearing), followed by high-N-demanding winter broccoli and watermelon. The third system consisted of sesbania ( Sesbania sesban ) during the summer, hairy vetch and rye (similar to the previous year rotati on of sunn hemp with hairy vetch and rye) followed by watermelon. Pearl millet was a suitable biomass accumulator for low fertility and high precipitation production environmen t. It provided a more gra dual nutrient re lease patterns (because it accumulated most of its N in l eaves), which enhanced broccoli dry matter accumulation. However, cowpea-based systems, resulted in earlier broccoli crown production and increased broccoli yield compared to pearl millet-based systems. In terms of its function as a cover crops, the variety used had a short growth cycle and accumulated only limited amounts of biomass and N and it was also adversely affected

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165 by diseases and use of late variety may be preferable. Sesbania was very sensitive to nematodes. But despite the intrinsic quali ties of the cover crops chosen and their corresponding growth cycles, in retrospect, us e of appropriate management practices is most critical for successful implementati on of cover crop-based production systems (Chapter 2). Low N use efficiencies and large amounts of N being poorly utilized by either sweet corn or watermelon wa s related to poor synchroniza tion between N realese and subsequent commercial crop N demand. Relativ ely large proportion of the cover crop N was either lost due to leaching or remain ed unavailable until the end of the cropping season (Chapter 3). So although cover crop N may enhance soil fertility at a later stage, it did not result in particularly efficient nu trient utilization. Ti ming of cover crop termination and crop planting, therefore is cr itical in assuring optimal synchronization and maximizing the benefits from potentially mineralizable N from crop residues. Results by Cherr (2004) in the same experimental ar ea showed that duri ng a-two-week period following crop senescence a great proportion of the ready available N was lost from the crop residue. This is especially true for summer legume cover crops. The warm temperatures, the coarse soils, and high rain fall intensities speeds up mineralization and subsequent N leaching from the effective root zone. Achieving early and adequately developed root systems of subs equent commercial crops (sweet corn, broccoli, and watermelon) was especially difficult in a non-tillage system. Presence of crop residue often hampered planting activities, and in the absence of appropriate zero-till equipment often resulted in heterogeneous germination of directseeded crops and increased mort ality of transplanted crops.

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166 From a N budget perspective, use of a summer leguminous cover crop in North Central Florida may not be appropriate, unles s a commercial crop like broccoli follows it directly during the winter. Use of a gramineous cover crop such as pearl millet may be be more desirable, especially if it is fo llowed by another cover crop during the winter. Since it is difficult to time cover crop ex termination and vegeta ble crop planting, intercropping may provi de a viable alternative. Howeve r, not all crops performed well under intercropping, and vigorous groundcovers su ch as hairy vetch may hamper crop development (Chapter 3) and delay fruit produc tion of a subsequent crop as was the case with watermelon, possibly due to the comp etition for light, water, and nutrients. Despite the rapid nutrient release dynamics of cover cropping systems in Florida production environments, a more long-term tren d in enhanced soil fertility was observed. This study was a continuation from a previous study and over a period of three years a gradual increase in sweet corn yield occurre d for non-fertilized cover crop treatments (Cherr 2004). From the second to the third year, marketable yields for CC-based systems fertilized with 133 kg N ha-1 increased 6 to19 %, and prod uctivity of cover crop-based systems fertilized with only 67 kg N ha-1 increased 19 to 45% (Chapter 3). In south Florida, sunn hemp enhanced yiel ds of tomato, pepper and sweet corn in a Community Supported Agriculture farm, augmen ting crop gross returns. It also was evident that increases in N fertilization for cover crop-based vegetable systems usually alters the marginal returns per kg of extr a fertilizer. The consumption of petroleumderived inputs was also reduced by the us e of cover crops, which also limits the dependency of external inputs and establishe s cover crops as a su stainable agricultural practice from an energetic po int of view (Chapter 4).

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167 For all of these reasons, our main hypothe sis, that cover crops can supplement vegetable crop N needs, enhance profitability of farm operation, and assure sustainability at a farm-level, is confirmed. Still, there is a need to enhance our understanding of how to improve the synchronization between nutrien t release and subsequent crop demand via improved timing of cover crop termination a nd subsequent planting of commercial crops. Other issues to be addres sed are how to improve crop establishment in non-tillage systems, and how to integr ate living mulch, (intercropping ) in commercial vegetable production systems.

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168 APPENDIX A EFFECT OF INTERACTIONS IN COVER CROPS DRY MATTER ACCUMULATION, N CONCENTRATION AND N ACCUMULATION Table A-1. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) inte raction (ST*Np) on dry weight, N concentration, and N accumulation of sunn hemp ( Crotalaria juncea ), during the summer/fall 2003. RootsShootsTotalRootsShootsTotalRootsShootsTotal 00.040.200.2321.639.9 a 36.8 a1.07.68.4 670.050.300.3419.640.1 a 36.4 a0.911.812.6 1330.040.210.2522.041.0 a 37.9 a0.98.49.3 00.261.411.679.627.6 b24.9 ab2.538.841.3 670.391.912.3010.925.0 b22.6 b4.147.351.5 1330.261.311.579.330.8 a27.1 a2.540.042.6 00.694.134.829.924.1 a22.3 a7.0100107 670.834.565.389.423.6 a21.6 a7.9109116 1330.744.765.499.622.3 a20.8 a7.5109116 00.705.075.778.817.3 a6.3 a6.386.092.4 670.866.787.648.916.4 a15.6 a7.5112120 1330.786.687.457.915.3 a14.5 a5.6100106 00.725.906.6210.914.8 a14.5 a 7.684.592.1 670.607.588.1912.017.1 a16.9 a7.5129136 1330.845.826.6612.416.5 a15.6 a9.695105 -----------kg ha-1 -----------Dry Weight Np ------------Mg ha-1 -----------WAE 2 N concentrationN accumulation -----------g kg-1 -----------WAE 14 WAE 11 WAE 5 WAE 8 WAE= weeks after emergence. Means followed by identi cal lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking.

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169 Table A-2. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) inte raction (ST*Np) on dry weight, N concentration, and N accumulation of cowpea ( Vigna unguiculata ), during the summer/fall 2004. RootsShootsTotalRootsShootsTotalRootsShootsTotal 00.03 a0.090.1030.742.340.91.28.18.8 670.04 a0.300.3033.844.142.91.311.513.0 1330.04 a0.300.2935.043.642.51.310.812.0 00.24 a2.980.3224.832.631.65.296.5102 670.21 a2.472.6926.732.231.85.679.084.3 1330.23 a2.662.8921.729.929.14.995.4101 00.38 a4.705.0813.820.219.55.493.098.3 670.34 a4.214.5513.421.721.14.590.094.5 1330.26 a4.124.3816.220.520.24.283.888.0 00.37 a2.402.7614.720.019.25.449.054.0 670.30 a2.352.6412.017.617.03.74.545.3 1330.48 a2.933.4013.019.018.15.955.061.3 WAE 11 N accumulation -----------kg ha-1 -----------WAE 5 N concentration -----------g kg-1 -----------Dry Weight Np WAE 2 ------------Mg ha-1 -----------WAE 8 WAE= weeks after emergence. Means followed by identi cal lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking.

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170 Table A-3. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) inte raction (ST*Np) on dry weight, N concentration, and N accumulation of pearl millet ( Pennisetum glaucum ), during the summer/fall 2004. RootsBiomassTotalRootsBiomassTotalRootsBiomassTotal 00.050.360.4015.325.624.50.7 a9.29.9 670.030.250.2715.428.527.10.5 a6.97.4 1330.040.350.3915.725.924.80.6 a9.09.7 00.242.793.0211.412.912.82.7 a38.040.6 670.202.582.789.813.112.82.0 a34.136.1 1330.192.833.0210.013.513.31.9 a38.440.2 00.395.896.286.68.07.92.6 b48.550.7 670.687.488.747.08.18.14.7 a60.266.7 1330.556.096.647.39.59.33.9 ab56.360.2 00.587.998.555.87.67.63.4 a60.968.9 670.7510.311.05.67.47.34.2 a75.980.2 1330.678.068.746.88.78.64.4 a70.274.7 ---------Mg ha-1 --------WAE 11 WAE 2 WAE 5 WAE 8 Np -----------g kg-1--------------------kg ha-1--------N concentrationN accumulation Dry Weight WAE= weeks after emergence. Mean s followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking.

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171 Table A-4. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applied to sweet corn (Np) inte raction (ST*Np) on dry weight, N concentration, and N accumulation of sesbania ( Sesbania sesban ), during the summer/fall of 2004. RootsBiomassTotalRootsBiomassTotalRootsBiomassTotal 00.020.070.0928.8 a34.933.60.52.43.0 670.010.630.0824.8 a34.632.80.42.12.5 1330.020.070.0923.0 a32.030.30.42.32.7 00.120.550.6713.7 a12.712.81.66.98.5 670.170.871.0414.3 a11.211.72.49.511.9 1330.221.361.5817.3 a12.613.53.717.721.4 00.070.390.4610.1 a9.99.90.73.94.6 670.130.570.6914.9 a9.810.51.95.77.3 1330.180.851.0313.0 a8.19.12.77.810.5 00.060.390.465.5 a6.26.10.42.73.1 670.070.350.417.9 a8.48.50.53.23.7 1330.191.031.219.9 a9.49.51.810.111.9 -----------Mg ha-1 --------------------g kg-1 -----------------------kg ha-1 -----------N accumulation WAE 8 WAE 11 N concentration WAE 5 Dry Weight WAE 2 Np WAE= weeks after emergence. Means followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking.

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172Table A-5. Effect of sampling time (ST) a nd residue [RES = residue of sunnhemp (SH ) or fallow vegetation (F) ] interaction (ST*RES) on dry weight, N concentration, and N accumulation of rye ( Secale cereale ), during the winter of 2003/04. SHFSHFSHFSHFSHFSHFSHFSHFSHF --------------------g kg -1---------------------------------------------kg ha-1 ----------------WAE 20.030.020.10A0.04A0.13A0.06A14.6A19.1A40.2A39.0A34.6A33.1A0.6A0.5B4.03.04.63.3WAE 50.300.100.68A0.22A0.98A0.32A8.3A 9.3A18.4A21.7A15.3A17.6A2.6A0.9B12.24.714.85.6WAE 80.300.161.47A0.61A1.77A0.77A8.9A8.8A17.5A17.5A16.0A15.6A2.6A1.4A26.210.728.812.1WAE 110.330.233.21A1.70A3.54A1.93A8.8A5.9B18.4A17.6A17.4A16.0A2.9A1.4B41.320.844.222.2WAE 140.390.265.54A2.85B5.93A3.10B5.7A6.2A12.5A 14.3A12.0A13.5A2.2A1.6A68.839.971.041.4WAE 170.400.256.40A3.49B6.80A3.74B8.3A7.2A9.2B1 4.8A9.2B14.2 A3.3A1.9B61.450.464.752.2RootsShootsTotal -------------------Mg ha-1 --------------------STDry WeightN ConcentrationN accumulation RootsShootsTotalRootsShootsTotal Sampling time in weeks after emergence (WAE ). Means followed by identical lower case letters in the same row are not significan tly different according to Tukeys test (p<0.05), A, B, C denote higher to lower ranking. SH= sunn hemp used as summer cover crop residue, F= summer fallow residue. Table A-6. Effect of sampling time (ST) and residue [ RES = residue of sunnhemp (S H) or fallow vegetation (F) ] interaction (ST*RES) on dry weight, N concentration, a nd N accumulation of hairy vetch ( Vicia villosa ), during the winter of 2003/04. SHFSHFSHFSHFSHFSHFSHFSHFSHF --------------------g kg -1---------------------------------------------kg ha-1 ----------------WAE 20.01A0.01A0.010.010.020.0217.2A17.1A----0.2A0.2A----WAE 50.04A0.03A0.050.040.090.0720.6A20.6A33. 443.329.831.01.0A1.0A1.91.53.02.6WAE 80.02A0.03A0.090.090.120.1132.1A31.7A39. 740.338.338.01.3A1.3A7.68.69.510.3WAE 110.03A0.03A0.330.340.360.3724.5A22.8A39.4 42.639.941.80.9A0.8A14.815.317.721.4WAE 140.08B0.23A0.961.431.041.6624.6B28.9A36.8 37.235.335.52.0B6.4A37.053.539.059.1WAE 170.15A0.19A2.322.362.452.5525.3A26.0A32.3 33.331.932.33.7A5.3A75.379.579.081.2RootsShootsTotal -------------------Mg ha-1 --------------------STDry WeightN ConcentrationN accumulation RootsShootsTotalRootsShootsTotal Sampling time in weeks after emergence (WAE ). Means followed by identical lower case letters in the same row are not significan tly different according to Tukeys test (p<0.05), A, B, C denote higher to lower ranking. SH= sunn hemp used as summer cover crop residue, F= summer fallow residue.

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173Table A-7. Effect of sampling time (ST) and residue [ RES = residue of sunnhemp (S H) or fallow vegetation (F) ] interaction (ST*RES) on dry weight, N concentration, and N accumulation of hairy vetch +rye, duri ng the winter of 2003/04. SHFShootsFSHFSHFSHFSHFSHFSHFSHF --------------------g kg -1---------------------------------------------kg ha-1 ----------------WAE 20.04A0.03A0.110.050.150.0817.4 A 16.9A36.4A33.0A30.2A26.0A0.7A0.5A4.05.34.65.3WAE 50.34A0.14B0.720.261.070.399.8A11.5A19.8A24.4 A 16.7A20.0A3.7A1.9A14.16.317.88.2WAE 80.32A0.18A1.560.701.880.8810.6A12.2A19.6A20.6A17.3A18.8A3.9A2.7A35.819.538.622.3WAE 110.36A0.26A3.532.073.872.3310.8A8.38A21.0A23.1A21.5A23.6A4.3A1.9A55.736.363.244.3WAE 140.48A0.49A6.504.286.984.769.2B16.5A16.6B22.1A16.1B21.4A4.3B8.1B10693.4110103WAE 170.55A0.45A8.635.779.176.2313.4 A15.4A16.7A21.7A16.6A21.1A7.0A7.2A137126144134RootsShootsTotal -------------------Mg ha-1 --------------------STDry WeightN ConcentrationN accumulation RootsShootsTotalRootsShootsTotal Sampling time in weeks after emergence (WAE ). Means followed by identical lower case letters in the same row are not significa ntly different according to Tukeys test (p<0.05), A, B, C denote higher to lower ranking. SH= sunn hemp used as summer cover crop residue, F= summer fallow residue.

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174Table A-8. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previous ly applied to sweet corn (Np) interaction (ST*Np) effect on dry weight, N concentra tion and N accumulation in rye ( Secale cereale ), during the winter of 2004/05. RootsShootsTotalRootsShootsTotalRootsShootsTotal 00.020.080.1020.941.335.20.73.35.8 670.020.110.1315.942.639.10.34.85.5 1330.020.120.1419.745.140.80.35.26.8 00.050.250.2910.325.422.70.56.26.6 670.050.320.379.425.122.70.58.08.5 1330.070.340.418.825.522.70.68.69.1 00.110.520.636.913.412.40.87.17.9 670.100.610.717.511.811.20.77.17.9 1330.110.740.869.011.311.01.18.39.4 00.191.441.635.810.19.61.114.816.1 670.221.361.586.79.69.21.512.714.2 1330.261.541.7913.69.710.13.314.918.2 00.201.822.029.56.56.81.812.113.9 670.212.002.228.36.06.21.612.013.6 1330.262.612.8610.17.27.52.418.821.2 00.422.132.557.46.97.03.114.717.8 670.492.523.007.86.76.83.516.820.3 1330.452.492.949.17.57.73.918.522.4 ----------kg ha-1 --------------------g kg-1 --------------------Mg ha-1 ----------Np Dry WeightN concentrationN accumulation WAE 14 WAE 17 WAE 2 WAE 5 WAE 7 WAE 11 WAE= weeks after emergence

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175Table A-9. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previous ly applied to sweet corn (Np) interaction (ST*Np) effect on dry weight, N concentratio n and N accumulation in hairy vetch ( Vicia villosa ), during the winter of 2004/05. RootsShootsTotalRootsShootsTotalRootsShootsTotal 00.010.04 a0.05 a46.743.944.00.40.51.7 670.000.03 a0.03 a46.351.850.00.22.22.0 1330.010.04 a0.05 a46.655.954.50.42.34.7 00.050.27 a0.32 a45.346.846.52.212.714.9 670.030.21 a0.25 a46.947.647.41.69.711.3 1330.050.25 a0.30 a46.243.443.82.310.913.1 00.131.45 a1.58 a38.226.127.35.140.845.9 670.131.65 a1.78 a39.519.925.16.234.348.1 1330.081.16 a1.24 a37.630.330.83.136.039.1 00.272.89 a3.64 a31.532.636.47.9103129 670.292.92a 3.21 a30.933.032.88.996.4105 1330.242.24a 2.49 a33.431.230.57.271.274.5 00.413.93 a4.35 a 28.631.831.512.0125137 670.334.17 a4.49 a35.430.631.011.4125137 1330.354.02 a4.37 a36.932.933.212.8132145 01.067.19 a8.24 b21.226.425.722.3188211 670.8810.9 b11.8 a28.223.123.625.1257282 1330.847.50 a8.30 b24.532.130.821.2220237 WAE 8 ----------kg ha-1 ----------Dry WeightN concentration WAE 2 Np WAE 5 N accumulation ----------Mg ha-1 --------------------g kg-1 ----------WAE 14 WAE 17 WAE 11 WAE= weeks after emergence. Me ans followed by identical lower case letters in the same row are not significantly different acc ording to Tukeys test (p<0.05), a, b, c denote higher to lower ranking.

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176Table A-10. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously applie d to sweet corn (Np) interaction (ST*Np) effect on dry weight, N concentration and N accumulation in rye+hairy, during the winter of 2004/05. RootsShootsTotalRootsShootsTotalRootsShootsTotal 00.030.13 a0.15 a26.941.439.31.14.85.7 670.030.14 a0.16 a21.744.741.40.55.96.8 1330.030.16 a0.19 a28.548.143.50.88.88.1 00.100.51 a0.61 a27.336.635.12.718.921.6 670.100.53 a0.62 a23.733.432.02.117.619.8 1330.120.59 a0.72 a24.033.032.92.819.423.0 00.241.98 a2.22 a24.422.622.85.847.953.7 670.232.25 a2.48 a26.617.520.86.941.457.7 1330.201.91 a2.10 a21.322.922.84.244.348.4 00.394.27 a4.83 a24.626.728.69.1115143 670.514.28 a4.79 a20.325.525.010.3109119 1330.503.78 a4.41 a23.122.719.311.786.181.6 00.615.75 a6.36 a22.523.923.813.8137151 670.546.17 a6.70 a24.522.022.213.0137150 1330.616.63 a7.23 a25.323.223.415.2151166 01.489.31 b10.8 b17.122.021.325.4203228 671.3713.4 a14.8 a21.020.120.328.6274302 1331.309.98 b11.3 b19.026.325.025.1238264 Np Dry WeightN concentration WAE 14 WAE 17 WAE 2 WAE 5 WAE 8 N accumulation ----------Mg ha-1 --------------------g kg-1 --------------------kg ha-1 ----------WAE 11 WAE= weeks after emergence. Me ans followed by identical lower case letters in the same row are not significantly different acc ording to Tukeys test (p<0.05), a, b, c denote higher to lower ranking.

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177 APPENDIX B CARBON AND NITROGEN CONCENTRATI ON IN DIFFERENT PLANT PARTS OF SUMMER AND WINTER COVER CROPS Table B-1. Carbon (C) to Nitrogen (N) relation (C:N ratio) for differe nt plant parts in summer cover crops. Tissues C concentration N concentration C:N ---g C kg-1 -----g N kg-1 ------ratio ----Sunn hemp Roots 4131.69 23.75.67 17.4 Stems 4441.95 11.90.769 37.4 Leaves 4347.26 37.82.04 11.5 Shoots 441 19 23.5 Cowpea Roots 4172.51 13.20.62 31.7 Stems 4213.92 17.31.66 24.3 Leaves 40313.0 45.53.46 8.9 Shoots 420 36.2 11.6 Pearl Millet Roots 41011.8 6.150.18 66.6 Stems 4111.28 4.980.29 82.6 Leaves 4156.40 11.61.04 35.8 Shoots 412 7.2 57.4 Sesbania Roots 4501.30 8.880.46 50.7 Stems 4415.52 6.950.37 63.5 Leaves 4510 41.60 10.8 Shoots 441 7.4 60.0

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178 Table B-2. Carbon (C) to Nitrogen (N) relation (C:N ratio) for differe nt plant parts in winter cover crops. Tissues C concentration N concentration C:N ---g C kg-1 ------g N kg-1 -------ratio ----Rye 04 Roots 31632.4 10.31.11 30.8 Stems 4325.02 9.190.69 47.0 Leaves 4264.63 21.31.66 19.9 Shoots 430 12.5 34.3 Rye 05 Roots 26927.4 8.580.18 31.3 Stems 4260.86 5.670.54 75.1 Leaves 35312.2 13.82.65 25.7 Shoots 413 7.1 58.0 Hairy 04 Roots 3963.14 24.21.18 16.4 Stems 4261.45 29.31.96 14.5 Leaves 4411.70 47.60.95 9.3 Shoots 431 35.9 12.0 Hairy 05 Roots 35721.4 28.02.50 12.7 Stems 3752.10 25.53.59 14.7 Leaves 36411.0 25.75.38 14.2 Shoots 369 25.6 14.4 Mix 2004 Roots 347 15.6 22.2 Stems 430 15.0 28.7 Leaves 431 31.1 13.9 Shoots 430 19.9 21.7 Mix 2005 Roots 321 20.0 16.1 Stems 393 18.4 21.4 Leaves 364 24.7 14.7 Shoots 379 21.6 17.5

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179 APPENDIX C WEATHER DATA FOR RESEARCH STATION Table C-1. Average temperature (at 60 cm he ight), minimum and maximum temperature (MinT and MaxT at 60 cm height), and average of solar radiation (AVGsolrd at 2 m height) for twelve months during 2003. 60cm 60cm 60cm 2m Year Month Average MinT MaxT AVGsolrd C C C (w m2) 2003 January 9.55 -6.63 25.78 146 2003 February 14.78 -0.52 29.53 141 2003 March 19.61 2.29 31.69 153 2003 April 20.44 -0.11 32.44 221 2003 Mayo 25.46 13.59 37.15 235 2003 June 25.92 18.04 36.4 192 2003 July 26.48 20.41 37.08 189 2003 August 26.15 20.72 35.42 178 2003 September 24.99 15.3 35.2 161 2003 October 21.7 9.32 32.61 138 2003 November 18.76 -0.23 32.36 127 2003 December 11.7 -5.25 27.03 121 Table C-2. Average temperature (at 60 cm he ight), minimum and maximum temperature (MinT and MaxT at 60 cm height), and av erage of solar radia tion (AVGsolrd at 2 m height) for twelve months during 2004. 60cm 60cm 60cm 2m Year Month Average MinT MaxT AVGsolrd C C C (w m2) 2004 January 12.33 -4.28 28.24 124 2004 February 13.88 -0.57 29.03 111 2004 March 17.64 -0.14 29.99 200 2004 April 19.31 4.54 32.77 231 2004 Mayo 24.58 8.8 36.81 248 2004 June 26.83 19.07 37.47 228 2004 July 26.82 19.73 36.74 204 2004 August 26.34 20.44 36.53 172 2004 September 25.84 17.73 36.32 154 2004 October 22.69 7.27 34.19 154 2004 November 18.7 6.24 32.22 135 2004 December 12.59 -3.67 29.06 111

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180 Table C-3. Average temperature (at 60 cm he ight), minimum and maximum temperature (MinT and MaxT at 60 cm height), and av erage of solar radia tion (AVGsolrd at 2 m height) for twelve months during 2005. 60cm 60cm 60cm 2m Year Month Average MinT MaxT AVGsolrd C C C (w m2) 2005 January 14.2 -2.42 30.19 118 2005 February 14.78 -0.48 28.15 136 2005 March 16.05 1.03 30.88 164 2005 April 18.52 4.28 31.15 236 2005 Mayo 22.53 9.93 33.39 216 2005 June 25.87 19.07 34.96 186 2005 July 27.74 19.38 37.57 215 2005 August 27.5 21.21 37.29 190 2005 September 26.11 17.95 36.26 180 2005 October 21.51 3.82 33.61 142 2005 November 18.02 1.11 31.31 131 2005 December 11.88 -1.3 27.3 105 Table C-4. Average rainfall fo r twelve months during 2003. Year Month Rainfall ---mm ---2003 January 4 2003 February 129 2003 March 182 2003 April 14 2003 Mayo 33 2003 June 238 2003 July 130 2003 August 148 2003 September 101 2003 October 114 2003 November 46 2003 December 22

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181 Table C-5. Average rainfall fo r twelve months during 2004. Year Month Rainfall ---mm ---2004 January 44 2004 February 143 2004 March 55 2004 April 25 2004 Mayo 70 2004 June 142 2004 July 272 2004 August 160 2004 September 420 2004 October 117 2004 November 35 2004 December 39 Table C-6. Average rainfall fo r twelve months during 2004. Year Month Rainfall ---mm ---2005 January 23 2005 February 65 2005 March 121 2005 April 148 2005 Mayo 163 2005 June 197 2005 July 102 2005 August 196 2005 September 102 2005 October 121 2005 November 58 2005 December 75

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182 APPENDIX D NITROGEN DYNAMICS AND INTERACT IONS FOR SWEET CORN, BROCCOLI AND WATERMELON Table D-1. Nitrogen app lied to sweet corn ( Zea mays var. Saturn Yellow) in form of NH4NO3 fertilizer and summer a nd winter cover crops residue and weeds, during the spring of 2004 (kg ha-1). Fixed Effects NAP* Residual N + Weeds --kg N ha-1--------kg N ha-1-----CS SW 248 a 181 a SF 122 b 55 b FW 207 a 141 a FF 152 b 18 b Significance *** *** N-rate* 0 100 c 100 67 170 b 103 133 228 ab 95 200 215 ab 15 267 288 a 21 Significance *** NS CS= cropping system, SW= sunn hemp used as summer cover crop followed by hairy vetch + rye winter cover crop mix, SF = sunn hemp used as summer cover crop combined with a winter fallow, FW = summer fallow combined with hairy vetch + rye winter cover crop mix, FF = summer and winter fallows. Means followed by identical lower case letters in the same column are not significan tly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking.* Nitrogen Applied to sweet corn in form of NH4NO3 fertilizer, residual summer and winter cover cr ops N, and end of the winter season weeds N (NAP).

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183 Table D-2. Effect of sampling time (ST or WAE) and kg ha-1of N fertilizer applied to sweet corn (N-rate) interaction effect (ST* N-rate) on dry weight, N concentration, N accumulation in shoots and SPAD readings (chlorophyll readings) of sweet corn leaves ( Zea mays ), during the spring of 2004. N-rate Dry matter content N concentration N content SPAD WAE 2 0 0.06 a 44.0 c 3.3 a 27.3 b 67 0.06 a 52.4 b 3.1 a 32.3 a 133 0.06 a 59.1 a 3.8 a 32.9 a WAE 4 0 0.68 a 24.2 c 16.4 b 30.6 b 67 0.8 a 37.2 b 29.6 a 34.5 ab 133 1.05 a 45.4 a 47.0 a 36.8 a WAE 6 0 2.9 c 12.9 b 37.4 c 29 c 67 5.24 b 15.4 b 80.6 b 42.3 b 133 6.35 a 19.2 a 120 a 47.2 a WAE 9 0 2.35 c 9.3 c 22.8 c 33.4 c 67 5.06 b 13.2 b 66.1 b 47.7 b 133 6.52 a 17.9 a 116 a 54.3 a WAE= weeks after emergence. Means followed by identi cal lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking.

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184 Table D-3. Sampling time (ST) and cropping sy stem (CS) interaction effects (ST*CS) on dry weight, N concentration, N accumu lation in shoots and SPAD readings (chlorophyll readings) of sweet corn leaves ( Zea mays ), during the spring of 2004. CS Dry matter content N concentration N content SPAD WAE 2 SW 0.06 a 52.3 a 3.4 a 30.5 a SF 0.07 a 52.4 a 4.1 a 32.4 a FW 0.06 a 51.9 a 3.6 a 31.5 a FF 0.05 a 50.6 a 2.7 a 29.0 a WAE 4 SW 0.89 a 38.1 a 34.3 a 35.3 a SF 0.96 a 32.1 b 31.7 a 33.6 a FW 0.76 a 36.9 a 29.8 a 34.7 a FF 0.75 a 34.8 ab 27.8 a 32.4a WAE 6 SW 5.28 a 16.9 a 93.3 a 41.5 ab SF 5.11 ab 14.2 a 73.6 b 37.3 b FW 4.37 b 16.9 a 76.0 b 42.2 a FF 4.57 ab 15.1 a 72.9 b 37.2 b WAE 9 SW 5.13 a 15.0 a 81.6 a 48.0 a SF 4.79 ab 11.7 a 60.6 b 42.9 b FW 4.39 ab 14.1 a 65.8 ab 46.7 ab FF 4.26 b 12.9 a 64.4 b 43.2 ab WAE= weeks after emergence. Means followed by identi cal lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking. SW = sunn hemp used as summer cover crop followed by hairy vetch + rye winter cover crop mix, SF= sunn hemp used as summer cover crop combined with winter fa llow, FW= summer fallow combined with hairy vetch + rye winter cover crop mix, FF = summer and winter fallow

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185Table D-4. Effect of kg ha-1of N fertilizer applied to watermelon (N-rate) and cropping system (CS) interaction (N-rate*CS) on dry weight, N concentration and N accumulation of sweet corn ( Zea mays ), during the spring of 2004. Cropping systems N-rate SW SF FW FF SW SF FW FF Dry matter content N concentration ------------------Mg ha-1--------------------------------------g N kg-1------------0 1.82Ac 1.71 ABb 1.43ABb 1.04Bc 23.1 21.9 23.2 21.5 67 2.97Ab 3.02Ab 2.65Aa 2.53Ab 30.9 27.5 30.3 28.6 133 3.74Aa 3.46Aa 3.11Aa 3.65Aa 37.9 32.6 35.9 34.7 N content SPAD ----------------g N kg-1-----------------0 25.1Ac 22.4 Ac 19.7Ac 12.7Ac 32.8Ab 29.6ABb 31.4Ab 26.5Bc 67 52.5Ab 44.4Ab 44.4Ab 39.0Ab 41Aa 38.4ABa 40.7ABa 37Bb 133 81.5Aa 61.6Ba 68.1ABa 75.2ABa 42.6Aa 41.7Aa 44.2Aa 42.9Aa Means followed by identical upper case letter in the same row, or identical lower case letters in the same column are not sign ificantly different according to Tukeys test (p<0.05), letters A or a, B or b, C or c denote higher to lower ranking. SW = sunn hemp used as summer cover crop followed by hairy vetch + rye winter cover crop mix, SF= sunn hemp summer cover crop combined with winter fallow, FW= summer fallow combined with hairy vetch + rye winter cover crop mix, FF = summer and winter fallow

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186 Table D-5. Effects of sampling time (ST) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (ST*N-rate) on marketable, fancy, non marketable (culls ) and total yield of sweet corn ( Zea mays ), during the spring of 2004. Cropping systems (CS) N-rate SW SF WF FF SW SF WF FF -----------------------------------------------kg ha-1---------------------------------------------------------------Marketable Fancy 0 2175 Ac 794 Abc 2408 Ac 122 Bc 455 Ac 228 Ac 506 Ac 0c 67 11371Ab 7001BCb 9352ABb 5182Cb 7597Ab 3946ABb 6260ABb 2543Bb 133 15848Aa 15723Aa 14618ABa14234Ba13743Aa 13501ABa11966ABa 11128Ba Culls Total 0 1591Aa 760 Aba 1641 Aa 452 Bb 3766Ac 1554Bc 4049 Ac 574 Bc 67 727Aa 1080Aa 912Aa 1665Aa 12283Ab 7728BCb 107432ABb6847Cb 133 2280Aa 672Aa 1939Aa 877Ab 18129Aa 16395ABa16556ABa 15111Ba Means followed by identical upper case letter in the same row, or identical lower case letters in the same column are not sign ificantly different according to Tukeys test (p<0.05), letters A or a, B or b, C or c denote higher to lower ranking. SW = sunn hemp used as summer cover crop followed by hairy vetch + rye winter cover crop mix, SF= sunn hemp used as summer cover crop combined with winter fallow, FW= summer fallow combi ned with hairy vetch + rye winter cover crop mix, FF = summer and winter fallow

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Table D-6. Equations for critical points of SPAD for sweet corn, SPAD, and NO3 for watermelon, and critical N concentration (g N kg-1) in broccoli leaves. Parameter* Sweet Corn (SPAD) Broccoli (N concentration) Watermelon (SPAD) Watermelon (NO3 -) A -18.4 4.30 -21.49 13.123 -68.5 31.7562 -6.706.953 B 0.63 0.0932 0.910-0.3903 2.56 1.1951 0.1110.058 CP 56.8 4.51 45.45 7.5574 33.7 2.3427 214.457.5 Parameter*= A is an estimate of the intercept, B is an estimate of the slope, and CP is an estimate of the critical point or plateau. Table D-7. N applied to broccoli ( Brassica oleracea var. Pac Man) in form of fertilizer (NH4 NO3), cover crops residue and weed s, during the winter of 2004/05. Fixed Effects NAP* N residue +Weeds ------kg N ha-1-------RES CP 189 80.4 PM 172 63.3 Significance NS NS N-rate 0 71.9 a 131 205.2 b 196 266 c Significance Np 0 71.9 67 74.2 133 69.5 Significance NS N-rate*RES NS Np RES NS RES= residue, CP= cowpea as a summer cover crop, PM = pearl millet as a summer cover crop. Means followed by identical lower case letters in the same column are not significan tly different according to Tukeys test (p<0.05), le tters a, b, c denote higher to lower ranking.* Nitrogen Applied to sweet corn in form of NH4NO3 fertilizer, residual summer and winter cover cr ops N, and end of the winter season weeds N (NAP). Nitrogen previously applied to sweet corn (Np).

