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Application of Statistical Techniques to Modeling Crop Growth


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APPLICATION OF STATISTICAL TECHN IQUES TO MODELING CROP GROWTH By BELKYS YASMIN BRACHO BRAVO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by Belkys Yasmin Bracho Bravo

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This dissertation is dedicated to my father Ivan E. Bracho, In memoriam 1931-2004.

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iv ACKNOWLEDGMENTS I would like to express my gratitude to my major professor, Dr. Raymond N. Gallaher, for giving me the necessary guidance and support to reach my goals and for his caring attitude during the course of my progr am. I would like to thank my committee, Dr. Robert McSorley for all his advice and useful comments not only about nematodes but also about agriculture research methods in general, Dr. Jerry Be nnett and Dr. Kenneth Boote for their guidance, support and for serving on my committee. Special thanks go to Dr. Ramon Littell, who was my major professo r for my masters degree, for making my arrival in the USA more straightforward and helping me from the first moment by giving me his support and invaluable advice. Thanks go to Howard Palmer, Jim Chichester and J.J. Frederick for their technical support in the field and laborat ory. I appreciate those invol ved with the fall 2002 and fall 2003 Field Plot Techniques class for assistan ce with much of the field work. I would also like to acknowledge Dea nye Overman for some of the data used in this work. I would like to thank the Universidad del Zu lia, Venezuela, and the University of Florida for providing financial support. I am grateful for receiving financial assistance through awards from the College of Agricultur e and Life Sciences (IFAS travel Grants) and the Family of Paul Robin Harris. I tha nk Dr. Tim White and Dr. Robert Schmidt for all their encouragement. I wish to express my gratefulness to Salva dor Pintos for encouraging me to come to this country, for all his statistical advice, and for his belief in me more than I believed in

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v myself. Many thanks go to my friends a nd fellow graduate students Veronica Emhart, Gabriela Luciani, and Kimberly Seaman for their daily companionship and lovely support. Special thanks go to Salvador Gezan for his helpful and loyal friendship. Most importantly, I thank the Lord for he lping me to withstand all worries and difficulties. I would like to thank my fam ily for their continuous encouragement and love. Finally, I am devoted to my Son Jhona tan Bracho for his help in the field, and for typing, plotting, and editing of this dissert ation, and for his unconditional love and presence in my life.

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vi TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES...........................................................................................................viii LIST OF FIGURES..........................................................................................................xii ABSTRACT....................................................................................................................xvi i CHAPTER 1 INTRODUCTION........................................................................................................1 2 OPTIMIZING TURNIP AND MUSTARD YIELDS IN RESPONSE TO PLANT POPULATION AND NITROGEN FERTILIZER USING RESPONSE SURFACE METHODOLOGY....................................................................................8 Introduction................................................................................................................... 8 Materials and Methods...............................................................................................10 Results and Discussion...............................................................................................15 Yield Results for 2002.........................................................................................15 Yield Results for 2003.........................................................................................31 Mineral Concentration Results............................................................................53 Summary.....................................................................................................................82 3 SWEET CORN YIELD, PLANT NUTRITION, AND NEMATODES AS AFFECTED BY COWPEA MULCH......................................................................102 Introduction...............................................................................................................102 Material and Methods...............................................................................................106 Results and Discussion.............................................................................................111 Yield Results.....................................................................................................111 Mineral Nutrition Results..................................................................................118 Nematode Results..............................................................................................123 Summary...................................................................................................................130

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vii 4 CORN GROWTH MODELING FO R TOTAL PLANT DRY MATTER...............141 Introduction...............................................................................................................141 Material and Methods...............................................................................................142 Results and Discussion.............................................................................................151 Summary...................................................................................................................163 5 CONCLUSION.........................................................................................................168 LIST OF REFERENCES.................................................................................................172 BIOGRAPHICAL SKETCH...........................................................................................180

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viii LIST OF TABLES Table page 2-1 Mineral sufficiency levels for turnips and mustard...............................................10 2-2 Soil test report and standard fert ilization recommendation (University of Florida, IFAS Extension Service, 2002)................................................................16 2-3 Analysis of variance for fresh a nd dry turnip yield affected by three population densities and five ra tes of nitrogen (2002)...........................................17 2-4 Analysis of variance for fresh and dr y turnip yield affected by four population densities and five rate s of nitrogen (2003).............................................................17 2-5 Analysis of variance for fresh a nd dry mustard yield affected by three population densities and five ra tes of nitrogen (2002)...........................................18 2-6 Analysis of variance for fresh a nd dry mustard yield affected by four population densities and five ra tes of nitrogen (2003)...........................................18 2-7 Analysis of variance for turnip mi neral concentration affected by three population densities and five ra tes of nitrogen (2002)...........................................19 2-8 Analysis of variance for turnip mi neral concentration affected by four population densities and five ra tes of nitrogen (2003)...........................................19 2-9 Analysis of variance for mustard mi neral concentration affected by three population densities and five ra tes of nitrogen (2002)...........................................20 2-10 Analysis of variance for mustard mi neral concentration affected by four population densities and five ra tes of nitrogen (2003)...........................................20 2-11 Yield of fresh turnip plant and it s parts for three (2002) and four (2003) population densities and five rates of nitrogen......................................................23 2-12 Yield of dry turnip pl ant and its parts for three population densities and five rates of nitrogen (2002)..........................................................................................24 2-13 Fresh and dry weight of turnip diagno stic leaf for three (2002) and four (2003) population densities and five rates of nitrogen......................................................25

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ix 2-14 Yield of fresh mustard plant and it s parts for three (2002) and four (2003) population densities and five rates of nitrogen......................................................26 2-15 Yield of dry mustard plant and its parts for three (2002) and four (2003) population densities and five rates of nitrogen......................................................27 2-16 Fresh and dry weight of mustard dia gnostic leaf for three (2002) and four (2003) population densities and five rates of nitrogen...........................................28 2-17 Multiple regression models for turnip yield, 2002-2003.......................................29 2-18 Multiple regression models for mustard yield, 2002-2003....................................30 2-19 ANOVA F-values of the effects of n itrogen rate and population densities on fresh turnip yields, 2002-2003...............................................................................33 2-20 ANOVA F-values of the effects of n itrogen rate and population densities on dry turnip yields, 2002-2003..................................................................................34 2-21 ANOVA F-values of the effects of n itrogen rate and population densities on fresh mustard yields, 2002-2003............................................................................35 2-22 ANOVA F-values of the effects of n itrogen rate and population densities on dry mustard yields, 2002-2003..............................................................................36 2-23 Mineral concentrations (Ca, Mg, and P) in turnip leaf for three (2002) and four (2003) population densities a nd five rates of nitrogen...................................72 2-24 Mineral concentrations (N, P, and Na) in turnip leaf for three (2002) and four (2003) population densities and five rates of nitrogen...........................................73 2-25 Mineral concentrations (Cu, Fe, and Mn ) in turnip leaf for three (2002) and four (2003) population densities a nd five rates of nitrogen...................................74 2-26 Mineral concentration (Z n) in turnip leaf for th ree (2002) and four (2003) population densities and five rates of nitrogen......................................................75 2-27 Mineral concentrations (Ca, Mg, and K) in mustard leaf for three (2002) and four (2003) population densities a nd five rates of nitrogen...................................76 2-28 Mineral concentrations (N, P, and Na ) in mustard leaf for three (2002) and four (2003) population densities a nd five rates of nitrogen...................................77 2-29 Mineral concentrations (Cu, Fe, and Mn ) in mustard leaf for three (2002) and four (2003) population densities a nd five rates of nitrogen...................................78 2-30 Mineral concentration (Zn) in mustar d leaf for three ( 2002) and four (2003) population densities and five rates of nitrogen......................................................79

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x 2-31 Multiple regression models for turnip leaf mineral concentration at 2 plants m2, 2002-2003...........................................................................................................80 2-32 Multiple regression models for mustard leaf mineral concentration at 2 plants m-2, 2002-2003.......................................................................................................81 2-33 Correlations coefficients between yi eld and leaf mineral concentration for turnip (2002)..........................................................................................................85 2-34 Correlations coefficients between yi eld and leaf mineral concentration for turnip (2003)..........................................................................................................86 2-35 Correlations coefficients between yi eld and leaf mineral concentration for mustard (2002).......................................................................................................87 2-36 Correlations coefficients between yi eld and leaf mineral concentration for mustard (2003).......................................................................................................88 3-1 Mineral sufficiency levels for macro a nd micronutrients for sweet corn at late tasseling................................................................................................................103 3-2 Green Cream 40 cowpea mulch treatments.......................................................107 3-3 Soil test report and standard fert ilization recommendation (University of Florida, IFAS Extension Service, 2002)..............................................................108 3-4 Analysis of variance for sweet corn nu mber and yield of total and fancy ears as affected by five rates of cowpea mulch...........................................................112 3-5 Analysis of variance for sweet corn affected by five rates of cowpea mulch......112 3-6 Sweet corn yield means a ffected by five mulch rates..........................................112 3-7 Sweet corn yield means a ffected by five mulch rates..........................................113 3-8 Quadratic models for sweet corn yields affected by mulch rates........................113 3-9 Linear-Plateau models for sweet co rn yields affected by mulch rates.................118 3-10 Analysis of variance for N, P, K, Ca, and Mg leaf concentrations as affected by five rates of cowpea mulch.............................................................................119 3-11 Analysis of variance for Fe, Mn, Zn, C u, and Na as affected by five rates of cowpea mulch......................................................................................................119 3-12 Sweet corn N, P, K, Ca, and Mg leaf concentrations affected by five mulch rates......................................................................................................................120

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xi 3-13 Sweet corn Fe, Mn, Zn, Cu, and Na leaf concentrations affected by five mulch rates......................................................................................................................122 3-14 Regression models for N, K, Zn, and Na leaf concentrations as affected by mulch rates...........................................................................................................122 3-15 Linear-plateau models for N, K, a nd Zn leaf concentration as affected by mulch rates...........................................................................................................124 3-16 Correlation coefficients between yield and leaf mineral concentration in sweet corn affected by five cowpea mulch rates............................................................125 3-17 Analysis of variance for ring, sting, stubby-root, spiral, lesion, and root-knot nematode population (nematodes cm3) affected by five rates of cowpea mulch and sampling time (day s after planting)...............................................................126 3-18 Nematode populations affected by five rates of cowpea mulch..........................127 3-19 Ring, stubby-root, and ro ot-knot populations affected by mulch rates................128 3-20 Ring, stubby-root, and ro ot-knot populations affected by sampling date............129 3-21 Ring, stubby-root, and ro ot-knot populations affected by mulch rates and sampling date.......................................................................................................130 3-22 Correlation coefficient among yield, l eaf mineral concentration, initial and final nematode population in sweet corn affected by five cowpea mulch rates..138 3-23 Correlation initial and final nematode population in sweet corn affected by five cowpea mulch rates.......................................................................................139 4-1 Analysis of variance for the field corn total plant dry matter general saturated model....................................................................................................................153 4-2 Model selection criteria for four covariance structures (smaller-is-better).........157 4-3 Decomposed spline model including genotype, planting date, and interaction effects over time...................................................................................................161 4-4 Final spline model for field corn to tal plant dry matter affected by genotype and planting date over time (t).............................................................................162 4-5 Estimates of total plant dry matter (D M) means at maximum growth of field corn......................................................................................................................163

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xii LIST OF FIGURES Figure page 2-1 Marginal quadratic polynomials for fresh turnip top yield as affected by N rate and plant population (2002).....................................................................................37 2-2 Surface response for fresh turnip top yield as affected by N rate and plant population (2002).....................................................................................................38 2-3 Marginal quadratic polynomials for fresh turnip root yield as affected by N rate and plant population (2002).....................................................................................39 2-4 Surface response for fresh turnip root yield as affected by N rate and plant population (2002).....................................................................................................40 2-5 Marginal quadratic polynomials for fres h turnip total plant yield as affected by N rate and plant population (2002)..........................................................................41 2-6 Surface response for fresh turnip total plan t yield as affected by N rate and plant population (2002).....................................................................................................42 2-7 Marginal quadratic polynomials for fres h turnip diagnostic (diag.) leaf yield as affected by N rate and plant population (2002)........................................................43 2-8 Surface response for fresh turnip diagnos tic (diag.) leaf yield as affected by N rate and plant population (2002)..............................................................................44 2-9 Marginal quadratic polynomials for fres h mustard top yield as affected by N rate and plant population (2002).....................................................................................45 2-10 Surface response for fresh mustard top yield as affected by N rate and plant population (2002).....................................................................................................46 2-11 Marginal quadratic polynomials for fres h mustard root yield as affected by N rate and plant population (2002)..............................................................................47 2-12 Surface response for fresh mustard root yield as affected by N rate and plant population (2002).....................................................................................................48 2-13 Marginal quadratic polynomials for fres h mustard total plant yield as affected by N rate and plant population (2002)..........................................................................49

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xiii 2-14 Surface response for fresh mustard total plant yield as affected by N rate and plant population (2002)............................................................................................50 2-15 Marginal quadratic polynomials for fres h mustard diagnostic (d iag.) leaf yield as affected by N rate and plant population (2002)........................................................51 2-16 Surface response for fresh mustard diagnos tic (diag.) leaf yield as affected by N rate and plant population (2002)..............................................................................52 2-17 Marginal quadratic polynomials for fres h turnip top yield as affected by N rate and plant population (2003).....................................................................................56 2-18 Surface response for fresh turnip top yield as affected by N rate and plant population (2003).....................................................................................................57 2-19 Marginal quadratic polynomials for fresh turnip root yield as affected by N rate and plant population (2003).....................................................................................58 2-20 Surface response for fresh turnip root yield as affected by N rate and plant population (2003).....................................................................................................59 2-21 Marginal quadratic polynomials for fres h turnip total plant yield as affected by N rate and plant population (2003)..........................................................................60 2-22 Surface response for fresh turnip total plan t yield as affected by N rate and plant population (2003).....................................................................................................61 2-23 Marginal quadratic polynomials for fres h turnip diagnostic (diag.) leaf yield as affected by N rate and plant population (2003)........................................................62 2-24 Surface response for fresh turnip diagnos tic (diag.) leaf yield as affected by N rate and plant population (2003)..............................................................................63 2-25 Marginal quadratic polynomials for fres h mustard top yield as affected by N rate and plant population (2003).....................................................................................64 2-26 Surface response for fresh mustard top yield as affected by N rate and plant population (2003).....................................................................................................65 2-27 Marginal quadratic polynomials for fres h mustard root yield as affected by N rate and plant population (2003)..............................................................................66 2-28 Surface response for fresh mustard root yield as affected by N rate and plant population (2003).....................................................................................................67 2-29 Marginal quadratic polynomials for fres h mustard total plant yield as affected by N rate and plant population (2003)..........................................................................68

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xiv 2-30 Surface response for fresh mustard total plant yield as affected by N rate and plant population (2003)............................................................................................69 2-31 Marginal quadratic polynomials for fres h mustard diagnostic (d iag.) leaf yield as affected by N rate and plant population (2003)........................................................70 2-32 Surface response for fresh mustard diagnos tic (diag.) leaf yield as affected by N rate and plant population (2003)..............................................................................71 2-33 Leaf N concentration in turnip and mustard planted at 2 plants m-2 as affected by N rates (2002-2003).................................................................................................83 2-34 Leaf P concentration in turnip and mustard planted at 2 plants m-2 as affected by N rates (2002-2003).................................................................................................84 2-35 Leaf K concentration in turnip and mustard planted at 2 plants m-2 as affected by N rates (2002-2003).................................................................................................89 2-36 Leaf Ca concentration in turnip and mustard plante d at 2 plants m-2 as affected by N rates (2002-2003)............................................................................................90 2-37 Leaf Mg concentration in turnip and mustard plante d at 2 plants m-2 as affected by N rates (2002-2003)............................................................................................91 2-38 Leaf Fe concentration in turnip and mustard plante d at 2 plants m-2 as affected by N rates (2002-2003)............................................................................................92 2-39 Leaf Mn concentration in turnip and mustard plante d at 2 plants m-2 as affected by N rates (2002-2003)............................................................................................93 2-40 Leaf Cu concentration in turnip and mustard plante d at 2 plants m-2 as affected by N rates (2002-2003)............................................................................................94 2-41 Leaf Zn concentration in turnip and mustard plante d at 2 plants m-2 as affected by N rates (2002-2003)............................................................................................95 2-42 Leaf Na concentration in turnip and mustard plante d at 2 plants m-2 as affected by N rates (2002-2003)............................................................................................96 2-43 Fresh turnip top yield in the Georgia piedmont experiment (Brantley, 1961) at 0, 34, 68, and 102 kg N ha-1 and Florida experiment at 0, 56, and 112 kg N ha-1.....100 2-44 Turnip N concentration in the Georgia piedmont experiment (Brantley, 1961) at 0, 34, 68, and 102 kg N ha-1 and Florida experiment at 0, 56, and 112 kg N ha-1 .100 3-1 Number of fancy and total ears as affected by five cowpea mulch rates...............114 3-2 Total and fancy ear yields as a ffected by five cowpea mulch rates.......................114

