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Adapting the CROPGRO Legume Model to Simulate Growth and Fresh Market Yield of Snap Bean

Permanent Link: http://ufdc.ufl.edu/UFE0022741/00001

Material Information

Title: Adapting the CROPGRO Legume Model to Simulate Growth and Fresh Market Yield of Snap Bean
Physical Description: 1 online resource (174 p.)
Language: english
Creator: Djidonou, Desire
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: bean, cropgro, florida, model, simulation, snap
Agronomy -- Dissertations, Academic -- UF
Genre: Agronomy thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Production of snap bean (Phaseolus vulgaris L.) faces various challenges with respect to crop management and decision-making throughout the growing season. As a vegetable grown for fresh market, commercial snap bean production is a trade-off between yield and quality. Crop growth models can be used to address many of the crop production management by increasing our understanding of crop growth, development and yield in relation to weather, soil, and management practices. However, existing simulation models that predict production on a dry matter basis have limited capability in addressing production of crops such as snap bean which are primarily grown for fresh market. The purpose of this study was therefore to develop a snap bean simulation model to predict the fresh market yield and quality of pods as affected by irrigation and nitrogen (N) levels. Snap bean cultivar Ambra was grown in a field study under three irrigation regimes (66, 100 and 133% of crop ET) and four N levels (37, 74, 111 and 148 kg ha-1) as sub-factors split within the irrigation regimes. Data were collected on crop growth and development, single pod growth and quality, fresh market yield, and N uptake. Weekly measurements of growth and development included canopy height and width, plant growth stages, leaf area index, pod and seed number, pod and seed fresh weight, and dry weight of plant components. For the single pod growth and quality aspects, 2-cm pods were tagged at 10 days after anthesis, and per-pod measurements were taken at 3-day intervals for pod sieve size, pod length, pod diameter, pod fresh weight, pod dry weight, number of seed, seed fresh weight and seed dry weight. For the final yield estimation, an area of 2.44 m2 was harvested and fresh weight of marketable pods and unmarketable pods categories were recorded. Using these data, a computer experiment was created in DSSAT Version 4.5 using one dry bean cultivar (erect, determinate plant type). Following a systematic approach, parameters of the species, ecotype and cultivar files (of the model) were calibrated to best fit the life cycle, dry matter accumulation, yield and yield components of snap bean. The data were also used to develop functional algorithms of pod dry matter concentration versus thermal time and pod size versus pod fresh weight and were introduced into the calibrated CROPGRO model to simulate fresh market yield and physical quality of snap bean. The interaction of irrigation and N was significant on fresh market production of snap bean. There was no yield benefit with N-rates over 111 kg ha-1 (IFAS recommended N rates for snap bean) at low or medium irrigation. However, at high irrigation (133% ET), increasing N fertilizer from 37 to 148 kg N ha-1 substantially increased fresh market yield. With calibration of genetic coefficients and addition of the fresh weight module, the CROPGRO Dry bean model had adequate capabilities to predict the life cycle, biomass accumulation, yield components, as well as fresh market yield and pod quality of snap bean over time. Pod dry matter concentration was well predicted but fresh market yield was somewhat under-predicted in late season at low N treatment. Simulation of pod sieve sizes and pod diameter which define pod quality was acceptable. However, there is a need to further examine the functional relationships between fresh market variables as well as soil N supplying and water balance aspects in order to improve simulation capability of the model.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Desire Djidonou.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: Boote, Kenneth J.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022741:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022741/00001

Material Information

Title: Adapting the CROPGRO Legume Model to Simulate Growth and Fresh Market Yield of Snap Bean
Physical Description: 1 online resource (174 p.)
Language: english
Creator: Djidonou, Desire
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: bean, cropgro, florida, model, simulation, snap
Agronomy -- Dissertations, Academic -- UF
Genre: Agronomy thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Production of snap bean (Phaseolus vulgaris L.) faces various challenges with respect to crop management and decision-making throughout the growing season. As a vegetable grown for fresh market, commercial snap bean production is a trade-off between yield and quality. Crop growth models can be used to address many of the crop production management by increasing our understanding of crop growth, development and yield in relation to weather, soil, and management practices. However, existing simulation models that predict production on a dry matter basis have limited capability in addressing production of crops such as snap bean which are primarily grown for fresh market. The purpose of this study was therefore to develop a snap bean simulation model to predict the fresh market yield and quality of pods as affected by irrigation and nitrogen (N) levels. Snap bean cultivar Ambra was grown in a field study under three irrigation regimes (66, 100 and 133% of crop ET) and four N levels (37, 74, 111 and 148 kg ha-1) as sub-factors split within the irrigation regimes. Data were collected on crop growth and development, single pod growth and quality, fresh market yield, and N uptake. Weekly measurements of growth and development included canopy height and width, plant growth stages, leaf area index, pod and seed number, pod and seed fresh weight, and dry weight of plant components. For the single pod growth and quality aspects, 2-cm pods were tagged at 10 days after anthesis, and per-pod measurements were taken at 3-day intervals for pod sieve size, pod length, pod diameter, pod fresh weight, pod dry weight, number of seed, seed fresh weight and seed dry weight. For the final yield estimation, an area of 2.44 m2 was harvested and fresh weight of marketable pods and unmarketable pods categories were recorded. Using these data, a computer experiment was created in DSSAT Version 4.5 using one dry bean cultivar (erect, determinate plant type). Following a systematic approach, parameters of the species, ecotype and cultivar files (of the model) were calibrated to best fit the life cycle, dry matter accumulation, yield and yield components of snap bean. The data were also used to develop functional algorithms of pod dry matter concentration versus thermal time and pod size versus pod fresh weight and were introduced into the calibrated CROPGRO model to simulate fresh market yield and physical quality of snap bean. The interaction of irrigation and N was significant on fresh market production of snap bean. There was no yield benefit with N-rates over 111 kg ha-1 (IFAS recommended N rates for snap bean) at low or medium irrigation. However, at high irrigation (133% ET), increasing N fertilizer from 37 to 148 kg N ha-1 substantially increased fresh market yield. With calibration of genetic coefficients and addition of the fresh weight module, the CROPGRO Dry bean model had adequate capabilities to predict the life cycle, biomass accumulation, yield components, as well as fresh market yield and pod quality of snap bean over time. Pod dry matter concentration was well predicted but fresh market yield was somewhat under-predicted in late season at low N treatment. Simulation of pod sieve sizes and pod diameter which define pod quality was acceptable. However, there is a need to further examine the functional relationships between fresh market variables as well as soil N supplying and water balance aspects in order to improve simulation capability of the model.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Desire Djidonou.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: Boote, Kenneth J.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022741:00001


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1 ADAPTING THE CROPGRO LEGUME MODEL TO SIMULATE GROWTH AND FRESH MARKET YIELD OF SNAP BEAN By DESIRE DJIDONOU 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 2008

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2 2008 Desire Djidonou

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3 ACKNOWLEDGMENTS I am greatly indebted to my advisor, Dr. Kenneth J. Boote, for his constant guidance, insight, encouragement, and continuous support. I would also like to thank Dr. Boote for providing a research assistantship to fund my M.S. program at the University of Florida. I would like to express my deep appreciation to Dr. James Jones, Dr. Jerry Bennett, and Dr. Eric Simonne for serving on my committee, contributing practical discussions, and providi ng their viewpoints on different problems and concerns. Thanks also go to Mr. Jason Hupp and Mrs. Susan Sorrell for their constant help during the field experimentation and sampling process. Without their tireless effort s, the field study would have been very difficult. Thanks also go to Dr. Jon Lizaso and Mrs. Cheryl Porter for their help in the model development process, particularly in developing relationships and outputs of pod cohorts.

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4 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................3 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................9 ABSTRACT...................................................................................................................................13 CHAP TER 1 INTRODUCTION..................................................................................................................15 2 LITERATURE REVIEW.......................................................................................................19 Introduction................................................................................................................... ..........19 Snap Bean Growth and Development..................................................................................... 19 Root Growth....................................................................................................................19 Plant Growth....................................................................................................................20 Flowering, Pod Set and Developm ent............................................................................. 23 Pod Quality.................................................................................................................... ..25 Crop Production................................................................................................................ ......26 Production Systems......................................................................................................... 26 Irrigation..........................................................................................................................27 Plant Nutrition.................................................................................................................31 Nitrogen Uptake..............................................................................................................33 Nitrogen Use Efficiency..................................................................................................35 Nitrogen Leaching...........................................................................................................35 Crop Modeling........................................................................................................................37 CROPGRO Model and Mode l Adaptation Approach .....................................................38 Previous Efforts of Modeling Horticultural Crops and Quality......................................41 3 GROWTH AND NITROGEN UPTAKE OF SNAP BE AN IN RESPONSE TO NITROGEN FERTILIZATION............................................................................................. 44 Introduction................................................................................................................... ..........44 Materials and Methods...........................................................................................................45 Cultural Practices.............................................................................................................45 Experimental Treatments................................................................................................. 46 Measurements.................................................................................................................. 48 Crop growth analysis................................................................................................ 48 Plant tissue nitrogen analyses................................................................................... 49 Data Analysis...................................................................................................................50 Results and Discussion......................................................................................................... ..50 Effects of N Fertilizer Ra tes on Canopy Characteristics .................................................50

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5 Effects on Leaf Area Index..............................................................................................51 Effects on Biomass Accumulation..................................................................................52 Effects on Plant Organ Mass and Distribution................................................................ 54 Effects of Nitrogen Supply on Nitrogen Uptake.............................................................55 Plant tissue nitrogen concentration.......................................................................... 55 Nitrogen accumulation by snap bean plants............................................................. 56 Effects of N supply on nitrogen distribution............................................................ 57 Conclusion..............................................................................................................................58 4 RESPONSES OF SNAP BEAN TO INTERACTIVE EFFECTS OF IRRIGATION AND NITR OGEN FERTILIZATION: YIELD, YIELD COMPONENTS AND QUALITY...............................................................................................................................74 Introduction................................................................................................................... ..........74 Materials and Methods...........................................................................................................75 Field Experiments............................................................................................................75 Final Yield Estimation..................................................................................................... 75 Plant Tissue Nitrogen Analyses....................................................................................... 76 Nitrogen in the Soil Profile.............................................................................................. 77 Data Analysis...................................................................................................................77 Results and Discussion......................................................................................................... ..78 Canopy Characteristics at Harvest Maturity.................................................................... 78 Fresh Marketable Yield, Crop Biomass, and Pod Harvest Index at Harvest Maturity ... 78 Yield Components and Pod Quality Parameters............................................................. 81 Total Nitrogen Accumulated and Analysis of Water and Nitrogen Use Efficiency for Snap Bean at Harvest .............................................................................................83 Seasonal Variations of Nitrate and Amm onium Contents in the Soil Profile................. 84 Conclusion..............................................................................................................................87 5 ADAPTING THE CROPGRO-DRY BEAN MO DEL TO SIMULATE THE GROWTH AND DEVE LOPMENT OF SNAP BEAN ( Phaseolus vulgaris L)......................................98 Introduction................................................................................................................... ..........98 Materials and Methods...........................................................................................................99 Snap Bean Field Experiments.........................................................................................99 Model Calibration..........................................................................................................100 Soil profile properties calibration........................................................................... 100 Approach for genetic coefficients calibration........................................................ 101 Crop life cycle........................................................................................................103 Dry matter accumulation and LAI......................................................................... 103 Yield and yield components................................................................................... 103 Results and Discussion......................................................................................................... 105 Predictions with Unmodi fied Model Param eters.......................................................... 105 Life Cycle and Canopy Growth.....................................................................................107 Biomass Accumulation and LAI................................................................................... 109 Timing of Pod Growth................................................................................................... 110 Distribution of Dry Matter to Leaf, S tem, Pod, and Seed............................................. 112

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6 Simulation of Nitrogen Accumulation in the Plant....................................................... 113 Conclusion............................................................................................................................115 6 DEVELOPING A SNAP BEAN SIMULATION MODEL TO PREDICT FRESH MARKET YIELD AND QUALITY OF PODS ................................................................... 129 Introduction................................................................................................................... ........129 Materials and Methods.........................................................................................................130 Model Structure.............................................................................................................130 Model Development......................................................................................................131 Model development data........................................................................................ 131 Dry matter concentration (DMC) and pod fresh weight........................................ 132 Pod quality.............................................................................................................. 132 Results and Discussion......................................................................................................... 133 Tagged Pod and Seed Dry Mass Simulation................................................................. 133 Tagged Pod DMC Simulation.......................................................................................134 Tagged Pod Fresh Weight Simulation........................................................................... 135 Total Pod Dry Matter Concentration Simulation.......................................................... 135 Total Pod Fresh Weight Simulation.............................................................................. 136 Pod Size Simulation......................................................................................................137 Simulation of the Interactive Effect of Irriga tion and Nitrogen Rates on Different Crop Variables...........................................................................................................139 Conclusion............................................................................................................................141 7 SUMMARY AND CONCLUSIONS...................................................................................151 Snap Bean Growth Study...................................................................................................... 152 Snap Bean Yield and Pod Quality Study.............................................................................. 153 CROPGRO Snap Bean Model Development Study............................................................. 154 Implications of the Research and Future Work.................................................................... 157 APPENDIX: EFFECT OF N TREATMENTS ON S NAP BEAN CR OP DRY MATTER ACCUMULATION, N CONCENTRAT ION AND N ACCUMULATION....................... 158 LIST OF REFERENCES.............................................................................................................162 BIOGRAPHICAL SKETCH.......................................................................................................174

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7 LIST OF TABLES Table page 3-1 Irrigation amounts (mm) and dates of a pplication of the three water m anagement treatments..................................................................................................................... ......60 3-2 Amounts and dates of N application of the four nitrogen treatm ents................................ 60 3-3 Probability levels (P) for the effects of nitrogen rate (N) and interaction (N*DAS) on canopy characteristics ........................................................................................................ 61 3-4 Probability levels (P) for the effects of nitrogen rate (N) and interaction (N*DAS) on plant m ass variables........................................................................................................... 61 3-5 Probability levels (P) for the effects of nitrogen rate (N) and interaction (N*DAS) on plant organ N concentrations ............................................................................................. 61 3-6 Probability levels (P) for the effects of nitrogen rate (N) and interaction (N*DAS) on total plant N m ass and plant organ N mass........................................................................ 61 4-1 Effects of irrigation and N fertilizer on canopy characterist ics of snap bean grown in Gainesville during spring 2007 at 64 DAS. ....................................................................... 89 4-2 Effects of irrigation and N fertilizer on fresh m arketab le yield, crop biomass and pod harvest index of snap bean grown in Ga inesville during spring 2007 at 64 DAS............. 90 4-3 Quadratic model regression equations for snap bean fresh marketable yield response (y, Mg ha-1) to fertilizer N rates (x, kg ha-1) under different irrigation regimes in Gainesville in spring 2007................................................................................................. 91 4-4 Effects of irrigation and N fertilizer on yield co mponents of snap bean grown in Gainesville during spring 2007 at 64 DAS........................................................................ 91 4-5 Effects of irrigation and N fertilizer on fresh m arket pod quality of snap bean grown in Gainesville during spring 2007......................................................................................92 4-6 Effects of irrigation and N fertilizer on total crop N uptake, pod WUE and pod NUE of snap bean grown in Gainesville during spring 2007.....................................................92 4-7 Irrigation effects on nitrate movement in the soil profile for snap bean grown in Gainesville during spring 2007 .......................................................................................... 93 4-8 Irrigation effects on ammonium presence in the soil prof ile for snap bean grown in Gainesville during spring 2007.......................................................................................... 93 5-1 Soil profile characteristics of the M illhopper fine sand, (hypertherm ic family of Grossarenic Paleudults) used dur ing the calibration process........................................... 116

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8 5-2 Genetic coefficients of cultivar Ambra for the CROPGRO model, after the calibration process, com pared to ge neric Andean dry bean cultivar............................ 117 A-1 Pair wise comparison of the effects of sa mpling days (DAS) and N treatments on total shoot dry weight of snap bean, grown in Gainesville during spring 2007..............158 A-2 Pair wise comparison of the effects of sa mpling days (DAS) and N treatments on pod dry weight of snap bean, grown in Gainesville during the spring 2007..........................158 A-3 Pair wise comparison of the effects of sa mpling days (DAS) and N treatments on seed dry weight of snap bean, grown in Gainesville during the spring 2007..................159 A-4 Pair wise comparison of the effects of sa mpling days (DAS) and N treatments on leaf N concentration of snap bean, grown in Gainesville during the spring 2007.................. 159 A-5 Pair wise comparison of the effects of sa mpling days (DAS) and N treatments on pod N concentration of snap bean, grown in Gainesville during the spring 2007.................. 160 A-6 Pair wise comparison of the effects of sa mpling days (DAS) and N treatments on shoot N mass of snap bean, grown in Gainesville during the spring 2007...................... 160 A-7 Pair wise comparison of the effects of sa mpling days (DAS) and N treatments on pod N mass of snap bean, grown in Gainesville during the spring 2007................................ 161 A-8 Pair wise comparison of the effects of sa mpling days (DAS) and N treatments on seed N concentration of snap bean, grow n in Gainesville during the spring 2007..........161

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9 LIST OF FIGURES Figure page 3-1 Number of nodes formed on snap bean ve rsus therm al time as affected by four N fertilization rates in Gaines ville FL during Spring 2007...................................................62 3-2 Canopy height and canopy width of snap bean versus therm al time as affected by N fertilization rates in Gaines ville FL during Spring 2007...................................................62 3-3 Leaf area index of snap b ean over tim e as affected by f our N fertilization rates in Gainesville FL during Spring 2007.................................................................................... 63 3-4 Shoot dry matter of snap b ean over tim e as affected by f our N fertilization rates in Gainesville FL during Spring 2007.................................................................................... 64 3-5 Plant dry matter of leaf, stem, pod, and seed of snap bean over time as affected by four N fertilization rates in Ga inesville FL during Spring 2007 ........................................ 64 3-6 Percentage of total plant biomass found in leaf, stem pod, and seed of snap bean over time as affected by 37 kg N ha-1, 74 kg N ha-1, 111 kg N ha-1 and 148 kg N ha-1.....66 3-7 Effects of N-fertilizer rates on N concen tration of leaf, stem, pod, and seed of snap bean grown in Gainesville FL in spring 2007....................................................................69 3-8 Shoot N of snap bean over time as affected by four N fe rtilization rates in Gainesville FL during Spring 2007.................................................................................... 71 3-9 Effects of N-fertilizer rates on fractio n of plant N found in plant com ponents over time for: 37, 74, 111 and 148 kg ha-1 treatments of snap bean grown in Gainesville FL in spring 2007...............................................................................................................72 4-1 Response (quadratic polynomial) for fresh marketable yield of snap bean as affected by N rate under different irrigation regim e s in Gainesville FL during spring 2007..........94 4-2 Distribution of sieve size of snap bean at harvest as affected by four N rates in Low, Medium and High irrigation regimes in Gainesville during spring 2007.......................... 95 4-3 Cumulative irrigation and precipitation during the growi ng season of snap bean in Gainesville in spring 2007 ................................................................................................. 96 4-4 Movement of mineral N (NO3-N + NH4-N ) below the root zone ( in 60-120 cm depth) over time as affected by irrigation regimes on snap bean grown in Gainesville in spring 2007....................................................................................................................96 4-5 Cumulative mineral N (NO3-N + NH4-N) in the soil profile (0-120 cm) over time as affected by irrigation regimes on snap bean grown in Gainesville in spring 2007............97

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10 5-1 Default model simulated (lines) and observ ed (sy mbols) leaf area index as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007.................................................................................. 118 5-2 Default model simulated (lines) and ob served (symbols) shoot dry m atter as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007..............................................................................118 5-3 Default model simulated (lines) and obse rved (symbols) pod dry m atter as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007.................................................................................. 119 5-4 Default model simulated (lines) and observed (symbols) accumulated shoot N as a function of days after sowing for snap bean cultivar Am bra grown under four N rates in Gainesville FL during spring 2007..............................................................................119 5-5 Simulated (lines) and observed (symbols) m ain stem node number as a function of days after sowing for snap bean cultiv ar Ambra grown under four N rates in Gainesville FL during spring 2007.................................................................................. 120 5-6 Simulated (lines) and observed (symbols) canopy height as a function of days after sowing for snap bean cultivar Am bra grow n under four N rates in Gainesville FL during spring 2007...........................................................................................................120 5-7 Simulated (lines) and observed (symbols) canopy width as a function of days after sowing for snap bean cultivar Am bra grow n under four N rates in Gainesville FL during spring 2007...........................................................................................................121 5-8 Simulated (lines) and observed (symbols) shoot dry m atter as a f unction of days after sowing for snap bean cultivar Ambra grow n under four N rates in Gainesville FL during spring 2007...........................................................................................................121 5-9 Simulated (lines) and observed (symbols) leaf area index as a f unction of days after sowing for snap bean cultivar Am bra grow n under four N rates in Gainesville FL during spring 2007...........................................................................................................122 5-10 Simulated (lines) and observed (symbols) pod dry m atter as a function of days after sowing for snap bean cultivar Ambra grow n under four N rates in Gainesville FL during spring 2007...........................................................................................................122 5-11 Simulated (lines) and observed (symbols) pod harvest index as a function of days after sowing for snap bean cultivar Am br a grown under four N rates in Gainesville FL during spring 2007.....................................................................................................123 5-12 Simulated (lines) and observed (symbols) we ight per seed as a function of days after sowing for snap bean cultivar Am bra grow n under four N rates in Gainesville FL during spring 2007...........................................................................................................123

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11 5-13 Simulated (lines) and observed (symbols) shelling percentage as a function of days after sowing for snap bean cultivar Am br a grown under four N rates in Gainesville FL during spring 2007.....................................................................................................124 5-14 Simulated (lines) and observed (symbols) fraction of biom ass in plant organs as a function of days after sowing for snap bean cultivar Ambra grown under 37, 74, 111, and 148 kg N ha-1 rates in Gainesville FL during spring 2007........................................ 124 5-15 Simulated (lines) and observed (symbols) to tal plant N accumulation as a function of days after sowing for snap bean cultiv ar Ambra grown under four N rates in Gainesville FL during spring 2007.................................................................................. 127 5-16 Simulated (lines) and observed (symbols) N in vegetative parts (leaf and stem ) as a function of days after sowing for snap b ean cultivar Ambra grown under under four N rates in Gainesville FL during spring 2007.................................................................. 127 5-17 Simulated (lines) and observed (symbols) grain N as a function of days after sowing for snap bean cultivar A mbra grown under four N rates in Gainesville FL during spring 2007.......................................................................................................................128 6-1 A relationship diagram of the crop model used in the present study... ............................143 6-2 Progression of single pod dry matte r concentration versus therm al time (photothermal days) for pods tagged 10 Days After Anthesis (DAA) on snap bean cultivar Ambra grown under four N rate s in Gainesville FL during spring 2007........... 143 6-3 Pod diameter versus single pod fresh weight for pods tagged 10 DAA on s nap bean cultivar Ambra grown under four N rate s in Gainesville FL during spring 2007........... 144 6-4 Model simulated (lines) and observed (s ym bols) average pod dry weight per pod as a function of days after anthes is for pods tagged 10 days after anthesis (DAA) on snap bean cultivar Ambra grown under four N ra tes in Gainesville FL during spring 2007... 144 6-5 Model simulated (lines) and observed (sym bols) average seed dry m atter per seed as a function of days after anthesis for pods tagged 10 DAA on snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007......................... 145 6-6 Model simulated (lines) and observed (s ym bols) single pod dry matter concentration as a function of days after anthesis fo r pods tagged 10 DAA for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007......................... 145 6-7 Model simulated (lines) and observed (s ym bols) average pod fresh weight per pod as a function of days after anthesis for p ods tagged 10 DAA for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007......................... 146 6-8 Model simulated (lines) and observed (s ym bols) total pod dry matter concentration as a function of days after anthesis for sn ap bean cultivar Ambr a grown under four N rates in Gainesville FL during spring 2007...................................................................... 146

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12 6-9 Model simulated (lines) and observed (s ym bols) total fresh market pod yield as a function of days after anth esis for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007...................................................................... 147 6-10 Model simulated (lines) and observed (sym bols) single pod diam eter as a function of days after anthesis for pods tagged 10 DAA on snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007................................................ 147 6-11 Model simulated (lines) and observed (s ym bols) single pod sieve size as a function of days after anthesis for pods tagged 10 DAA on snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007................................................ 148 6-12 Cumulative progression of total pod fresh weight and fresh pod weight in pod sieve sizes 3 and 4 as a function of days after an thesis for snap bean cultivar A mbra grown under N rate of 148 kg ha-1 in Gainesville FL during spring 2007..................................148 6-13 Mode simulated (lines) and observed (s ym bols) shoot dry weight of snap bean cultivar Ambra at fresh harvest date (64 DAS) as affected by N rates under low, medium, or high irrigation regimes in Gainesville FL during spring 2007..................... 149 6-14 Model simulated (lines) and observed (sym bols) pod dry weight of snap bean cultivar Ambra at fresh harvest date (64 DAS) as affected by N rates under low, medium, or high irrigation regimes in Gainesville FL during spring 2007..................... 149 6-15 Model simulated (lines) and observed (s ym bols) pod fresh weight of snap bean cultivar Ambra at fresh harvest date (64 DAS) as affected by N rates under low, medium, or high irrigation regimes in Gainesville FL during spring 2007..................... 150 6-16 Model simulated (lines) and observed (s ym bols) fresh pod diameter of snap bean cultivar Ambra at fresh harvest date (64 DAS) as affected by N rates under low, medium, or high irrigation regimes in Gainesville FL during spring 2007..................... 150

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13 Abstract of Thesis Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Master of Science ADAPTING THE CROPGRO LEGUME MODEL TO SIMULATE GROWTH AND FRESH MARKET YIELD OF SNAP BEAN By Desire Djidonou December 2008 Chair: Kenneth J. Boote Major: Agronomy Production of snap bean ( Phaseolus vulgaris L.) faces various chal lenges with respect to crop management and decision-making throughout the growing season. As a vegetable grown for fresh market, commercial snap bean production is a trade-off between yield and quality. Crop growth models can be used to address many of the crop production management by increasing our understanding of crop growth, development a nd yield in relation to weather, soil, and management practices. However, existing simula tion models that predict production on a dry matter basis have limited capability in addressing production of crops such as snap bean which are primarily grown for fresh market. The purpose of this study was therefore to develop a snap bean simulation model to predict the fresh mark et yield and quality of pods as affected by irrigation and nitrogen (N) levels. Snap bean cultivar Ambra was grown in a field study under three irrigation regimes (66, 100 and 133% of crop ET) and four N levels (37, 74, 111 and 148 kg ha-1) as sub-factors split within the irrigation regimes. Data were collected on crop growth and development, single pod growth and quality, fresh market yield, and N uptake. Weekly measurements of growth and development included canopy height and width, plant growth stages, leaf area index, pod and se ed number, pod and seed fresh we ight, and dry weight of plant components. For the single pod growth and quality aspects, 2-cm pods were tagged at 10 days

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14 after anthesis, and per-pod measurements were taken at 3-day intervals for pod sieve size, pod length, pod diameter, pod fresh wei ght, pod dry weight, number of seed, seed fresh weight and seed dry weight. For the final yi eld estimation, an area of 2.44 m2 was harvested and fresh weight of marketable pods and unmar ketable pods categories were recorded. Using these data, a computer experiment wa s created in DSSAT Version 4.5 using one dry bean cultivar (erect, determ inate plant type). Following a syst ematic approach, parameters of the species, ecotype and cultivar files (of the model) were calibrated to best fit the life cycle, dry matter accumulation, yield and yield components of snap bean. The data were also used to develop functional algorithms of pod dry matter concentration versus thermal time and pod size versus pod fresh weight and were introduced in to the calibrated CROPGRO model to simulate fresh market yield and physical quality of snap bean. The interaction of irrigation and N was signi ficant on fresh market production of snap bean. There was no yield benef it with N-rates over 111 kg ha-1 (IFAS recommended N rates for snap bean) at low or medium irrigation. Howe ver, at high irrigation (133% ET), increasing N fertilizer from 37 to 148 kg N ha-1 substantially increased fresh market yield. With calibration of genetic coefficients and additi on of the fresh weight module, the CROPGRO Dry bean model had adequate capabilities to predict the life cy cle, biomass accumulation, yield components, as well as fresh market yield and pod quality of sn ap bean over time. Pod dry matter concentration was well predicted but fresh market yield was some what under-predicted in late season at low N treatment. Simulation of pod sieve sizes and pod diameter which define pod quality was acceptable. However, there is a need to furthe r examine the functional relationships between fresh market variables as well as soil N supplying and water balance aspects in order to improve simulation capability of the model.

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15 CHAPTER 1 INTRODUCTION Snap bean ( Phaseolus vulgaris L.) is a food legum e that pr ovides an important dietary source of carbohydrates and prot eins in both developing and de veloped countries (Yamaguchi, 1983). For instance, about 2% of Americans cons ume daily fresh snap beans, also known as green beans or string beans (Lucier and Lin, 2002) Florida is the largest snap bean producing state in the United States. In 2007, Florida accounte d for 35% of the total harvested U.S. fresh market snap bean and 55% of the overall U.S. crop value (USDA, 2007). As a vegetable grown for fresh market, managing snap bean production to meet market standa rds is a complex tradeoff between yield and quality; growers select a time for harvest that produces the highest possible yield and the optimum quality parameters before quality deteriorates to an unacceptable level. As any commodity with increasing commerci al importance, tools and techniques are always needed to assist in deve loping strategies that can enable maintaining a higher productivity based on better understanding of th e effects of weather, pests, soil, and management practices. For these purposes, crop simulation models, which dynamically simulate crop growth by numerical integration of constitu ent processes with the aid of computer (Sinclair and Seligman, 2000), are valuable tools developed around the world for a wide array of crops to predict crop growth, development and yield in relation to the weather and management practices and help in decision-making processes throughout the cr op growing season. The use of crop models, incorporating climatic conditions and management practices, may assist in making more timely and better management decisions during the crop growing season (Singh and Jones, 2000). Additionally, well-calibrated crop simulation models can be useful in reducing the cost and time

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16 of field experiments, gaining insights into relevant crop growth and development processes, and predicting long-term trends in the production system. Many studies have demonstrated or discussed the capability of these models to predict crop yield, plant growth and development, and nu trient and water dynamics. Indeed, models such as CROPGRO (Hoogenboom et al 1994, Boote et al. 1998a,b), CERES (Ritchie, 1998), and InfoCrop (Aggarwal et al ., 2006), developed over the past decad es integrate the influence of different factors on productivity and have been used to predict the po tential production, optimize crop response to nitrogen fertilization, and quantify yield gaps. Nitrogen accumulation and uptake has been modeled for soybean ( Glycine max L.) by Sinclair et al. (2003) and for wheat ( Triticum spp) by Jamieson and Semenov (2000). However, when a vegetable crop grown for fr esh market like snap bean is to be simulated, most of these dynamic models present some limitations because they only take into account the dry matter accumulation to generate th e final dry weight grai n yield as affected by different management practices. Furthermore, Gary et al. (1998) mentioned that the modeling of quality aspects which determine vegetable market value is still in its infancy in most simulation models. For example, the grain legume model CROPGRO enables predicting growth and yield of crops such as soybean ( Glycine max L. Merr), peanut ( Arachis hypogaea L.), dry bean ( Phaseolus vulgaris L.), chickpea ( Cicer arietinum L.), cowpea ( Vigna unguiculata ) (Jones et al., 2003), but does not simulate legumes harvested for fresh market such as snap bean. The research conducted by Ferreira et al. (1997) tried to predict phasic development of green beans using a model with thermal time accumulation to optimum harvest time, but that work did not predict fresh market yield or pod quality, notably pod sizing over time.

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17 Owing to its generic and versatile structure, the CROPGRO model ha s the ability to be adapted to new species or different environmenta l conditions to meet specific interests. Indeed, based on values and relationships reported from the literature and obser ved experimental data, Boote et al. (2002) were able to adapt the CROPGRO model to simulate the growth and yield of faba bean ( Vicia faba L.). This approach has also been implemented for velvet bean ( Mucuna pruriens ; Hartkamp et al 2002) as well as non leguminous crops such as tomato ( Lycopersicon esculentum L.; Scholberg, 1997) and bahiagrass ( Paspalum notatum ; Rymph, 2004). Consequently, the goal of this study is to use the CROPGRO mode l for legumes as a framework to develop a simulation model to predict the growth, development, fresh market production, and quality of snap bean as affect ed by different irrigati on regimes and nitrogen rates. More specifically, this study aims to Evaluate the growth and N uptake response of snap bean to nitrogen fertilization; Evaluate the response of snap bean to inter active effects of irrigation regimes and nitrogen fertilization on yield, yield components and quality; Adapt the CROPGRO-Dry bean model to simulate the growth and development of snap bean; and Add a new module to the calibrate d model to simulate fresh mark etable yield and quality of snap bean. The following hypotheses were be tested: 1) In creased N rates stimulate crop growth and increase N accumulation in snap bean, 2) Interactive effect of irrigation regime and nitrogen fertilization will enhance snap bean yield a nd pod quality, and 3) CROPGRO dry bean model can be used to develop a si mulation model for snap bean. This thesis is organized in seven sections. In form of a general in troduction, this current Chapter 1 outlines the rationale and background underlying the goals and objectives proposed in

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18 this study. Chapter 2 presents literature pertaining to snap bean growth, development and yield as influenced by management practices, namely irrigation and nitrogen fertilization. Additionally, this chapter provides a conceptual framework and the scope of prev ious research efforts related to crop simulation models in general and snap bean simulation in particular Chapter 3 is oriented at crop physiology and assesses crop growth pr ocesses at a plant level. It describes experimentally observed biomass and nitrogen accumulation patterns of snap bean as affected by nitrogen fertilization. Chapter 4 discusses the interactive eff ects of irrigation regimes and nitrogen (N) fertilizer applic ation rates on yield, yield components and pod quality. Chapters 5 and 6 are oriented to crop model prediction and present the process used to develop a CROPGRO Snap Bean Model from the CROPGRO Dry Bean model as a starting point. Finally, Chapter 7 provides summary and conclusions and offe rs suggestions for future research activities in order to improve the current version of this model.