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188 Table D. Effect of sampling time (ST) and kg ha-1of N fertilizer applied to broccoli (Nrate) interaction (ST*N-rate) effect on dry weight, N concentration and N accumulation in broccoli ( Brassica oleraceae ), during the winter of 2004/05. Dry matter content N concentration N accumulation Nrate Roots Shoots Crowns Roots Shoots CrownsRoots Shoots Crowns ---------------Mg ha-1----------------------------g N kg-1-------------------------kg N ha-1-------------WAE 3 0 0.00 a 0.02 a 18.4 a 34.0 b 0.06 a 0.7 b 131 0.00 a 0.07 a 24.2 a 58.7 a 0.12 a 3.9 a 196 0.00 a 0.06 a 26.0 a 60.5 a 0.15 a 4.0 a WAE 6 0 0.01 a 0.12 b 14.6 b 31.2 c 0.20 b 3.7 c 131 0.05 a 0.56 a 19.6 b 45.6 b 0.98 a 24.8 b 196 0.06 a 0.73 a 26.2 a 53.8 a 1.55 a 39.1a WAE 9 0 0.03 b 0.32 b 0.01 b 11.0 b 20.4 b 16.2 0.30 b 5.9 c 1.7 b 131 0.14 ab 1.41 a 0.25 a 14.1 b 31.9 a 58.1 2.32 a 44.5 b 14.0 a 196 0.16 a 1.55 a 0.35 a 21.2 a 39.3 a 61.9 3.27 a 63.7 a 21.5 a WAE 13 0 0.10 b 0.43 b 0.1 a 7.92 b 14.6 b 37.5 0.73 b 6.4 b 4.1 a 131 0.30 ab 1.72 a 0.2 a 11.0 ab20.9 ab 54.3 3.2 a 35.6 a 10.2 a 196 0.28 a 2.09 a 0.16 a 14.7 a 26.9 a 60.4 3.48 a 55.7 a 9.5 a WAE 16 0 0.33 b 1.14 b 0.01 b 5.03 b 9.60 b 0.00 1.6b 11.2 b 0.0 b 131 0.65 a 2.92 a 0.04 a 6.69 ab13.7 b 45.5 4.38 a 39.8 a 3.4 a 196 0.62 a 3.19 a 0.11 a 9.83 a 18.9 a 52.6 6.05 a 61.0 a 6.3 a WAE 19 0 0.27 b 0.99 b 0.1 b 5.51 a 10.8 c 30.6 1.76 b 11.8 b 3.1 b 131 0.65 a 2.87 a 0.25 a 5.53 a 14.9 b 37.2 3.55 ab 43.8 a 9.4 a 196 0.51 a 3.02 a 0.3 a 8.85 a 17.2 a 41.1 4.58 a 57.5 a 12.5 a WAE = weeks after emergence. Means followed by identical lower case letters in the same column are not significantly different according to Tu keys test (p<0.05), letters a, b, c denote higher to lower ranking.

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189 Table D-9. Effect of sampling time (ST) and residue [ RES = residue of cowpea (CP) or residue of pearl millet (P) ] interaction (ST*RES)effect on dry weight, N concentration and N accumulation in broccoli ( Brassica oleraceae ), during the winter of 2004/05. WAE = weeks after emergence. Means followed by identical lower case letters in the same column are not significantly different according to Tu keys test (p<0.05), letters a, b, c denote higher to lower ranking. Dry matter content N concentration N accumulation RES Roots Shoots CrownRootsShoots Crown Roots Shoots Crown --------------Mg ha-1----------------------g N kg-1--------------------kg N ha-1------------WAE 2 CP 0.00 0.06 a 23.5 53.8 0.2 3.4 PM 0.00 0.04 a 22.2 48.3 0.1 2.3 WAE 5 CP 0.05 0.56 a 19.9 43.0 0.1 26.4 PM 0.03 0.38 a 20.4 44.1 0.7 18.7 WAE 8 CP 0.14 1.28 a 0.27 15.4 29.9 49.5 2.5 45.1 16.4 PM 0.09 0.90 b 0.13 15.5 31.0 41.3 1.4 31.0 8.4 WAE 11 CP 0.24 1.46 a 0.15 10.7 21.0 52.4 2.8 34.0 8.4 PM 0.21 1.37 a 0.15 11.7 20.5 49.0 2.2 31.1 7.5 WAE 14 CP 0.57 2.58 a 0.07 7.1 13.9 32.9 4.1 38.9 3.6 PM 0.50 2.25 a 0.06 7.3 14.2 32.5 3.9 35.7 2.8 WAE 17 CP 0.58 2.92 a 0.26 6.5 16.2 35.4 3.7 49.9 9.6 PM 0.38 1.67 b 0.18 6.8 12.5 37.2 2.9 25.5 7.1

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190Table D-10. Effect of kg ha-1of N fertilizer applied to broccoli (N-rate) and residue residue [ RES = residue of cowpea (CP) or residue of pearl millet (P) ] interaction (N-rate*RES) on dry weight, N c oncentration and N accumulation in broccoli ( Brassica oleraceae ), during the winter of 2004/05. CPPMCPPMCPPMCPPMCPPMCPPMCPPMCPPMCPPM 0 0.160.08 0.680.33 0.070.04 25.725.751.647.656.547.61.10.410.82.43.31.2 131 0.350.25 1.780.39 0.220.16 32.634.375.377.51251162.62.236.028.110.67.9 296 0.280.27 1.961.58 0.280.18 44.243.792.885.41341323.42.952.141.614.610.3 RootsShoots ----kg N ha-1---ShootsCrown Crown N content Roots N concentration Shoots Roots ----g N kg-1---N-rate Dry matter content ----Mg ha-1---Crown

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191 Table D-11. N applied to watermelon ( Citrullus lanatus var. Mardi Gras) in form of fertilizer (NH4NO3), cover crops residue and we eds, during the spring of 2005. Fixed Effects NAP* N residue +Weeds ----kg N ha-1---------kg N ha-1----CS CP+B 141 b 445 b PM+B 137 bc 41 b SB+W 389 a 293 a FF 108 c 1 c Significance *** *** N-rate 0 84 d 84 b 84 187 c 103 a 124 223 bc 97 ab 168 272 b 104 a 210 316 a 106 a Significance *** ** N-rate*CS NS Means followed by identical lower case letters in the sa me column are not significa ntly different according to Tukeys test (p<0.05), lette rs a, b, c denote higher to lower ranking. Nitrogen Applied to sweet corn in form of NH4NO3 fertilizer, residual summer and winter cover cr ops N, and end of the winter season weeds N (NAP)

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192 Table D-12. Effect of sampling time (ST) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (ST* N-rate) on dry weight, N concentration, and N accumulation in watermelon shoots and fruits ( Citrullus lanatus ), during the spring 2005. Dry matter content N concentration N content Shoots Fruits Total Shoots Fruits Shoots Fruits Total N-rate -------------Mg ha-1------------------g N kg-1---------------kg N ha-1----------WAE 3 0 0.00 0 a 0.02 a 28.1 b 0 a 0 0 a 0.0 a 84 0.00 0 a 0.00 a 39.5 a 0 a 0.3 0 a 0.2 a 168 0.00 0 a 0.00 a 46.9 a 0 a 0.5 0 a 0.5 a WAE 6 0 0.02 0 a 0.02 a 23.7 b 0 a 0.4 0 a 0.4 a 84 0.13 0 a 0.13 a 35.2 a 0 a 4.8 0 a 4.8 a 168 0.11 0 a 0.11 a 35.2 a 0 a 3.5 0 a 3.6 a WAE 9 0 0.09 0 a 0.09 b 22.5 a 0 a 2.0 0 a 2.0 b 84 0.58 0 a 0.58 a 21.3 a 0 a 11 0 a 11 ab 168 0.79 0 a 0.79 a 27.7 a 0 a 21.6 0 a 21.6 a WAE 12 0 0.52 0.03 c 0.54 b 17.5 a 25.2 a 11.1 0.8 c 11.9 b 84 1.18 0.32 b 1.50 a 19.9 a 25.0 a 21.8 7.8 b 29.6 a 168 0.54 0.55 a 1.09 a 17.6 a 26.7 a 10.3 15.0 a 25.3 a WAE = weeks after emergence. Means followed by identical lower case letters in the same column are not significantly different according to Tu keys test (p<0.05), letters a, b, c denote higher to lower ranking.

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193 Table D-13. Effect of sampling time and cropping system interaction (ST*CS) effect on dry weight, N concentration and N accumulation in watermelon shoots and fruits( Citrullus lanatus ), during the spring of 2005. Dry matter content N concentration N content CS Shoots Fruits Total Shoots Fruits Shoots Fruits Total ---------------Mg ha-1-------------g N kg-1------------kg N ha -1-------WAE 3 CP+B 0 0 a 0.02 33.5 a 0 a 0.11 a 0 a 0.1 a PM+B 0 0 a 0.00 29.4 a 0 a 0.3 a 0 a 0.3 a SB+W 0 0 a 0.02 27.8 a 0 a 0.1a 0 a 0.1 a FF 0 0 a 0.00 20.3 a 0 a 0.6 a 0 a 0.6 a WAE 6 CP+B 0.11 0 a 0.10 40.5 a 0 a 3.6 a 0 a 3.6 a PM+B 0.12 0 a 0.12 27.4 a 0 a 3.5 a 0 a 3.5 a SB+W 0.02 0 a 0.02 18.6 a 0 a 0.8 a 0 a 0.8 a FF 0.1 0 a 0.10 13.3 a 0 a 3.7 a 0 a 3.7 a WAE 9 CP+B 0.37 0a 0.37 38.1 a 0 a 10.8 a 0 a 10.8 a PM+B 0.51 0 a 0.52 35.3 a 0 a 10.5 a 0 a 10.5 a SB+W 0.52 0 a 0.52 25.7 a 0a 14.2 a 0 a 14.2 a FF 0.54 0 a 0.54 20.2 a 0 a 10.6 a 0 a 10.6 a WAE 12 CP+B 0.82 0.25 b 1.07 40.6 a 28.0 a 17.7 ab 6.8 b 24.5 a PM+B 0.56 0.41 a 0.97 33.4 ab 23.7 b 8.5 bc 10.7 a 19.2 a SB+W 1.14 0.22 b 1.36 23.4 b 26.8 ab 22.2 a 5.8 b 28.1 a FF 0.45 0.32 ab 0.77 19.4 b 24.1 b 9.1 c 8.0 ab 17.2 a WAE = weeks after emergence. Means followed by identical lower case letters in the same column are not significantly different according to Tu keys test (p<0.05), letters a, b, c denote higher to lower ranking. CP+B = cowpea used as summer cover crop followed by winter broccoli, PM+B= pearl millet used as a summer cover crop followed by winter broccoli, SB+W= sesbania used as a summer cover crop followed by hairy vetch + rye winter cover crop mix, FF = summer and winter fallow.

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194Table D. Effect of cropping system (CS) and kg ha-1of N fertilizer applied to watermelon (N-ra te) interaction (CS*N-rate) effect on dry weight, N concentration and N accumu lation in watermelon shoots and fruits ( Citrullus lanatus ), during the spring of 2005. Means followed by identical upper case letter in the same row, or identical lower case letters in the same column are not sign ificantly different according to Tukeys test (p<0.05), le tters a, b, c denote higher to lower ranking. CP+B = cowpea used as a summer cover crop followed by winter broccoli, PM+B= pearl millet used as a summer cover crop mixed followed by winter broccoli, SB+W= sesbania used as a summer cover crop mi xed followed by hairy vetch + rye winter cover crop mix, FF = summer and winter fallow. Dry matter content N concentration N content Nrate CP+B PM+B SB+W FF CP+B PM+B SB+W FF CP+B PM+B SB+W FF --------------------Mg ha-1--------------------------------------g N kg-1--------------------------------------kg N ha-1-----------------Shoots 0 0.06 0.11 0.4 0.05 21.7 20.5 24.7 24.8 1.22 1.8 9.7 0.90 86 0.6 0.52 0.48 0.29 28.1 29.1 30.7 27.5 15.7 9.9 7.9 4.8 168 0.32 0.27 0.39 0.47 33.3 24.7 33.8 34.9 7.54 5.6 10.6 12.7 Fruits 0 0.00Aa 0.00Ab 0.03Aa 0.00Ab 7.1 Aa 5.2 Aa 6.9 Aa 6.1 Aa 0.0AAa 0.0Ab 0.7Aa 0.0Ab 86 0.08Aa 0.09Ab 0.07Aa 0.08Aab 6.3 Aa 5.8 Aa 7.0Aa 5.9Aa 2.0Aa 2.0Ab 1.9Aa 1.9Aab 168 0.1 Ba 0.22Aa 0.07Ba 0.16ABb 7.5Aa 6.8Aa 6.2Aa 6.1Aa 3.1ABa 6.0ABa 1.7Ba 4.1Ba

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195 Table D. Effect of cropping system (CS) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (CS*N-ra te) on dry weight, N concentration and N accumulation in watermelon total tissues ( Citrullus lanatus ), during the spring of 2005. CP+BPM+BSB+WFFCP+BPM+BSB+WFF----kg N ha-1----00.07ABb0.11ABb0.44Aa0.05Bb 1.2 Abb1.8ABb10.5Aa0.9Bb840.68Aa0.61Aa0.55Aa0.37Aa 17.7Aa11.9Aa9.8Aa6.7Aa1680.42Aa0.49Aa0.46Aa0.63Aa 10.7Aa11.6Aa12.4Aa16.8Aa Cropping System (CS)Dry matter contentN accumulation N-rate----Mg ha-1---Means followed by identical upper case letter in the sa me row, or identical lower case letters in the same column are not significantly different according to Tukeys test (p<0.05) letters a, b, c denote higher to lower ranking. CP+B = cowpea used as a summer cover crop followed by winter broccoli, PM+B= pearl millet used as a summer cover crop followed by winter broccoli, SB+W= sesbania used as a summer cover crop followed by hairy vetch + rye winter cover crop mix, FF = summer and winter fallow. Table D-16. Effect of croppi ng system (CS) and kg ha-1of N fertilizer applied to watermelon (N-rate) interaction (C S*N-rate) on weeds dry weight accumulation, N concentration and accumulation, during the spring of 2005 (July after watermelon final harvest). Fixed Effects Dry matter content N concentration N content -------kg ha-1-----------g N kg-1-------kg N ha-1----N-rate 0 4.8 b 11.3 b 50.8 b 133 8.23 a 12.3 ab 94.6 a 196 7.48 ab 14.8 a 93.1 a Significance * CS CP+B 7.19 ab 12.8 b 84.3 PM+B 8.41 a 11.2 b 95.4 SB+W 4.04 b 17.7 a 63.7 FF 7.26 ab 10.0 b 74.6 Significance *** NS N*Res NS NS NS Means followed by identical lower case letters in the same column are not significantly different according to Tukeys test (p<0.05), letters a, b, c denote higher to lower ranking. CP+B = cowpea used as a summer cover crop followed by winter broccoli, PM+B= pearl millet used as a summer cover crop followed by winter broccoli, SB+W= sesbania used as a summer cover crop followed by hairy vetch + rye winter cover crop mix, FF = summer and winter fallow.

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196 APPENDIX E COST EFFECTIVENESS ANALYSIS Table E-1. Generic expenses for tomato or pepper and sweet corn production systems (2003-2004). Item Unit Quantity (# units) Cost ($) Total ($ ha-1) Input Plastic mulch $ for an acre 81 142 Equipment Rent Truck 983 Bedding equipment and tractor 4 15 55 Labor Costs Field cleaning h 59 7 413 Bedding labor h 46 7 321 Bedding (tractor driver) h 4 9 33 Hole punching tractor driver) h 1 9 7 Total $ 1955

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197 Table E-2. Tomato crop production expenses (2003-2004). Item Unit Quantity (# units) Cost ($ unit) Total ($ ha-1) Input Seeds 1000 sd 11 38 429 Transplant production seedlings 10764 0.07 753 Stakes Unit 3229 0.75 2422 Pesticide materials 208 208 Pesticides applications 34 74 2520 Additive 37 2 64 Potasium fertilizer 252 0.42 105 Irrigation month 3 49 148 Subtotal 6651 Machinery Pesticide application h 28 15.0 420 Mowing h 1 15.0 19 Subtotal 439 Labor Planting h 37.1 7 259 Pesticides application h 28.0 9 252 Staking h 123.6 7 865 Removing stakes h 59.3 7 415 Mowing h 1.2 9 11 Subtotal 1803 Total $8892

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198 Table E-3. Bell pepper crop production expenses average years 2003-2004. Item Unit Quantity (# units) Cost ($ unit) Total ($ ha-1) Input Seeds 1000 sd 29.7 38 1127 Transplant production seedling 29652.6 0 2076 Pesticide materials 208 208 Pesticides applications application 37.0 74 2743 Additive application 37.0 2 64 Potasium fertilizer kg 252.1 0.4 106 Irrigation month 3.0 49 148 Subtotal 6471 Machinery Planting h 0.8 15 12 Pesticides application h 30.5 15 457 Mowing h 1.2 15 19 Subtotal 488 Labor Planting h 123.6 7 865 Pesticides application h 16.5 9 148 Weeding h 44.5 7 311 Mowing h 1.2 9 11 Subtotal 1336 Total $ 8295

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199 Table E-4. Sweet corn crop producti on expenses average years 2003-2004. Item Unit Quantity (# units) Cost ($ unit) Total ($ ha-1) Input Seed 1.0 214 214 Pesticide application application28 49 1376 Fertigation day 60.0 2 94 Micronutrient application1.0 86 86 Subtotal 1770 Machinery rent Planting h 49.1 15 737 Pesticide application h 22.9 15 344 Mowing h 1.2 15 18 Subtotal 1100 Labor costs Planting h 49.1 7 344 Pesticide application h 22.9 9 206 Weeding h 44.2 7 310 Mowing h 1.2 9 11 Subtotal 871 Total $ 3741 Table E-5.Sensitivity analysis for the eff ect of product price on revenues from specific pepper treatments based on aver age pepper yield (2004 and 2005). Price ($ kg-1) SH0 SH112 SH224 F0 F112 F224 0.65 6260 11507 11552 1606 6981 11510 0.75 8600 14657 14722 3236 9451 14690 0.85 10940 17807 17892 4866 11921 17870 0.95 13280 20957 21062 6496 14391 21050 1.05 15620 24107 24232 8126 16861 24230 1.15 17960 27257 27402 9756 19331 27410 Prices are based on IFAS budgets (2003-2004) fo r Palm Beach County, Florida, for pepper bushel. Bushel = 28 lb (USDA, 2006). SH0= sunn hemp summer cover crop and 112 kg N ha-1 ; SH112 = sunn hemp summer cover crop and 112 kg N ha-1 ; SH224= = sunn hemp summer cover and 224 kg N ha-1. F0 =summer fallow and 0 kg N ha-1 ; F112=summer fallow and 112 kg N ha-1 ; F224= summer fallow and 112 kg N ha-1

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200 Table E-6.Sensitivity analysis for the eff ect of product price on revenues from specific tomato treatments, based on average tomato yield (2004 and 2005). Price ($ kg-1) SH0 SH112 SH224 F0 F12 F224 0.45 1879 62432 9741 6836 8114 11583 0.55 4419 10232 14051 10486 11971 16293 0.75 9499 17432 22671 17786 19811 25713 0.85 12039 21032 26981 21436 23731 30423 0.95 14579 24632 31291 25086 27651 35133 1.05 17119 28232 35601 28736 31571 39843 Prices are based on IFAS budgets (2003-2004) for Palm Beach County, Florida, fo r tomato carton. Bushel = 25 lb (USDA, 2006). ). SH0= sunn hemp summer cover crop and 112 kg N ha-1 ; SH112 = sunn hemp summer cover crop and 112 kg N ha-1 ; SH224= = sunn hemp summer cover crop and 224 kg N ha-1. F0 = summer fallow and 0 kg N ha-1 ; F112= summer fallow and 112 kg N ha-1 ; F224= summer fallow and 112 kg N ha-1 Table E-7.Sensitivity analysis for the eff ect of product price on revenues from specific sweet corn treatments, based on aver age sweet corn yield (2004 and 2005). Price ($ kg-1) SH0 SH112 SH224 Fallow0 Fallow112 Fallow224 0.35 -2005 -1312 -942 -2451 -1770 -1383 0.45 -1357 -439 63 -1931 -1018 -493 0.55 -710 433 1068 -1411 -265 397 0.65 -62 1306 2073 -891 487 1287 0.75 585 2178 3078 -371 1240 2177 0.85 1233 3051 4083 149 1992 3067 Prices are based on IFAS budgets (2004-2005) for Pa lm Beach County, Florida, for sweet corn crates. Crate = 42 lb (USDA, 2006). ). SH0= sunn hemp summer cover crop and 112 kg N ha-1 ; SH112 = sunn hemp summer cover crop and 112 kg N ha-1 ; SH224= = sunn hemp summer cover crop and 224 kg N ha-1. F0 =summer fallow and 0 kg N ha-1 ; F112= summer fallow and 112 kg N ha-1 ; F224= summer fallow and 112 kg N ha-1

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201Table E-8.Budget analysis for the different manage ment scenarios without synthetic N fertilizer CostsTomatoPepper Sweet CornTomatoPepper Sweet CornTomatoPepper Sweet CornTomatoPepper Sweet Corn I nputs Nitrogen Fert. Potassium Fert. 105106105106105106105106 Micronutrients 86868686 Irrigation cost 148148148148148148148148 Pesticide Initial 208208208208208208208208 Pesticide Appl. 252027431376252027431376252027431376252027431376 Pesticide Appl. 827866827866827866827866 Additive 6464646464646464 Seeds 4291127214429112721442911272144291127214 Transpl. Prod. 7532076753207675320767532076 Stakes 2422242224222422Cover Crop35635653 Weed control 243243243243243243243243243 Ammendment 779779162542542113 Plastic mulch 717171717171717171717171 Subtotal 704668632056715969771866782576422217758874052168 Labor Planting 259865344259865344259865344259865344 Pesticide Appl. 252148206252148206252148206252148206 Weeding 311310311310311310311310 Staking 865865865865 Stakes removal 415415415415 Field cleaning 138138138138138138138138138138138138 Bedding labor 161161161161161161161161161161161161 Mowing 111111111111111111111111 Punching holes 444444444444 Ammend. Aplic 383838383838 Subtotal 210416371173210416371173214316761211214316761211 Machinery rent Planting tractor 12737127371273712737 Propelled Spray. 420457344420457344420457344420457344 Mower 1919191919191919 Subtotal 4394881081439488108143948810814394881081 Total 95898989431097029102412010406980645101017095694461 Wihtout cover cropWith cover cropCompost ---------------------------------------------------------------------------$ ha-1 --------------------------------------------------------------------Broiler litter

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202Table E-9.Budget analysis for the different management scenarios with 112 kg N ha-1 N fertilizer CostsTomatoPepper Sweet CornTomatoPepper Sweet CornTomatoPepper Sweet CornTomatoPepper Sweet Corn Inputs Nitrogen Fert. 868694868686868694868694 Potassium Fert. 105106105106105106105106 Micronutrients 86868686 Irrigation cost 148148148148148148148148 Pesticide Initial 208208208208208208208208 Pesticide Appl. 252027431376252027431376252027431376252027431376 Pesticide Appl. 827866827866827866827866 Additive 6464646464646464 Seeds 4291127214429112721442911272144291127214 Transpl. Prod. 7532076753207675320767532076 Stakes 2422242224222422Cover Crop35635653 Weed control 243243243243243243243243243 Ammendment 779779162542542113 Plastic mulch 717171717171717171717171 Subtotal 713269492150724570621952791077282312767474912263 Labor Planting 259865344259865344259865344259865344 Pesticide Appl. 252148206252148206252148206252148206 Weeding 311310311310311310311310 Staking 865865865865 Stakes removal 415415415415 Field cleaning 138138138138138138138138138138138138 Bedding labor 161161161161161161161161161161161161 Mowing 111111111111111111111111 Punching holes 444444444444 Ammend. Aplic 383838383838 Subtotal 210416371173210416371173214316761211214316761211 Machinery rent Planting tractor 12737127371273712737 Propelled Spray. 420457344420457344420457344420457344 Mower 1919191919191919 Subtotal 4394881081439488108143948810814394881081 Total 96759074440497889188420610492989146041025596554555 ---------------------------------------------------------------------------$ ha-1 --------------------------------------------------------------------Wihtout cover cropWith cover cropCompostBroiler litter

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203Table E-10.Budget analysis for the differe nt management scenarios with 224 kg N ha-1 N fertilizer Costs TomatoPepper Sweet Corn TomatoPepper Sweet Corn TomatoPepper Sweet Corn TomatoPepper Sweet Corn Inputs Nitrogen Fert. 171171189171171189171171189171171189 Potassium Fert. 105106105106105106105106 Micronutrients 86868686 Irrigation cost 148148148148148148148148 Pesticide Initial 208208208208208208208208 Pesticide Appl. 252027431376252027431376252027431376252027431376 Pesticide Appl. 827866827866827866827866 Additive 6464646464646464 Seeds 4291127214429112721442911272144291127214 Transpl. Prod. 7532076753207675320767532076 Stakes 2422242224222422Cover Crop35635653 Weed control 243243243243243243243243243 Ammendment 779779162542542113 Plastic mulch 717171717171717171717171 Subtotal 721770352244733071482054799678132406775975772357 Labor Planting 259865344259865344259865344259865344 Pesticide Appl. 252148206252148206252148206252148206 Weeding 311310311310311310311310 Staking 865865865865 Stakes removal 415415415415 Field cleaning 138138138138138138138138138138138138 Bedding labor 161161161161161161161161161161161161 Mowing 111111111111111111111111 Punching holes 444444444444 Ammend. Aplic 383838383838 Subtotal 210416371173210416371173214316761211214316761211 Machinery rent Planting tractor 12737127371273712737 Propelled Spray. 420457344420457344420457344420457344 Mower 1919191919191919 Subtotal 4394881081439488108143948810814394881081 Total 97609160449898749273430810577997746981034197414649 ---------------------------------------------------------------------------$ ha-1 --------------------------------------------------------------------With cover cropCompostBroiler litter Wihtout cover crop

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APPENDIX F ENERGY AND EMERGYANALYSIS

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205 Table F-1.Energy analysis for the different management scenarios for tomato production. ItemUnits Energy Units (J)Fallow (J)Sunn hemp (J)Compost (J)Broiler litter (J) P reoperat i on Summer maintenance F a ll ow-wee d contro l 1 10E + 11 1 10E + 11 1 10E + 11 C over crop-sunn h emp 4 84E + 09 F ield La b o r h 5. 23 E+ 0 5 2 .55E+ 0 7 2 .55E+ 0 7 2 .55E+ 0 7 2 .55E+ 0 7 Bedding FuelL3.57E+072.67E+082.6 7E+082.67E+082.67E+08 Machiner y 7.44E+077.44E+077.44E+077.44E+07 Labo r h5.23E+051.99E+071.99E+071.99E+071.99E+07 P unc hi ng h o l es F ue l L 3 57E + 07 1 90E + 07 1 90E + 07 1 90E + 07 1 90E + 07 Machiner y 7.44E+077.44E+077.44E+077.44E+07 La b o r h 5.23E+053.51E+053.51E+053.51E+053.51E+05 Tota l 1.10E+115.32E+091.10E+111.10E+11 Operat i on Pl ant i ng La b o r h 5.23E+051.94E+071.94E+071.94E+071.94E+07 Sta k ing La b o r h 5.23E+056.46E+076.46E+076.46E+076.46E+07 R emov i ng sta k e La b o r h 5.23E+053.10E+073.10E+073.10E+073.10E+07 Compost /l itter app l ication l a b or+ f ue l s C ompost 0 N k g 4 30E + 09 Compost 150 k g 4.30E+09 Compost300 k g4.30E+09 Bro il er li tte r 0 N k g2.44E+09 Bro il er li tte r 150 k g 2.44E+09 Bro il er li tte r 300 k g 2.44E+09 Pestici d es ap l ication Fue l L3.57E+079.33E+089.33E+089.33E+089.33E+08 Mac hi ner y 8.98E+078.98E+078.98E+078.98E+078.98E+07 La b o r h 5.23E+051.47E+071.47E+071.47E+071.47E+07 M owing Fue l L3.57E+071.44E+081.44E+081.44E+081.44E+08 Mac hi ner y 1.03E+091.03E+091.03E+091.03E+091.03E+09 La b o r h 5.23E+056.46E+056.46E+056.46E+056.46E+05 I rrigation Equ i pment h a wee k 6.85E+089.38E+099.38E+099.38E+099.38E+09 T ota l 1 17E + 10 1 17E + 10 1 17E + 10 1 17E + 10

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206 Table F-1.Continued. Item Units Energy Units (J) Fallow (J) Sunn hemp (J) Compost (J) Broiler litter (J) Inputs Fertigation NH4NO3 0 kg 6.51E+07 0.00E+00 0. 00E+00 0.00E+00 0.00E+00 NH4NO3 112 kg 6.51E+07 2.14E+10 2. 14E+10 2.14E+10 2.14E+10 NH4NO3 224 kg 6.51E+07 4.29E+10 4. 29E+10 4.29E+10 4.29E+10 K2O kg 2.89E+06 7.29E+08 7. 29E+08 7.29E+08 7.29E+08 Replacement of sunn hemp C:N Compost 0 kg 3.71E+06 5.09E+10 Compost 112 kg 3.71E+06 5.09E+10 Compost 224 kg 3.71E+06 5.09E+10 Broiler litter 0 kg 2.14E+08 1.67E+12 Broiler litter 112 kg 2.14E+08 1.67E+12 Broiler litter 224 kg 2.14E+08 1.67E+12 Cover crop residue 8.04E+09 Pesticides kg 3.26E+07 1.11E+09 1.11E +09 1.11E+09 1.11E+09 Plastic mulch kg 8.50E+07 8.68E+09 8.68E +09 8.68E+09 8.68E+09 Stakes unit 1.60E+07 5.17E+10 5. 17E+10 5.17E+10 5.17E+10 Oil 10% of fuels 1.08E+08 1.30E+08 5.37E+08 3.51E+08 E lectricity kWh 1.32E+00 1.09E+10 1.09E +10 1.09E+10 1.09E+10 Total 7.32E+10 8.13E+10 7.37E+10 7.35E+10

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207 Table F-2.Energy analysis for the different management scenarios for pepper production. ItemUnits Energy Units (J)Fallow (J) Sunnhemp (J)Compost (J) Broiler litter (J) Preoperation S ummer ma i ntenance Fa ll ow-wee d contro l 1.10E+111.10E+111.10E+11 Cover crop-sunn h emp4.84E+09 Fie ld c l eaning L a b o r h 5 23E+05 2 55E+07 2 55E+07 2 55E+07 2 55E+07 B e ddi ng Fue l L3.57E+072.67E+082.67E +082.67E+082.67E+08 Machiner y 7.44E+077.44E+077.44E+077.44E+07 La b o r h 5.23E+051.99E+071.99 E+071.99E+071.99E+07 P unc h ing h o l es F ue l L 3 57E+07 1 90E+07 1 90E+07 1 90E+07 1 90E+07 Machiner y 7 44E+07 7 44E+07 7 44E+07 7 44E+07 L a b o r h 5 23E + 05 3 51E + 05 3 51E + 05 3 51E + 05 3 51E + 05 Tota l 1.10E+115.32E+091.10E+111.10E+11 Operat i on Pl anting La b o r h 5.23E+056.46E+076.46 E+076.46E+076.46E+07 Wee d ing L a b o r h 5 23E+05 2 33E+07 2 33E+07 2 33E+07 2 33E+07 Compost /l itter app l ication l a b or+ f ue l s Compost 0 N k g4.30E+09 Compost 150 k g 4.30E+09 Compost300 k g4.30E+09 Bro il er li tte r 0 N k g2.44E+09 Bro il er li tte r 150 k g 2.44E+09 Bro il er li tte r 300 k g 2.44E+09 P est i c id es ap li cat i on Fue l L3.57E+071.01E+091.01E +091.01E+091.01E+09 Mac hi ner y 8.98E+078.98E+078.98 E+078.98E+078.98E+07 La b o r h 5.23E+058.62E+068.62 E+068.62E+068.62E+06 M owing Fue l L3.57E+071.44E+081.44E +081.44E+081.44E+08 M ac hi ner y 1 03E+09 1 03E+09 1 03E+09 1 03E+09 1 03E+09 La b o r h 5.23E+056.46E+056.46 E+056.46E+056.46E+05 I rrigation Equ i pment h a wee k 6.85E+089.38E+099.38E+099.38E+099.38E+09 T ota l 1 18E+10 1 18E+10 1 18E+10 1 18E+10

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208 Table F-2.Continued. Item Units Energy Units (J)Fallow (J) Sunnhemp (J) Compost (J) Broiler litter (J) Inputs Fertigation Ammonium nitrate 0 kg N 6.51E+07 0.00E+00 0.00E+00 0.00E+00 0.00E+00 Ammonium nitrate, kg112 kg N 6.51E+ 07 2.14E+10 2.14E+10 2.14E+10 2.14E+10 Ammonium nitrate 224 kg N 6.51E+07 4.29E+10 4.29E+10 4.29E+10 4.29E+10 Potassium kg K2O 2.89E+06 7.29E+08 7.29E+08 7.29E+08 7.29E+08 Replacement of sunn hemp C:N Compost 0 kg N 3.71E+06 5.09E+10 Compost 112 kg N 3.71E+06 5.09E+10 Compost 224 kg N 3.71E+06 5.09E+10 Broiler litter 0 kg N 2.14E+08 1.67E+12 Broiler litter 112 kg N 2.14E+08 1.67E+12 Broiler litter 224 kg N 2.14E+08 1.67E+12 Cover crop residue 8.04E+09 Pesticides application times3.26E+07 2. 98E+09 2.98E+09 2.98E+09 2.98E+09 Plastic mulch kg 8.50E+07 8.68E+09 8.68E +09 8.68E+09 8.68E+09 Oil 10% of fuels 1.51E+08 1.38E+08 5.80E+08 3.94E+08 Electricity kWh 1.32E+00 1.09E+10 1.09E +10 1.09E+10 1.09E+10 Total 2.35E+10 3.15E+10 2.39E+10 2.37E+10

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209 Table F-3.Energy analysis for the different management scenarios for sweet corn production. ItemUnits Energy Units (J)Fallow (J) Sunnhemp (J)Compost (J) Broiler litter (J) Preoperation S ummer ma i ntenance F a ll ow-wee d contro l 2 28E + 10 2 28E + 10 2 28E + 10 C over crop-sunn h emp 1 01E + 09 F ield L a b or h 5 23E + 05 5 31E + 06 5 31E + 06 5 31E + 06 5 31E + 06 B e dd ing Fue l L3.57E+075.55E+075.55E+075.55E+075.55E+07 Machiner y 1.55E+071.55E+071.55E+071.55E+07 La b or h 5.23E+054.13E+064.13E+064.13E+064.13E+06 P unc hi ng h o l es Fue l L3.57E+073.95E+063.95E+063.95E+063.95E+06 Machiner y 1.55E+071.55E+071.55E+071.55E+07 La b or h 5.23E+057.30E+047.30E+047.30E+047.30E+04 Tota l 2.29E+101.11E+092.29E+102.29E+10 Operation Planting Laborh5.23E+052.57E+072.57E+072.57E+072.57E+07 Compost/litter application labor+fuels Compost 0 N k g8.94E+08 Compost 150 k g 8.94E+08 Compost300 k g8.94E+08 B ro il er li tte r 0 N k g 5 07E + 08 Bro il er li tte r 150 k g 5.07E+08 Bro il er li tte r 300 k g 5.07E+08 Dewe d ing La b or h 5.23E+052.31E+072.31E+072.31E+072.31E+07 Pestici d es ap l ication Fue l L3.57E+071.19E+101.19E+101.19E+101.19E+10 Mac hi nery8.98E+078.98E+078.98E+078.98E+078.98E+07 La b or h 5.23E+052.57E+072.57E+072.57E+072.57E+07 M owing Fue l L3.57E+076.40E+086.40E+086.40E+086.40E+08 Mac hi nery1.03E+091.03E+091.03E+091.03E+091.03E+09 La b or h 5.23E+056.43E+056.43E+056.43E+056.43E+05 Irrigation E quipment h a yea r 3.56E+101.19E+101.19E+101.19E+101.19E+10 Tota l 2.56E+102.56E+102.56E+102.56E+10

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210 Table F-3.Continued. Item Units Energy Units (J)Fallow (J) Sunnhemp (J) Compost (J) Broiler litter (J) Inputs Seeds bushel ha 1.89E+081.89E+081.89E+08 1.89E+08 1.89E+08 Fertigation Ammonium nitrate 0 kg 6.51E+070.00E+000.00E+00 0.00E+00 0.00E+00 Ammonium nitrate 112 kg 6.51E+072.14E+102.14E+10 2.14E+10 2.14E+10 Ammonium nitrate 224 kg 6.51E+074.29E+104.29E+10 4.29E+10 4.29E+10 Potassium kg 2.89E+061.78E+081.78E+08 1.78E+08 1.78E+08 Replacement of sunn hemp C:N Compost 0 kg 3.71E+06 1.06E+10 Compost 112 kg 3.71E+06 1.06E+10 Compost 224 kg 3.71E+06 1.06E+10 Broiler litter 0 kg 2.14E+08 3.46E+11 Broiler litter 112 kg 2.14E+08 3.46E+11 Broiler litter 224 kg 2.14E+08 3.46E+11 Cover crop residue 1.20E+09 Pesticides kg 3.26E+079.13E+089.13E+08 9.13E+08 9.13E+08 Plastic mulch kg 8.50E+078.68E+098.68E+09 8.68E+09 8.68E+09 Oil 10% of fuels 1.27E+091.26E+09 1.36E+09 1.32E+09 Electricity kWh 1.32E+00 8.99E+098.99E+09 8.99E+09 8.99E+09 Total 2.02E+102.14E+10 2.03E+10 2.03E+10

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211 Table F-4. Energy coefficients calculated of gather from literature for the energy analysis Energy Input Energy Units (J) Source Broiler litter, kg 2.14E+08 Calculated based on Midwest Plan Service 1985; Proudfoot et al. 1991; Douglas and Ouart 1992; British Columbia Extension Service 2005. Broiler manure produced per life cycle 1.72E+08 Calculated based on Midwest Plan Service 1985; Proudfoot et al. 1991; Douglas and Ouart 1992; University of Minnesota n.a Corn Stover, kg dry weight 1.79E+04 Idris et al., n.a. Diesel, L 3.57E+07 Conwell, 2005 Distribute manure, hr 6.26E+08 Lazarous and Selley, 2005. Drip irrigation operation, year 3.56E+10 Stout, 1991. Electricity, kWh 3.60E+06 Edisson Electric, 2003. Energy of carbohidrates, proteins and fats Depending on the crop Paul and Southgate, 1978. Grow Sunn hemp, ha 5.85E+09 Calculated based on Pimentel 1980; Dows and Hansen, 1994. Herbicides g a.i. miscible oil 3.93E+06 Pimentel, 1980. Herbicides miscible oiltransport 4.05E+06 Pimentel, 1980. Horse litter, kg 1.77E+06 Calculated based on Rutgers Cooperative Extension 2004; Alberta Government, 2004 Horse manure compost, kg 3.71E+06 Calculated based on National Renewable Energy Laboratory, 1995; Trautman and Richard, 2000; Rutgers Cooperative Extension, 2004; Alberta Government, 2005. Human labor, h 5.23E+05 Department of Food Drug Administration K2O, packed and transported kg 2.89E+06 Mundahar and Hineth, 1987; Bhat,1994 Machinery for 1 kg compost 1.43E+04 Komlis and Ham 2004 Machinery, depreciation depending on machinery Calculated based on Pimentel 1989; Dows and Hansen 1994, NH4NO3, kg bagged 2.25E+07 Lewis, 1982; Stout, 1991, Pepper stover, kg dry weight 6.78E+06 Calculated based on Martin, 1996. Plastic mulch ( PCL), kg 8.50E+07 Gengross and Slater, 2000. Plastic mulch (PCL), kg 8.50E+07 Calculated based on Gengross and Slater 2000; Gross and Kalra. Sweet corn seed, ha 1.89E+08 Tomato stover, kg dry weight 4.50E+ 06 Calculated based on Martin, 1996. Water, m3 6.30E+05 Ozkan et al., 2003.