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xv 3-3 Total plant and stalk yields as affected by five cowpea mulch rates.....................115 3-4 Fresh diagnostic (diag.) leaf yields as affected by five cowpea mulch rates.........115 3-5 Diagnostic (diag.) leaf area as a ffected by five cowpea mulch rates.....................116 3-6 Quadratic and linear-plat eau models for total ear yi elds as affected by five cowpea mulch rates................................................................................................116 3-7 Quadratic and linear-plat eau models for fancy ear yi elds as affected by five cowpea mulch rates................................................................................................117 3-8 Quadratic and linea r-plateau models for fresh diagnos tic leaf yields as affected by five cowpea mulch rates....................................................................................117 3-9 Quadratic and linear-plateau models for leaf N concentration as affected by five cowpea mulch rates................................................................................................120 3-10 Quadratic and Linear-Plateau models for leaf K concentration as affected by five cowpea mulch rates................................................................................................121 3-11 Quadratic and linear-plateau models for leaf Zn concentration as affected by five cowpea mulch rates................................................................................................121 3-12 Marginal cubic polynomials for ring ne matode population as affected by days after planting and cowpea mulch rates...................................................................131 3-13 Surface response for ring nematode populat ion as affected by days after planting and cowpea mulch rates.........................................................................................132 3-14 Marginal cubic polynomials for stubby -root nematode population as affected by days after planting and cowpea mulch rates...........................................................133 3-15 Surface response for stubby-root nematode population as affected by days after planting and cowpea mulch rates...........................................................................134 3-16 Marginal cubic polynomials for rootknot nematode population as affected by days after planting and cowpea mulch rates ..........................................................135 3-17 Surface response for root-knot nematode population as affected by days after planting and cowpea mulch rates...........................................................................136 3-18 Effect of sting nematodes final popul ation (Pf) on number of total ears...............137 3-19 Effect of stubby-root nematodes fina l population (Pf) on num ber of total ears....137 4-1 Observed field corn total plant dry matter growth profile of Pioneer Brand 3320 planted in March, May, and August ............................................................149

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xvi 4-2 Pioneer Brand 3320, Pioneer Brand X304C, and FLOPUP averaged field corn total plant dry matter yields planted in March, May, and August .................154 4-3 Residual analysis for the dry matter (DM) general saturated model including genotype, planting date, sampling da te, and their interactions..............................155 4-4 Covariance structure analysis for the dry matter genera l saturated model including genotype, planti ng date, sampling date, and their interactions. Averaged correlation against time interval (lag) and variance against days after planting...................................................................................................................156 4-5 Predicted and observed field corn to tal plant dry matter for Pioneer Brand 3320 planted in March (cubic model)...................................................................159 4-6 Residual analysis for the cubic mode l for three genotypes planted in March........159 4-7 Predicted and observed field corn to tal plant dry matter for Pioneer Brand 3320 planted in March (spline model)..................................................................164 4-8 Residual analysis for th e spline model for three ge notypes planted in March.......164 4-9 Field corn total plant dry matter aff ected by genotype and sampling date when planted in March.....................................................................................................165 4-10 Field corn total plant dry matter aff ected by genotype and sampling date when planted in May........................................................................................................165 4-11 Field corn total plant dry matter aff ected by genotype and sampling date when planted in August...................................................................................................166 4-12 Maximum field corn total plant dry matter affected by genotype and planting date.........................................................................................................................16 6

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xvii Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy APPLICATION OF STATISTICAL TECHN IQUES TO MODELING CROP GROWTH By Belkys Yasmin Bracho Bravo May 2005 Chair: Raymond N. Gallaher Major Department: Agronomy Crop models that incorporated genetics, climate, and management operations may be useful tools to make better decisions throughout the growing season for turnip ( Brassica rapa L.), mustard ( Brassica juncea L.), sweet and field corn ( Zea mays L.). Experiments were conducted near Gainesville Florida, to test plant populations and N fertilizer on turnip and mustard; to determine the effect of cowpea ( Vigna unguiculata (L.) Walp.) mulch on sweet corn yield, plan t nutrition, and nematode populations, as well as the effect of nematodes on yield; and to develop dry matter growth models over time for field corn as affected by genotypes and planting dates. Variance, correlation, linear and non-linear regression, surface response, ridg e, and longitudinal analysis were used to optimize management decisions. Ridge analysis predicted that a combination of 168 kg N ha-1 and more than 6 plants m-2 will produce maximum mustard and turn ip shoot as well as total biomass yields. Sweet corn yield responded to increasing cowpea mulch, peaking at rates corresponding to at least 201 kg N ha-1. Quadratic and linear-plateau models agreed with

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xviii the critical N levels reported by others and co uld be used as tools to select N fertilizer requirements. Diagnostic leaf area and weight as well as N, K, Zn, and Na concentrations of the leaf increased as to tal and fancy ears increas ed and all could be good predictors of sweet corn yield. Fanc y ear yield reached maximum levels around 63 days after planting (DAP) and betw een 4.4 and 6.6 kg of cowpea mulch m-2. Nematode population also peaked at 63 DAP but there was little impact of nematodes on ear yield. Analysis showed that the N-sufficient corn plants were able to sustain greater nematode densities compared to N-deficient plants. For field corn, genotype and planting date affected total dry matter accumulation over the life cycle of the field corn. At early planting (March) Pioneer 3320, a temperate hybrid, had greater yield than Pioneer X304C and FLOPUP. Delaying planting until May favored Pi oneer X304C, a tropical hybrid. When planted in August FLOPUP, an open pollinated variety deve loped under Florida conditions, performed better than the other corn hybrids. FLOPUP had more resistance to late season diseases, especially compared to Pioneer 3320.

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1 CHAPTER 1 INTRODUCTION Agricultural producers, researchers, cons ultants, and industry representatives are faced with crop management and decisions throughout the growing season. The need to manage and predict crop behavior over a wide range of plant densit ies, planting dates, mineral fertilization, climate, pest contro l, among others has become increasingly important. Crop growth models have traditi onally been used to address many of these research problems by increasing our unders tanding of crop growth, development and yield. The emphasis in production agriculture is to obtain the maximum yield possible, or at least to obtain the mo st economical yield. Final cr op yield depends mainly on the management decisions made throughout th e growing season. The use of crop models, incorporating climatic conditi ons and management operations, may assist in making more timely and better management decisions (A ikman and Scaife, 1993; Singh and Jones, 2000). The term model has many meanings in agronomy. Representative plants are commonly referred to as model plants; qual itative hypotheses are referred to as models, and the mathematical simulation of crop growth, development, and yield is referred to as a crop simulation model. This dissertation considered the statistical analysis of experimental data, in which mathematical equations give very precise and concise descriptions of data and how de pendent variables are related to independent variables. In particular, the goals were to find model parame ters with useful interpretations relative to the growth of crops studied. Models can be used to calculate and estimate relative

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2 maximums, predicted or out-of-range values th at would be useful for the optimization of management decisions, such as fertilization, plant population, genetics or planting dates, providing a framework for comparison. A crop model is usually more accurate in predicting crop growth than in predicting final yields. This is because that most plan ts can be effectively ch aracterized for growth based on accumulated growing time. Crop yi eld is an accumulation and integration of physiological processes through time. Numer ous errors are associated with yield predictions and these errors are cumulati ve through time (Vagts 2004). Crop modeling is important to enhance the understanding of plants because they are complex and dynamic, and plant growth is difficult to address only by empirical approaches. Modeling is a useful tool for understanding th e developmental mechanisms of plants as they interact with environmental variations (Wann, 1984). In this dissertation, statisti cal modeling was used to examine the effect of different management inputs on the growth of four impor tant crops in Florida. A goal of this research was to develop a theoretical mode l to obtain maximum or optimum yields by determining the factors affecti ng those yields. A number of models have been developed to predict plant growth and yields. These m odels may be classified as empirical models; they describe sigmoid functi ons that approximate plant gr owth curves versus time or optimize crop responses (Hanway, 1963). Howe ver, the usefulness of empirical models is limited, frequently providing parameters with no agronomic or biologic meaning. Mechanistic models may be developed from theoretical or can be experimentally determined from data describing the cause or mechanism behind the dynamic changes observed in an experimental system (Breidt and Fleming, 1998). Models in this research

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3 may be classified as partially mechanisti c, based on fertilization, plant population, genetics, and planting and sampling dates as variables that affect crop growth. In general, N fertilization, plant population densities, cowpea ( Vigna unguiculata (L.) Walp.) mulches, genetics, planting and sampli ng dates were important in the growth of turnip ( Brassica rapa L.), mustard ( Brassica juncea L.), and sweet and field corn ( Zea mays L.). By incorporating these factors as parameters into models, values for these parameters could be estimated to gain insight into their relative importance in the growth process, and ultimately on the final yields of those crops. Turnip is a biennial cool season crop, resistant to frost and mild freezes (Duke, 1983). This plant is native to Western Asia a nd is grown today in the United States, Asia and Europe. In Florida it is grown commerc ially and by home gardeners. Its roots, swollen white, pink or yellow-fleshed tubers, are eaten raw or cooked. Its thin, light to dark green leaves are also eat en raw or cooked like spinach ( Spinacia oleracea L.). Turnip may be intercropped with corn or used as a catch crop af ter vegetables. In Florida, turnip is planted from August to February, with a growing period of 40 to 50 d (Maynard et al., 2002). Mustar d is an annual cool season ve getable, native to central Asia (Northwest India), and widely grown for its seed. However, its glabrous young leaves are extensively eate n raw or cooked like spinach or turnip (Duke, 1983). More than half of the total leaf N is in components associated with photosynthesis, which is positively associated with leaf N c oncentration (Alt et al., 2000b). Increases in green leaf yields and hence in rates of photos ynthesis are often expected with the addition of N fertilizer. Quantifying the interaction between N concentration in leaf tissue and plant productivity under specific conditions will result in optimum N fertilizer

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4 recommendations, avoiding both environmental concerns and also N deficiency (Alt et al., 2000a). Although biomass on a land area basis can be manipulated through varying population density; in general, when density is increased, competition for nutrients, light, and water also increases. At low densities an adequate economical yield may not be reached. On the other hand, high densities may increase production cost without significant increases in yi eld (Hay and Walker, 1989). Response surface methodology is a statisti cal technique used for optimization studies when two or more independent variab les have a combined effect on the desired response (Hunter, 1959). A three-dimensional surface response graph can be obtained by use of a multiple regression polynomial equati on to describe two or more independent variables that have a combined effect on the desired response (Little and Hills, 1978). The objectives of the second ch apter of this dissertation we re to use this methodology to establish the relationship betw een population density and N rates affecting mustard and turnip yields and to establish a set of op timum recommendations of those variables for obtaining maximum yields. Corn is a crop of New World origin. It is one of the most important crops in providing human sustenance, directly or i ndirectly as feed for domestic animals (Jugenheimer, 1976; Hochmuth et al., 2002). Co rn is a major crop on cultivated land of the United States and has become an importa nt crop because of its productivity and great adaptability (Duncan, 1975; Fraz ier, 1983). Florida ranks nu mber one nationally in the production and value of fresh market sweet corn, typically accounting for approximately 25% of both national sweet corn production a nd of U.S. cash receipts for fresh sales

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5 (FASS, 2000). Field corn is typically pl anted as a spring crop and has shown little success when planted in the fall in Florida (Bustillo and Gallaher, 1989). Nitrogen is the nutrient that most limits corn yield. Low cost of N fertilizer and the lack of a yield loss from over-application of N has led to a management approach emphasizing generous applications (Br ouder et al., 2000; Vanotti and Bundy, 1994). Nitrogen management in corn is important because excessive N can result in contamination of the environment (NO3-N leaching) and inadequa te N can result in yield and profit losses to the grower (Doerge, 2002; Mamo et al., 2003; Scharf et al., 2002; Toth and Fox, 1998). Residue mulches protect the soil from wi nd and water erosion, conserve moisture, control weeds, often increase crop production, but also delay soil warming in the spring (Swan et al. 1996; Gallaher, 1978). Rivero (1997) has shown that cation exchange capacity (CEC), organic carbon, and available P a nd K in soil as well as dry matter yield, and N, P, and K concentration in the leaf tis sue of corn increase with mulch applications. Statistically significant increases were observed when sorghum ( Sorghum bicolor L.) and sunn hemp ( Crotalaria juncea L.) mulches were used. Crop production was very dependent on soil-residue interaction but e ffects were for a short period of time. Nematodes injure sweet corn by reducing co rn root growth, stalk height, and stalk diameter. In most cases, plants weakened by nematodes produce smaller and fewer ears, and plants that are heavily parasitized may produce no ears, resulting in up to 100% crop loss. General symptoms of nematode in jury include stunting, wilting, and nutrient deficiency symptoms, often in patches throughout the field due to irre gular distri bution of nematodes. Some nematodes affecting sweet corn in Florida include sting ( Belonolaimus

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6 spp.), stubby root ( Trichodorus spp., Paratrichodorus spp.), lesion ( Pratylenchus spp.), and occasionally root-knot ( Meloidogyne spp.). Corn yield reduction from those nematodes is generally higher in sandier so ils (Christie, 1959; Noling, 1997; 1999). The objectives addressed in the third chapter were to determine the effect of five cowpea mulch rates on sweet corn yield and plant nu trition, sting, stubby-r oot, lesion, and rootknot nematode populations, as well as the eff ect of nematode densities on sweet corn yield. Field corn production occurs mostly in central and north Florida. Corn is used for both silage and grain. Tropical corn hybrids are more resist ant to insects and diseases than temperate hybrids. Therefore tropical co rn hybrids should be used if planting is delayed until late April or May. With the long growing season in Florida, double cropping of corn is possible. Temperate co rn hybrids tend to yield higher in early plantings, while tropical corn hybrids should be selected over temperate varieties for late planting. Summer-planted corn will partition dry matter to vegetative and reproductive tissues in a different manner compared to spring-planted corn. A study conducted by Overman and Gallaher (1989) evaluated the pr oper management required for temperate, tropical and subtropical field co rn growing in Florida. The pattern of corn growth is often represented as a sigm oid curve with time. This S shaped curve of growth is a result of th e differential rates of growth during its life cycle. As young seedlings expand leaf area, accumulation of crop biomass per unit area increases exponentially until the canopy develo ps sufficient leaf area to intercept approximately 100 % of available radiation. Once a complete leaf canopy is developed, the accumulation of biomass is quite linea r for most of the growth period until

PAGE 25

7 approximately grain maturity. Biomass accu mulation then slows as leaf senescence occurs late in the season. Longitudinal data analysis is frequently used in agricultural studies. Longitudinal data consists of repeated observations of a given conti nuous characteristic over time (Zimmerman and Nuez-Anton, 2001). Growth curve data over time would be such longitudinal data. The mixed methodology appr oach for longitudinal analysis includes modeling the covariance stru cture and mean or treatment effects. Modeling those structures allows one to find a functional re lationship between the covariance and mean of any two observations and the times of their measurement and possibly other covariates. The general object ive presented in the fourth chapter was to develop dry matter accumulation growth models over time, for total biomass provided by field corn, as affected by genotypes and planting dates.