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19 CHAPTER 2 LITERATURE REVIEW Introduction Common beans ( Phaseolus vulgaris L.) are nativ e to southern Mexico, Guatemala, Honduras and Costa Rica (Rubatzky and Yamaguc hi, 1997). They are an annual twining vine with alternate trifoliate leaves grown now in the tropics, subtro pics and, during warm months, in the temperate regions of the world (Yamaguchi 1983). Beans are usually grown in tropical countries for dry seeds and in temperate coun tries for dry seeds as well as for fresh pod consumption or fresh processed as frozen vegetables (Fageria et al., 1997). Snap bean is a type of co mmon bean grown for fresh market consumption selected for tasty pods with reduced fiber (Silbernagel and Drake, 1978). Snap bean is an economically important vegetable with a U.S. fresh market crop value of more than $390 million (USDA, 2007). Florida is the largest snap bean producin g state in the United States. In 2007, Florida accounted for 35% of the total harvested U.S. sn ap bean fresh market and 55% of the overall U.S. crop value (USDA, 2007). Florida has a significant comparative advantage among other states in the production of horticultural vegetable crops in general and snap bean in particular due to its mild winters. This review presents information on the characteristics of snap bean plants and the growth and development as influenced by enviro nmental factors (water and nitrogen namely). Importance of crop simulation models and pr evious research on their development and application are also reviewed. Snap Bean Growth and Development Root Growth Snap bean, like m any other legumes, has a taproot system with extensive lateral roots. The roots may grow to a depth of 1 m, but the late ral root system is mainly confined to the top 25

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20 cm of the soil profile (Rubatzky and Yamaguchi, 1997). In general, during germination and emergence, the radicle grows rapidly, developing a primary root. Subsequently, the primary root growth subsides, and many laterals develop an ex tensive fibrous root system in the upper 15 to 25 cm of soil. In the presence of Rhizobium bacteria, nodules develop on lateral roots (Rubatzky and Yamaguchi, 1997). These lateral roots are less effective in extr acting water stored deep in the soil. The pattern of root development is de termined by the growth habit (determinate or indeterminate) of the w hole plant. Indeed, Kelly et al. (1999) mentioned th at early flowering erect determinate cultivars tend to develop sha llow root systems, while erect indeterminate cultivars tend to have a more prominent tap root that can better exploit deep soil layers and can be effective with terminal drought s. More prostrate indeterminate cultivars tend to have a more sprawling fibrous root system which can be effective under intermittent droughts. Plant Growth Common bean in general is a highly polym orphi c species showing considerable variation in growth habit, vegetative characters, flower co lor, and the size, shape, and color of both seeds and pods (Laing et al. 1984). Growth and development of sn ap beans are divided into vegetative and reproductive stages. The vegetative (V) stag es are defined on the basis of number of nodes on the main stem, including the primary leaf node whereas reproductive (R) stages are defined on the basis of pod and seed characteristi cs in addition to nodes (Fageria, 1997). Based on plant growth characteristics, bean ecotypes can be classified into four groups (Davis et al. 1984): type 1a, de terminate bush, erect, with 5 to 7 nodes on the main stem; 1b, determinate bush with more than 7 nodes on the main stem, erect, prostrate or climbing; 2a, indeterminate bush with erect main stem and branches; 2b, indeterminate bush with erect main stem and branches but with medium to elongate d development; 3a, indeterminate sprawling type with little or nor climbing tendency; 3b, a f acultative climber less than 1.5-m high with little

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21 branching and pod bearing on the lo wer part of the plant; 4a, indeterminate climber, 1.5-2.0 m high with little branching, medium vigor, and pods evenly distri buted along the whole length of the plant; and 4b, a vigorous indeterminate climber, more than 2-m high, little branching, and pod bearing on the upper portion of the plant. These four major classes are defined using the type of terminal bud (vegetative versus reproductive), stem strength (weak ve rsus strong), climbing ability (non-climber versus strong climber), a nd fruiting patterns (mostly basal versus along entire stem length or only in the upper part) (Singh, 1982). Indeterminate cultivars can grow 2-3m in height, while determinate cultivars reach on ly 20-60 cm, with the stems terminating in an inflorescence. Most snap bean cultivars, espe cially for fresh market and processing, have a determinate bush (type I) growth ha bit. The type I growth habit is easier to handle in mechanized agricultural systems and lends itself to a once-over mechanical harvest (Fernandez et al., 1986). A classification based on the temperatur e requirement for optimum growth and development is valuable in determining which cr ops may be planted in a given region and at what time during the year. Studies on the ecophysiol ogy of snap bean revealed that snap bean is classified as a warm-season vegetable and sub-grouped as tender, implying that the young plants are susceptible to damage dur ing cold weather. In fact, the b ean plant is intolerant to frost and a short exposure to 0 oC or below will kill bean tissue (Wa llace, 1980). The intensity of light and the relative length of the light and dark pe riods can dramatically a ffect the pattern of crop development and yield. Long daylen gths result in high rates of net photosynthesis, favoring high growth and yield potentials. The relative length of day and night influences the initiation of flowers and storage organs for many vegetable crops. Daylength is a major factor affecting adaptation of the common bean. Although some temperate zone cultivars are relatively insensitive to daylength, many ge notypes of tropical origin are sens itive to daylength for both the initiation and development of flower buds (Ojehomon et al. 1973). In general, later-maturing,

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22 indeterminate genotypes are more sensitive to photoperiod than are determinate or determinate bush types (Purseglove, 1968; Wallace et al., 1982). In most climates, the date of sowing is such that flowering occurs when temperatures are within 2oC of the apparent optimum of 21oC for flowering and the suppl y of water is adequate for growth (Laing et al., 1984). Seedlings emerge af ter about 17 days when soil temperatures are 10-11oC, after 6-8 days at 13-14oC, and after only 5 days at 15-16oC (Scarisbrick et al., 1976). The optimum air temperature range for th e germination of co mmon bean is 20-30 oC (Scully and Waines, 1987). Temperature has a pronounced effect on the photoperiod response. The effect of increasing temperature on completely day neutral cultivars is to decrease the number of days to flowering (Hall, 2004). Wallace (1980) found that at daylength of 9 to 12 h, days to first flower varied from 37 to 42. As the night temperature rose from 18 to 21 to 24 oC, the days to first flower decreased. Foliage is pinnately trifolia te and somewhat hairy, and each leaf has a well-developed pulvinus at the base (Summerfield and Roberts, 1984). Present-day cultiv ars have small leaves which improve light penetration into the canopy, es pecially for high density plantings. However, Rubatzky and Yamaguchi (1997) mentioned that a lthough this characteristic tends to increase total yield, small leaf size is linked to small pod size. Snap bean stems and petioles are slender, tw isted, angled, and ribbe d; in climbing forms they have more nodes, which are further apart th an in determinate bush types (Summerfield and Roberts, 1984). Growth in plants can be measured in many ways. The most direct method is by measuring the increase in plant dry weight over time, which in turn reflects the potential for assimilation of photosynthates by the plant (S ader, 1980). The growth analysis procedure requires only measurements of plan t dry weight and leaf area on sa mples of the crop at intervals

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23 throughout its growth. Parameters of growth analys is such as crop growth rate (CGR), relative growth rate (RGR), leaf area (LA), leaf area index (LAI) and others with their respective formulae have been described by Evans ( 1972) and Radford (1967). Maximum dry matter accumulation of tops as well as roots occurred in the time period of 30-60 days after sowing (Fageria, 1997). The decrease in top dry matter at the later growth stage may be related to more photosynthate translocation to gr ains and senescence and abscission of old leaves. Sader (1980) observed that snap bean leaf area index increa sed rapidly, attaining maximum values of 3.0 to 4.2 by 63 days after emergence depending to N fertilization. Flowering, Pod Set and Development Generally, s nap bean cultivars are selected fo r a near-simultaneous bl oom of the terminal and branch node racemes, and for pod fill to oc cur over a short period of time (Singh, 1999). Beans, in general, are normally self-pollinati ng and generally little out-crossing occurs. Flowers are large and showy and may be white, pink, or purple. In some determinate cultivars flower primordia are formed on the raceme in the axil of the uppermost leaf of the mainstem first and differentiation then proceeds in a downward dire ction and along the branch es (Ojehomon et al., 1973). The proportion of opened flowers that do not form mature pods is influenced by a very wide range of environmental and bi otic factors. Even in favorable field environments about 60 to 70 % of the flowers are ultimately shed. Additiona lly, Gross and Kigel, (1994) found that raising night temperature from 17C to 27C strongly reduced pod producti on by causing a reduction of buds and flowers, mature pod size and seeds per pod, while an increase in day temperature from 22C to 32C had smaller and less consistent effects. Th e preferential mobili zation of available photosynthate to first-formed reproductive struct ures and the associated abscission of ones formed later suggest that in Phaseolus vulgaris as in other species of grain legume, the plant is

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24 conservative in the allocation of resources and gi ves survival of a few fruits precedence over productivity (Fageria, 1997). Pods are narrow (1-1.5 cm wide) and long (8-20 cm). Depending on cultivar, pod ends may have a pointed or blunt tip ; cross-sectional shapes vary from round to elongated oval, and some are heart shaped. The number of seed depends on the cultivar and can vary from 3 to 7, while dry bean types may have several more (Rubatzky and Yamaguchi, 1997). At maturity, seed size can vary from 0.7 to 1.5 cm in length, and weigh from 0.2 to 0.6 g each, while the form can be globular to kidney shape (Rubatzky a nd Yamaguchi, 1997, Yamaguchi, 1983). Pod traits are the most important aspect of snap bean cultiv ars. Traits of importan ce include color (relative internal and external color, a nd uniformity of color), pod shape, length, cross-sectional shape, straightness, smoothness, fiber content, rate of seed development, and point of detachment (Silbernagel, 1986, Myers and Baggett, 1999). Within snap beans, fiber content appears to be quantitatively in herited, with reported values from 0.02% to about 3.0% of pod fresh weight for pods with acceptable quality (Silbernagel and Drake, 1978). Fibe r content increases with sieve size and maturity, with some cultivars having as high as 20% fiber in ma ture six-sieve (Silbernagel and Drake, 1978). Yield is more complex in a vegetable crop such as snap bean, compared to a seed crop. Both physiological and morphologi cal characteristics of the bean plant are thought to play a major and interdependent role in determining yields (Denis and Adams, 1978). Nienhuis and Singh (1986) observed that yield was positively correlated with pod density, seeds per pod, and all architectural traits except br anches per plant. A detailed an alysis of plant structure, pod development and ripening indicated that the high er pod yields are related to a shorter flowering stage, a more homogeneous pod development and an advanced ripening of the pods (Deproost et al., 2004).

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25 Pod Quality The greatest quality of green bean s is reach ed well in advance of maximum yield (de Varennes et al., 2002). Pods harden as they mature becoming rich in fiber and less palatable. As the pods of snap bean plants approach optim um harvest time for fresh market, the quality decreases while the yield of pods increases (Robinson et al., 1965). A lthough pods of different sizes are processed for various uses and styles of product, the market value of the pods decreases as the pods become larger and more mature (Peck and MacDonald, 1983). The quality of snap bean pods grown for processing and fresh market is defined by many chemical and physical properties of the pods including diameter of the pods and weight of the seeds in the pods (Peck and VanBuren, 1975; Robinson et al ., 1965). The fresh weight of the seeds as a percentage of the fresh weight of the pods (including seeds) is used as a determination of quality of the pods for processing (Robinson et al., 1965). Unlike legumes harvested for seed, snap bean pods should have less than 10% seed in the pods on a fres h weight basis at optimum harvest time for processing for human consumption (Peck and M acDonald, 1983). For best quality, pods should be about half-grown to about three-fourths of maximum length (before the pods reach full size and while the seeds are succulent and not starc hy) (Watada and Morris, 1966). Yield and quality have an inverse relationship. Th ere is no clear defined point at which yield and quality are maximized. To a grower or proces sor, quality in snap bean is defined in terms of sieve size, percent seed by weight of total pod weight, pod fiber content, pod smoothness and straightness, pod color, and flavor. Sieve size is probably the si ngle most important qualit y factor in processed and fresh market snap beans. The sieve distribu tion may vary from the expected normal when pod set is disrupted by hot weather, resulting in a split set. Smaller siev e beans have straighter pods because pods are inherently shorter. Maturity for snap bean harvest is based on the diameter of the bean pod. USDA has six standard size desi gnations for snap beans. Size 1 and 2 are any

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26 pod less than 18.5/60 inch in diamet er and they are considered t oo small, and size 6 is any pod more than 27/64 inch in diameter and is considered overmature (Gast, 1994). With a once-over mechanical harvest, growers must select a time for harvest that produces the highest possible yiel d and the optimum percentages of sieve sizes before quality deteriorates to an unacceptable level. Small-si eve cultivars are those that maximize quality and yield at a smaller sieve size. Parameters such as percentage of fiber in th e pod, size of seeds, and pod sieve size are all important to the marketability of the crop (Bonanno and Mack, 1983). Crop Production Growth and developm ent of a plant is influe nced by various environmental factors such as temperature, water, and nitrogen. Among these factors, effect s of water supply and nitrogen deficiency have been studied extensively. This se ction presents the effects of water and nitrogen on snap bean and different techniques of mana ging these two important crop production factors starting out first with a brief review of the production systems currently used in Florida. Production Systems Snap beans grow best on soils that have higher water holding capacity and have good air and water in filtration. Snap beans require a c onstant supply of moisture during the growing season. In a sandy soil prevailing in Florida, irri gation is important to ensure optimum plant growth, a uniform pod set, and robust developm ent (Mossler and Nesheim, 2000). Snap bean production is typically intensively managed with high inputs of fertilizers and irrigation water. Snap bean seeds are planted 5-10 cm apart in rows 60-90 cm apart and 2-4 cm in depth. Snap beans are planted in Florida be tween August 15 and April 1, with some variation by region. In north Florida, usual planting da tes are from March to April an d from August to September. Planting occurs in central Florida from February to April and from August to September, while in south Florida snap beans may be planted anyt ime from September to April. Approximately 33

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27 percent of snap bean acreage is harvested during the winter season (January to March from South Florida fields), with approximately 44 percent ha rvested during the spring season (April to May) and 23 percent harvested during the fall se ason (October to December) (Pernezny, 1997; FDACS, 1998, Olson et al., 2007). Crop production is fully mechaniz ed, and the entire yield is harvested by a single picking. Irrigation Snap beans have high water requirem ents and in general, irrigation is necessary for successful vegetable crop production. The growth and development of a pl ant is significantly dependent on the moisture levels in the soils as water deficit can reduce growth. Irrigation water management always aims at providing sufficient water to replenish depleted soil water in time to avoid physiological water stress in growing plants. Due to its re latively shallow water-extracting root system, snap bean is very responsive to frequent irrigations (Smittle, 1976). Water uptake occurs mainly from the first 0.5 to 0.7 m depth (FAO, 2008). High irrigation frequencies generally favored strong vegetative developmen t and stimulate the generation of flowers and pods (Deproost et al., 2004). Plant growth stages are modified by the moisture availability patterns during the growth cycl e (Ramirez and Kelly, 1998). Wate r requirements are extremely important at all stages of plant development (Sezen et al. 2005). But, controversy exists as to what development stage is mo st susceptible. Doorenbos and Kassam (1979) reported yield response coefficients of 0.2, 1.1 and 0.75 for the vegetative stage, flowering, and pod development, respectively, indicating that flower ing is the most sensitive period. Also, Dubetz et al. (1969) and Kattan and Flemi ng (1956) found that water stre ss during the flowering phase results in the greatest yield re duction. Water stress during fruit development reduced both yield and pod quality (Kattan and Fleming, 1956). Water deficit during the yield formation period gives rise to small, short discol ored pods with malformed beans. Also, the fiber content of the

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28 pods is higher and seeds lo se their tenderness (Rubatzky and Yamaguchi, 1997, FAO, 2008). Bonanno and Mack (1983) observed that pod quality was less sensitive to water stress than was pod yield. Low irrigation level reduced total leaf area/plant and number of leaves (Bonanno and Mack, 1983, Nielsen and Nelson, 1998) and caused reduction in the crop growth (Bergamaschi et al., 1988). Singh (1989) observed that vegetative growth (number of leaves, leaf area and leaf dry weight) increased linearly with irriga tion amounts from 0 to 100 % pan evaporation. On the other hand, Deproost et al. (2004) repor ted that observed that moderate drought stress during flowering induced yi eld increases of 30 to 70% as co mpared to frequently irrigated snap beans. Other researchers concluded that fr equent irrigation was necessary during the entire growth cycle in order to obtain optimal yiel d levels (Stansell and Smittle, 1980; Bonnano and Mack, 1983). Irrigation method and scheduling affect crop growth and development, fruit yield and water use efficiency. For example, Locascio (200 5) mentions that the use of frequent but relatively low water application volumes gives generally greater advantage to plant productivity as opposed to the more traditional scheduling of fe w applications of large irrigation volumes. In a study involving different irriga tion regimes and crop coefficients Sezen et al. (2005) found that maximum yields of beans (20,558 kg ha-1) were obtained from a treatment consisting of 13-17 mm every 2 to 3 days with a crop pan coefficient of Kcp of 1 as opposed to treatments irrigated 58-62 mm every 10 to 12 days with a Kcp of 0.50 which yielded 12,243 kg ha-1. Several methods have been proposed for scheduling irrigation in sn ap bean based on environmental factors. These methods usually evaluate the soil water content and soil water potential. Soil water content can be measured by a neutron probe, which directly measures soil water on a volumetric basis. But, Bonnano and Mack (1983) reported that radiat ion hazards, high cost, and the need for calibrations restrict its use prin cipally to a research tool. Irri gation scheduling methods based on

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29 pan evaporation are widely used. Plant response to irrigation is better corr elated with soil water potential than with soil content (Bonnano and Mack, 1983). Soil water pote ntial at which water should be applied for maximum yields of snap bean grown in deep, well drained and fertilized soils is between -75 to -200 kPa (Wallace, 1980). The use of tensio meters enables one to monitor soil water tension at a given depth and allows scheduling irrigation applications with more efficient use of water. Soil water tension (S WT) represents the magnitude of the suction (negative pressure) the plant roots have to create to free soil water from the attraction of the soil, and move it into the root cells (Simonne et al., 2007). The tensiometer consists of a tube with a porous cup at the bottom and a wate r reservoir and vacuum gauge at the top. Field investigations carried out in Bangalore (India) with French bean crops, indi cated that irrigation when soil matric potential at 0.15 m depth reached -45 kPa re sulted in highest dry matter production, green pod yield, nutrient uptake and wate r use efficiency (WUE) as comp ared to irrigations scheduled at -65 or -85 kPa (Hegde and Srinivas, 1990). Fo r most vegetable crops grown on the sandy soils of Florida, soil water tension in the rooting zone should be maintained between 6 (field capacity) and 15 kPa (Simonne et al., 2007). Furthermore, soil water balance and evapot ranspiration measurements which are an assessment of water loss by a cr opped surface can also be used to predict irrigation needs for crop. According to Ritchie (1998), this method is based on the water balance in the soil-plantatmosphere continuum and can be calculated as follows: PSEEDRIP dt dW where, dW/dt = Net rate of change in stored soil water, in units of mm3[water] mm-2[area] d-1, or mm d-1; P = Precipitation on day t, mm d-1; I = Irrigation amount on day t, mm d-1; R = Surface runoff on day t, mm d-1; D = Drainage from bottom of profile on day t, mm d-1; Es = Soil evaporation on day t, mm d-1; and Ep = Plant evaporation (trans piration) on day t, mm d-1.

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30 Bonanno and Mack (1983) reported that one of the most commonly used methods for predicting irrigations by evapot ranspiration is a computerized irrigation scheduling program developed by Jensen et al. (1971), commonly referred to as the USDA-ARS Irrigation Scheduling Program. This program provides estimates of the timing and amount of irrigation water based on weather data and specific charac teristics of the crop and soil. Under conditions when maximum evapotranspiration (ETmax) is 5 to 6 mm/day, 40 to 50 percent of the total available soil water can be depl eted before water uptake is aff ected (FAO, 2008). But, this is related to the type of soil. Cr op coefficient (kc) relating refere nce evapotranspiration (ETo) to water requirements (ETm) for diffe rent development stages is, for snap bean: during the initial stage 0.3-0.4 (15 to 20 days); the early-season development stage 0.65-0.75 (next 15 to 20 days); the mid-season stage 0.95-1.05 (next 20 to 30 days); the late-season stage 0.9-0.95 (next 5 to 20 days) and at harvest 0.85-0.9 (FAO, 2008). Smittle et al. (1990) used pan evaporation with continuous crop factor function to estimate ET and a dynamic root depth function to sc hedule irrigation for snap bean in Georgia, USA. Also, Bharat (1989) compar ed different pan coefficients and suggested a pan coefficient of 0.80 for optimal yield in Fort Valley, Georgia USA. Another method is the one developed by FAO which consists of establishing the irriga tion schedule using a computer model (Cropwat) based on Penman equation. The peak water use for gr een beans use is approximately 4 to 5 mm per day for April and June plantings, respectively. Weekly irrigation during the peak is adequate, however with sandy and sandy loam soils, irrigation may be required as frequently as every 3 to 4 days. Also, under conditions when ETm is 5 to 6 mm/day, 40 to 50 percent of the total available soil water can be depleted before water uptake is affected. Once the irrigation scheduling is determined, va rious methods or techniques are currently available for providing irrigation water to snap bean in Florida. These methods include the

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31 seepage system, overhead irrigation system and drip irrigation system. Smajstrla and Haman (1997) reported that nearly 43% of the total irrigated land in Florida utili zes seepage irrigation system, while about 30% and 25% of irrigated land is under center pivot and linear moves and drip system, respectively. With the application efficiency ar ound 60 to 80%, overhead irrigation represents the most common i rrigation method practiced for snap bean in Florida. Efficiency of the method and technique used to manage irrigation in the cropping system is estimated with two coefficients called wate r use efficiency (WUE) and irrigation water useefficiency (IWUE). In a specific case of sn ap bean, WUE and IWUE values are usually calculated as fresh market yi eld divided by seasonal ET and total seasonal irrigation water applied, respectively (Tanner and Sinclair, 1983). The water utilizat ion efficiency for harvested yield (Ey) for fresh bean containing 80 to 90% moisture is 1.5 to 2.0 kg/m3. Good commercial yield in favorable environments under irrigation is 6 to 8 ton/ ha fresh bean (FAO, 2008). Plant Nutrition Nitrogen is an essential com ponent of protoplasm, chloroph yll m olecules, nucleic acids (DNA and RNA), and amino acids, from which pr oteins are made. Nitrogen enhances the vegetative parts to produce large, green leaves, and is also necessary for pod filling period (Neeteson, 1995; Brady and Weil, 1997, Foth a nd Ellis, 1997). An appropriate supply of N stimulates root growth and development, as well as the uptake of other nutrients. Healthy plant foliage generally contains 2.5 to 4.0% N, depe nding on the age of the leaves and whether the plant is a legume (Brady and Weil, 1997). In contra st, plants deficient in N tend to have a pale yellowish green color (chlorosis), a stunted ap pearance, and develop th in, spindly stems (Foth and Ellis, 1997). Nitrogen depriv ation decreased shoot to root ratio as a consequence of a decreased weight of above ground organs, especially of leaves, wh ile chlorophyll content declined significantly (Lima et al., 2000).

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32 As a legume, the enzymatic reduction of at mospheric nitrogen to ammonia and protein N in lateral root nodules by the symbiotic bacterium Rhizobium, may supply part of the N requirements of beans. However, snap beans are generally considered to be weak in N2 fixation and show a variable response to inoculati on (Vincent, 1974, Calvache and Reichardt, 1999). Poor N2 fixation by P. vulgaris has been attributed to the difficulty of establishing effective symbioses in the field and to genetic variabil ity in the capacity to fix N (Graham, 1981; Rubatzky and Yamaguchi, 1997). Owing to this ineffective potentiality of fixing N2, supplemental N fertilizer is always required in commercial production of snap bean for vigorous crop development (Graham, 1981, Piha and Munns, 1987, Calvache and Reichardt, 1999, Redden and Herridge, 1999). More specifically, N fertilization affects the ve getative growth of snap bean plants as well as the pod set and development (Peck and MacDonald, 1983). The nitrate form of N is preferred to the ammonium form. In experimental conditio ns where nitrification is limited, plants supplied with moderate concentrations of NH4 + generally do not grow as well as plants supplied with equal amount of N as NO3 or NH4 + and NO3 combined (Kirkby, 1981). In Florida, the recommended N rate for snap bean is 110 kg N ha-1, and recommended K2O and P2O5 (for soil testing low in P and K) are 120 kg ha-1 for P2O5 and K2O, respectively (Olson et al., 2007). Typically, all P2O5 is broadcasted at planti ng and 20 to 50% of N and K2O are banded also at planting. The remaining N and K2O is sidedressed at pre-bloom st age. Nitrogen application rates should consider plant populations because high-density plantings generally require higher levels of supplemental fertilizer. In a study involving different N rates, Peck and Macdonald (1983) observed that dry matter accumulation in the snap bean plants was slow from planting to the seedling stage, more rapid from the seedling to the bloom stage and reached a rapid rate of 22 g m-2 day-1 from the bloom stage to the pod stage. In the study conducted by Nicholaides et al.

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33 (1985), total snap bean fresh we ight yield response to N fe rtilization was quadratic with maximum yield at 168 kg N ha-1. The 112 kg N ha-1 rate produced greater yield when 66% rather than 50% or 100% of the N was applied preplant. Yield increased as the level of NPK increased while pod quality, i.e. pod length, thickness and fi ber content were not significantly affected by the level of NPK application (Abdel Mawgoud et al., 2006). In the study car ried out by Tewari and Singh (2000), successive increase in the doses of N (up to 120 kg ha-1) as well as P increased plant height, number of branches length of pod and seed yield. Nitr ogen fertilization at 40 to 80 kg ha-1 produced the optimum quality and yiel d of snap bean pods for processing. Nitrogen Uptake Understanding elem ental accumulation in horticultural crops is important for optimizing growth and development and designing fertility practices for nutrient management programs. Nutrient uptake from soils is essentially the pr oduct of the nutrient c oncentration of the soil solution and the absorbing power of roots when there is no other limiting factor. Root-absorbing power is affected by root length and/or su rface area, kind and age of roots, plant age, temperature, plant species, and ion competition or interaction (Foth and Ellis, 1997). Both NH4 + and NO3 are commonly present in soil solutions, and both are readily taken up by roots but plants absorb predominately NO3 -. Nitrate remains soluble in the soil solution and is readily moved to plant roots by mass flow. Nitrogen is highly mobile in plants. It can be translocated from older leaves to newly developing leaves, and to storage or reproductive organ. This ability helps plants to optimize their N use and plays an important role in plant development. As the crop matures, N is re-mobilized from leaves to the developing grain (Lawlor et al., 2001). In general, the N concentration of plants within dense canopie s declines throughout ontogeny, even when N supply is not limiting growth. Lemaire et al. (2007) reported that this phenomenon has usually been interpreted as a dire ct result of plant ageing and has often been

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34 related simply to phenology, leading to large diffe rences in N concentration between species or genotypes within a given environment, and betwee n different environments for a given genotype. There is a relationship between crop N uptake (N, kg ha-1) and crop biomass accumulation (W, t ha-1) expressed as follows: baWN where parameter a represents the amount of N accumulated by the crop at W = 1 t ha-1 and coefficient b represents the ratio between the relative accumulation rates of N and biomass. In a stea dy state non-limiting supply of N, the relationship between N uptake and biomass accumulation reflects the feed-back regulation of N absorption capacity of roots by shoot growth itself (Lemaire et al., 2007). For grain legumes, Ney et al. (1997) observed that until th e pod setting, plant N content declines as a consequence of the increase in crop mass, but after pod set and during seed filling, the accumulation of N in reproductive organs with a high N concentration counterbalances the decline in N% of the vegetative plant component s (leaves and stems), and therefore total plant N% remains almost constant. Application of N fe rtilizer generally increas ed the concentrations of nitrate N and total N in all plant parts at a ll stages of growth, development, and maturation. Nitrate N represented 4.6 to 7.2% of the total N in the small pods. Fertilizer N increased the content of total N in the plants at the pod stag e. Partitioning of dry weight and N in the plant parts depends upon time after planting or stage of physiological growth. Concentrations of nitrate N and total N generally decrease in all plant part s with later stages of growth. Accumulation of total N in the plants was similar to, but preced ed dry weight accumulation. Total N represented 3.5 to 4.6% of the dry weight at the seedling stage, 3.1 to 3.8% at the bloom stage and 2.5 to 3.1% at the pod stage (Peck and MacDonal d, 1983). When N supply is non-limiting, an empirical linear relationship between the amount of N accumulated in the above ground biomass and the crop leaf area index (LA I) during the period of leaf area expansion has been proposed for maize (Plenet and Lemaire, 2000).

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35 Nitrogen Use Efficiency Different definitions are used in literature to describe the agro nom ic and physiological range of N efficiency referring to external and internal N-status. Among these different definitions exhaustively reviewed by Rathke et al. (2006), two are emphasized in this study. Nuptake efficiency (the efficiency with which so il-applied N can be taken up by the plant) and N utilization efficiency (the seed dry weight or pod fresh weight per unit of absorbed fertilizer-N). N-uptake efficiency depends substantially on sp atial root development (rooting depth, rooting intensity) and thus governs uptake of the total amount of N (capacity of the uptake system). From an agronomic perspective, Below (2002) mentioned that NUE refers to three main functions detailing the relations hips between N availability an d yield, N availability and N recovered, and yield and N recovered. High N-utilization efficiency can result from effective remobilization of N from vegeta tive parts of the plan t to developing tissues representing strong sinks for N but also from reduced N-demand of th ese tissues. It is a meas ure of the extent to which a crop transforms available N to economic yield (Ma et al., 1999). Nitrogen use efficiency is generally highest where little N has been applied. The NUE declines considerably as the amount of applied N is increased. This is especi ally true once the plant N concentration exceeds the level required for optimal dry matter growt h. Improved N use efficiency (NUE: capacity to produce a supplement of yield for each added unit of N fertilizer) has the potential to enhance yield under low N supply and thereby improve crop nutritional quality while reducing ground water contamination by nitrates. Nitrogen Leaching Undoubtedly, intensive agricu lture has led to an increase in N leaching in the environment, particularly as nitrate. Ammonium and nitrate are all availa ble in the rooting zone to be taken up. In contrast to ammonium i ons, which carry a positive charge, the negatively

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36 charged nitrate ions are not adsorbed by the nega tively charged soil colloids. Therefore, nitrate ions move downward freely with drainage water, and are thus readily leac hed from the root zone (Brady and Weil, 1997). Nitrate may reach domes tic wells, and may also eventually flow underground to surface waters such as streams, la kes, and estuaries. The nitrate may contaminate drinking water and cause eutroph ication and associated problem s. Excessive N fertilizer application (especially common for some vegetables) when combined with high intensity rainfall events and poor water and nutrien t holding capacity of soils may result in N leaching below the active root zone (Prakash et al., 1999). Very sandy soils such in Florida are particularly susceptible to nitrate leaching lo sses. Various factors influence the rate of nitrate loss below the rooting zone. Actual N leaching losses depend on N source and app lication rates, crop removal capacity, and water displacement below the rooti ng zone (Zotarelli et al., 2007b). Also, plants that have high aboveground biomass and adequate root density are genera lly more effective at reducing residual soil NO3 (Sainju et al., 1998). Therefore, with its relatively shallow rooting system and combined with the low water-holding capacity of sandy Florida soils, the snap bean cropping system is expected to i nduce a higher nitrat e leaching rate. A number of different methods have been used to study the rate of in situ soil nitrate leaching in different cropping systems. Zotarell i et al. (2007a) compared three methods to monitor nitrate leaching in sandy soils grown with different vegetables crops. These methods include the ceramic suction cup lysimeters, s ubsurface drainage lysimeters and soil cores. Soil coring is simple, relatively cheap, widely used, and applicable to most soils. Regardless the technique used, these authors found that applying N rates in excess of standard recommendations increased N leaching by 64, 59, and 32%, respectively, for pepper, tomato, and zucchini crops.