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212 Table F-5 .Emergy memory or calculations (Not all the calculations are applicable to the different scenarios or crops). 1 Sun, J Total energy1.67E+13J/cycle/ haCalculated Life cycle of crops1.37E+01average 03-04-05Calculated Yearly energy6.35E+13J/ ha/ yearBrandt-Williams, 2002 Transformity1.00E+00sej /Jby definition. 2 Water, J Total Energy6.19E+09JCalculated Water per ha1.25E+09gCalculated Energy input4.94E+00J/gOdum, 1996 Transformity2.27E+04sej/JOdum, 1996 3 Evapotranspiration, J Tomato crop evapotranspiration Total energy 6.18E+10J Water used by plant per ha1.25E+10g Energy input4.94E+00J/g Transformity1.54E+04sej/JOdum, 1996 Pepper crop evapotranspiration Total energy3.09E+10J /ha ha2.47E+00acre Energy used per acre1.25E+10J/acreSmajstrla,1990 Transformity1.80E+04sej/J Cover crops Total energy9.13E+09JCalculated ha2.47E+00acreCalculated Energy acre2.10E+00J/gBrandt-Williams, 2001 Transformity1.54E+04sej/JBrandt-Williams, 2002 Soybean evapotranspiration was used (Brandt-Williams, 2002). Soybean evapotranspiration was used instead. Total energy = ((2.49 E10 J acre year) (2.47 acres ha) / (365 days/ year) )*62.3 days per cycle)*(0.87 ratio) Transformity 1 by definition. Solar insolation calculated using the solar constant of 2 Langleys/sec and integration over changing surface for a one year period at latitude 27.00 N, longitudd 82.00W: 6.9 Jm2/yr. Albedo 8% (Nasaoesweb). Annual energy = (Avr. Total annual insolation J/yr/m2)(Area m2) (1-albedo). Energy input = (Annual energy/ 52 weeks) (13.7 weeks tomato life cycle, based on 3 years average). Does not vary with crops or scenarios. Transformity for groundwater Odum (1996 ) corrected by a factor of 1.68. Water per ha =(1.31E7 lb water day 7 d / week*13.7 weeks g in lb of water) (J g of water). Energy per input = ( J needed to evaporate 1 g water). Cycle energy =water per hectare*energy input. Does not vary with the different crops or scenarios. Transformity 15423 sej/J (Odum, 1996) corrected by a factor of 1.68 (Odum et al., 2002).Water used by plant = (maximum water use by the plant 1250 kg/m2) (10000 m2/ha*1000 g/kg). Annual energy for fallow scenarios, compost and broiler litter = (crop maximum water use)*(J to evaporate 1 g water). Annual energy for cover crop scnearios = ((crop maximum water use)+(cover crop water use) )*(J to evaporate 1 g water).

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213 Table F-5.Continued. 1 Sun, J Total energy1.67E+13J/ha/cycleCalculated Life cycle of crops1.37E+01average 03-04-05Calculated Yearly energy6.35E+13J/ha/yearBrandt-Williams, 2002 Transformity1.00E+00sej/Jby definition. 2 Water, J Total Energy6.19E+09JCalculated Water per ha1.25E+09gCalculated Energy input4.94E+00J/gOdum, 1996 Transformity2.27E+04sej/JOdum, 1996 3 Evapotranspiration, J Tomato crop evapotranspiration Total energy 6.18E+10J Water used by plant per ha1.25E+10g Energy input4.94E+00J/g Transformity1.54E+04sej/JOdum, 1996 Pepper crop evapotranspiration Total energy3.09E+10J /ha ha2.47E+00acre Energy used per acre1.25E+10J/acreSmajstrla,1990 Transformity1.80E+04sej/J Cover crops Total energy9.13E+09JCalculated ha2.47E+00acreCalculated Energy acre2.10E+00J/gBrandt-Williams, 2001 Transformity1.54E+04sej/JBrandt-Williams, 2002 Soybean evapotranspiration was used (Brandt-Williams, 2002). Soybean evapotranspiration was used instead. Total energy = ((2.49 E10 J acre year) (2.47 acres ha) / (365 days/year) )*(62.3 days/cycle)*(0.87 ratio) Transformity 1 by definition. Solar insolation calculated using the solar constant of 2 Langleys/sec and integration over changing surface for a one year period at latitude 27.00 N, longitudd 82.00W: 6.9 Jm2/yr. Albedo 8% (Nasaoesweb). Annual energy = (Avr. Total annual insolation J/yr/m2)*(Area m2)* (1-albedo). Energy input = (Annual energy/ 52 weeks) (13.7 weeks tomato life cycle, based on 3 years average). Does not vary with crops or scenarios. Transformity for groundwater Odum (1996 ) corrected by a factor of 1.68. Water per ha =(1.31E7 lb water day 7 d / week*13.7 weeks g in lb of water) (J g of water). Energy per input = ( J needed to evaporate 1 g water). Cycle energy =water per hectare*energy input. Does not vary with the different crops or scenarios. Transformity 15423 sej/J (Odum, 1996) corrected by a factor of 1.68 (Odum et al., 2002).Water used by plant = (maximum water use by the plant 1250 kg/m2)*(10000 m2/ha*1000 g/kg). Annual energy for fallow scenarios, compost and broiler litter = (crop maximum water use)*(J to evaporate 1 g water). Annual energy for cover crop scnearios = ((crop maximum water use)+(cover crop water use) )*(J to evaporate 1 g water).

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214 Table F-5.Continued. 4 Soil Loss, J Total energy in soil used or loss1.00E+09J/haCalculated Erosion rate7.00E+00g/m2/yearPimentel et al., 1995 Farmed Area1.00E+04m2/haCalculated Net top soil loss7.00E+04g/ha/ yearCalculated Net top soil loss cycle1.84E+04g/ha/cycleCalculated % OM, for fallow2.40E+00From farm data % OM, for cover croped plots2.57E+00From farm data Energy content in soil5.40E+00kcal/gUligati et al., 1992 Transformity7.38E+04sej/JOdum, 1996 5 Fuel, J Total energy fallow scenario1.82E+09J/ha/cycleCalculated Total energy in cover crop scenario1.71E+09J/ha/cycleCalculated Total energy for broiler litter scenario4.49E+09J/ha/cycleCalculated Total energy in compost scenario6.54E+09J/ha/cycleCalculated Transformity6.60E+04sej/JOdum, 1996 6 Electricity, J Total energy1.09E+10J Calculated Electricity consumption1.32E+00kWhCalculated Cycle2.30E+03hrCalculated Energy input 3.60E+06J/kWhEdisson Electric, 2003 Transformity1.60E+05sej/JOdum, 1996 7 Potash, g Total input8.49E+04g/cycle Transformity1.74E+09sej/g KOdum, 1996 8 Nitrogen, g Total input"x"g/ha/cycleCalculated Transformity2.41E+10sej/gBrandt-Williams, 1999 Fuel includes diesel and oils and uses petroleum products transformity 6.60E4 sej/J (Odum, 1996) corrected by a factor of 1.68 (Odum et al., 2000). Fuels used include diesel and 10% oil over all diesel used = (fuel and oil L/ha/cycle) + (fuel used for summer weed control or sunn hemp cultivation L/ha/summer cycle) (0.83 which is the percentage of benefits for tomato and pepper). Energy in diesel 3.57E7 J/L (Conwell, 2005). Totall energy = ( Fuels use ) ( ener gy in diesel ) Transformity for electricicty from average U.S. coal plant 1.60 E5 sej/J (Odum, 1996). Energy consumption by the farm 1.32 kWh. Hours in a production cycle= (13.7 weeks)*(7 days/week)*(24 h/day) Total energy= (energy consumption by farm)* (hours in a production cycle)* ( J/ kWh). Transformity of potash (K2O) 1.74 E9 sej/g K (Odum, 1996) corrected by a factor of 1.68 (Odum et al., 2002). Total input of K2O = K2O g/cycle, converted to g K = (kg K2O ) *(1000 g/ kg)*(78 g mol K/94 g mol K2O). Transformity of N fertilzer (N) 2.41 E10 sej/g DAP (Brandt-Williams, 1999) corrected by a factor of 1.68 (Odum et al., 2002). Total input = grams of NH4NO3cycle used converted to g N = (kg NH4NO3) (1000 g/kg)*(34% N). The grams of fertilizer will vary from the scenarios from 0 N, 1.22 E5 and 2.24 E5 g/ha/cycle, for both crops. Tranformity for organic soil 7.38E4 sej/J (Odum, 1996) corrected by a factor 1.68 Odum et al., (2002). Energy content on organic soil 5.4 kcal/g (Uligati et al, 1992). Net top soil loss year = (farmed area)* (erosion rate). Net top soil loss cycle = (net top soil loss ha/yr)* (0.0192308 year/week)*(13.7 weeks/cycle). Erosion rate estimated at 7.0 g/m2/yr (Pimentel et al., 1995) with 0.04% organics in the soil. Total energy of soil used or lost =(net top soil loss ha/cycle)*(% organic matter)*(5.4 kcal/g)*(4186 J/kcal). For cover crop, broiler litter and compost the % OM value for cover cropped plots was used.

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215 Table F-5.Continued. 9 Pesticides, g Total preplanting pesticide 1.01E+03g/ha/cycleCalculated Tomato Total pesticide used for crops1.72E+04g/cycleCalculated Tranformity1.48E+10sej/gBrown and Arding, 1999 Pepper Total pesticide used for crops1.87E+04g/ha/cycleCalculated Tranformity1.48E+10sej/gBrown and Arding, 1999 10 Production Labor, J Tomato Fallow Total energy1.80E+08J/ha/cycleCalculated Transformity2.46E+07sej/JOdum, 1996 Cover crops (considers only 87%) Energy input1.79E+08J/ha/cycleCalculated Transformity2.46E+07sej/JOdum, 1996 Compost Total energy1.87E+08J/ha/cycleCalculated Transformity2.46E+07sej/JOdum, 1996 Broiler litter, J Total energy1.84E+08J/ha/cycleCalculated Transformity2.46E+07sej/JOdum, 1996 Pepper Fallow Total energy1.46E+08J/ha/cycleCalculated Transformity2.46E+07sej/JOdum, 1996 Cover crops (considers only 87%) Energy input1.46E+08J/ha/cycleCalculated Transformity2.46E+07sej/JOdum, 1996 Compost Total energy1.54E+08J/ha/cycleCalculated Transformity2.46E+07sej/JOdum, 1996 Broiler litter, J Total energy1.50E+08J/ha/cycleCalculated Transformity2.46E+07sej/JOdum, 1996 Transformity for pesticides 1.48E10 sej/J (Brown and Arding, 1991) including herbicides and insecticides. Pesticide use was calculaded from initial pesticides application, and "x" applications along the season (available in economic analysis) for each crop. Standard ammount/application = 506 g/ha/application. Refer to energy calculation methodology for details about the quantity of pesticide (g) used per cycle. Preplanting pesticide was not accounted in the cover crop scenarios. Transformy for labor with middle school degree 2.46 E7 sej J. Total daily consumption of 3000 kcal/day (FAO & WHO, 1979) divided by 24 hours and multiplied by the # hour required for each task/ha. Total energy = sum of energy expenditures for all tasks. Energy expenditure per task = (((3000 kcal/day)*(4186 J/kcal)) / (24 hr/day) )* (hours person/ha). Total energy expenditures are different for each scenario.

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216 Table F-5.Continued. 11 Plastic, J Total input1.02E+05g/cycle Transformity3.80E+08sej/JOdum and Odum, 1983 12 Compost, g Total input"x"g/ha/cycle Transformity3.27E+09sej/gAvila, 2006 13 Broiler litter, g Total input"x"g/ha/cycle Transformity2.37E+10sej/gAvila, 2006 14 15 Machinery rent ,$ Total cost machinery rent4.39E+02$Calculated Transformit1.00E+12sej/$ Total cost machinery rent3.94E+01$Calculated Transformit1.00E+12sej/$ 16 Labor cost, $ Total input Fallow1.85E+03$ Tota input Cover crops1.84E+03$ Total input Broiler litter1.90E+03$ Total input Compost1.94E+03$ Emergy per unit input =1.00E+12sej/$ 17 Total fresh yield, g 18 Total yield dry weight, g Cro p s, with fallow, broiler litter, com p ost Only cover crop operation (cosiders only 87%) Total fresh yields for each crop, for the six treatments were calculated from the average yields from 2004 and 2005 (Appendix) in kg/ha and was multiplied by 1000 g/kg Transformity used for a emergy/dollar ($) ratio 1 E12 sej/J$ (Brown, 2003). Total input= cost of all labor services under the different management scenarios, including the labor for ammendments distribuition if applicable. Same for both crops. Total cover crop stover residue energy Total yield energy was calculated by multipliying the total stover dry weigh (g/ha) by the energy content J/g stover. Average DM accumulation per cycle (average of three cycles) was of 3.82 E6 g. Total energy in sunn hemp stover = weight of sunn hemp stover dry matter (g/ha) ( 1.61% N concentration in aboveground dry matter; Avila, 2006)*(6.25; factor for converting % N to protein)* (24 KJ g protein; Paul and Southgate, 1978). Total fresh yield for each crop for the six treatments (Table) was calculated from the average yields from 2004 and 2005 in kg/ha and were multiplied by 1000 g/kg and by their dry matter content (water content 93% for tomato and 97% for peppers, according to Paul and Southgate, 1979 and calculated data). Transformity of broiler litter was calculated (Avila, 2006) as 2.37 E10 sej/g. Total input= broiler for replacing sunn hemp cover crop mineralized N (7775 kg broiler litter/cycle). This value wil be "0" for fallow, cover crops and com p ost scenario. Transformity used for a emergy/dollar($) ratio 1 E12 sej/J (Brown, 2000). Total cost = cost of all machinery services for crop production. For cover crop scenario the total cost = to the sum of total cost for crop+ total cost for cover crop cultivation. Tranformity for plastic 3.80 E8 sej J. (Odum and Odum, 1983). Plastic used in the farm was calculated based on a biodegradable plastic PLC with a density of 1.45 g cm3 (Gross and Karla, ). Plastic used = It was assumed that 33% of the land was covered by the plastic mulch. Energy per unit input = 8.50 E7 J/kg. (Gengross and Slater, 2000) Total energy = (plastic used) (energy per unit input). Same for all scnearios and crops Transformity of horse manure compost was calculated (Avila, 2006) as 3.27 E9 sej/g. Total input = compost for replacing sunn hemp cover crop mineralized N (13720 kg compost/cycle). This value wil be "0" for fallow, cover crops and broiler litter scenario.

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217 Table F-5. Continued. 19 Total dry stover residue, g Sunn hemp (considers only 87%)3.17E+06g Tomato3.27E+06g Pepper2.55E+06g 20 Total yield energy 21 Total crop stover residue energy 22 23 Emergy in fruit mass, g 24 Transformity of fruit 25 Transformity for plant dry weight 26 Empower density Total yield energy was calculated by multipliying the total yield fresh weigh (g/ha) by the energy content in tomato fruits 1.23 E3 J g (Tomato, 93.4% water 24% protein 24KJ g, 76% carbohidrates 17 KJ; Paul and Southgate, 1978). For pepper the dry weight of fruits was multiplied by the energ in dry weigth (1.67 E3 J/g; Fluck 1992 ) Total emergy in sej calculated in this analysis, divided by the tomato yield dry weight in g. Total yield energy was calculated by multipliying the total stover dry weigh (g/ha) by the energy content in per g of stover. Average dry matter accumulation per cycle (average of three cycles) was of 3.82 E6 g. The energy is sunn hemp stover was calculated based = weight of sunn hemp stover dry matter (g/ha) (0.61% N concentration in aboveground dry matter; Avila, 2006)*(6.25; factor for converting % N to protein)* (24 KJ g protein; aul and Southgate, 1978). Total energy = weight of dry stover (g/ha)*(energy g). Total yield energy was calculated by multipliying the total stover dry weigh (g/ha) by the energy content in per g of stover (4.50 E6 J/g for tomato and 6.78 E6 J/g for pepper) Energy in residue for pepper and tomato was calculated as 18% protein in tissues multiplied by 24 KJ g protein, plus 0.15 g of starch per tissue with 1.02E4 J per g starch. Tomato and pepper fresh yields are shown in Table Total cover crop stover residue energy Total emergy in sej divided by the energy of yield in J Total emergy in sej divided by the energy in dry weight of stover and fruits yield in J. This is the total annual flows of emergy into an unit area over a year. In this case the emergy for the analysis was for a cycle of crop. A cycle of tomato and peppers was established as 13.7 weeks, based in average cultivation from years 2003, 2004-2005. Therefore the annual flow would be the total emergy divided by 13.7 weeks and multiplied by 52 weeks/year. Cultivated area= 1 ha. Total dry weight of residues for each crop for the six treatments (data from year 2003) was used for this calculation and multiplied by 1000 g/kg

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218 Table F-6. Emergy indicators calculation 26 27 28 Sustainability index (SI) Environmental loading ratio (ELR) Emergy yield ratio (EYR) The Environmental Loading Ratio (ELR) is the ratio of purchased (F) and indigenous non-renewable emergy (N) to free environmental emergy (R). It is an indicator of the amount stress that a production process places on the local environment. The Sustainability Index (SI) = EYR/ELR and is an aggregate measure of yield and sustainability that assumes that the objective function for sustainability is to obtain the highest yield ratio at the lowestenvironmental load. The Emergy Yield Ratio (EYR) is the ratio of the emergy of the output (Y), divided by the emergy of those inputs (F) to the process that are fed back to the system from outside. Stated otherwise: "the emergy yield ratio of each system output is a meas ure of its net contribution to the economy beyond its own operation" (Odum, 1996, pp. 71).

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219 Table F-7. Emergy analysis for tomato production scenario Fallow 0 N-rate. Data Transformit y Emergy Em$ Value Not e Item Unit (Unit ha -1 cycle -1) (sej unit-1) (1013 sej yr-1) (2000 $ yr1) RENEWABLE RESOURCES 1 Sun J 1.67E+13 1.00E+00 2 17 2 Water J 6.19E+09 3.81E+04 24 236 3 Evapotranspiration J 6.18E+10 2.59E+04 160 1600 4 Net top soil loss J 1.00E+09 7.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 1.82E+09 1.11E+05 20 202 6 Electricity J 1.09E+10 2.69E+05 293 2933 7 Potash g 8.49E+04 2.92E+09 25 248 8 Nitogen g 0.00E+00 4.05E+10 0 0 9 Pesticides g 1.82E+04 1.48E+10 27 270 10 Production Labor J 1.80E+08 4.13E+07 742 7419 11 Plastic J 1.02E+05 3.80E+08 4 39 12 Compost g 0.00E+00 5.49E+09 0 0 13 Broiler litter g 0.00E+00 3.97E+10 0 0 15 Machinery rent for cover crops $ 0.00E+00 1.00E+12 0 0 Machinery rent $ 4.39E+02 1.00E+12 44 439 16 Labor cost $ 1.85E+03 1.00E+12 185 1848 Sum of purchased inputs +renewables 1500 15323

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220 Table F-8. Emergy analysis for tomato production scenario Compost 0 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha -1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr1) RENEWABLE RESOURCES 1 Sun 1.67E+13 1.00E+00 2 17 2 Water J 6.19E+09 3.81E+04 24 236 3 Evapotranspiration J 6.18E+10 2.59E+04 160 1600 4 Net top soil loss J 1.00E+09 7.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 6.54E+09 1.11E+05 73 725 6 Electricity J 1.09E+10 2.69E+05 293 2933 7 Potash g 8.49E+04 2.92E+09 25 248 8 Nitrogen g 0.00E+00 4.05E+10 0 0 9 Pesticides g 1.82E+04 1.48E+10 27 270 10 Production Labor J 1.87E+08 4.13E+07 772 7716 11 Plastic J 1.02E+05 3.80E+08 4 39 12 Compost J 1.37E+07 5.49E+09 7538 75380 13 Broiler litter g 0.00E+00 3.97E+10 0 0 15.1 Machinery rent for cover crops $ 0.00E+00 1.00E+12 0 0 15.2 Machinery rent $ 4.39E+02 1.00E+12 44 439 16 Labor cost $ 1.94E+03 1.00E+12 194 1944 Sum of purchased inputs +renewables 1591 16240 With Compost 9129 91620

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221 Table F-9. Emergy analysis for tomato produc tion scenario Bro iler litter 0 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.18E+102.59E+04 160 1600 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 4.49E+091.11E+05 50 498 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 0.00E+004.05E+10 0 0 9 Pesticides g 1.82E+041.48E+10 27 270 10 Production Labor J 1.84E+084.13E+07 759 7587 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 0.00E+005.49E+09 0 0 13 Broiler litter g 7.77E+063.97E+10 30897 308966 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 4.39E+021.00E+12 44 439 16 Labor cost $ 1.90E+031.00E+12 190 1902 Sum of purchased inputs +renewables 1552 15843 With Broiler litter 32448 324808

PAGE 241

222 Table F-10. Emergy analysis for tomato production scenario Cover Crop 0 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+13 1.00E+00 2 17 2 Water J 6.19E+09 3.81E+04 24 236 3 Evapotranspiration J 6.93E+10 2.59E+04 180 1796 4 Net top soil loss J 4.07E+09 7.38E+04 30 300 PURCHASED INPUTS 5 Fuel J 1.71E+09 1.11E+05 19 190 6 Electricity J 1.09E+10 2.21E+00 0 0 7 Potash g 8.49E+04 2.92E+09 25 248 8 Nitogen g 0.00E+00 4.05E+10 0 0 9 Pesticides g 1.72E+04 1.48E+10 25 255 10 Production Labor J 1.79E+08 4.13E+07 739 7387 11 Plastic J 1.02E+05 3.80E+08 4 39 12 Compost g 0.00E+00 5.49E+09 0 0 13 Broiler litter g 0.00E+00 3.97E+10 0 0 14 Sunn hemp, residue J 9.24E+09 4.04E+05 373 3731 Seeds cover crop J 3.70E+08 3.84E+04 1 14 15 Machinery rent for cover crops $ 3.94E+01 1.00E+12 4 39 Machinery rent $ 4.39E+02 1.00E+12 44 439 16 Labor cost $ 1.84E+03 1.00E+12 184 1837 Sum of purchased inputs +renewables 1223 12230 With Sunnhemp 1598 15976

PAGE 242

223 Table F-11. Emergy analysis for tomato production scenario Fallow 112 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.18E+102.59E+04 160 1600 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 1.82E+091.11E+05 20 202 6 Electricity J 1.32E+002.69E+05 0 0 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 1.12E+054.05E+10 453 4535 9 Pesticides g 1.82E+041.48E+10 27 270 10 Production Labor J 1.80E+084.13E+07 742 7419 11 Plastic J 1.02E+053.80E+08 4 38.844 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 0.00E+003.97E+10 0.0 0 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 4.39E+021.00E+12 44 439 16 Labor cost $ 1.85E+031.00E+12 185 1848 Sum of purchased inputs +renewables 1659.8 16924.51

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224 Table F-12. Emergy analysis for tomato production scenario Compost 112 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.18E+102.59E+04 160 1600 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 6.54E+091.11E+05 73 725 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 1.12E+054.05E+10 453 4535 9 Pesticides g 1.82E+041.48E+10 27 270 10 Production Labor J 1.87E+084.13E+07 772 7716 11 Plastic J 1.02E+053.80E+08 4 38.844 12 Compost J 1.37E+075.49E+09 7538 75380 13 Broiler litter g 0.00E+003.97E+10 0 0 15.1 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 15.2 Machinery rent $ 4.39E+021.00E+12 44 439 16 Labor cost $ 1.94E+031.00E+12 194 1944 Sum of purchased inputs +renewables 2045 20774 With Compost 9583 96154

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225 Table F-13. Emergy analysis for tomato pr oduction scenario Bro iler litter112 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.18E+102.59E+04 160 1600 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 3 Fuel J 4.49E+091.11E+05 50 498 4 Electricity J 1.32E+002.69E+05 0 0 5 Potash g 8.49E+042.92E+09 25 248 6 Nitogen g 1.12E+054.05E+10 453 4535 7 Pesticides g 1.82E+041.48E+10 27 270 8 Production Labor J 1.84E+084.13E+07 759 7587 9 Plastic J 1.02E+053.80E+08 4 39 10 Compost g 0.00E+005.49E+09 0 0 11 Broiler litter g 7.77E+063.97E+10 30897 308966 12 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 13 Machinery rent $ 4.39E+021.00E+12 44 439 14 Labor cost $ 1.90E+031.00E+12 190 1902 Sum of purchased inputs +renewables 1712 17444 With Broiler litter 32608 326410

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226 Table F-14. Emergy analysis for tomato production scenario Cover Crop 112 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.93E+102.59E+04 180 1796 4 Net top soil loss J 4.07E+097.38E+04 30 300 PURCHASED INPUTS 5 Fuel J 1.71E+091.11E+05 19 190 6 Electricity J 1.09E+102.21E+00 0 0 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 1.12E+054.05E+10 453 4535 9 Pesticides g 1.72E+041.48E+10 25 255 10 Production Labor J 1.79E+084.13E+07 739 7387 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 0.00E+005.49E+09 0 0 13 Broiler litter g 0.00E+003.97E+10 0 0 14 Sunn hemp residue J 9.24E+094.04E+05 373 3731 Seeds cover crop J 3.70E+083.84E+04 1 14 15 Machinery rent for cover crops $ 3.94E+011.00E+12 4 39 Machinery rent $ 4.39E+021.00E+12 44 439 16 Labor cost $ 1.84E+031.00E+12 184 1837 Sum of purchased inputs +renewables 1678 17318 With Sunnhemp 2052 21063

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227 Table F-15. Emergy analysis for tomato production scenario Fallow 224 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun J 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.18E+102.59E+04 160 1600 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 1.82E+091.11E+05 20 202 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 2.24E+054.05E+10 907 9069 9 Pesticides g 1.82E+041.48E+10 27 270 10 Production Labor J 1.80E+084.13E+07 742 7419 11 Plastic J 1.02E+053.80E+08 4 38.844 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 0.00E+003.97E+10 0.0 0 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 4.39E+021.00E+12 44 439 16 Labor cost $ 1.85E+031.00E+12 185 1848 Sum of purchased inputs +renewables 2407 24392

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228 Table F-16. Emergy analysis for tomato production scenario Compost 224 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.18E+102.59E+04 160 1600 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 6.54E+091.11E+05 73 725 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 2.24E+054.05E+10 907 9069 9 Pesticides g 1.82E+041.48E+10 27 270 10 Production Labor J 1.87E+084.13E+07 772 7716 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost J 1.37E+075.49E+09 7538 75380 13 Broiler litter g 0.00E+003.97E+10 0 0 15.1 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 15.2 Machinery rent $ 4.39E+021.00E+12 44 439 16 Labor cost $ 1.94E+031.00E+12 194 1944 Sum of purchased inputs +renewables 2498 25309 With Compost 10036 100689

PAGE 248

229 Table F-17. Emergy analysis for tomato pr oduction scenario Bro iler litter 224 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.18E+102.59E+04 160 1600 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 4.49E+091.11E+05 50 498 6 Electricity J 1.32E+002.69E+05 0 0 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 2.24E+054.05E+10 907 9069 9 Pesticides g 1.82E+041.48E+10 27 270 10 Production Labor J 1.84E+084.13E+07 759 7587 11 Plastic J 1.02E+053.80E+08 4 38.844 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 7.77E+063.97E+10 30897 308966 15.1 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 4.39E+021.00E+12 44 439 16 Labor cost $ 1.90E+031.00E+12 190 1902 Sum of purchased inputs +renewables 2165 21979 With Broiler litter 33062 330945

PAGE 249

230 Table F-18. Emergy analysis for tomato production scenario Cover Crop 224 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.19E+093.81E+04 24 236 3 Evapotranspiration J 6.93E+102.59E+04 180 1796 4 Net top soil loss J 4.07E+097.38E+04 30 300 PURCHASED INPUTS 5 Fuel J 1.71E+091.11E+05 19 190 6 Electricity J 1.09E+102.21E+00 0 0 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 2.24E+054.05E+10 907 9069 9 Pesticides g 1.72E+041.48E+10 25 255 10 Production Labor J 1.79E+084.13E+07 739 7387 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 0.00E+003.97E+10 0.0 0 14 Sunn hemp residue J 9.24E+094.04E+05 373 3731 Seeds cover crop J 3.70E+083.84E+04 1 14 15 Machinery rent for cover crops $ 3.94E+011.00E+12 4 39 Machinery rent $ 4.39E+021.00E+12 44 439 16 Labor cost $ 1.84E+031.00E+12 184 1837 Sum of purchased inputs +renewables 2131 21852 With Sunnhemp 2506 25598

PAGE 250

231 Table F-19. Emergy analysis for pepper production scenario Fallow 0 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) (E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 3.09E+103.02E+04 93 934 4 Net top soil loss J 1.00E+097.34E+04 7 73 PURCHASED INPUTS 5 Fuel J 1.94E+091.11E+05 22 215 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 0.00E+004.05E+10 0 0 9 Pesticides g 1.97E+041.48E+10 29 292 10 Production Labor J 1.46E+084.13E+07 605 6048 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 0.00E+003.97E+10 0.0 0 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.38E+031.00E+12 138 1381 Sum of purchased inputs +renewables 1233.3 12664

PAGE 251

232 Table F-20. Emergy analysis for pepper production scenario Compost 0 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 3.09E+103.02E+04 93 934 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 6.67E+091.11E+05 74 739 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 0.00E+004.05E+10 0 0 9 Pesticides g 1.97E+041.48E+10 29 292 10 Production Labor J 1.54E+084.13E+07 634 6344 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 1.37E+075.49E+09 7538 75380 13 Broiler litter g 0.00E+003.97E+10 0 0 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.48E+031.00E+12 148 1477 Sum of purchased inputs +renewables 1325 13581 With Compost 8863 88961