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8 CHAPTER 2 OPTIMIZING TURNIP AND MUSTARD YIELDS IN RESPONSE TO PLANT POPULATION AND NITROGEN FERTILIZER USING RESPONSE SURFACE METHODOLOGY Introduction Turnip ( Brassica rapa L.) is a biennial cool season crop, resistant to frost and mild freezes (Duke, 1983). This plant is native to Western Asia and is grown today in the United States, Asia and Eur ope. In Florida it is grow n commercially and by home gardeners. Its roots, swollen white, pink or yellow-fleshed tube rs, are eaten raw or cooked. Its thin, light to da rk green leaves are also eaten raw or cooked like spinach ( Spinacia oleracea L.). Turnip roots and leaves have been used as a folk remedy for cancer (Hartwell, 1982). Turnip ma y be intercropped with corn ( Zea mays L.) or used as a catch crop after vegetables. In Florida, turnip is planted from August to February, with a growing period of 40 to 50 d (Maynard et al ., 2002). Turnip tops are harvested when plants are young and tender. Roots may be harvested in 40 to 60 d for bunching when they are 5 cm in diameter and for topping when they are 7.5 cm in diameter (Duke, 1983). Fresh yield range between 12.5 to 25 MT ha-1, depending upon if harvested as bunched or topped roots. Mustard ( Brassica juncea L.) is an annual cool season vegetable, native to central Asia (Northwest India) and widely grown for its seed. However, its glabrous young leaves are extensively eaten raw or cooked like spinach or turnip. Mustard has been reported as a natural remedy to relieve h eadache, inflammations and hemorrhages, and their ingestion may impart a body odor to repel mosquitoes (Duke, 1983; Burkill, 1966,

PAGE 27

9 Hartwell, 1967). The growing period is 40 to 50 d (Maynard et al ., 2002) and its leaves are harvested tender when they are 15 to 30 cm long. In the United States fresh yields of mustard greens average about 12 MT ha-1 (Duke, 1983). More than half of the leaf N is in components associated with photosynthesis, which is positively associated with leaf N c oncentration (Alt et al., 2000b). High leaf N also increases respiration, which may partially compensate for the increase in net C accumulation rates (Penning de Vries et al., 1974 ). Increases in green leaf yields and hence in rates of photosynthesis are expected with increases in N fertilization. The study of the interaction between N c oncentration in the diagnostic leaf and plant yield not only helps determine crop N fertilizer demands unde r specific environment conditions but also helps avoid environmental concerns and N deficiency (Alt et al., 2000a). The N fertilization recommendation for turnip and mustard is 135 kg ha-1 (University of Florida, IFAS Extension Service, 2002). The N sufficiency ranges in the youngest mature leaves as reported Mills and Jones ( 1996) and in the whole plant to ps as reported by Hochmuth et al. (1991) are shown in Table 2-1. Although biomass can be manipulated through population density; in general, when density is increased, competition for nutrients, light, and water also increases. At low densities an adequate economical yield ma y not be reached. On the other hand high densities may increase production cost without significant increases in yield (Hay and Walker, 1989). Maynard et al. (2002) reco mmended row widths of 0.30 to 0.90 m and plant spacing in the row of 0.13 to 0.26 m fo r turnip and 0.05 to 0.15 m for mustard. Given the importance of these two vegetables as possible cool season crops in Florida,

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10 optimum plant population and N fertilizer re quirements are important in order to maximize their yields. Table 2-1. Mineral sufficiency levels for turnips and mustard. Turnip Mustard Plant mineral Mills and Jones, 1996 Hochmuth et al., 1991 Mills and Jones, 1996 Macronutrients (g kg-1) N 35.0 50.0 30.0 50.0 29.7 38.5 P 3.3 6.0 2.5 8.0 4.1 6.4 K 35.0 50.0 25.0 40.0 31.8 43.9 Ca 15.0 40.0 8.0 15.0 15.2 25.1 Mg 3.0 10.0 2.5 6.0 2.1 3.6 Micronutrients (mg kg-1) Fe 40.0 300.0 30.0 100.0 76.0 209.0 Mn 40.0 250.0 30.0 100.0 40.0 52.0 Cu 6.0 25.0 5.0 10.0 3.0 5.0 Zn 20.0 250.0 20.0 40.0 20.0 36.0 Na 361.0 No data 193.0 417.0 Response surface methodology is a statisti cal technique used for optimization studies when two or more independent variab les have a combined effect on the desired response (Hunter, 1959). A three-dimensional surface response graph can be obtained by use of a multiple regression polynomial equati on to describe two or more independent variables that have a combined effect on the desired response (Little and Hills, 1978). The objective of this study was to use this methodology to establish the relationship between population density and N rates affecting turnip and mustard yields. Materials and Methods Two independent field experiments with turnip cv. Shogoin and mustard cv. Florida Broadleaf were conducted as split-pl ot designs at the Un iversity of Florida Statistical Design Field Teaching Lab in Gainesv ille, Florida. The experimental site is characterized by USDA-NRCS (2003) as a Millhopper fine sand (loamy, siliceous,

PAGE 29

11 semiactive, hyperthermic Grossarenic Paleudults). Plant populations of 2, 4, and 6 plants m-2 in 2002 and 2, 4, 6, and 8 plants m-2 in 2003 were main plot treatments. Five N rates (0, 56, 112, 168, and 224 kg N ha-1) were sub-treatments. Sub-plots were 2 rows 1.5 m wide and 2 m long. Experiments were replicated 5 times in 2002 and 4 times in 2003. For both years the experiments were mechani cally over-planted after conventional tillage and thinned to establish populat ion treatments. Ammonium n itrate (34% N) was applied in two equal splits as the N source. Weeds we re controlled by mechanical cultivation and by hand. A minimum of 30 mm water was app lied each week, either from rainfall or overhead sprinkler irrigation. A soil sample was collect ed prior to initiating the experiment and analyzed for Mehlich I extr actable minerals (Mehlich, 1953), pH, and lime requirements (Adams and Evans, 1962; Hanlon, 1996) (Table 2-2). In order to enhance soil fertility, K was applied according to soil test results. The labeled rate of LannateLV {S-methyl-N-[(methyl-carbam oyl)oxy]thioacetimidate}, manufactured by E.I. DUPONT DE NEMOURS and CO. (Inc.) was applied twice to control leaf feeding insects. Six most recently fully expanded leaves were collected on 28 October in 2002 and on 27 October in 2003, from each treatment, to test for essential minerals (N, P, K, Ca, Mg, Cu, Fe, Mn, and Zn) and Na. Leaves were weighed fresh, dried at 70C and reweighed. Dry leaf samples were ground in a Wiley mill to pass through a 2 mm stainless steel screen. The ground samples were re-dried at 70C in a convection oven for 2 h to standardize tissue mo isture content among all samples and stored in sterile containers until lab analysis.

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12 Leaf N analysis was by modified micro-Kj edahl procedure. A mixture of 100 mg of tissue sample, 3.2 g salt-catalyst (9:1 K2SO4:CuSO4), 2 to 3 Pyrex beads and 10 ml of H2SO4 were vortexed in a 100 ml Pyrex test tube. To reduce frothing, 2 ml 30% H2O2 was added in 1 ml increments and tubes were digested in an aluminum block digester (Gallaher et. al., 1975b) at 370C for 3.5 hours. Tubes were capped with small Pyrex funnels that allowed evolving gases to escap e while preserving refluxing action. Cool digested solutions were vortexed with approxi mately 30 ml of de-ionized water, allowed to cool to room temperature, brought to 75 ml volume, transferred to storage bottles (glass beads were filtered out), sealed, mixed and stored. Nitrogen trapped as (NH4)2SO4 was analyzed on an automatic solution sampler and a proportioning pump. A plant standard with a long history of recorded N concentration values was subjected to the same procedure and used as a check. Plant material was prepared for mineral an alysis by dry ashing procedure. A 1.0 g sample of each cultivar was placed in a beak er and ashed in a muffle furnace for 4 hours at 480C. After removal from the furnace, 5 ml of concentrated HCl and 15 to 20 ml of deionized water was added to each beaker. Samples were boiled to dryness on a hot plate to precipitate Si. Each beaker was rehydrated with 5 ml of concentrated HCl and 15 to 20 ml of deionized water, covered with a watch glass and heated until boiling. After cooling samples were brought to volume with deionized water and a po rtion was transferred to a vial. Potassium and Na were determined by flame emission spectrophotometry, while Ca, Mg, Fe, Mn, Cu, and Zn were determined by flame absortion spectrophotometry. Phosphorus was determined by colorimetry procedure.

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13 Turnip experiment was harvested from 3.0 m2 plots on 4 November 2002 and 2003. Mustard experiment wa s harvested from 3.0 m2 plots on 14 November 2002 and 6 November 2003. After weighing plots for fresh top and root yields, they were dried at 70C in a forced air oven to determine dry matter yield. Statistical analyses of the data were pe rformed using GLM procedure in SAS (SAS Institute, 2000). Analysis of variance show ed that plant populati on density and N rates affected yields in both crops, and suggested that a quadratic polynomial would provide a good approximation of the true relations hip between yields and both factors. A second order polynomial regression model, shown in [1], was fitted to the data using the response surface re gression procedure, PROC RS REG, included in SAS (SAS Institute, 2000). Multivariate second-order models have been used successfully in the past for determining the impact of three variables on crop yield (Gallaher et al., 1972; 1975a). 2 1 5 2 2 4 2 1 3 2 2 1 1 0X X X X X X Y [1] For the general statistical model in [1], Y denotes yield, X1 and X2 denote N rate and plant density, respectively, while = ( 0, 1, 2, 3, 4) represents the regression coefficient vector and stands for the error term. Treatment means averaged over repetitions were used, because, as Gomez and Gomez (1984) stated, variation between experimental units receiving the same treatmen t is not needed to evaluate the association between yields and treatments (plant population and N rates). Final models were selected using the st epwise backward method and examined to determine adequacy in predicting the response. Three-dimensional plots were generated

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14 using MATLAB (MathWorks, 2001). Optimum conditions were investigated through partial differentiation of [1], finding potential critical points: 4 3 2 5 5 1 3 2 14 2 X [2] 5 2 1 4 22 X X [3] 2 5 1 3 1 12 X X X Y 1 5 2 4 2 22 X X X Y 3 2 1 22 X Y 4 2 2 22 X Y 5 2 1 2 X X Y The second partial derivatives test, (Abr amowitz and Stegun, 1972; Stewart, 1995) was used to discern if the critical points Xj j=1 or 2 in equations [2] and [3] are maximum or minimum. The discriminant D is defined as: 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 22 1 2 1 2 1 X X Y X Y X Y X Y X X Y X X Y X Y D For our model [1] the discriminant is: 2 5 4 3 4 5 5 34 2 2 D

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15 The second derivatives test uses D and classifies the point Xj as a maximum or minimum according to the following statements: (a) If D>0 and 2 2iX Y < 0, then it is a local maximum. (b) If D>0 and 2 2iX Y > 0, then it is a local minimum. (c) If D < 0, then it is a saddle point. (d) If D = 0, higher order test must be used. Results and Discussion Yield production and mineral concentration in turnip and mustard harvested in 2002 differed from those harvested at 2003 (p < 0.0001). Therefore, yield and plant nutrition data were analyzed and will be discus sed separately for each year. Analysis of variance with the corresponding df breakdown a nd significance level for treatments are included in Tables 2-3 to 2-10. Data from soil tests (Table 2-2) resulted in fertilizer recommendations only for N and K for both crops. Yield Results for 2002 Fresh top, root, total plant (Table 2-11), and fresh diagnostic leaf yield (Table 2-13) increased with increases in N rates and populatio n density for turnip. Fresh top, root, and total plant yield at 4 and 6 plants m-2 were significantly different (p < 0.05) from those at 2 plants m-2. However, data suggested that turnip growing at densities as high as 6 plants m-2 still might have not achieved optimum yield. Fresh diagnostic leaf yield decreased as plant population increa sed, weighing an average of 1.5 g leaf-1 less at 6 plants m-2 than at 2 or 4 plants m-2. These results illustrated the imp act of plant competition on the size of

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16 the leaf. Dry diagnostic leaf yields responded inversely to population density, with yield at 6 plants m-2 of 0.2 g leaf-1 less than for the other tw o densities (Table 2-13). Significant differences (p < 0.05) were f ound in dry yields among turnip growing at higher densities compared with turnip growing at 2 plants m-2 Table 2-2. Soil test report and standard fertilization r ecommendation (University of Florida, IFAS Extension Service, 2002). 2002 2003 Soil property Turnip Mustard Turnip Mustard pH 7.1 7.1 7.2 7.3 BpH ----7.9 7.9 OM (%) 1.4 1.4 1.5 1.4 CEC (Cmol kg-1) ----6.4 5.8 Macronutrients (mg kg-1) Phosphorus 143 143 >164 84 Potassium 33 33 48 39 Magnesium 52 52 56 48 Calcium 872 872 >1136 924 Macronutrients (mg kg-1): Iron 18.8 18.8 18.2 13.8 Manganese 5.68 5.68 5.00 4.36 Copper 0.28 0.28 0.22 0.20 Zinc 5.64 5.64 1.40 0.86 Sodium 13.6 13.6 4.8 3.2 Recommendations : Lime 0 0 0 0 Nitrogen (kg N ha-1) 135 135 135 135 Phosphorus (kg P2O5 ha-1) 0 0 0 0 Potassium (kg K2O ha-1) 135 135 112 112 One composite sample over experimental site Average of two composite sa mple over experimental site

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17 Table 2-3. Analysis of va riance for fresh and dry turnip yield affected by three population densities and five rates of nitrogen (2002). Source of Variation df Fresh top Fresh root Fresh total Fresh diag. leaf Dry top Dry root Dry total Dry diag. leaf Total 74 ----------------Replicate 4 ----------------Plant Population (X1) 2 + * + ** ** Error(a) 8 ----------------Nitrogen (X2) 4 ** *** *** ** ** ** ** X1X2 8 NS NS NS NS NS NS NS NS Error(b) 48 ----------------Coefficient of Variation, % 26.5 26.0 25.0 13.7 26.2 27.0 25.3 11.6 + Significant at the 0.10 level. Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant. Table 2-4. Analysis of vari ance for fresh and dry turnip yi eld affected by four population densities and five ra tes of nitrogen (2003). Source of Variation df Fresh top Fresh root Fresh total Fresh diag. leaf Total 79 --------Replicate 3 --------Plant Population (X1) 3 + + ** Error(a) 9 --------Nitrogen (X2) 4 *** *** *** X1X2 12 NS NS NS NS Error(b) 48 --------Coefficient of Variation, % 25.6 22.0 22.0 23.0 + Significant at the 0.10 level. Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant.

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18 Table 2-5. Analysis of va riance for fresh and dry must ard yield affected by three population densities and five rates of nitrogen (2002). Source of Variation df Fresh top Fresh root Fresh total Fresh diag. leaf Dry top Dry root Dry total Dry diag. leaf Total 74 ----------------Replicate 4 ----------------Plant Population (X1) 2 * * * *** Error(a) 8 ----------------Nitrogen (X2) 4 *** ** *** *** *** *** *** *** X1X2 8 NS NS NS NS NS NS NS NS Error(b) 48 ----------------Coefficient of Variation, % 35.3 40.0 34.2 15.5 13.3 33.1 27.3 30.7 Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant. Table 2-6. Analysis of va riance for fresh and dry must ard yield affected by four population densities and five rates of nitrogen (2003). Source of Variation df Fresh top Fresh root Fresh total Fresh diag. leaf Dry top Dry root Dry total Dry diag. leaf Total 79 ----------------Replicate 3 ----------------Plant Population (X1) 3 ** + * + + * Error(a) 9 ----------------Nitrogen (X2) 4 *** + *** *** *** + *** *** X1X2 12 NS NS NS NS NS NS NS NS Error(b) 48 ----------------Coefficient of Variation, % 25.4 41.5 25.0 15.1 26.1 41.4 25.5 16.4 + Significant at the 0.10 level. Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant.

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19 Table 2-7. Analysis of variance for turn ip mineral concentration affected by three population densities and five rates of nitrogen (2002). Source of Variation df Ca Mg K N P Na Cu Fe Mn Zn Total 74 --------------------Replicate 4 --------------------Plant Population (X1) 2 NS NS + NS NS NS NS + NS NS Error(a) 8 --------------------Nitrogen (X2) 4 NS *** ** *** NS ** + *** X1X2 8 NS NS NS NS NS NS NS NS NS NS Error(b) 48 --------------------Coefficient of Variation, % 16.5 9.2 11.7 16.9 7.8 25.6 36.7 24.2 17.4 19.3 + Significant at the 0.10 level. Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant. Table 2-8. Analysis of variance for turn ip mineral concentra tion affected by four population densities and five rates of nitrogen (2003). Source of Variation df Ca Mg K N P Na Cu Fe Mn Zn Total 79 --------------------Replicate 3 --------------------Plant Population (X1) 3 NS + + NS NS NS NS + NS NS Error(a) 9 --------------------Nitrogen (X2) 4 NS *** *** ** *** + NS NS ** X1X2 12 NS NS NS NS NS NS NS NS NS NS Error(b) 48 --------------------Coefficient of Variation, % 21.7 12.0 12.6 12.8 6.1 19.6 44.9 26.1 22.9 17.1 + Significant at the 0.10 level. Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant.