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37 The challenge is to define cropping techniqu es which will give the best compromise between good agricultural production (in quantity and quality) and an acce ptable level of N losses. Crop Modeling Crop sim ulation models are mathematical repr esentations of plant growth processes as influenced by interactions among genotype, environment, and crop management (Yang et al., 2004). They may provide quantitative information fr om which decisions, such as crop timing, irrigation, fertilization, cr op protection, and climate control, can be taken at the field scale (Gary et al., 1998). Models can be used to estimate valu es that could be useful for the optimization of management decisions, such as fertilization, plant population, genetics, or planting dates, providing a framework for compar ison. Some process-based crop gr owth models use the concept of radiation use efficiency (RUE) and inter cepted solar radiation for computing biomass accumulation (Monteith, 1977, Sinclair and Shir aiwa, 1993), and others consider detailed processes of gross photosynthesis and respiration (maintenance and growth) to estimate biomass accumulation (van Keulen et al., 1982, Penning de Vries and van Laar, 1982). Based on either of these two approaches, a wide range of crop simula tion models have been developed for different crops under various environment conditions. The concept of Radiation Use Efficiency (RUE) is followed in CERES crop models (Ritchie, 1998). In the maize model (CERES-Maize), growth of organs is primarily driven by temperature, and dry matter production is computed directly from absorbed solar radiation by means of a fixed value for RUE that accounts for respiration costs implicitly. In generic models such as WOFOST (Van Diepen et al., 1989) and INTERCOM, growth of plant organs is driven primarily by the availability of assimilates from simulation of canopy photosynthesis, and both growth and maintena nce respiration are exp licitly accounted for to predict dry matter produc tion. Among the photosynthesis-driven models, the CROPGRO

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38 model (Hoogenboom et al., 1994) can simulate the impacts of genetics, weather, soil, management practices, and their dynamic interactions on crop growth, development, and yield based on C, N and water balance principles (Bat chelor et al., 2002). Both the radiation use efficiency and photosynthesis-d riven models allow predicting potential production, but also include modules to account for water and/or N limitation and/or modules describing effects of pests, diseases or weeds (v an Ittersum et al. 2003). The purpose in the next section is to review the major features of CROPGRO pertinent to adapting the model for a new crop and pres ent some previous adaptation efforts. CROPGRO Model and Model Adaptation Approach CROPGRO is a process desc riptive model that considers crop phenology and canopy development and crop carbon, nitrogen and water balances. Crop phenology includes the rates of vegetative and reproductive development that govern the partitioning of C and N to plant organs over time. Crop N balance incl udes daily soil N uptake, N2 fixation, N mobilization from vegetative to storage tissues and N loss in abscised parts. Soil water balance includes infiltration of rain and irrigation, crop and soil evaporation, root uptake, and water drainage and distribution within the soil profile. State variables are the amounts, masses and numbers of tissues, and rate variables are the rates of inputs transformations, and losses from state variable pools (Boote et al. 1998a). Crop development in CROPGRO during the va rious growth phases is differentially sensitive to temperature and photoperiod. In CRO PGRO there are 13 possible life-cycle phases from sowing to maturity, each with its own unique development rate. A developmental phase change occurs when the integrated development rate reaches a cultivar-dependent threshold and, for example, seed growth starts.

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39 The physiological development rate, expressed as physiological days per calendar day, is modeled as a function of temperature, photoperiod, water deficit, and N deficit. If conditions are optimal, one physiological day is accumulated per calendar day and the number of physiological days equals the number of calendar days for a development phase. If conditions are not optimal (water or N stress, for instance) for developmen t, the physiological days per calendar day will be less than 1.0 and the crop will require more calendar days to achieve the physiological day threshold for a given development phase. The soil water balance processes include in filtration of rainfall and irrigation, soil evaporation, crop transpiration, di stribution of root water uptake and drainage of water through the soil profile (Ritchie, 1998). The crop N balance processes include N uptake, N2 fixation, N mobilization from vegetative tissues, rate of N us e for new tissue growth and rate of N loss in abscised parts (Boote et al., 1998a,b). CROPGRO was developed as a generic approach for modeling crops in the sense that it has one common source code, yet it can predict the growth of a number of different crops (Jones et al., 2003). Currently, it simulates ten crops ; including seven grain le gumes (soybean; peanut; dry bean; chickpea; cowpea; velvet bean (Hartkamp et al., 2002); and faba bean (Vicia faba L.)) (Boote et al. 2002),; non-legumes such as tomato (Lycopersicon esculentum Mill.) (Scholberg et al., 1997; Boote et al., 1998a,b); and forages (Brachiaria decumbens) Rymph et al., 2004; Giraldo et al., 1998). This versatility is achieved through three input files that define species traits, ecotypes and cultivar traits (Boote et al., 2002). The species file contains information on base temperatures (Tb) and optimum temperatur es (Topt) for developmental processes (rate of emergence, rate of leaf appearance, and rate of progress toward flowering and maturity) and growth processes (photosynthesis, nodule growth, N2-fixation, leaf expansion, pod addition, seed growth, N mobilization, etc.). These parameters are set duri ng model development for a

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40 particular crop and are not generally changed by th e user. The ecotype file contains information that describes broad groups of cultivars, such as determinate vs. indeterminate growth habit groups. Cultivar differences are represented in a file containing 15 coe fficients (Boote et al., 2003). The cultivar file allows users to specify how cultivars differ in life cycle progression, daylength sensitivity, canopy and fru it growth characteristics. Th e cultivar traits lead to differences in yield potential in different envi ronments (Boote and Tollenaar, 1994; Boote et al., 2003) and the traits can be solved from field trial or growth analyses data (Mavromatis et al., 2001). Parameters in these files include factors such as physiological time between growth stages, relative differences in photosynthetic rate, and leaf size, among others. The CROPGRO model uses a 1-day time step, except for hourly time steps for development rate leaf-to-canopy assimilation calculation. Owing to the structure of the CROPGRO model, the model is well suited to be adapted to new species. Literature reports several cases of adaptation processes on different crops including tomato (Scholberg et al., 1997), faba bean (Boote et al., 2002), and chic kpea (Singh and Virmani, 1994) and bahiagrass (Rymph et al., 2004). These ad aptations and parameterizations followed a systematic approach as proposed by Boote (1999 ) and Boote et al. (2002). First, cardinal temperatures, light, and daylengt h dependencies of various processes (leaf development, leaf photosynthesis, respiration, onset of anthesis, onset of fruit grow th, rate of fruit growth, and photothermal time to maturity) are obtained from the literature where possibl e. The second step is calibration of model paramete rs using growth data, whether available from published literature or new experiments for representa tive production environments to derive model parameters that can not be directly obtained from the literat ure. Calibration is the process by which model parameters are adjusted to give the best fit between simulated results and observed data at a particular site. In other words, calibration i nvolves adjusting certain model parameters by

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41 systematically comparing simulated results with observations while model structure remains the same (Jones and Luyten, 1998). It is hypothesized that this approach should also be successful in adapting the CROPGRO for another horti cultural crop such as snap bean. Previous Efforts of Modeling Horticultural Crops and Quality Several process-oriented crop growth mode ls have been extensively developed and validated for use in pred icting dry matter accumu lation on the pod or seed weight basis. As Boote and Scholberg (2006) pointed it out, th ese models fall short of permitting accurate simulation of growth and development of vegeta ble crops such snap bean which are primarily marketed based on fresh weight, pod size. When fresh vegetables are the crops to be modeled, the approach of photosynthesis-based or RUE-base d models has to be altered to account for the fact that these crops are nearly 90 to 95% water and therefore the fresh we ight growth is related to the flows of water and carbon in to individual harvestable organs such as immature pod in snap bean. For such crops, model predicted fruit dry matter needs to be converted to fresh weight and/or fruit size (Marcelis et al., 1998) as yield is predominantly determined by the water content. These authors summarized in concise way, the different approaches to simulate the fresh market yield with the photosynthesis-based models First, the intercepti on of light by the leaf area is calculated to simulate the production of photosynthates. Subsequently, the use of photosynthates for respiration, conv ersion into structural DM, the partitioning of assimilates or DM among the different plant organs is calculated and finally the fresh weight can be estimated from the dry weight. More specifically for th e CROPGRO model use in predicting fresh market and size of fruit, Boote and Scholberg (2006) envisioned that since the CROPGRO model already predicts explicit fruit a ddition and fruit growth rates over time for specific cohorts, this model ability can be adapted for predicting fres h market yields and i ndividual fruit quality aspects (fruit sizing) over time.

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42 Following these steps, few mechanistic mode ls have been developed for horticultural crops. Among these, the model KOS I was developed to predict the yield and quality of cucumber fruits (Marcelis and Gijzen, 1998). These authors showed that with such a modeling approach, fruit size distribution in cucumber could be predicted in good agreement with observed distribution for commercial situ ation. Also, Tan et al. (2000a,b) developed a model to predict broccoli development which showed that the development is predominantly determined by temperature rather than photoperiod. Although quality is now the compulsory mo tto of every advertising campaign for horticultural products and hence a co mponent of their price (Gary et al., 1998), efforts to include product quality aspects in crop m odeling is still in its infancy (H euvelink et al., 2004). There are various dimensions of quality, such as shape, co lor, taste, composition, and shelf-life (Marcelis and Gijzen 1998, Gary et al., 1998). In the case of snap bean, Ferreira et al. ( 1997, 2006) illustrated sparse literature in which they attempted to develop a simu lation model of this vegetable crop. Several other studies have addressed the growth and development of dry bean (Hoogenboom et al., 1994; Gutierrez et al. 1994). Indeed, Ferreira et al. (1997) predicted phasic development of green beans using a model with thermal time accumulation, understanding the importance of developmental stages to monitor and predict with accuracy, especially ha rvest time date which is determinant for pod quality. Ferreira et al. (2006), on the other hand, integrated in thei r model some internal as well as external variables such as alcohol-insoluble solids, dry matter content, seed: pod ratio, fiber content, length of 10 seeds, Kramer shear press, color, lipid content and mineral composition, to evaluate quality and maturity of snap bean pod. This chapter reviewed literature pertaining to snap bean growth, development and yields as influenced by management practices, namely irrigation and N fertilization. Additionally, this

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43 chapter provides a conceptual framework and the scope of previous research efforts related to crop simulation models in general and snap bean simulation in particular.

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44 CHAPTER 3 GROWTH AND NITROGEN UPTAKE OF SNAP BE AN IN RESPONSE TO NITROGEN FERTILIZATION Introduction Intensification of cropping system s to attain higher crop yields per unit of land area is typically achieved through the use of high yielding varieties, which usuall y require larger doses of inorganic fertilizers. Under optimal water s upply, crop performance is influenced substantially by the supply of N from soil and fertilizer sour ces (Below, 2002). Therefore, soil and fertilizer management must be designed to furnish a con tinuous supply of availabl e N and other nutrients to produce high yield and quality while withstan ding unpredictable plant stress conditions (Peck and MacDonald, 1983). Nitrogen is essential for crop growth, being a constituent of proteins, amino acids, chlorophyll, nucleic acids, and cell walls (Neeteson, 1995) Nitrogen nutrition influences leaf growth and leaf area duration and thus the size of the photosynthetic system, the photosynthetic rate per unit of leaf area as well as the generative storage organs which is the sink capacity (Below, 2002). Assessing plant response to nutrients in genera l, and to nitrogen (N) in particular, is an important step towards understanding plant growth and development patterns and developing appropriate nutrient management techniques in cropping systems. More specifically, knowledge of crop N demand is essential in predicting crop N uptake and, therefore, in developing reliable N fertilization recommendati ons for growers (Nkoa et al., 2001). Analysis of plant growth response to N fertilization involves quantifying pa tterns of crop growth parameters (dry matter production in plants and leaf area index for instance) which allow computing different crop growth coefficients such as the Crop Gr owth Rate (CGR) (Radford, 1967; Hunt, 1982). Generally, varying N rates from low to high as we ll as using different N sources can be used on the same type of soil to evaluate crop res ponse to N (Peck and MacDonald, 1983; Sader, 1980).

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45 Although snap beans are legumes with symbiotic N fixation capability, the literature contains different N recommendation rates on snap bean in different environments. Nitrogen fertilization affects the vegetative growth of snap bean plan ts as well as the pod set and development (Peck and MacDonald, 1983). Further, Sader (1980) re ported that green manure and inorganic nitrogen fertilization affected gr owth of dry bean during the earl y phases of plant development, causing a significant increase in leaf area index (L AI) and crop growth rate up to the time of flowering, but decreased growth in the later stages. The present chapter describes field experiments conducted in Gainesville, Florida, in Spring 2007 based on the hypothesis that higher n itrogen rates stimulate greater crop growth. The underlying objective in this study was to determine the effects of N fertilization on growth attributes of snap bean plants, N uptake and N partitioning within plan t parts over the growing period. Materials and Methods Cultural Practices Snap bean was grown in the field on a Millhopper fine sand soil (loam y, siliceous, hyperthermic Arenic Paleudult) during spring grow ing season 2007 at the Plant and Soil Science Field Teaching Laboratory at the campus of the Un iversity of Florida, Gainesville (29 38 N, 82 22 W). Before sowing (9 March 2007), the experimental area was moldboard plowed and 925 kg ha-1 of 4-12-12 (N-P2O5-K2O) commercial fertilizer was broadcast. Also, weeds were controlled with herbicides and hand weeding. Sowing occurred on 15 March 2007 and was performed with a custom no-till planter equippe d with notched, double disk openers and springloaded angled closing wheels. An in-row sowi ng density of 21 seeds pe r meter of cultivar 'Ambra' snap bean was adopted. Spacing betw een rows was 0.61 m and the sowing depth was 25.4 mm, resulting in a final plant population of 34 plant m-2.

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46 Daily weather data, including daily ET0, minimum temperature, and maximum temperature were obtained from the weather database of the Florida Automated Weather Network (FAWN) located at the Citra site. Also, rainfa ll data were also collected on-site. Experimental Treatments Treatm ents were imposed in a 3 x 4 factoria l split plot design with water management treatments as main plots and N levels as subplots. All treatments were replicated four times. Main plots (13.7 m 13.7 m) consisted of the following three water-management treatments: (I1) medium irrigation which was 100% of crop evapotranspiration; (I2) low irrigation which received 1/3 less the amount received by the medium regime; and (I3) high irrigation which received 1/3 more water than the medium tr eatment. Water was applied by overhead sprinkler irrigation on all the treatments on the same day with the frequency set by the medium irrigation regime, and the amount of water for the medium irrigation was determined from water balance developed by Ritchie (1998). The water balance in the soil-plant-atm osphere continuum was calculated as follows: PSEEDIP dt dW where, dW/dt = Net rate of change in stored soil water, in units of mm3[water] mm-2[area] d-1, or mm d-1; P = Precipitation on day t, mm d-1; I = Irrigation amount on day t, mm d-1; D = Drainage from bottom of profile on day t, mm d-1; Es = Soil evaporation on day t, mm d-1; and Ep = Plant evaporation (transpira tion) on day t, mm d-1. Depletion of 60% of the available soil water (ASW) was allowed for the 20 cm soil profile depth; th en irrigation up to drai ned upper limit (DUL) was scheduled. The calculation accounts for the soil wa ter content at satura tion (SAT), the lower limit of plant water availability (LL) and th e drained upper limit (DUL). Values of these parameters appropriate for this soil were taken from Carstile et al. ( 1981). Daily crop water use (ETcrop) was computed using values of potentia l evapotranspiration (ETp ot) collected from the

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47 FAWN web site and values of the crop coefficient (Kcp) which is function of development stage. The depth increment used in the water balance and irrigation scheduling procedure was 20 cm. Drainage from a soil profile takes place when th e soil water content (SW) is above the drained upper limit (DUL). Thus, the drainage (D) used above occurs when the precipitation (P) and irrigation (I) minus Es and Ep exceed the DUL on a given date. These equations and parameters were entered in EXCEL Spreadsheet and allowe d to develop the schedule and amount of water required at each irrigation even t. Differential irrigation star ted 14 DAS after applying three uniform irrigations for germination and crop esta blishment. Irrigation amounts and dates applied for each of the three water management treatments are shown in Table 3-1. Within each water-management treatment, the following four N treatments were randomly imposed: (N37) low N, consisting of a total of 37 kg N ha-1 corresponding to the preplant N application described above; (N74) Medium N, consisting of a total of 74 kg N ha-1 applied in three side-dress applications. (N111) recommended N, consisting of a total of 111 kg N ha-1 applied in three side-dre ss applications, and (N148) High N, consisting of a total of 148 kg N ha-1 applied in three side-dress applications. The first applicati on of N to the three higher N (N74, N111 and N148) treatments was same as the N37 treatment, which corresponded to the amount of N applied as preplant application. The remaining amount of N for the other three treatments was applied in two equal applications at the rate of 18.5, 37 and 55.5 kg ha-1 for the treatments 74, 111 and 148 kg ha-1, with the last applica tion occurring right after th e flowering. The remaining N for the treatments N74, N111 and N148 was supplied as ammonium n itrate (34-0-0) and was sidedressed between rows according to the schedule shown in Table 3-2. Nitrogen treatments were applied to 9 subplot rows, each 7.5 m long. Subse quently, there were 12 combination treatments consisting of three irrigation regimes and four N rates. It should be noted that the (N111) rate of

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48 111 kg N ha-1 is the current nitrogen reco mmendation rate for snap bean by the Institute of Food and Agricultural Sciences (IFAS) of the Un iversity of Florida (Olson et al., 2007). Measurements During the experim ent, the following data we re collected on crop growth and nitrogen uptake. Crop growth analysis The growth analysis study em phasized the sn ap bean response to N fertilization (four treatments) only studied in the medium irrigation treatment in order to reduce the workload. Growth analysis samples were collected at 7-day intervals beginning 14 DAS and, first measurements involved taking the c anopy height and width on two random plants within 1 m of row Then, all the plants from th is 1-m of row were cut off at ground level and the number of plants recorded. From these harvested plants, four plan ts of median size were selected and vegetative and reproductive stages were measured based on soybean development stages established by Fehr et al. (1971). Then the four sub-sampled plants were partitioned into leaves, stems, and pods. Leaf area was determined with a LI-COR 3100 area meter. Total number of main stem nodes was determined. Also, total nu mber and fresh weight of pods were measured and a sub sample of 10 median pods was taken from the total pods and their fresh weight was taken. These sub-sampled pods were partitioned into seeds and podwall, and the fresh weight and total number of the seeds was recorded. The rest of the sampled plants from the 1-m row, the leaves of the four sampled plants, the stems of the four plants, th e total rest of pods, the podwalls and the seeds of the 10 sampled pods were separately conserved and dried at 60o C in a forced-air oven for at least 72 hours and dry weights were measured. Fraction allocation of assimilates among plant organs was calculated as the ratio of dr y weight of individual pa rts to that of total plant dry matter at each sampling date. Leaf, stem, pod, and seed dry matter (DM) mass (kg DM

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49 ha-1) was calculated from the respective ratios multiplied by the total crop land-area DM (kg DM ha-1) from the combined sample and sub sample ma sses of leaf, stem, pod, and seed, respectively (Boote, 1999). Also, specif ic leaf area (SLA) (m2 leaf kg-1 leaf) was calculated from the measured leaf area and leaf mass for each sub sample. Leaf area index (LAI) (m2 leaf m-2 land) was then calculated by multiplying the SLA by the total leaf mass. Crop growth rates (CGR) were comput ed for each treatment as follows: 12 12tt WW CGR (3-1) where W1 and W2 represent the biomass weights at time t1 and t2, respectively. Using a base temperature for snap bean of 4 oC (Ferreira et al., 1997), thermal time was calculated as follows: n i bia TTT1 ,)( (3-2) Where iaT, is the daily mean air temperature of day i, Tb is the base temperature at which development stops, and n is the number of days used in the summation. Plant tissue nitrogen analyses Oven-dried samples of component plant parts ( leaf, stem, pods and seed) from each sampling date were ground in a Wiley mill to pass through a 1-mm screen. For N analysis, samples were digested using a modification of the aluminum block digestion procedure of Gallaher et al. (1975). Sample weight was 0.25 g, catalyst used was 1.5 g of 9:1 K2SO4:CuSO4, and digestion was conducted for at le ast 4h at 375C using 6 ml of H2SO4 and 2 ml H2O2. Nitrogen in the digestate was determined by semiautomated colorimetry (Hambleton, 1977). Shoot N accumulation was computed by multiplying dry mass of leaves, stem, pod and seed by the corresponding N concentrations. The distribution among plan t organs (leaf, stem,

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50 pod and seed) was calculated as the ratio of N accumulation of indivi dual parts to that of shoot N accumulation at each sampling date. Data Analysis Statistical analysis was performed with SAS (Statistical Analysis Systems, Cary, NC). Since sampling dates were correlated over time (covariance), the Proc Mixed procedure of SAS was used to analyze results with sampling date (DAS), N, and the DAS by N interaction being the main fixed effects in the model. Repetition (block) and its interaction with N were included in the random effects term. Also, linear, quadratic and cubic trends were tested for sampling time (DAS). This is a repeated measur es statistical analysis where repetition (block) and its interaction with N were included in the random effects term. When the interaction N*DAS was significant for a give n response variable, the LSMEANS differences were used to compare the N treatments at each sampling date. Shoot and N shoot growth rates were estimated with linear regressions of shoot and N shoot mass and the resulting slopes were compared for differences between N rates. Response variables tested included node numbe r, canopy height and width, LAI, plant and organs dry matter (DM) accumulation (kg ha-1), tissue N concentration (g N kg-1), and crop and organs N accumulation (kg N ha-1). Results and Discussion Effects of N Fertilizer Rates on Canopy Characteristics Numbers of nodes formed we re not significantly affect ed by the N rates and the interactions between day after sowing and N trea tments (P-values = 0.76 and 0.68, respectively) (Table 3-3). Regardless of the N treatments, th e number of nodes formed on the main stem increased linearly with th ermal time (up to about 750 oC d-1) (Figure 3-1). This number of nodes produced was stable for all treatments with the maximum number of 7 followed by a plateau

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51 which presumably marked the end of leaf appear ance on the main axis and associated initiation of a terminal inflorescence. The leaf appearance rate for snap bean as affected by N rates was compared by plotting accumulative node number ve rsus thermal time. The slopes taken in the linear portion of the acc umulative node number against th ermal time, corresponded to the development rate (rate of leaf appearance) These were 0.010, 0.009, 0.009 and 0.009 node oC d-1 for 37, 74, 111, and 148 kg N ha-1, respectively. Similarly, repeated-measures analysis of variance performed on the canopy height and width did not show a significant effect of th e individual of N treatments (P-values = 0.88 and 0.82, respectively, Table 3-3). The interaction e ffect between N treatments and days after sowing was not significant either on the canopy height and width (P-values = 0.63 and 0.23, respectively, Table 3-3). Figures 3-2A and 3-2B indicate that canopy height and canopy width increased linearly with thermal time to a maximum at about 750 oC d-1. The maximum height reached on the recommended N rate (N111) was about 46 cm as opposed to 37 cm for the lowest N rate (N37). These values of height and width are typi cal to a type 1 determinate snap bean as reported by Fageria (1997). Regard less of the N rates, flowering occurred 40 to 42 days after planting. Effects on Leaf Area Index The effects of N treatments on leaf area in dex (LAI) averaged over the sampling dates were not significant (Pr = 0.86, Ta ble 3-3). The interaction of N ra te and time (days after sowing) were not significant for leaf area index (Pr = 0.11, Table 3-3). Seasonal patterns of the effect of N rates on the development of LAI are shown in Figure 3-3. The LAI increased rapidly, with minor ef fects of N rates. The exponentially increasing phase of LAI represents the period of fast de velopment of the canopy, which determines light interception and photosynthetic ac tivity, resulting in higher carbon assimilation of leaf canopies.

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52 The time course of the LAI appeared to be s ubdivided into three sub phases as follows. A lag phase from emergence to crop establishment (0-2 0 days) which was a peri od of slow growth; the second sub phase (20-35 days) was characterize d by almost linear growth and finally a more rapid linear phase (35-50 days) duri ng which the LAI increased at a constant rate leading to a full canopy. Higher photosynthesis production can be expected during this phase. After about 34 days after sowing (DAS), the lowest N rate (N37) started to show a slower increase compared to the other three rates. This slow er increase was marked by symptoms of N stress and was visible by the yellow color of the leaves. The maxi mum LAI was approximately 2.6 and 2.0 for N111 and N37, respectively, at 55 DAS. Thereafter, a declin e in LAI was observed which was relatively more rapid at the lowest nitrogen level N37. This decline of leaf growth was associated with increased leaf senescence and abscission of th e lower leaves. Penning de Vries and van Laar (1982) estimated the re lative leaf senescence rate during this stage at 3% per day. Similar overall responses of LAI to N fertilization have been reported for dry beans (Montojos and Magalhaes, 1971; Wallace and Munger, 1965). However, the valu es of LAI measured in this study were lower than the ones reported by th e above authors. It is of interest to note that the peak LAI observed for the highest N treat ment was only numerically similar to the one observed on the lowest N rate (2.2 versus 2.1). This may be due to over-fertilization which to some extent limited plant growth and foliar expansion, especially under high evaporat ive conditions with marginally adequate irrigation regime where any excessive N was not leached. Effects on Biomass Accumulation Significant effects of N treatments on tota l dry matter accumulation averaged over the growing period were highly significant (Pr = 0.00 1, Table 3-4). A similar result was also found with respect to the inte raction between N rate and days af ter sowing (Pr < 0.0001); implying that there was a significant differe nce among N treatments at indi vidual time points (days after

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53 sowing). Indeed, significant diffe rences in shoot dry matter were observed mostly from mid to late season (50 to 83 days after sowing), notably between the lowest N rate and the three higher N rates which did not show any significant diffe rence from each other (Appendix Table A-1). Figure 3-4 illustrates the seasona l patterns of shoot dry matter in relation to the N rates. Regardless of the N rates applied, sh oot dry matter (DM) accumulation (kg ha-1) increased continuously throughout the growing season until reaching its maximum value near maturation (77 days after sowing). As shown by the statistical analysis, the dry matter accumulation of snap bean did not differ across N rates until about 50 DAS a nd thereafter the lowest N rate began to show a lower dry matter accumulation rate than th e three higher N rates which did not show any significant difference from each other (Appendix Table A-1). The greatest amount of DM (5000 kg ha-1) was produced at the 148 kg ha-1 N rate. A similar trend was observed by Peck and MacDonald (1983) who found that dr y weight accumulation in the snap bean plants was slow from planting to the seedling stage, more rapid from seedling to the bloom stage and reached a rapid rate of 22 g m-2 day-1 from the bloom to the pod stage. Increase in DM production as a consequence of N can be explained by increase in Net Assimilation Rate (NAR) (Sader, 1980). Average crop growth rates estimated from the sl opes of near-linear periods of total biomass increase (34-76 DAS) were 80.7, 107.9, 108.6 and 109.0 kg ha-1 d-1 for 37, 74, 111 and 148 kg N ha-1, respectively, with significant difference obser ved only between the lo west N rate (37 kg ha1) and the three higher N rates (74, 111 and 148 kg ha-1) which did not show any significant different from each other (Pr = 0.003, 0.89, 0.99 and 0.99, respectively). With differences between N rates typically becoming more evid ent over time, final DM accumulation was 26% higher for the three higher N-fertilizer rates than at the lowest N rate. The decrease in dry matter

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54 at the later growth stage may be related to more photosynthate transl ocated to grain and abscission of old leaves (Fageria, 1997). Effects on Plant Organ Mass and Distribution Differences between N treatments on the leaf and stem dry weights measured in this study were not significant across the overall growing pe riod (Pr = 0.94 and 0.74, respectively, for leaf and stem dry weights; Table 3-4). The interac tion of time (DAS) and N was not significant on these two response variables either (Table 3-4) On the contrary, the effect of N and the interaction effect of N and DAS showed highly significant difference for the pod mass and seed mass (Pr < 0.0001 and 0.04, respectively, Table 3-4) The pair wise comparison of N treatments showed significant differences in pod dry mass mostly during late season from 62 to 83 days after sowing, notably between the lowest N rate and the three higher N rates (Appendix Table A2). Similarly, Appendix Table A-3 indicated that significant differences between N treatments for the seed dry weights were observed from 69 days after sowi ng through the end of season, mostly between the lowest N rate and the three higher N rates which did not show any significant difference from each other. The time course of dry matter accumulation in di fferent plant organs is presented in the Figures 3-5A, 3-5B, 3-5C, and 3-5D. As indicated by the stat istical analysis, dry matter accumulation in different plant organs under the lowest N rate (N37) was slightly lower than the three other rates especially towards the end of the growing season. This difference was more pronounced on the reproductive organs (pods and s eeds). The pattern of DM increase over time included linear, quadratic, and cubic terms. Figures 3-6A, 3.6B, 3.6C, and 3.6D present the fraction of the total plant dry matter in the respective plant organs over time. Inspection of th ese figures revealed that approximately 80% of the plant dry matter was present in leaf during initial growth comp ared to roughly 20% in stems.

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55 But throughout the growing season, the fraction of plant dry matte r in leaf and stem declined continuously. About 50 DAS, assimilates were pre dominantly directed to the newly-developing pods, resulting in a rapid increase in fraction of total dry matter found in pod and later in seed. Finally, when pods reached physiological matu rity about 75 to 80 DAS, pods and seeds accounted for most of the top dry weight, with pods accounting for 75 to 80%, and seeds, 55 to 60%, respectively, of the total shoot dry matter. Gutierrez et al. (1994) ar gued that the switch in dry matter allocation from vegetativ e growth to fruit growth is s een as plateau in stem growth, and also seen as a decline in leaf mass due to leaf abscission and to the slowing of leaf initiation. While N rate had no effect on fraction dry matter in plant organs/components early in the season, it was observed later in the season that the lowest N rate (N37) had a lower fraction of dry matter in stem and leaf towards the end of the growing season. Effects of Nitrogen Supply on Nitrogen Uptake Plant tissue nitrogen concentration Knowledge of nutrient concentr ations and distribution in plant parts is important for a basic understanding of plant nutriti on (Fageria, 1997). Tissue N concentrations were variably affected by the N treatments. Except for the stem and seed, the N concentrations in the leaf and pod were significantly different as result of N treatments with P values of 0.02 and < 0.0001 for leaf and pod, respectively, (Table 3-5). The N* DAS interaction effect was also significant only for leaf and pod N concentration (Table 3-5). The pa ir wise contrast analysis based on the least square means of leaf N concentration and pod N concentration are presented in Tables A-4 and A-5, respectively. These tables showed that hi ghly significant differences between the lowest N treatment (37 kg ha-1) and the three higher N treatments (74, 111 and 148 kg ha-1) were observed from 50 days after sowing through the end of seas on for the leaf N concentration; and from 50 to 69 days after sowing for the pod N concentration.