PAGE 252

233 Table F-21. Emergy analysis for pepper pr oduction scenario Bro iler litter 0 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 3.09E+103.02E+04 93 934 4 Net top soil loss J 1.00E+097.34E+04 7 73 PURCHASED INPUTS 5 Fuel J 4.62E+091.11E+05 51 512 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 0.00E+004.05E+10 0 0 9 Pesticides g 1.97E+041.48E+10 29 292 10 Production Labor J 1.50E+084.13E+07 622 6216 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 0.00E+005.49E+09 0 0 13 Broiler litter g 7.77E+063.97E+10 30897 308966 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.38E+031.00E+12 138 1381 Sum of purchased inputs +renewables 1280 13129 With Compost 32176 322095

PAGE 253

234 Table F-22. Emergy analysis for pepper production scenario Cover Crop 0 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 6.93E+103.02E+04 210 2096 4 Net top soil loss J 4.07E+097.34E+04 30 298 PURCHASED INPUTS 5 Fuel J 1.80E+091.11E+05 20 200 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 0.00E+004.05E+10 0 0 9 Pesticides g 1.01E+041.48E+10 15 150 10 Production Labor J 1.46E+084.13E+07 602 6016 11 Plastic J 1.02E+053.80E+08 4 38.844 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 0.00E+003.97E+10 0.0 0 14 Sunn hemp J 9.24E+094.04E+05 373 3731 Seeds cover crop J 3.70E+083.84E+04 1 14 15 Machinery rent for cover crops $ 3.94E+011.00E+12 4 39 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.37E+031.00E+12 137 1371 Sum of purchased inputs +renewables 1333 13891 With Compost 1708 17637

PAGE 254

235 Table F-23. Emergy analysis for pepper production scenario Fallow 112 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 3.09E+103.02E+04 93 934 4 Net top soil loss J 1.00E+097.34E+04 7 73 PURCHASED INPUTS 5 Fuel J 1.94E+091.11E+05 22 215 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 1.12E+054.05E+10 453 4535 9 Pesticides g 1.97E+041.48E+10 29 292 10 Production Labor J 1.46E+084.13E+07 605 6048 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 0.00E+005.49E+09 0 0 13 Broiler litter g 0.00E+003.97E+10 0 0 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.38E+031.00E+12 138 1381 Sum of purchased inputs +renewables 1687 17199

PAGE 255

236 Table F-24. Emergy analysis for pepper production scenario Compost 112 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 3.09E+103.02E+04 93 934 4 Net top soil loss J 1.00E+097.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 6.67E+091.11E+05 74 739 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 1.12E+054.05E+10 453 4535 9 Pesticides g 1.97E+041.48E+10 29 292 10 Production Labor J 1.54E+084.13E+07 634 6344 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost J 1.37E+075.49E+09 7538 75380 13 Broiler litter g 0.00E+003.97E+10 0 0 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.48E+031.00E+12 148 1477 Sum of purchased inputs +renewables 1778 18116 With Compost 9316 93496

PAGE 256

237 Table F-25. Emergy analysis for pepper pr oduction scenario Bro iler litter 112 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 3.09E+103.02E+04 93 934 4 Net top soil loss J 1.00E+097.34E+04 7 73 PURCHASED INPUTS 5 Fuel J 4.62E+091.11E+05 51 512 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 1.12E+054.05E+10 453 4535 9 Pesticides g 1.97E+041.48E+10 29 292 10 Production Labor J 1.50E+084.13E+07 622 6216 11 Plastic J 1.02E+053.80E+08 4 38.844 12 Compost g 0.00E+005.49E+09 0 0 13 Broiler litter g 7.77E+063.97E+10 30897 308966 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.38E+031.00E+12 138 1381 Sum of purchased inputs +renewables 1733 17664 With Compost 32630 326630

PAGE 257

238 Table F-26. Emergy analysis for pepper production scenario Cover Crop 112 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 6.93E+103.02E+04 210 2096 4 Net top soil loss J 4.07E+097.34E+04 30 298 PURCHASED INPUTS 5 Fuel J 1.80E+091.11E+05 20 200 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 1.12E+054.05E+10 453 4535 9 Pesticides g 1.01E+041.48E+10 15 150 10 Production Labor J 1.46E+084.13E+07 602 6016 11 Plastic J 1.02E+053.80E+08 4 38.844 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 0.00E+001.24E+05 0.0 0 14 Sunn hemp J 9.24E+094.04E+05 373.1 3731 Seeds cover crop J 3.70E+083.84E+04 1 14 15 Machinery rent for cover crops $ 3.94E+011.00E+12 4 39 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.37E+031.00E+12 137 1371 Sum of purchased inputs +renewables 1787 18426 With Compost 2161 22171

PAGE 258

239 Table F-27. Emergy analysis for pepper production scenario Fallow 224 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 3.09E+103.02E+04 93 934 4 Net top soil loss J 1.00E+097.34E+04 7 73 PURCHASED INPUTS 5 Fuel J 1.94E+091.11E+05 22 215 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 2.24E+054.05E+10 907 9069 9 Pesticides g 1.97E+041.48E+10 29 292 10 Production Labor J 1.46E+084.13E+07 605 6048 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 0.00E+003.97E+10 0.0 0 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.38E+031.00E+12 138 1381 Sum of purchased inputs +renewables 2140 21733

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240 Table F-28. Emergy analysis for pepper production scenario Compost 224 N-rate. Data Transformit y Emergy Em$ Value Not e Item Unit (Unit ha 1 cycle -1) (sej unit-1) (E13 sej yr-1) (2000 $ yr1) RENEWABLE RESOURCES 1 Sun 1.67E+1 3 1.00E+00 2 17 2 Water J 6.32E+0 9 3.81E+04 24 241 3 Evapotranspiration J 3.09E+1 0 3.02E+04 93 934 4 Net top soil loss J 1.00E+0 9 7.38E+04 7 74 PURCHASED INPUTS 5 Fuel J 6.67E+0 9 1.11E+05 74 739 6 Electricity J 1.09E+1 0 2.69E+05 293 2933 7 Potash g 8.49E+0 4 2.92E+09 25 248 8 Nitogen g 2.24E+0 5 4.05E+10 907 9069 9 Pesticides g 1.97E+0 4 1.48E+10 29 292 10 Production Labor J 1.54E+0 8 4.13E+07 634 6344 11 Plastic J 1.02E+0 5 3.80E+08 4 39 12 Compost g 1.37E+0 7 5.49E+09 7538 75380 13 Broiler litter g 0.00E+0 0 3.97E+10 0 0 15 Machinery rent for cover crops $ 0.00E+0 0 1.00E+12 0 0 Machinery rent $ 2.43E+0 2 1.00E+12 24 243 16 Labor cost $ 1.48E+0 3 1.00E+12 148 1477 Sum of purchased inputs +renewables 2232 22650 With Compost 9770 98030

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241 Table F-29. Emergy analysis for pepper pr oduction scenario Bro iler litter 224 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr-1) RENEWABLE RESOURCES 1 Sun 1.67E+131.00E+00 2 17 2 Water J 6.32E+093.81E+04 24 241 3 Evapotranspiration J 3.09E+103.02E+04 93 934 4 Net top soil loss J 1.00E+097.34E+04 7 73 PURCHASED INPUTS 5 Fuel J 4.62E+091.11E+05 51 512 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 2.24E+054.05E+10 907 9069 9 Pesticides g 1.97E+041.48E+10 29 292 10 Production Labor J 1.50E+084.13E+07 622 6216 11 Plastic J 1.02E+053.80E+08 4 39 12 Compost g 0.00E+005.49E+09 0 0 13 Broiler litter g 7.77E+063.97E+10 30897 308966 15 Machinery rent for cover crops $ 0.00E+001.00E+12 0 0 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.38E+031.00E+12 138 1381 Sum of purchased inputs +renewables 2187 22198 With Compost 33083 331164

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242 Table F-30. Emergy analysis for pepper production scenario Cover Crop 224 N-rate. Data TransformityEmergy Em$ Value Note Item Unit (Unit ha 1 cycle -1) (sej unit-1) ( E13 sej yr-1) (2000 $ yr1) RENEWABLE RESOURCES 1 Sun 2.14E+131.00E+00 2 21 2 Water J 0.00E+003.81E+04 0 0 3 Evapotranspiration J 6.93E+103.02E+04 210 2096 4 Net top soil loss J 4.07E+097.34E+04 30 298 PURCHASED INPUTS 5 Fuel J 1.80E+091.11E+05 20 200 6 Electricity J 1.09E+102.69E+05 293 2933 7 Potash g 8.49E+042.92E+09 25 248 8 Nitogen g 2.24E+054.05E+10 907 9069 9 Pesticides g 1.01E+041.48E+10 15 150 10 Production Labor J 1.46E+084.13E+07 602 6016 11 Plastic J 1.02E+053.80E+08 4 38.844 12 Compost g 0.00E+005.49E+09 0.0 0 13 Broiler litter g 0.00E+003.97E+10 0.0 0 14 Sunn hemp J 9.24E+094.04E+05 373 3731 Seeds cover crop J 3.70E+083.84E+04 1 14 15 Machinery rent for cover crops $ 3.94E+011.00E+12 4 39 Machinery rent $ 2.43E+021.00E+12 24 243 16 Labor cost $ 1.37E+031.00E+12 137 1371 Sum of purchased inputs +renewables 2240 22724 With Compost 2615 26469

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268 BIOGRAPHICAL SKETCH Laura vila was born in the southern region of Costa Rica, on March 8th, 1980. She received a Licenciatura degree in Agr onomy at EARTH University, in 2001. After graduation she completed an internship at the American University of Beirut, Lebanon, as a researcher in humic substances extracti on. After the completion of her program, she continued collaborating with the American University for the promotion of organic vegetable home gardens in the Bekaa Valle y, Lebanon. Upon her return to Costa Rica, she collaborated with EARTH University for the coordination of international workshops in animal homeopathy and precision agriculture After her time at EARTH, Laura went to work with CoopeAgropal R.L., an African palm production and extraction cooperative, located in her home town. During that time, Laura designed a waste management plan for industrial organic residues, through composting and on-farm management. She left Costa Rica in order to initiate her Master of Sc ience program in 2003, thanks to a research assistantship offered by the Agronomy Departme nt and School of Natural Resources and Environment of the University of Florida. During her Master of Science program Laura presented her research at the national meeti ngs of the American Society of Agronomy and Horticultural Society. Through her pr ogram, she gained e xperience in personnel management; experimental research; farm research; economic, energy, and emergy evaluations; statistics, and systems analysis.


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Title: Potential Benefits of Cover Crop Based Systems for Sustainable Production of Vegetables
Physical Description: Mixed Material
Copyright Date: 2008

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Table of Contents
    Title Page
        Page i
        Page ii
    Dedication
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    Acknowledgement
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    List of Tables
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    List of Figures
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    Abstract
        Page xviii
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    Introduction
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    Cover crop: Biomass and nitrogen accumulation
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    Growth, N accumulation, and yield of vegetable crops as affected by crop residues and N-fertilizer rate
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    Cost, energy, and emergy analysis of cover crop-based production systems
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    Conclusion
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    Appendices
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    References
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    Biographical sketch
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Full Text












POTENTIAL BENEFITS OF COVER CROP BASED SYSTEMS FOR
SUSTAINABLE PRODUCTION OF VEGETABLES
















By

LAURA MATILDE AVILA SEGURA


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2006

































Copyright 2006

by

Laura Matilde Avila Segura




























A mis queridisimos padres, en honor a sus ensefianzas, sacrificios, carifio y apoyo
incondicional.















ACKNOWLEDGMENTS

This research would have not been possible without the support of a diverse group

of committed people.

I would like to thank my advisor, Dr. Johannes Scholberg, for providing me with an

opportunity to come to the University of Florida and for his help and guidance during the

past three years. I also want to thank Andy Scheffler, Kari Reno, Huazhi Liu, Alicia

Lusiardo, Hannah Snyder, Susan Sorell, Jose Linares, Corey Cherr, among others, for

their assistance with field and laboratory work; but particularly for their friendship, which

permitted me to learn by doing. Special thanks go to Green Cay Farms, Nancy Roe and

UF-IFAS Plant Science Research and Education Unit in Citra.

Thanks go to Meghan Brennan and Dr. Ramon Littell, for their great help with

statistical analysis. I greatly value the help of Dr. Robert McSorley for his assistance with

data presentation and manuscript corrections and Dr. Clyde Kiker for his help with

economics and for encouraging me to look at systems from different scales.

Last but not least, I want to acknowledge Wesley Ingwersen, not only for

supporting and helping me through this research, but for walking with me the challenging

path of professional definition.

This research was funded by a grant from the Sustainable Agriculture Research and

Education program of the United States Department of Agriculture (grant number LS02-

140, "A System Approach for Improved Integration of Green Manure in Commercial

Vegetable Production Systems").
















TABLE OF CONTENTS



A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES ............. ................... .. .......... ...................................... ix

LIST OF FIGURES ........... ... ................... .......... .................................... xvii

ABSTRACT ........................................... ............. ................. xviii

CHAPTER

1 IN TR O D U C T IO N ........ .. ......................................... ..........................................1.

Conceptual Approach ............ ............. .. .......... ............................ .. 1
R rationale .................................................................................. . .. ...............2
M an ag em ent .................................................... ........................................... . 5
Knowledge Gaps ...............................................7
Experimental Design and Measurements .................... ...................................8
E x p erim ental U n it ............................................................................ ...............8
M easurem ents .......................................................................................... . 9
O n Farm Experim ent ........................................................................................10
M easurem ents ................................................................................ ..................... 11
H ypotheses .................................................................................................. ...............11
O objectives ................................................................................................... ...............11
G general O objective .............................................................................................. 11
Specific O objectives ........................................................................................... 12

2 COVER CROP: BIOMASS AND NITROGEN ACCUMULATION.......................18

Introduction and Literature Review 1............................. ..................... 18
M materials and M ethods ............................................................................................22
S et-u p an d D e sig n ................................................................................................ 2 2
Tim eline of O operations ..................................................................................23
2003-04 ................................................................................................... 23
2004-05 ................................................................................................... 24
Sam pling Procedures ........................................................................................24
2003-04 ................................................................................................... 24
2004-05 ................................................................................................... 25
Sam ple Processing ....... ................................ ........................................ 25


v









Statistical A analysis .............. ...... ............ ................................................ 26
R results ................................................................. ..... ..................... 27
Sum m er Cover Crops (SCC) ........................................................ 27
Sunn hem p 2003 ............... ................ ............................................. 27
C ow pea 2004 .............................................................................................28
P e a rl m ille t ...................................................................................................2 8
Sesbania ................................................................................................... 29
Species com prison ..................................... .. ............ ............ ............ 29
W inter C over C rops (W C C ) ........................................................... ................ 30
W inter ry e 2004 ......................................... .. ....................... .. ........... .. 30
H airy vetch 2004 ........................................... .. .... .......... ...................... 31
Overall winter cover crop system performance 2004 ................................32
W inter ry e 2 00 5 ......................................... .. ....................... .. ........... .. 33
H airy vetch 2005 ............................................ .... .. .. ... ....... ................. 34
Overall winter cover crop system performance 2005 ................................34
Species C om prison ....................................... ...................... ................ 35
D discussion ....................................................... ......... ..................... 36
Sum m er C over C rop System s ........................................................ ................ 36
Sunn hem p 2003 ............... ................ ............................................. 36
C ow pea ................................................................................................... 38
P e a rl m ille t ...................................................................................................4 0
Sesbania ................................................................................... ................. 4 1
Overall summer cover crop growth dynamics ........................... ...............43
W inter Cover Crop System s.............................................................................44
W in te r ry e .....................................................................................................4 4
H a iry v e tc h ...................................................................................................4 6
C conclusion .............. ........................................................................ . ..... 50

3 GROWTH, N ACCUMULATION, AND YIELD OF VEGETABLE CROPS AS
AFFECTED BY CROP RESIDUES AND N-FERTILIZER RATE .........................65

Introduction ................................................................................... ...................... 65
M materials and M ethods ............................................................................................69
Set-U p and D esign .........................................................................................69
Tim eline of O operations ..................................................................................71
2004 ....................................................................................................... 7 1
2004-05 ................................................................................................... 72
Sam pling Procedures ........................................................................................73
2004 ....................................................................................................... 73
2004-05 ................................................................................................... 73
Sam ple Processing .........................................................................................74
N itrogen A applied to Crops ...............................................................................75
Statistical A naly sis ........................................................................................... 75
R esu lts ...................................................... ....................... .......................76
Sw eet Corn (Spring 2004) ................................................................................76
Sw eet corn grow th ..................................................................................77
Sw eet corn yield ........................................................................................78









B roccoli (Fall 2004) .............. ...... ............ .............................................. 79
B broccoli grow th .............................................. ....................... .... ......... 80
B ro cco li y ield .......................................................................... ............... 8 1
W aterm elon (Spring 2005) ..................................... ..................... ................ 82
W term elon grow th .................................... ....................... ................ 82
W aterm elon yields.................................................................................. 83
D discussion ......................................................................................85
Sw eet C orn G row th ................ .............. ............................................ 85
Sw eet Corn Y ields .. ................................................................................. 85
B broccoli G row th ............... ................ ............................................... 89
B ro c c o li Y ie ld s .................................................................................................... 9 1
W aterm elon G row th .................................................................... ................ 92
W aterm elon Y ield ................ .............. ............................................ 94
C conclusion .............. ........................................................................ . ..... 96

4 COST, ENERGY, AND EMERGY ANALYSIS OF COVER CROP-BASED
PR OD U CTION SY STEM S.................................... ....................... ............... 114

Introduction...................... ....... ............... ............................ 114
Florida Farm ing System Characteristics ...................................... ................ 117
Economics and Energy Dynamics of Cover Crops................ .................. 118
M eth o d o lo g y ............................................................................................................. 12 0
Farm Description ................................................................... 120
Experim ental Set-up .................. ............................................................ 121
M easurem ents .............. .................... .. ....................... ...................... 122
Cost-Effectiveness Analysis...... ............ .......... ..................... 123
E energy A naly sis ... ... ......................................... ....................... . .......... 124
O operational expenses....................................................... ............... 124
In p u ts .......................................................................................................... 12 5
E m ergy A analysis ............... .... ............. ................................................ 126
Sunn Hemp Replacement Scenarios............... ........................ 127
R e su lts.............................................................................................. ........ .......... 12 8
Cost- Effectiveness Analysis...... ............ .......... ..................... 128
E energy A naly sis ... ... ......................................... ....................... . .......... 132
E m ergy A analysis ............. .. ............... .............................................. 134
D isc u ssio n ............... ....................................... .... ................................................ .. 1 3 5
Cost-Effectiveness A nalysis...... ............ .......... ..................... 135
E energy A naly sis ... ... ......................................... ....................... . .......... 139
E m ergy analy sis ... ... ......................................... ....................... . ........... 14 1
G general discussion ........................................................................ ............... 143
C o n c lu sio n s.............................................................................................................. 14 6

5 C O N C L U SIO N ............... .. .................. .................. ................. ..... .... .... ........... 162

APPENDIX









A EFFECT OF INTERACTIONS IN COVER CROPS DRY MATTER
ACCUMULATION, N CONCENTRATION AND N ACCUMULATION ........... 168

B CARBON AND NITROGEN CONCENTRATION IN DIFFERENT PLANT
PARTS OF SUMMER AND WINTER COVER CROPS ................................... 177

C WEATHER DATA FOR RESEARCH STATION..........................................179

D NITROGEN DYNAMICS AND INTERACTIONS FOR SWEET CORN,
BROCCOLI AND WATERMELON .......................................... 182

E COST -EFFECTIVENESS ANALYSIS ....... .......... ....................................... 196

F ENERGY AND EMERGYANALYSIS........................................204

REFERENCES ............ ................... .. ........... .....................................243

BIOGRAPHICAL SKETCH ..................................................... 268















LIST OF TABLES


Table page

1-1. Cover crop research focus over time in Florida and Georgia, a small sample......... 13

1-2. Outline of crop rotation and experimental treatments......................... ................ 17

2-1. Outline of crop rotations and experimental treatments during the research
period (03-05) ........................................................................... ... . ............... 52

2-2. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) main effect, along with ST*Np interaction effect on dry weight,
N concentration, and N accumulation of sunn hemp (Crotalariajuncea) ...............53

2-3. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) main effect, along with ST*Np interaction effect on dry weight,
N concentration, and N accumulation of cowpea (Vigna unguiculata) ................54

2-4. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) main effect, along with ST*Np interaction effect on dry weight,
N concentration, and N accumulation of pearl millet (Pennisetum glaucum) .........55

2-5. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) main effect, along with ST*Np interaction effect on dry weight,
N concentration, and N accumulation of sesbania (Sesbania sesban) ..................56

2-6. Total dry weight accumulation and dry matter allocation to different plant parts
for sum m er/fall cover crops ..................................... ....................... ................ 57

2-7. Total Nitrogen (N) accumulation and N allocation to different plant parts for
sum m er/fall cover crops ...................................................................... ................ 57

2-8. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) and residue [RES = residue of sunnhemp (SH) or fallow
vegetation (F)] main effect on rye (Secale cereale), during summer/fall 04. ..........58

2-9. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) and residue [RES = residue of sunn hemp (SH) or fallow
vegetation (F)] main effect on hairy vetch (Vicia villosa), during summer/fall 04..59









2-10. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np and residue [RES = residue of sunn hemp (SH) or fallow
vegetation (F)] main effect on hairy vetch and rye, during summer/fall 04 ..........60

2-11. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) main effect and ST*Np interaction effect on dry weight, N
concentration, and N accumulation of rye (Secale cereale), winter 04/05. ............61

2-12. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) main effect and ST*N-p interaction effect on dry weight, N
concentration, and N accumulation of hairy vetch (Vicia villosa), winter 04/05.....62

2-13. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet
corn crop (Np) main effect and ST*Np interaction effect on dry weight, N
concentration, and N accumulation of hairy vetch + rye, during winter 04/05........ 63

2-14.Total dry weight accumulation and dry matter allocation different plant parts for
winter cover crops. ........ .. ................ .... ........ ...... ...............64

2-15. Total Nitrogen (N) and N allocation to different plant parts for winter cover
crops, studied during 04 and 05 .......................................................... ................ 64

3-1. Outline of crop rotations and experimental treatments used during 03-05..............98

3-2. Effects of sampling time (ST), kg ha-&ofN fertilizer applied to sweet corn (N-
rate) and cropping system (CS) main effect; along with ST*N-rate, ST*CS, N-
rate*CS interactions on sweet (Zea mays) corn shoots, during the spring of 04. ....99

3-3. Effect of kg ha-lofN fertilizer applied to sweet corn (N-rate) and cropping
system (CS) interaction (N-rate*CS) on shoot dry weight, N concentration, N
accumulation and SPAD readings of sweet corn (Zea mays), spring 04. .............100

3-4. Pair-wise contrast comparison by treatment for dry weight, N concentration and
N accumulation in sweet corn (Zea mays) shoots, during the spring of 04. .........101

3-5. Effects of kg ha-lofN fertilizer applied to sweet corn (N-rate) and cropping
system (CS), along with CS*N-rate interaction on total, marketable, fancy and
culls yield of sweet corn (Zea mays), during the spring of 04. .............................103

3-6. Pair-wise comparison of selected treatments for total, marketable and culls yield,
total N applied to sweet corn (N applied), nitrogen use efficiency (NUE), and
un-utilized applied nitrogen (UAN), during the spring of 04. ............................. 104

3-7. Regression equation for total and marketable yields of sweet corn for a
conventional sweet corn treatment (FF) amended with 5 different levels of N
fertilization, during the spring of 04................ ........................ 104









3-8. Effects of sampling time (ST), kg ha-lof N fertilizer applied to broccoli (N-rate)
and summer cover crop residue (RES), along with ST*RES and N-rate*RES
interaction effect on broccoli, during the winter of 04/05.................................105

3-9. Pair-wise contrast comparison by treatment for dry weights, N concentration and
N accumulation along sampling times (in weeks after transplanting [WAT]) in
broccoli (Brassica oleracea), during the winter of 04/05 .................................106

3-10. Effects of kg ha-lof N fertilizer applied to broccoli (N-rate) and cover crop
residue (RES), along with RES*N-rate interaction effect on yields of winter
broccoli yields, during the 04/05 ........ ...... ........ .......... ............. 108

3-11. Pair wise comparison between cowpea and pearl millet based systems amended
with different N-fertilizer rates for fresh marketable, process marketable, total
marketable, culls marketable, and culls process categories of broccoli.............. 109

3-12. Effects of kg ha-lof N fertilizer applied to watermelon (N-rate) and cropping
system (CS), along with CS*N-rate interaction on dry matter accumulation, N
concentration and N accumulation of watermelon during the spring of 05 .........110

3-13. Effect of kg ha-lof N fertilizer applied to watermelon (N-rate) and cropping
system (CS) interaction (N-rate*CS) on shoot dry weight, and N accumulation
of watermelon (Citrullus lanatus) for last sampling date. .................................. 111

3-14. Effects of kg ha-lof N fertilizer applied to watermelon (N-rate) and cropping
system (CS), along with CS*N-rate interaction on total, marketable, and non
marketable (culls) yield of watermelon during the spring of 05 ..........................112

3-15. Pair-wise contrast comparison by treatment for fresh marketable, total
marketable and non marketable (culls) of watermelon during the winter of 04.... 113

3-16. Regression equation for total and marketable yields of watermelon for a
conventional treatment (FF), with 5 levels of N fertilization, during the spring of
0 5 .................................................................................................. ....... . ........ 1 1 3

4-1. Overview of cropping sequence and experimental treatments at Boynton Beach
(0 2 -0 5 ) ................................................................................................................ ... 1 4 8

4-2. Summary of yields for tomato, pepper and sweet corn as affected by summer
cover crop (sunn hemp) and N-fertilizer rate (04 and 05).................................148

4-3. Average cost of growing sunn hemp (03 and 04). .................... ................148

4-4. Average summer weed control production expenses (03 and 04)....................... 149

4-6. Energy analysis summary for tomato production per ha at Boynton Beach,
Florida, for 12 different (hypothetical) production scenarios (03-04). .................151









4-7. Energy distribution among different production components for tomato
production per ha at Boynton Beach, Florida, for 12 different (hypothetical)
sc e n a rio s ............................................................................................................ .. 1 5 2

4-8. Energy distribution from the energy analysis for pepper production per ha at
Boynton Beach, Florida, forl2 different (hypothetical) scenarios.......................153

4-9. Energy distribution from the energy analysis for pepper production per ha at
Boynton Beach, Florida, for 12 different (hypothetical) scenarios......................154

4-10. Energy analysis summary for sweet corn production per ha at Boynton Beach,
Florida, for 12 different (hypothetical) scenarios...................... ................... 155

4-12. Energy distribution from the energy analysis for crop production per ha at
Boynton Beach, Florida, for four different (hypothetical) scenarios ...................156

4-13. Emergy analysis main indicators from energy analysis for tomato production per
ha at Boynton Beach, Florida, for twelve different (hypothetical) scenarios......... 157

4-14. Emergy analysis main indicators from energy analysis for pepper production per
ha in Boynton Beach, Florida, for 12 different (hypothetical) scenarios.............159

A-1. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously
applied to sweet corn (Np) interaction (ST*Np) on dry weight, N concentration,
and N accumulation of sunn hemp (Crotalariajuncea), during summer/fall 03... 168

A-2. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously
applied to sweet corn (Np) interaction (ST*Np) on dry weight, N concentration,
and N accumulation of cowpea (Vigna unguiculata), during summer/fall 04 ......169

A-3. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously
applied to sweet corn (Np) interaction (ST*Np) on dry weight, N concentration,
and N accumulation of pearl millet (Pennisetum glaucum), summer/fall 04......... 170

A-4. Effects of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously
applied to sweet corn (Np) interaction (ST*Np) on dry weight, N concentration,
and N accumulation of sesbania (Sesbania sesban), during summer/fall of 04..... 171

A-5. Effect of sampling time (ST) and residue [RES = residue of sunnhemp (SH) or
fallow vegetation (F)] interaction (ST*RES) on dry weight, N concentration, and
N accumulation of rye (Secale cereale), during the winter of 03/04 .................. 172

A-6. Effect of sampling time (ST) and residue [RES = residue of sunnhemp (SH) or
fallow vegetation (F)] interaction (ST*RES) on dry weight, N concentration, and
N accumulation of hairy vetch (Vicia villosa), during the winter of 2003/04. ......172









A-7. Effect of sampling time (ST) and residue [RES = residue of sunnhemp (SH) or
fallow vegetation (F)] interaction (ST*RES) on dry weight, N concentration, and
N accumulation of hairy vetch +rye, during the winter of 2003/04 .................... 173

A-8. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously
applied to sweet corn (Np) interaction (ST*Np) effect on dry weight, N
concentration and N accumulation in rye (Secale cereale), winter 04/05........... 174

A-9. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously
applied to sweet corn (Np) interaction (ST*Np) effect on dry weight, N
concentration and N accumulation in hairy vetch (Vicia villosa), winter 04/05.... 175

A-10. Effect of sampling time (ST or WAE) and kg ha-1 of N fertilizer previously
applied to sweet corn (Np) interaction (ST*Np) effect on dry weight, N
concentration and N accumulation in rye+hairy, winter 04/05........................... 176

B-1. Carbon (C) to Nitrogen (N) relation (C:N ratio) for different plant parts in
sum m er cover crops. ............. ................ .............................................. 177

B-2. Carbon (C) to Nitrogen (N) relation (C:N ratio) for different plant parts in winter
c o v er cro p s. ............................................................................................................ 17 8

C-1. Average temperature (at 60 cm height), minimum and maximum temperature
(MinT and MaxT at 60 cm height), and average of solar radiation (AVGsolrd at
2 m height) for twelve months during 2003 ....................... .......... ................ 179

C-2. Average temperature (at 60 cm height), minimum and maximum temperature
(MinT and MaxT at 60 cm height), and average of solar radiation (AVGsolrd at
2 m height) for twelve months during 2004. ...... ... ...................................... 179

C-3. Average temperature (at 60 cm height), minimum and maximum temperature
(MinT and MaxT at 60 cm height), and average of solar radiation (AVGsolrd at
2 m height) for twelve months during 2005 ....................... .......... ................ 180

C-4. Average rainfall for twelve months during 2003 ........................ ................180

C-5. Average rainfall for twelve months during 2004. ............................ ............. 181

C-6. Average rainfall for twelve months during 2004. ............................ ............. 181

D-1. Nitrogen applied to sweet corn (Zea mays var. "Saturn Yellow") in form of
NH4N03 fertilizer and summer and winter cover crops residue and weeds,
during the spring of 2004 (kg ha-1)....... ... .......................... 182

D-2. Effect of sampling time (ST or WAE) and kg ha-lof N fertilizer applied to sweet
corn (N-rate) interaction effect (ST*N-rate) on dry weight, N concentration, N
accumulation in shoots and SPAD readings (chlorophyll readings) of sweet corn
leaves (Zea mays), during the spring of 2004. ......................................183









D-3. Sampling time (ST) and cropping system (CS) interaction effects (ST*CS) on
dry weight, N concentration, N accumulation in shoots and SPAD readings
(chlorophyll readings) of sweet corn leaves (Zea mays), during the spring of
2 0 0 4 .................................................................................................... ........... 18 4

D-4. Effect of kg ha-lof N fertilizer applied to watermelon (N-rate) and cropping
system (CS) interaction (N-rate*CS) on dry weight, N concentration and N
accumulation of sweet corn (Zea mays), during the spring of 2004 ....................185

D-5. Effects of sampling time (ST) and kg ha-lof N fertilizer applied to watermelon
(N-rate) interaction (ST*N-rate) on marketable, fancy, non marketable (culls)
and total yield of sweet corn (Zea mays), during the spring of 2004 .................. 186

D-6. Equations for critical points of SPAD for sweet corn, SPAD, and NO3- for
watermelon, and critical N concentration (g N kg-1) in broccoli leaves ..............187

D-7. N applied to broccoli (Brassica oleracea var. "Pac Man") in form of fertilizer
(NH4 NO3), cover crops residue and weeds, during the winter of 2004/05 .........187

D-8. Effect of sampling time (ST) and kg ha-lof N fertilizer applied to broccoli (N-
rate) interaction (ST*N-rate) effect on dry weight, N concentration and N
accumulation in broccoli (Brassica oleraceae), during the winter of 2004/05......188

D-9. Effect of sampling time (ST) and residue [RES = residue of cowpea (CP) or
residue of pearl millet (P)] interaction (ST*RES)effect on dry weight, N
concentration and N accumulation in broccoli (Brassica oleraceae) ..................189

D-10. Effect of kg ha-lof N fertilizer applied to broccoli (N-rate) and residue residue
[RES = residue of cowpea (CP) or residue of pearl millet (P)] interaction (N-
rate*RES) on dry weight, and N accumulation in broccoli...............................190

D-11. N applied to watermelon (Citrullus lanatus var. "Mardi Gras") in form of
fertilizer (NH4NO3), cover crops residue and weeds, during the spring of 2005...191

D-12. Effect of sampling time (ST) and kg ha-lof N fertilizer applied to watermelon
(N-rate) interaction (ST*N-rate) on dry weight, N concentration, and N
accumulation in watermelon shoots and fruits (Citrullus lanatus), spring 2005...192

D-13. Effect of sampling time and cropping system interaction (ST*CS) effect on dry
weight, N concentration and N accumulation in watermelon shoots and
fruits(Citrullus lanatus), during the spring of 2005. ................. ................193

D-14. Effect of cropping system (CS) and kg ha-lof N fertilizer applied to watermelon
(N-rate) interaction (CS*N-rate) effect on dry weight, N concentration and N
accumulation in watermelon shoots and fruits (Citrullus lanatus), during the
sp rin g o f 2 0 0 5 ......................................................................................................... 19 4









D-15. Effect of cropping system (CS) and kg ha- of N fertilizer applied to watermelon
(N-rate) interaction (CS*N-rate) on dry weight, N concentration and N
accumulation in watermelon total tissues (Citrullus lanatus), spring 05 .............195

D-16. Effect of cropping system (CS) and kg ha-lof N fertilizer applied to watermelon
(N-rate) interaction (CS*N-rate) on weeds dry weight accumulation, N
concentration and accumulation, during the spring of 05 .................................195

E-1. Generic expenses for tomato or pepper and sweet corn production systems
(2 0 0 3 -2 0 0 4 )............................................................................................................ 19 6

E-2. Tomato crop production expenses (2003-2004)......................... ...................197

E-3. Bell pepper crop production expenses average years 2003-2004 ........................198