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20 Table 2-9. Analysis of variance for must ard mineral concentration affected by three population densities and five rates of nitrogen (2002). Source of Variation df Ca Mg K N P Na Cu Fe Mn Zn Total 74 --------------------Replicate 4 --------------------Plant Population (X1) 2 NS + + NS ** NS + NS + Error(a) 8 --------------------Nitrogen (X2) 4 ** NS NS + + *** ** *** *** + X1X2 8 NS NS NS NS NS NS NS NS NS NS Error(b) 48 --------------------Coefficient of Variation, % 11.1 9.0 14.5 90.4 8.1 20.0 15.4 35.4 12.6 15.4 + Significant at the 0.10 level. Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant. Table 2-10. Analysis of variance for must ard mineral concentration affected by four population densities and five rates of nitrogen (2003). Source of Variation df Ca Mg K N P Na Cu Fe Mn Zn Total 79 --------------------Replicate 3 --------------------Plant Population (X1) 3 + + NS+ NS NS NS + NS NS Error(a) 9 --------------------Nitrogen (X2) 4 ** NS *** + + *** NS + *** NS X1X2 12 NS NS NS NS NS NS NS NS NS NS Error(b) 48 --------------------Coefficient of Variation, % 20.7 72.4 17.0 16.2 8.2 31.8 26.1 35.5 20.0 15.7 + Significant at the 0.10 level. Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant.

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21 Dry top, root, and total pl ant yields at 2 plants m-2 were 72.7, 51.8 and 124.4 g m-2, respectively, indicating larger pl ants at that population density (Table 2-12). Fresh yields of mustard at 6 plants m-2 presented significant increases (p < 0.05) of 1090.3 and 783.5 g m-2 for top weight; and 1112.5 and 792.5 g m-2 for total plant compared to fresh yields at 2 and 4 plants m-2, respectively (Table 2-14). Root yields of 58.1 g m-2 at 2 plants m-2 was significantly smaller than root yields obta ined at higher densities. Dry top, root, and total mustard plant weights at 6 plants m-2 were 321.6, 30.2, and 351.4 g m-2, respectively. These yields were significantly greater (p < 0.05) than yields at the lower population densities (Table 2-15). Table 2-16 shows that fresh dia gnostic leaf weight decreased more than 3 g leaf-1 when density increased from 2 plants m-2 to 4 or 6 plants m-2. Dry diagnostic leaf yiel d per leaf decreased as popul ation densities increased, indicating a reduction of leaf size at higher densities. In Tables 2-11 to 2-16 it can be observed that as N fertilization increased, fresh and dry yields increased for both crops. Thes e data fit quadratic polynomial regression models with coefficients of determination (R2) greater than 86% for turnip and 89% for mustard. Multiple regression models were selected through a stepwise backward procedure with factors entering into the model at the 0. 05 level of probability (Rawling et al., 1998). Tables 2-17 and 2-18 show the final equations for turnip and mustard, respectively. Table 2-19 shows that N rates linear and quadratic factors affected turnip diagnostic leaf yields (p < 0.1), with little or no plan t population linear effect. In mustard (Table 219), linear and quadratic eff ects were important in explaining the differences in diagnostic leaf yields (p < 0.1), with models accounting for 93, 86, 91, and 94% of the

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22 variation of fresh and dry dia gnostic leaf yields for both cr ops, respectively (Tables 2-17 and 2-18). The examination of F-values from regression analysis of variance was done as reported by Gallaher et al. (1972; 1975a). Table 2-19 shows that both linear and quadratic effects of N rates and plant population are important (p < 0.05) on influencing turnip yields, while the interaction between linear effects occurred at the 0.05 level of probability for fresh top yields but not for yields of other variables. Interaction of quadratic effects was not significant. The models accounted for 94, 90, and 92% of the variation in top, root, and total fresh yields, respec tively. For dry top, root, and total yields models explained 89, 91, and 92%, respectively of the variation in turnip response (Table 2-20). Table 2-21 indicate that N ra tes linear, N rates and pl ant population quadratic, and the interaction effects were significant (p < 0.05) factors in explaining differences in mustard top and total plant yi elds, accounting for 96% of th e variation in both fresh and dry yields. For root yields, the most im portant factors were the N rates and plant population linear and the N rates quadratic e ffects (p < 0.01), expl aining 89 and 91% of the variation for fresh and dry yields, respectiv ely. The plant populati on linear effect did not influence mustard root yields as it did in turnip root yields, suggesting that for the latter, root competition must be considered (Table 2-20). Response surface methodology was used to optimize population densities and N rates (Figures 2-1 to 2-16). For mustard t op, total, and diagnostic leaf yields canonical analysis gave saddle-points indicating that for those variables th e predicted response surface did not have a unique optimum.

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23Table 2-11. Yield of fresh turnip plant and its parts for three (2002) and four (2003) population densities and five rates of nitrogen. Year 2002 2003 Nitrogen Plants m-2 Plants m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Fresh turnip tops, g m-2 0 300.2 773.6 691.2 588.3 285.8 200.0 390.0 430.0 326.5 56 531.6 988.0 915.4 811.7 403.3 485.8 746.67 700.0 584.0 112 750.4 923.2 1180.8 951.5 513.3 706.7 813.3 753.3 696.7 168 596.6 977.2 1278.0 950.6 603.3 688.3 800.0 941.7 758.3 224 542.8 989.4 1237.6 923.3 739.2 700.0 753.3 708.3 725.2 Average 544.3 930.3 1060.6 509.0 556.2 700.7 706.7 Fresh turnip roots, g m-2 0 415.6 972.0 770.2 719.3 503.3 350.0 440.8 325.0 404.8 56 841.8 1315.2 1226.2 1127.7 560.0 510.8 686.7 661.7 604.8 112 1129.6 1303.6 1209.0 1214.1 608.3 823.3 725.8 701.9 168 990.2 1353.4 1483.0 1275.5 840.0 510.8 776.7 753.3 769.7 224 853.2 1449.4 1177.4 1160.0 955.8 746.7 768.3 550.0 755.2 Average 846.1 1278.7 1173.2 693.5 593.2 699.2 603.2 Fresh turnip total plant, g m-2 0 715.4 1745.6 1461.4 1307.5 789.2 550.0 830.84 755.0 731.3 56 1373.2 2303.2 2141.8 1939.4 963.3 996.7 1433.3 1361.7 1188.8 112 1880.0 2227.0 2389.8 2165.6 1122.7 1356.7 1636.7 1479.2 1398.5 168 1587.2 2330.4 2761.0 2226.2 1443.3 1396.7 1576.7 1695.0 1527.9 224 1396.0 2438.6 2414.6 2083.1 1695.0 1446.7 1521.7 1258.3 1480.4 Average 1390.4 2208.9 2233.7 1202.5 1149.3 1399.8 1309.8 LSD @ p = 0.05 for population (2002): top = 146.07; root = 182.32; total= 315.71 LSD @ p = 0.05 for population (2003): top = 103.42; root = 97.84; total= 179.16 LSD @ p = 0.05 for nitrogen (2002): top = 188.58; root = 235.38; total= 407.58 LSD @ p = 0.05 for nitrogen (2003): top = 115.62; root = 109.39; total= 200.31

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24Table 2-12 Yield of dry turnip plant and its parts for three population densiti es and five rates of nitrogen (2002). Year 2002 Nitrogen Plant m-2 Rate 2 4 6 Average kg ha-1 Dry turnip tops g m-2 0 40.4 103.8 92.6 78.9 56 74.6 138.8 128.8 114.1 112 94.4 116.4 148.6 119.8 168 84.4 138.0 180.6 134.3 224 69.6 123.0 158.8 117.1 Average 72.7 124.0 141.9 Dry turnip roots, g m-2 0 30.8 71.6 56.4 52.9 56 55.4 86.4 80.2 74.0 112 49.4 86.6 80.0 72.0 168 66.6 90.8 99.6 85.7 224 56.8 96.2 78.2 77.1 Average 51.8 86.3 78.9 Dry turnip total plant, g m-2 0 70.8 175.4 149.4 131.9 56 130.0 225.2 209.2 188.1 112 144.0 202.6 228.8 191.8 168 150.8 229.0 280.2 220.0 224 126.2 219.0 236.8 194.0 Average 124.4 210.2 220.9 LSD @ p = 0.05 for population (2002): t op = 19.4; root = 12.4; total = 40.0 LSD @ p = 0.05 for population (2003): t op = NS; root = NS; total = NS LSD @ p = 0.05 for nitrogen (2002): top = 25.0; root = 16.3; total = 40.0 LSD @ p = 0.05 for nitrogen (2003): top = NS; root = NS; total = NS

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25Table 2-13. Fresh and dry weight of turn ip diagnostic leaf for thr ee (2002) and four (2003) populat ion densities and five rate s of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Fresh turnip diagnostic leaf, g leaf-1 0 8.5 8.9 7.3 8.2 11.5 10.6 9.4 9.8 10.3 56 10.7 10.4 8.0 9.7 15.0 8.7 9.7 10.8 11.1 112 11.0 10.4 9.1 10.2 13.9 12.8 11.7 11.2 12.4 168 10.8 10.7 9.1 10.2 15.5 11.9 10.7 12.7 12.7 224 9.9 10.5 9.9 10.1 13.9 13.4 12.7 11.3 12.8 Average 10.2 10.2 8.7 14.0 11.5 10.8 11.2 Dry turnip diagnostic leaf, g leaf-1 0 1.10 1.08 0.85 1.01 1.53 1.18 1.15 1.13 1.25 56 1.27 1.18 0.96 1.14 1.96 1.31 1.16 1.11 1.39 112 1.28 1.20 1.02 1.16 1.66 1.53 1.36 1.43 1.50 168 1.25 1.14 0.99 1.13 1.85 1.50 1.28 1.52 1.54 224 1.16 1.17 1.10 1.14 2.14 1.58 1.54 1.25 1.63 Average 1.21 1.16 0.98 1.83 1.42 1.30 1.29 LSD @ p = 0.05 for population (2002): Fresh diagn. leaf = 0.77; Dry diagn. leaf = 0.07 LSD @ p = 0.05 for population (2003): Fresh diagn. leaf = 1.65; Dry diagn. leaf = 0.17 LSD @ p = 0.05 for nitrogen (2002): Fresh diagn. leaf = 0.99; Dry diagn. leaf = 0.09 LSD @ p = 0.05 for nitrogen (2003): Fresh diagn. leaf = 1.85; Dry diagn. leaf = 0.19

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26Table 2-14. Yield of fresh mu stard plant and its parts for th ree (2002) and four ( 2003) population densitie s and five rates of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Fresh mustard tops, g m-2 0 665.4 564.8 999.2 743.1 586.7 770.0 843.3 866.7 766.7 56 987.0 1436.0 2000.8 1474.6 918.3 1220.0 1000.0 1291.7 1107.5 112 1452.8 1956.2 2516.0 1975.0 905.0 1328.3 1486.7 1478.3 1299.6 168 1600.0 1876.0 2678.0 2051.3 1193.3 1461.7 1370.0 1428.3 1363.3 224 1416.8 1823.2 3379.6 2206.5 980.0 973.3 1596.6 1343.3 1223.3 Average 1224.4 1531.2 2314.7 916.7 1150.7 1259.3 1281.6 Fresh mustard roots, g m-2 0 27.4 37.6 49.0 38.0 61.4 62.9 69.2 98.2 72.0 56 54.6 79.0 66.0 66.5 79.7 70.7 69.3 70.1 72.5 112 59.4 85.6 96.2 80.4 63.6 109.3 91.4 104.9 92.3 168 73.4 86.8 98.0 86.1 55.7 101.3 115.5 123.3 98.9 224 75.6 66.6 91.6 77.9 86.6 74.2 112.4 105.5 94.7 Average 58.1 71.1 80.2 69.4 83.7 91.6 100.4 Fresh mustard total plant, g m-2 0 692.8 602.4 1048.4 781.2 648.0 832.88 912.5 964.9 839.6 56 1041.2 1515.0 2066.8 1541.0 998.1 1290.7 1069.3 1361.7 1180.0 112 1511.8 2041.6 2612.2 2055.2 968.6 1437.7 1578.1 1583.2 1391.9 168 1673.4 1962.6 2776.0 2137.3 1249.0 1562.9 1485.5 1551.6 1462.3 224 1492.4 1890.0 3471.0 2284.5 1066.6 1047.6 1709.1 1448.9 1318.0 Average 1282.3 1602.3 2394.8 986.1 1234.4 1350.9 1382.1 LSD @ p = 0.05 for population (2002): t op = 387.1; root = 15.6; total= 392.9 LSD @ p = 0.05 for population (2003): t op = 175.8; root = 22.2; total= 186.7 LSD @ p = 0.05 for nitrogen (2002): top = 500.5; root = 20.1; total= 507.2 LSD @ p = 0.05 for nitrogen (2003): top = 196.5; root = 24.8; total= 208.8

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27Table 2-15. Yield of dry mustar d plant and its parts for three (2002) and four (2003) population densities and five rates of n itrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Dry mustard tops, g m-2 0 97.0 82.2 145.8 108.3 41.4 88.0 62.8 98.0 72.4 56 155.2 226.0 314.8 232.0 78.8 96.7 97.7 143.5 104.2 112 202.2 272.2 350.4 274.9 85.2 118.9 105.3 126.1 108.9 168 221.2 259.4 370.6 283.7 110.9 141.4 126.4 145.1 131.0 224 178.8 229.8 426.2 278.3 81.7 79.3 144.8 103.7 102.4 Average 170.9 213.9 321.6 79.6 104.9 107.4 123.3 Dry mustard roots, g m-2 0 10.60 12.2 19.2 14.0 11.9 10.2 13.6 18.1 13.4 56 24.0 30.6 29.4 28.0 17.7 13.7 15.4 14.7 15.4 112 24.0 29.8 33.8 29.2 12.8 19.1 16.0 19.1 16.7 168 29.0 34.4 34.8 32.7 11.0 20.1 20.5 23.4 18.8 224 22.4 24.0 33.6 26.6 12.8 13.4 20.6 18.0 16.2 Average 22.0 26.2 30.2 13.3 15.3 17.2 18.7 Dry mustard total plant, g m-2 0 107.6 94.8 164.8 122.4 53.3 98.2 76.4 116.1 86.0 56 179.2 256.4 344.0 259.9 96.5 110.4 113.1 158.2 119.6 112 226.0 302.0 383.8 303.9 98.0 137.9 121.4 145.2 125.6 168 250.4 293.8 405.2 304.8 121.9 161.5 146.9 168.5 149.7 224 200.8 254.2 459.4 316.5 94.5 92.6 165.4 121.7 118.5 Average 192.8 240.2 351.4 92.8 120.1 124.6 141.9 LSD @ p = 0.05 for population (2002): top = 51.3; root = 4.2; total = 53.2 LSD @ p = 0.05 for population (2003): top = 16.3; root = 4.2; total = 18.6 LSD @ p = 0.05 for nitrogen (2002): top = 66.2; root = 5.4; total = 68.6 LSD @ p = 0.05 for nitrogen (2003): t op = 18.2; root = 4. 6; total = 20.8

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28Table 2-16. Fresh and dry weight of mustard diagnostic leaf for three (2002) and four (2003) pop ulation densities and five rat es of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Fresh mustard di agnostic leaf, g leaf-1 0 16.5 11.1 11.9 13.2 13.4 12.9 10.5 12.0 12.2 56 21.0 18.7 17.3 19.0 23.0 14.4 12.8 14.7 16.2 112 23.3 19.4 18.0 20.2 20.0 18.3 14.9 17.9 17.8 168 23.0 21.0 20.9 21.6 23.1 20.0 19.4 17.5 19.9 224 21.5 16.7 20.5 19.6 22.4 20.1 17.3 18.8 19.6 Average 21.1 17.7 17.4 20.4 17.2 15.0 16.2 Dry mustard diagnostic leaf, g leaf-1 0 2.56 1.80 1.72 2.03 2.58 1.99 1.83 1.96 2.09 56 3.21 2.65 2.26 2.71 4.05 2.61 2.22 2.23 2.78 112 3.45 2.93 2.36 2.91 4.13 2.90 2.48 2.95 3.11 168 3.25 2.90 2.57 2.91 3.72 3.49 3.00 2.83 3.26 224 3.21 2.43 2.56 2.73 3.75 3.44 2.76 2.95 3.23 Average 3.13 2.54 2.29 3.65 2.90 2.46 2.58 LSD @ p = 0.05 for population (2002): Fresh diagn. leaf = 1.73; Dry diagn. leaf = 0.20 LSD @ p = 0.05 for population (2003): Fresh diagn. leaf = 1.70; Dry diagn. leaf = 0.30 LSD @ p = 0.05 for nitrogen (2002): Fresh diagn. leaf = 2.23; Dry diagn. leaf = 0.25 LSD @ p = 0.05 for nitrogen (2003): Fresh di agn. leaf = 1.89; Dry diagn. leaf = 0.34