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56 Tissue N concentrations over time for snap bean grown at four N rates are shown in Figures 3-7A, 3-7B, 3-7C, and 3-7D. Irrespective of the tissue types, N concentration showed a decreasing trend over the growing season. Typically, initial values of N concentration were higher early in the season acro ss all the N rates with reproductive organs (pod and seed) having relatively higher values. Similar declines in shoot N concentration have been commonly observed (Peck and MacDonald, 1983; Fageria et al., 1997; Barker and Bryson, 2006; Lemaire et al., 2007). The N concentration in leaf decr eased from 6% to about 2% near the end of the growing season irrespective of N treatments. Similarly, the N concentration in stem decreased from 4.5% during initial growth to 0.7% in the end of season. Respective values for pod and seed were 5 to 1%, and 7 to 3%. But of all organs, the N concentratio ns in the seed at the end of the season were higher. Lemaire et al. (2007) reported that this observation of tissue N decline has usually been interpreted as a direct result of plant ageing and has often been related simply to phenology, leading to large differences in N concentrati on between species or genotypes within a given environment, and between different environments for a given genotype. Mobilization of N from old leaves to meristems, young leaves, and fruits leads to a diminished N concentration in old, lower leaves of plants (Barker and Bryson, 2006). Nitrogen accumulation by snap bean plants There was a highly significant differen ce between N treatments for the total N accumulation in snap bean (P = 0.0001). The interaction of N and days after sowing was also highly significant, showing th at there was a significant di fference among N treatments at individual time points (days after sowing). Indeed, highly significan t differences in total shoot N were observed mostly during late season from 50 DAS towards the end of season (83 DAS), notably between the lowest N rate and the thre e higher N rates (Appendix Table A-6). Seasonal total N accumulation pattern over time is presente d in Figure 3-8. The N accumulation in the

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57 plant followed a linear increase over time similar to total plant dry matter accumulation. From germination until about 34 DAS, the response of N accumulation to the four N rates was similar with no obvious treatment effect. The average N accumulated during this early growth ranged from 11 to 13 kg N ha-1. The maximum N accumulation occurred late in the season at 76 DAS and was 95 and 149 kg N ha-1 for the lowest and highest N rates, respectively. Similar seasonal accumulation pattern of N by crop plants was reported by Below (2002). At an N rate of 300 kg ha-1, Sader (1980) found a maximum N accumulation in the seed of 125 kg ha-1. These values compared fairly well with those observed in this study. Similarly, during the vegetative growth peri od from sowing to 40 DAS, the four N rates accumulated on average 1.10, 1.38, 1.30, and 1.37 kg N ha-1 d-1 for 37, 74, 111 and 148 kg ha-1, respectively, indicating little effect of observed N-rates until th at time. Later in the growing season, the average daily N accumulation rate was 1.71, 2.45, 2.31 and 3.04 kg N ha-1 d-1, for the treatments 37, 74, 111 and 148 kg N ha-1, respectively. However, Peck and MacDonald (1983) observed that during the 20 days fr om the bloom to the pod stage, the total N in the plants grown with soil N but without fer tilizer N increased an av erage rate of 4.4 kg N ha-1 d-1 while plants grown with soil N plus fertilizer N at 120 kg N ha-1increased on average rate of 6.6 kg N ha-1 d-1. Effects of N supply on nitrogen distribution Unlike the seed, the N accumula tion in the leaf, stem and pod did not show any significant difference with N treatments. The P-values we re 0.93, 0.96, 0.10 and 0.003 for leaf, stem, pod and seed, respectively (Table 3-5). The DAS*N interaction term was significant for pod N mass and seed N mass response variables with differe nces between N rates based on the pair wise comparison typically becoming more pronounced over time (Tables A-7 and A-8). The effects of N rates on cumulative N-distribution for snap bean are shown in Figures 3-9A, 3-9B, 3-9C and 3-9D. Independent of the N rate applied, during vegeta tive growth about 70 to 80% of the N was

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58 accumulated in the leaves compared to 20 to 30% in stems. While the fracti on of N present in the leaves declined throughout the season, the fracti on of N found in the stem initially followed an increase to 40 to 50 DAS and thereafter showed decline until the end of season. The lowest Nrate showed greater decline in the N fraction found in the vegetative organs while the three other rates followed a slow decrease. At the end of the growing season, N accumulation in the fruit accounted for 64% at the highest N rate and 72% at the lowest N rate. Sader (1980) observed that re-mobilization of N from vegetative plant material to the snap bean seed occurred regardless of the rate of fertilizer application. Conclusion The purpose of our experiment was to evaluate the pattern of some crop growth variables over the full growth life cycle to maturity for field-grown snap bean plants under different N rates. Snap bean growth parameters analyzed in this study responded variab ly to N fertilization. Number of nodes on the main stem, canopy heig ht and width did not show any significant difference with N treatments. Maximum values of LAI for treatments of 37 and 111 kg N ha-1 were 2.0 and 2.6, respectively, at 55 days af ter planting. Thereafter, a decline in LAI was observed which was relatively more rapid at th e lowest N rate. The dry matter accumulation of snap bean did not differ statistically across N rates until about 55 days after planting but thereafter the lowest N rate showed lower dr y matter accumulation rate as opposed to the three higher N rates which did not show any apparent differences from each other. Generally, the temporal pattern of plant total, leaf, and stem mass showed an initial exponential increase and a near linear increase to the peak followed by a decline phase later during the end of the growing season irrespective of N treatments. Fractional distribution of total dry matter in aerial plant parts indicated that irrespective of N treatments, a ll treatments produced maximum allocation to the leaves followed by the stem during the early st age of development, and after the onset of

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59 reproductive organs, more dry matter was alloca ted to the pod and seed towards the end of growing season. This study confirmed the pos itive relationship between biomass accumulation and N accumulation in the plant. Indeed, the pattern of total N accumulated over time was closely associated with aerial biomass. Nitr ogen distribution in plan t components was largely affected by N fertilization. N accumulated in vegetati ve parts at all treatments was relatively high at very early stages of plant development, and started declini ng throughout the growing season. A sustained slower increase in N accumulation occurred after 50 DAS due to remobilization of N to other plant parts, mainly pod. Total N accumulated in plant was considerably affected by N fertilization. At the end of gr owing cycle, the majority of th e N was allocated to the seed irrespective of the N treatments.

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60 Table 3-1. Irrigation amounts (mm) and dates of application of the three water management treatments Water Management Treatments (mm) Date DAS Low Medium High 03/14/2007 0 15.2 15.2 15.2 03/15/2007 1 13.2 13.2 13.2 03/22/2007 7 5.8 5.8 5.8 03/26/2007 12 8.2 8.2 8.2 03/30/2007 16 7.9 9.7 13.0 04/03/2007 20 7.5 9.7 17.7 04/12/2007 29 5.8 7.4 11.0 04/14/2007 31 7.4 9.1 12.6 04/20/2007 37 7.5 9.0 12.4 04/24/2007 41 9.8 11.7 20.6 04/27/2007 44 11.2 14.8 22.3 04/30/2007 47 11.9 15.3 19.9 05/03/2007 50 14.6 15.4 26.9 05/06/2007 53 13.4 17.4 24.2 05/10/2007 57 11.4 14.9 22.9 05/13/2007 60 17.0 21.0 31.6 05/15/2007 62 3.1 3.3 3.7 05/20/2007 67 11.8 13.3 20.2 05/24/2007 71 14.6 15.2 23.1 05/25/2007 72 6.5 7.1 11.7 05/30/2007 77 12.4 14.2 21.5 Total 216 251 354 Table 3-2. Amounts and dates of N applic ation of the four nitrogen treatments Nitrogen Rates (kg ha-1) Date DAS 37 74 111 148 03/09/2007 Preplant 37.0 37.0 37.0 37.0 03/29/2007 15 18.5 37.0 55.5 04/26/2007 43 18.5 37.0 55.5

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61 Table 3-3. Probability levels (P) for the effects of nitrogen rate (N) and interaction (N*DAS) on canopy characteristics Source of variation Number of Nodes Height Width LAI DAS-L <0.0001 0.55 0.58 <0.0001 DAS-Q <0.0001 <0.0001 <0.0001 <0.0001 DAS-C 0.10 <0.0001 <0.0001 <0.0001 N 0.76 0.88 0.82 0.86 N*DAS 0.68 0.63 0.23 0.11 L= Linear, Q= Quadratic and C= Cubic Table 3-4. Probability levels (P) for the effects of nitrogen rate (N) and interaction (N*DAS) on plant mass variables Source of variation Shoot biomass Leaf mass Stem mass Pod mass Seed mass DAS-L <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 DAS-Q <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 DAS-C <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 N 0.001 0.94 0.74 <0.0001 0.04 N*DAS 0.0001 0.08 0.16 <0.0001 0.04 L= Linear, Q= Quadratic and C= Cubic Table 3-5. Probability levels (P) for the effects of nitrogen rate (N) and interaction (N*DAS) on plant organ N concentrations Source of variation Leaf N conc. Stem N conc. Pod N conc. Seed N conc. DAS-L 0.51 0.0002 <0.0001 0.34 DAS-Q 0.002 0.84 <0.0001 0.82 DAS-C 0.007 0.84 <0.0001 0.73 N 0.02 0.21 <0.0001 0.30 N*DAS 0.02 0.49 0.005 0.46 L= Linear, Q= Quadratic and C= Cubic Table 3-6. Probability levels (P) for the effects of nitrogen rate (N) and interaction (N*DAS) on total plant N mass and plant organ N mass Source of variation Shoot N mass Leaf N mass Stem N mass Pod N mass Seed N mass DAS-L 0.0004 0.03 0.78 0.009 <0.0001 DAS-Q <0.0001 0.02 0.001 0.01 <0.0001 DAS-C <0.0001 <0.0001 <0.0001 0.07 0.02 N 0.0001 0.93 0.96 0.10 0.003 N*DAS 0.0001 0.21 0.06 0.003 0.01 L= Linear, Q= Quadratic and C= Cubic

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62 0 1 2 3 4 5 6 7 8 02004006008001000120014001600 Thermal Time (oC d-1)Number of Nodes 37 74 111 148 Figure 3-1. Number of n odes formed on snap bean versus thermal time as affected by four N fertilization rates in Gain esville FL during Spring 2007 0 5 10 15 20 25 30 35 40 45 02004006008001000120014001600 Thermal Time (oC d-1)Canopy height (cm ) 37 74 111 148 Figure 3-2. Canopy height (A) a nd canopy width (B) of snap bean versus thermal time as affected by N fertilization rates in Gainesville FL during Spring 2007 A

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63 0 5 10 15 20 25 30 35 40 45 50 02004006008001000120014001600 Thermal Time (oC d-1)Canopy width (c m 37 74 111 148 Figure 3-2. Continued 0 1 2 3 020406080100 Days after sowingLeaf Area Inde x 37 74 111 148 Figure 3-3. Leaf area index of sn ap bean over time as affected by four N fertilization rates in Gainesville FL during Spring 2007 B

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64 0 1000 2000 3000 4000 5000 6000 020406080100 Days after PlantingShoot Dry Matter (kg ha-1) 37 74 111 148 Figure 3-4. Shoot dry matter of sn ap bean over time as affected by four N fertilization rates in Gainesville FL during Spring 2007 0 200 400 600 800 1000 1200 1400 020406080100 Days after plantingLeaf Mass (kg ha-1) 37 74 111 148 Figure 3-5. Plant dry matter of A) leaf, B) stem, C) pod, and D) seed of snap bean over time as affected by four N ferti lization rates in Gainesvi lle FL during Spring 2007 A

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65 0 200 400 600 800 1000 1200 020406080100 Days after plantingStem Mass (kg ha-1) 37 74 111 148 Figure 3-5. Continued 0 500 1000 1500 2000 2500 3000 3500 4000 020406080100 Days after plantingPod Mass (kg ha-1) 37 74 111 148 Figure 3-5. Continued B C

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66 0 500 1000 1500 2000 2500 3000 020406080100 Days after plantingSeed Mass (kg ha-1) 37 74 111 148 Figure 3-5. Continued 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 020406080100 Days after plantingFraction of plant biomas s Leaf Stem Pod Seed Figure 3-6. Percentage of total plant biomass found in leaf, stem, pod, and seed of snap bean over time as affected by A) 37 kg N ha-1, B) 74 kg N ha-1, C) 111 kg N ha-1 and D) 148 kg N ha-1 D A

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67 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 020406080100 Days after plantingFraction of Plant biomas s Figure 3-6. Continued 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 020406080100 Days after plantingFraction of plant biomas s Figure 3-6. Continued B C

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68 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 020406080100 Days after plantingFraction of plant biomas s Figure 3-6. Continued D

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69 0 1 2 3 4 5 6 702 04 06 08 01 0 0Days after plantingN concentration in Leaves (%) 37 74 111 148 Figure 3-7. Effects of N-fertilizer rates on N concentration of A) leaf, B) stem, C) pod, and D) seed of snap bean grown in Gainesville FL in spring 2007 0 1 2 3 4 5 6 02 04 06 08 01 0 0 Days after plantingN concentration in Stem (% ) 37 74 111 148 Figure 3-7. Continued B A

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70 0 1 2 3 4 5 6 02 04 06 08 01 0 0 Days after plantingN concentration in Pod (% ) 37 74 111 148 Figure 3-7. Continued 0 1 2 3 4 5 6 7 8 020406080100 Days after PlantingN concentration in Seed (% ) 37 74 111 148 Figure 3-7. Continued C D

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71 0 20 40 60 80 100 120 140 160 0102030405060708090 Days after plantingShoot Nitroggen (kg ha-1) 37 74 111 148 Figure 3-8. Shoot N of snap bean over time as af fected by four N fertilization rates in Gainesville FL during Spring 2007

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72 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 020406080100 Days after plantingFraction of total plant N Leaf Stem Pod Seed Figure 3-9. Effects of N-ferti lizer rates on fraction of plant N found in plant components over time for: A) 37, B) 74, C) 111 and D) 148 kg ha-1 treatments of snap bean grown in Gainesville FL in spring 2007 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 020406080100 Days after plantingFraction of total plant N Leaf Stem Pod Seed Figure 3-9. Continued A B

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73 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 020406080100 Days after plantingFration of total plant N Leaf Stem Pod Seed Figure 3-9. Continued 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 020406080100 Days after plantingFraction of total plant N Leaf Stem Pod Seed Figure 3-9. Continued C D

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74 CHAPTER 4 RESPONSES OF SNAP BEAN TO INTERACTIVE EFFECTS OF IRRIGATION AND NI TROGEN FERTILIZATION: YIELD, YIELD COMPONENTS AND QUALITY Introduction Yield is the integrated manifest ation of various phys iological processes occurring in plants and these processes influence the development of observed plant traits, which can be modified by imposed management practices (Gill and Na rang, 1993). The management of water and fertilizers are among the management practices instrumental to cr op performance and are vital to the high productivity of vegetable crops in the commercial production system. Achieving the correct rate of fertilizer application is an esse ntial part of optimal fertilizer management. This however needs to be coupled with good irrigation practices as the applicat ion of fertilizer and water are interlinked. Indeed, a combination of optimal irrigation and N management is considered critical to improve crop N uptake effi ciency, so as to maintain optimal crop yield, while minimizing NO3 leaching below the root zone (Quinon es et al., 2007). Efficient use of water and fertilizers are thus highly critical for the sustainability of agriculture in increasingly competitive local and world markets, and in comp etition with urban environments for resources (Hebbar et al., 2004). Several studies showed direct relationships between the addition of N in intensive agriculture and excessive soil N accumulation and losses to surface and ground water with potential long term environmental hazards (P aramasivam et al., 2001; Quinones et al., 2007; Zotarelli et al., 2007a). Like other vegetable crop s in the commercial production system, snap bean production in Florida is intensively manage d with relatively high in puts of fertilizers and irrigation water. Obtaining high fresh market snap bean yiel ds in these production systems requires intensive sprinkler irrigation and ferti lizer application. Zotarelli et al. (2007a) reported that excessive irrigation and/or N application rate combined with intense rainfall on excessively

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75 drained sandy soils with low wa ter-holding capacity great ly enhances the potential risk of N leaching. Sustainability of these cropping syst ems requires developing ma nagement techniques that maintain or increase N and water use effi ciency while sustaining environmental quality. Thus, this research was intended to: (i) determine the effects of interactions of irrigation amounts and N fertilization on snap bean fresh market yield, quality, N uptake, water use efficiency, nitrogen use efficiency, and (ii) evaluate th e effect of irrigation regimes on the seasonal distribution of N in the soil profile. Materials and Methods Field Experiments A detailed description of the field experiments (cultural practices and field treatments) is given in Chapter 3. Final Yield Estimation On 17 May (64 days after sowing), a sample ar ea in the center of each sub-plot consisting of 2 rows of 2 m length was marked. First, canopy height and width was measured at two random sites in this area. Then, all plants within this harvest area were cut off at ground level and the number of plants recorded. A sub-sample of two median pl ants was taken and growth and development parameters (number of nodes, developm ent stage, leaf area, and stem length) were recorded. Pods were picked from all plants. Pods were then sorted visually into the marketable pods (sieve sizes of 3 to 5) and unmarketable pods (culls made up with small pods less than 3 sieve size, plus damaged pods) and the fresh we ight of each category was recorded. From the marketable pods, a sub-sample of 20 pods was taken and fresh weight of the 20 pods, and the sieve size, length, and diameter of each pod were recorded. Also, the total number of seed and the fresh weight of the seed from these 20 pods were recorded. Finally, the harvested plants (minus pods), marketable pods, unmarketable pods and the podwall and the seeds of the 20-pod

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76 subsample were dried at 60o C for at least 72 hours and their dry weights were recorded. From the two sub-sampled plants, the leaf and stem were dried and weighed. Leaf, stem, pod, and seed dry matter (DM) mass (kg DM ha-1) were calculated from the combined sample and sub sample masses of leaf, stem, pod, and seed, respectivel y. Pod harvest index was calculated as pod to above ground biomass ratio on dry weight basis. Specific leaf area (SLA) (m2 leaf kg-1 leaf) was calculated from the measured leaf area and leaf mass for the two sub-sample plants. Leaf area index (LAI) (m2 leaf m-2 land) was then calculated by multiplying the SLA by the total leaf mass (g m-2 ) from each sample. Parameters such as size of seed, pod sieve si ze, pod length, pod diameter, number of seed, and average seed weight are important to appr eciate the marketability of snap bean (Bonanno and Mack, 1983; Peck and MacDonald 1983). Intera ctive effects of irrigation and N rates were thus evaluated through computati on of these parameters on the fr esh market harvest date (64 days after sowing). Plant Tissue Nitrogen Analyses Oven-dried samples of component plant parts ( leaf, stem, podwall, and seed) were ground in a Wiley mill to pass through a 2-mm screen. Fo r nitrogen analysis, samples were digested using a modification of the aluminum block di gestion procedure of Ga llaher et al. (1975). Sample weight was 0.25 g, the catalyst used was 1.5 g of 9:1 K2SO4:CuSO4, and digestion was conducted for at least 4 h at 375C using 6 ml of H2SO4 and 2 ml H2O2. Nitrogen in the digestate was determined by semiautomated colorimetry (Hambleton, 1977). Shoot N accumulation was computed by multiply ing tissue weights of leaves, stem, pod and seed by the corresponding N concentrations.

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77 Nitrogen in the Soil Profile For monitoring N movement within the soil profile over time as affected by different irrigation regimes, soil cores (4cm diameter) were taken three times at 0.3 m depth increments to a depth of 1.20 m on the subplot trea tment receiving the N rate of 111 kg ha-1 and refrigerated until further analysis. A 10-g sub sample was ex tracted with 100 mL of 1 M KCL and filtered by gravity with distilled water (Q8, Fisher Scientific Inc., Pittsburgh, PA). Soil core extracts were stored at -18oC until they were analyzed for NO3-N and NH4-N using an air-segmented semiautomated colorimetric analysis (EPA Method 353.2) in the media extr act with Technicon II Auto-Analyzer (Mylavarapu and Kennelley, 2002) Soil moisture was determined by drying a 30-g subsample at 105C for 24 h in a forced-air oven. The soil bulk density was used to convert soil NO3-N and NH4-N to a mass-per-land-area basis. Data Analysis All the plant data were statistically analyzed as a split plot design with four replications using the General Linear Models program of SAS statistical software (SAS Institute Inc., 2000). Response variables tested include d fresh marketable yield (Mg ha-1), crop dry matter (DM) accumulation (kg ha-1), crop N accumulation (kg N ha-1), nitrogen use efficiency (NUE) (kg kg1), and water use efficiency (WUE) (kg ha-1 mm-1). WUE was calculated as the ratio of the Fresh Marketable Yield (kg ha-1) to the seasonal water use (effectiv e amount of water applied in mm). Also, NUE was calculated as the ra tio of fresh marketable yield to applied N. The main fixed effects used in the model were irrigation regime (I) and N-rate (N). Interaction effects included in the model were I*N-rate. Linear and quadrat ic trends of N and I treatments were also evaluated using orthogonal contrasts. Means among treatments were compared using Least Significant Difference (LSD) at P 0.05 probability. Regression analyses between amounts of N applied and fresh marketable yield were performed.

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78 Results and Discussion Canopy Characteristics at Harvest Maturity The canopy vegetative parameters of snap bean responded as expected to individual effects of irrigation levels and N rates and the interaction of these two factors (Table 4-1). The number of nodes formed on the main stem was approximate ly 7 at harvest maturity for all treatments, confirming that final node number on this very determinant snap bean is unresponsive (as expected) to the N-rates and irrigation levels in this study. Measured plant height and width were significantly responsive to the effects of irrigation regime. Plan t height was not significantly responsive to the N rates but plant width was. The interactive effect of bo th irrigation levels and N rates was significant on the plant height res ponse (P< 0.05) while plant width did not show any interaction. Further, the hi gh irrigation treatments used in this study increased plant growth parameters such as the leaf area index (LAI). The increas e in LAI would be exp ected to lead to a higher light interception and photosynthesis. Increasing irrigation rate would have increased water availability in the root zone resulting in improving plant wa ter status and be tter stomatal conductance which eventually reflects in photoassimilate production (Abdel-Mawgoud, 2006). As can be seen in Table 4-1, response of leaf area index (LAI) showed no significant interactive effect of irrigation levels and N rates. Similar to plant height and width responses, only the lowest irrigation and N rates showed significant difference from the remaining higher treatments. The response of LAI to indivi dual N or irrigation effects was linear. Fresh Marketable Yield, Crop Biomass, a nd Pod Harvest Index at Harvest Maturity Fresh marketable yield and shoot DM accumulation increased linea rly with irrigation regimes and N-rates, while they showed a quadratic response to N rate (Table 4-2). Interactive effect of irrigation levels and N rates was significant on the fre sh marketable yield (P< 0.05) while interaction effect on shoot DM was not significant. At the low irrigation regime (66% ET),

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79 fresh market yields were 8. 5, 11.8, 15.7, and 10.6 Mg ha-1 for the 37, 74, 111, and 148 kg N ha-1 treatments, respectively. At the medium irrigation treatment (100% ET), yields were on the order of 10.1, 14.8, 17.5, and 17.1 Mg ha-1 and finally, at the high irri gation level (133% ET), they were 12.9, 16.3, 15.2 and 18.9 Mg ha-1. Numerically, the incremental differences in fresh marketable yield among N rates for each irrigati on regime were small at the low irrigation regime (3.3 versus 3.9 and 2.1 Mg ha-1 for N37 versus N74, N74 versus N111 and N37 versus N148, respectively) while they were larger at high irrigation regime (3.4 and 6 Mg ha-1 for N37 versus N74, and N37 versus N148, respectively). Results of fresh marketable yield to different N rates compared fairly well with yields repor ted by Hochmuth and Cordasco (2000) who found that marketable yields increased quadrat ically with average yield of 17 Mg ha-1 at N rates of 110 kg ha-1. These authors also observed that there was no yield advantage from application of N in excess of recommended rates in experiments in nor th Florida. A similar conclusion was made by Dufault et al. (2000) who observed that snap bean fresh marketable yields were similar with 60 to 120 kg N ha-1. The higher irrigation (medium or high) enhanced the fres h market yield significantly compared to low irrigation levels (Table 4.2). Th ese results illustrate the impact of irrigation regime on snap fresh market production and would s uggest that irrigation is an important factor in snap bean production system and increased growth is expected w ith increased irrigation rate at recommended N application rates. This is in agreement with Abdel-Mawgood (2006) who quantified the interactive effects of irrigation le vel and compost applicatio n rate (as N source) on different plant growth parameters of snap b ean. Additionally, in a sub-Saharan environment, Pandey et al. (2000) concluded th at, generally, the gr eater the N supply, the more yield was reduced by deficit irrigation in maize production. High irrigation frequencies generally favored

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80 strong vegetative development and stimulated the generation of flowers and pods (Deproost et al., 2004). Sezen et al. (2005) found that maximum fresh market yields (20,558 kg ha-1) of snap bean were obtained from irrigation treatments consisting of 13-17 mm every 2 to 3 days with a crop pan coefficient (Kcp) of 1 as opposed to the treatments consisting of 58-62 mm every 10 to 12 days with a Kcp of 0.50 which yielded 12,243 kg ha-1. This confirms again the positive effect of adequate soil water on N availability and th e capacity that the plant has for a simultaneous uptake of water and N leading to their more effective use when both are at a satisfactory level (DiPaolo and Rinaldi, 2008). In this study, fresh marketable yield was increased 26% for High versus Low regime and only 6% for High versus Medium regime. As for the N rates, the response to N fertilizer level was less consistent Fresh marketable yield increments were 35% for N111 versus N37 and only 4% for N148 versus N111. Individual regression analysis for N rates at each irrigati on regime was performed on the fresh marketable yield. Table 4-3 presents the quadratic model regression equations to N fertilization. Predicted N rates required to attain maximum yi eld under each irrigation regime condition were calculated and showed that across all three irrigation re gimes, greater N rates were required to achieve the maximum yields, at successively higher irrigation regimes. Figure 4-1 shows the significant inter action of Irrigation x N on fresh ma rket yield with a second order relationship, confirming that the soil water (irr igation regime) is the most fresh market yield limiting factor, followed by N effect. Values of Nm ax could be used as a reference point for determining optimum N rate accounting for variou s production costs. With the coefficient of determination R2 values of 0.52, 0.70 and 0.32 for Low, Medium and High irrigation regimes, respectively, it appeared that a re latively large fraction of the ove rall variability in yield could

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81 not be accounted for by N rates, most notably at high irrigation which showed the lowest coefficient of determination value. While water and nitrogen availa bility had significant effects on fresh marketable yield and plant biomass, the response was not reflect ed on the pod harvest index which showed nonsignificant difference with respect to the individual effects of water and irrigation as well as the interaction. The observed harves t index for all treatments was w ithin the range of 0.4 to 0.6 reported as typical by Fageria et al. (1997). Yield Components and Pod Quality Parameters Analysis of yield attributes in this study (number of pod and number of seed) showed that the pod number and seed number presented a linear response to the irrigation regimes while these variables both responded linearl y and quadratically to the N ra tes. The interaction I*N-rate effect was not significant for thes e yield components (Table 4-4). There were significant differen ces in pod diameter, pod length and number of seed per pod while other pod quality parameters such as perc entage of seed weight per total pod weight on fresh weight basis and average weight per se ed (mg) remained statistically unaffected by irrigation and N levels (Table 4-5). Pod quality para meters for fresh marketable yield such as pod diameter responded linearly to both irrigation and N rates while pod length and number of seed per pods were linearly responsive to the irriga tion regimes and quadratically to the N rates. Overall, there was no interacti on of irrigation and N rates on these pod quality parameters. Pod diameter decreased with increa sing irrigation level while it increased with increasing N rates. Pod length increased with increa sing irrigation regimes while it first increased with increasing N rate to the rate of 111 kg ha-1, then it was reduced when N ra te was further increased to 148 kg ha-1. A similar trend was observed on the average number of seed per pod. Despite these small significant differences, it appeared from these resu lts that pod quality parameters overall were

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82 not affected in a major way by irrigation and N e ffects. This is in lin e with Bonnano and Mack (1983) who concluded that pod quality in snap bean was less sensit ive to water deficits than was pod yield. Findings of Sezen et al. (2005) suggest ed that higher Kcp coefficients with lower irrigation frequency (2 to 3 days interval) resulted in better quality green beans. The percentages of seed weight to the total pod weight on a fres h weight basis were in the range of 10 to 11%. This compared fairly well with Peck and M acDonald (1983) who indicat ed that unlike legumes harvested for seed, snap bean pods should have le ss than 10% seed in the pods on a fresh weight basis at optimum harvest time for processing for human consumption. Further analysis of the average weight per seed versus N rate revealed that increasing the N rate increased average weight per seed, but this may be a general statement of relative maturity, especially if seed growth and maturation is delayed by N deficit. Quality in snap bean is defined in terms of sieve size. Sieve sizes, which are usually used as a primary measure of quality and therefore mark et price of snap bean pods, are actually based on the range of diameter of the pods. For this pu rpose, standard sieve si ze ranges were developed by USDA to grade snap bean fresh marketable quality. The U.S. standards for grades of fresh market snap bean separate pods into six main classes based mostly on pod diameter also called pod sieve size as follows: Size 1 (diameter be tween 5.1 and 7.3 mm), Size 2 (diameter between 7.3 and 8.3 mm), Size 3 (diameter be tween 8.3 and 9.5 mm), Size 4 (diameter between 9.5 and 10.7 mm) and Size 5 (diameter greater than 10.7 mm). Of these five categories, Size 3 and Size 4 are considered appropriate for fresh ma rket. Distributions of the pod sieve size at harves t are presented in Figures 4-2 A, B, and C. Analyses of these figures revealed that across all irrigati on treatments, pods were between the sieve sizes 2 to 5 but the largest proportion of pods were in the sieve si ze 4 category. Regardless the N rate applied,

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83 relatively higher yield would then be expected as opposed to the case where most pods fall in the sieve size 3 for example because the bigger is the sieve size, the heavier is the pod. There was no difference among the four N rates with regard s to pod sieve size dist ribution. At the low irrigation regime and the rate of 148 kg N ha-1, 60% of pods were in sieve size 4. At the medium irrigation rate and the lowest N rate (N37), more than 60% of pods were in sieve size 4. Finally, at the highest irrigation regime w ith the optimum N rate of 111 kg N ha-1, the sieve size 4 predominated. These results would indicate th at that maturation was delayed under lower N (more categories 2 and 3, and less category 5 pods). Total Nitrogen Accumulated and Analysis of Water and Nitrogen Use Efficiency for Snap Bean at Harvest Nitrogen accumulated in the snap bean crop di d not respond significan tly to the individual effect of irrigation regimes but did respond linearly to N rate. Increasing N rates significantly increased the total N accumulated in the plants. The N-rate*Irrigation regime interaction effect was not significant for total N accumulated (Table 4-6). Apparent N recovery (ANR) is defined as the amount of N taken up by the crop in a given N treatment minus the amount taken up in the zero N treatment divided by the amount of N applied. If we consider the lowest N rate (N-37) as a zero N treatment, values of ANR averaged over a ll the irrigation levels were 30, 34 and 31% for 74, 111, 148 kg ha-1, respectively. These values of fertilizer N recovery by snap bean could have been affected by such factors as various sources of N such as nitrogen fixation, mineralization of residues and soil organic matter and also initial soil ammonium and nitrate; irrigation, precipitation and even soil type. The NUE values, expressed as kg of fresh marketable yield per kg of N applied, are also reported in Table 4-6. Analysis of these values revealed that the N use efficiency was linearly increased with irrigation, but linearly decreased with higher N rate. Ther e was no interaction of

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84 these two factors on the efficien cy of N use. In line with Peck and McDonald (1985), these results showed that NUE increased linearly with soil water availability and decreased with applied N. The NUE of the two higher irrigation regimes were similar, with 15% lower value at the low irrigation. On the contrary, the NUE of the first three N rates (N37, N74 and N111) were also similar, with a 19% lower NUE at the highest N rate. There was a quadratic response of water use e fficiency to the irri gation regimes and WUE responded both linearly and quadrati cally to the N rates. Water use efficiency was overall greater at higher N rates and under medium irrigation regimes. With the us e of SDI (sub-drip irrigation) as irrigation technique, Gencogl an et al (2006) observed in the Mediterranean region that increasing applied irrigation water increased irri gation water use efficiency. On the other hand, Stansel and Smittle (1980) reported that in ge neral, WUE values decreased with increasing irrigation interval and list ed WUE value of 4-6 kg m-3 for green bean in Georgia, USA. Seasonal Variations of Nitrate and A mmonium Contents in the Soil Profile In this study, the effect of the three diffe rent irrigation regimes was assessed under the IFAS recommended N rate (N111 kg N ha-1) on NO3-N and NH4-N distribution at different soil depths at various times during the snap bean growing season. It is of interest to note that the snap bean root system is a relatively shallow, well-branched lateral forming system with extensive fibrous roots (Rubatzky and Yama guchi, 1997). Nitrate concentrati on in soil in the upper 60 cm of the soil profile was thus considered the potential N available for root uptake while NO3-N concentration detected below 60 cm depth was an indication of potential NO3-N leaching into groundwater. Contents of NO3-N and NH4-N in the soil profile at each soil sampling date (18, 49 and 87 days after preplant fert ilization or 13, 44 and 82 days after sowing) are presented in Tables 4-7 and 4-8, respectively. Analyzing these values revealed that 18 days after the first fertilizer application, there was no effect of irrigation on soil NO3-N and NH4-N content at any

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85 soil depth but there was a significant depth effect on the amount of NO3-N and NH4-N. Irrespective of the irrigation regimes, higher values of NO3-N and NH4-N were observed in the rooting zone (0-60 cm) and were potentially available for plant uptake compared to below the rooting zone (60-120 cm) which could be susceptible to leaching. About 10 kg ha-1 of NO3-N was observed in these deeper soil profile layers (60-120) in addition to about 27 kg ha-1 in that zone as NH4-N. Nitrification is a micr obial process by which reduced N compounds (primarily ammonia) are sequentially oxidized to nitrite and nitrate. Therefore, the bulk of the NH4-N present in the profile is expected to undergo nitrification to result into NO3-N which may be available for plant uptake or readily leach out fr om the soil profile. Indeed, results from different experiments suggest that approximately half of the applied ammonium has been reported to be converted to NO3-N in sandy soils in Flor ida and Turkey (Sato and Morgan, 2007; Unlu et al., 1999). At 18 days after preplant fertil ization, values of mineral N (NO3-N + NH4-N) below the root zone (60-120 cm), thus potentially leachable, were 37, 31 and 34 kg ha-1 for the Low, Medium and High irrigation regimes, respectivel y. At the same period, cumulative irrigation water and precipitation received by the crop were about 54 mm for all three irrigation regimes (Figure 4-4). The similar amounts of mineral N found in deeper soil s layer at this stage may be explained in part by the relati vely low amount of water applie d in this period which did not induce any significant N leaching. By 49 days after first fertiliz er application (31 days after the second application), nitrate and ammonium in the soil prof ile followed a non-uniform dist ribution across the soil depths similar to the trend observed at the first soil sampling. It appeared that irrigation effect on nitrate in the soil profile was not significant but this effect was significant on NH4-N content in the soil profile. Depth effects were sign ificant for both nitrate and amm onium. However, there is more

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86 NO3-N available in the rooting zone of the soil pr ofile (0-60 cm) compared to what was available at the first sampling while values of NH4-N appeared to be relatively similar to the first sampling. The result would suggest that a proportion of the NH4-N from applied fertilizer was nitrified into nitrate and the plant uptake rate w ould be expected to be higher during this period which coincided to the period of active growth and plant N uptake. From the second fertilizer application to the second soil sa mpling (31 days after fertilizer application), amounts of water (irrigation plus precipitations) applied to the plants were 73, 86 and 125 mm for Low, Medium and High irrigation regimes, re spectively (Figure 4-4). Within the same period, amount of mineral N (NO3-N + NH4-N) below the root zone (60-120 cm) was 32, 50 and 31 kg ha-1 for the Low, Medium and High irrigation regimes, respec tively. This movement of mineral N below the rooting zone shows that relatively more minera l N was observed below the rooting zone in the medium irrigation regime than in the high irriga tion. This difference in mineral nitrogen may be due to the surplus in amount of water received in high irrigation which may have induced much of the N to leach into the deepest depth before so il samples were taken given the high mobility of nitrate in sandy soil. Examining values of the downward movement of mineral N in the soil at the end of the growing season (82 days after sowing or 38 days afte r the last fertilizer application) revealed that there was a relatively large amount of NO3-N and NH4-N below the root-zone (60-120 cm) compared to within the root-zone (0-60 cm), irrespective of irrigation treatment. This would imply that potential nitrate and ammonium was l eached below the snap bean root-zone (Figure 46). However, there was no signifi cant difference in the amount of NO3-N and NH4-N remaining in the entire soil profile due to irrigation treatm ent implying that all irrigation treatments most likely had leached NO3-N essentially equally. This confirmed our hypothesis and common

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87 assumption that as more water is applied, more N is expected to be leached out from the soil profile. Similarly, in a mass balance approach to model the annua l N cycle under soybean, Gibson et al. (2007) found that average residual soil NO3N to a depth of 120 cm was in the range of 70 to 80 kg ha-1 after soybean grown at two locations in Iowa. Although there was no significant difference be tween the residual n itrate and ammonium due to the irrigation regimes, an overview of the time course of the mineral N (NO3-N + NH4-N) across the soil profile presented in Figure 4-6, showed greater reductions in residual soil NO3N and NH4-N in soil profile at the end of growing season for the highest irrigation regime (133% Et) as opposed to the two other irrigation regimes, indicating that increasing the irrigation rate enhanced N movement to soil layers below 120 cm. Therefore, management practices that increase downward water flux increases the risk of loss of NO3-N below the crop root-zone. Conclusion This chapter examined how management of irrigation regimes and N rates influenced snap bean growth, fresh marketable yield production and quality, a nd movement of nitrate and ammonium in the soil profile. According to these results, fresh marketable yield of snap bean, crop biomass, and N uptake were all significantly increased with irrigation regime or N rates (each individual factor) but the interaction of both factors was only significant for the fresh marketable yield. Analyzing more closely this interaction revealed that at the low Irrigation regime, increasing N rates did not increase linearly the fresh marketable yi eld; there was no yield benefit with N-ra tes over 111 kg ha-1. Additionally, application of N in excess of that recommended in medium irrigation regime did not significantly increase the fresh marketable yield. The result at medium irriga tion regime showed that 74 kg N ha-1 was enough to provide statistically acceptable fresh marketab le yield quantity close to the rate observed under the N rate of 148 kg N ha-1.