E-4. Sweet corn crop production expenses average years 2003-2004 .........................199

E-5. Sensitivity analysis for the effect of product price on revenues from specific
pepper treatments based on average pepper yield (2004 and 2005).................... 199

E-6. Sensitivity analysis for the effect of product price on revenues from specific
tomato treatments, based on average tomato yield (2004 and 2005). ...................200

E-7. Sensitivity analysis for the effect of product price on revenues from specific
sweet corn treatments, based on average sweet corn yield (2004 and 2005) .........200

E-8. Budget analysis for the different management scenarios without synthetic N
fe rtiliz e r ............................................................................................................. . 2 0 1

E-9. Budget analysis for the different management scenarios with 112 kg N ha-1 N
fe rtiliz e r ............................................................................................................. . 2 0 2

F-1. Energy analysis for the different management scenarios for tomato production...205

F-2. Energy analysis for the different management scenarios for pepper production...207

F-3. Energy analysis for the different management scenarios for sweet corn
p ro d u ctio n .............................................................................................................. 2 0 9

F-4. Energy coefficients calculated of gather from literature for the energy analysis...211

F-5 Emergy memory or calculations (Not all the calculations are applicable to the
different scenarios or crops) ....................................................... ................ 212

F-7. Emergy analysis for tomato production scenario Fallow 0 N-rate.......................219

F-8. Emergy analysis for tomato production scenario Compost 0 N-rate ...................220

F-9. Emergy analysis for tomato production scenario Broiler litter 0 N-rate..............221









F-10. Emergy analysis for tomato production scenario Cover Crop 0 N-rate ...............222

F-11. Emergy analysis for tomato production scenario Fallow 112 N-rate...................223

F-12. Emergy analysis for tomato production scenario Compost 112 N-rate ...............224

F-13. Emergy analysis for tomato production scenario Broiler litter 12 N-rate...........225

F-14. Emergy analysis for tomato production scenario Cover Crop 112 N-rate ...........226

F-15. Emergy analysis for tomato production scenario Fallow 224 N-rate.................. 227

F-16. Emergy analysis for tomato production scenario Compost 224 N-rate ...............228

F-17. Emergy analysis for tomato production scenario Broiler litter 224 N-rate..........229

F-18. Emergy analysis for tomato production scenario Cover Crop 224 N-rate ...........230

F-19. Emergy analysis for pepper production scenario Fallow 0 N-rate.......................231

F-20. Emergy analysis for pepper production scenario Compost 0 N-rate....................232

F-21. Emergy analysis for pepper production scenario Broiler litter 0 N-rate ..............233

F-22. Emergy analysis for pepper production scenario Cover Crop 0 N-rate ...............234

F-23. Emergy analysis for pepper production scenario Fallow 112 N-rate...................235

F-24. Emergy analysis for pepper production scenario Compost 112 N-rate................236

F-25. Emergy analysis for pepper production scenario Broiler litter 112 N-rate ..........237

F-26. Emergy analysis for pepper production scenario Cover Crop 112 N-rate ...........238

F-27. Emergy analysis for pepper production scenario Fallow 224 N-rate...................239

F-28. Emergy analysis for pepper production scenario Compost 224 N-rate................240

F-29. Emergy analysis for pepper production scenario Broiler litter 224 N-rate ..........241

F-30. Emergy analysis for pepper production scenario Cover Crop 224 N-rate ...........242















LIST OF FIGURES


Figure page

3-1. Calculated N accumulation for different N-rates for cropping systems (CS) as a
function of weeks after emergence (WAE) for A) sweet corn amended with 0 kg
N ha-1; B) sweet corn amended with 67 kg N ha-1; C) sweet corn amended with
133 kg N ha-'; D) sweet corn amended with 200 kg N ha-'; and E) sweet corn
am ended w ith 267 kg N ha-1. ........................................................102

3-2. Nitrogen accumulation in different cropping systems (RES) as a function of
days after emergence (DAP) for A) broccoli amended with 0 kg N ha-1; B)
broccoli amended with 131 kg N ha- ; C) broccoli amended with 196 kg N ha- .. 107

4-1. Overview of inter-relation between processes and economic scales using an
Object-Oriented programming approach outlining how cover crop best
management practices at a micro scale interact with meso scales .......................161















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

POTENTIAL BENEFITS OF COVER CROP-BASED SYSTEMS FOR
SUSTAINABLE PRODUCTION OF VEGETABLES

By

Laura Matilde Avila Segura

August, 2006

Chair: Johannes Scholberg
Major Department: Agronomy

Although cover crops (CC) historically were an integral part of cropping systems,

there is limited information on how to best integrate CC in current production operations,

especially in transitional environments. Moreover, on-farm cost-effectiveness analysis are

needed for evaluating the benefits from CC in vegetable production systems. At Citra,

Florida, we conducted a 2-year field study to evaluate yield response of spring sweet corn

(Zea mays var. Saturn Yellow) to a summer CC (sunn hemp [Crotalariajuncea]) and/or

winter CC (hairy vetch [Vicia villosa] and rye [Secale cereale]) during 2003/04. We also

evaluated the response of watermelon (Citrullus lanatus var. Mardigrass) in a crop

rotation with summer CC (pearl millet [Pennisetum glaucum var. Tifleaf], cowpea [Vigna

unguiculata var. Zipper Cream] or sesbania [Sesbania sesban], followed by either winter-

planted broccoli (Brassica oleracea var Pac Man) or a winter CC (hairy vetch and winter

rye mix) during 2004/05. We conducted a farm-based cost, energy, and emergy analysis

for tomato (Lycopersicon esculentum), pepper (Capsicum annum), and sweet corn


xviii









production systems at a Community Supported Agriculture farm, located in Boynton

Beach, Florida, during 2003/04. At Citra, sweet corn planted in CC residues received 0,

67, or 133 kg N ha-1, whereas sweet corn non-CC (control) systems received 0, 67, 133,

200, or 267 kg N ha-1. Broccoli was considered amended with 0, 131, or 196 kg N ha-1

The CC-based watermelon systems received either 0, 84, or 168 kg inorganic N ha-1,

while non-CC systems received either 0, 84, 126, 168, or 210 kg N ha-1. Sunn hemp

accumulated 7.2 Mg ha-1 and 111 kg N ha-1, but continuous cultivation resulted in build

up of soil-borne-disease. Pearl millet performed well in low fertility and high

precipitation environment, accumulating 9.4 Mg DM ha-1 and 75 kg N ha-1. Cowpea, on

the other hand, tended to be sensitive to high humidity, and early senescence reduced

biomass yield (2.9 Mg ha-1 and 54 kg N ha-1). Sesbania stands were affected by

nematodes causing this crop to perform very poorly. The winter CC mix produced 7.7

Mg ha-1 and 139 kg N ha-1 and 12.3 Mg ha-1 and 264 kg N ha-1 during 2004 and 2005,

respectively. A double cropping system fertilized with 133 kg N ha-1 produced

comparable yields to fallow sweet corn fertilized at 200 kg N ha-1 (15.8 vs. 17.3 Mg ha-1,

respectively). Pearl millet enhanced broccoli biomass accumulation while yields were not

affected by summer cover crop at high N-fertilizer rates. In contrast, non-fertilized

cowpea-based systems had greater and earlier broccoli yields compared to pearl millet-

based systems. Watermelon initial growth and fruit development was hampered by cold

and wet conditions and continuous growth of hairy vetch after mowing. The cost, energy,

and emergy analysis concluded that when CC enhanced yields, they provide higher gross

returns compared to conventional management, further reducing the dependency on fossil

fuel-derived inputs, and helping achieve farm sustainability














CHAPTER 1
INTRODUCTION

Conceptual Approach

This chapter will outline the scope of work of the research program underlying this

thesis and provides a conceptual framework of subsequent chapters of this thesis, along

with a brief discussion of how these chapters are interrelated. The first chapter also

includes initial hypotheses and a brief overview of experiments, treatments, and

measurements. This thesis aims to look at processes at different scales and a number of

system components as well. Chapter 2 is mainly physiologically oriented and looks at

processes at a plant level. It outlines biomass and nitrogen accumulation patterns of cover

crops. Chapter 3 discusses the interactive effects of cover crops and nitrogen (N) fertilizer

application rates on the growth and yield of subsequent vegetable crops in North Central

Florida (NCF), such as sweet corn in spring 2004, broccoli in winter 2004/2005, and

watermelon in Spring 2005. Chapter 4 presents a much broader framework and assesses

the potential benefits of cover crops on a farm scale for a commercial vegetable operation

in South East Florida (SEF). For this location the effects of cover crop on sweet corn,

tomato and peppers yield, production cost and profitability were examined. Energy and

emergy analysis of the cover cropping system is also included using farm records and

values obtained from the literature. In the last section of this chapter a meso-scale

theoretical evaluation framework is presented which allows for improved assessment of

the importance of cover cropping systems in the context of sustainable small farming









operations. Chapter 5 synthesizes information from previous chapters and also provides

suggestions for future program activities.

Rationale

Complex biological systems such as agroecosystem require a systems approach in

order to fully appreciate their structure and function. The underlying assumption is that

agroecosystems are complex and interrelationships among environmental conditions,

management, and biological processes are important in determining outcomes such as

yield, pest pressure, and environmental impacts (Drinkwater, 2002), which might serve as

indicators of the agroecosystem sustainability. It is also important to evaluate the

economic component of managed agroecosystems (Ante and Capalbo, 2002). Recent

environmental guidelines and regulations can only be integrated into agricultural

management practices when farmers can also sustain long term profitability of their

operations (Baggs et al., 2000).

Florida possesses a large and stable agricultural economic base. According to the

Florida Department of Agriculture and Consumer Services (2003) Florida has 44,000

commercial farms, occupying 4.13 million hectares. Most of these operations use

conventional production practices.

Floridian agriculture occurs mainly on very sandy (>98% sand) soils, with low soil

organic matter content and low inherent soil fertility. This implies that frequent

application of synthetic nitrogen fertilizers is required for optimal production (Hochmuth,

2000). Excessive N fertilizer application when combined with high intensity rainfall

events and poor water and nutrient holding capacity of soils may result in N leaching

below the active root zone (Prakash et al,. 1999). For example, groundwater nitrate (NO3-

N) values in excess of the maximum contaminant limit (MCL) of 10 mg NO3-N L-1 are









commonly found in citrus production areas of central Florida (Mattos et al., 2003).

Florida appears to be following a general global trend, as mentioned by Nair and Graetz

(2004), and it is projected that by 2020 the contribution to crop nutrient requirements

from the soil will be as low as 21%, compared to 9% from organic amendments and 70%

from an inorganic fertilizer. These projections show the need for more sustainable

practices. In Florida for example, The Office of Agricultural Water Policy (OAWP) of

the Florida Department of Agriculture and Consumer Services (FDACS) has been

developing Best Management Practices (BMPs), addressing both water quality and water

conservation on a site-specific, regional, and watershed basis (OAWP, 2005).

The dependence of conventional agriculture on inorganic fertilizers and thereby in

fossil fuels, may constrain production in the near future. Today, as in the early 1970s,

fossil fuels supply is uncertain (Huilsbergen et al., 2001). For example in 1997 the total

energy inputs necessary to cultivate one hectare of maize in the Unites States was about

10 million kCal, or 1000 liters of oil (Pimentel et al., 1998). Reduced fuel availability and

sharply increasing fuel prices may favor replacement of chemical fertilizers by manures

and other organic amendments in agriculture. Use of leguminous cover crops, that via

symbiotic N fixation use solar energy to generate on-site N in a sustainable fashion, can

be seen as a strategy for decreasing energy invested per output of crop. Similarly non-

tillage systems can also greatly reduce machinery use, and thereby energy expenditures in

cultivation (Conservation Technology Information Center, 2002).

Among other alternatives to fossil fuels, yard compost or biosolids appear to be

more widespread, but it has been documented that compost or chicken manure, may

contain small amounts of heavy metals and also may result in hyper-accumulation of









phosphorous. In this regard, no tillage system combined with cover crop rotations

represents a more sustainable management strategy to secure production while

minimizing externalities.

An improved understanding of soil-plant-environment interactions is necessary

when selecting cover crops (Cobo et al., 2002). Cover crops enhance soil quality by

attracting beneficial insects (Bugg et al.,1991), improving "peds" aggregation (Gregory et

al., 2005), infiltration capacity, increasing organic matter (Salinas-Garcia et al., 1997),

stimulating microbial activity during first weeks after incorporation (Lundquist et al.,

1999), and reducing nematode populations (Abawi and Widmer, 2000). The latter

characteristic is especially important in sandy soils where nematodes tend to proliferate

easily (Griffin, 1996). However, it is very difficult to quantify economic benefits from

cover crops use in vegetable production systems, since their benefits are cumulative over

time and soil quality improvement might not be evident in the short term. Another

difficulty in economic evaluation of cover crops is the appropriate allocation of

establishment cost of the cover crop (Klonsky, 2003). From the strict economic stand

point, the use of cover crops is only cost effective if production input requirements

decrease and/or results in a significant increase in crop yields. Therefore yield decrease in

cover cropping systems may reduce profits, due to the cover crop high establishment cost

(Baldwin and Creamer, 1999). For all these reasons, the real benefits of cover crops may

be masked when just focusing on short-term economic results, and this underlines the

importance of also comparing the energetic cost of cover cropping systems versus

conventional cropping systems. It is also desirable to also assess the economic value of

all the environmental long term services provided by cover crops.









Management

Environmental characteristics, as well as soil type, are crucial factors when

managing cover crops. In North Central Florida, fall cover crops have to withstand high

temperatures in the months of July, August and September, while frost may occur starting

the beginning of November. The summer cover crop that was used in this study for the

first three years is commonly referred to as "sunn hemp" (Crotalaria juncea). This

fabacea is native to India and Pakistan, and cultivated in Southeast Asia for fiber and as

live mulch (Li et al., 2006). Its positive attributes include potential nematicidal action

(McSorley, 1999), erosion and weed control, and high biomass and nitrogen

accumulation capacity (Li et al., 2000). However, sunn hemp is sensitive to Verticillium

spp. (Cherr, 2003) and frost (Mansoer, 1997). It has been shown that under Florida

conditions, sunn hemp can cause NO3-N leaching, compared to non legumes, when

incorporated into the soil (Wang et al., 2003), therefore a non tillage system may be more

appropriate.

In California, summer cover crops often require supplemental irrigation during its

establishment (Van Horn, 2003) to attain maximum growth. In Illinois, no-till corn

following ryegrass as a cover crop for three years, yielded 5.2 Mg ha-1 under water

limited conditions, compared to 4.1 Mg ha-1 for no-till without a cover crop on a fragipan

soil. Corresponding results for a silt loam soil were 8.7 Mg ha-1 versus 1.4 3.5 Mg ha-1

(Collins, 2003).

Poor summer cover crop development means a reduction in the biomass added to

the soil, and thereby a decrease in soil organic matter accumulation. In this case a

vigorously growing winter cover crop may be required to sustain soil organic matter.









According to Weinert et al., (2002) over-wintering non-leguminous crops prevent

N movement through the soil. This supports the hypothesis that in order to avoid N

leaching and to enhance soil nutrient retention. Therefore, in excessively drained soils

and warm climates it is better to plant a non-leguminous crop or cover crop after a

leguminous cover crop (Kuo and Jellum, 2002). Leguminous cover crops can

substantially reduce N fertilizer requirements. However, poor synchronization between

cover crop residue mineralization and subsequent peak N demand of a commercial crop

may either reduce N availability and/or the risk of excessive N leaching and thereby

hamper efficient N utilization (Sperow, 1995; Weinert et al,. 2000; Logsdon et al., 2002).

In the Central Corn Belt of the United States of America (Missouri, Illinois,

Indiana, and Ohio) hairy vetch (Vicia villosa) is a commonly used legume, while winter

rye (Secale cereale) is a preferred non-leguminous cover crop. Hairy vetch is mainly used

as a nitrogen source for cash crops. Rye is utilized as a catch crop for residual nitrates and

due to its vigorous growth in the fall and its winter hardiness, it also provides an excellent

soil cover that can both prevent soil erosion and suppress weeds. Maize growing after

hairy vetch had a higher yield than when following rye (Bollero et al., 1994).

Mixtures of cover crops appear to be more suitable for improving soil N retention

and crop N availability. Winter rye performs well when mixed with hairy vetch, rye can

tolerate a wide variety of soil types and climatic conditions (Creamer and Baldwing,

1999). For example, a mix of hairy vetch and rye can create an optimal C:N ratio, which

decreases the risk of N leaching and at the same time may enhance N supply for a

subsequent crop (Ruffo and Bollero, 2003), especially when overhead irrigation is

provided. When alive, non-legume cover crops, such as rye, may be more effective in









reducing residual NO3- and potential leaching from the soil early in the season, compared

to legumes, such as hairy vetch and crimson clover (Sainju et al., 1998).

Another management consideration is the method used for suppressing the cover

crop, for example spraying with herbicide and mowing enhances inorganic N availability

in the short-term while simultaneously reducing carbon and N inputs (Snapp and Borden,

2005).

Knowledge Gaps

Several studies have been carried out in northern states pertaining to cover crop

physiology, ecology, mineralization and weed suppression in non-tillage systems

(Carrera et al. 2005?; Rosecrance et al., 2000). Florida research has contributed to

generating knowledge about cover crops rotations for tomato, peppers, field corn and or

nematodes suppression; but there is no or little information on temporal dynamic of cover

crops growth, and their effect on soil organic matter build-up and/or soil nitrogen

dynamics (Table 1-1). Few studies have looked at intercropping of green manures or

brassicae and cucurbitae behavior under non-tillage system, or timing for planting and

elimination of the cover crops (Table 1.1). Although information on non-tillage systems

nitrogen dynamics and carbon accumulation for Georgia is readily available (Kuo et al.,

1997, Sainju et al., 2002; Sainju et al., 2003; Sainju et al., 2005), these results may not be

pertinent to directly applicable to North Central Florida systems because both soil and

climatic condition differ between these two regions.

Analyses looking at energy expenditures and economics for non-tillage cover

cropped systems have been developed for Minnesota, Maryland, California, Wisconsin

and Tennessee (Gregory et al., 2005; Lu et al., 2003; Andraski and Bundy, 2005; Wyland









et al., 1996; Ogbuchiekwe et al. 2004; Stute 1995; Roberts et al. 1998). However, there is

a critical need for similar information for Florida cover crop systems.

The experiments presented in this thesis aimed to enhance our understanding of

improved use of cover cropping in vegetable production systems in North Central Florida

and provide information on C and N dynamics, as well as energetic and economic

considerations for on-farm cover crop use at South Florida.

Experimental Design and Measurements

Experimental Unit

Studies were conducted at the Plant Science Research and Education Unit (PSREU)

near Citra, Marion County, FL. The prevailing soil type of the research area were a

Candler fine sand (Typic Quarzipsamments, hyperthermic, uncoated; 98% sand in the

upper 15 cm) and Lake fine sand (Typic Quarzipsamments, hyperthermic, coated; 97%

sand in the upper 15 cm) (Carlisle et al.,1988).

This study provides a continuation and also complements two previous years of

research in cover cropping systems and aimed to evaluate if improved integration of

cover crops can increase soil organic matter and reduce inorganic N-fertilizer demand of

subsequent vegetable crops. During the summer of 2003, sunn hemp was planted for a

third consecutive year. Since its continuous cultivation resulted in a build-up of

Verticillium spp., alternative summer cover crops species including sesbania (Sesbania

sesban) and cowpea (Vigna unguiculata) were evaluated during summer 2004. In order to

avoid anticipated N loses during the fall season (as shown by Cherr, 2004), pearl millet

(Pennisetum glaucum) was also included as a summer cover crop because it was assumed

that its higher C:N ratio would reduce mineralization and enhance N retention. Winter

cover crops used included a mix of hairy vetch and winter rye during 2003 and 2004. But









during the latter year, broccoli was also planted in the late fall following previous summer

cover crops cowpeaa and pearl millet). In this case, it was expected that broccoli would

directly benefit from N mineralized from summer cover crops residues. During the spring

commercial vegetable crops were grown including sweet corn (Zea mays) during the

spring of 2004 and watermelon (Citrullus lanatus) during the spring of 2005. Both are

crops with high nitrogen demands. In addition to cover crops and vegetable treatments a

non-planted (complete control) plot was also included which was managed as a

controlled weed fallow via 3-monthly application of herbicides. All leguminous seeds

were inoculated with proper rhizobium, before planting. Fertilizer rates differed for sweet

corn, broccoli and watermelon, and were based on University of Florida Institute of Food

and Agricultural Sciences (IFAS) fertilizer recommendations.

Treatments were replicated four times and arranged in a complete randomized

block design and total plot number equaled sixty plots. The dimensions of each plot were

7.62 m x 9.14 m (69.7 m2). Total area of the plots and "alleyways" was approximately

one hectare (or 2.5 acres). An outline and more detailed description of experimental

treatments is presented in Table 1-2.

Measurements

* Cover crop biomass sampling: sunn hemp (2003) and hairy vetch/ rye (2003/04)
Plots were sampled every 3 weeks using a sample area of 0.23 m2. Fresh and dry
weight of leaves, stems, roots and flowers along with leaf area, leaf number, plant
height, and plant density were determined from a representative subsample. For
sesbania, pearl millet, cowpea (2004) and hairy vetch/rye (2004) total fresh and dry
weight of shoots, roots and reproductive organs (flowers and/or pods) were
determined. Dried tissue was ground and analyzed for N concentration.

* Vegetable crop sampling: sweet corn (2004), broccoli (2004), and watermelon
(2005) were sampled every 3 weeks. Fresh and dry weigh of shoots, roots and
reproductive (flower, ear, fruit or head) organs were determined for representative
areas of 0.23 m2, 0.31 m2, and 1.86 m2, respectively. Total and marketable yield
was determined for net harvested plots at the end of the growing season using a









harvesting area of 19 m2of 12 m2, 70 m2 for sweet corn, broccoli, and water melon,
respectively.

* Diagnostic tissue sampling: for broccoli total leaf N concentration was determined
at 11 weeks. For watermelon, chlorophyll readings and NO3-N concentration in the
petioles of 6 representative leaves were determined, along with the N concentration
of diagnostic leaves.

* Weeds: total weed above ground biomass and N concentration was determined at
the end of season (Data not included in this thesis, to be published as a separate
paper which will also outline the effects of cover crop treatments on changes in
nematode numbers over time).

* Nematodes: nematode counts were determined for composite samples collected
from 5 different points at end of season of each crop for all plots (Data not
discussed in this thesis, to be published as a separate paper which will also include
weed data).

* Soil: the soil pH (2:1 water extract) was measured at end of season for the 0-15 and
15-25 cm for cover crops treatments, and for the 0-7.5 and 7.5-15 cm soil depth for
sweet corn (2004) and watermelon (2005). Nitrate leaching from selected sweet
corn plots was measured using suction lysimeters placed at 0.3 and 1.2 m; soil
coring (0.3 m increments to a soil depth of 1.2 m); and resin traps (0.9 m depth).
Nitrate leaching from selected watermelon plots was determined by soil coring.
Soil particulate organic matter (POM) was determined during the spring of 2004.
Total soil C and N concentrations were determined during the spring of 2004 and
2005 (Data not discussed in this thesis and will be included in a separate
publication).

On Farm Experiment

This part of the program was carried out on a Community Supported farm located

in Boynton Beach Florida and this operation managed by Dr. Nancy Roe. This farm is

not certified organic, but sustainable practices drive the production process. Moreover the

product is sold under the modality of Community Supported Agriculture (CSA), where

costumers have a subscription and pay for their produce in advance, resulting in unique

economic characteristics. Crop rotations included sunn hemp as a summer cover crop

during 2003 and 2004; tomatoes and peppers as fall vegetable crops during 2003 and









2004, and sweet corn grown during the spring of 2003, 2004, and 2005. Sampling

procedures were similar as those described for the studies at PSREU.

Measurements

* Summer cover crops: fresh and dry shoot weights were determined at the end of the
growing season.

* Sweet corn: fresh and dry weights of stover and ears were determined at the end of
the season, along with chlorophyll readings of diagnostic leaves.

* For tomato and pepper: fresh and dry weights of fruits and stover were measured at
the end of growing season

* Weeds: dry weights were determined at the end of the 2003 growing season.

* Economic and energetic parameters: production cost data were gathered by Nancy
Roe during 2003/2004.

Hypotheses

* Including leguminous cover crops during the summer and/or fall season will
provide additional nitrogen (N) via symbiotic N fixation and improved soil N
retention and their use will reduce supplemental synthetic nitrogen requirements.

* Non-leguminous winter cover crop will sequester N that is being mineralized from
summer cover crop residues.

* A fall vegetable crop directly following a summer cover crop will make more
efficient use of mineralized N, because during the fall growing season in non-
tillage systems cover crop biomass decomposes slower and is not as lost as readily.

* Use of cover crops will reduce farm dependence on external resources and overall
farm energy consumption.

* Appropriate use of cover crops can enhance the sustainability of existing
agroecosystem.

Objectives

General Objective

Determine if a combination of cover crops will reduce supplemental nitrogen

fertilizer requirements and improve soil and/or environmental quality of vegetable


production systems, in North Central and South East Florida









Specific Objectives

* Evaluate the performance of selected cover crops in terms of their potential to
accumulate biomass and/or nitrogen in North Central Florida and South Florida
(Chapter 2).

* Determine if the use of cover crops will result in maximum sweet corn, broccoli
and water melon yields, while reducing crop N-fertilizer requirements (Chapter 3).

* Evaluate the economic feasibility of the cover crop based systems for a Community
Supported Agriculture farm in South Florida (Chapter 4).

* Perform an energy balance and emergy analysis to determine the ecological
sustainability of the cover crop based vegetable crop production systems (Chapter
4).

* Measure the potential environmental benefits of cover crops due to reduced N
leaching and increased carbon sequestration and soil quality (Not included in
thesis).

* Synthesize research findings and outline the pertinence and potential use of cover
crops in southeaster U.S.A with special reference to future research needs, suitable
management practices, and farm adoption (Chapter 5)










Table 1-1. Cover crop research focus over time in Florida and Georgia, a small sample.
Cover crops studied Main crop Focus of the study Location of experiments Source


Legumes

Summer cover crops


Rye


Sudangrass hybrid


Corn


Cabbage, field corn


Cowpea


Soybean


Potato


Soybean, velvet bean,
cowpea, 'Asgrow Chaparral'
sorghum

Hairy indigo



Soybean, corn, cowpea,
velvet bean, sorghum


Cowpea


Intercropping and double cropping of
corn with green manures.
Effects of fallowing, summer cover
crops, and fenamiphos on nematode
populations and yields.
Preharvest infestation of weevil and
population trends.
Population dynamics soil-borne fungi
in multi-cropped field, under reduced
tillage.
Effects of planting date and mowing
interval of the summer cover crop on
the abundance of wireworms, and
subsequent damage to tubers in the
following crop cycle.

Densities of plant-parasitic nematodes
on crops grown for forage.

Response of hairy indigo to water
deficits in a greenhouse experiment
Change in nematodes population
densities from winter to summer
cover crops. Dry matter yields and
levels of Ca, Mg, K, P, N, Cu, Fe,
Mn, and Zn in leaves of summer
cover crops.
Testing cowpea varieties for
nematode resistance.


North Florida


Florida


Florida


Florida


Southern Florida



North Central Florida -
seven sites

North Central Florida -
Gainesville



North Florida


Florida sandy soils


Smith and Prine
(1982)

Rhoades (1984)


Hagstrum (1985)


Ploetz et al.,
(1985)


Jansson and
Lecrone (1991)



McSorley and
Gallaher (1992)

Winzer et al.,
(1992)


McSorley and
Gallaher (1993)



Gallaher and
McSorley (1993)










Table 1-1. Continued.
Cover crops studied Main crop Focus of the study Location of experiments Source


Rye


Lupine rye hairy vetch,
crimson clover

Cowpea
Castor, velvet bean, cowpea,
American j ointvetch,
sorgum-sudangrass, rye
Hairy vetch, crimson clover,
wheat


Rye


Browntop miller, 'Iron Clay'
cowpea, marigold


Sorghum-sundangrass,
velvet bean


Corn


Cotton, okra,
soybean, eggplant,
corn, sesame


Soybean


Tomato, pepper

Sorgum -
sudangrass,
cabbage and potato


Potato


Hairy vetch


Nematode population changes during
winter.
Effects of management (winter cover
crop and tillage) on nematode
densities for an associated corn crop.
Research on precision seeding and
row spacing.
Cover cropping system and its effect
on parasitic nematodes.
Abundance of thrips during winter
and early spring.
Densities of nematode in six trophic
groups, in rows and between rows of
soybean. Nematodes population
density after cover crop.
Production systems (including cover
crops) for managing plant-parasitic
nematodes in a double-crop system.
Parasitic nematodes niche
distribution.
Nematodes population densities and
crop yields from different potato
cropping systems with summer cover
crops.
Climatic conditions influence in the
proliferation of thrips in its host hairy
vetch.


North Florida


North Florida (five sites)


Florida-Forth Pierce


Florida


North and Central Florida


Florida



Southwest Florida


Florida



Florida Hastings



North Florida


McSorley (1994)

McSorley and
Gallaher (1994)

Kahn (1995)

McSorley and
Dickson (1995)
Toapanta et al.,
(1996)

McSorley and
Frederick (1996)


McSorley et al.,
(1999)

Perez et al.,
(2000)


Crow et al.,
(2001)


Toapanta et al
(2001)










Table 1-1. Continued.
Cover crops studied Main crop


Rye, hairy vetch and crimson
clover

Wheat, rye, oat, lupine, hairy
vetch, crimson clover
Hairy vetch, rye, hairy
vetch/rye mixture

Sunn hemp, velvet bean, and
cowpea


Sunn hemp, 'Iron
Clay'cowpea



Cowpea


Hairy vetch, rye, hairy
vetch/rye mixture
Rye


Tomato, eggplant
and field corn


Tomato


Tomato



Pepper



Basil, Chinese
cabbage


Cotton and
sorghum
Peanut


Focus of the study
Management practices (non tillage,
chisel plowing, moldboard plowing
and cover crops) and their influence
on soil C and N, and yield.
Invertebrate community.
Cover crops and nitrogen fertilization
effects on soil aggregation, C and N
pools.
Evaluate the effects of three legume
cover crops on populations of
nematodes in the successive crop.
Impact of alternative crop production
practices, among them cover crops,
on soil quality and yields.
Field experiments were conducted to
evaluate three non-chemical
alternatives to methyl bromide, for
the management of plant-parasitic
nematodes.
Influence of tillage, cover crops and
fertilization on soil carbon.
Conservation tillage systems and
intercropping effect on yield.


Location of experiments Source


Georgia Greenville and
Forth Valley- fine sandy
loam

North Central Florida
Georgia Greenville and
Forth Valley- fine sandy
loam

Florida- Homestead



Florida




Florida


Central Georgia- Dothan
- sandy loam
Florida Alachua


Sainju et al.,
(2002b)NIR?

Tremelling et al.,
(2003)
Sainju et al.,
(2003)

Wang et al.,
(2003)
Chellemi and
Rosskopf (2004)




Wang et al.,
(2004)


Sainju et al.,
(2005)
Tubbs and
Gallaher (2005)











Table 1-1 Continued.


Cover crops studied Main crop Focus of the study Location of experiments Source
Use of sunn hemp hay as organic N
fertilizer compared to synthetic Wang et al.
fertilizer, and its effects on nematode (2006)
communities.





Table 1-2. Outline of crop rotation and experimental treatments.
YEAR 1
Trt. Fall Winter Spring N ratio Fall Winter
2003 2003 2004 (kg ha-1) 2004 2004
1 S H+R SC 0 CP B
2 S H+R SC 67 CP B
3 S H+R SC 133 CP B
4 S F SC 0 PM B
5 S F SC 67 PM B
6 S F SC 133 PM B
7 F H+R SC 0 SB H+R
8 F H+R SC 67 SB H+R
9 F H+R SC 133 SB H+R
10 F F SC 0 F F
11 F F SC 67 F F
12 F F SC 133 F F
13 F F SC 200 F F
14 F F SC 267 F F
15 F F F None F F


YEAR 2
N rate Spring
(kg ha-1) 2005
0 W
131 W
196 W
0 W
131 W
196 W
0 W
0 W
0 W
0 W
0 W
0 W
0 W
0 W
0 F


N rate
(kg ha-1)
0
84
168
0
84
168
0
84
168
0
84
126
168
210
None














CHAPTER 2
COVER CROP: BIOMASS AND NITROGEN ACCUMULATION

Introduction and Literature Review

Cover crops (CC) have been used extensively throughout the world and they may

provide a myriad of services including nitrogen (N) fixation and improved nutrient

recycling/retention (Ibewiro et al., 2000; Weinert et al., 2002), nematode control

(McSorley 1999, Wang et al., 2002), erosion prevention (Sainju et al., 2005), insect

trapping and/or pest inhibition (Bottenberg et al., 1997; Hooks et al., 1998), allelopathic

weed suppression (Caamal-Maldonado et al., 2001; Hartwig and Ammon, 2002), water

conservation (Schonbeck et al., 1993) while they also may enhance soil organic matter

and beneficial soil organism activity (Roldan et al., 2003). Successful CC systems require

that CC complement commercial crops in space and/or time (Derksen et al., 2002;

Carrera et al., 2005).

Synchronization between CC nutrient release and commercial crop nutrient demand

are the base for designing CC-based systems (Thonnissen et al., 2000; Fortuna et al.,

2003). Based on a greenhouse study, researchers concluded that the benefits obtained by

rapeseed (Brassica napus) and wheat (Triticum aestivum) depended on precedent type of

leguminous crop and their N fixation capacity (Mayer et al., 2003). On sandy loam soils

wheat and canola recovered 8 to 12% of the residual N at maturity. However, on loamy

sand soils in a semi-arid region in Mali use of cowpea as a CC increased sorghum

(Sorghum spp.) and pearl millet (Pennisetum glaucum) stover and grain yields by 25









andl8%, while corresponding increases for sesbania (Sesbania sesban) ranged from 32 to

45% (Kouyat et al., 2000).

Nutrients release to subsequent crops will be also affected by how the CC is

terminated (killed) and on residue placement. Reduced tillage and surface application of

residues will decrease mineralization rates. In a seven-year study on a silt loam at

Pennsylvania, tilling legume cover cropped plots with chisel-disc and moldboard plow,

enhanced initial N mineralizing (Drinkwater et al., 2000). However, results for reduced

tillage systems may be inconsistent. For example, yields of crops such as peanuts have

shown improved or comparable yields compared to conventional tillage systems (Tubss,

2005). However, no-tillage tomato (Lycopersicum esculentum) and eggplant (Solanum

melongena) systems on the similar soils did not increase yields (Sainju et al., 2002).