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29 Table 2-17. Multiple regression m odels for turnip yield, 2002-2003. Response Model R2 (p 0.05) 2002 Fresh tops, g m-2 -169.7 + 3.1X1 + 339.5X2 0.01X1 2 32.0 X2 2 + 0.4X1X2 0.94 Fresh root, g m-2 -483.9 + 7.3X1 + 620.0X2 0.02X1 2 67.3 X2 2 0.90 Fresh plant, g m-2 -834.8 + 12.0X1 + 1004.7X2 0.04X1 2 99.2 X2 2 0.92 Dry top, g m-2 -45.1 + 0.7X1 + 50.7X2 0.002X1 2 4.2 X2 2 0.89 Dry root, g m-2 -42.9 + 0.3X1 + 48.7X2 0.001X1 2 5.2 X2 2 0.91 Dry plant, g m-2 -88.0 + 1.0X1 + 99.4X2 0.003X1 2 9.4 X2 2 0.92 Fresh diagn. leaf, g leaf-1 7.8 + 0.03X1 + 1.1X2 0.0001X1 2 0.2 X2 2 + 0.000001X1X2 0.93 Dry diagn. leaf, g leaf-1 1.1 + 0.002X1 + 0.1X2 0.000007X1 2 0.01 X2 2 0.86 2003 Fresh tops, g m-2 57.7 +5.0X1 + 62.6 X2 0.01X1 2 1.2 X2 20.0001X1 2X2 2 0.89 Fresh root, g m-2 408.7 +3.9X1 -0.01X2 0.70 Fresh plant, g m-2 776.7 +3.9X1 58.6X2 +1.0 X1X2 0.01X1 2 + 8.6 X2 20.001X1 2X2 2 0.89 Fresh diagn. leaf, g leaf-1 97.7 + 0.07X1 13.2X2 + 1.05X2 2 0.73 X1: N rates (0, 56, 112, 168, and 224 kg ha-1) X2: Plant population densities: 2002 (2, 4, and 6 plants m-2) 2003 (2, 4, 6, and 8 plants m-2) Maximum yield may have not been achieve d even at the highest plant population and N rate. A ridge analysis confirmed that maximum yields coul d be obtained with a combination of higher population densities and higher N rates. The functional form of the model given in [1] was solved using pa rtial differentiation, in order to find the combination of factors that would provide highest yields. The combination of 168 kg N ha-1 and 6 plants m-2 produced maximum fresh and dry turnip top and total yields while maximum root yields could be achieved at 168 kg N ha-1 and 4 plants m-2. The highest mustard fr esh and dry top, root, and total yields were

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30 also found at 168 kg N ha-1 and 6 plants m-2. Fresh and dry diagnosti c leaf yields for both crops reached maximum values at 168 kg N ha-1 and 2 plants m-2. Table 2-18. Multiple regression m odels for mustard yield, 2002-2003. Response Model R2 (p 0.05) 2002 Fresh tops, g m-2 1132.7 + 8.4X1 370.2X2 0.04X1 2 + 59.6X2 2 + 1.5X1X2 0.96 Fresh root, g m-2 16.2 + 0.6X1 + 5.5X2 0.002X1 2 0.89 Fresh plant, g m-2 1142.1 + 8.9X1 360.6X2 0.04X1 2 + 59.1 X2 2 + 1.5X1X2 0.97 Dry top, g m-2 149.7 + 1.5X1 46.3X2 0.007X1 2 + 8.1 X2 2 + 0.2X1X2 0.96 Dry root, g m-2 6.5 + 0.2X1 + 2.0X2 0.0009X1 2 0.91 Dry plant, g m-2 156.3 + 1.7X1 43.7X2 0.007X1 2 + 7.9 X2 2 +0.2X1X2 0.96 Fresh diagn. leaf, g leaf-1 23.4 + 0.1X1 4.8X2 0.004X1 2 + 0.5 X2 2 0.91 Dry diagn. leaf, g leaf-1 3.5 + 0.01X1 0.6X2 0.00004X1 2 + 0.04 X2 2 0.94 2003 Fresh tops, g m-2 295.6 +11.5X1 +288.7X2 0.04X1 2 19.8X2 2 0.83 Fresh root, g m-2 23.5 +0.1X1 2.5X2 0.53 Fresh plant, g m-2 252.9 +3.9X1 + 32.6X2 0.01X1 2 0.80 Dry top, g m-2 38.4 + 0.7X1 6.7X2 0.002X1 2 0.69 Dry root, g m-2 9.8 + 0.02X1 0.9X2 0.44 Dry plant, g m-2 47.0 + 0.7X1 7.6X2 0.003X1 2 0.69 Fresh diagn. leaf, g leaf-1 21.5 + 0.1X1 3.5X2 0.0002X1 2 + 0.3 X2 2 0.87 Dry diagn. leaf, g leaf-1 4.1 + 0.01X1 0.7X2 0.00004X1 2 + 0.06 X2 2 0.89 X1: N rates (0, 56, 112, 168, and 224 kg ha-1) X2: Plant population densities: 2002 (2, 4, and 6 plants m-2) 2003 (2, 4, 6, and 8 plants m-2) Individual regression analys is for N rates and plant popul ation, as well as their correspondent plots were performed on the va riables described above as a preliminary

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31 analysis. Based on those analyses and usi ng the models provided by the optimization, surface responses for fresh and dry tops, root s, total plant, and diagnostic leaf were plotted for turnip and must ard (Figures 2-1 to 2-16). Analyzing the marginal N effect on turnip and mustard, increase s in dry diagnostic leaf matter were obtained as N fertilization increased, fitting a quadratic polynomial with R2 values of 0.98 and 0.84, respectively. Ma ximum dry diagnostic l eaf yield was reached at 145 and 147 kg N ha-1 for turnip and mustard, respectively. Yield Results for 2003 Fresh yields for 2003 presented a similar tr end to those for 2002 with increases as N rate and population densities increased. However, yields for both crops in 2003 were reduced (p < 0.001) when compared to the previous year. This reduction was probably due to disease and nematode build up from th e previous crop season. Turnip had a high incidence of crown rot caused either by Erwinia carotovora or Phytophthora spp. This disease attacks the roots and lower parts of turnip leaf stems (Shattuck and Mayberry, 1998). Turnip yields were more affected th e second year than mu stard which did not show the disease. Table 2-11 show differences in fresh turnip top and total yields for 6 and 8 plants m-2 compared to those for 4 and 6 plants m-2 (p < 0.0001). Turnip grown at the 8 plants m-2 density produces more tops and to tal plant yield by 197.7 and 107.3 g m-2 when compared to the 2 plants m-2 density. Fresh turnip root yi elds were seriously reduced by the crown rot disease attack r eaching a maximum of only 699.2 g m-2 at 6 plants m-2. Fresh yield maximum seemed to be reached at about 168 kg N ha-1. Increases of fresh turnip yields were steady up to this rate. Fr esh top, root, and total plant yield decreases of

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32 33.1, 14.5, and 47.5 g m-2 were found when compar ing yields at 168 kg N ha-1 with 224 kg N ha-1. Turnip dry top, root, and total plant yield did not show statisti cal treatment effects (Table 2-12). Fresh and dry diagnostic leaf we ight (Table 2-13), also increased as N rate increased as much as 19.9 and 3.26 g leaf-1, respectively at 168 kg N ha-1. However, diagnostic leaf weight was greater wh en turnip was grown at 2 plants m-2. Table 2-14 shows how mustar d fresh yields responde d to N rates and plant population density. Top, root, and total pl ant yield increased as density increased, weighing 364.9, 31.0, and 396 g m-2 more at 8 plants m-2 than at 2 plants m-2. The highest fresh yields were r eached at a rate of 168 kg N ha-1, with differences of 596.6, 26.9, and 622.7 g m-2, when compared with yields at 0 kg N ha-1. In 2003, as plant population de nsity increased mustard dry top, root, and total plant yields also increased, reach ing 123.3, 18.7, and 141.9 g m-2 when planted at 8 plants m-2 (Table 2-15). Dry yields of 131.0, 18.8, and 149.7 g m-2 for top, root, and total plant were reached at 168 kg N ha-1. Diagnostic leaf weighed more when planted at 2 plants m-2 than at 8 plants m-2, with differences of 4.2 and 1.07 g leaf-1 for fresh and dry yield, respectively (Table 2-16). Dry weight fo llowed the same pattern as fresh yields. Tables 2-17 and 2-18 show the final regres sion models for turnip and mustard in 2003. F-values from the analysis of variance in Table 2-19 show that the linear effect of X1 was important for turnip fresh top and diagnos tic leaf yields while its quadratic effect affected only fresh top yield (p < 0.001).

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33 Table 2-19. ANOVA F-values of the effects of nitrogen rate (X1) and population densities (X2) on fresh turnip yields, 2002-2003. 2002 2003 Source of Variation df top root pl ant diag top root Plant diag X1 1 6.95* 35.10** 34.10** 35.04** 44.77** 31.72*** 1.73 NS 15.57** Linear X2 1 12.78** 27.21** 25.63** 6.61* 2.19NS --0.35 NS 13.19* X1 2 1 12.49** 21.39** 21.39** 26.17** 13.13** 6.35** 0.95 NS --Quadr. X2 2 1 7.70* 20.94** 20.94** 15.02** 0.09 NS --0.70 NS 8.58* Lin .Inter X1X2 1 5.79* ----------3.45* --Quad.Inter X1 2X2 2 1 ----8.39* --3.17* --6.74* --* Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant.

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34 Table 2-20. ANOVA F-values of the effects of nitrogen rate (X1) and population densities (X2) on dry turnip yields, 2002-2003. 2002 2003 Source of Variation df top root plant diag diag X1 1 15.04** 16.80** 21.45** 9.82* 20.12* Linear X2 1 9.54* 39.55** 23.16** 0.85NS 21.54** X1 2 1 8.94* 8.34* 12.07** 6.23* --Quadr. X2 2 1 4.23+ 29.93** 13.55** 3.61+ 12.18** Lin .Inter X1X2 1 ----------Quad.Inter X1 2X2 2 1 ----------+ Significant at the 0.10 level. Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant.

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35 Table 2-21. ANOVA F-values of the effects of nitrogen rate (X1) and population densities (X2) on fresh mustard yields, 2002-2003. 2002 2003 Source of Variation df top root pl ant diag top root plant diag X1 1 10.89** 44.37** 12.10** 50.65** 33.11** 8.48* 28.72** 23.83** Linear X2 1 3.27NS 20.28** 3.01NS 11.33** 6.44* 10.98* 19.49** 16.02** X1 2 1 16.22** 23.71** 17.39** 30.49** 19.03** --16.15** 7.96* Quadr. X2 2 1 5.77* --5.50* 7.79* 3.14* ----10.31** Lin .Inter X1X2 1 16.81** --16.31** ----------Quad.Inter X1 2X2 2 1 ----------------* Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant.

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36 Table 2-22. ANOVA F-values of the effects of nitrogen rate (X1) and population densities (X2) on dry mustard yields, 2002-2003. 2002 2003 Source of Variation df top root pl ant diag top root plant diag X1 1 17.82** 65.31** 21.56** 55.57** 14.37* 3.68* 15.25* 26.88** Linear X2 1 2.61NS 23.63** 2.11NS 11.55** 13.73* 9.71* 15.96* 25.30** X1 2 1 28.42** 43.46** 32.74** 36.08** 9.34* --9.83* 11.58* Quadr. X2 2 1 5.39* --4.76* 4.56+ ------14.81* Lin .Inter X1X2 1 11.62** --10.84** ----------Quad.Inter X1 2X2 2 1 ----------------* Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant.

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37 (a) 0 400 800 1200 1600 056112168224 Nitrogen rate, kg ha-1Fresh top yield, g m -2 2 4 6Plants m-2 (b) 0 400 800 1200 1600 246 Plant population, plants m-2Fresh top yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-1. Marginal quadratic polynomials fo r fresh turnip top yiel d as affected by (a) N rate and (b) plan t population (2002).

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38 Figure 2-2. Surface response for fresh turnip top yield as affected by N rate and plant population (2002).

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39 (a) 0 400 800 1200 1600 056112168224 Nitrogen rate, kg ha-1Fresh root yield, g m -2 2 4 6 Plants m-2, (b) 0 400 800 1200 1600 246 Plant population, plants m-2Fresh root yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-3. Marginal quadratic polynomials fo r fresh turnip root yield as affected by (a) N rate and (b) plan t population (2002).

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40 Figure 2-4. Surface response for fresh turnip root yield as affected by N rate and plant population (2002).

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41 (a) 0 400 800 1200 1600 2000 2400 2800 056112168224 Nitrogen rate, kg ha-1Fresh total plant yield, g m -2 2 4 6 Plants m-2, (b) 0 400 800 1200 1600 2000 2400 2800 246 Plant population, plants m-2Fresh total plant yield, g m-2 0 56 112 168 224 N rate, kg ha-1, Figure 2-5. Marginal quadratic polynomials fo r fresh turnip total pl ant yield as affected by (a) N rate and (b) plant population (2002).

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42 Figure 2-6. Surface response for fresh turnip to tal plant yield as affected by N rate and plant population (2002).

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43 (a) 5 10 15 20 056112168224 Nitrogen rate, kg ha-1Fresh diag. leaf yield, g leaf -1 2 4 6 Plants m-2, (b) 5 10 15 20 246 Plant population, plants m-2Fresh diag. leaf yield, g leaf -1 0 56 112 168 224 N rate, kg ha-1, Figure 2-7. Marginal quadratic polynomials fo r fresh turnip diagnostic (diag.) leaf yield as affected by (a) N rate and (b) plant population (2002).

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44 Figure 2-8. Surface response for fresh turnip diagnostic (diag.) leaf yield as affected by N rate and plant population (2002).

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45 (a) 0 400 800 1200 1600 2000 2400 2800 3200 3600 056112168224 Nitrogen rate, kg ha-1Fresh top yield, g m -2 2 4 6 Plants m-2, (b) 0 400 800 1200 1600 2000 2400 2800 3200 3600 246 Plant population, plants m-2Fresh top yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-9. Marginal quadratic polynomials fo r fresh mustard top yield as affected by (a) N rate and (b) plan t population (2002).

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46 Figure 2-10. Surface response for fresh mustard top yield as affected by N rate and plant population (2002).

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47 (a) 0 50 100 150 200 056112168224 Nitrogen rate, kg ha-1Fresh root yield, g m -2 2 4 6 Plants m-2, (b) 0 50 100 150 200 246 Plant population, plants m-2Fresh root yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-11. Marginal quadratic polynomials fo r fresh mustard root yield as affected by (a) N rate and (b) pl ant population (2002).

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48 Figure 2-12. Surface response for fresh mustard root yield as affected by N rate and plant population (2002).

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49 (a) 0 400 800 1200 1600 2000 2400 2800 3200 3600 056112168224 Nitrogen rate, kg ha-1Fresh total plant yield, g m -2 2 4 6 Plants m-2, (b) 0 400 800 1200 1600 2000 2400 2800 3200 3600 246 Plant population, plants m-2Fresh total plant yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-13. Marginal quadratic polynomial s for fresh mustard total plant yield as affected by (a) N rate and (b) plant population (2002).

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50 Figure 2-14. Surface response for fresh mustar d total plant yield as affected by N rate and plant population (2002).