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88 Analyzing the temporal and spatial distributi on of both nitrate and ammonium contents in the soil profile showed that nitrate and ammonium were higher in the deepest layers (90-120 cm) at the end of the season irrespec tive of irrigation regimes, impl ying that accumulation of these nutrients leading to possibl e leaching into ground water. Results presented in this chapter may help improve irrigation a nd nitrogen management within whole farm level and also assist effo rts towards development of Best Management Practices for snap bean in particular and Florida vegetable crops in general.

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89 Table 4-1. Effects of irrigation and N fertilizer on canopy characteri stics of snap bean grown in Gainesville during spring 2007 at 64 DAS. Treatments # Nodes Height (cm) Width (cm) LAI Irrigation Low 7.0 A 35.8 B 44.4 B 1.8 B Medium 7.0 A 39.7 A 50.5 A 2.1 A High 7.0 A 41.2 A 53.0 A 2.2 A Significance ns L*** L*** L*** N Rates N-37 7.0 A 37.9 A 44.8 B 1.7 B N-74 7.0 A 40.0 A 50.5 A 1.9 A N-111 7.0 A 39.5 A 51.6 A 2.3 A N-148 7.0 A 38.1 A 50.1 A 2.2 A Significance ns Q* L*** Q** L*** ''Water x N" ns ns ns NS,*,**,*** Non-significant or si gnificant at the p<0.05, 0.01, 0.001 level, respectively, and linear (L), quadratic (Q) for each effect (Irrigation regime and N-rate). Means followed by identical lower case letters in the same colu mn are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking. Irrigation treatment Low, Medium and High are 66%, 100% and 133% of crop evapotranspira tion rates (ETc), respectively.

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90 Table 4-2. Effects of irrigation and N fertilizer on fresh marketable yield, crop biomass and pod harvest index of snap bean grown in Gainesville during spring 2007 at 64 DAS. Treatments Fresh marketable yield (Mg ha-1) Total dry matter (kg ha-1) Pod harvest index Irrigation Low 11.7 B 2.7 B 0.42 A Medium 14.9 A 3.3 A 0.44 A High 15.9 A 3.4 A 0.45 A Significance L*** L*** ns N Rates N-37 10.5B 2.5 B 0.41 A N-74 14.3A 3.2A 0.45 A N-111 16.2 A 3.5A 0.45 A N-148 15.6A 3.3A 0.45 A Significance L***Q** L***Q** ns 'Water x N" ns ns Low x 37 8.5C Low x 74 11.8B Low x 111 15.7A Low x 148 10.6BC Med x 37 10.1B Med x 74 14.8A Med x 111 17.5A Med x 148 17.1A High x 37 12.9B High x 74 16.3AB High x 111 15.2AB High x 148 18.9A NS,*,**,*** Non-significant or si gnificant at the p<0.05, 0.01, 0.001 level, respectively, and linear (L), quadratic (Q) for each effect (Irrigation regime and N-rate). Means followed by identical lower case letters in the same colu mn are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking. Irrigation treatment Low, Medium and High are 66%, 100% and 133% of crop evapotranspira tion rates (ETc), respectively.

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91 Table 4-3. Quadratic model regression equations for snap bean fresh ma rketable yield response (y, Mg ha-1) to fertilizer N rates (x, kg ha-1) under different irrigation regimes in Gainesville in spring 2007. Irrigation regime N-response curve Nmax Ymax R2 Low y = -1421.61 + 312.26x 1.53x2 103 14.5 0.52 Medium y = 2380.89 + 241.39x 0.95x2 120 16.78 0.70 High y = 12194 + 31.20x 0.077x2 200 22.0 0.32 Table 4-4. Effects of irrigation and N fertilizer on yield components of snap bean grown in Gainesville during spring 2007 at 64 DAS. Treatments Pod Number Seed Number Irrigation Low 191 B 891 B Medium 235 A 1226 A High 248 A 1331 A Significance L*** L*** N Rates N-37 178 B 907 B N-74 236 A 1222 A N-111 249 A 1291 A N-148 236 A 1176 A Significance L*** Q* L*** Q*** ''Water x N" ns ns NS,*,**,*** Non-significant or si gnificant at the p<0.05, 0.01, 0.001 level, respectively, and linear (L), quadratic (Q) for each effect (Irrigation regime and N-rate). Means followed by identical lower case letters in the same colu mn are not significantly different according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking. Irrigation treatment Low, Medium and High are 66%, 100% and 133% of crop evapotranspira tion rates (ETc), respectively.

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92 Table 4-5. Effects of irrigation and N fertilizer on fresh market pod quality of snap bean grown in Gainesville during spring 2007. Treatments Pod Diameter (mm) Pod Length (cm) # Seed/Pod % Seed Fwt/total Pod Avg Fwt/seed (mg) Irrigation Low 9.2 A 13.8 B 4.9 B 10.9 A 131.5 A Medium 9.1 AB 14.5 A 5.5 A 10.7 A 119.7 A High 8.9 B 14.7 A 5.7 A 11.1 A 119.9 A Significance L* L*** L*** ns ns N Rates N-37 8.9 B 14.1 B 5.3 A 10.5 A 115.3 A N-74 9.0 AB 14.3 AB 5.5 A 10.8 A 118.8 A N-111 9.1 AB 14.7 A 5.6 A 11.2 A 127.7 A N-148 9.3 A 14.3 AB 5.2 B 11.1 A 133.1 A Significance L* Q* Q* ns ** ''Water x N" ns ns ns ns ns NS,*,**,*** Non-significant or significant at the p<0.05, 0.01, 0.001 level, respectively, and linear (L), quadratic (Q) for each effect (Irrigation regime a nd N-rate). Means followed by identical lower case letters in the same column are not significantly diffe rent according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking. Irrigation treatment Low, Medium and High are 66%, 100% and 133% of crop evapotranspiration rates (ETc), respectively. Table 4-6. Effects of irrigation and N fertili zer on total crop N uptake, pod WUE and pod NUE of snap bean grown in Gainesville during spring 2007 Treatments Accumulated N (kg ha-1) Pod WUE (kg ha-1 mm-1) Pod NUE (kg kg-1) Irrigation Low 81.5 A 46.4 B 147.1 B Medium 88.4 A 52.7 A 173.7 A High 92.8 A 43.8 B 173.5 A Significance ns Q** L** N Rates N-37 60.8 C 35.1 C 173.1 A N-74 82.9 B 48.2 B 177.7 A N-111 98.8 A 55.5 A 164.9 A N-148 107.7 A 51.8 AB 143.4 B Significance L*** L***Q** L** 'Water x N" ns ns NS,*,**,*** Non-significant or significant at the p<0.05, 0.01, 0.001 level, respectively, and linear (L), quadratic (Q) for each effect (Irrigation regime a nd N-rate). Means followed by identical lower case letters in the same column are not significantly diffe rent according to Tukeys test (p<0.05), a, b, c denote higher to lower ranking. Irrigation treatment Low, Medium and High are 66%, 100% and 133% of crop evapotranspiration rates (ETc), respectively.

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93 Table 4-7. Irrigation effects on nitrate movement in the soil pr ofile for snap bean grown in Gainesville during spring 2007 Days after First Fertilization 18 49 87 Treatments N-NO3 (kg ha-1) Irrigation Low 15.6 AB 24.5 A 15.6 A Medium 17.1 A 21.9 A 10.8 A Over 11.7 B 23.8 A 8.0 A Depth (cm) 0-30 34.7 A 57.0 A 11.6 A 30-60 13.5 B 24.4 B 3.5 B 60-90 6.3C 7.7 C 9.4 B 90-120 4.6 C 4.4 C 21.4 A Irrigation (I) ns ns ns Depth (D) *** *** *** I x D ns ** ns NS,**,*** Non-significant or significant at the p<0.05, 0.01, 0.001 level, respectively. Means within columns followed by the same lowercase letters are not significantly different (p < 0.05) according to Least Significant Difference test. Table 4-8. Irrigation effects on ammonium presence in the soil pr ofile for snap bean grown in Gainesville during spring 2007 Days after First Fertilization 18 49 87 Treatments N-NH4 (kg ha-1) Irrigation Low 19.5 A 12.9 B 28.5 A Medium 17.3 A 19.8 A 25.5 A Over 17.6 A 13.9 B 19.1 A Depth (cm) 0-30 27.5 A 24.5 A 24.7 A 30-60 18.1 B 11.8 B 18.6 A 60-90 13.9 B 13.2 B 19.9 A 90-120 13.1 B 12.6 B 34.2 A Irrigation (I) ns ns Depth (D) *** *** ns I x D ns ns ns NS,*,*** Non-significant or signi ficant at the p<0.05, 0.001 level, respectively. Means within columns followed by the same lowercase le tters are not significantly different (p < 0.05) according to Least Significant Difference test.

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94 0 2 4 6 8 10 12 14 16 18 20 37 74 111 148 Nitrogen rates (kg ha-1)Fresh Marketable Yield (Mg ha-1) Low Medium High Poly. (Low) Poly. (Medium) Poly. (High) Figure 4-1. Response (quadratic pol ynomial) for fresh marketable yiel d of snap bean as affected by N rate under different irrigation regimes in Gainesville FL during spring 2007

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95 A 0 20 40 60 80 2345 Sieve SizePod Sieve Size Distribution (%) 37 74 111 148 B 0 20 40 60 80 2345 Sieve sizePod Sieve size Distribution (%) C 0 10 20 30 40 50 60 70 80 2345 Sieve sizePod sieve size distribution (%) Figure 4-2. Distribution of sieve size of snap bean at harvest as affected by four N rates in A) Low, B) Medium and C) High irrigation regimes in Gainesville during spring 2007

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96 0 100 200 300 400 500 600 020406080100 Days after Preplant FertilizationCumulative Irrigation and Precipitatio n (mm) Low Medium High Figure 4-3. Cumulative irrigation and precipitation during the growing season of snap bean in Gainesville in spring 2007 0 20 40 60 80 100 120 020406080100 Days after fertilization applicationNO3-N+NH4-N (kg ha-1) Low Medium High Figure 4-4. Movement of mineral N (NO3-N + NH4-N) below the root zone ( in 60-120 cm depth) over time as affected by irrigation regimes on snap bean grown in Gainesville in spring 2007

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97 0 20 40 60 80 100 120 140 160 180 200 18 36 54 72 Days after applicationSoil NO3-N+ NH4-N (kg ha-1) Low Medium Over Figure 4-5. Cumulative mineral N (NO3-N + NH4-N) in the soil profile (0-120 cm) over time as affected by irrigation regimes on snap b ean grown in Gaines ville in spring 2007

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98 CHAPTER 5 ADAPTING THE CROPGRO-DRY BEAN MODEL TO SIMULATE THE GROWTH AND DEVEL OPMENT OF SNAP BEAN (PHASEOLUS VULGARIS L) Introduction Crop simulation models are mathematical repr esentations of plant growth processes as influenced by interactions among genotype, e nvironment, and crop management (Yang et al., 2004). A wide array of c rop simulation models are increasingly used to assess crop performance in various environments and management strate gies, and to assist decision-making processes such as crop timing, irrigation, fe rtilization, crop protection, and to facilitate optimization of the crop and its management strategies. The CROPGRO model (Hoogenboom et al., 1994; Boote et al ., 1998ab), embedded in the Decision Support System for Agrotechnology Tran sfer Cropping System Model (DSSAT-CSM), is a process-oriented, dynamic, a nd generic crop simulation model. It is designed to simulate the effects of weather, soils, and agronomic management (including planting, nitrog en, residues and irrigation) on daily crop growth and development, carbon balance, crop and soil N balance, and soil water balance. Its generic, process-oriented design has allowed it to be adapted to model a variety of different species including tomato (Lycopersicon esculentum Mill.) (Scholberg et al., 1997), faba bean (Vicia faba L.) (Boote et al., 2002), velvet bean (Mucuna pruriens) (Hartkamp et al., 2002), (chickpea (Cicer arietinum L.) (Singh and Vermani, 1994), and bahiagrass (Paspalum notatum) (Rymph, 2004). This versatility is ach ieved through three input files that define species traits, ecotypes and cultivars, along with code improvement in the basic model (Boote et al., 2002). The species file contains information on base temperatures (Tb) and optimum temperatures (Topt) for developmental pr ocesses (rate of emerge nce, rate of leaf appearance, and rate of progress toward fl owering and maturity) and growth processes (photosynthesis, nodule growth, N2-fixation, leaf expansion, pod addition, seed growth, N

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99 mobilization, etc.). These parameters are set during model development and are not generally changed by the user. The ecotype file contains information that desc ribes broad groups of cultivars, such as determinate vs. indeterminat e growth habit groups. Cultivar differences are represented in a file containing 15 coefficients (Boote et al., 2003; Hartkamp et al., 2002) that allows users to specify how cultivars differ in life cycle progression, daylength sensitivity, canopy and fruit growth characteristics. Knowledge of bean genetics s uggests that snap beans were derived from dry beans because more genetic changes would be required to derive snap beans from the wild bean than from dry beans (Myers and Baggett, 1999). Moreove r, growth habit and plant architecture in snap bean fall into a range simila r to that found in dry beans. Most cultivars, especially for fresh market and processing, have determinate bush (t ype I) growth habit (Fernandez et al., 1986). It could thus be hypothesized that the CROPGRO-Dry bean model is a good starting point for the development of a snap bean growth and development simulation model. Consequently, the objective of this chapte r was to adapt the CRO PGRO-Dry bean model to simulate the growth and development of snap bean. Materials and Methods Snap Bean Field Experiments The adjustment of the parameters in mechan istic crop models requires measured data. In this study, the experimental data used for ca libration of the CROPGRO Dry bean model were collected from field experiments presented in Chapter 3. In order to assure relatively good precision in the parameters calibrated, field da ta used in the species, ecotype, and cultivar calibration procedure should generally be collec ted under optimal water and nitrogen supply and in absence of pests and diseases. Thus, we calibrated the species, ecotype, and cultivar

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100 parameters, with growth analys is data collected from the Me dium (100 % crop ET) irrigation regime and High nitrogen rate (N-148 kg N ha-1) treatment. Subsequently, in order to enable the model to respond accurately to vari ous nitrogen fertilization rates, field data collected from the two lower nitrogen treatment (37 and 74 kg N ha-1) were used to calibrate parameters (soilrelated, as well as some plant-related N balan ce parameters) which influenced nitrogen uptake. Model Calibration Soil profile properties calibration The CROPGRO simulation model uses a soil file which describes the soil profile properties and is created based on measured values or information gathered in the literature. The soil in our experimental site is classified as a Millhopper fine sand, a member of the loamy, hyperthermic family of Grossarenic Paleudults. Carstille et al. (1981) presented some soil physical characteristics such as so il texture (fractions of the clay silt, and sand) which influence the soil water balance traits and soil water hol ding capacity. The soil fi le in CROPGRO-DSSAT also lists information of each layer which in cludes the drained upper limit (DUL) or field capacity, the lower limit (LL) or permanent wilting point, and the saturated soil water content (SAT). Default values of these parameters were available for Mill hopper sand soil in the SOIL.SOL file in DSSAT and were used during this calibration pr ocess. Also, in our calibration procedure of soil parameter valu es, we varied the soil organi c matter fractions (SOM1, SOM2 and SOM3) to minimize the Root Mean Square E rror (RMSE) of the plan t nitrogen uptake for the two low N treatments (37 and 74 kg N ha-1). Based on the RMSE-plant N uptake, we selected the appropriate values of the soil organic matter fractions which were then used in the overall model calibration procedure. In the soil layer (0-30 cm), the default initial ratio of soil organic matter fractions (SOM1:SOM2:SOM3 ) were adjusted from 0.02:0. 54:0.44 to 0.01:0.70:0.29,

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101 respectively, and in the soil laye r (30-150 cm), these values were set from 0.02, 0.64, and 0.34 to 0.01, 0.60, and 0.39, respectively (Table 5-1). It is of interest to note that the soil organic matter is a changeable nutrient reservoir that may function both as source and sink for N through the competing effects of mineraliza tion and immobilization (Boone, 1990). Additionally, the CROPGRO model in the DSSAT requires information on the initial status of mineral N in the soil profile before planting. Measurements were not made to evaluate initial soil nitrate and ammonium values in our field before planting. Therefore, these initial values were set from data found in the technical report produced by Gr aetz (2007) on a study of similar soils conducted at the North Florida REC. Table 5-1 presents profil e characteristics of a Millhopper fine sand, (hyperthermic family of Grossarenic Paleudults) used during our calibration process, which includes these initial soil nitrate and ammonium values for respective layers. It should be noted that the model was run with symbiotic nitrogen fixatio n routines turned off given that snap bean has poor N fixation capabil ity and no rhizobium treatment was provided in this experiment, and the soil was not known to have bean rhizobium applied previously. Furthermore, the CROPGRO model requires weather data as inputs. The w eather file contains daily maximum and minimum air temperature, solar radiation collected from the Florida Automatic Weather Network (FAW N) web page from Citra site. Data on precipitation were based on a rain gauge placed in the field experiment. Approach for genetic coefficients calibration The CROPGRO dry bean model requires genetic coefficients that de scribe durations of phases of the crop life cycle, vegetative growth traits, and reproductive tr aits unique to a given cultivar. The calibration of the CROPGRO-dry b ean model to accurately simulate snap bean

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102 growth was carried out using the systematic a pproach described by Boote (1999) and led to the development of the required cultiv ar and ecotype files. In essence, this approach starts off with the life cycle by adjusting the appropriate genetic coefficients in the cultivar file in order to match the date of flowering, and the date of physiological maturity (f irst mature pod stage). Indeed, variation in life cycle and duration of different phases are among the most important genetic variations contributing to yield potential of different cult ivars (Boote et al. 2003). Then, the dry matter accumulation (biomass and leaf area index) was adjusted and finally yield and the yield components parameters were calibrated. The generic Andean Habit cultivar with generic Andean ecotype present in the CROPGRO-dry bean model version 4.5 was used as our initial starting point to define the appropriate cultivar and ecotype files for our snap bean cultivar Ambra. It was anticipated that the species file would not require much modification in this adaptation process because dry bean and sn ap bean are both in the same species Phaseolus vulgaris and therefore developmental processes in re lation to cardinal temperatures and growth processes (photosynthesis, nodule growth, N2 fixation, pod addition, and seed growth, etc.), which characterize the species files, would fundamentally not change. However, some parameters in the species files which define the re lative carbon and nitrogen mobilization rates of vegetative tissues (CMOBMX, NMOBMX, NVSMOB) were modified to minimize computed N stress, and the rate of N uptake per unit root length (RTNO3 and RTNH4) were modified to optimize N uptake for the two low N fertilization treatments. Coefficients CMOBMX, NMOBMX, NVSMOB were increased from 0.03 0.10 and 0.36 to 0.07, 0.16 and 0.70, respectively. Additionally, RTNO3 and RTNH4 were set from their initial default values 0.006, and 0.006 to 0.015 and 0.015, respectively. Values of these parameters are presented in Table 5-

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103 2. In addition, as the CO2 level is no longer at 330 ppm CO2 value used for the default model; the CO2 level was increased from 330 to 383 ppm which is the current CO2 level. Crop life cycle Photothermal day (PD) threshold values we re adjusted to accurately predict crop life cycle, anthesis and maturity dates, each in sequence. First the EM-FL parameter (photothermal days between plant emergence and flower appearan ce) was adjusted until the simulated date of flowering matched the observed date. Then, the SD-PM (photothermal days between first seed and physiological maturity) was adjusted until the simulated date of maturity was correct. Calibration of phenology was conducted by mini mizing the error between observed and simulated flowering and maturity dates. Dry matter accumulation and LAI Genetic coefficients calibrated in order to minimize the error between observed and simulated dry matter accumulation and LAI included specific leaf area (SLAVR), time to cessation of leaf area expansion (FL-LF), light -saturated leaf photosynthesis (LFMAX) and the specific leaf weight at which st andard leaf photosynthesis is defi ned (SLWREF in species file). More specifically, SLWREF was increased from 0.030 to 0.033 g cm-2 and LFMAX was reduced from 1 to 0.95 mg CO2 m-2 s-1 in order to compensate for the increase in the CO2 level from 330 to 383 ppm, and/or because snap bean is somewh at less productive than the previously modeled dry bean cultivars. In addition, the timing of pod formation and seed formation influence LAI, but those traits are listed below. Yield and yield components The seed size, seeds per pod, and single-seed growth duration (SFDUR) were adjusted to reproduce observed seed size and seed growth duration. The threshing percentage (THRESH)

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104 was not changed. Parameters accounted for in th is step included WTPSD (maximum weight per seed), SDPDV (average seeds per pod), SFDUR (seed filling duration for pod cohort), PODUR (duration of pod addition), time to onset of pod addition (FL-SH), and time to onset of seed growth (FL-SD). The latter three (PODUR, FL-S H, and FL-SD) also influence onset of pod and seed growth. There was iteration between this pro cedure and the prior proce dures relative to dry matter accumulation. Two statistical indices were used to comp are observed and model-simulated values: the Root Mean Square Error (RMSE) and the Willmotts index of agreement (d). The RMSE was calculated as: 5.0 1 2)( 1 n i iiOS n RMSE (5-1) where Si and Oi are a corresponding pair of simulated and observed values, respectively, and n is the number of observations included in the evaluation. The parameter d or Willmotts index was calculated as: 2 1 1 2)''( )( 1n i i i n i iiOS OS d (5-2) where OSSii and OOOii '. Parameter d lies within the range 0 to 1 with higher values indicating more accurate simulations.

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105 Results and Discussion Predictions with Unmodi fied Mod el Parameters The model was run with the default genetic co efficient values (generic Andean Habit cultivar with generic Andean ecotype and defau lt V4.0 species traits) and initial soil profiles and the outcomes were compared with data collected on the N treatment of 148 kg ha-1. The default version slightly over-predicted the anth esis date (43 versus 41) and the physiological maturity date (82 versus 76). Figure 5-1 presents the default model performance in simulating the time course of leaf area index. The default model slightly over-predict ed the leaf area index during the exponential phase, but the over-predi ction was more pronounced during later stages, when the maximum leaf area index value was reac hed, caused in part because reproductive onset and maturity were predicted to occur too late. These high simulated values of LAI were resolved in the calibrated model, by the simulated earl ier onset of reproductive growth, as well as by reducing the coefficient SLAVAR as shown in Table 5-2. For LAI, the Root Mean Square Error (RMSE) values of the default model were 0.82, 0.54, 0.74 and 0.53 m2 m-2 and the d-statistic was 0.79, 0.91, 0.93 and 0.91, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1 indicating relatively poor prediction of LAI especially at the lowest N rate. On the contrary, the default model showed relatively good ability in predicting the accumulation of shoot dry matter over time. Indeed Figure 5-2 reveals that the simulation had good agreement with observed shoot biomass irrespective of N rates until about 60 DAS, and thereafter, the model consistently over-predicted the shoot dry ma tter accumulation, more so at the lowest N rate. The Root Mean Square Error (RMSE) values of the default model were 1000, 652, 532 and 563 kg ha-1and the d-statistic was 0.91, 0.97, 0.98 and 0.98, respectively, for the N rates of 37, 74, 111, and 148 kg ha-1. The RMSE values were incr eased at decreasing N rates,

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106 implying that the model performed relatively po orly at low N rate. Additionally, simulation of the time course of pod dry weight grown under four different N rates is il lustrated on Figure 5-3. In general, the model consistently under-p redicted the time-course of pod dry weights irrespective of N rates, in part because of incorrect phenological timing, and partly because of inadequate pod growth at lower N rates. The R oot Mean Square Error (RMSE) values of the default model were 728, 957, 907 and 708 kg ha-1and the d-statistic was 0.83, 0.83, 0.87 and 0.92, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. Seasonal patterns of simulated and observed total plant N accumulated with the default model under the different N rates are shown in the Figure 5-4. The simulated N accu mulation closely matched with the observed values during the lag phase (about 35 DAS ) but onwards, the model under-predicted N accumulation. The Root Mean Square Error (RMS E) values of the default model were 12.26, 19.66, 17.18 and 12.26 kg N ha-1 and the d-statistic was 0.95, 0.93, 0.96 and 0.98, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. These results, while reasonably close, showed insufficient N uptake in late season, especially u nder low N rates, which illustrate the need to modify parameters that influence N uptake in the model. In essence, with the default values of genetic, ecotype and species coefficients in the CROPGRO Dry bean model, the life cycle was longer (late to set pods and late to reach physiological maturity), the leaf area index a nd the shoot dry matter were too high, but the pod dry matter was consistently too low irrespective of N rates and the total plant N accumulation was relatively lower than the observed values. These results with the default dry bean model justify various calibrations of the genetic coefficients desc ribed above in or der to accurately mimic the simulated and observed values of different crop variables for snap bean.

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107 Life Cycle and Canopy Growth The calibration process involve d changes and adjustments of different ecotype and cultivar coefficients which govern the life cycle duration, dry ma tter accumulation and partitioning. Table 5-2 presents the adjusted values of these coefficients which were used for the subsequent simulations. Phototherm al day requirements presented in Table 5-2 are equivalent to calendar days, if the temperature is at the optimum 28oC for the entire 24-h day, where snap beans base and optimum temperatures are 5 and 27oC, respectively. Calibration of phenology to accurately simulate the life cy cle and duration of development phases is important as these genetic variations lead to yield potential of different cultivars. It should be noted that the module of life cycle simulation in the CROPGRO model wa s not intrinsically built to be sensitive to different nitrogen rates. Therefore, discussions in this sect ion relative to reproductive and vegetative phenology stage apply to all the N rates. The model predictions of flowering (anthesis) and maturity dates accurately agr eed with the observed measurements. Under the weather conditions prevailing in our study area (Gai nesville), this snap bean cultivar (Ambra) flowered 41 days after sowing and the physiological maturity was reached 77 days after sowing, and there was no observed effect of N rates. Days from emergence to anthesis and days from first seed to maturity are controlled in CROPGRO by EM-FL and SD-PM which were set respectively to 21 and 14 phototherm al days (PD). It is of intere st to note that snap bean, like many dry bean cultivars, has a day-neutral re sponse to photoperiod. Therefore setting the daylength sensitivity coefficient and thresholds determining flowering and maturity for this cultivar was not necessary. The slope of the re lative response of deve lopment to photothermal period (PP-SEN) and the critical short daylength (CSDL) (not listed in Table 5-2) for this cultivar were maintained to their respective default value of 0.0 and 12.17 h (0.0 meaning no sensitivity).