Soil fertility issues may also interfere with CC performance. When nutrients (N, P,

K) are readily available, CC tend to allocate more resources to aboveground biomass than

to roots formation. In the absence of fertilizer application, root N content of tropical

leguminous CC was relatively stable while shoot N content increased by 30% when

supplemental fertilizer was applied (Tian and Kang, 1998). Nitrogen accumulation by

hairy vetch ranged from 45 to 224 kg N ha-(Sustainable Agriculture Network, 2001).

Although symbiotic fixation can contribute a substantial fraction of this N, excessive

residual soil N levels reduce the efficiency of N fixation (Hartwig and Ammon, 2002).

Studies in Denmark showed that N biomass accumulation and yield of legumes such as

Pisum sativum decreased with N fertilizer rate (Ghaley et al., 2005).

Low inherent soil organic matter (SOM) in sandy soils prevailing in Central

Florida requires integration of suitable CC species into existing production systems in









order to maintain SOM. Use of cereal CC may be the most effective in enhancing SOM

(Snapp et al., 2005). High temperatures and radiation levels during the summer/fall

fallow season in Florida, combined with adequate rain result in high biomass production

potential. Although gramineous C4 crops are considered to prolific biomass producers in

high radiation environments (Fageria et al., 1997), in certain case legumes may actually

exceed gramineous growth performance. Sunn hemp (Crotalaria juncea) for example,

accumulated 5.9 Mg ha-1 of DM and 126 kg N ha-lon a sandy loam soils in Alabama in a

9-12 week period and 59-63% of this N was released during winter (Reeves et al, 1996).

In Homestead Florida, sunn hemp produced between 12.2 Mg ha-1 of dry biomass and

accumulated 351 kg N ha-1 (Li et al., 2006).

Residue lignin content and soil environmental conditions may also affect CC

mineralization. Therefore carbon to nitrogen (C:N) ratio alone may not provide an

accurate predictor of subsequent residue N release rates (Ruffo and Bollero, 2003). In the

southeastern U.S., N from legumes terminated right before corn cultivation exhibited C:N

ratios around 10 to 20 (Ranells and Wagger, 1997). Mixing gramineous crops with

legumes increases the C:N ratio, thereby reducing initial mineralization rates. Use of a

mix of non-legumes and legumes cover crops such as rye (Secale cereale) and hairy

vetch (Vicia villosa) on sandy soils with poor nutrient retention capacities thus can reduce

both N leaching during rainy fallow periods and fix additional N for subsequent

commercial crops (Kuo and Sainju 1998, Ruffo and Bollero, 2004, Sainju et al., 2005).

Moreover, residual N from leguminous CC can enhance N accumulation and crop growth

of subsequent gramineous crops (Glasener et al., 2002). However, on poor sandy soils

intercropping cowpea with high biomass accumulator, such as pearl millet, led to a









decrease in overall biomass production (Zegada-Lizarazu and Iijima, 2005), and

intercropping it with sesbania did not provide extra biomass accumulation benefits

compared to pure sesbania stands (Toomsan et al., 2000). Both overly low and high C:N

ratio associated with mono-cropped leguminous and gramineous CC systems may require

the addition of supplementary inorganic N to make up for N losses due to leaching and/or

immobilization (Creamer and Baldwin, 2000).

Although several studies have outlined end-of-season DM and N content for

different CC-based systems, most of these studies do not address temporal time trends,

nor do they address how cover crop residue affects crop N requirements of subsequent

cover cropping systems, nor N losses from summer cover crop residues during winter

fallows.

Suitable cover crops for Florida vegetable production systems include sunn hemp, a

native from India, which has a high capacity for both C and N sequestration (Cherr,

2004). Cowpea (Vigna unguiculata) is a prospective cover crop due to its symbiotic N

fixation and capacity to generate economic returns (Toomsan et al., 2000). Pearl millet, is

widely used in Africa (Maman et al., 1999; Bationo and Ntare, 2000; Buerkert et al.,

2000) but it is also adapted to Coastal sandy soils of the South East U.S. (Menezes et al.,

1999), which could help retain and recycle residual soil nutrients, build up soil OM via

the accumulation of large amounts of recalcitrant biomass (Kennedy et al., 2002).

Sesbania, is widely cultivated in tropical Africa (Kwesiga et al., 1999; Phiri et al., 2003;

Mudhara et al., 2003), and it is a prolific biomass producer (Stihl et al., 2005), and due to

its symbiotic N fixation capacity has the potential to also increase both soil C and N pools

and thus further enhance SOM.









The specific objective for this component of the study was to evaluate the

performance of selected single summer CC or winter CC mixes in terms of their potential

to accumulate biomass and/or (to partly) meet nitrogen requirements of subsequent

vegetable crops in North Central Florida.

The hypotheses of the study were 1) through the use of leguminous cover crops

during the summer and/or fall season, N can be fixed and therefore supplemental

synthetic nitrogen applications to a spring crop can be reduced; 2) a winter non-legume

cover crop will recover N that is being mineralized from the summer cover crop residues

and may also provide a more stable N source for spring vegetable crop.

Materials and Methods

Set-up and Design

Research was conducted at the Plant Science Research and Education Unit near

Citra, Florida (University of Florida, Gainesville). The dominant soil types at this site

were a Candler fine sand (Typic Quarzipsamments, hyperthermic, uncoated) and Lake

fine sand (Typic Quarzipsamments, hyperthermic, coated). Both soil types contained

more than 95% sand in the upper 1-2 m of the soil profile (Carlisle et al., 1988).

The study included selected cropping systems consisting of a combination

summer and/or winter cover crops residues amended with different N fertilizer rates and

these combinations were compared with conventional (without CC residues) production

systems. Summer CC included sunn hemp (2003), cowpea, pearl millet and sesbania

(2004) and during winter a hairy vetch / rye mix was planted (2004 and 2005). By

following summer CC with a mix of legume and gramineous CC we aimed to improve

the C:N ratio of the CC residue and the N retention from N released by sunn hemp,

cowpea and pearl millet, while also facilitating additional N fixation.









The crops succeeding winter cover crops during spring were sweet corn (2004)

and watermelon (2005). Sweet corn has a high demand for inorganic N (>200 kg N ha-1)

and served as a as a biological indicator of overall residue N availability and also

provided a common component for the different cropping systems outlined in Table 2-1.

Each cropping system was amended with 3 inorganic N fertilizer rates (0, 0.33,

0.67 times IFAS N recommendation for sweet corn (Olson and Simonne, 2005); and 0,

0.5, and 1.0 times N recommendation for watermelon and broccoli (Olson and Simonne,

2004). For systems that did not include a CC, two additional N rates (1.00, and 1.33 vs

0.75 and 1.25 times IFAS recommendation) were included for sweet corn and

watermelon, respectively. An overview of experimental treatments is provided in Table

2-1. All treatments were arranged in a randomized complete block design with four

replicated blocks.

Timeline of Operations

2003-04

During the last week of July 2003, sunn hemp (SH) was planted following

herbicide application and mowing of the field. Seed was inoculated with cowpea-type

rhizobium and planted at 30 mm depth using an in-row spacing of 0.03 m and between-

row spacing of 0.76 m. The crop was terminated on 23 October with an application of

ammonium sulfate 50% (1.2 L ha-1), Mirage Plus (Glyphosate 41.0%) at a rate of 1.2 L

ha-'(Loveland Products, INC., Greeley, CO), and Remedy (Triclopyr 61.6%) at a rate of

1.2 Lha-1 (Dow AgroSciences, Indianapolis, IN).

Hairy vetch was inoculated with hairy-vetch type rhizobium and the winter CC mix

was planted at a rate of 56 kg ha-1 rye and 22 kg ha-1 hairy vetch on 13 November of

2004, with a rip-strip planter using a row spacing of 0.19 m and planting depth of 13 mm.









Hairy vetch and rye emerged December 7th of 2003 and all plots were mowed and

sprayed on April 2nd of 2004 with Pendimethalin (BASF, Florham Park, NJ) and Atrazine

(Syngentha, Basel, Switzerland) and on April 6th of 2004 with Ammonium sulfate 50%

(applied at a rate of 1.2 L ha-1), Mirage Plus (Glyphosate 41.0%) at a rate of 2.4 L ha-1

(Loveland Products, INC., Greeley, CO) and on.

2004-05

Cowpea (variety Zipper Cream) and sesbania were inoculated at recommended

rates prior to planting. Pearl millet (PM), cowpea (CP), and sesbania (SB) were planted

on July 8th 2004 with a rip-strip planter at the spacing of 0.38 m using a plant depth of 13,

19 and 38 mm, respectively. Corresponding seed rates were 34, 56, and 28 kg ha-1,

respectively. Plants emerged on July 15th and grew until October 10th of 2004. After

mowing, they were sprayed with Ammonium sulfate 50% (at a rate of 2.3 L ha-1), Mirage

Plus (Glyphosate 41.0% at a rate of 9.4 L ha-1, Loveland Products, INC., Greeley, CO) on

14 October and with Ammonium sulfate 50% (at a rate of 1.2 L ha-1) and GLY-4 Plus

(Glyphosate 41.0% at a rate of 4.7 L ha-1 Albaugh Inc., Valdosta, GA) on 20 October of

2004. Hairy vetch was inoculated with rhizobium and mixed with rye and planted with a

zero-till grain-drill at a seed rate of 56 and 22 kg ha-1 on October 28th, of 2004 and plots

were strip tilled on March 22nd 2004, no herbicides were applied, before intercropping the

watermelon seedlings.

Sampling Procedures

2003-04

All 24 plots planted with sun hemp were sampled at 3-wk intervals and sampling

dates were expressed in weeks after emergence (WAE). At each sampling, a

representative 0.6 m long row section was clipped at the soil level (sampling area 0.46









m2). Total fresh weight was determined and a representative sub-sample was used for

growth analysis. The root system for this sample was excavated carefully and plant

material was stored in coolers during transportation and refrigerated until further analysis.

Hairy vetch was sampled from all 24 plots, except for the 8 and 11 WAE samplings when

only 8 plots were harvested. In this case, a representative 0.6-meter-long row section was

clipped at the soil level (sampling area 0.12 m2). Total sample leaf number and area and

leaf, stem, root, and reproductive (flowers) fresh weights were taken for each sample,

except at WAE 8 and 11.

2004-05

Summer CC (CP, PM and SB) were sampled from all 24 plots every three weeks

until WAE 11. At each sampling, a representative 0.6-m-long row section was clipped at

the soil level (sampling area 0.23 m2) and roots were excavated with a shovel. Because of

the "viny" nature of the hairy vetch, overlapping between rows occurred and a sampling

frame of 0.31 x 0.76 m (0.23 m2) was used for the sampling of winter CC in 2004/05 to

ensure a more representative sample. Samplings were repeated at 3-wk intervals for a

representative plot section.

Sample Processing

Plants were separated into leaves, stems, roots, and reproductive tissues (flowers

and pods, when present). Roots were carefully rinsed to remove soil and debris above a

1-mm sieve. Leaf area was determined with an LI-3000 (Li-cor; Lincoln, NE) using a

representative sub-sample. Dry weights were recorded for sub-samples and roots after

oven drying at 65 C for at least 72 hours. For all sampling dates, except the last one,

plants parts were recombined and then ground in a Wiley mill to pass through a 2 mm

screen. For end-of-season samplings plant organs were processed separately. Grindings









were then subjected to a wet-acid Kjeldahl digestion, diluted, filtered, and analyzed for

total Kjeldahl nitrogen at the UF-IFAS Soils Laboratory and at the Agronomy Physiology

Laboratory (University of Florida, Gainesville, FL) using EPA Method 351.2 (Jones and

Case, 1991). Final growth samples for selected treatments were also analyzed for total C

and N. Roots, stems and leaves tissue-material were re-ground in a Willey mill and

passed trough a 1-m screen, weighed in an analytical balance, and then analyzed for C

and N using a Carlo Erba CN analyzer (Carlo Erba Reagenti, Milan, Italy).

Statistical Analysis

Growth data was recorded on datasheets, and organized, and converted to a hectare

basis using EXCEL (Microsoft, Corporation, Los Angeles, CA). Statistical analysis was

performed with SAS (Statistical Analysis Systems, Cary, NC). Since sampling dates were

correlated over time covariancee), the "Proc Mixed" procedure of SAS was used to

analyze results with sampling date (ST) being the main fixed effect in the model.

Since all summer CC were planted in sweet corn residue, it was hypothesized that

the different N-fertilizer rates previously applied to sweet corn (Np) may potentially

affect the growth of the subsequent summer CC and therefore Np was included in the

model along with a ST*Np interaction term.

During the 2004 and 2005, winter rye was intercropped with hairy vetch and

system components were analyzed both separate and in conjunction with each other. In

2004, winter CC followed either sunn hemp or a summer fallow. This approach allowed

us to evaluate both the effects from sunn hemp residue (RES) and Np and in this case

both the effect of residue (RES) and Np were included in the model Random variation

was attributed to replicates (blocks) and sampling time (ST) and this was a common

component for the statistical model used for both winter and summer CC. During 2005,









winter CC always followed sesbania, and therefore only ST and Np were included as

model effects.

Mean separation was performed by the Tukey's T-statistic (p < 0.05). Response

variables tested included dry matter accumulation (Mg ha-1), tissue N concentration (g N

kg-1), and crop N accumulation (kg N ha-1) for all sampling dates. For general

comparisons among cover crops within the same season, another model was employed

that included block (Rep) and species (CP vs SH vs PM or HV04-05 vs R04-05) as main

effects. In this case the random and repeated statements were dropped from the model,

since only end-of-season values were used.

Results

Summer Cover Crops (SCC)

Sunn hemp 2003

Root dry weight of sunn hemp increased quadratically over time while

corresponding responses for shoots and total biomass were cubic (Table 2-2). Root and

shoot DM tended to "level off' after WAE 11 and maximum observed total DM was 7.0-

7.2 Mg ha-1. Maximum dry matter (DM) and N accumulation rates were 161 and 3.2 kg

ha-1 d-1 at WAE 8 and DM allocation to roots was relatively low (-10%). The N

application rate to the preceding sweet corn crop (Np) did not affect crop DM

accumulation.

Root, shoot and total N tissue concentrations decreased quadratically over time

(Table 2-2). Overall crop tissue N concentration decreased from 37 (WAE 2) to 16 g N

kg-1 (WAE 17) and roots had a 25% lower N concentration compared to shoots. The

ST*Np interaction was significant for both shoot and total N tissue concentration during

WAE 5 and values for the Np=133 treatment were highest. In all other cases tissue N









concentration was not affected by Np (Table A-1). Nitrogen accumulation mimicked

biomass accumulation patterns and crop N increased quadratically from 10 kg ha-1 at

WAE 2 to 111 kg ha1 at WAE 14 (Table 2-2). By WAE 14, shoots accounted for 93% of

total crop N.

Cowpea 2004

Root, shoot and total DM increased quadratically with time, while previously

applied fertilizer did not affect cowpea growth (Table 2-3). Maximum total DM occurred

at WAE 8 (4.7 Mg ha-1). End-of-season shoot and root N concentration were 54 to 60%

lower compared to initial values and roots had a 30% lower N concentration compared to

shoots. Calculated daily N accumulation rates reached maximum values of 4 kg N ha-1 d-1

at 5 WAE resulting in overall N accumulation of 94 kg N ha-1, with 95% of this amount

being allocated to above-ground biomass. For the purpose of consistency, the ST*Np

interaction effects are outlined in Table A-2, although none of these interaction terms

were significant.

Pearl millet

Root, shoot, and total DM of pearl millet (PM) increased linearly with time (Table

2-4). Total DM accumulation rate was 9.4 Mg ha-1 at WAE 11 and the maximum

calculated rate calculated of DM accumulation was 204 kg ha-1 d-1 at WAE 8. Preceding

N application (Np) rates did not affect plant growth nor tissue N concentration. Towards

the end of the growing season, root and shoot N concentration decreased by 61 and 70%,

while overall crop N concentration decreased over time from 26 to 8 g N kg-'.

Total crop N accumulation showed a linear increase. Maximum N accumulation

was attained between WAE 8 and 11, following the biomass accumulation trend. At

WAE 11 total N accumulation was 75 kg N ha-1, with 93% coming from shoots (Table 2-









4). The interaction of ST*Np was only significant for roots N accumulation and at WAE

8, with plants growing in residual 67 kg ha-1 N-fertilizer accumulating more N compared

to non-fertilized treatment (Table A-3).

Sesbania

Shoot, root and total DM accumulation followed a cubic trend (Table 2-5). Total

dry weight accumulation peaked at WAE 5 (1.1 Mg ha-1). Roots accounted for 29% of

total DM at WAE, while Np did not affect overall crop DM accumulation or allocation.

Nitrogen concentration in the plant decreased over time from 32.2 to 8.0 g N kg-1

exhibited a cubic trend. Shoot N concentration showed a 74% decline which is greater

than any of the other systems. The ST*Np interaction was significant for root N

concentration but means for Np treatments were similar for each sampling date (Table A-

4). Total N accumulated by the crop followed a cubic increase. Maximum N

accumulation was reached at WAE 5 for both roots and biomass, while 82% of the N was

accumulated in the above-ground parts. Root, shoot and total N accumulation was

greatest for the Np=133 treatment (Table 2-5).

Species comparison

In order to compare the growth characteristics and overall performance of the three

legumes (SH, SB, CP) and one gramineous (PM) summer CC species under local

conditions, DM and N content and allocation was compared among these species at WAE

11 (Table 2-6 and 2-7).

Pearl millet had the highest (9.4 Mg ha-1) biomass production followed by sunn

hemp, while the productivity of cowpea was intermediate, and sesbania performed very

poorly (0.7 Mg ha-1). Overall DM allocation to roots and stems was highest for SB and

lowest for PM, while SH and PM had the highest DM allocation to leaves. Overall N









accumulation was as follows: SH > CP ~ PM >> SB. But it should be noted that early-

season N accumulation for cowpea was comparable or higher than that for sunn hemp

(Tables 2-2 and 2-3). Overall N root content was similar for all crops except for sesbania

(Table 2-7). Both SH and PM appeared to allocate less N to stems and more to leaves

compared to other crops. But this may be related to the poor performance of sesbania and

the early onset of leaf sensescence for cowpea. Nitrogen allocation to reproductive

growth was similar for both cowpea and pearl millet and relatively low for sunnhemp and

sesbania.

Winter Cover Crops (WCC)

Winter rye 2004

Root and shoot DM accumulation for winter rye during 2004 (R04) increased

quadratically over time (Table 2-8). Maximum DM accumulation was 5.3 Mg ha1 at

WAE 17 and the maximum observed daily DM accumulation rate was 85 kg ha1 d1

(WAE 14). Dry matter allocation to roots decreased from 20% (WAE 2) to 6% (WAE

17). Although Np had no effect on dry matter accumulation, the ST*RES interaction was

significant for total dry weight (Table 2-8). Root, shoot, and total DM content of winter

rye were greater in plots following SH compared to fallow. However, for shoot and total

DM content, the ST*RES interaction was significant and benefits from sunn hemp

residue become more evident toward the end of the growing season (Table A-5). At the

end of the season DM accumulation of rye doubled with SH residue (6.8 for fallow

versus 3.7 Mg ha1 for SH).

Nitrogen concentration in below-ground tissue showed a quadratic response and

decreased from 16.9 to 7.7 g N kg-1 over time, whereas shoots and total tissue followed a

cubic trend diminishing from 32 to 12 g N kg1 (Table 2-8). Residue treatment affected N









concentration differentially over time. By the end of the season, overall N concentration

in SH-amended plots was lower compared to fallow plots and corresponding overall N

concentrations were 9.2 versus 14.2 g kg-1 (Table A-5).

Root and shoot N content showed a cubic increase over time where as shoot DM

increased linearly (Table 2-8). Total N content was greatest at WAE 17, but N

accumulation rates were highest at WAE 11. Shoots accounted for 96% of overall crop N

accumulation. While shoot and total biomass N accumulation across time was greatest in

sunnhemp plots. Although the ST*RES interaction effect was significant, SH-based

systems had either similar or higher root N accumulation rates than fallow plots (Table

A-5).

Hairy vetch 2004

Root, shoot and total dry weight accumulation for hairy vetch increased

quadratically reaching a maximum value of 2.5 Mg ha-1 at WAE 17 (Table 2-9). Roots

accounted for 50% of total biomass at WAE 2, while the root fraction was reduced to 7%

at WAE 17. Maximum observed DM accumulation rates were 54 kg ha-1 d-1, occurring at

the end of the growing season.

The ST*Np interaction term had a significant effect on root weight and at WAE 14

fallow plots had higher (p<0.05) root dry weights (Table A-6). However, at the end of the

season, root and total DM accumulation was similar for SH-amended and fallow

treatments.

Root and overall N concentrations exhibited a cubic trend, while shoot values

decreased quadratically with time (Table 2-9). Compared to other crops, hairy vetch

retained relatively high and/or constant N tissue concentrations and overall N

concentration ranged from 30 to 41 g N kg-1, while root tissue maintained fairly high N









concentrations until the end of the growing season (26 g N kg-1 for hairy vetch vs 8 g N

kg-1 for rye). The ST*Np interaction term had a significant effect on root N concentration

and at WAE 14 fallow plots had lower root N concentrations (Table A-6).

Root, shoot and total N concentration increased quadratically with time attaining

values of up to 80 kg N ha-1 at WAE 17, of which 97% came from above-ground biomass

(Table 2-9). Overall, N accumulation rate attained a maximum value of 1.5 kg N ha-1 d-1

at WAE 17. Similarly to root weights, the ST*Np interaction terms was significant and at

WAE root N accumulation was greater in fallow plots (Table A-6).

Overall winter cover crop system performance 2004

Since winter rye and hairy vetch were grown as an intercropped system, overall

system performance characteristics are also presented for the combined system

components. Root dry weight followed a linear trend, where as biomass and total

accumulation increased quadratically. Dry matter reached its maximum at 14 WAE with

7.7 Mg ha-1, while daily DM accumulation rates also reached maximum values of 133 kg

ha-1 d-1 at WAE 14. Roots accounted for 27 and 7 % of total biomass at WAE 2 and 17,

respectively (Table 2-10). The ST*RES interaction effect for roots dry weight was

significant, with difference between residue types being most articulated early in the

season (WAE 5). The winter CC mix growing on SH residue accumulated 0.34 vs 0.14

Mg ha-1 for systems following a summer fallow, but this difference dissipated during

subsequent samplings (Table A-7).

Root and total N concentration followed a cubic trend, while shoot N

concentration decreased quadratically over time (Table 2-10). Overall tissue N

concentration decreased by 33% throughout the growing season to final N concentration

of 18.8 g N kg-'. Root, shoot and overall N concentrations showed a significant ST*RES









interaction effect. At WAE 5, shoot N concentrations were greater for SH-based systems,

whereas at WAE 14 fallow treatments had much greater root and total tissue N

concentrations (Table A-7).

Root N accumulation followed a linear trend whereas shoot and total N

accumulation increased quadratically. Maximum total accumulation was attained at WAE

17 and by that time the total biomass contained 139 kg N ha-1, with 95% being allocated

to above-ground plant parts. Similar to root growth, interaction effects of ST*RES were

significant for root N accumulation and at WAE 14, fallow based systems had higher

overall root N accumulation rates (Table A-7). However, final root N accumulation

values were not affected by residue treatments.

Winter rye 2005

During 2005, winter rye was always preceded by sesbania. As a result, only

sampling time (ST) and N application rate applied to the previous corn crop (Np) are

included in the statistical analysis (Table 2-11). Root dry weight accumulation increased

quadratically with time, while shoot and total DM accumulation followed a cubic and

linear trend, respectively. Maximum DM was 2.8 Mg ha-1 at WAE 17, while growth rates

attained maximum values of 44 kg ha-1 d-1 at WAE 11 Proportional changes in DM

accumulation were similar for roots and shoots and at the end-of growing season, shoots

and roots accounted for 84% of the total crop dry weight biomass.

Root, shoot, and total N concentrations showed cubic decreases over time, with

total N concentrations decreased from from 38.4 to 7.2 g N kg-1. Total shoot content was

affected by Np and was greatest for the Np=133 treatment (Table 2-11).

Total N accumulation followed a linear trend over time. Overall N accumulation

rates were greatest at WAE 11 and subsequent N accumulation values was not









significant. At the end of the growing season, overall N content was 20 kg ha-1 and shoots

contributed 83% of the overall N accumulation (Table 2-11).

Hairy vetch 2005

Hairy vetch root, shoot, and total dry matter accumulation followed a cubic trend

across sampling dates (Table 2-12). Total DM accumulation was 9.4 Mg ha-1 in the 15

weeks between 2 to 17 WAE. In contrast with rye, maximum DM accumulation rates

were greatest (240 kg ha-1 d-) towards the end of the growing season. Due to

unseasonably cool weather, growth even continued after final mowing. Roots represented

10% of the biomass by the end of the season. There was no significant Np effect on root

growth but the ST*Np interaction was significant for total DM, and was the greatest for

Np= 67 treatment on DM for both shoot and total biomass (Table A-9).

Decreases in N concentrations over time were linear, cubic, and quadratic for root,

shoot, and overall tissue N, respectively. However, in comparison with winter rye, end-of

the season N concentrations remained relatively high for all the tissues and overall N

concentration between WAE 2 and 17 decreased by only 42% (Table 2-12).

Nitrogen accumulation in roots and shoots over time followed quadratic and cubic

trends, respectively. Overall N accumulation was 235 kg N ha-1 by the end of the season.

Overall winter cover crop system performance 2005

In order to assess overall winter CC system performance, both species were also

analyzed together. Root, shoot, and total DM accumulation exhibited a cubic increases

over time, reaching their highest points at WAE 17 with 12.3 Mg ha-1 of total DM and

10.5% of total biomass was allocated to roots (Table 2-13). Due to increased growth

vigor of vetch toward the end of the growing season, overall DM accumulation rates

attained maximum values of 262 kg ha-1 d-1 at WAE 17.









Total N concentration for all shoots and total dry weight followed a cubic trend.

Overall shoot N tissue concentration decreased from 45 to 23 g N kg-1 between WAE 2

and 17. Due to the higher fraction of vetch in the 2005 crop mix, N concentration in roots

was similar to overall biomass N concentration. Crop N accumulation increased

quadratically and maximum total N accumulation was 264 kg N ha-1, with 90% being

accrued above-ground. Overall N accumulation rates attained a maximum value of 5.7 kg

N ha-1 d-1 at WAE 17 (Table 2-13).

Species Comparison

Total biomass accumulation of winter rye was 5.4 and 3.0 Mg ha-1 during 2004 and

2005, respectively (Table 2-14). While corresponding values for vetch were 2.5 and 9.6

Mg ha-1 (Table 2-14). During 2004, rye roots represented 6% of the biomass, while in

2005 there was an increase to 16%. Hairy vetch had an intermediate, yet more constant

root DM allocation percentage. For rye, DM allocation to other tissues also differed

between years and values decreased from 23% in 2004 to 10% in 2005. Stems and leaves,

on the other hand, accounted for 56-58% and 6-8% of the final biomass. It should be

noted that leaves accounted for most of the senescent tissue so the overall leaf fraction for

rye would be on the order of 15 to 26%.

Rye roots accumulated 4-18% of N, while stems accounted for 37 to 52% and

leaves (including senescent tissue) accounted forl4 to 21% of crop N. During 2005, N

allocation to roots and reproductive structures was increased, while stems and total leaf N

allocation was being reduced (Table 2-15).

Hairy vetch partitioned more DM toward building stems than to other plant parts

(Table 2-14). However leaves accounted for 47-58% of the N allocation. Nitrogen

contained in leaves could function as a readily available N source to the succeeding crop.









During the second year, hairy vetch had not allocated any assimilates towards the

construction of reproductive structures at final sampling (WAE 17).

The C:N ratios of different tissue materials for different cover crops are presented

in Table B-1. The significance of C:N ratios is that they may provide better insight into

the likelihood of mineralization rates for each tissue. Species with a C:N ratio of >25 may

increase the potential risk of (initial) N immobilization. Gramineous crops had higher

C:N ratios compared to leguminous crops, while for plant tissue types C:N ratios ranked

as follows: stems> roots > leaves, indicating that leaves and root's propensity to faster

mineralization than stems (C:N = 61).

Discussion

Summer Cover Crop Systems

Sunn hemp 2003

Total biomass and N accumulation of sunn hemp (SH) in 2003 (Table 2-2) were

lower compared to 12.3 Mg ha-1 produced during the 2003 cropping season (Cherr,

2004). In Homestead Florida, SH also performed better and accumulated 12.2 Mg ha-1

and provided up to 351 kg N ha-1 (Li et al., 2006). Reduced DM accumulation during

2003 was related to cultivation of sunn hemp for three consecutive years in the same

plots resulting in an accumulation of Verticillium sp., a soil-borne disease. This hampered

biomass production since up to 70% of the plants presented disease symptoms by 14

WAE. Continuous use of sunn hemp as a summer CC appeared to have resulted in

fungus population surpassing the infestation threshold (Abawi and Widmer, 2000). Other

researchers have also shown that population densities of Pythium spp and Rhizoctonia

solani were greater following legumes and those levels decreased in mixtures of legume-

grass or crucifers, compared to legumes (Sumner et al., 1995). Soil-borne diseases may









thus pose challenges for continuous cover cropping, and may require appropriate changes

in cover crop rotation. However, even during the third year, biomass accumulation by SH

was still acceptable compared to results reported for Southeastern U.S. In Alabama sandy

loam soils for example, in a 9-12 week period, respective DM and N content rates were

5.9 Mg ha-1 and 126 kg N ha-1 (Reeves et al., 1996). Corresponding values for North

Carolina were 7.6 Mg ha-1 and 144 kg N ha-1 (Balkcom and Reeves, 2005).

Although sunn hemp is a leguminous crop, it can also utilize residual soil N

(Mendonca and Schiavinato, 2005). In the current study, residual N from a previous

sweet corn planting affected shoot and total N concentration of sunn hemp at WAE 5,

when dry matter accumulation was the highest for Np 133 treatment but not for any of the

other sampling dates (Table A-1). The increase in N concentration may have resulted

from mineralization of sweet corn stover resulting in increased N availability but this

effect was not consistent through the growth cycle

Nitrogen concentration in roots was lower than in shoots since leaves contain large

amounts of N rich compounds. As a result, leaves make up an appreciable fraction of the

above-ground nitrogen. Since growth virtually peaked at WAE 8 while N concentration

slightly decreased, total N accumulation stabilized after this time (Table 2-2). This may

have consequences for the management of SH as a summer cover crop. Over time, a

greater proportion of SH dry matter is partitioned to stems (Cherr, 2004), and the high

C:N ratio associated with stems (Table B-1) results in more recalcitrant crop residue that

can be fairly effective in suppressing weeds. If N accumulation is the main objective, SH

should be mowed in WAE 8, whereas a more prolonged growth cycle may contribute to

increasing soil organic matter. However in other systems, including mulched production









beds, large stems may interfere with cultural production practices, including bed

formation and can also damage plastic mulch.

Cowpea

Cowpea (CP) was used as a double purpose cover crop, which could also provide

extra income to farmers, during the summer-fall season in north central Florida. In this

experiment, CP did not appear to benefit from residual N from sweet corn (Table 2-3).

The apparently poor utilization of residual soil N may be attributed to the following

issues: 1) residual N may already been leached prior to the establishment of an

adequately deep cowpea root system; 2) slow and/or incomplete N mineralization from

sweet corn stover; 3) reduction in N fixation in plots with higher residual soil N levels.

Soil N may provide up to 80% of CP's aboveground needs during its first 42 days of

growth (Awonaike et al., 1991), supporting the idea that effects of Np would be most

obvious during initial growth. A study in Oklahoma also showed that residual N did not

alter cowpea rooting patterns at pod setting stage (Kanh and Schroeder, 1999). In

chickpeas grown in silt clay soils in Syria, at physiological maturity 60% of accumulated

nitrogen had been derived from N fixation, 35% from the soil and 5% from fertilizer

(Kurdali, 1996). Other studies also have shown that the efficiency of N fixation decreases

with an increase in residual soil N levels (Ghaley et al., 2005). Based on this, residual N

may not affect overall growth and/or N accumulation by cowpea.

While shoot DM reached a maximum of 4.3 Mg ha-1 at WAE 8, roots continued

growing for three more weeks (Table 2-3). This can be explained by the heavy rainfall

events experienced in experimental area, due to hurricane Frances, in September 2004

(Table C-5). During this time leaves, stems and reproductive structures became damaged

and combined with wet conditions, this may have enhanced fungal growth and early crop









senescence. Similar findings were reported by Creamer (1999) when cowpea biomass

accumulation reached only 4.0 Mg ha-1, after enduring two hurricanes. Maximum DM

accumulation (4.3 Mg ha-1 at WAE 8) by cowpea were similar to values reported by

Schroeder et al., (1998), but 59% below those for SH (at WAE 14) reported by Cherr

(2004). Overall DM accumulation was below the 6.9 Mg ha-1 reported by Harrison et al.,

2004 for 'Iron Clay' CP. This variety has a longer growing season, is less compact in its

growth habit, and thus appears to be a more prolific biomass producer (Linares et al.,

2005).

The decrease in overall shoot N concentration from 43.3 to 18.9 g N kg-1 (Table

2-3) may be related to a dilution of nutrients in DM associated with rapid growth, an

increase in stem fraction of cover crops over time (Cherr, 2004), and the N translocation

from other tissues to pods (Douglas, 1993). However for greenhouse grown mungbean

(Vigna radiata L. Wilczek), blackgram (Vigna mungo L. Hepper), cowpea (Vigna

unguiculata L. Walp.), and peanut (Arachis hypogaea L.), N translocation was only

significant for mungbean (Senaratne and Ratnasinghe, 1993). Cowpea stems accounted

for the highest DM fraction, but due their relatively low C:N ratio, the N from stems

should be readily available to succeeding crops. However, combined with lower overall

DM production capacity of this crop, it may not be as effective as sunn hemp in

sustaining soil organic matter and nitrogen.

Average dry pod yields were similar or slightly below those a study in Thailand

on sandy soils (Toomsan et al., 2000). Low productivity levels may have been related to

unfavorable production conditions as mentioned previously.









Maximum total N accumulation (94 kg N ha-1) occurred when N concentrations in

both roots and biomass were high and total biomass accumulation was the second highest

from all the sampling dates (Table 2-3). Reported values in the literature ranged from 68

kg N ha-1 (John et al., 1992) to 261 kg N ha-1 (Piha and Munns, 1987). Although CP

accumulated 20 kg N ha-1 less N, actual N accumulation rates were 25% greater for CP

compared to SH. It appears that cowpea may be more suitable as a short-term (< 6 wk)

summer cover crop if grown as an N source or green manure In addition seed cost of

cowpea may be also lower ($210 ha-1 vs $408 ha-1 for sunnhemp), while cowpea may

also provide a marketable edible seed.