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51 (a) 5 10 15 20 25 056112168224 Nitrogen rate, kg ha-1Fresh diag. leaf yield, g leaf -1 2 4 6 Plants m-2, (b) 5 10 15 20 25 246 Plant population, plants m-2Fresh diag. leaf yield, g leaf -1 0 56 112 168 224 N rate, kg ha-1, Figure 2-15. Marginal quadratic polynomials for fresh mustard diagnostic (diag.) leaf yield as affected by N rate and plant population (2002).

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52 Figure 2-16. Surface response for fresh mustard diagnostic (diag.) leaf yield as affected by N rate and plant population (2002). The linear and quadratic eff ect of plant population aff ected only fresh diagnostic leaf weight (p < 0.05) in 2003. Fresh root and total plan t did not respond to linear or quadratic effects in both variable s but the variables interacted with each other (p = 0.05). Similarly, the quadratic inte raction between N rates and plant population was important for fresh top yield. The models accounted for 89, 70, and 89% of the variation in turnip top, root, and total fresh yields, re spectively (Table 2-17) in 2003. Table 2-21 shows the F-value for mustard yields. Fresh top and diagnostic leaf weights were affected by the linear and quadratic effects of N rates and plant population (p < 0.05) accounting for 83 and 87% of the vari ation. Fresh root and total plant were

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53 affected by the linear effect of N rates and pl ant population but total plant yield reflected the quadratic effect of N (p < 0.05). Inte raction effects were not observed in mustard yields planted in 2003. Models for fresh root and total plant explaine d 53 and 80% of the variation in yields. Dry wei ght models followed the same trend as fresh yield models, accounting for 69, 44, 69, and 89% for dry top, root, total plant, and diagnostic leaf, respectively in 2003. Turnip responses in 2003 were affected by the incidence of crown rot. Maximum fresh yields were predicted to be outside our treatment leve l ranges. In general, ridge analysis suggested that high plant population density and high N rates will produce the highest top and total plant yiel ds. In order to reach maxi mum root and diagnostic leaf yields low plant population densities and hi gh N rates should be researched further (Figures 2-17 to 2-24). Mustard fresh top and total reached their maximum around 8 plants m-2 and 160 kg N ha-1. Fresh root and diagnostic leaf yield analysis produced saddl e points, but ridge analysis suggested that high plant population densities and high N rates might result in highest root yields. Th e same type of analysis suggested that diagnostic leaf yields might increase at low plant population densities but with increasing N rate s (Figures 2-25 to 232). Mineral Concentration Results Nitrogen leaf concentrations indicated th at both crops responded to 56 kg N ha-1 but still did not reach sufficiency levels (Table 2-1) for that mineral in any treatment. Potassium leaf concentration was also very low. Tables 2-23 to 2-30 show mineral concentrations in turnip and mustard dia gnostic leaves over the 2 yr of the study. Nitrogen concentration in diagnos tic leaves for both crops incr eased as N rates increased,

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54 reaching maximum values when the Brassicas were planted at 2 plants m-2. Cubic and quadratic models presented a good fit for the data in 2002 and 2003 at this plant population density (Tables 2-31 and 2-32). The R2 values for models in both years were 86 and 97% for turnip and 87 and 92 for must ard, respectively. Nitrogen sufficiency levels were not reached in bot h years at any N rate for eith er of the crops (Table 2-1). In 2002, turnip leaf N concentration was pos itively correlated with leaf Mg, P, Na, Cu, Mn, and Zn concentrations (Table 2-33) For 2003, N was positively correlated with fresh top and total yields, P, Cu, Fe, Mn, and Zn (Table 2-34) but in both years N correlated negatively with Ca. For 2002 and 2003, N in mustard leaf was positively correlated with fresh top, root total plant yields and fres h and dry diagnostic weights. Leaf N concentration increased when Ca, Mg, Na, Cu, Mn, and Zn leaf concentration increased in 2002, but decreased when Fe con centration increased (Table 2-35). In 2003, N concentration was positively correlated with Na and Mn concentrations but was negatively correlated with Ca, K, P, and Fe leaf concentrations (Table 2-36). In Figure 2-33 it can be observed that pr edicted turnip leaf N concentration increased with N fertilization (p < 0.0 01) reaching a maximum around 56 to 112 kg N ha-1 in 2002 and outside the range of our e xperiment in 2003. Figure 2-33 shows maximum mustard leaf N concentration wa s reached in both years around 168 kg N ha-1. At the peak response, leaf N concentration in the diagnostic leaf was lower than the sufficiency levels for these crops (Tables 2-24 and 2-28) as reported by Mills and Jones (1996) and Hochmuth et al (1991) (Table 2-1). Turnip and mustard P leaf concentrations reached their maximum at the same N rates as N leaf concentrati on (Figure 2-34) showing respons es inside the range of the

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55 sufficiency range reported for this element in both years (Table 2-1). Cubic models fit (p < 0.05) P concentration in mustar d leaves for both years with R2 values over 78%. Turnip results in 2002 and 2003 also f it a cubic model (p < 0.05) with R2 values of 85 and 53%, respectively (Tables 2-31 and 2-32). In Tables 2-33 and 2-34, it can be observed that P in turnip was positively correlated with K, N, Mn, and Zn in both y ears, but showed a negative correlation with Fe in 2002 and with Na in 2003. Mustard P l eaf concentration increased as K, Cu, and Zn increased in both years. However, in 2003 P concentration increased as Fe concentrations decreased (Table 2-35 a nd 2-36). As Mill and Jones (1996) reported interaction among mineral are expe cted and some of them [NH4:K, N:S or K:(Ca+Mg)] may be speculated to be possibl e causes of turnip and mustard yield decreases at highest N rates. Figure 2-35 shows that maximum K concentr ation in diagnostic leaf was reached by N rates as low as 56 kg ha-1 but even those values were still outside the sufficiency ranges (Table 2-1). The study site was fert ilized following soil test recommendations (Table 2-2); therefore sufficiency ranges (T able 2-1) indicated by Mill and Jones (1996) for these crops may be higher than requi rements for greens growing under Floridas sandy soil conditions.

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56 (a) 0 400 800 1200 056112168224 Nitrogen rate, kg ha-1Fresh top yield, g m -2 2 4 6 8 Plants m-2, (b) -200 200 600 1000 1400 2468 Plant population, plants m-2Fresh top yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-17. Marginal quadratic polynomials fo r fresh turnip top yiel d as affected by (a) N rate and (b) plan t population (2003).

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57 Figure 2-18. Surface response for fresh turnip top yield as affected by N rate and plant population (2003).

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58 (a) 0 400 800 1200 1600 056112168224 Nitrogen rate, kg ha-1Fresh root yield, g m -2 2 4 6 8 Plants m-2, (b) 0 400 800 1200 1600 2468 Plant population, plants m-2Fresh root yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-19. Marginal quadrati c polynomials for fresh turnip r oot yield as affected by (a) N rate and (b) plan t population (2003).

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59 Figure 2-20. Surface response for fresh turnip root yield as affected by N rate and plant population (2003).

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60 (a) 0 400 800 1200 1600 2000 2400 2800 056112168224 Nitrogen rate, kg ha-1Fresh total plant yield, g m -2 2 4 6 8 Plants m-2, (b) 0 400 800 1200 1600 2000 2400 2800 2468 Plant population, plants m-2Fresh total plant yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-21. Marginal quadrati c polynomials for fresh turnip total plant yield as affected by (a) N rate and (b) plant population (2003).

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61 Figure 2-22. Surface response for fresh turnip total plant yield as affected by N rate and plant population (2003).

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62 (a) 5 10 15 20 056112168224 Nitrogen rate, kg ha-1Fresh diag. leaf yield, g leaf -1 2 4 6 8 Plants m-2, (b) 5 10 15 20 056112168224 Nitrogen rate, kg ha-1Fresh diag. leaf yield, g leaf -1 2 4 6 8 Plants m-2, Figure 2-23. Marginal quadratic polynomials fo r fresh turnip diagnostic (diag.) leaf yield as affected by (a) N rate and (b) plant population (2003).

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63 Figure 2-24. Surface response for fresh turnip diagnostic (diag.) leaf yield as affected by N rate and plant population (2003).

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64 (a) 0 400 800 1200 1600 2000 2400 2800 3200 3600 056112168224 Nitrogen rate, kg ha-1Fresh top yield, g m -2 2 4 6 8 Plants m-2, (b) 0 400 800 1200 1600 2000 2400 2800 3200 3600 2468 Plant population, plants m-2Fresh top yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-25. Marginal quadratic polynomials fo r fresh mustard top yield as affected by (a) N rate and (b) pl ant population (2003).

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65 Figure 2-26. Surface response for fresh mustard top yield as affected by N rate and plant population (2003).

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66 (a) 0 50 100 150 200 056112168224 Nitrogen rate, kg ha-1Fresh root yield, g m -2 2 4 6 8 Plants m-2, (b) 0 50 100 150 200 2468 Plant population, plants m-2Fresh root yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-27. Marginal quadratic polynomials fo r fresh mustard root yield as affected by N rate and plant population (2003).

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67 Figure 2-28. Surface response for fresh mustard root yield as affected by N rate and plant population (2003).

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68 (a) 0 400 800 1200 1600 2000 2400 2800 3200 3600 056112168224 Nitrogen rate, kg ha-1Fresh total plant yield, g m -2 2 4 6 8 Plants m-2, (b) 0 400 800 1200 1600 2000 2400 2800 3200 3600 2468 Plant population, plants m-2Fresh total plant yield, g m -2 0 56 112 168 224 N rate, kg ha-1, Figure 2-29. Marginal quadratic polynomial s for fresh mustard total plant yield as affected by (a) N rate and (b) plant population (2003).

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69 Figure 2-30. Surface response for fresh mustar d total plant yield as affected by N rate and plant population (2003).

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70 (a) 5 10 15 20 25 056112168224 Nitrogen rate, kg ha-1Fresh diag. leaf yield, g leaf -1 2 4 6 8 Plants m-2, (b) 5 10 15 20 25 056112168224 Nitrogen rate, kg ha-1Fresh diag. leaf yield, g leaf -1 2 4 6 8 Plants m-2, Figure 2-31. Marginal quadratic polynomials for fresh mustard diagnostic (diag.) leaf yield as affected by N rate and plant population (2003).

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71 Figure 2-32. Surface response for fresh mustard diagnostic (diag.) leaf yield as affected by N rate and plant population (2003).

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72Table 2-23. Mineral concentrations (Ca, Mg, and P) in turnip leaf for three (2002) and four (2003) populat ion densities and fi ve rates of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Calcium, g kg-1 0 15.0 15.2 15.2 15.1 17.8 20.8 20.5 20.4 19.8 56 14.4 14.7 12.4 13.9 14.9 14.6 16.7 14.2 15.1 112 14.0 14.6 12.5 13.7 14.6 16.0 17.0 14.1 15.4 168 13.7 12.4 13.8 13.7 18.1 17.4 20.0 17.9 18.3 224 14.5 13.7 13.7 14.0 14.1 16.7 15.8 19.0 16.3 Average 14.3 14.1 13.5 17.0 17.0 18.0 17.1 Magnessium, g kg-1 0 2.34 2.45 2.29 2.36 2.13 2.40 2.32 2.06 2.36 56 2.36 2.41 2.21 2.32 1.92 2.24 2.23 2.00 2.10 112 2.38 2.16 2.19 2.24 1.94 2.16 2.20 2.33 2.16 168 2.23 2.04 2.14 2.14 2.12 2.26 2.52 2.12 2.25 224 2.22 2.13 2.15 2.17 1.86 2.24 2.27 2.38 2.19 Average 2.31 2.24 2.20 1.99 2.26 2.31 2.18 Potassium, g kg-1 0 17.2 16.1 17.9 17.1 30.0 28.6 26.0 33.1 28.9 56 21.1 18.4 18.4 19.3 24.5 24.7 22.3 23.9 23.8 112 20.0 19.4 18.1 19.1 22.4 23.9 22.7 23.1 23.0 168 15.2 15.6 15.3 15.3 24.6 19.7 22.1 24.3 22.7 224 17.2 15.1 14.5 15.6 19.7 22.8 20.9 23.1 21.6 Average 18.1 16.9 16.8 23.8 23.9 22.8 25.5 LSD @p=0.05 for population (2002): Ca=N S; Mg=NS; K=1.27. LSD @p=0.05 for population (2003): Ca=N S; Mg=0.17; K=2.14 LSD @p=0.05 for nitrogen (2002): Ca=NS; Mg=0.15; K=1.63. LSD @p=0.05 for nitrogen (2003): Ca=2.78; Mg=NS; K=2.39

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73Table 2-24. Mineral concentrations (N, P, and Na) in turnip leaf for three (2002) and four (2003) populati on densities and fiv e rates of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Nitrogen, g kg-1 0 25.3 23.8 26.3 25.1 22.2 21.4 19.6 22.1 21.3 56 27.5 28.2 24.9 26.9 22.1 24.9 25.6 24.9 24.4 112 28.1 29.7 30.1 29.3 24.6 28.4 22.7 26.9 25.6 168 22.6 24.6 23.0 23.4 25.6 29.4 28.2 27.8 27.8 224 23.0 25.2 23.4 23.9 28.8 28.0 32.0 31.0 29.9 Average 25.3 26.3 25.6 24.7 26.4 25.5 26.6 Phosphorus, g kg-1 0 3.74 3.58 3.71 3.68 3.59 3.45 3.48 3.69 3.55 56 4.31 4.08 4.11 4.17 3.58 3.74 3.68 3.74 3.68 112 4.54 4.37 4.28 4.37 3.50 3.77 3.66 3.78 3.67 168 4.01 4.94 3.90 3.95 3.87 3.79 3.94 3.86 3.86 224 4.31 4.16 3.92 4.13 3.59 4.16 3.92 3.89 3.89 Average 4.18 4.03 3.98 3.62 3.78 3.74 3.79 Sodium, g kg-1 0 0.60 0.77 0.87 0.74 0.59 0.54 0.55 0.62 0.57 56 0.64 0.70 0.61 0.65 0.95 0.83 0.80 0.83 0.85 112 0.50 0.63 0.71 0.61 0.85 0.83 0.94 0.83 0.86 168 0.80 0.65 0.72 0.72 0.99 0.95 0.97 0.86 0.94 224 0.69 0.99 0.73 0.80 0.77 0.91 0.99 1.01 0.92 Average 0.65 0.75 0.73 0.83 0.81 0.85 0.83 LSD @p=0.05 for population (2002): N=NS; P=NS; Na=NS. LSD @p=0.05 for population (2003): N=NS; P=NS; Na=NS LSD @p=0.05 for nitrogen (2002): N=3.58; P=0.25; Na=0.14. LSD @p=0.05 for nitrogen (2003): N=2.33; P=0.17; Na=0.12

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74Table 2-25. Mineral concentrations (Cu, Fe, and Mn) in turnip leaf for three (2002) and four (2003) popula tion densities and f ive rates of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Copper, mg kg-1 0 3.40 2.80 3.60 3.27 3.50 3.75 3.50 3.50 3.56 56 3.80 4.40 3.20 3.80 3.75 4.00 3.75 4.00 3.88 112 4.00 3.80 3.60 3.80 3.50 3.75 4.00 3.75 3.75 168 3.20 4.60 3.20 3.67 4.00 4.00 4.00 3.75 3.94 224 3.20 3.40 2.80 3.13 4.00 4.00 4.00 4.25 4.06 Average 3.52 3.80 3.28 3.75 3.85 3.85 3.85 Iron, mg kg-1 0 140.0 158.0 136.0 144.7 100.0 125.0 100.0 110.0 111.3 56 162.0 200.0 140.0 167.3 97.5 110.0 97.5 87.5 98.1 112 144.0 140.0 116.0 133.3 97.0 115.0 87.5 95.0 98.1 168 156.0 138.0 120.0 138.0 92.5 142.5 112.5 105.0 113.1 224 110.0 106.0 110.0 108.7 90.0 110.0 100.0 82.5 95.6 Average 142.4 148.4 124.4 95.0 120.5 101.5 96.0 Manganese, mg kg-1 0 15.6 13.6 15.0 14.7 10.2 13.5 11.3 11.0 11.5 56 15.0 16.4 14.2 15.2 14.8 15.8 12.5 15.5 14.6 112 14.2 16.6 13.2 14.7 14.3 15.3 15.3 17.8 15.6 168 14.2 15.0 12.6 13.9 25.0 23.5 21.8 21.3 22.9 224 15.6 15.4 17.0 16.0 24.5 22.3 22.5 21.5 22.7 Average 14.9 15.4 14.4 17.8 18.1 16.7 17.4 LSD @p=0.05 for population (2002): Cu=NS; Fe=20.77; Mn=NS. LSD @p=0.05 for popul ation (2003): Cu=NS; Fe=17.46; Mn=NS LSD @p=0.05 for nitrogen (2002): Cu=NS; Fe=26.81; Mn=2.41. LSD @p=0.05 for nitrogen (2003): Cu=0.35; Fe=NS; Mn=NS