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108 Snap bean has relatively slow growth upon seedling emergence. Using the calibrated coefficients, the number of leaves on main stem (expressed as the number of nodes on the main stem) during early season was slightly overestimated at all four N rates. Subsequently, the rate of main stem leaf appearance was also slightly t oo rapid, as noted by 45 days after planting (Figure 5-5). The Root Mean Square Error (RMSE) valu es of calibration were 0.59, 0.48, 0.55 and 0.57 and the d-statistic values were 0.96, 0.97, 0.96 a nd 0.97, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. These coefficients values indicated a good prediction of this variable and no meaningful response to N fer tilization rate was observed. Several parameters controlling canopy width and height such as RWIDTH and RHIGHT, ecotype coefficients for relative canopy width and height, and in ternode length (in species) were changed even though the default dry bean cultivar values used for the calibration procedure were selected from the growth habit type I (determinate). These adjustments were made in order to more accurately mimic the simulated and observe d canopy height and width, basically increasing height and width about 10% more over time compared to initial default simulations. Observed and simulated canopy heights expone ntially increased to a maximum at 50 days after sowing and were maintained constant thereafter (Figure 56). The end of the exponential phase coincided with time to end of main stem appearance, time of flowering and early pod development. During this phase, the simulated height closely agreed wi th the measured values of heights, but also when the maximum plant height was reached (pla teau phase), the simulation was accurate except at the end of growing season where the model seem ed not to capture the slight decrease in plant height which may be caused by canopy loss due to senescence. The average maximum height of the simulated canopy height was 0.30 m versus 0. 36 m for the measured one. The Root Mean Square Error (RMSE) values of the canopy height calib ration were 0.02, 0.02, 0.0.03 and 0.02 m

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109 and the d-statistic values were 0.99, 0.99, 0.99 a nd 0.99, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. Similarly, the time-course of canopy wi dth followed the same time trend as the canopy height, and reached a maximum widt h at the time of end of main stem node appearance and onset of pod formation (Figure 5-7). Biomass Accumulation and LAI Upon successfully calibrating anthesis and maturity parameters and the plant N uptake parameters as presented above, the model was run to simulate the biomass accumulation. The temporal changes in observed crop canopy dry matter along with the predicted values are presented in Figure 5-8. Simulated changes in dry matter accumulation globally matched well with observed measurements. Comparisons showed that the slope of dry matter accumulation rose smoothly after a short lag pha se early in the vegetative growth period. Both the simulation and the observations for the high N treatment used for calibration indicated a peak of vegetative dry weight above 5000 kg ha-1. The Root Mean Square Error (R MSE) values of calibration data were 390, 129, 164 and 196 kg ha-1 and the d-statistic was 0.98, 0.99, 0.99 and 0.99, respectively, for the N rates of 37, 74, and 111 and 148 kg ha-1 indicating good prediction of this variable. A somewhat higher RMSE value was observed at the lowest N level indicating that N stress resulted in relatively poor predic tion of crop dry matter accumulation at low N. Comparison of the seasonal patterns of simulated biomass accumulation among the N treatments did not show any difference until about 60 days after sowing. Thereafter, the simulated dry matter accumulation at the lowest N treatment showed lower dry matter accumulation as opposed to the three higher N treatme nts. On the contrary, plants at the three higher N treatments maintained dry matter accumu lation longer into pod filling, suggesting that

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110 the higher N rates enhanced the photosynthetic apparatus, thus maintaining longer the source of plant structural dry matter. The temporal changes in LAI are presented in Figure 5-9. Analysis of these plots showed that the CROPGRO model performed well in pr edicting the time courses of LAI especially during the initial exponen tial increasing phase and slightly un der-predicting LAI near the end of season. Simulated maximum value of LAI under lo west N treatment was reached around 50 days after sowing whereas under the three higher N treatments, maximum value occurred after 55 days after sowing. The peak in the LAI proba bly marked the end of the leaf expansion development and thereafter, the CROPGRO model predicted a reduction in LAI due to natural senescence. Gutierrez et al. (1994) observed that maximum LAI occurred when pod filling began in their study on development of a simulation model for beans. Leaf senescence in the model is dependent on the mobilization of protein (C and N) from vegetative tissues to reproductive tissues and is closely related to predictions of onset of pod initia tion during reproductive development in other legumes (Alagarswamy et al., 2000). Plants at the three higher N treatments started showing leaf senescence later in the season (about 5 days difference) suggesting that a smaller frac tion of N might be mobilized from older leaves during the vegetative phase. The Root Mean Square Error (RMSE) values of calib ration data were 0.16, 0.31, 0.28 and 0.21 m2 m-2 and the d-statistic was 0.98, 0.96, 0.97 and 0.98, respectively, for the N rates of 37, 74, and 111 and 148 kg ha-1 indicating that the model performed well in simulating this variable irrespective of N rates. Timing of Pod Growth The CROPGRO model begins to add pods at the beginni ng pod stage (R3) which occurred at 4 photothermal days (PD) from first flower to first pod (FL-SH) for snap bean (Table 5-

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111 2). The coefficients SFDUR (seed filling duration for one cohort of pods) and PODUR (time required for cultivar from first to final pod load ) were also set to 18 and 6 photothermal days, respectively. After these adjust ments of appropriate genetic co efficients defining reproductive behavior of the plant, the CROPGRO model showed relatively good performance in predicting pod and grain dry matter at various N treatments (Figure 5-10). Th e RMSE and d-statistic of pod weight for the calibration were 150, 191, 224 and 315 kg ha-1 and 0.99, 0.99, 0.99 and 0.98, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. Time between first flower and first seed (R5) was set to 13 photothermal days (PD) and the time between first seed (R5) and physiological maturity (R7) (f irst pods beginning to mature, color turning brown) was set to 14 PD. Based on th ese adjustments, seed growth initiated at 64 days after sowing. Similar to th e prediction of pod dry matter growth pattern, the simulated grain weight time course was in good agreement with observed values, irrespectively of the nitrogen levels (Figure not shown). Pod harvest index is the ratio of pod mass to total aboveground mass and illustrates the onset and degree of partitioni ng to reproductive organs. The comparison of simulated vs. observed pod harvest index (Figur e 5-11) showed relatively good pred iction of this variable at high N rate (148 kg N ha-1) used for the model calibration and for the model evaluation under other N treatments as well. The RMSE and d-st atistic of pod harvest i ndex for the calibration were 0.10, 0.06, 0.06 and 0.06 and 0.96, 0.98, 0.98 and 0.98, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. While these values are relatively close, it appears that the model showed relatively poorer prediction capability of pod harvest index at low N due to a concept deficiency revealed in the model code in simulating shoot biomass at low N. The CROPGRO model, under N stress, accumulates non-structural carbohydrates in stems mainly, but also in leaves, which

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112 causes dry matter accumulation in vegetative organs th at appears to be more than observed in the real crop. The result is the incorrect appearance of sustained dry matter growth, and a lower (than observed) pod harvest index, for the low N treatment. The calibration of seed characteristics (seed size, threshing percentage and seed fill duration) involved adjustment of different genetic coefficients controlling these variables. To mimic the final mass per seed at harvest, the coefficient maximum weight per seed (WTPSD) was set to 0.255 g seed-1. Also, the threshing percentage (seed divided by pod wall plus seed) was set to 78% (same as default value) and the individual seed-filli ng duration (SFDUR) was increased from 14 to 18 PD. With these different adjustments, the model seemed to well estimate the weight per seed during early seed growth pe riod but under-estimated it near the end of the growing season (Figure 5-12). Also analysis of the time-courses of the threshing percentage presented in the Figure 5-13 showed a trend simila r to the weight per seed simulation. However, as opposed to the weight per seed simulation at the end of growing season, the model performed better in simulating the threshing percentage at the end of growing season. The RMSE and dstatistic of threshing percentage for the calibration were 8.12, 3.77, 3.55 and 5.16 and 0.97, 0.99, 0.99 and 0.99, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. Distribution of Dry Matter to Leaf, Stem, Pod, and Seed Evaluation of distribution of dry matter among differe nt aboveground organs was achieved by comparing simulated vs. observed fr action leaf, fraction st em, fraction pod, and fraction seed. The model performance in simu lating plant biomass distribution among plant organs is presented in Figure 5-14A, B, C, D. Across all the N rates, simulation of the time courses of dry matter distribution was in general in accordance with the patte rn of distribution of measured dry matter partitioning. Inspection of the figures indicated that snap bean partitions

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113 more assimilates to vegetative growth (leaf and stem) early in the life cycle until the onset of reproductive organs which become potential si nk strength, thus decr eased the partitioning intensity to vegetative organs. Indeed, Boote and Scholberg (2006) mentioned that partitioning of dry matter to leaf, st em, and root in CROPGRO initially varies as a function of main-axis leaf appearance (related to photothermal time), but then dry matter allocation begins to be reduced in a transition after anthesis, contro lled by the fact that reproductive sinks are given first priority once they are added. Assimilate allocation to vegetative growth progressively declines and will cease if full fruit load is attained, depending on the fruit assim ilate allocation coefficient (XFRT). Leaves accounted for a very low fraction of the total dry matter accumulation at the end of the growing season due to the natural senescence. In termediate amounts were allocated to the stem, slightly higher than leaf dry matter accumula tion. The switch in dry matter allocation from vegetative growth to pod and seed growth is expressed as a plateau in root and stem growth according the bean model developed by Gutierrez et al. (1994). The simulation of plant dry matter allocation to plant component s was reasonably well predicted across all four N rates. This is a direct consequence of a relatively good pred iction of shoot and organs mass described above. Simulation of Nitrogen A ccumulation in the Plant After calibration of parameters which infl uence plant N uptake as described in the Methods (calibrated to the two low N treatm ents) and calibration of cultivar, ecotype, and species parameters to the high N treatment, the following seasonal patterns of simulated and observed total plant N accumulation for the different N treatments resulted (Figure 5-15). After a short lag phase, a rapid and linear N accumula tion was observed between 35 to 60 days after sowing under the different N treatments. During this period, the data indicate relatively close agreement between predicted and measured va lues, with differences among N treatments

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114 becoming apparent around 45 days after sowing. The model simulati ons captured the decrease in N uptake as a result of low N conditions fairly we ll after calibration. Afte r 45 days after sowing, increase in N uptake was less rapid under the lowest N treatments, with peak values reached at 55 days after sowing, followed by a plateau phase until the end of season suggesting that the crop accumulated almost all its N prior to 55 days after sowing for the low N treatments. Accordingly, N allocated to the reproductive tissues ( pod and seed) during this phase had to be remobilized from vegetative tissues, mainly stem s and leaves. Similar patterns were observed under the three higher N treatments, but respective peak values were reached differently. Analysis of model performance with different N treatments showed that the N uptake was accurately predicted under these N treatments. More closely, the Root Mean Square Error (RMSE) values were 9.80, 15.10, 13.16 and 9.56 kg N ha-1 and the d-statistic values were 0.97, 0.97, 0.98 and 0.99, respectively, for the N rates of 37, 74, and 111, and 148 kg ha-1. Thus, with the adjustments of parameters c ontrolling plant N uptake and the fractions of soil organic matter (SOM), the CROPGRO simulation m odel was able to accurately ca pture the dynamics of plant N uptake over the growing season. Inde ed, availability of N in the soil is determined by the balance between N supply and mineralization, and between N immobilization and losses. Figure 5-16 shows the simulated and measured time course of total N accumulated in the vegetative parts (leaf and stem). Nitrogen accu mulated in these vegetative parts increased exponentially for the first 40 days and the peaks for leaf and stem were reached at 50 and 58 days after sowing, respectively for th e lowest (37) and highest (148 kg N ha-1) with progressively greater N accumulation with increasing N fertilizer rates. The model sli ghtly under-predicted the N uptake notably at higher N treatments. The RM SE values were 3.5, 8.8, 9.5 and 9.7 kg ha-1. N accumulated in vegetative parts declined sharply after the peak towards the end of the growing

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115 season and the decline was more apparent at th e three higher N treatments. The decline phase observed is ontogenic and corresp onded to the setting of repr oductive organs (pod and seed namely) which became sinks for N. Nitrogen mobili zation from vegetative parts to reproductive tissues is expected to be higher during this period. Figure 5-17 illustrates the simulated and measured time course of N accumulated in the grain. The overall trend was relatively well simulated except an under-predicti on was observed at later stages. Conclusion This study illustrates the adapta tion of the CROPGRO dry bean m odel to simulate the growth, development and dry matter accumulation of sn ap bean. With the calibration of several coefficients in the ecotype file and genetic co efficients in cultivar file, CROPGRO Dry Bean model was able to capture most of the patterns of growth and development in snap bean. In addition, N mobilization aspects in the species file were modified (accelerated) to minimize computed N stress and improve simulations of N balance under low soil N supply. We suspect that these modifications may also be necessary fo r the dry bean model as well, as it has not been tested under low N supply and non-n odulation. The model has ade quate capabilities to predict the life cycle (anthesis and matu rity dates) and biomass accumula tion irrespective of different N rates. Furthermore, with adjustments of parameters defining N uptake and mobilization, the simulated yield and yield components were generally in good agreement with the data obtained under different N s upply conditions. Additional model components such as fruit fresh weight, fruit size distribution, fruit quality, and fruit maturity can be included into the structure of this calibrated CROPGRO dry bean model in order to simulate the fresh marketab le yield and quality of snap bean.

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116Table 5-1. Soil profile characteristics of the Millhopper fine sand, (hyperthermic family of Grossarenic Paleudults) used durin g the calibration process SLB SLLL SDUL SSAT SBDMSLOCSH2O SNH4 SNO3 SOM1 SOM2 SOM3 5 0.023 0.086 0.23 1.47 0.9 0.086 1.03 0.7 0.01 0.70 0.29 15 0.023 0.086 0.23 1.47 0.69 0.086 1.03 0.7 0.01 0.70 0.29 30 0.023 0.086 0.23 1.41 0.28 0.086 1.03 0.7 0.01 0.70 0.29 45 0.023 0.086 0.23 1.43 0.2 0.086 1.30 0.95 0.01 0.60 0.39 60 0.023 0.086 0.23 1.43 0.2 0.086 1.30 0.95 0.01 0.60 0.39 90 0.021 0.076 0.23 1.52 0.09 0.076 1.40 1.4 0.01 0.60 0.39 120 0.02 0.076 0.23 1.52 0.03 0.076 1.10 0.8 0.01 0.60 0.39 150 0.027 0.13 0.23 1.46 0.03 0.13 1.10 0.8 0.01 0.60 0.39 180 0.07 0.258 0.36 1.46 0.03 0.258 1.10 0.8 0.01 0.60 0.39 SLB, depth to base of soil layer (cm); SLLL, soil lower limit (cm3 cm-3); SDUL, soil drained upper limit (cm3 cm-3); SSAT, soil saturated upper limit (cm3 cm-3); SBDM, soil bulk density, moist (g cm3), SLOC, soil organic carbon (%); SH2O, Initial soil water content, (cm3 cm-3); SNH4, Initial ammonium, (g elemental N Mg-1 soil); SNO3, Initial soil nitrate, (g elemental N Mg-1 soil); SOM1, Initial microbial soil organic matter fractional composition (un itless); SOM2, Initial intermed iate soil organic matter fractio nal composition (unitless); SOM3, Initial passive soil organic ma tter fractional composition (unitless); SOM1 + SOM2 + SOM3 = 1.0. (Compiled from Carstile et al., 1981 and Graetz, 2007)

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117Table 5-2. Genetic coefficients of cultiv ar Ambra for the CROPGRO model, after the calibration process, compared to generic Andean dry bean cultivar Genetic Coefficients Abbreviation Andean Ambra Photothermal days from emergence to flower appearance EM-FL 22.6 21 Photothermal days from first flower to first pod FL-SH 3 4 Photothermal days from first flower to first seed FL-SD 12 13 Photothermal days from first seed to physiological maturity SD-PM 18.40 14.00 Photothermal days from first flower (R1) and end of leaf expansion FL-LF 10 16 Maximum leaf photosynthesis rate, mg CO2 m-2 s-1 LFMAX 1 0.95 Specific leaf area of cultivar under standard growth conditions cm-2 g-1 SLAVR 305 210 Maximum size of full leaf, cm-2 SIZELF 133 135 Maximum fraction of daily grow th partitioned to seed + shell XFRUIT 1 1 Maximum weight per seed, g WTPSD 0.600 0.255 Photothermal days for seed filling for pod cohort at standard growth conditions SFDUR 15 18 Average seed per pod under standard growing conditions no. pod-1 SDPDV 3.50 5.60 Photothermal days to reach final pod load PODUR 10 6 Ecotype Coefficients Photothermal days from first flower to main stem termination FL-VS 0 1.50 Weight percentage of seeds in pods (shelling percentage) THRESH 78 78 Fraction protein in seeds (g(protein)/g(seed)) SDPRO 0.235 0.200 Fraction oil in seeds (g(oil)/g(seed)) SDLIP 0.030 0.030 Photothermal days required fo r growth of individual shells LNGSH 8 11.3 Species Coefficients Maximum rate of mobilization of carbohydrat e from veg. tissues (Fraction of pool/day) CMOBMX 0.03 0.07 Maximum rate of mobilization of protein from ve g. tissues during reproductive growth (Fraction of available protein pool/day) NMOBMX 0.10 0.16 Relative rate of mobilization of protein from veg. tissues (compared to rate in reproductive phase) NVSMOB 0.36 0.70 Specific leaf weight at which LFMAX is defined (g dry weight cm-2) SLWREF 0.0030 0.0033 Maximum uptake of NO3 per unit root length (mg N per cm root length) RTNO3 0.006 0.015 Maximum uptake of NH4 per unit root length (mg N per cm root length) RTNH4 0.006 0.015

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118 0 0.5 1 1.5 2 2.5 3 3.5 020406080100 Days after sowingLeaf area index 37 74 111 148 Figure 5-1. Default model simulated (lines) and ob served (symbols) leaf area index as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007 0 1000 2000 3000 4000 5000 6000 7000 020406080100 Days after sowingShoot dry matter (kg ha-1) 37 74 111 148 Figure 5-2. Default model simulated (lines) a nd observed (symbols) shoot dry matter as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007

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119 0 500 1000 1500 2000 2500 3000 3500 4000 4500 020406080100 Days after sowingPod dry matter (kg ha-1) 37 74 111 148 Figure 5-3. Default model simulated (lines) and observed (symbols) pod dry matter as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007 0 20 40 60 80 100 120 140 160 020406080100 Days after sowingAccumulated Shoot N (kg ha-1) 37 74 111 148 Figure 5-4. Default model simulated (lines) and observed (symbols) accumulated shoot N as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007

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120 0 1 2 3 4 5 6 7 8 020406080100 Days after sowingLeaf number (# Nodes ) 37 74 111 148 Figure 5-5. Simulated (lines) and observed (sym bols) main stem node number as a function of days after sowing for snap bean cultiv ar Ambra grown under four N rates in Gainesville FL during spring 2007 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 020406080100 Days after sowingCanopy Height (m ) 37 74 111 148 Figure 5-6. Simulated (lines) and observed (symbol s) canopy height as a function of days after sowing for snap bean cultivar Ambra grow n under four N rates in Gainesville FL during spring 2007

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121 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 020406080100 Days after sowingCanopy Width (m ) 37 74 111 148 Figure 5-7. Simulated (lines) and observed (sym bols) canopy width as a function of days after sowing for snap bean cultivar Ambra grow n under four N rates in Gainesville FL during spring 2007 0 1000 2000 3000 4000 5000 6000 020406080100 Days after sowingShoot dry matter (kg ha-1) 148 37 74 111 Figure 5-8. Simulated (lines) and observed (symbol s) shoot dry matter as a function of days after sowing for snap bean cultivar Ambra grow n under four N rates in Gainesville FL during spring 2007

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122 0 0.5 1 1.5 2 2.5 3 020406080100 Days after sowingLeaf area index 37 74 111 148 Figure 5-9. Simulated (lines) and observed (symbol s) leaf area index as a function of days after sowing for snap bean cultivar Ambra grow n under four N rates in Gainesville FL during spring 2007 0 500 1000 1500 2000 2500 3000 3500 4000 4500 020406080100 Days after sowingPod Dry Matter (kg ha-1) 37 74 111 148 Figure 5-10. Simulated (lines) and observed (symbols) pod dry matter as a function of days after sowing for snap bean cultivar Ambra grow n under four N rates in Gainesville FL during spring 2007

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123 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 020406080100 Days after sowingPod Harvest Index 37 74 111 148 Figure 5-11. Simulated (lines) and observed (symbols) pod harvest index as a function of days after sowing for snap bean cultivar Ambr a grown under four N rates in Gainesville FL during spring 2007 0 50 100 150 200 250 020406080100 Days after sowingSeed dry weight (mg seed-1) 37 74 111 148 Figure 5-12. Simulated (lines) and observed (symbols) weight per seed as a function of days after sowing for snap bean cultivar Ambra grow n under four N rates in Gainesville FL during spring 2007

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124 0 10 20 30 40 50 60 70 80 020406080100 Days after sowingShelling % 37 74 111 148 Figure 5-13. Simulated (lines) and observed (symbol s) shelling percentage as a function of days after sowing for snap bean cultivar Ambr a grown under four N rates in Gainesville FL during spring 2007 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 71727374757677787 Days after plantingFraction of plant biomas s Leaf Stem Pod Seed Figure 5-14. Simulated (lines) and observed (symbol s) fraction of biomass in plant organs as a function of days after sowing for snap b ean cultivar Ambra grown under (A) 37, (B) 74, (C) 111, and (D) 148 kg N ha-1 rates in Gainesville FL during spring 2007 A

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125 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 71727374757677787 Days after plantingFraction of plant biomas s Leaf Stem Pod Seed c Figure 5-14 Continued 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 71727374757677787 Days after plantingFraction of plant biomas s Leaf Stem Pod Seed Figure 5-14 Continued C B

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126 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 71727374757677787 Days after plantingFraction of plant biomas s Leaf Stem Pod Seed Figure 5-14 Continued D

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127 0 20 40 60 80 100 120 140 160 020406080100 Days after sowingShoot accumulated N (kg ha-1) 37 74 111 148 Figure 5-15. Simulated (lines) and observed (s ymbols) total plant N accumulation as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007 0 10 20 30 40 50 60 70 80 90 020406080100 Days after sowingLeaf+ Stem accumulated N (kg ha-1) 37 74 111 148 Figure 5-16. Simulated (lines) and observed (symbol s) N in vegetative parts (leaf and stem) as a function of days after sowing for snap b ean cultivar Ambra grown under under four N rates in Gainesville FL during spring 2007

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128 0 10 20 30 40 50 60 70 80 90 100 020406080100 Days after plantingGrain accumulated N (kg ha-1) 37 74 111 148 Figure 5-17. Simulated (lines) and observed (symbol s) grain N as a function of days after sowing for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007

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129 CHAPTER 6 DEVELOPING A SNAP BEAN SIMULATION MODEL TO PREDICT FRESH MARKET YI ELD AND QUALITY OF PODS Introduction Snap bean (Phaseolus vulgaris L.) is an economically impor tant vegetable with a U.S. fresh market crop value of more than 390 million (U SDA, 2007). Florida is the largest snap bean producing state in the United States. In 2007, Flor ida accounted for 35% of the total harvested U.S. fresh market snap bean and 55% of the overall U.S. crop value (USDA, 2007). Like production of most vegetables, snap bean producti on faces different challenges with respect to crop management and decision-making throughout th e growing season. As a vegetable grown for fresh market, managing snap bean production to m eet market standards is a complex trade-off between yield and quality; growers select a time for harvest that produc es the highest possible yield and the optimum quality parameters before quality deteriorates to an unacceptable level. Several process-oriented crop growth models have been ex tensively developed and used for predicting crop growth, devel opment and yield in relation to the weather, pests, soil, and management practices. In the case of snap bean, Ferreira et al. (1997) attempted to predict phasic development of green beans us ing a model with thermal time accumulation concept, but their work did not predict fresh market quality. Also, Ferreira et al. ( 2006) accounted for some internal as well as external quality variables such as al cohol-insoluble solids, dry matter concentration, seed: pod ratio, fiber concentration, length of 10 seeds, Kramer shear press, color, lipid concentration and mineral composition. They de termined regression analyses between these variables versus thermal time in order to evaluate quality and maturity of snap bean pods. These empirical approaches based on statistical analys es to model snap bean may not be of great interest for growers and researchers in that they did not account for underlying causes and mechanisms which regulate the temporal cha nges and distribution of fresh weight and pod

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130 quality in relation to environmental factors a nd management practices (water and nitrogen influence). For modeling results to be meaningful or useful for producers or extension agents, additional modules including fruit fresh weight, fruit size distribu tion, fruit quality, and fruit maturity should be incorporated into exis ting crop simulation models which predict crop production on a dry matter basis as affected by crop management strategies and environmental factors. For such crops, Marcelis et al. (1998) suggested that mode l-predicted fruit dry matter needs to be converted to fresh weight and/or fruit si ze as yield is predominantly determined by the water concentration. The CROPGRO model is a pr ocess-oriented model that uses daily weather and management inputs to predict daily changes in pl ant growth to the point of final yield on dry matter basis. Boote and Scholberg (2006) envisi oned that since the CRO PGRO model predicts explicit fruit addition and fruit growth rates ov er time for specific cohor ts, this model ability could be valuable for predicting fresh market yields over time. Achieving these processes required modification and new algorithms in the existing CROPGRO code. Therefore, our objective in this study was to add a new module to the calibrated CROPGRO model to simulate the fresh market production and quality of snap bean. It is of interest to note that to a grower or processor, quality in snap bean is defined in te rms of sieve size, percent seed by weight of total pod weight, pod fiber content, pod smoothness and straightness, pod color, and flavor. This study was only interested in pod sieve size distribut ion to simulate the snap bean pod quality. Materials and Methods Model Structure Figure 6-1 illustrates a conceptu al model for simulating snap bean fresh marketable yield and quality useful to understa nd how temporal patterns of pod distribution and quality aspects (dry matter concentration, pod diameter) are impacted by environment factors and management

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131 practices. This conceptual model diagram is de signed based on previous visuals of Jones and Luyten (1998), and Marcelis et al. (1998). In essence, the model is photosynthesis-based, and first calculates th e interception of light by the leaf area and then simula tes the production of photosynthate s. Subsequently, the use of photosynthates for respiration, c onversion into structur al dry matter (DM), the partitioning of assimilates or DM among the differe nt plant organs is calculated. Finally, the fresh weight can be estimated from the dry weight based on the dry matter concentration algorithm. In this model, plant biomass production is mainly driven by climatic factors (CO2, solar radiation, and temperature) and is affected by growth reduction factors, namely nitrogen and water stress (not represented on the diagram). Model Development In Chapter 5, the CSM-CROPGRO-Dry bean Mod el was calibrated to simulate the growth and development of snap bean under optimum water and nitrogen conditions. The calibrated model resulting from this process was modified to simulate the fresh market production and quality of snap bean. For this purpose, additiona l source code was incorpor ated into the general model structure and was focused on two co mponents: pod fresh weight and pod quality. Model development data Data used to develop this model were collected in the same field e xperiment presented in Chapter 2. More specifically, data for this chapter relate to s ingle pod growth, fresh market weight and quality For this purpose, 50 small pods (about 2-cm length) were tagged all on the same day per replicate from the four N treatments receiving the medium irrigation (100% Et). Subsequently, at interval of 3 days, 4 pods per replicate (4 replicates total) were randomly harvested from the tagged pods and the following data were measured on a per pod basis: pod diameter (mm), pod fresh weight (g ), pod wall dry weight, number of seed, seed fresh weight and

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132 seed dry weight. Also, pod sizes were determined with the green snap bean sieve size grader. These data were used to devel op functional relationships between fresh weight and dry weights versus thermal time and also pod size (diameter) vers us fresh weight with the use of the software package Microcal Origin Version 6.0 which provided the best fit and equations of our numerical data. Dry matter concentration (DMC) and pod fresh weight Fresh weight and dry weight of pods and seeds measured were used to develop a Dry Matter Concentration algor ithm (DMC) as a function of thermal time as follows: )*161.0exp(*0116.00465.0PAGE DMC (6-1) The model simulates the increase in Pod Dry Matter Concentration (DMC), following an exponential pattern with thermal time (physiological days) (Figure 6-2). It should be noted that the thermal time values in this function were derived from the thermal time module in CROPGRO which accounts for the cardinal te mperatures (base temperature, optimum temperature and maximum temperat ure) to calculate values of physiological days accumulated by a given observation date. Fresh weight of pods [FWpod (grams per pod)] is thus calculated as follows: DMC DWpod FWpod (6-2) where DWpod is the pod dry weight (grams per pod). Do ing this for all pod cohorts on the crop for which DWpod is positive and integrating the resulting FWpod over all pods, the model calculates total fresh we ight yield of pods [TotFW (kilograms per hectare)]. Pod quality Upon developing the fresh weight relationship, a functional relationship between measured pod diameter (which defines pod quality) and meas ured pod fresh weight was developed, which

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133 subsequently allows predicting the U.S. standard grades of fresh market of snap bean (USDA, 1997). The pod diameter versus FWpod relationship is described as follows: ))48.0 *460.0exp(1(*737.9 FWPod PodDiam (6-3) This is a monomolecular growth relationship of the pod fresh weight with a sharp increase of pod diameter early in the pod growth period up to the point where the pod diameter follows a steady and constant plateau phase (Figure 6-3). The U.S. standards for grades of fresh market snap bean separate snap bean into six main classes based on pod diameter also called pod sieve size as follows: Size 1 (diameter between 5.1 and 7.3 mm), Size 2 (diameter between 7.3 and 8.3 mm), Size 3 (diameter between 8.3 and 9. 5 mm), Size 4 (diameter between 9.5 and 10.7 mm) and Size 5 (diameter greater than 10.7 mm). Of these five categorie s, Size 3 and Size 4 are considered appropriate for fresh market. A new module to simulate the aggregated time-courses of fresh market yield (kg ha-1) and pod quality was developed through introduction of the different algorithms and code added to the CROPGRO simulation model stru cture. Therefore, the mode l outputs daily pod dry matter concentration, pod fresh weight (kg ha-1) and individual pod dry and fresh weight (g pod-1), pod diameter (mm) and pod sieve size. An aggregati ng function in that modu le integrates over all single pod cohorts, to compute tota l fresh market yield falling in to respective pod size classes. Results and Discussion Tagged Pod and Seed Dry Mass Simulation In the CROPGRO simulation model, the time step of the modules for simulation of assimilate partitioning, dry matter production, pod growth, dry matter concentration and pod fresh mass is 1 day. The numbers of pods, seeds and flowers that are initia ted within a given day are stored in arrays that allo w knowing not only the numbers of pods etc., on the plants, but also

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134 the calendar time ages of the pods and their physiological ages (Boote, 1999). The model predicts pods formed on each day, and a specific days pods can thus be identified. The function for individual pod growth rate (which affects final pod size) depends on maximum weight per seed (g) (WTPSD), the maximum ratio of (seed/( seed+shell)) at maturity (THRSH), and the average seed per pod under standard growing co nditions (SDPDV), as well as the duration of shell growth (LNGSH), which is also sensitive to temperature. Seasona l dry weight progression of individual pods formed in the model on the same day as the observed tagged pods (10 days after anthesis) are compared in Figure 6-4. The single pod dry weight simulations were reasonably close, but these also required some calibration of th e length of shell growth period (LNGSH) which was increased from 8 to 11.3 PD. Analyzing these plots show a linear increase in single pod dry weight up to 23 days after anthesis and thereafte r, the trend shows a more rapid increase in pod dry weight which is related to the accumulation of dry weight in seed within the pods, as can be seen in Figure 6-5. Simulations of single seed growth under the different N treatments indicated that the CROPGRO snap b ean model has a general tendency to set seed within the pod a little late, but gives relativel y good prediction of seed dry weight during the initial growth of seed but unde r-predicts them later. A similar trend was also observed with respect to the single pod dry we ight simulations. The RMSE of pod dry weight prediction and the corresponding d-statistic values were 0.21, 0.12, 0.14 and 0.08 g pod-1 and 0.93, 0.98, 0.97 and 0.99 for the N treatments of 37, 74, 111 and 148 kg ha-1. Tagged Pod DMC Simulation Figure 6-6 shows the time course of dry ma tter concentration of individual pods as a function of days after anthesis. After an initial lag-period phase of approximately 5 days, the pod showed a steady almost linear increase (linear growth phase) of pod dry matter concentration over time. The DMC was relatively constant at about 0.06 during the init ial lag phase period.