Pearl millet

During 2003, pearl millet (PM) was the most prolific biomass producer and

surpassed SH DM accumulation by 1.6 Mg ha-1 (Tables 2-2 and 2-4). Observed linear

growth patterns are indicative of continuous and rather constant root and shoot growth

throughout the entire season and similar results were reported by Bruck et al. (2003).

Biomass dry matter reached 8.8 Mg ha-1. It was expected that PM would recover

mineralized N and would benefit from Np, as was shown for other gramineous crops

(Sainju et al., 1998, Paponov et al.,1999). However, Np did not have a significant effect

on any of the studied response variables. In other studies, when PM was planted as a

grain crop, N fertilization did not affect stover weight (Maman et al., 1999), nor did it

dramatically increase shoot N concentration (Kennedy et al., 2002). In South Carolina,

PM yielded up to 6.7 Mg ha-1 of DM, even after two hurricanes (Creamer and Baldwin,

1999). PM thus appears to be a rather robust crop. In loamy sand Indian soils, DM

accumulation of rainfed PM without fertilization reached 0.8 Mg ha-1when growing after

fallow and 1.1 Mg ha-1 when following PM residue (Aggarwal et al., 1997).









The pronounced decrease in tissue N concentrations after initial growth (Table 2-4)

may be related to the low SOM and very low inherent soil fertility and nutrient retention

capacity of Florida sandy soils. Other researchers reported a more gradual decline (Payne

et al., 1995) unless lack of readily available soil N induced a more drastic drop in tissue

N concentration (Kennedy et al., 2002). The decline in shoot N concentration may also be

partly caused by N remobilization before flowering (Diouf et al., 2004).

Despite high DM accumulation, PM only accrued 75 kg N ha-1 which was 39 kg N

ha-1 less than SH, but it could be argued that symbiotic N fixation in SH may have

accounted for this difference. Maximum N and biomass accumulation were better

synchronized and both occurred at WAE 11 and PM thus may be better suited as a

medium term (> 11 wk) summer cover crop. This has implications for winter crops,

because use of a summer CC with a longer growth cycle will reduce potential N losses

(Weinert et al., 2002). The C:N ratios for PM were relatively high (Table B-1), which

may be related to it being a C4 gramineous crop (Loomis and Connor, 1992). High C:N

ratios can be beneficial in sandy soils, because nutrients and specially N are released

more slowly, decreasing potential N leaching risk (Kuo et al., 2002). However, use of

more recalcitrant residues can result in a relatively large fraction of the labile N pool tied

up in microorganism biomass, thus compromising N availability for a succeeding crop

(Creamer and Baldwin, 2000).

Sesbania

Sesbania (SB) was severely affected by root-knot nematode (Meloidogyne

incognita) infestation (data not shown) which hampered its initial growth, nodulation, and

overall N accumulation. Sesbania is very susceptible to the root-knot nematode

(Meloidogynejavanica) which greatly affects its growth (Desaeger and Rao, 2001). As a









result, leaves showed N deficiency symptoms and crop growth declined after WAE 5

(Table 2-5). Overall biomass and N accumulation by SB was thus only a fraction of that

for other summer CC crops. Li et al., (no date) reported similar results at Homestead,

Florida, on a calcareous soil.

Initial shoot N concentrations, when most of the N is obtained from seed and soil N

storage pools, was 33.9 g N kg-1 (Table 2-5) and values were similar to those reported by

Mafongoya and Dzowela (1999). However, in the absence of successful nodulation, shoot

N levels rapidly dropped to values that are indicative of N deficiency (Zhiznevskaya et

al., 1997). Since up to 70-90% of N accumulated by SB is produced via symbiotic N

fixation, this underlines the critical role of root health to optimize the performance of

leguminous cover crops (Stihl et al., 2002). Incidence of nematodes in the current study

would have reduced assimilate availability for nodule development, thereby hampering N

fixation. As a consequence, in the absence of external soil N, leaf tissue N concentrations

dropped, thus greatly reducing photosynthesis and overall shoot growth. Although some

residual N might have been captured by the root systems, presence of nematodes may

also have reduced overall root growth and effective root depth (Araya and Caswellchen,

1994). In the absence of effective nodulation, the crop appeared to be greatly limited for

N. As a result, crop N accumulation was greatly affected by Np. Similar to PM, stems

had the highest C:N ratios (Table B-1), and stems represented half of the DM

accumulation. Since overall biomass and N accumulation of sesbania was rather poor and

this CC is also very susceptibility to a commonly occurring root knot nematode, it may

not be the most suitable summer CC for vegetable cropping systems in Florida.









Overall summer cover crop growth dynamics

Overall biomass accumulation could be ranked as follows: PM > SH > CP >> SB.

Overall N accumulation patterns were: SH > CP > PM >> SB (Table 2-6).

Cowpea and pearl millet had a precocious growth; both accumulated 123 kg-1 d-1

ha-1 by 5 WAE. In contrast, DM production of SH and PM peaked at 161 and 204 kg-1 d-1

ha-1 later in the season (WAE 8). The major drop in DM and N concentration after 8

WAE provides a justification for mowing both crops at that time.

For legumes and gramineous CC, stem DM allocation was the greatest. Allocation

towards roots was similar for PM, SH, CP, except for SB, as discussed above. Under

Florida conditions when N leaching can be appreciable, it is may be desirable when dry

matter is partitioned towards more recalcitrant above-ground tissues, presuming a slower

C mineralization thereby potentially increasing particulate organic matter (POM).

However, presence of adequate N in crop residues may also be important since steady

state soil OM levels may also be affected by overall system N inputs (Jenkinson et al.,

1985; Raum et al., 1998). Alternatively, it could be argued that a crop residue with

adequately high (>30) C:N ratio may function as "sponge" inmobilizing labile N from

fertilizer materials, thus functioning as an on-site slow-release nutrient source (Janzen et

al., 1992; Thompson et al., 2002). Overall N allocation to leaves was the highest for SH

and PM. Since leaves have low C:N ratio, this N pool is more prone to rapid

mineralization compared to other plant structures. Therefore, N from leaves is more

likely to be lost via leaching from the system if a winter crop is not planted directly after

the extermination of the summer CC. Both SH and PM allocated similar (24 vs 30%)

percentage of dry matter to leaves, but N concentration was lower, while C:N ratio was

higher in PM leaves compared to SH. (Table 2-7).









Cover crop phenology is an important issue for North Florida conditions, since it

would be desirable to plant a cover crop that will fix or scavenge residual N from July to

October, until winter cover crops and/or commercial fall crops can be planted. In this

study, the species that could meet this goal with appear to be sunn hemp and pearl millet.

Although sesbania may potentially recover residual N, it is rather susceptible to parasitic

nematodes. Cowpea also provides the potential benefit of symbiotic N fixation during dry

summers while also providing direct economic yield returns. Use of a late maturity

variety with a bush type growth habit would be more suitable for CC-based systems

compared to 'zipper cream'. Both sunn hemp and sesbania present the potential for

building up soil-borne diseases, and therefore it is critical to implement a sound crop

rotation.

Winter Cover Crop Systems

Winter rye

Winter rye is one of the most commonly used winter CC in temperate regions of the U.S.

As expected, the residual N from fertilizer applied to a previous corn crop had no effect

on any variable measured, but cropping system treatments did (Table 2-8). The quadratic

increase in DM and N accumulation was related to a gradual decrease in crop growth and

N uptake as the crop matured. Presence of SH residue almost doubled DM and N

accumulation by winter rye and overall DM and N accumulation for this system was 6.4

Mg ha-1 and 65 kg N ha-1, which was higher than the 1.0 Mg ha-1 and 27 kg N ha-1

reported by Garwood et al. (1999). In another study, rye was reported to recover up to 30

kg N ha-1 from residual inorganic fertilizer (Cline and Silvernail, 2001). According to

Cherr (2004), 64 % of the N from SH was lost within two weeks after crop senescence,

while the remaining fraction is relatively stable up to 28 weeks after death. The N release









from this residue thus appeared to benefit rye DM accumulation. In contrast with this, rye

N concentration in SH-based systems were lower which may be related to N dilution in

the dry matter associated with enhanced rye growth for SH-based systems (Table 2-8).

Similar results were reported for other cover crops (Derksen et al., 2002). Rye plants

growing in SH-based systems also may have been more precocious and the decline in

tissue N concentration associated with crop maturation was reached faster a similar

phenomenon has been described for DM accumulation (Paponov et al., 1999).

During 2005, winter rye was planted exclusively in sesbania-based systems and

residue effects were not tested. In order to attain higher N accumulation rates, a more

vigorous rye variety was used (Florida 401), and the rye to vetch ratio, was also reversed

(30% rye and 70% vetch). As a result, overall rye DM content was lower than during the

previous year, while for vetch the reverse was true. During 2005, the root system was

much vigorous compared to previous winter season, and despite a lower plant density,

root biomass was greater which may be related to genotypic difference and increased

competition between species. Total biomass and N content in 2005 were 46 and 65%

lower, respectively (Table 2-8 and 2-10). The lower biomass accumulation was related to

a two-fold reduction in plant densities and also to the fact that N accumulation associated

with the sesbania crop was only 5 to 12 kg N ha-1 (Table 2-5). As a result, rye did not

benefit much from residual crop residues and DM and N content results appear to be

similar to those reported for the fallow treatment in 2004 (Appendix A-5). The

disproportionably large reduction in N accumulation provides further indication that

despite luxurious growth of hairy vetch, soil N availability appeared to be the limiting

factor for the growth of winter rye.









Since N supply from sesbania was limited, the ST*Np interaction effect became

more apparent and total tissue N concentration was highest for the Np 133 treatment

(Table A-4). This carryover effect was unexpected, principally because the heavy rains of

September 2005 during Hurricane season may have displaced most of the residual soil N

below the surface soil (Table C-5). However, since SB had very poor growth, weeds may

have tied up residual N or N mineralizing from sweet corn, and released it after herbicide

application. Alternatively, due to the more vigorous root growth in 2005, rye may have

been able to make more efficient use of NO3- located at deeper soil layers as was

proposed by Thorup-Kristensen (2001). But from a practical perspective, the increase in

the N concentration associated with Np was relatively small and overall N content was

not affected.

Overall, most (56-59%) of the rye DM was partitioned to stems (Table 2-14).

Stems are recalcitrant and also provide a good control against weeds when left as a

surface residue. The relatively high root DM accumulation in 2005, was related to a

'Florida 401' having a much greater root allocation for both DM (16% vs 6%) and N

(18% vs 4%) compared to rye accumulation during 2004.

Hairy vetch

In Florida, hairy vetch has a short growing season compared to other regions of

the United States. Guldan et al., (1996) reported dry weight accumulations of 1.5 to 2.8

Mg ha-1 after 17 weeks of growth in sandy loam soils in New Mexico. During 2004, root

growth of hairy vetch was initially enhanced in SH-based systems, but over time this

trend was reversed (Table A-6). This may be related to increased competition with winter

rye (which was favored by the SH-residue). By the end of the season, N accumulation in

roots of hairy vetch growing after fallow was slightly higher than in plants growing on









SH residues; this effect might be the result of biological N fixation inhibition by residual

NO3-N from SH (Ledgard and Steele,, 1992; Mengel, 1994). Usually exogenous N does

not inhibit legume growth, but N coming from biological fixation decreases its efficiency

(Sanginga, 1996). In 2005, this effect was apparent for residual N from previously

planted sweet corn (Table A-9).

The tremendous increase in DM during 2005 (Table 2-12) was related to higher

seed rates (70 vs 30 kg ha-1) combined with unseasonably cool (Table C-1, C-2 and C-3)

and relatively wet spring (Table C-4 and C-5), which extended the rapid growth phase of

hairy vetch thereby greatly enhancing overall growth. This vigorous re-growth after final

mowing was not expected, since studies have shown that hairy vetch does not vigorously

re-grow after mowing, even when temperatures range from 5 to 10 C (Bransaeter and

Netland, 1999). It has been shown that hairy vetch performs best when soil temperature is

about 100 C (Zachariassen and Power, 1991), and air temperature is about 200 C

(Teasdale et al., 2004). Hairy vetch can resist frost better than many other template

adapted legumes (Bransaeter et al., 2002), which is why its growth is enhanced during

spring time (Teasdale et al., 2004). It may also be that continuous cultivation of hairy

vetch may have resulted in a gradual build of soil rhizobial inoculum and better initial

growth because vetch growth continued to improve each year (Cherr, 2004).

Nitrogen fixation during the second year (Table 2-12) was also high compared to

values reported in the literature for northern southeastern states (Abdul-Baki et al., 1996;

Cline and Silvernail, 2001) but similar to values reported for Georgia (Sainju and Singh,

2001). Hairy vetch nodulation and N fixation could also have been benefited from rains









during its establishment, since nodulation is susceptible to water stress (Hungria and

Vargas, 2000).

Overall winter cover crop growth dynamics

The 59% and 70% increases in overall DM and N accumulation during 2005

(Table 2-13) show that increased seed rates for vetch did greatly enhance the overall

system performance. Compared to leguminous summer CC, it appears that this mixed

winter cover crop system is very successful in recovering residual soil N and fixing

additional N. An explanation for the success of intercropping is that the non-legume

component more effectively utilizes residual soil N, forcing the legume to fix additional

N (Hardarson and Atkins, 2003). The system components also appear to complement

each other very well. The erect structure of rye allowed the vetch to more rapidly expand

its canopy volume and rye and thereby intercept more light and maintained higher growth

rates (Odhiambo and Bomke, 2001). Rye also had higher initial DM accumulation rates

whereas vetch had the greatest DM accumulation rates toward the end of the growing

season. It thus appears that the different canopy, shoot, and root growth characteristics of

these species allow a mixed system to be more efficient in water, nutrient, and radiation

utilization (Karpenstein-Manchan and Stuelpnagel, 2000).

During the second year of the rotation, stem and leaf N concentrations in rye

appeared to decrease compared to the previous year. This may be related to hairy vetch

competing for light, water, and nutrients. Although, vetch accumulated substantial

amounts of N (80 and 243 kg N ha-1), most of this N was tied up in shoot growth and due

to favorable growth, less then 5% of this N pool was available for uptake by rye.









Although root exudates and root sloughing may result in high C losses and release of N

(Grayston et al.,1997), rye did not appear to benefit from this potential N source.

The steep increase in DM accumulation rate of hairy vetch toward the end of the

growing season during 2005, is rather unique and may be related to unseasonly cool

weather combined with a relatively wet spring (Cl, C-2 and C-3). Regardless of this, the

mixed rye/vetch system appears to be a suitable winter CC system for north central

Florida since it clearly outperforms mono-cropped leguminous CC systems (Cherr,

2004). In Kentucky, DM accumulation rates for a similar system were 3.8 Mg ha-1 with

no residual N and almost 5.8 Mg ha-1 in presence of residual N. This study also showed

that rye was more dependent on residual N than hairy vetch, as shown by Cline and

Silvernail (2001). In Denmark, DM accumulation for a 64:36 rye:hairy vetch mix was 4.7

Mg ha-1, while in Georgia a 68:32 ratio rye:hairy vetch mix, yielded 6.6 Mg ha-1 (Sainju

et al., 2005). Higher potential production for our system may be related to warmer

winters and higher radiation levels.

Using gramineous and legumes mixes enhances the balance between C pool build

up, and N retention in the soil (Kuo and Sainju, 1998). In the case of the hairy vetch and

rye mix overall C:N ratios were similar for both years and based on the low values it

appears that overall mineralization of hairy vetch residue would be very fast (Table B-2).

Moreover, CC mixes may benefit from summer cover cropping, by either

scavenging residual N or from cover crop stover that preserves soil moisture. In

temperate zones, only 9-29% of the N added through cover cropping is recovered by the

following crop, while in other regions use of appropriate crop rotations is considered to

be more sustainable than intercropping (Dakora and Keya, 1997). Cover crops mixes thus









could enable farmers and cover crop users to accomplish the goal of fixing N and

accumulating biomass; however this should be studied more extensively under Florida

conditions.

Conclusion

Summer cover crops may provide a number of services and benefits and may fit

into different production systems depending on their growth cycle and tissue

composition characteristics. Sunn hemp and pearl millet are suitable cover crops for

summer-fall cultivation in north central Florida, due to their prolonged growth cycle and

prolific biomass accumulation. Sunn hemp, accumulated high amounts of C and N, but

should be followed by either a commercial fall vegetable crop or a suitable winter cover

crop system, to ensure that the N is not lost via leaching after plant senescence. The C:N

ratio of pearl millet is relatively high due to the recalcitrant stem fraction, thus holding

promise for enhancing soil organic matter build up and also could act as a slow-release

source of nutrients. Due to its short growing cycle and high initial N and DM

accumulation rates, commercial cowpea, such as 'zipper cream' may be most suitable to

take advantage of short summer fallow periods. Use of late maturing varieties, such as

'iron clay' may be more desirable in order to achieve satisfactory N accumulation.

Although sesbania has good potential for N-recovery (Ruffo and Bollero, 2004), it

appeared to be overly susceptible to plant-parasitic nematodes, especially root-knot

nematodes. Although sunn hemp was shown to be the most prolific biomass producer

among summer cover crops (use different reference), continuous cultivation may not be

desirable due to the potential for build up of soil-borne diseases, such as Verticillium sp.









Use of winter cover crop mixes appeared to greatly enhanced the performance of

these cropping systems. As documented in the literature, during the first year of our trials

rye scavenged N from residual sunn hemp. However, N benefits appeared to be greatest

when hairy vetch was the predominant species, and based on our results it appears that

most of the N will only become available after the senescence of hairy vetch. However,

more detailed information is required pertaining to the quality and degradation of

structural compounds, such as lignin, and how these processes are affected by

environmental conditions and cultural practices, in order to improve our understanding of

subsequent N release patterns.

It is also important to keep in mind that environmental conditions may vary on

temporal and spatial scales, influencing the performance of cover crop-based systems. As

a result, long-term field studies with larger production units that are replicated both in

space and time may be required to fully understand the more subtle system dynamics.










Table 2-1. Outline of crop rotations and experimental treatments during the research
period (2003-2005).


Year 1
Winter
2003
H+R
H+R
H+R
F
F
F
H+R
H+R
H+R
F
F
F
F
F
F


Fall
2004
CP
CP
CP
PM
PM
PM
SB
SB
SB
F
F
F
F
F
F


Winter
2004
B
B
B
B
B
B
H+R
H+R
H+R
F
F
F
F
F
F


Trt. Fall
2003
1 S
2 S
3 S
4 S
5 S
6 S
7 F
8 F
9 F
10 F
11 F
12 F
13 F
14 F
15 F


Spring N rate
2004 (kg ha1)
SC 0
SC 67
SC 133
SC 0
SC 67
SC 133
SC 0
SC 67
SC 133
SC 0
SC 67
SC 133
SC 200
SC 267
F None


Trt. = Treatment, S = sunn hemp, F = fallow, H+R = hairy vetch/rye mix, SC = sweet
corn, CP= cowpea, PM = pearl millet, SB= sesbania, B= broccoli, W = watermelon.


Year 2
N rate Spring N rate
(kg ha') 2005 (kg ha')
0 W 0
131 W 84
196 W 168
0 W 0
131 W 84
196 W 168
0 W 0
0 W 84
0 W 168
0 W 0
0 W 84
0 W 126
0 W 168
0 W 210
0 F None










Table 2-2. Effects of sampling time (ST) and kg ha- N fertilizer applied to preceding sweet corn crop (Np) main effect, along
with ST*Np interaction effect on dry weight, N concentration, and N accumulation of sunn hemp (Crotalaria
jjuncea), during summer/fall 2003.
Fixed Effects Dry Weight N concentration N accumulation
Roots Shoot Total Roots Shoot Total Roots Shoot Total
---------------------- Mg ha1 ---- -- ------------- g kg -------- -- ------------- kg ha ---------
ST'
WAE2 0.04c 0.24d 0.27d 21.1a 40.3d 37.3a 0.9b 9.3c 10.1c
WAE 5 0.31b 1.54c 1.84c 9.9b 27.8c 24.9b 3.0b 42b 45.lb
WAE 8 0.75a 4.48b 5.23b 9.6b 23.5b 21.5c 7.5a 106a 113a
WAE 11 0.78a 6.18a 6.95a 8.5bc 16.3a 15.5d 6.5a 99a 106a
WAE 14 0.72a 6.43a 7.15a 11.8c 16.1a 15.7d 8.3a 103a lila
Significance L*Q* LmQ"C" LmQ C* L*Q* L*Q* L*Q* L*Q* L*Q* L*Q*


0 0.48 3.39 3.82 12.2 24.7 22.9 4.9 63.4 b 68.3 b
67 0.55 4.23 4.77 12.2 24.5 22.8 5.6 81.7 a 87.2 a
133 0.53 3.75 4.25 12.2 25.2 23.2 5.2 70.6 ab 75.9 ab
Significance NS NS NS NS NS NS NS *

ST*Np NS NS NS NS NS NS NS
t Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote higher to lower ranking.









Table 2-3. Effects of sampling time (ST) and kg ha- N fertilizer applied to preceding sweet corn crop (Np) main effect, along
with ST*Np interaction effect on dry weight, N concentration, and N accumulation of cowpea (Vigna unguiculata)
during summer/fall 2004.
fxed Effets DyWeight N concentration Naccunulation
Roots Shoots Total Roots Shoots Total Roots Shoots Total

IVIha gkg kgha

Week2 0.04c 0.20c 0.23c 33.2a 43.3a 42.la 1.2b 10. c 11.3c
Week 5 0.23bc 271b 293b 24.4a 31.6b 30.8b 5.2a 90.3a 95.7a
Week 8 0.33ab 4.34a 4.67a 14.5b 20.8c 20.3c 4.7a 88.9a 93.6a
Week 11 0.38a 256b 2.94b 13.2c 18.9c 18. d 8.0a 48.5b 53.5b
Significace L*Q* LCQ** L*Q* LQ* LQ*C* LQC* LQC* L*Q(** LQ**


0 0.26 2.54 2.79 21.0 28.8 27.8 4.3 61.7 65.8
67 0.22 2.32 2.55 21.5 28.9 28.2 3.8 55.5 59.3
133 0.25 2.49 2.74 21.5 28.2 27.5 4.1 61.2 65.6
Significance S S NS NS NS NS NS NS NS

SPrNj N S NS NS NS NS NS NS NS
Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking.










Table 2-4. Effects of sampling time (ST) and kg ha-' N fertilizer applied to preceding sweet corn crop (Np) main effect, along
with ST*Np interaction effect on dry weight, N concentration, and N accumulation of pearl millet (Pennisetum
glaucum) during 2004.
a]_ Effe:-rc D i V ei;ht -10 croncentr ation 11 accumulation
S-------- 3------------ --- k:, ------------------ ------------------ k I -
E.:c:Rls E i':oin i Total F.or!:i:'Eloiiin TorJl F.co':m' F nw-'i- :sTc:ral


ST
W eek 1: 14,1 -.. d 1I : d It 5 -a "- 1 l .c :; 4,: r CIr
Week f 0 21c 2-:.c 9- 10 4b 1231 1, Ob -l. 36 3b 39 01
6e'ek ; :1.. r." 4'b "n., I,: t :,: :; 4,: ", 7 ",'. (I-l "9 a
Week 11 0 6,a L a r a4 61 la '9 3, -41 Ga
iu,nficnce L "C L L L L CQ"C LL ; L""C' L L"

II -pre
1:1 1:1 31 4 4 :'r 1:1 I-, is 1:1 3- 2 42 :
66 0 42 5 14 -0 03 141 1"3 3C 44 3 147
1 -_ ,l: -- 4 3-.- 4 I: 1:1 144 4 14 :1 2 4-, :5 456 '
Inimficance 113 IT ItS 113 T3 HT ITS Ci 113




t Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking.









Table 2-5. Effects of sampling time (ST) and kg ha- N fertilizer applied to preceding sweet corn crop (Np) main effect, along
with ST*Np interaction effect on dry weight, N concentration, and N accumulation of sesbania (Sesbania sesban),
during summer/fall 2004.

FixedEffect Dy Weight Nconcentration Naccunulation
Roots Shoots Total Roots Shoots Total Roots Shoots Total
M-ha1 gkg -- kgha1
STt
Week2 0.02c 0.07c 0.08c 25.5a 33.8a 32.2a 0.4b 2.3b 2.7b
Week 5 0.17a 0.93a 1.09a 15. lb 12.2b 12.7b 2.6a 11.4a 13.9a
Week 8 0.12ab 0.61b 0.72b 127b 9.2c 9.8c 1.7ab 5.8b 7.5a
Week 11 0.10b 0.59b 0.69b 7.8c 8.0c 8.0c 0.9b 5.3b 6.2b
SignificanceLQ**C* L*QC* LQC* LQC* LQC LQC QC QC QC*


0 0.07 0.35 0.42 14.5 15.9 15.6 0.8b 40b 4.8b
67 0.09 0.46 0.55 15.5 16.0 15.8 1.3ab 5.2ab 6.4ab
133 0.15 0.83 0.97 15.8 15.6 15.6 2.1a 9.5a 11.6a
Significance N N NS NS *

ST*P1 NS NS NS N NS N NS
Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking.










Table 2-6.Total dry weight accumulation and dry matter allocation to diff


S. Biomass Allocation
Specie Total Biomass Biomass Allocation
Root Stem Leaf Reproductive


-- Mg ha -- -------------------------%--------------------------
Sunn hemp 7.16 b 10 bc 61 bc 24 a 6 b
Cowpea 3.37 c 11 ab 70 ab 5 b 14 a
Pearl Millet 9.44 a 7 c 51 c 31 a 11 a
Sesbania 0.70 d 14 a 79 a 1 b 5 b
Means followed by identical lower case letters in the same column are not significantly different according to Tukey's test (p<0.05), letters "a", "b", "c"
denote a higher to lower ranking.


Table 2-7. Total Nitrogen (N) accumulation and N allocation to different p


specie Total N Nitrogen allocation
Specie
Accumulation Root Stem Leaf Reproductive
1


)lant parts for summer/fall cover crops.


-- kg N ha -- ----------------------- %--------------------------
Sunn hemp lila 7b 38b 42 a 14 c
Cowpea 53.5 b 7b 56 a 10 b 27 a
Pearl Millet 74.9 b 6 b 30 b 41 a 24 ab
Sesbania 6.3 c 16 a 69 a 2 b 13 bc
Means followed by identical lower case letters in the same column are not significantly different according to Tukey's test (p<0.05), letters "a", "b", "c"
denote a higher to lower ranking.


rent plant parts for summer/fall cover crops.










Table 2-8. Effects of sampling time (ST), kg ha-' N fertilizer applied to preceding sweet corn crop (Np) and residue [RES =
residue of sunnhemp (SH) or fallow vegetation (F)] main effect, along with ST*Np, ST*RES, Np*RES interactions
effects on dry weight, N concentration, and N accumulation of rye (Secale cereale), during summer/fall 2004.
Fixed Effect Dry Weight N concentration N accumulation

-- --- c- hk "1 .......----------- ----------- k ----------- ----------- k l 1 -----------


STt
WAE 2
WAE 5
WAE 8
WAE 11
WAE 14
WAE 17


0.02 c
0.20 b
0.23 b
0.28ab
0.32 a
0.33 a


Significance L Q


0
67
133
Significance


RES'
SH
F
Significance


0.21
0.25
0.23
NS


0.29 a
0.17b
***


0.07 c
0.45 c
1.04 c
2.45 b
4.19 a
4.94 a


L



2.06
2.21
2.30
NS


2.90 a
1.49 b
***


0.09 c
0.65 c
1.27 c
2.73 b
4.52 a
5.27 a
L*



2.28
2.45
2.54
NS



3.20 a
1.65 b
***


17.1 a
8.8 b
8.8 b
7.4 bc
5.9 c
7.7 b


5 g5


39.6 a
20.0 b
17.5 c
18.0 bc
13.4 d
12.0 d


33.8 a
17.0 b
15.8 b
16.7 b
12.8 c
11.7 c


0.4 b
1.8 ab
2.0 a
2.1 a
1.9 a
2.6 a


L Q C L Q C L Q C Q'C


20.1
20.7
19.4
NS



19.4 b
20.8 a
*


1.6
1.8
1.9
NS



2.4 a
1.2 b
***


3.5 c
8.5 c
18.5 bc
31.1 b
54.3 a
55.9 a


L



29.2
29.3
27.3
NS



35.7 a
21.6b
*


3.9 c
10.2 c
20.4 bc
33.2 b
56.2 a
58.4 a


30.8
31.1
29.3
NS


38.0 a
22.8 b
*


ST*Np
ST*RES
RES*Np


t Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking. SH= summer sunn hemp cover crop residue, F= summer fallow residue.


NS NS









Table 2-9. Effects of sampling time (ST), kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) and residue [RES =
residue of sunn hemp (SH) or fallow vegetation (F)] main effect, along with ST*Np, ST*Res, Np*RES interactions
effects on dry weight, N concentration, and N accumulation of hairy vetch (Vicia villosa), during summer/fall 2004.
Fixed Effect Dry Weight N concentration N accumulation
Roots Shoots Total Roots Shoots Total Roots Shoots Total
----------- Mg ha ----------- ----------- gkg ----------- ----------- kg ha -----------
STt
WAE 2 0.01 b 0.01 c 0.02 c 17.1 e 0.2 c -
WAE 5 0.03 b 0.05 c 0.08 c 20.6 d 38.3 a 30.4 b 1.0 bc 1.7 c 2.7 c
WAE8 0.02 b 0.09 c 0.11 c 31.9 a 40.0 a 38.2 a 1.3 b 8.2 c 9.9 c
WAE 11 0.03b 0.34c 0.36 c 23.6c 41.0a 40.8a 0.8bc 15.1 c 19.6 c
WAE 14 0.16 a 1.19 b 1.35 b 26.8 b 37.0 ab 35.4 a 4.2 a 45.3 b 49.1 b
WAE 17 0.17 a 2.34 a 2.49 a 25.6 bc 32.8 b 32.1 ab 4.5 a 77.4 a 80.1 a
Significance L"Q... L "Q L "Q..." Q ...C L Q L"Q C L Q L"Q... L Q

Np
0 0.07 0.63 0.69 24.7 a 39.2 36.4 2.0 27.2 32.2
67 0.07 0.67 0.75 23.1 b 35.7 34.0 2.1 30.5 32.0
133 0.07 0.70 0.77 25.0 a 38.6 35.8 2.0 30.8 33.6
Significance NS NS NS ns NS NS NS NS


RESIN
SH 0.05 b 0.63 0.68 24.0 36.3 b 35.1 1.5 b 27.3 29.6
F 0.09 a 0.71 0.80 24.5 39.3 a 35.7 2.5 a 31.7 34.9
Significance NS NS NS NS ** NS NS

ST*Np NS NS NS NS NS NS NS NS NS
ST*RES *** NS NS NS NS *** NS NS
RES*Np NS NS NS NS NS NS NS NS NS
t Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking. SH= summer sunn hemp cover crop residue, F= summer fallow residue.









Table 2-10. Effects of sampling time (ST), kg ha1 N fertilizer applied to preceding sweet corn crop (Np and residue [RES =
residue of sunn hemp (SH) or fallow vegetation (F)] main effect, along with ST*Np, ST*RES, Np*RES interactions
effects on dry weight, N concentration, and N accumulation of hairy vetch and rye, during summer/fall 2004.

Fixed Effect Dry Weight N concentration N accumulation
Roots Shoots Total Roots Shoots Total Roots Shoots Total
----------- Mg ha ----------- ----------- g kg ----------- ----------- kg ha -----------
ST'
WAE2 0.03 c 0.08d 0.11 d 17.1 a 34.7 a 28.1 a 0.6c 4.6 d 4.9d
WAE 5 0.24 b 0.49 d 0.73 d 10.6 c 22.1 b 18.3 b 2.8 b 10.2 d 13.0 d
WAE8 0.25 b 1.13d 1.38 d 11.4c 20.0 b 18.0 b 3.3 b 27.7 cd 30.4 cd
WAE 11 0.31b 2.80 c 3.09 c 9.6 c 22.0 b 22.0 b 3.1b 46.0 c 53.7 c
WAE 14 0.48 a 5.39 b 5.87 b 12.9 bc 19.4 b 18.7 b 6.2 a 99.6 b 106 b
WAE 17 0.50 a 7.20 a 7.70 a 14.4 b 19.2 b 18.8 b 7.1 a 132 a 139 a
Significance L L Q L L***Q*** Q***C* L***Q***C*** L***Q***C*** L L***Q*** L***Q**



0 0.28 2.67 2.95 12.5 23.5 21.3 3.6 53.4 55.8
67 0.32 2.88 3.20 12.4 23.2 21.0 3.9 55.5 59.5
133 0.31 2.99 3.29 13.1 22.1 20.1 4.0 52.1 58.2
Significance NS NS NS NS NS NS NS NS NS

RESIN
SH 0.35 a 3.51 a 3.85 a 11.9 b 21.7 19.7 b 4.0 58.8 62.0
F 0.26 b 2.19 b 2.44 b 13.5 a 24.2 21.8 a 3.7 47.9 52.7
Significance *** ** NS NS NS

ST*Np NS NS NS NS NS NS NS NS NS
ST*RES NS NS *** *** NS NS
RES*Np NS NS NS NS NS NS NS NS NS
t Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking. SH= summer sunn hemp cover crop residue, F= summer fallow residue.









Table 2-11. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect and
ST*Np interaction effect on dry weight, N concentration, and N accumulation of rye (Secale cereale), during winter
2004/05.
Fixed Dry Weight N concentration N accumulation
Effect Roots Shoots Total Roots Shoots Total Roots Shoots Total
----------- Mg ha ----------- ----------- g kg ----------- ---- kg ha1 -----
STt
WAE 2 0.02 c 0.10 c 0.12c 18.8 a 43.0 a 38.4 a 0.4 c 4.4 b 6.0 b
WAE 5 0.06c 0.30 c 0.36 c 9.5 b 25.3 b 22.7 b 0.5 c 7.6 b 8.1 b
WAE 8 0.11 c 0.62 c 0.73 c 7.8 b 12.2 c 11.5 c 0.9 c 7.5 b 8.4 b
WAE 11 0.22b 1.44b 1.67b 8.7b 9.8 d 9.6 d 1.9b 14.1 a 16.2 a
WAE 14 0.22 b 2.14 a 2.36 a 9.3 b 6.6 e 6.8 e 1.9 b 14.3 a 16.2 a
WAE 17 0.45 a 2.38 a 2.83 a 8.1 b 7.0 e 7.2 e 3.5 a 16.7 a 20.2 a
Effect LQ LC L LQC L Q C L Q C LQ L L

Nn
0 0.16 1.04 1.20 10.1 17.3 15.6 b 1.3 9.7 11.4
67 0.18 1.15 1.34 9.3 17.0 15.9 ab 1.4 10.2 11.7
133 0.19 1.30 1.50 11.7 17.7 16.6 a 1.9 12.4 14.5
Significance NS NS NS NS NS NS NS NS

ST*Np NS NS NS NS NS NS NS NS NS
t Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking.