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75Table 2-26. Mineral concentrati on (Zn) in turnip leaf for three (2002) and four (2003) popul ation densities and five rates of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Zinc, mg kg-1 0 31.0 28.8 32.6 30.8 29.5 26.8 25.3 25.3 26.7 56 34.6 30.6 32.4 32.5 24.8 35.3 29.3 28.8 28.4 112 41.4 43.8 44.2 43.1 27.3 29.5 26.5 32.5 30.4 168 33.0 35.6 36.4 35.0 28.8 34.0 34.0 32.8 32.9 224 32.8 33.6 35.4 33.9 26.8 34.5 34.5 37.0 33.7 Average 34.6 34.5 36.2 29.1 31.4 29.9 31.3 LSD @ p = 0.05 for population (2002): Zn = NS. LSD @ p = 0.05 for popul ation (2003): Zn = NS LSD @ p = 0.05 for nitrogen (2002): Zn = 5.22. LSD @ p = 0.05 for nitrogen (2003): Zn = 4.99

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76Table 2-27. Mineral concentrations (Ca, Mg, and K) in mustard leaf for three (2002 ) and four (2003) popula tion densities and f ive rates of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Calcium, g kg-1 0 15.6 16.0 17.8 16.4 15.3 21.3 16.8 25.2 19.6 56 15.2 14.7 14.7 14.9 19.9 21.2 16.8 19.7 19.4 112 14.0 14.0 15.6 14.5 16.6 15.9 16.1 20.4 17.2 168 13.9 13.5 13.4 13.6 16.2 13.6 17.8 18.2 16.5 224 13.0 14.2 14.9 14.0 15.6 14.5 16.4 14.9 15.4 Average 14.4 14.5 15.3 16.7 17.3 16.8 19.7 Magnesium, g kg-1 0 1.87 1.91 2.09 1.96 1.76 2.28 1.92 2.50 2.11 56 2.01 2.06 1.94 2.01 2.14 2.47 2.19 2.34 2.28 112 1.94 1.99 2.26 2.07 1.98 1.99 2.10 2.31 2.10 168 2.11 2.07 2.11 2.10 2.04 1.87 2.15 2.28 2.08 224 2.07 2.06 2.21 2.12 2.07 5.60 2.30 1.87 2.96 Average 2.00 2.02 2.13 2.00 2.84 2.13 2.26 Potassium, g kg-1 0 19.3 17.3 20.3 18.9 20.5 25.5 22.5 28.0 24.1 56 16.6 17.1 18.1 17.3 21.3 21.1 18.6 19.6 20.1 112 17.0 17.2 17.3 17.2 18.8 20.2 19.5 19.0 19.3 168 18.2 16.4 18.3 17.7 18.8 18.0 17.3 16.6 17.7 224 17.4 16.1 18.6 17.3 14.9 13.4 17.8 16.1 15.5 Average 17.7 16.8 18.5 18.8 19.6 19.1 19.8 LSD @p=0.05 for population (2002): Ca=NS; Mg=0.10; K=1.54. LSD @ p=0.05 for popul ation (2003): Ca=1.24; Mg=0.13; K=NS LSD @p=0.05 for nitrogen (2002): Ca=2.36; Mg=1.06; K=NS. L SD @ p = 0.05 for nitrogen (2003): Ca=2.63; Mg=NS; K=2.36

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77Table 2-28. Mineral concentrations (N, P, and Na) in mustard leaf for three (2002) and four (2003) popula tion densities and fi ve rates of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Nitrogen, g kg-1 0 16.9 15.0 16.2 16.0 14.4 19.3 18.2 17.5 17.3 56 19.8 55.8 19.5 31.7 15.8 15.8 15.7 17.0 16.0 112 20.1 17.5 23.8 20.4 17.4 20.1 17.0 16.1 17.6 168 24.8 22.8 26.6 24.7 16.8 15.5 19.0 18.9 17.6 224 22.8 22.8 26.6 24.4 17.0 18.0 21.1 18.2 18.6 Average 20.9 26.8 22.7 16.3 17.7 18.2 17.5 Phosphorus, g kg-1 0 5.03 4.34 4.75 4.71 3.51 4.05 3.66 4.30 3.88 56 4.70 4.58 4.54 4.62 4.12 3.98 3.60 3.99 3.92 112 4.77 4.42 4.58 4.59 3.82 3.98 3.69 3.75 3.81 168 5.17 4.69 4.79 4.88 3.86 3.68 3.85 3.88 3.82 224 4.72 4.76 4.79 4.76 3.80 3.39 3.76 3.50 3.61 Average 4.89 4.96 4.56 3.82 3.81 3.71 3.89 Sodium, g kg-1 0 0.51 0.45 0.53 0.50 0.44 0.62 0.47 0.42 0.49 56 0.57 0.57 0.67 0.60 0.49 0.54 0.85 0.70 0.65 112 0.60 0.66 0.78 0.68 0.62 0.66 0.61 0.78 0.67 168 0.82 0.61 0.77 0.73 0.88 0.75 0.85 1.04 0.88 224 0.76 0.67 0.78 0.74 1.00 0.95 1.02 0.89 0.96 Average 0.65 0.59 0.71 0.69 0.70 0.76 0.77 LSD @p=0.05 for population (2002): N= NS; P= 0.21; Na= 0.07. LSD @p = 0.05 for population (2003 ): N = 1.89; P = NS; Na = NS LSD @p=0.05 for nitrogen (2002): N= 15.50; P= 0.27; Na= 0.09. LSD@ p = 0.05 for nitrogen (2003): N= 2.12; P= 0.27; Na= 0. 16

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78Table 2-29. Mineral concentrations (Cu, Fe, and Mn) in mustard leaf for three ( 2002) and four (2003) popul ation densities and five rates of nitrogen. Year 2002 2003 Nitrogen Plant m-2 Plant m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Copper, mg kg-1 0 2.40 2.40 2.40 2.40 3.00 3.25 3.00 3.25 3.13 56 3.00 2.40 2.60 2.67 3.00 3.00 2.75 2.75 2.88 112 2.80 2.40 2.80 2.67 3.00 3.25 3.50 3.00 3.19 168 3.40 3.00 3.00 3.13 3.00 2.75 3.00 2.50 2.81 224 2.60 2.80 2.80 2.73 2.50 2.50 3.50 2.50 2.75 Average 2.84 2.60 2.72 2.90 2.95 3.15 2.80 Iron, mg kg-1 0 298.0 298.0 216.0 270.7 410.0 237.5 247.5 225.0 280.0 56 136.0 106.0 100.0 114.0 280.0 257.5 205.0 182.5 231.3 112 118.0 92.0 90.0 100.0 227.5 215.0 210.0 197.5 212.5 168 100.0 102.0 84.0 95.3 195.0 240.0 207.5 162.5 201.3 224 128.0 112.0 88.0 109.3 225.0 282.5 192.5 162.5 215.6 Average 156.0 142.0 115.6 267.5 246.5 212.5 186.0 Manganese, mg kg-1 0 16.6 16.2 17. 4 16.7 18.0 18.3 15.8 16.0 17.0 56 15.0 15.4 13.8 14.7 19.5 20.3 17.8 17.5 18.8 112 15.4 14.8 16.4 15.5 19.0 20.0 19.3 23.0 20.3 168 18.2 18.2 17.4 17.9 19.3 19.3 22.3 23.0 20.9 224 20.6 19.0 17.2 18.9 28.5 26.8 28.0 19.0 25.6 Average 17.2 16.7 16.4 20.9 20.9 20.6 19.7 LSD @p=0.05 for population (2002): Cu=NS; Fe=29.01; Mn=NS. LSD @p=0.05 for popul ation (2003): Cu=NS; Fe=56.47; Mn=NS LSD @p=0.05 for nitrogen (2002): Cu= 0.35; Fe=34.45; Mn=1.71. LSD @p=0.05 for nitrogen (2003): Cu=NS; Fe=64.25; Mn=3.04

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79Table 2-30. Mineral concentra tion (Zn) in mustard leaf for three (2002) and four (2003) popula tion densities and five rates of nitrogen. Year 2002 2003 Nitrogen Plants m-2 Plants m-2 Rate 2 4 6 Average 2 4 6 8 Average kg ha-1 Zinc, mg kg-1 0 28.6 26.4 25.4 26.8 23.8 27.0 26.0 27.5 26.1 56 28.0 25.2 24.0 25.7 27.5 26.0 21.3 23.0 24.4 112 30.8 24.2 24.6 26.5 23.5 25.8 23.8 27.8 25.2 168 31.8 27.2 28.4 29.1 25.8 24.0 24.0 22.5 24.1 224 30.6 27.4 30.4 29.5 26.0 23.5 25.5 20.5 23.9 Average 30.0 26.1 26.6 25.3 25.3 24.1 24.3 LSD @ p = 0.05 for population (2002): Zn = 2.55. LSD @ p = 0.05 for popul ation (2003): Zn = NS LSD @ p = 0.05 for nitrogen (2002): Zn = 3.29. LSD @ p = 0.05 for nitrogen (2003): Zn = NS

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80 Table 2-31. Multiple regression models for turnip leaf mineral concentration at 2 plants m-2, 2002-2003. Response Model R2 (p 0.05) 2002 Nitrogen, g kg-1 25.1 + 0.13X1 0.0014X1 2 + 4*10-6 X1 3 0.86 Phosphurus, g kg-1 3.7 + 0.02X1 0.0002X1 2 + 6*10-7X1 3 0.85 Potassium, g kg-1 17.1 + 0.18X1 0.002X1 2 + 610-6X1 3 0.95 Calcium, g kg-1 15.1 0.02X1 + 7*10-5X1 2 0.90 Magnesium, g kg-1 2.3 + 0.002X1 3*10-5X1 2 + 7*10-8X1 3 0.85 Iron, mg kg-1 142.3 + 0.19X1 + 0.0005X1 2 9*10-6X1 3 0.78 Manganese, mg kg-1 15.6 0.005X1 0.0001X1 2 + 8*10-7X1 3 0.99 Copper, mg kg-1 3.4 + 0.02X1 0.0002X1 2 + 5*10-7X1 3 0.82 Zinc, mg kg-1 30.4 + 0.18X1 0.0013X1 2 + 2*10-6X1 3 0.62 Sodium, g kg-1 0.62 0.003X1 0.0002X1 2 + 5*10-7X1 3 0.82 2003 Nitrogen, g kg-1 22.5 + 0.004X1 + 0.0001X1 2 0.97 Phosphurus, g kg-1 3.6 0.006X1 + 9*10-5X1 2 3*10-7X1 3 0.53 Potassium, g kg-1 28.8 0.08X1 + 0.0002X1 2 0.86 Calcium, g kg-1 18.0 0.14X1 + 0.002X1 2 5*10-6X1 3 0.84 Magnesium, g kg-1 2.14 0.009X1 + 0.001X1 2 3*10-7X1 3 0.93 Iron, mg kg-1 99.8 0.02X1 9*10-5X1 2 0.97 Manganese, mg kg-1 10.8 0.01X1 + 0.001X1 2 3*10-6X1 3 0.88 Copper, mg kg-1 3.53 + 0.001X1 + 6*10-6X1 2 0.64 Zinc, mg kg-1 29.4 0.16X1 + 0.0018X1 2 5*10-6X1 3 0.97 Sodium, g kg-1 0.61 + 0.007X1 -4*10-5 X1 2 + 5*10-8X1 3 0.76 X1: N rates (0, 56, 112, 168, 224 kg ha-1)

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81 Table 2-32. Multiple regression models for mustard leaf mineral concentration at 2 plants m-2, 2002-2003. Response Model R2 (p 0.05) 2002 Nitrogen, g kg-1 17.2 + 0.005X1 + 0.0005X1 2 2*10-6X1 3 0.87 Phosphurus, g kg-1 5.05 0.017X1 + 0.002X1 2 6*10-7X1 3 0.90 Potassium, g kg-1 19.3 0.09X1 + 0.0009X1 2 2*10-6X1 3 0.99 Calcium, g kg-1 15.7 0.012X1 + 2*10-6X1 2 0.95 Magnesium, g kg-1 1.88 + 0.002X1 3*10-6X1 2 0.69 Iron, mg kg-1 295.3 3.9X1 + 0.02X1 2 5*10-5X1 3 0.98 Manganese, mg kg-1 16.7 0.06X1 + 0.0006X1 2 1*10-6X1 3 0.99 Copper, mg kg-1 2.45 + 0.004X1 + 5*10-5X1 2 3*10-7X1 3 0.65 Zinc, mg kg-1 28.5 0.04X1 + 0.0008X1 2 3*10-6X1 3 0.97 Sodium, g kg-1 0.52 0.002X1 + 4*10-5X1 2 1*10-7X1 3 0.90 2003 Nitrogen, g kg-1 14.4 + 0.03X1 0.0001X1 2 0.92 Phosphurus, g kg-1 3.5 + 0.02X1 0.0002X1 2 + 4*10-7X1 3 0.78 Potassium, g kg-1 20.6 + 0.01X1 0.0002X1 2 0.91 Calcium, g kg-1 15.5 + 0.13X1 0.001 X1 2 + 4*10-6X1 2 0.80 Magnesium, g kg-1 1.8 + 0.01X1 96*10-57X1 2 + 2*10-7X1 3 0.83 Iron, mg kg-1 407.1 2.54X1 + 0.008X1 2 0.99 Manganese, mg kg-1 17.9 + 0.09X1 0.0014X1 2 + 5*10-6X1 3 0.99 Copper, mg kg-1 2.9 + 0.003X1 2*10-5X1 2 0.85 Zinc, mg kg-1 24.1 + 0.08X1 0.0009X1 2 + 3*10-6X1 3 0.35 Sodium, g kg-1 0.4 0.002X1 + 4*10-5X1 2 1*10-7X1 3 0.99 X1: N rates (0, 56, 112, 168, 224 kg ha-1)

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82 In general, K concentration increased with increases in P leaf concentration and decreases in Na concentrations (Tables 2-33, 2-34, 2-35, and 2-36). Figures 2-36, 2-38 and 2-41 show that Ca, Fe, and Zn leaf concen trations in general were affected by N rates in both 2002 and 2003, reaching sufficiency le vels reported by Mills and Jones (1996) and Hochmuth et al. (1991) (T able 2-1) for both crops. Magnesium and Cu leaf concentrations did not reach the sufficiency levels (Table 2-1) but are close to them (F igure 2-37 and 2-40). Manganese leaf concentration for both turnip an mustard (Figure 2-39) differed from the sufficiency levels reported by Mill and Jones (1996) and Hochmuth, et al. (1991). Figu re 2-42 shows that turnip and mustard Na leaf concentrations were higher than t hose suggested by Mills and Jones (1996); and Hochmuth et al. (1991) (Table 2-1). Summary Analyses indicated that N rates and plan t population dens ities are important factors in predicting turnip and must ard yields, with expected impr ovements in growth at their optimum levels. These results are consistent w ith data reported by othe rs in the literature where individual effects of N rates and plant population densities ha ve been reported to affect yields (Alt et al ., 2000a; 2000b; Maynard et al ., 2002; Moore and Guy, 1997; Momoh and Zhou, 2001). Response surface methodology and multiple regression models are important techniques to model growth in vegetables. Those techniques were used in this study to find the relationship between population density and N rates affecting turnip and mustard yields and to establish a set of recommendations in order to maximize yields.

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83 (a) 10 20 30 40 50 056112168224 N rates, Kg ha-1Leaf N, g kg -1 (b) 10 20 30 40 50 056112168224 N rates, Kg ha-1Leaf N, g kg-1 2002 2003 Mill and Jones, 1996 N sufficiency levels (turnip: 35.0 to 50.0 g kg-1, mustard: 29.7 to 38.5 g kg-1). Hochmuth et al., 1991 N sufficiency levels (turnip: 30.0 to 50.0 g kg-1). Figure 2-33. Leaf N concentra tion in turnip (a) and mustar d (b) planted at 2 plants m-2 as affected by N rates (2002-2003).