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135 Based on these plots, it appears that the mode l performed relatively well in simulating the dry matter concentration of individu al pods. Simulation was relatively accurate with RMSE being 0.039, 0.029, 0.044 and 0.027 and the d statistic was 0.98, 0.98, 0.97 and 0.99 for the N treatments of 37, 74, 111 and 148 kg ha-1, respectively. Tagged Pod Fresh Weight Simulation Fresh weight of fruit is calculated from the dry weight and dry matter concentration relationship. The ability of th e snap bean model to simulate seasonal progression of single pod growth is shown in Figure 6-7. Analysis of the accumulation of single pod fresh weight showed a linear increase during th e initial pod development (from onset of pod growth at about 10 days lasting until about 20 days after anthesis). From day 20 after an thesis onwards, the simulation quickly approached a peak pod fresh weight followed by a slow decline phase. The peak illustrates attainment of full pod size, also charac terized by accumulation of assimilates into the growing seed inside the pod. The period of rapi d pod fresh weight growth is indicative of the intense growth activity associat ed with cell division and elongation. The RMSE values of pod fresh weight predictions were 1.04, 0.94, 0.97 and 0.955 g pod-1 and the d-statistic values were 0.78, 0.88, 0.87 and 0.87 for the N treatments 37, 74, 111 and 148 kg ha-1, respectively. Based on these model evaluation coefficients, it is concl uded that the model perfor med relatively poorly in simulating the individual pod fresh weights, which could possibly be improved with better single pod DM growth algorithms and possibly improved DMC algorithms. Total Pod Dry Matter Concentration Simulation Implementation of the single pod dry matter concentration algorithm described above is used in the model to simulate the aggregated pod dry matter concentration. The CROPGRO Snap bean model exhibited good performance in simu lating the seasonal pod dry matter concentration (Figure 6-8). Indeed, the simulated dry matter concentration matched we ll with the observed

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136 values, irrespective of the N rates. This result in dicates therefore that dry matter concentration of snap bean pods appears to be relatively una ffected by N stress. However, Scholberg (1997) suggested that increased N suppl y may result in a reduction in dry matter percentage, due to increased plant growth and a dilu tion of dry matter concentration of tomato fruit. Similar to our results, Kenig et al. (1993) observed that pod DMC for soyb ean increased exponentially over time, with the later rapid phase caused by the drying of pods during seed maturation. Harvest for the fresh market occurred at 23 da ys after anthesis (indicated on figure by the arrow). At this time, the pod dry matter concentr ation was around 10% regardless the N rates. As many horticultural crops grown for fresh market are characterized by a low DM concentration, this compares well with values reported by Gardiner and Prendiville (1970) and Ferreira et al. (2006). Based on this pod quality variable, it can be concluded that in our experiments and the model prediction, the harvest for fr esh market occurred at the appropria te period. It is of interest to note that Marcelis et al. ( 1998) observed that the relationshi p between growth in fresh matter and DM, which determines the DM concentration, is still poorly understood and reported that to some extent the accumulation of water might be independent of the accumulation of DM and suggested that a model in which carbon production and partitioning are combined with water uptake and transpiration may be th e first step in direction of a more mechanistic model for DM concentration. Total Pod Fresh Weight Simulation Simulation of the time course of total aggreg ated pod fresh weight of snap bean grown under four N rates is illustrated on Figure 6-9. At high N rate, the model predicted well the accumulation of pod fresh weight, but slightly unde r-predicted this variable as the N supply was reduced. During the initial expolinear phase, th e model showed good prediction ability for the progression of pod fresh mass for th e three highest N treatments, but for the low N treatment, it

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137 began to under-predict the pod fresh weight from 20 days after anthesis until the end of growing season, although this may not be important afte r that time. To place this analysis into perspective, consider that harvest for fresh mark et production in this study occurred 23 days after anthesis or 64 days after sowing. Data observations after this peri od are irrelevant to fresh market harvest because pods are already too mature and beginning to dry out. Therefore, when analysis accounts for only the period up to fresh mark et yield production, the model shows good prediction capability irrespective of N treatme nts. The simulated maximum pod fresh weights were reached slightly later than the observed maximum value. The Root Mean Square Error (RMSE) values of pod fresh weight pr ediction were 1673, 1814, 1948 and 1365 kg ha-1 and the d-statistic values were 0.95, 0.97, 0.97 and 0.98, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. Data on simulation of pod dry matter pres ented in Chapter 5 were relatively consistent with this analysis showing relatively good prediction capability of the model. Given that the fresh market pod yiel d is derived from the pod dry matter concentration functional relationship, good prediction of pod dry matter shoul d also ultimately result in good prediction of pod fresh weight, if the environmental conditions and management practices remain equal. Pod Size Simulation Pod sieve size is considered an important qua lity aspect of snap bean grown for fresh market. Snap bean pods are marketed based on the U.S. grade standards defined in terms of the pod sieve size which itself is related to the pod diameter. Figures 6-10 and 6-11 illustrate the time courses of simulated and measured individual pod diameter and pod si eve size, respectively. In essence, simulations of single pod diameter and sieve size progression over time appeared to relatively follow the general trend of the meas ured values under di fferent N treatments. However, it appears that the model was initially too rapid in predicting the growth pattern of fresh pod diameter but later consistently matc hed the dynamics of measured pod diameter. The

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138 simulated pod diameter during this initial growth phase appeared not to be affected by different N treatments, but showed slight differences during later stages. The RMSE values of pod diameter simulation were 1.55, 1.41, 1.39 and 1. 32 and the d-statistic values were 0.73, 0.79, 0.80 and 0.82 for N treatments 37, 74, 111 and 148 kg ha-1. These values suggest that the model slightly under-estimated the pod diameter when N application was low. Simulation of fresh weight of individual pod presente d in Figure 6-7 above is consiste nt with this analysis which showed relatively poor predicti on of pod fresh weight at low n itrogen. Accordingly, given the functional relationship between pod diameter and pod fresh weight, poor prediction of pod diameter was just a direct consequence of poor performance of the model in simulating the pod fresh weight notably at low N rate. After the linear increa se phase of pod diameter from 10 to 20 days after an thesis, the time course of pod diameter showed a steady plateau phase which indicates that the pod had reached its full size. The subsequent calculated decrease phase of diameter is related to pod drying and loss of fresh weight, and is thus not as important to predicting pod quality. The linear growth phase of the pods corresponds to the period of cell enlargemen t and higher water import and assimilates which leads to the maximum diameter (pod size) when seed development may still be insignificant. When a pod reaches its full size, the import of water into the pod ceases and subsequently pod DMC continues to increase. The temporal distribution patte rns of pod sieve size presented in Figure 6-11 show step increases in pod size similar to, and triggered by, pod diameter w ith short plateau phases which indicates the time for passage from one sieve size to another one. The increase in pod size corresponds to the period of intense accumulation of wate r and assimilates into the pods; implying that assimilates and water import play a dominant role in snap bean quality aspects.

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139 The model did not show any differe ntial response to N treatments relative to the time course of pod size until 15 days after anthes is. Thereafter, the lowest N tr eatment (N-37 kg ha-1) showed a steady plateau phase at the pod sieve size 3 whil e the three higher N treatments followed an increase in sieve size up to 4. A steady plateau ph ase shown on the plots at 20 days after anthesis illustrates that pod reached the full size which is according to the results. The decline phase observed later in the season explains a decrease in pod diameter as a result of pod drying and loss of water. The prediction of appropriate ha rvest date of snap bean for fresh market is important to guarantee both optimum yield and quality of b ean. Figure 6-12 illustrates concurrently the simulated progression of snap bean total fresh ma rket yield and fresh yield in pod sieve sizes 3 and 4 over time. The arrow in the figure indicates the date of harvest for higher yield and higher bean quality which occurred ar ound 23 days after anthesis. Ou r harvest was 23 days after anthesis, but optimum harvest could be 1 to 2 days earlier. After this period, fresh yield decreases rapidly (due to increase in dry matter concentration) and quality declines rapidly (pods with higher fiber). Therefore, the optimum harvest date is a compromise between two opposing components: optimum yield and optimum quality. Simulation of the Interactive Effect of Ir rigation and Nitrogen Rates on Different Crop Variables The capability of the CROPGRO Snap bean simu lation model to predict various snap bean crop variables under different irri gation regimes was evaluated th rough prediction of total shoot dry weight, pod dry weight, pod fr esh weight and average pod diameter. Figure 6-13 illustrates the effect of irrigation and N treatments on snap bean total shoot dry weight at fresh market harvest date. In general, the model appears to sl ightly over-predict the shoot biomass irrespective

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140 of different irrigation regimes, but it did not show any differe nce among irrigation treatments notably at the three higher N rates. On the other hand, analysis of the simula ted pod dry weights versus measured pod dry weights at harvest shows a discrepancy in the model performance with respect to irrigation regimes effects. Irrespective of the N trea tments, the simulated pod dry weight under low irrigation regime was higher than the simu lated pod dry weight under high irrigation. Additionally, when these simulated pod dry weights were compared with observed pod dry weights under the respective irrigation conditions, it appears th at the model over-predicted pod dry weight under low irrigation while under-predic ting it at high irrigation, notably at the lower nitrogen rates (37 and 74 kg ha-1). This simulated result coul d be related to more N being available for uptake under low irrigation than high irrigation. Results of simulated N uptake from the soil under low and high irrigation regimes at the N rate of 148 kg N ha-1 show that total simulated N uptake from the soil was higher in low irrigation than high irrigation (160.0 versus 125.9 kg ha-1). Less N uptake simulated in the high irriga tion treatment can also be a result of higher simulated N leaching from the root zone. Analysis of the amount of simulated N leached at the N rate of 148 kg ha-1 shows that the model is predicting higher N leaching at high irrigation (99.8 versus 27.6 kg ha-1). The differential performance of the model with regard to the irrigation regimes may be due to a combination of deficiencies in model inputs or parameters which influence water uptake and ET in the model. It is important to highlight that our initial model calibration was performed under medium i rrigation regime (100% ET) and initial soil N and soil organic matter fractions and N uptake rate s per unit root length were the only N-related variables adjusted during the calibration pro cedure. Therefore, it appears that further

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141 modification and adjustment of parameters are still needed to better capture N uptake and transpiration parameters in the model. With no surprise, the trend for pod dry weight s was similar to that observed for the pod fresh weights with the simulation in low irriga tion performing better th at the high irrigation treatment (Figure 6-15). Given the functional relationship between pod dry weight and pod fresh weight, this deficiency in pod fresh weight simulation result ed from the discrepancy in pod dry weight reported previously. Finally, Figure 6-16 illustrate s the interactive e ffect of simulated and observed fresh pod diameter at fresh market harvest. Inspection of these plots did not reveal any meaningful difference in response to the in teractive effect of irrigation and N treatments on the simulated versus observed pod diameter. Conclusion To simulate fresh market production and pod quality of snap bean under different N and irrigation regimes, new algorithms of pod dry matter concentration and pod diameter were developed and incorporated into the CROPGRO dry bean model calibrated for snap bean. Although the proposed model for snap bean fresh market production and quality is still in its initial development stage, preliminary modeling re sults appear to be encouraging. Indeed, the pod dry matter concentration was well predicted a nd appeared to be unaffected by differential N rates. In this model, single pod fresh weight wa s derived from dry matte r concentration function and the total fresh market pod production was co mputed on the land area basis. Results of simulation indicate that at high N rate, the m odel predicted well the accumulation of total pod fresh weight, but slightly under-predicted it at low N treatment. Further, simulation of pod sieve size and pod diameter, important snap bean quality aspects, were also acceptable and were not significantly affected by various N treatments.

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142 Simulating the seasonal changes in crop pr oduction variables under different irrigation regimes and nitrogen rates helped to evaluate the model stability and performance. An analysis of the influence of interactiv e effect of irrigation regimes and N rates on fresh weight pod indicated that the model predic ted too high under low irrigation regime and predicted too low under high irrigation regime in si mulating pod fresh weight at fr esh harvest date. This finding was attributed to relatively higher predicti on of N leaching in high irrigation regime and consequently lower available N for uptake as opp osed to low irrigation. This discrepancy is associated in part to deficiency and inaccuracy of parameters and inputs in the model which influence water balance and N uptake. Therefor e, further model improvements are thus still needed for a better understanding of some underlyi ng processes in order to overcome limitations and to enhance the accuracy of our model predictions, notably under differential irrigation conditions.

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143 Assimilates Plant dry matter Leaf Stem Pod Seed Development stage Pod fresh weight Pod Dry Matter Concentration Temperature Growth Growth respiration Photosynthesis Maintenance respiration CO2 Radiation Leaf area Development rate Partitioning Conversion efficiency Figure 6-1. A relationship diagra m of the crop model used in th e present study. Boxes are state variables, valves are rate variables, circles are parame ters, solid lines represent carbon flow, and dashed lines represent informa tion flow. Adapted from Jones and Luyten (1998) and Marcelli s et al. (1998). 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 051015202530 Thermal time (pd)Pod DM C 37 74 111 148 Figure 6-2. Progression of si ngle pod dry matter concentration versus thermal time (photothermal days) for pods tagged 10 Days After Anthesis (DAA) on snap bean cultivar Ambra grown under four N ra tes in Gainesville FL during spring 2007

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144 0 1 2 3 4 5 6 7 8 9 10 024681012 Pod Fresh Weight (g)Fresh Pod Diameter (m m) 148 37 74 111 Figure 6-3. Pod diameter versus single pod fresh weight for pods tagged 10 DAA on snap bean cultivar Ambra grown under four N ra tes in Gainesville FL during spring 2007 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 01020304050 Days after anthesisPod Dry Weight (g pod-1) 37 74 111 148 Figure 6-4. Model simulated (lines) and observe d (symbols) average pod dry weight per pod as a function of days after anthes is for pods tagged 10 days after anthesis (DAA) on snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007

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145 0 50 100 150 200 250 01020304050 Days after anthesisSeed Dry Weight (mg seed-1) 37 74 111 148 Figure 6-5. Model simulated (lines) and observed (symbols) average seed dry matter per seed as a function of days after anthesis for pods tagged 10 DAA on snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 01020304050 Days after anthesisPod Dry Matter Concentratio n 37 74 111 148 Figure 6-6. Model simulated (lines) and observe d (symbols) single pod dry matter concentration as a function of days after anthesis fo r pods tagged 10 DAA for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007

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146 0 1 2 3 4 5 6 01020304050 Days after anthesisPod Fresh Weight (g pod-1) 37 74 111 148 Figure 6-7. Model simulated (lines) and observe d (symbols) average pod fresh weight per pod as a function of days after anthesis for p ods tagged 10 DAA for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 01 02 03 04 05 0 Days after anthesisPod Dry Matter Concentratio n 37 74 111 148 Harvest FW Figure 6-8. Model simulated (lines) and observed (symbols) total pod dry matter concentration as a function of days after anthesis for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007

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147 0 5000 10000 15000 20000 25000 01020304050 Days after anthesisTotal Pod Fresh Weight (kg ha-1) 37 74 111 148 Figure 6-9. Model simulated (lines) and observe d (symbols) total fresh market pod yield as a function of days after anthes is for snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007 0 1 2 3 4 5 6 7 8 9 10 01020304050 Days after anthesisPod diameter (mm pod-1) 37 74 111 148 Figure 6-10. Model simulated (lines) and observe d (symbols) single pod diameter as a function of days after anthesis for pods tagged 10 DAA on snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007

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148 0 1 2 3 4 5 01020304050 Days after anthesisPod sieve siz e 37 74 111 148 Figure 6-11. Model simulated (lines) and observe d (symbols) single pod sieve size as a function of days after anthesis for pods tagged 10 DAA on snap bean cultivar Ambra grown under four N rates in Gainesville FL during spring 2007 Figure 6-12. Cumulative progression of total pod fresh weight and fresh pod weight in pod sieve sizes 3 and 4 as a function of days after an thesis for snap bean cultivar Ambra grown under N rate of 148 kg ha-1 in Gainesville FL during spring 2007 0 5000 10000 15000 20000 01020304050 Days after anthesisPod FW (kg ha-1) PodFW Size 4 Size 3 Optimum harvest

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149 0 500 1000 1500 2000 2500 3000 3500 4000 4500 3774111148 Nitrogen Rates (kg ha-1)Shoot Dry Weight (kg ha-1) Low Medium High Figure 6-13. Mode simulated (lines) and observe d (symbols) shoot dry weight of snap bean cultivar Ambra at fresh harvest date ( 64 DAS) as affected by N rates under low, medium, or high irrigation regimes in Gainesville FL during spring 2007 0 200 400 600 800 1000 1200 1400 1600 1800 2000 37 74 111 148 Nitrogen Rates (kg ha-1)Pod Dry Weight (kg ha-1) Low Medium High Figure 6-14. Model simulated (lines) and obser ved (symbols) pod dry weight of snap bean cultivar Ambra at fresh harvest date ( 64 DAS) as affected by N rates under low, medium, or high irrigation regimes in Gainesville FL during spring 2007

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150 0 2 4 6 8 10 12 14 16 18 20 37 74 111 148 Nitrogen rates (kg ha-1)Pod Fresh Marketable Yield (t ha-1) Low Medium High Figure 6-15. Model simulated (lines) and observe d (symbols) pod fresh weight of snap bean cultivar Ambra at fresh harvest date ( 64 DAS) as affected by N rates under low, medium, or high irrigation regimes in Gainesville FL during spring 2007 0 2 4 6 8 10 12 37 74 111 148 Nitrogen Rates (kg ha-1)Fresh Pod Diameter (m m) Low Medium High Figure 6-16. Model simulated (lines) and observe d (symbols) fresh pod diameter of snap bean cultivar Ambra at fresh harvest date ( 64 DAS) as affected by N rates under low, medium, or high irrigation regimes in Gainesville FL during spring 2007

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151 CHAPTER 7 SUMMARY AND CONCLUSIONS Snap bean (Phaseolus vulgaris) is an economically important vegetable grown in Florida which accounts for 35% of the total harvested U.S. fresh market of snap bean and 55% of the overall U.S. crop value. Comput er simulation models have become valuable management tools for assessing crop growth, yield and nutrient movement in plant and soil in relation to the weather, soil and management practices. Existi ng simulation models have limited capability in assisting production of crops su ch as snap bean which are primarily grown for fresh market because most models only predict yield on dr y matter basis. The purpose of this study was therefore to develop a snap bean simulation mode l to predict the fresh market yield and quality of pods as affected by irrigation and nitrogen le vels. In this effort, a field experiment was conducted at the Irrigation Research and Education Park located on the University of Florida in Gainesville, Florida, during Marc h-June 2007 to study the growth and N uptake response of snap bean (Phaseolus vulgaris L.) to N fertilization (Chapter 3) and to evaluate the yield response of snap bean to the interactive effect of irrigati on regimes and N fertiliza tion (Chapter 4). Data collected in this experiment were used to adapt the CSM version of the CROPGRO Dry bean model to simulate the growth and development of snap bean (Chapter 5) and finally to develop a fresh weight and pod-size module in order to simulate fresh mark et yield and quality using the calibrated CROPGRO model as star ting point (Chapter 6). Four N fertilization rates (37, 74, 111 and 148 kg N ha-1) were applied to snap bean cultiv ar Ambra grown under three irrigation regimes (Low (66% crop Et, Medium (100% Et) and High (133% crop Et)) as main plots distributed in a split plot de sign, with four replications.

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152 Snap Bean Growth Study The first objective of this study was to provide insight into the pa ttern of crop physiological variables over the full growth period to maturity for field-gr own snap bean under different N rates at medium irrigation regime. Measurements of canopy growth, leaf area index, dry matter and N distribution in plant parts were performed weekly, starting at 14 days after sowing. Results indicated that snap bean growth parameters analyzed in th is study responded consistently to N fertilization. Leaf area index, aboveground biom ass and distribution, plant N accumulation and allocation among plant tissues all showed consistent patterns w ith increasing N rates. More specifically, the leaf area index (LAI) increased rapidly, with minor effect of N rates. Maximum values of LAI for treatments of 37 and 111 kg N ha-1 were 2.0 and 2.6, respectively, at 55 days after planting. Thereafter, a dec line in LAI was observed which was relatively more rapid at the lowest N rate. The dry matter accumulation of sn ap bean did not differ meaningfully across N rates until about 55 days after pl anting but thereafter the lowest N rate showed lower dry matter accumulation rate as opposed to the three high er N rates which did not show any apparent differences from each other. Average crop growth rates computed from the slopes of near-linear periods of total biomass increase ( 34-76 DAP) were 81, 108, 109 and 109 kg ha-1 d-1 for 37, 74, 111 and 148 kg N ha-1, respectively. With differences betw een N rates typically becoming more evident over time, towards the end of the growing season, final DM accumulation was 26% higher for the three higher N-fertilizer rates than the lowest N rate. Fractional distribution of total dry matter among above-ground plant parts indicated that across all the N treatments, snap bean allocated maximum dry matter to the seeds, followed by pods, stems and leaves towards the end of growing season. Nitrogen distribution in plant components was largely affected by N fertilization. Leaf N concentration in all treatments was relatively high at very early stages of plant development, and

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153 started declining consistently due to remobilization of N to other plant parts, notably to seed. N uptake was considerably increased by N fertilization. The crop absorbed almost of its N prior to 57 DAS. Snap Bean Yield and Pod Quality Study Proper management of water and fertilizers are vital to the high productivity of snap bean in the commercial production system while concurrently reducing the environmental consequences of intensive management practices. This study evaluated the effects of interaction of water irrigation and inorgani c N-fertilizer on fresh market production and quality of snap bean. Harvest for fresh market yield and pod quali ty aspects occurred at 64 days after sowing. Results indicated that individual effects of irriga tion and N fertilizer levels showed significant influence on the fresh market yield of snap bean and the interaction of irrigation and N rates was also significant. More specifically, at the low irrigation regime (66% ET) fresh market yields were 8.5, 11.8, 15.7, and 10.6 Mg ha-1 for the 37, 74, 111, and 148 kg N ha-1 treatments, respectively. At the medium irri gation treatment (100% Et), yields were in the order of 10.1, 14.8, 17.5, and 17.1 Mg ha-1 and finally, at the high irrigation level (133% ET), they were 12.9, 16.3, 15.2 and 18.9 Mg ha-1, respectively. Therefore, irrigatio n amplified the N effect except at the low irrigation at which the difference between the lowest N rate (N37) and the highest rate (N148) was very small. Furthermore, at low irri gation regime, increasing N rates did not increase linearly the fresh market yield. There was no yield benefit with N-rates over 111 kg ha-1 (IFAS recommended N rates for snap bean) at low or me dium irrigation. However, at high irrigation regime, increasing N fertilizer from 37 to 148 kg N ha-1 substantially increased fresh marketable yield, confirming that the soil wa ter content (irrigation regime) is a very important limiting factor to fresh market yield, followed by N effect. Additionally, results illustrated that pod quality parameter for fresh marketable yield such as pod diameter responded linea rly to both irrigation

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154 and N rates while pod length and number of seed per pods were linearly responsive to the irrigation regimes and quadratically to the N rates. Overall, there was no interaction of irrigation and N rates on these pod quality parameters. De spite these small significant differences, it appeared from these results that pod quality parameters overall were not affected in a major way by irrigation and N effects. Finall y, analyzing the temporal and sp atial distribution of both nitrate and ammonium contents in the so il profile showed that nitrate a nd ammonium were higher in the deepest layers (90-120 cm) at the end of the seas on irrespective of irrigation regimes, implying accumulation of these nutrients leading to possible leaching into ground water. CROPGRO Snap Bean Model Development Study Crop modeling is a mathematical method developed for predicting the growth, development, and yield of a crop, given a set of genetic coefficients and relevant environmental variables. In this study, the CROPGRO Dry Bean simulation model embedded in DSSAT 4.5 was adapted to accurately simulate the shoot dry matter accumulation and pod dry matter, and a new module was added to the calibrated model to simulate the fresh market yield and pod quality of snap bean. The approach of model adaptation involved itera tive calibration of the species, cultivar, and ecotype files of dry bean to para meterize CROPGRO for snap bean. The measured data from the field experiments described in prev ious sections were used during this procedure. Calibration was conducted by minimizing the error between observed and simulated variables. As a result, CROPGRO Dry Bean captured most of the patterns of crop growth and development in snap bean and has adequate capabilities to predict the life cycle, biomass accumulation and yield components of snap bean over time, sugges ting thus that the physiology of snap bean is relatively similar to other legumes simulated by the model, such as dry bean. Simulated changes in dry matter accumulation globally matched well with observed measurements. The Root Mean Square Error (RMSE) values of calibration data was 390, 129, 164 and 196 kg ha-1 and the d-

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155 statistic was 0.98, 0.99, 0.99 and 0.99, respectively for the N rates of 37, 74, and 111 and 148 kg ha-1 indicating good prediction of this variable. The model predicted well th e distribution of total dry matter within plant. Across all N rates, simulation of the time courses of dry matter partitioning was in general in accordance with the pattern of distribution of measured dry matter partitioning. With adjustments of parameters de fining N uptake and mobilization in the species files, the simulated yield and yield components were generally in good agreement with the data obtained under different N rates. More specifically, RMSE and dstatistic of pod weight for the calibration were 150, 191, 224 and 315 kg ha-1 and 0.99, 0.99, 0.99 and 0.98, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. The main goal of this study was to develop a snap bean simulation model to predict fresh market yield and quality of pods. In this effort pods of 2-cm length and near uniform size were tagged at 10 days after anthesis and time-se quence measurements were made to evaluate pod growth and quality aspects. With data collected on the tagged pods, a Dr y Matter Concentration algorithm (DMC) was developed as a function of thermal time and enabled computing the pod fresh weight. Also, a functional relationship between measured pod diameter (which defines pod quality) and measured pod fresh weight was devel oped, subsequently allo wing prediction of the U.S. standard grades of fresh market of snap bean which depend on pod diameter. A new module to simulate the aggregated time-c ourses of fresh market yield (kg ha-1) and pod quality was thus developed through introduction of the different algorithms and code added to the calibrated CROPGRO simulation model structure. As a result, the CROPGRO Snap Bean model exhibited good performance in simulating the seasonal pod dry matter concentration irrespective of the N rates. This result indicates that dry matter conc entration of snap bean pods appears to be relatively unaffected by N stress. Additionally, simulation of the time course of total aggregated

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156 pod fresh weight of snap bean grown under four N rates indicated that at high N rate, the model predicted well the accumulation of pod fresh wei ght, but slightly under-predicted pod fresh weight as the N supply was reduced. More spec ifically, during the earl y pod growth phase, the model showed good prediction ability for the prog ression of pod fresh mass for the three highest N treatments, but for the low N treatment, it bega n to under-predict the pod fresh weight from 20 days after anthesis until the e nd of growing season, although this may not be important to fresh weight prediction after that time. The Root Mean Square Erro r (RMSE) values of pod fresh weight prediction were 1.67, 1.81, 1.95 and 1.36 Mg ha-1 and the d-statistic values were 0.95, 0.97, 0.97 and 0.98, respectively, for the N rates of 37, 74, 111 and 148 kg ha-1. Simulation of pod diameter and pod sieve sizes which define snap bean pod quality was also acceptable. Single pod diameter and sieve size progression over time appeared to relatively follow the general trend of the measured values under different N treatments, except that the model was initially too rapid in predicting the pattern of fresh pod diam eter but this later consistently matched the dynamics of measur ed pod diameter. The RMSE values of pod diameter simulation were 1.55, 1.41, 1.39 and 1. 32 and the d-statistic values were 0.73, 0.79, 0.80 and 0.82 for N treatments 37, 74, 111 and 148 kg ha-1. Analyzing concurrently the simulated progression of snap bean total fresh ma rket yield and fresh yield in pod sieve sizes 3 and 4 over time for this variety of snap bean under the agroclimatic conditions prevailing in Gainesville, indicates that the period of harvest for higher yiel d and higher bean quality should have occurred around 21 days after anthesis Our harvest was 23 days after anthesis, i.e. 2 days later. Finally, simulating the seasonal changes in crop produc tion variables under different irrigation regimes and N rates helped to evaluate the model stability and performance. An analysis of the influence of interactive effect of irrigation regimes and N rates on fresh weight

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157 pod indicated that the model predicted too high under low irriga tion regime and predicted too low under high irrigation regime in simulating po d fresh weight at fresh harvest date. This finding was attributed to relatively higher predic tion of N leaching in hi gh irrigation regime and consequently lower available N for up take as opposed to low irrigation. This may indicate that the model is not properly accounting for water uptake and transpiration or possibly not correctly accounting for N mineralization aspects under the differential soil water regimes. Implications of the Research and Future Work A crop simulation model capable of predicti ng growth and fresh market production and quality of snap bean as affected by irrigation and N fertilization was developed based on the existing CROPGRO Dry Bean model in DSSAT 4.5. Although the proposed model for snap bean fresh market production and quality is still in it s initial development stag e, preliminary modeling results appear to be encouraging. As the first working version, the model could provide potential users with relatively a ccurate prediction of yield and period of harvest for pod quality for crops grown under high N fertility, and ther efore could be used as an a pplication tool or in decisionsupport process. However, the current level of accuracy and prediction capability in the model must be enhanced through further testing a nd validation studies base d on field experiments conducted in different agroclimatic zones (South Florida for instance) and also different snap bean cultivars. Besides a dry matter concentratio n approach for simulati ng fresh weight, further viable mechanisms that influence accumulation of fresh weights in snap bean should be also investigated with the ultimate goal of improving the fresh weight module and the robustness of the model for general use in the future. The di screpancy results obtained from the effect of irrigation levels on simulated pod yields, especially at lower N rates, need to be tested to improve the robustness of the model under high and low irrigation conditions.

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158 APPENDIX EFFECT OF N TREATMENTS ON SNAP BEAN CROP DRY MATTER ACCUMULATION, N CONCE NTRATION AND N ACCUMULATION Table A-1. Pair wise comparis on of the effects of sampling days (DAS) and N treatments on total shoot dry weight of snap bean, grown in Gainesville during spring 2007 Pair wise Comparison of N treatments DAS 37 vs 74 37 vs 111 37 vs 148 74 vs 111 74 vs 148 111 vs 148 14 0.27 0.21 0.21 0.87 0.87 0.99 20 0.54 0.54 0.44 0.94 0.86 0.92 27 0.87 0.81 0.99 0.94 0.86 0.80 34 0.22 0.14 0.30 0.79 0.85 0.65 41 0.01 0.002 0.02 0.61 0.86 0.50 50 < 0.0001 < 0.0001 < 0.0001 0.42 0.88 0.34 55 < 0.0001 < 0.0001 < 0.0001 0.36 0.90 0.30 62 < 0.0001 < 0.0001 < 0.0001 0.32 0.93 0.26 69 < 0.0001 < 0.0001 < 0.0001 0.31 0.96 0.29 76 < 0.0001 < 0.0001 < 0.0001 0.32 0.98 0.31 83 < 0.0001 < 0.0001 < 0.0001 0.33 0.99 0.33 Table A-2. Pair wise comparison of the effect s of sampling days (DAS) and N treatments on pod dry weight of snap bean, grown in Gainesville during the spring 2007 Pair wise Comparison of N treatments DAS 37 vs 74 37 vs 111 37 vs 148 74 vs 111 74 vs 148 111 vs 148 14 20 27 34 41 50 0.69 0.36 0.36 0.75 0.80 0.92 55 0.10 0.001 0.002 0.40 0.53 0.77 62 0.0001 < 0.0001 < 0.0001 0.05 0.18 0.50 69 < 0.0001 < 0.0001 < 0.0001 0.0005 0.05 0.32 76 < 0.0001 < 0.0001 < 0.0001 0.005 0.04 0.29 83 < 0.0001 < 0.0001 < 0.0001 0.008 0.04 0.31

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159 Table A-3. Pair wise comparis on of the effects of sampling days (DAS) and N treatments on seed dry weight of snap bean, grown in Gainesville during the spring 2007 Pair wise comparison of N treatments DAS 37 vs 74 37 vs 111 37 vs 148 74 vs 111 74 vs 148 111 vs 148 14 20 27 34 41 50 55 0.91 0.39 0.12 0.58 0.19 0.36 62 0.04 0.01 0.39 0.84 0.21 0.23 69 < 0.0001 < 0.0001 < 0.0001 0.67 0.39 0.17 76 < 0.0001 < 0.0001 < 0.0001 0.36 0.85 0.24 83 < 0.0001 < 0.0001 < 0.0001 0.28 0.29 0.36 Table A-4. Pair wise comparison of the effects of sampling days (DAS) and N treatments on leaf N concentration of snap bean, grown in Gainesville during the spring 2007 Pair wise comparison of N treatments DAS 37 vs 74 37 vs 111 37 vs 148 74 vs 111 74 vs 148 111 vs 148 14 0.43 0.06 0.11 0.002 0.02 0.53 20 0.69 0.005 0.03 0.001 0.01 0.60 27 0.84 0.0006 0.005 0.001 0.009 0.71 34 0.31 < 0.0001 0.0002 0.002 0.006 0.87 41 0.04 < 0.0001 < 0.0001 0.005 0.006 0.88 50 0.001 < 0.0001 < 0.0001 0.04 0.01 0.56 55 0.0002 < 0.0001 < 0.0001 0.14 0.03 0.42 62 < 0.0001 < 0.0001 < 0.0001 0.47 0.12 0.32 69 < 0.0001 < 0.0001 < 0.0001 0.92 0.28 0.27 76 < 0.0001 < 0.0001 < 0.0001 0.71 0.48 0.25 83 < 0.0001 0.0002 < 0.0001 0.48 0.68 0.24

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160 Table A-5. Pair wise comparison of the effect s of sampling days (DAS) and N treatments on pod N concentration of snap bean, grown in Gainesville during the spring 2007 Pair wise comparison of N treatments DAS 37 vs 74 37 vs 111 37 vs 148 74 vs 111 74 vs 148 111 vs 148 14 20 27 34 41 50 < 0.0001 < 0.0001 < 0.0001 0.55 0.15 0.39 55 < 0.0001 < 0.0001 < 0.0001 0.46 0.05 0.23 62 0.0001 < 0.0001 < 0.0001 0.33 0.005 0.007 69 0.04 0.003 < 0.0001 0.30 0.001 0.04 76 0.98 0.38 0.008 0.40 0.007 0.07 83 0.20 0.66 0.19 0.51 0.02 0.13 Table A-6. Pair wise comparis on of the effects of sampling days (DAS) and N treatments on shoot N mass of snap bean, grown in Gainesville during the spring 2007 Pair wise comparison of N treatments DAS 37 vs 74 37 vs 111 37 vs 148 74 vs 111 74 vs 148 111 vs 148 14 0.06 0.06 0.05 0.86 0.90 0.75 20 0.29 0.58 0.34 0.61 0.87 0.71 27 0.88 0.14 0.37 0.31 0.56 0.07 34 0.08 < 0.0001 0.001 0.09 0.25 0.62 41 0.0003 < 0.0001 < 0.0001 0.01 0.07 0.57 50 < 0.0001 < 0.0001 < 0.0001 0.001 0.009 0.55 55 < 0.0001 < 0.0001 < 0.0001 0.0005 0.004 0.57 62 < 0.0001 < 0.0001 < 0.0001 0.0003 0.002 0.61 69 < 0.0001 < 0.0001 < 0.0001 0.0005 0.002 0.66 76 < 0.0001 < 0.0001 < 0.0001 0.0008 0.003 0.71 83 < 0.0001 < 0.0001 < 0.0001 0.001 0.005 0.74

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161 Table A-7. Pair wise comparison of the effect s of sampling days (DAS) and N treatments on pod N mass of snap bean, grown in Gainesville during the spring 2007 Pair wise comparison of N treatments DAS 37 vs 74 37 vs 111 37 vs 148 74 vs 111 74 vs 148 111 vs 148 14 20 27 34 41 50 0.98 0.004 0.07 0.009 0.10 0.72 55 0.42 < 0.0001 0.002 0.002 0.01 0.95 62 0.02 < 0.0001 < 0.0001 0.0004 0.0002 0.34 69 0.0005 < 0.0001 < 0.0001 0.0006 < 0.0001 0.06 76 < 0.0001 < 0.0001 < 0.0001 0.007 < 0.0001 0.03 83 < 0.0001 < 0.0001 < 0.0001 0.04 0.0002 0.03 Table A-8. Pair wise comparis on of the effects of sampling days (DAS) and N treatments on seed N concentration of snap bean, gr own in Gainesville during the spring 2007 Pair wise comparison of N treatments DAS 37 vs 74 37 vs 111 37 vs 148 74 vs 111 74 vs 148 111 vs 148 14 20 27 34 41 50 55 0.95 0.57 0.62 0.66 0.70 0.95 62 0.08 0.05 0.12 0.83 0.88 0.98 69 0.0009 0.0001 0.0008 0.022 0.33 0.95 76 0.0002 < 0.0001 0.0001 0.08 0.15 0.91 83 < 0.0001 < 0.0001 0.0002 0.07 0.14 0.90

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162 LIST OF REFERENCES Abdel-Maw goud, A.M.R. 2006. Growth, yield and quali ty of green bean (Pha seolus vulgaris) in response to irrigation and co mpost applications. J. Applied Sci. Res. 2: 443-450. Aggarwal, P.K., N. Kalra, S. Chander, and H. Pathak. 2006. InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description. Agric. Syst. 89: 1-25. Alagarswamy G. P. Singh, G. Hoogenboom, S.P. Wani, P. Pathak, and S.M. Virmani. 2000. Evaluation and application of the CROPGRO -Soybean simulation model in a Vertic Inceptisol. Agri. Sys. 63: 19-32. Barker AV and G.M. Bryson. 2007. Nitrogen p. 21-50. In Barker and Pilbeam (ed.) Handbook of Plant Nutrition. Taylor & Francis. Boca Raton, FL. Batchelor, W.D., B. Basso, and J.O. Paz. 2002. Ex amples of strategies to analyze spatial and temporal yield variability using crop models Europ. J. Agron. 18: 141-158. Below, E.F. 2002. Nitrogen metabo lism and crop productivity. pp 385-406. In P. Mohammad (ed.) Handbook of plant and crop physiology. Second edition. Marcel Dekker, INC. New York. Bergamaschi, H., H.J. Vieira, J.C. Ometto, L.R. Angelocci and P.L. Libardi, 1988. Water deficit in beans. I. Growth analysis and phenology. Pesquisa Agropecuaria Brasileira. 23: 733743. (c.f. Field Crop Abst. 1990 43:5966). Bharat, P.S. 1989. Irrigation water requirement fo r snap bean production. Hort. Sci. 26: 9-70. Bonanno, A.R. and H.J. Mack. 1983. Yield comp onents and pod quality of snap beans grown under differential irrigation J. Amer. Soc. Hort. Sci. 108: 832-836. Boone, R.D. 1990. Soil organic matter as a potential net nitrogen sink in a fe rtilized corn field, South Deerfield, Massachusetts, US A, Plant and Soil. 28: 191-198. Boote, K.J. 1999. Concepts for calibrati ng crop growth models. DSSAT version 3.5 Documentation Volume 4-6 179-200. Boote, K.J., J.W. Jones, W.D. Batchelor, E.D. Nafziger, and O. Myers. 2003. Genetic coefficients in the CROPGROSoybean model: Links to field performance and genomics. Agron. J. 95:32-51. Boote, K.J., J.W. Jones, and G. Hoogenboom 1998a. Simulation of crop growth: CROPGRO model. p. 651. In R.M. Peart and R.B. Curry (ed.) Agricultural systems modeling and simulation Marcel Dekker, New York.