Table 2-12. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect and
ST*N-p interaction effect on dry weight, N concentration, and N accumulation of hairy vetch (Vicia villosa), winter
during 2004/05.
Fixed Effect Dry Weight N concentration N accumulation
Roots Shoots Total Roots Shoots Total Roots Shoots Total
----------- Mg ha ----------- ----------- g kg ----------- ----------- kg ha ------
STt
WAE 2 0.01 c 0.04 e 0.04 e 46.6 a 50.6 a 49.5 a 0.35 c 2.3 d 2.8 c
WAE 5 0.04 bc 0.24 e 0.28 e 46.1 a 45.9 a 45.9 a 2.03 c 11.1 d 13.1 c
WAE8 0.12 bc 1.42 d 1.53d 38.4 b 25.4 b 27.7 b 4.77 c 37.0 d 44.3 c
WAE 11 0.27 b 2.68 c 3.11 c 31.9 c 32.3 b 33.2 b 7.98 bc 82.2 c 103 b
WAE 14 0.36 b 4.03 b 4.40 b 33.6 bc 31.8 b 31.9 b 12.1 b 127 b 139 b
WAE 17 0.93 a 8.52 a 9.44 a 24.6 d 27.2 b 26.5 b 20.9 a 221 a 243 a
Significance L***Q***C** L***Q***C* L***Q***C*** L*** LQC L***Q L**Q L**Q L**Q

Nn
0 0.32 2.63 b 3.03 ab 35.2 b 34.6 35.3 8.3 78.3 89.7
67 0.28 3.31 a 3.58 a 37.9 a 34.3 35.0 8.9 87.5 97.6
133 0.26 2.54 b 2.80 b 37.5 a 37.6 37.3 7.8 78.8 85.5
Significance NS NS NS NS NS NS

ST*Np NS *** ** NS NS NS NS NS NS
t Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking









Table 2-13. Effects of sampling time (ST) and kg ha-1 N fertilizer applied to preceding sweet corn crop (Np) main effect and
ST*Np interaction effect on dry weight, N concentration, and N accumulation of hairy vetch + rye, during winter
2004/05.
Fixed Effect Dry Weight N concentration N accumulation
Roots Shoots Total Roots Shoots Total Roots Shoots Total
----------- Mg ha ----------- ----------- g kg ----------- ---- kg ha ------
STt
WAE 2 0.03 d 0.14 e 0.17 e 25.7 a 44.8 a 41.4 a 0.8 c 6.5 d 6.8 d
WAE 5 0.10 c 0.54 e 0.65 e 25.0 a 34.3 b 33.3 b 2.6 c 18.6 d 21.4 cd
WAE 8 0.22 c 2.04 d 2.27 d 24.1 a 21.0 c 22.1 c 5.6 c 44.5 d 53.3 c
WAE 11 0.47 bc 4.11 c 4.68 c 22.7 a 25.0 c 24.3 c 10.4 bc 104 c 115 b
WAE 14 0.59 b 6.18 b 6.77 b 24.1 a 23.0 c 23.1 c 14.0 b 142 b 156 b
WAE 17 1.38 a 10.9 a 12.3 a 19.0 a 22.8 c 22.2 c 25.4 a 238 a 264 a
Significance L***Q***C* LQ LQC L LQC LQC LQ LQ LQ

Nn
0 0.47 3.66 b 4.16 23.8 28.9 28.5 9.7 86.7 100
67 0.46 4.46 a 4.91 23.0 27.2 26.9 10.2 97.5 109
133 0.46 3.84 b 4.32 23.5 29.4 27.8 10.0 91.3 98.0
Significance NS NS NS NS NS NS NS NS

ST*Np NS *** NS NS NS NS NS NS
t Sampling time in weeks after emergence (WAE). NS,*,**,*** Non-significant or significant at the p<0.05, 0.001, 0.001 level, respectively, and linear
(L), quadratic (Q), or cubic ( C). Means followed by identical lower case letters in the same column are not significantly different according to Tukey's
test (p<0.05), letters "a", "b", "c" denote a higher to lower ranking.










Table 2-14.Total dry weight accumulation and dry matter allocation different plant parts for winter cover crops.


.. Biomass Allocation
Specie Total Biomass Biomass Allocation
Root Stem Leaf Reproductive Sensc. Tissue
---Mg ha--- ------------------------------ % -------------------
Rye, 2004 5.35 b 6 c 58 a 3 c 9b 24 a
Rye, 2005 2.96 c 16 a 56 a 5 c 13 a 10 b
Hairy vetch, 2004 2.50 c 8 bc 57 a 30 b 2 c 3 c
Hairy vetch, 2005 9.58 a 7b 38 b 55 a 0 c 0 c


Means followed by identical lower case letters in the same column are not significantly different according to Tukey's test (p<0.05), letters "a", "b", "c"
denote a higher to lower ranking.

Table 2-15. Total Nitrogen (N) and N allocation to different plant parts for winter cover crops, studied during 2004 and 2005.


pecie Total N Nitrogen Allocation
Specie Accumulation Root Stem Leaf Reproductive Sens. Tissue
Accumulation Root Stem Leaf Reproductive Sensc. Tissue


-- kg N ha1 -- ---------------------------- %------------
Rye, 2004 58.8 bc 4 c 53 a 6 c 24 b 15 a
Rye, 2005 20.45 c 18 a 37 bc 8 c 31 a 6 b
Hairy vetch, 2004 80.2 b 7 bc 41 b 47 b 3 c 2 c
Hairy vetch, 2005 246a 10b 31 c 59a 0c 0 c
Means followed by identical lower case letters in the same column are not significantly different according to Tukey's test (p<0.05), letters "a", "b", "c"
denote a higher to lower ranking














CHAPTER 3
GROWTH, N ACCUMULATION, AND YIELD OF VEGETABLE CROPS AS
AFFECTED BY CROP RESIDUES AND N-FERTILIZER RATE

Introduction

A continuous expanding global population forces agriculture to meet the world's

calorie intake at the expense of natural resource depletion (Ehrlich et al., 1993; Matson et

al., 1997). Agricultural soil fertility was traditionally replenished by the use of crop residues

and legume rotation and via use of integrated farming systems (Bohlool et al., 1992;

Howarth et al., 2002; Tonnito et al., 2006). Currently, higher crop yields per unit area in

developed countries are typically achieved through use of high yielding varieties, which

usually demand large doses of nitrogen (N) fertilizer (Novotny, 1999). Conventional

production systems, depend greatly on external inputs and agrochemicals and thus

compromise the long-term sustainability of agriculture. Attaining sustainability requires

revisiting traditional agricultural practices in a so called "second green revolution"

(Giampietro, 1997; Welch and Graham, 1999; Altieri, 2004). This process entails reviewing

agro-ecosystems production capabilities (Robertson and Swinton, 2005), enhancing

agricultural biodiversity (Altieri 2000), and using sound crop rotations (Caporali and Onnis,

1992; Gregory et al., 2005).

Many current vegetable production systems are characterized by an intense use of

pesticides as well as chemical fertilizers (Rice et al., 2001). However, market demand for

organic produce and more environmentally sound production practices is expected to

increase farmer's interest in using cover crops (Cline and Silvernail, 2001). In Florida,

spring vegetable crops such as sweet corn (Zea mays) and watermelon (Citrullus lanatus)

65









are very important for the local agricultural economy. For example, Florida provided about

32% of the U.S. watermelons and 81% of spring grown sweet corn in 2000 (Sargent, 2000;

Stevens et al., 2003).

Florida sweet corn production maximum recommended N fertilizer dose is 224 kg N

ha-1 (Olson and Simonne, 2005). Corresponding values for broccoli (Brassica oleracea) and

watermelon are 196 and 168 kg N ha-1 (Olsen et al, 2005). Some authors suggest that

meeting crop N demands requires a combination of both external N fertilizer and symbiotic

N-fixation (Cambell et al., 1995; Bockman, 1997). Although legumes use symbiotic N

fixation, it is necessary to place symbiotic N fixation in perspective, since legumes can be

both sources and/or sinks of N depending on residual soil N status (Isse et al., 1999). Even

though N from legumes is more stable in the soil (Crews and Peoples 2005), this N source

still contributes, along with fertilizers and manure, to increases in N incorporated into the

biosphere (Goulding et al., 1998; Mosier et al., 2001).

On a silty clay soil in Colorado, maximum sweet corn yield was obtained at a total

(residual + fertilizer) of 258-265 kg N ha-1 (Halvorson et al., 2005). In a study carried out on

a silt loam soil in Maine, alfalfa (Medicago sativa), winter rye (Secale cereale) and hairy

vetch (Vicia villosa Roth) replaced 50 to 156 kg N ha-1 of synthetic fertilizer, thus providing

almost all N required by a subsequent sweet corn crop (Griffin et al., 2000). In the coastal

plain area in Maryland, economic N-fertilizer rates for sweet corn following vetch were 30

to 76 kg N ha-1, compared to 65 to 193 kg N ha-1 for rye and vetch mixture, 161 to 247 kg N

ha-1 for rye and 201 kg N ha-1 in a fallow system (Clark, 1997). Andraski and Bundy (2005)

found that on a Wisconsin loamy sand soil, corn yields were significantly higher following

non-leguminous cover crops compared to winter fallow.









In Virginia, watermelon had higher fruit yields when it followed hairy vetch (49.8 Mg ha-1)

compared to hairy vetch/rye (45.5 Mg ha-1) or crimson clover (Trifolium incarnatum) and

rye (43.9 Mg ha-1).

In order to avoid excessive N fluxes through the soil, quality of cover crop (CC)

biomass and timing of extermination should be linked with the nutritional needs of

subsequent commercial crops as affected by environmental conditions. A Danish research

group reported that residue composition is perhaps even more influential than temperature

(Magid et al., 2001), while N mineralization is also less affected by temperature changes

than C mineralization (Magid et al., 2004). Because crop N demand of Brassicae is high

(Kage et al., 2003), they are considered to be very effective in scavenging residual nitrogen

(Dabney et al., 2001). Since they are well-adapted to low temperatures, they could be

successfully used following a summer legume rotation in subtropical and temperate

environments. Brassicae-derived residues in turn, mineralize faster compared to gramineous

residues due to their higher N concentration and lower C:N ratio (Garwood et al., 1999). For

this reason, it is preferable to follow a Brassicae directly with another crop to ensure

optimal N retention.

Central Florida sandy soils are very prone to nitrogen leaching (Alva, 1992; Perrin et

al., 1998). Although there is no specific information in the literature about N leaching from

sweet corn production systems in Florida, it has been documented that N uptake from

fertilizer is typically on the order of 50% (Bundy and Andraski, 2005), and that N leaching

potential is high (Isse, 1999). In Wisconsin, 71% of the applied N eventually reached the

groundwater (Kraft and Stites, 2003). In the Florida production environment, a more stable

source of N could be provided by using a mix of gramineous and leguminous cover crops.

Use of cover crops can also play an important role in intercepting nitrates (NO3-) from









residual N fertilizer or crop residues (Kristensen and Thoroup-Kristensen, 2004). McDonald

et al., (2005) reported that the presence of weeds and/or winter rye significantly decreased

nitrate leaching on the sandy loam compared with a bare fallow. For catch crops to be

effective they should be prolific biomass producers and should have rapidly growing deep

root systems (Thoroup-Kristensen, 2001).

Winter cauliflower and broccoli (Brassica oleracea var. botrytis), are examples of

double- purposed crops that can be used as both catch crop and cash crop. It was reported

that broccoli yields were higher when broccoli was planted into cowpea (Vigna

unguicuolata) residue compared to bare soil systems (Harrison et al., 2004). In a study

carried out in Virginia, yields of non-tillage broccoli planted in mulches of foxtail millet

(Setaria sp.) and/or soybean (Glycine max) residue were equal or higher compared to clean

cultivation controls (Abdul-Baki et al., 1997). In an organic farm study in New England, no-

tillage vs conventional tillage did not affect broccoli and cabbage (Brassica oleracea var.

capitata) performance on a sandy loam soil (Schonbeck et al., 1993).

Additional studies are needed for identifying productive, yet environmentally sound,

cropping systems suitable for North Central Florida. There is also a need to enhance our

understanding of how soil N and C cycles are affected by cover crops, and in what manner

plant nutrition and weed control can be enhanced via improved integration of cover crops in

vegetable minimum-tillage systems. Cropping system components that were of special

interest included graminaeous summer cover crops, leguminous and graminaeous winter

cover crop mixes and brassicae crops suitable for double cropping following summer cover

crops.

As part of a larger study to promote the improved use of cover crops in vegetable

cropping systems in Florida, this chapter aims to enhance our understanding of the









interaction between cover crop performance and inorganic N-fertilizer requirements of

commercial vegetables in Florida. The specific objective for this research component was to

determine if the use of cover crops will result in maximum sweet corn, broccoli, and

watermelon growth and yields, while reducing supplemental N-fertilizer requirements.

Our hypotheses were that: 1) A fall/winter vegetable crop following a summer cover

crop will utilize most of the mineralized N efficiently, because during the fall growing

season in minimum-tillage systems cover crop residues decompose slower; 2) Cropping

systems and N fertilizer rate will affect the growth, N accumulation, yield, and quality of

sweet corn, broccoli and watermelon; 3) Use of cover crops will reduce farm dependence on

external inorganic N-fertilizer inputs; and 4) Appropriate use of cover crops can enhance

the sustainability of existing agroecosystems.

Materials and Methods

Set-Up and Design

The research was conducted at the University of Florida, Plant Science Research and

Education Unit near Citra, Florida. The dominant soil types at this site were a Candler fine

sand (Typic Quarzipsamments, hyperthermic, uncoated) and a Lake fine sand (Typic

Quarzipsamments, hyperthermic, uncoated). Both soil types contained more than 95% sand

in the upper 1-2 m of the soil profile (Carlisle et al., 1998).

The experiment consisted of 14 treatments and a complete control, arranged in a

factorial randomized complete block design. Each treatment was replicated four times and

each replicate was considered a block. Treatments were the combination of two factors:

cropping system and N fertilizer rate. There were four levels of cropping systems, which

denoted the presence or absence (fallow) of summer and winter cover crops residues. There

were several levels of fertilizer rates. During spring 2004 all treatments were planted with









sweet corn (Zea mays var. "Saturn Yellow"), because this crop has a high demand for

inorganic N (224 kg N ha-) and served as biological indicator of overall residue N

availability. Cropping systems for sweet corn were:

1. A summer cover crop of sunn hemp (Crotalariajuncea) in 2003 + a winter cover
crop mix of hairy vetch (Vicia villosa) and rye (Secale cereale) during 2003/04. This
system is referred to as SW, or double CC system with "S" for summer cover crop
and "W" for winter cover crop mix.

2. Summer cover crop (sunn hemp) during 2003 + winter fallow 2003/04. With "SF"
referring to Summer cover crop and "F" in 2nd position to winter fallow.

3. Fallow during summer 2003+ winter cover crop mix (hairy and rye mix). This
systems is denoted as FW, with "F" for summer fallow and "W" for winter cover
crop mix.

4. Fallow + Fallow, denoted as FF, "F" for summer fallow and the second "F" for
winter fallow.

The following year, after the completion of 2003/04 summer sunn hemp, hairy vetch

+ rye mix, and spring sweet corn cycle, four different cropping systems were established.

Instead of one summer cover crop, three different summer cover crops were planted. During

the winter 2004/05, the hairy and rye mixed was planted again, but broccoli (Brassica

oleracea var "Pac Man") was also tested, replacing some of the winter fallow treatments.

Broccoli was used because it's potential as high value cash crop and moderate biomass

accumulation. During spring 2005 watermelon (Citrullus lanatus var. Mardigrass) was

included as nematode and weed sensitive high value crop instead of sweet corn.

Watermelon was proceeded by:

1. Summer pearl millet (Pennisetum glaucum var. Tifleaf) in 2004 + winter broccoli
2004/05.This system was called PM+B, "PM" for pearl millet and "B" for broccoli.

2. Summer cowpea (Vigna unguiculata var. Zipper Cream) in 2004 + winter broccoli
during winter 2004/05. This system was called CP+B, "CP" for cowpea and "B" for
broccoli.









3. Summer sesbania (Sesbania sesban) in 2004 + a winter cover crop mix of hairy vetch
and rye during 2004/05. The system was called SW, "S" for sesbania and "W" for the
winter CC crops).

4. Fallow during the summer 2004 + Fallow during the winter 2004/05. The systems
was denoted as FF or double fallow. Same as previous the system during the previous
year.

Sweet corn planted in CC residues received 0, 67 or 133 kg N ha-'(No, N67, and N133)

whereas sweet corn growing in double fallow received 0, 67 133, 200 or 267 kg N ha-1 (No,

N67, N133, N200, and N267). Broccoli was considered a commercial crop and was therefore

amended with 0, 131, or 196 kg N ha-1 fertilizer (No, N131, and N196). The CC-based

watermelon systems received either 0, 84, or 168 kg inorganic N ha-1 (No, N84, and N168)

while double fallow plots received either 0, 84, 126, 168, or 210 kg N ha-1 (No, N84, N126,

N168, and N210).

Timeline of Operations

2004

Sweet corn was planted on 14 April 2004, following summer sunn hemp and winter

hairy vetch and rye mix (22 and 56 kg seed ha-1, respectively). Planting was done by a rip-

strip planter, with in-row spacing of 0.18 m and between-row spacing of 0.76m (73,100

plants ha-1) and seeds were planted 30 mm deep. Sweet corn emerged on 21 April, 2004.

For each N fertilizer rate (N-rate) 20, 40, and 40% of the total doses were applied to sweet

corn at 1, 3, and 7 wk after emergence (WAE), respectively. Fertilizer was applied as

NH4N03 for all cropping systems. Plant biomass was determined on WAE 2, 4, 6, and 9

while final harvest occurred at 10 WAE. After final harvest, sweet corn was mowed and

Glyphosate 41% (Roundup Ultra, Monsanto Company, D.C., at a rate of 1.2 L ha-1) was

applied to all plots on July 6th of 2004.









2004-05

A total of 24 out of 56 cropped plots were planted with broccoli on November 1st

2004, following either summer pearl millet or cowpea (Refer to Chapter 2). Plant spacing

was 0.3 m x 1.0 m (3,333 plants ha-1). The remaining plots were planted with a mix of hairy

vetch and rye (56 and 22 kg seed ha-1, respectively) which were planted on October 28th of

2004 and exterminated on March 22nd of 2005. Gaps were replanted 1 and 2 wk after initial

planting. For each N fertilizer level, 25.0, 37.5 and 37.5% of total N doses were applied to

broccoli at 1, 6 and 9 wk after the initial transplanting (WAT). Biomass samplings was

determined at 3, 6, 9, 13, 16 and 19 WAT and plots were harvested at 6, 8 and 11 WAT .

Broccoli plots were sprayed on March 23rd of 2005 with Glyphosate Isopropylamine

Salt 41% (Roundup Original, Monsanto Company, D.C.) at a rate of 5.0 L ha-1.

Hairy vetch-rye plots were strip-tilled on March 22nd of 2005 but no herbicides were

applied before planting watermelon seedlings. Holes for the watermelon transplants were

placed into the tilled strips. Watermelon was planted on April 4th of 2005, at a plant spacing

of 1.52 m x 1.22 m (5,405 plants ha-1). Gaps were replanted within a week after initial

transplanting. Nitrogen fertilizer was split into three doses (25, 37.5, and 37.5%) applied at

1, 4, and 9 WAT.

Biomass samplings were collected at 3, 6, 9, and 12 WAT and watermelon fruits were

harvests at 11 and 13 WAT. Watermelon plants were mowed and sprayed with Dicamba

dimethylamine (Banvel, Micro Flow, Memphis, TN) at a rate of 1.22 L ha-1, Ammonium

Sulfate 50%, and Glyphosate 53.6% (Durango, Dow AgroScience, Indianapolis, IL) at a

rate of 3.7 L ha-1 on July 12th of 2005, with Dicamba dimethylamine and Glyphosate 53.6%

(at a rate of 2.4 L ha-1) on July 21st of 2005, and with Paraquat dichloride 43.8%









(Gramoxone Max, Syngenta Crop Protectionat, Greensboro, NC, a rate of 3.7 L ha-1 on July

29th of 2005 prior to planting subsequent summer cover crops.

Sampling Procedures

2004

Sweet corn plant counts were determined 1 wk after emergence. Biomass samples

were obtained outside the inner area (4.6 x 4.6 m) used for yield sampling but away from

plot edges (same as for the other two crops), using a representative 0.91 m of row length

(0.69 m2). In order to minimize disturbance, the root systems of one representative plant

was carefully excavated to assess root weights while all other plants were clipped at ground

level. Clipped plants were weighed and kept refrigerated until further processing in the

Agronomy Physiology Laboratory in Gainesville, FL. Final biomass samplings were taken

the day before harvesting ears (WAE=10). Ears were harvested at maturity from the inner

plot area (21.2 m2) and ears were graded using USDA standards (United States Department

of Agriculture, 1997) while representative sub-samples were kept for further growth and

tissue analysis.

2004-05

A row length of 0.61 m (0.61 m2) of broccoli was sampled using the procedure

outlined above. On 19 January 2005, diagnostic leaf samples were collected and analyzed

for leaf N concentration. Broccoli plots were harvested on January 14th and 16th (WAT=10

and 12) and February 11th (WAT=14) and yield was determined for the inner plot (6.1 m x

3.0 m = 18.3 m2). Broccoli crowns were graded in the field according to USDA standards

(United States Standards for Grades of Bunched Italian Sprouting Broccoli, 1997, USDA)

and a representative harvest sub-sample was used for growth and tissue analysis. .









One representative plant from the outer watermelon rows was selected at each

sampling and samples were processed using procedures outlined above. Due to the viny

nature of the crop delimitation of a net fruit-harvest-area was not feasible and the entire

experimental unit (plot), approximate 69.7 m2 was harvested instead. On 2 and 25 June

2005, chlorophyll and petiole sap nitrates readings of diagnostic leaves were determined

using a Minolta SPAD-502 and Horiba Cardy NO3-meter (Spectrum Technologies;

Plainfield, IL).

Mature fruits were picked on 23 June (WAT=11) and 5 July 2004 (WAT=13) and fruits

were graded using standard procedures (United States Standards for Grades of

Watermelons, 1997, USDA). Representative fruit samples were used for dry matter

determination and N analysis.

Sample Processing

Weed and/or organic debris were removed at the lab before recording tissue fresh

weight. Samples were separated in shoots, roots and reproductive parts (inflorescence or

"crowns" in the case of broccoli, fruits in the case of watermelon, and ears for sweet corn

were included in the shoot tissue. If shoot samples weight exceeded approx. 1000 g, a

representative sub-samples was used for DW determination. Roots were rinsed with tap

water and blotted before recording fresh weights. In the case of watermelon, fruits were cut

into small pieces and processed to a slurry using a blender (brand, model, location

manufacturer). Approximately, 100 g of the sub-sample liquid was decanted into a

graduated beaker; fresh weight was recorded and then set to dry at 500 C for more than 96

hours.

Shoot and root tissues of all other tissues were dried for a minimum of 72 hr at 65 C

before recording dry weights. Dried tissue material was ground in a Wiley mill to pass









through a 1-mm screen, and a thoroughly mixed 10 g portion of each grinding was

subsequently stored. Ground samples were digested using a modification of a procedure

developed by Gallaher et al. (1975) and diluted samples were then analyzed for total

Kjeldahl N (TKN) at the UF Analytical Research Laboratory (University of Florida,

Gainesville, FL) using EPA Method 351.2 (Jones and Case 1991).

Nitrogen Applied to Crops

Nitrogen applied (NA) to corn and watermelon was calculated as follows: NAx =

Chemical-Nx + Residue-Nx; where Chemical-Nx = N applied as NH4NO3 to corn in plot "x"

and Residue-Nx 0.2 total N content summer CC (based on N decomposition curves by

Cherr, 2004) + N content winter CC at last sampling + winter weeds, prior to planting.

Nitrogen-uptake efficiency (NUE) was calculated as: NUEx = (Total N Contentx -

Total N Content FFo) / NACx; where Total N Contentx = TKN present in total spring crop

biomass in plot "x" and Total N Content FFo= average TKN present in total crop biomass of

FFo treatment.

Unaccounted applied N (UAN) was calculated as: UANx = NACx Total N Contentx.

Statistical Analysis

Growth data were recorded in datasheets, organized, and standardized to a per hectare

basis using EXCEL (Microsoft Corporation, Los Angeles, CA). SAS software (Statistical

Analysis Systems, Cary, NC ) was used for statistical analysis. Since plant growth was

correlated over time covariancee), the "Proc Mixed" procedure in SAS was utilized.

Response variables tested included dry matter (DM) accumulation (Mg ha-1), tissue N

concentration (g N kg-1), crop N accumulation (kg N ha-1), NUE, NAP, UAN, and yield (kg

ha-1) The main fixed effects used in the model were sampling time (ST), N-rate and

cropping system (CS). Additional interactions effects included in the model were ST*N-









rate, ST*CS, and N-rate*CS. Linear, quadratic and cubic trends were tested for sampling

time and N-rate, whenever this was appropriate.

Random variation was attributed to plots (replicates*block) and replicates (blocks).

Mean separation was performed by the Tukey's T- statistic (p < 0.05). Yield models did not

include the time component, but included all the other parameters, and in this case the "Proc

GLM" function in SAS was used for the analysis of variance. For the statistical analysis of

broccoli data, the term "CS" was substituted by "RES" the residue material of the preceding

summer cover crop. To test the selected hypotheses, pair-wise comparisons were performed

for different yield categories, dry matter accumulation, N accumulation, SPAD readings,

NUE, and UAN for pertinent treatments.

Yield response of sweet corn and watermelon systems that did not include cover crops

for different fertilizer rates was assessed to test for significant trends were fitted with

appropriate regression equations using a regression function (Proc Reg) in SAS and both

significance level and model fit (r 2) are briefly discussed in the results section. Linear

plateau yield response functions were developed for chlorophyll readings for sweet corn and

watermelon and for leaf N concentration in broccoli using Proc Nlin of SAS.

Results

Sweet Corn (Spring 2004)

During the spring 2004 season, average N derived from residues and weeds was

greatest for SW (181 kg N ha-1) and FW (141 kg N ha-1), intermediate for SF (55 kg N ha-)

and lowest for FF (18 kg N ha-1; Table D-1). The sum of N derived from residues and

weeds across systems were 100, 170, and 228 kg N ha-1 for the No, N67, and N133 treatments,

respectively.









Sweet corn growth

Sweet corn shoot dry matter (DM) content, N concentration, and total N accumulation

increased cubically over time, while N concentration was lowest at the end of the season

(Table 3-2). Maximum DM and N accumulation occurred at 6 WAE corresponding to daily

DM and N accumulation rates of 266 and 3.2 kg ha-1 d1, respectively. Overall DM and

SPAD values increased quadratically with N rate, while N concentration and N content

showed a linear response. Overall growth and N accumulation was highest for the SW

system, while the FF and SF treatments had the lowest N concentrations and SPAD

readings.

The ST*N-rate interaction term was significant for all response variables with

differences between N rates typically becoming more evident over time (Table D-2).

Towards the end of the growing season, DM content, N concentration, and N accumulation

were 54, 30, and 65% higher for the first N-fertilizer increment (No -- N67). Corresponding

values for the second N-fertilizer increment (N67 -- N133) were 22, 26, and 43%.

Based on the ST*CS interaction term, it appears that effects of CS systems generally

became more pronounced over time (Table D-3). The SW system had significantly greater

DM content and numerically higher N concentrations thus resulting in augmented N

accumulation (28% higher than FF) by the end-of-season (Table D-3).

The N-rate*CS interaction effect was significant for all parameters except for N

concentration (Table 3-2). Analysis of the end-of-season N-rate*CS interaction term

showed that for double cropping system either N67 or N133 performed best, while the FF

system was typically inferior to CC-based systems at lower (No and N67) N rates (Table 3-

3). Pair wise comparisons allowed more detailed evaluation of DM and N content

differences across cropping systems and N rates that were of special relevance (Table 3-4).









This analysis showed that DM and N content was the same for FF200 and FF267. By the end

of the season, DW content for SW133 and SF133 were also similar DW compared to FF200

and FF267. Overall N concentration in shoots was the same for SW133, WFN33, FF200 and

FF267. Shoot DM content, N concentration and N content was lower for CC-based systems

amended with 67 kg N ha-1 compared to treatments receiving higher N rates.

Overall daily N uptake varied in different systems was affected by N-rate. Maximum

daily N uptake was reached by SW133 with 8 kg N d-1 ha-1. For all treatments and all N-rates

N daily uptake dropped off after 6 WAE. Daily N uptake for SW133 was higher than FF200.

Sweet corn yield

A non-linear model fitted to SPAD values for diagnostic leaf tissue testing showed

that the critical chlorophyll content for maximum yield was 56.8 4.5 (Table D-6). Overall

yield and dry weight increased linearly with N rate (Table 3-5). Overall yields and DM

content tended to be highest for the double CC (SW) system and the summer fallow-winter

CC (FW) system, intermediate for the summer CC-winter fallow (SF) system, and lowest

for the summer + winter fallow (FF) system. However, the interaction between the CS and

N-rate affected all yield categories (Table 3-6). Although in all cases there was a significant

response to each N-rate increment, differences among cropping systems (CS) became less

pronounced as N rate increased (Table D-4).

Use of pair-wise contrast also allowed comparisons between CC-based systems with

FF treatments receiving highest (200-267 kg N ha-1) N rates (Table 3-6). Despite the fact

that SW133 treatment produced the highest total and marketable yield among the cover crop

treatments, its yield was only 8% higher. Similarly, the productivity of SW133 was

comparable to FF200, but still 18 % lower than FF267 yields while the FF267 treatment had

10% higher yields than FF200.









When comparing the FS133 system against treatment FF133, benefits from CC residues

ranged between 8-17% and 3-10 %, for total and marketable yields, respectively. The

double cropping system SW67 produced 41 and 34% lower yield than FF200 and FF267. Low

marketable yields were also obtained with treatment FW67 (46% and 52% less than FF200

and FF267, accordingly). Even lower yields were obtained with treatment SF67 (60 and 64%

lower yield than FF200 and FF267 respectively).

Based on calculated N use efficiency (NUE) values at harvest, it appears that among

the selected treatments, the most effective N use was achieved by treatment FF133

(NUE=0.77). For the FF systems, NUE decreased with N rate, while for the CC-based

systems, the reverse appears to be true. Among the CC-based systems, SF133 appeared to be

the most efficient (NUE=0.48). Overall NUE for other CC-based systems was comparable

to treatment FF267. In general, cover cropping systems including a hairy vetch and rye mix

tended had NUE as low as those for the FF267 treatment and relatively high corresponding

un-utilized applied nitrogen (UAN) values. The N response model developed for

conventional (FF) system showed that a majority of the variability in yield difference was

related to fertilizer applications (Table 3-7). Cubic fit of the models was good for total and

marketable yield prediction (r2= 0.97).

Broccoli (Fall 2004)

Broccoli received 80 kg N ha-1 vs 63 kg N ha-1 from cowpea (CP) and pearl millet

(PM) residues (Table D-7). When adding up both N fertilizer and N derived from cover

crop residues, broccoli received 189 and 172 kg N ha-1 from CP and PM, respectively. The

total amount of N coming from cover crops (averaged across systems) and N-fertilizer was

72, 133, and 265 kg N ha-1 for the No, N131, and N196 treatments, respectively.









Broccoli growth

Root and crown DM content followed a cubic trend, while shoot growth increased

quadratically over time (Table 3-8). Both roots and shoots reached maximum DM content at

16 WAT (2.9 Mg ha-1). The 21% decline in crown N concentration between WAE 9 and 19

was relatively low compared to the 54-57% decrease in roots and shoots concentration.

Maximum shoot N accumulation occurred at WAT 9.

Crown and shoot DM content increased quadratically with N-rates, while roots

showed a linear response. Nitrogen content increased cubically for roots, while shoot and

crown N concentrations showed a linear response to N rate. Shoot N accumulation leveled

off prior to the first harvest (WAT 9), but DM and N content of roots increased up to WAT

16. Shoot and crown N concentration increased linearly with N rate, while for root tissue

this increase was quadratically. Shoot N accumulation was highest for the N196 treatment,

while N content of roots and crowns were similar for N131 and N196 treatments.

In general CP-based systems had 23, 23, and 27% greater root, shoot, and crown DM

content compared to PM-based systems. Although N concentrations where similar, plants in

CP-based systems also had 18-21% higher N content.

Overall, growth showed differential responses to N-rate over time as indicated by

significant ST*N-rate interaction effects (Table 3-8). By the end of the season, shoot DM

content was lowest for No and corresponding reductions in comparison with N131 were 58,

66, and 60% less for roots, shoots and crowns, respectively (Table D-7) starting at WAT 9.

In general, DM content was similar for both N131 and N196 treatments (Table D-8). Towards

the end of the growing season, tissue N concentrations in shoots showed an incremental

increase up to 196 kg N ha-1, while root and crown N concentration were not affected by N-









rate. Overall differences between N-rate treatments were greatest at WAT 6 and 9,

coinciding with previous N-fertilizer applications.

The significant ST*RES interaction effect on shoot DM content (Table 3-8) was

related to CP-based systems having 43% greater shoots DM content at the end of the season

(Table D-9).

When comparing shoot DM accumulation for specific systems across N-rates via pair

wise comparisons, it was observed that non-fertilized treatments for CP-based systems

accumulated 45% more shoots than PM-based systems (Table 3-9). At the same time, CP196

and PM196 had similar shoot DM content, while CP131 produced as much biomass as CP196.

By the end of the season, the only treatment that had a different performance was CP196,

with a 33% higher DM content than PM. Tendencies in N content at final sampling were

not representative of overall dynamics across the growing season. Most of the N benefits

from residual cover crops occurred during initial growth (WAT 3) in lower N-rates (No and

N131). Throughout the season, N concentrations for CP at No and N131 were lower than CP196

(Table 3-9).

Weekly N uptake rates based on crop N accumulation showed two distinct peaks in N

uptake rates (Figure 3-2). The first peakoccurring at WAT= 6 and a subsequent one toward

the end of the growing season (WAT=16) were associated with the bolting of the crop.

Overall maximum N uptake rates were on the order 3 kg N ha-1 d1, which is low compared

to sweet corn.

Broccoli yield

Broccoli yield was separated into fresh market and process market and further divided

into marketable and non-marketable (culls). Based on diagnostic leaf N concentration, it