PAGE 163

163 Boote, K.J., J.W. Jones, G. Hoogenboom, N. B. Pickering. 1998b. The CROPGRO model for grain legumes. p. 99-128. In G.Y. Tsuji et al. (ed.) Unde rstanding Options for Agricultural Production. Kluwer Academic Publis hers, Dordrecht, The Netherlands. Boote, K.J., M.I. Minguez, and F. Sau. 2002. Adapting the CROPGRO legume model to simulate growth of faba bean. Agron. J. 94:743-756. Boote, K.J. and J.M.S. Scholberg. 2006. Developing, parameterizing, and testing of dynamic crop growth models for horticultura l crops. Acta Hort. 718: 23-35. Boote, K.J., and M. Tollenaar. 1994. Modeling genetic yield potential p. 533. In K.J. Boote et al. (ed.) Physiology and determination of crop yield. ASA, CSSA, and SSSA, Madison, WI. Brady, N. C. and R.R. Weil. 1996. Th e nature and properties of soil. 11th edition, Prentice-Hall, Inc, New Jersey, 740p. Calvache, M. and K. Reichardt, 1999. Effects of water stress imposed at different plant growth stages of common bean (Phase olus vulgaris) on yield and N2 fixation. pp 121-127. In Kirda et al. (ed.) Crop Yield Response to defi cit irrigation. Kluwer Academic Publishers Dordrecht, The Netherlands. Carstille, V.W., C.T. Hallmark, F. Sodek, R. E. Caldwell, L.C. Hammond, and V.E. Berkheiser. 1981 Characterization data for select ed Florida soils. IFAS UF, 304p. Davis, J. H., C.L. Beuningen, M. V. Ortiz, and C. Pino. 1984. Effect of growth habit of beans on tolerance to competition from maize when intercropped. Crop Sci. 24: 751-755. de Varennes, A., J.P. de Melo-Abreu and M.E. Ferreira. 2002. Predicting the concentration and uptake of nitrogen, phosphorus and potassi um by field-grown green beans under nonlimiting conditions. Europ. J. Agron. 17: 63-72. Denis, J.C. and M.W. Adams. 1978. A factor analysis of plant va riables related to yield in dry beans. I. Morphological traits. Crop Sci. 18: 74-78. Deproost, P., F. Elsen, and M. Geypens. 2004. High yi elds of mechanically harvest snap beans as induced by moderate water stress during fl owering. Acta Hort. 664 ISHS pp. 205-212. Di Paolo, E. and M. Rinaldi. 2008. Yield response of corn to irrigation a nd nitrogen fertilization in a Mediterranean environment. Field Crops Res. 105: 202-210. Diepen C.A. van, C. Rappoldt, J. Wolf, H. Van Keulen, 1989. Crop growth simulation model WOFOST. Documentation version 4.1, CW FS, Wageningen, The Netherlands. 299pp. Doorenbos, J., and A. H. Kassam. 1979. Yield res ponse to water. F.A.O. Rome Italy. Irrigation and Drainage Paper no. 33.

PAGE 164

164 Dubetz, S. and P. S. Nathalle. 1969. Effect of soil water stress on bush beans Phaseolus vulgaris L. at three stages of growth. J. Amer. Soc. Hort. Sci. 94: 479-481. Dufault, R.J., D.R. Decoteau, J.T. Garrett, K.D. Batal, D. Granberry, J.M. Davis, G. Hoyt, and D. Sanders. 2000. Influence of cover crops and inorganic nitrogen fertilization on tomato and snap bean production and soil nitrate distri bution. J. Vege. Crop. Prod. 6: 13-25. Evans, G.C. 1972. The quantitative analysis of plant growth. Blackwell Sc ientific Publications, Oxford, 734 pp. Fageria, N.K., V.C. Baligar, and C.A. Jones. 1997. Corn. p.345-383. In N.K. Fageria (ed) Growth and mineral nutrition of field crops. Marcel Dekker, Inc., New York. FAO. 2008. Crop Water ManagementBean. http://www.fao.org/AG/aGL/ aglw/cropwater/bean.stm (last accessed April 16 2008). FDACS Florida Dept. of Agricult ure & Consumer Services. 1998. Florida Agricultural Facts. Florida Dept. of Agriculture & Consumer Services, Tallahassee, FL. Fehr, W.R., C.E. Caviness, D.T. Burmood, a nd J.S. Pennington. 1971. Stage of development descriptions for soybeans, Glycine max (L.) Merrill. Crop Sci. 11 929-931. Fernandez, F.P., P. Gepts and M. Lopez. 1986. Stages of development of the common bean plant. CIAT, Cali, Colombia. Ferreira, M.E., A. de Varennes, J.P. de Me lo-Abreu, and M. I. Viei ra. 2006. Predicting pod quality of green beans for pro cessing. Hort. Sci. 109: 207-211. Ferreira, M.E., J.P. de Melo-Abreu, V.V. Bi anco and A. Monteiro. 1997. Predicting phasic development of green beans for processing using a model with high temperature reduction of thermal time accumulation. Hort. Sci. 69: 123-133. Foth, H. D. and B.G. Ellis. 1997. Soil fertility 2nd edition, CRC Press, Inc, 290p. Gallaher, R. N., C. O. Weldon and J. G. Futral. 1975. An aluminum block digester for plant and soil analysis. Soil Sci. Soc. Amer. Proc. 39:803-806. Gardiner, K.D. and M.D. Prendiville. 1970. Seed pe rcentage, seed length and shear press values in the evaluation of quality and maturity in French beans. J. Hort. Sci. 45: 303-314. Gary, C., J.W. Jones, and M. Tchamitchian. 1998. Crop modeling in horticultur e: state of the art. Sci. Hortic 74: 3-20. Gast, K.L.B. 1994. Containers a nd packaging fruits and vegetabl es. Postharvest management of commercial horticultural crops. Kansas State Un iversity Agricultural Experiment Station and Cooperative Extension Service Available at http://www.oznet.ksu.edu /library/hort2/m f979.pdf (last accessed May 14 2007).

PAGE 165

165 Gencoglan, C., H. Altunbey, and S. Ge ncoglan. 2006. Response of green bean (P. vulgaris L.) to subsurface drip irrigation and partial rootzone -drying irrigation. Agri. Water. Management 84: 274-280. Gibson, L.R., C.D. Nance, and D.L. Karlen. 2007. Winter triticale response to nitrogen fertilization when grown after corn or soybean. Agron. J. 99:49-58. Gill, M.S. and R.S Narang. 1993. Yield analysis in gobhi sarson (Brassica napus ssp. oleifera var. annua) to irrigation and nitrogen. I ndian J. Agron. 38: 257-265. Giraldo, L.M., Lizcano, L.J., Gijsman, A.J., Rivera, B., Franco, L.H., 1998. Adaptation of the DSSAT model for simulation of Brachiaria decumbens production. Pasturas Tropicales 20, 2-12. Graetz, D.A. 2007. Evaluating effectiveness of best management practices for animal waste and fertilizer management to reduce nutrient i nputs into ground water in the Suwannee River Basin. Technical Report, IFAS University of Florida. Graham, P. H. 1981. Some problems of nodul ation and symbiotic nitrogen fixation in Phaseolus vulgaris L.: A review. Field Crops Res. 4: 93-112. Gross, Y. and J. Kigel. 1994. Differential se nsitivity to high temperature of stages in the reproductive development of common bean (Phaseolus vulgaris L.). Field Crops Res. 36: 201-212. Gutierrez, A.P., E.J. Mariot, J.R. Cure, C.S. Wagner Riddle, C.K. Ellis and A.M. Villacorta. 1994. A model of bean (Phaseolus vulgaris L.) growth types I-III: fact ors affecting yield. Agr. Syst. 44: 35-63. Hall, A. E. 2004. Comparative ecophysiology of cowpea, common bean, and peanut, pp.271325. In H.T. Nguyen and A. Blum (ed) Physiolo gy and Biotechnology Integration for Plant Breeding. Marcel Dekker, Inc., New York. Hambleton, L.G. 1977. Semiautomated method for simultaneous determination of phosphorus, calcium and crude protein in anim al feeds. J.A.O.A.C. 60:845-852. Hartkamp, A.D., G. Hoogenboom, J.W. White. 2002. Adaptation of the CROPGRO growth model to velvet bean (Mucuna pruriens) I. Model development. Field Crops Res. 78: 9-25. Hebbar, S.S., B.K. Ramach andrappa, H.V. Nanjappa, and M. Prabhakar. 2004. Studies on NPK drip fertigation in field grown tomato (Lycopersicon esculentum Mill.). Europ. J. Agronomy 21: 117. Hegde, D.M. and K. Srinivas. 1990. Plant water re lations and nutrient uptake in French bean (Abstract). Irrigation Science 11: 51-56. Heuvelink, E., P. Tijskens, M.Z. Kang. 2004. M odeling product quality in horticulture: an overview. Acta Hort. 654, 19-25.

PAGE 166

166 Hochmuth, G. J., and K. Cordasco. 2000. A summary of N, P, and K research with snap bean in Florida, HS 757, Fla. Coop. Ext. Ser., IFAS, Univ. of Fla. Hoogenboom, G., White, J. W., Jones, J.W. a nd Boote, K.J., 1994. BEANGRO: A processoriented dry bean model with a versatile user interface. Agron. J. 86: 182-190. Hunt, R. 1982. Plant growth curves: The functiona l approach to plant growth analysis. Arnold, London, and Univ. Park Press, Baltimore, MD. 248p. Jamieson, P.D. and M.A. Semonov. 2000. Modeling nitrogen uptake and redistribution in wheat. Field Crops Res. 68: 21-29. Jensen, M. E., J. L. Wright, and B. J. Pratt. 1971. Estimating soil moisture depletion from climate, crop, and soil data. Trans. of ASAE 14: 954-959. Jones, J. W. and J. C. Luyten.1998. Simulation of biological processes. In R. M. Peart and R. B. Curry (ed.) Agricultural systems modeling a nd simulation, Marcel Dekker, Inc. Madison New York, USA.pp. 19-62. Jones, J.W., G. Hoogenboom, C. H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. The DSSAT cropping system model. Europ. J. Agronomy 18: 235-265. Kattan, A. A. and J. W. Fleming. 1956. Effect of irrigation at specific st ages of development on yield, quality, growth, and composition of snap beans. Proc. Amer. Soc. Hort. Sci. 68: 329342. Kelly, J.D., K.A. Schneider, and J.M. Kolkman. 1999. Breeding to improve yield. p. 185-222. In S.P. Singh (ed.). Common Bean Improvement in the Twenty-First Century. Dordrecht: Kluwer Acad. Publ. Kenig, A., J.W. Mishoe, K.J.Boote, P.W. C ook, D.C. Reicosky, W.T. Pettigrew, and H.F. Hodges. 1993. Development of soybean fresh a nd dry weight relationships for real time modeling calibration. Agron. J. 85: 140-146. Kirkby, E.A. 1981.. Plant growth in relation to nitrogen supply. pp. 249-267. In F.E. Clark and T. Rosswald, (eds.). Terrestrial nitrogen cy cles: processes, ecosystem strategies and management impacts. Ecol. Bull 33. Swedish Natural Science Research Council, Stockholm. Laing, D. R., P.G. Jones, and J.H.C. Davis. 1984. Common bean (Phaseolus vulgaris L.) p. 305351. In P. R. Goldsworthy & N. M. Fisher (e d.) The Physiology of Tropical Crops, New York: John Wiley and Sons Ltd. Lawlor, D.W., G. Lemaire, and F. Gastal. 2001. Nitrogen, plan t growth and crop yield. p.343367 In P.J. Lea, J.F. Morot Gaudry, (eds.) Plant nitrogen, Berlin: Springer-Verlag.

PAGE 167

167 Lemaire, G., E. van Oosterom, J. Sheehy, M.H. Jeuffroy, A. Massignam and L. Rossato. 2007. Is crop N demand more closely related to dry matter accumulation or leaf area expansion during vegetative growth? Fi eld Crops Res. 100: 91-106. Lima, J.D., F.M. Da-Matta, and P.R. Mosquim. 2000. Growth attributes, xylem sap composition and photosynthesis in common bean as affected by nitrogen and phosphorus deficiency. J. Plant Nutr. 23:937-947. Locascio, S.J. 2005. Management of irrigation for vegetables: Past, present, and future. HortTechnology 15: 482-485. Lucier, G. and Lin, B.H. 2002. Fresh snap bean s: No strings attached. Commodity spotlight. Agricultural Outlook. Available at http://www.ers.usda.gov/publications/AgOutlook/Mar2002/ao289b.pdf (last acces sed June 23, 2008). Ma, B.L., L.M. Dwyer, and E.G. Gregorich. 1999. Soil nitrogen amendment effects on nitrogen uptake and grain yield of maize. Agron. J. 91:650-656. Marcelis, L.F.M and H. Gijzen. 1998. Evaluati on under commercial conditions of a model of prediction of the yield and quality of cucumber fruits. Scientia Horticulturae 76: 171-181. Marcelis, L.F.M., E. Heuvelink, and J. Goudria an. 1998. Modeling biomass production and yield of horticultural crops: a revi ew. Sci. Hort. 74: 83-111. Mavromatis, T., K.J. Boote, J.W. Jones, A. Irmak, D. Shinde, and G. Hoogenboom. 2001. Developing genetic coefficients for crop simulation models with data from crop performance trials. Crop Sci. 41:40. Monteith, L.J., 1977. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. Lond. 284: 277. Montojos, C. and A.C. Magalhaes. 1971. Growth analysis of dry beans (Phaseolus vulgaris L. var. Pintado) under varying conditions of solar radiation and nitr ogen application. Plant and Soil 35: 217-223. Mossler, M.A. and O. Norman Nesheim. 2003. Fl orida Crop/Pest Management Profiles: Snap Beans. CIR 1231 Food Science and Human Nu trition Department, Florida Cooperative Extension Service, Institute of Food and Agri cultural Sciences, University of Florida. Myers, J.R. and J.R. Baggett. 1999. Im provement of Snap bean p. 289-329. In S.P. Singh (ed.). Common Bean Improvement in the Twenty-fir st century. Dordrecht: Kluwer Acad. Publ. Mylavarapu, R. and E. Kennelly. 2002. UF/IFAS Extension Soil Testing Laboratory (ESTL) Analytical Procedures and Training Manual. http://edis.ifas.ufl.edu/SS312.

PAGE 168

168 Neeteson, J.J. 1995. Nitrogen management for intensively grown arable crops and field vegetables. In: P. B. Edward (ed.) Nitrogen Fertilization in the Environment. Marcel Dekker, New York, USA, pp. 295-325. Ney, B., T. Dore, and M. Sagan. 1997. The nitr ogen requirement of ma jor agricultural crops. Grain legumes pp. 107. In: Gilles Lemaire (eds.), Diagno sis of the Nitrogen Status in Crops. Springer-Verlag, Heidelberg. Nicholaides, J.J., H.R. Chancy, L.H. Nilson, a nd J.E. Shelton. 1985. Snap bean grade and yield response to N rate and time of application and P and K rate (Abstract). Comm. Soil Sci. Plant Analy. 16: 741-757. Nielsen, D. C. and N. O. Nels on. 1998. Black bean sensitivity to water stress at various growth stages. Crop Sci. 38: 422-427. Nienhuis, J. and S. P. Singh. 1986. Combining ab ility analyses and relationships among yield, yield components and architectural traits in dry bean. Crop Sci. 26: 21-27. Nkoa, R., J. Coulombe, Y. Desjardins and N. Tremblay. 2001. Towards optimization of growth via nutrient supply phasing: nitrogen supply phasing increases broccoli (Brassica oleracea var. italica) growth and yiel d. J. Exp. Bot. 52: 821-827. Ojehomon, O. O., M. S. Zehi, and D. G. Morg an. 1973. The effects of photoperiod on flower bud development in Phaseolus vulgaris L. Ann. Bot. 37: 871-879. Olson, S.M., E.H. Simonne, A.J. Palmateer, W. M. Stall, S.E. Webb, T.G. Taylor, and S.A. Smith. 2007. Legume production in Florida: Sn ap bean, Lima bean, Southern pea, Snowpea, pp. 253-267. In S. M. Olson and E. Simonne (ed) Vegetable production handbook for Florida Horticultural Science De partment, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences Gainesville, FL:, University of Florida. Pandey, R.K., J.W. Maranville and A. Admou. 20 00. Deficit irrigation an d nitrogen effects on maize in a Sahelian environment I. Grain yield and yield compone nts. Agri. Water. Management. 46: 1-13. Paramasivam, S., A.K. Alva, A. Fares, and K.S. Sajwan. 2002. Fate of nitrate and bromide in an unsaturated zone of a sandy so il under citrus production. J. Environ. Qual. 31:671-681. Peck, N.H. and G.E. MacDonald. 1983. Snap bean plant response to nitr ogen fertilization. Agron. J. 76: 247-253. Peck, N.H. and J.P. VanBurem. 1975. Plant responses to concentrated superphosphate and potassium chloride fertilizers V. Snap bean (Phaseolus vulgaris var. humilis). New York State Agric. Exp. Stn 5: 1-32. Penning de Vries, F.W.T. and H.H. van Laar 1982. Simulation of plant growth and crop production. Simulation Monographs Series. Pudoc, Wageningen, the Netherlands. 308p.

PAGE 169

169 Pernezny, K. 1997. Disease control for Florid a snap beans. Plant Pathology Department Document PPP 38. Florida Cooperative Exte nsion Service, Institute of Food and Agricultural Sciences, University of Florida. Available: http://edis.ifas.ufl.edu/VH055 (last accessed February 17 2008). Piha, M. I. and D. N. Munns. 1987. Nitrogen fixa tion capacity of field grown bean compared to other grain legum es. Agron. J. 79: 690-696. Plenet, D., and G. Lemaire. 2000. Relationships between dynamics of nitrogen uptake and dry matter accumulation in maize crops. Determinati on of critical N concentration. Plant Soil 216: 65. Prakash, O., A.K. Alva, and S. Paramasivam. 1999. Use of the urease inhibitor N-(n-BUTYL)thiophosphoric triamide decreased nitrogen leach ing from urea in a fine sandy soil. Water Air Pollut. 116:587-595. Purseglove, J. W. 1968. Tropi cal crops: Dicotyledons. Vol. 1 719 p. Longman, London. Quinones, A., A., B. Mart nez-Alcantara, and F. Legaz. 2007. Influence of irrigation system and fertilization management on seasonal distribution of N in the soil profile and on N-uptake by citrus trees. Agri. Ecosystems and Env. 122: 399. Radford, P.J. 1967. Growth analysis formulaetheir use and abuse. Crop Sci. 7: 171-175. Ramirez, V.P. and J.D. Kelly, 1998. Traits re lated to drought resistance in common bean. Euphytica 99: 127-136. Rathke GW, T. Behrens,and W. Diepenbrock. 2006. Integrated management strategies to improve seed yield; oil content and nitrog en efficiency of winter oilseed rape (Brassica napus L.): a review. Agri. Eco. and Env. 117: 80. Redden, R.J. and D.F. Herridge. 1999. Evalua tion of genotypes of navy and culinary bean (Phaseolus vulgaris L.) selected for superior growth a nd nitrogen fixation. Australian J. Exp. Agri. 39:975-980. Ritchie, J.T., 1998. Soil water balance and water stress. In: Tsuji, G.Y., Hoogenboom, G. Thornton, P.K. (Eds.), Understanding Opti ons for Agricultural Production. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 41-54. Robinson, W.B., D.E. Wilson, J.C. Moyer, J.D. Atkin, and D.B. Hand. 1965. Quality versus yield of snap beans for processing. Am. Soc. Hort. Sci. 84:339-347. Rubatzky, V. E. and M. Yamaguchi. 1997. World Vegetables: Princi ples. Production, and Nutritive Values, 2nd Edition, Chapman & Hall, New York, 843p. Rymph S.J. 2004. Modeling growth and composition of perennial tropical forage grasses. Ph.D. dissertation. Gainesville, Fl: University of Florida 316p.

PAGE 170

170 Sader, R. 1980. Effect of N and P fertilizers on growth, nitrate reductase activity, seed production, and seed quality of snap bean (Phaseolus vulgaris L.). Oregon State University PhD, 132p. Sainju, U. M., B.P. Singh, and W.F. Whitehead. 1998. Crop root distribu tion and its effects on soil nitrogen cycling, Agron. J. 90: 511-518. SAS Institute, Inc. 2000. SAS/STAT Users guide. Release 8.02. SAS Inst., Cary, NC. Sato, S. and K.T. Morgan 2007. Nitrogen recove ry and transformation from a surface or subsurface application of controlled-release fert ilizer on a sandy soil (Abstr act). Water, Air, & Soil Pollution (in press). Scarisbrick, D. H., M.K.V. Carr, and J.M. Wilks. 1976. The effect of sowing date and season on the development and yield of navy beans (Phaseolus vulgaris) in South East England, J. Agric. Sci., 86: 65-76. Scholberg, J.M.S. 1997. Adaptive use of crop growth model to simulate the growth of field grown tomato. Doctoral Thesis. Graduate School of the University of Florida. 282 pp. Scully, B. and J. G. Waines. 1987. Germinati on and emergence response of common and tepary beans to controlled temperature. Agron. J. 79: 287-291. Sezen, S.M., A. Yazar, M. Canbolat, S. Eker, and G. Celikel. 2005. Effect of drip irrigation management on yield and quality of field grown green beans. Agri. Water. Management 71: 243-255. Silbernagel, M.J. 1986. Snap bean breeding. p. 243. In Basset, M.J. and J. Mark (ed.) Breeding vegetable crops. AVI Publ. Co., WestPort, CT. Silbernagel, M.J., and S.R. Drake. 1978. Seed inde x, an estimate of snap bean quality. J. Am. Soc. Hort. Sci. 103: 257-260. Simonne, E. H., M. D. Dukes, and D. Z. Haman. 2007. Principles and practices of irrigation management for vegetables. pp.33-39. In S. M. Olson and E. Simonne (ed) Vegetable production handbook for Florida Horticultural Science Department, Florida Cooperative Extension Service, Institute of Food and Agricu ltural Sciences Gainesville, FL: University of Florida. Sinclair, T.R. and N. Seligman. 2000. Criteria for publishing papers on crop modeling. Field Crops Res. 68: 165-172. Sinclair, T.R., J.R. Farias, N. Neumaier, and A.L. Nepomuceno. 2003. Modeling nitrogen accumulation and use by soybean. Field Crops Res. 81: 149-158. Sinclair, T.R., and T. Shiraiwa. 1993. Soybean radiation-use efficien cy as influenced by nonuniform specific leaf nitrog en distribution and diffuse radiation. Crop Sci. 33: 808 812.

PAGE 171

171 Singh, B.P. 1989. Irrigation water management for bush snap bean production. HortScience. 24: 69-70. Singh, M., and M.J. Jones. 2000. Statistical esti mation of time trends in two-course crop rotations. J. Appl. Stat. 27:589-597. Singh, P. and S.M. Virmani. 1994. Modeling growth and yield of chickpea (Cicer arietinum L.). Field Crops Res. 46: 1-29. Singh, P. S. 1999. Integrated genetic improve ment p. 133-165. In S.P. Singh (ed.). Common Bean Improvement in the Twenty-First Ce ntury. Dordrecht: Kluwer Acad. Publ. Singh, S. P. 1982. A key for identification of different growth habits of frijol Phaseolus vulgaris L. Annu. Rep. Bean Improv. Coop. 25: 92-95. Smajstrla, A.G. and D.S. Haman. 1997. Irrig ated acreage in Florida: A summary through 1998. CIR 1220 Agricultural and Biological Engine ering Department, Florida Cooperative Extension Service, Institute of Food and Agri cultural Sciences, University of Florida. Original publication date June 1997. Reviewed December 2005. Available at http://edis.ifas.ufl.edu (last accessed April 27, 2008). Sm ittle, D.A. 1976. Response of snap bean to irrigation, nitrogen fertilization, and plant population. J. Amer. Soc. Hort. Sci. 101: 37-40. Smittle, D.A., W. L. Dickens, and J.R. Stansell. 1990. An irrigation scheduling model for snap bean. J. Amer. Hort. Sci. 115: 226-230. Stansell, J.R. and D.A. Smittle. 1980. Effects of irrigation regimes on yield and water use of snap bean (Phaseolus vulgaris L.). J. Amer. Soc. Hort. Sci. 105: 869-873. Summerfield, R. J. and E. H. Roberts. 1984. Phaseolus vulgaris, pp. 139-148. In A. H. Haveley (ed.). Handbook of flowering, Vol. 1, CRC Press, Boca Raton, Florida. Tan, D.K.Y., C.J. Birch, A.H. Wearing, a nd K.G. Rickert. 2000a. Predicting broccoli development I. Development is predominantly determined by temperature rather than photoperiod. Scientia Horticulturae 84: 227-243. Tan, D.K.Y., C.J. Birch, A.H. Wearing, a nd K.G. Rickert. 2000b. Predicting broccoli development II. Comparison and validation of thermal time models. Scientia Horticulturae 86: 89-101. Tanner, C.B., and T.R. Sinclair. 1983. Efficient water use in crop production: research or research? pp. 1-27. In H.M. Taylor et al. (eds), Limitations to efficient water use in crop production. ASA, Madison, WI. Tewari, J.K. and S.S. Singh. 2000. Effect of nitrogen and phosphorus on growth and seed yield of French bean (Phaseolus vulgaris L.), Vegetable Sci. 27:172-175.

PAGE 172

172 Unlu, K., G. Ozenirler, C. Yurteri. 1999. Nitr ogen fertilizer leaching from cropped and irrigated sandy soil in Central Turkey. Europ. J. of Soil Sci. 50: 609-620. USDA. 1997. United States standards for grades of snap beans for processing and fresh m arket, USDA, Washington D.C. USDA, 2007. Census, US-State Data Table, 35. Vegetables and melons harvested for sale: http://www.nass.usda.gov:8080/Census/Pull_Data_Census (last accessed March17, 2008). van Ittersum M.K., P.A. Leffelaar, H. van Keulen M.J. Kropff, L. Bastiaans, and J. Goudriaan. 2003. On approaches and applic ations of the Wageningen crop models. Europ. J. Agron. 18: 201-234. van Keulen, H., Penning de Vries, F.W.T ., Drees, E.M., 1982. A summary model for crop growth. In: Penning de Vries, F.W.T., Laar va n, H.H. (Eds.), Simulation of Plant Growth and Crop Production. Simulation Monographs, Pudoc, Wageningen. Vincent, J. M. 1974. Root nodule symbioses with rhizobium, pp. 266-341. In: A. Guispel (ed.). The biology of nitrogen fixation. North Holland, Amsterdam. Wallace, D. and H.M. Munger. 1965. Studies of the physiological basis for yield differences. Growth analysis of six dry bean varieties Crop Sci. 5:343-48. Wallace, D. H. 1980. Adaptation of Phaseolus to different environments, pp. 349-357. In: In: R. J. Summerfield and A. H. Bunting (eds.). Advances in legumes science. Royal Botanic Gardens, Kew, England. Wallace, D. H. 1980. Daylength a nd temperature effects on days to flowering of early and late maturing beans. J. Am. Soc. Hort. Sci. 105: 583-589. Wallace, D. H., P. Garrett, R. F. Sandsted, H. C. Wien, P. N. Masaya, and S. Arreigo. 1982. Agronomic, sociological a nd genetic aspects of bean yield and adaptation. In: Bean/cowpea collaborative Research Support Pr ogram, 1982, Ann. Report, East Lansing, Michigan State University, pp. 48-52. Watada, A.E. and L.L. Morris. 1966. Post-harvest behavior of snap bean cu ltivars. J. Amer. Soc. Hort. Sci. 89: 375-380. Yamaguchi, M. 1983. World Vegetables. Van No strand Reinhold Company, New York, pp. 267270. Yang, H.S., A. Dobermann, J.L. Lindquist, D.T. Walters, T.J. Arkebauer, K.G. Cassman. 2004 Hybrid-maizea maize simulation model that combines two crop modeling approaches. Field Crops Res. 87: 131-154. Zotarelli, L., J.M. Scholberg, M.D. Dukes and R. Munoz-Carpena. 2007a. Monitoring of nitrate leaching in sandy soils: comparison of three methods (In press).

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173 Zotarelli, L., M.D. Dukes, J. M. Scholberg, T. Hanselman, K. L. Femminella, and R. MunozCarpena. 2007b. Nitrogen and water use efficien cy of zucchini squash for a plastic mulch bed system on a sandy soil. Sci. Hort (In press).

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174 BIOGRAPHICAL SKETCH Desire Djidonou is a native of Benin, a republic in western A frica. He earned an Agronomist Engineer Degree (Major: Crop Pro duction) from the College of Agronomic Sciences of the National University of Benin. He then worked 2 years on a cowpea research project to promote an Integrated Pest Manage ment approach for controlling the pests and diseases that inflict this legume. Upon completion of these project activities, Desire enrolled in graduate studies in the Management of Animal and Vege table Resources in the Tropics at the University College of Agronomic Sciences of Gembloux (Belgium). This year-long program enabled him to deal with various topics such as intensif ication and sustainability of fa rming systems through the analysis of mixed crop and livestock production. One year later, he began an agricu ltural internship with Glades Crop Care, Inc., based in Jupiter, Flor ida. This company offers to Florida vegetable growers, an agricultural cons ultancy through integrated pest management programs. As a member of the consulting team, Desire carried out scouting responsibilities on vegetables such as tomatoes, peppers, potatoes and cucurbits. He also had the opportunity to be involved in contract research activities where he pa rticipated in the evaluation of pesticide efficacy in different cropping systems. In August of 2006, Desire began a Master of Science program in the Agronomy Department of the University of Florida under the direction of K.J. Boote. His major field of study was ecology and physiology with an emphasis on crop modeling.