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84(a) 2 4 6 8 056112168224 N rates, Kg ha-1Leaf P, g kg -1 (b) 2 4 6 8 056112168224 N rates, Kg ha-1Leaf P, g kg -1 2002 2003 Mill and Jones, 1996 P sufficiency levels (turnip: 3.3 to 6.0 g kg-1, mustard: 4.1 to 6.4 g kg-1). Hochmuth et al., 1991 P sufficiency levels (turnip: 2.5 to 8.0 g kg-1). Figure 2-34. Leaf P concentra tion in turnip (a) and mustar d (b) planted at 2 plants m-2 as affected by N rates (2002-2003).

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85Table 2-33. Correlations coefficien ts between yield and leaf mineral concentration for turnip (2002). TOP FRESH ROOT FRESH TOTAL FRESH FRESH DIAG LEAF DRY DIAG LEAF Ca Mg K N P Na Cu Fe Mn Zn TOP FRESH YIELD 1 ROOT FRESH YIELD 0.86 *** 1 TOTAL FRESH 0.96 *** 0.97 *** 1 FRESH DIAG LEAF YIELD 0.29 0.38 *** 0.35 ** 1 DRY DIAG LEAF YIELD 0.04 NS 0.21 + 0.13 NS 0.84 *** 1 Ca -0.13 NS -0.11 NS -0.13 NS -0.06 NS 0.01 NS 1 Mg -0.23 -0.21 + -0.23 -0.04 NS 0.13 NS 0.54 *** 1 K -0.16 NS -0.11 NS -0.14 NS 0.09 NS 0.10 NS 0.13 NS 0.16 NS 1 N 0.05 NS 0.05 NS 0.05 NS 0.05 NS 0.14 NS -0.20 + 0.26 0.18 NS 1 P 0.09 NS O.18 NS 0.14 NS -0.15 NS 0.36 ** -0.10 NS 0.12 NS 0.40 *** 0.58 *** 1 Na 0.07 NS 0.09 NS 0.08 NS -0.15 NS 0.01 NS -0.10 NS 0.17 NS -0.47 *** 0.29 0.03 NS 1 Cu 0.04 NS 0.05 NS 0.05 NS 0.33 ** 0.26 -0.14 NS -0.01 NS 0.17 NS 0.30 ** 0.18 NS -0.09 NS 1 Fe -0.11 NS -0.07 NS -0.09 NS 0.20 + 0.18 NS 0.09 NS 0.13 NS 0.15 NS -0.17 NS -0.25 -0.29 0.40 *** 1 Mn 0.03 NS 0.06 NS 0.05 NS 0.04 NS 0.10 NS 0.01 NS 0.11 NS -0.19 NS 0.53 *** 0.26 0.31 ** 0.15 NS -0.07 NS 1 Zn 0.20 + 0.15 NS 0.18 NS 0.13 NS 0.16 NS -0.16 NS -0.04 NS 0.07 NS 0.45 *** 0.53 *** 0.16 NS 0.18 NS -0.15 NS 0.31 ** 1 +, *, **, *** Significant at the 0.1, 0.05, 0.01, and 0.001 level, respectively. NS no significant.

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86Table 2-34. Correlations coefficien ts between yield and leaf mineral concentration for turnip (2003). TOP FRESH ROOT FRESH TOTAL FRESH FRESH DIAG LEAF DRY DIAG LEAF Ca Mg K N P Na Cu Fe Mn Zn TOP FRESH 1 ROOT FRESH 0.71 *** 1 TOTAL FRESH 0.93 *** 0.92 *** 1 FRESH DIAG LEAF 0.14 NS 0.32 ** 0.24 1 DRY DIAG LEAF 0.13 NS 0.36 ** 0.26 0.76 *** 1 Ca -0.17 NS -0.21 + -0.21 + 0.10 NS 0.02 NS 1 Mg -0.08 NS 0.13 NS -0.11 NS -0.24 -0.34 ** 0.55 *** 1 K -0.30 ** -0.50 *** -0.43 *** -0.20 + -0.32 ** 0.14 NS 0.14 NS 1 N 0.32 ** 0.15 NS 0.26 -0.09 NS -0.11 NS -0.20 + 0.12 NS -0.02 NS 1 P 0.13 NS -0.04 NS 0.06 NS -0.20 + -0.27 -0.08 NS 0.26 0.42 *** 0.66 *** 1 Na 0.36 ** 0.43 *** 0.42 *** 0.27 0.29 ** 0.20 + 0.15 NS -0.47 *** -0.15 NS -0.22 1 Cu -0.10 NS -0.08 NS -0.10 NS -0.22 + -0.24 -0.06 NS 0.08 NS 0.08 NS 0.46 *** 0.61 *** -0.32 ** 1 Fe -0.25 -0.22 -0.26 -0.26 -0.17 NS 0.13 NS 0.20 + 0.13 NS 0.19 + 0.24 -0.30 ** 0.37 *** 1 Mn 0.33 ** 0.40 *** 0.39 *** 0.27 0.23 -0.12 NS 0.05 NS -0.22 0.59 *** 0.43 *** 0.07 NS 0.31 ** 0.14 NS 1 Zn 0.05 NS -0.15 NS -0.05 NS -0.11 NS -0.22 + -0.04 NS 0.18 NS 0.30 ** 0.58 *** 0.73 *** -0.28 0.54 *** 0.21 + 0.50 *** 1 +, *, **, *** Significant at the 0.1, 0.05, 0.01, and 0.001 level, respectively. NS no significant.

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87Table 2-35. Correlations coefficien ts between yield and leaf minera l concentration for mustard (2002). TOP FRESH ROOT FRESH TOTAL FRESH FRESH DIAG LEAF DRY DIAG LEAF Ca Mg K N P Na Cu Fe Mn Zn TOPFRESH 1 ROOT FRESH 0.59 *** 1 TOTAL FRESH 0.99 *** 0.61 *** 1 FRESH DIAG LEAF 0.45 *** 0.40 ** 0.46 *** 1 DRY DIAG LEAF 0.04 NS 0.20 0.04 NS 0.85 *** 1 Ca -0.28 -0.13 NS -0.28 -0.25 -0.13 NS 1 Mg 0.54 *** 0.45 *** 0.54 *** 0.23 -0.02 NS 0.16 NS 1 K 0.43 ** 0.03 NS 0.43 ** 0.14 NS -0.19 + -0.02 NS 0.22 + 1 N 0.47 *** 0.41 ** 0.47 *** 0.47 *** 0.37 ** 0.40 ** 0.28 -0.13 NS 1 P 0.14 NS 0.14 NS 0.14 NS 0.21 + 0.18 NS 0.32 + 0.16 NS 0.31 0.11 NS 1 Na 0.32 + 0.39 ** 0.32 ** 0.34 0.27 -0.05 NS 0.26 -0.18 NS 0.51 *** 0.19 NS 1 Cu 0.16 NS 0.20 + 0.16 NS 0.35 0.31 + 0.12 NS 0.15 NS 0.06 NS 0.23 0.57 *** 0.21 + 1 Fe -0.50 *** -0.56 *** -0.51 *** -0.53 *** -0.36 0.17 NS -0.36 -0.03 NS -0.57 *** -0.21 + -0.41 ** -0.23 1 Mn 0.30 ** 0.12 NS 0.23** 0.02 NS -0.10 NS -0.31 0.50 *** 0.06 NS 0.34 ** 0.01 NS 0.09 NS 0.01 NS 0.10 NS 1 Zn 0.20 + 0.08 NS 0.20 + 0.34 0.22 + -0.16 NS 0.22 + 0.27 0.25 0.52 *** 0.09 NS 0.59 *** -0.04 NS 0.32 1 +, *, **, *** Significant at the 0.1, 0.05, 0.01, and 0.001 level, respectively. NS no significant.

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88Table 2-36. Correlations coefficien ts between yield and leaf minera l concentration for mustard (2003). TOP FRESH ROOT FRESH TOTAL FRESH FRESH DIAG LEAF DRY DIAG LEAF Ca Mg K N P Na Cu Fe Mn Zn TOP FRESH 1 ROOT FRESH 0.57 *** 1 TOTAL FRESH 0.99 *** 0.63 *** 1 FRESH DIAG LEAF 0.33 ** 0.12 NS 0.32 ** 1 DRY DIAG LEAF 0.19 0.06 NS 0.18 NS 0.86 *** 1 Ca -0.04 NS 0.05 NS -0.04 NS -0.12 NS -0.08 NS 1 Mg 0.01 NS 0.04 NS 0.02 NS 0.08 NS 0.07 NS -0.03 NS 1 K -0.23 -0.14 NS -0.23 -0.32 ** -0.36 ** 0.44 *** -0.04 NS 1 N 0.43 *** 0.27 0.43 *** 0.59 *** 0.48 *** -0.37 *** 0.13 NS -0.63 *** 1 P 0.03 NS 0.10 NS 0.04 NS 0.05 NS -0.06 NS 0.61 *** -0.02 NS 0.63 *** -0.23 1 Na 0.24 0.10 NS 0.24 0.24 0.13 NS -0.11 NS 0.18 NS -0.34 ** 0.60 *** -0.08 NS 1 Cu -0.06 NS -0.17 NS -0.07 NS 0.002 NS -0.07 NS 0.23 -0.11 NS 0.30 ** -0.16 NS 0.30 ** -0.14 NS 1 Fe -0.28 ** -0.18 NS -0.29 ** -.0.07 NS 0.17 NS 0.06 NS -0.02 NS -0.06 NS -0.20 + -0.24 -0.24 -0.03 NS 1 Mn 0.02 NS 0.06 NS 0.03 NS 0.20 + 0.23 0.09 NS 0.25 -0.35 ** 0.51 *** -0.02 NS 0.49 *** -0.15 NS 0.23 + 1 Zn -0.12 NS -0.09 NS -0.12 NS 0.01 NS 0.04 NS 0.43 *** 0.05 NS 0.41 ** -0.14 NS 0.52 *** 0.01 NS 0.23 0.35 ** 0.42 *** 1 +, *, **, *** Significant at the 0.1, 0.05, 0.01, and 0.001 level, respectively. NS no significant.

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89(a) 10 20 30 40 50 056112168224 N rates, Kg ha-1Leaf K, g kg -1 (b) 10 20 30 40 50 056112168224 N rates, Kg ha-1Leaf K, g kg -1 2002 2003 Mill and Jones, 1996 K sufficiency levels (turnip: 35.0 to 50.0 g kg-1, mustard: 31.8 to 43.9 g kg-1). Hochmuth et al., 1991 K sufficiency levels (turnip: 25.0 to 40.0 g kg-1). Figure 2-35. Leaf K concentra tion in turnip (a) and mustar d (b) planted at 2 plants m-2 as affected by N rates (2002-2003).

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90(a) 5 10 15 20 25 30 35 40 056112168224 N rates, Kg ha-1Leaf Ca, g kg -1 (b) 5 10 15 20 25 30 35 40 056112168224 N rates, Kg ha-1Leaf Ca, g kg -1 2002 2003 Mill and Jones, 1996 Ca sufficiency levels (turnip: 15.0 to 40.0 g kg-1, mustard: 15.2 to 25.1 g kg-1). Hochmuth et al., 1991 Ca sufficiency levels (turnip: 8.0 to 15.0 g kg-1). Figure 2-36. Leaf Ca concentration in turnip (a) and mustard (b) pl anted at 2 plants m-2 as affected by N rates (2002-2003).

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91(a) 0 1 2 3 4 5 6 056112168224 N rates, Kg ha-1Leag Mg, g kg -1 (b) 1 2 3 4 5 6 056112168224 N rates, Kg ha-1Leaf Mg, g kg -1 2002 2003 Mill and Jones, 1996 Mg sufficiency levels (turnip: 3.0 to 10.0 g kg-1, mustard: 2.1 to 3.6 g kg-1). Hochmuth et al., 1991 Mg sufficiency levels (turnip: 2.5 to 6.0 g kg-1). Figure 2-37. Leaf Mg concentration in turnip (a) and mustard (b) planted at 2 plants m-2 as affected by N rates (2002-2003).

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92(a) 0 100 200 300 400 500 056112168224 N rates, Kg ha-1Leaf Fe, mg kg -1 (b) 0 100 200 300 400 500 056112168224 N rates, Kg ha-1Leaf Fe, mg kg -1 2002 2003 Mill and Jones, 1996 Fe sufficiency levels (turnip: 40.0 to 300.0 g kg-1, mustard: 76.0 to 209.0 g kg-1). Hochmuth et al., 1991 Fe sufficiency levels (turnip: 30.0 to 100.0 g kg-1). Figure 2-38. Leaf Fe concentr ation in turnip (a) and mustar d (b) planted at 2 plants m-2 as affected by N rates (2002-2003).

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93(a) 5 15 25 35 45 55 056112168224 N rates, Kg ha-1Leaf Mn, mg kg -1 (b) 5 15 25 35 45 55 056112168224 N rates, Kg ha-1Leaf Mn, mg kg -1 2002 2003 Mill and Jones, 1996 Mn sufficiency levels (turnip: 40.0 to 250.0 g kg-1, mustard: 40.0 to 52.0 g kg-1). Hochmuth et al., 1991 Mn sufficiency levels (turnip: 30.0 to 100.0 g kg-1). Figure 2-39. Leaf Mn concentration in turnip (a) and mustard (b) planted at 2 plants m-2 as affected by N rates (2002-2003).

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94(a) 2 4 6 8 10 056112168224 N rates, Kg ha-1Leaf Cu, mg kg -1 (b) 2 4 6 8 10 056112168224 N rates, Kg ha-1Leaf Cu, mg kg -1 2002 2003 Mill and Jones, 1996 Cu sufficiency levels (turnip: 6.0 to 25.0 g kg-1, mustard: 3.0 to 5.0 g kg-1). Hochmuth et al., 1991 Cu sufficiency levels (turnip: 5.0 to 10.0 g kg-1). Figure 2-40. Leaf Cu concentration in turnip (a) and mustard (b) pl anted at 2 plants m-2 as affected by N rates (2002-2003).

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95(a) 15 20 25 30 35 40 056112168224 N rates, Kg ha-1Leaf Zn, mg kg -1 (b) 15 20 25 30 35 40 056112168224 N rates, Kg ha-1Leaf Zn, mg kg -1 2002 2003 Mill and Jones, 1996 Zn sufficiency levels (turnip: 20.0 to 250.0 g kg-1, mustard: 20.0 to 36.0 g kg-1). Hochmuth et al., 1991 Zn sufficiency levels (turnip: 20.0 to 40.0 g kg-1). Figure 2-41. Leaf Zn concentration in turnip (a) and mustard (b) pl anted at 2 plants m-2 as affected by N rates (2002-2003).

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96(a) 0 200 400 600 800 1000 1200 056112168224 N rates, Kg ha-1Leaf Na, mg kg -1 (b) 0 200 400 600 800 1000 1200 056112168224 N rates, Kg ha-1Leaf Na, mg kg -1 2002 2003 0 Mill and Jones, 1996 Na sufficiency levels (turnip: 361.0 g kg-1, mustard: 193.0 to 417.0 g kg-1). Hochmuth et al., 1991 Na sufficiency levels (turnip: no data). Figure 2-42. Leaf Na concentration in turnip (a) and mustard (b) pl anted at 2 plants m-2 as affected by N rates (2002-2003).

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97 Figure 2-35 shows that maximum K concentr ation in diagnostic leaf was reached by N rates as low as 56 kg ha-1 but even those values were still outside the sufficiency ranges (Table 2-1). The study site was fert ilized following soil test recommendations (Table 2-2); therefore sufficiency ranges (T able 2-1) indicated by Mill and Jones (1996) for these crops may be higher than requi rements for greens growing under Floridas sandy soil conditions. The highest mustard fresh and dry top, root, and total yi elds were also found at 168 kg N ha-1 and 6 plants m-2. Fresh and dry diagnostic leaf yields for both crops reached maximum values at 168 kg N ha-1 and 2 plants m-2. Turnip responses in 2003 were affected by the incidence of crown rot. Maximum fresh yields were predicted to be outside our treatment leve l ranges. In general for 2003 data, N rates greater than 224 kg N ha-1 and plant populations gr eater than 8 plants m-2 would produce the highest top and total plant yields. Maximum diagnostic leaf yields occurred at low plant population densities. Mustard fresh top and total plant yields reached their maximum around 8 plants m-2 and 160 kg N ha-1. Fresh root and diagnostic leaf yield analysis produced saddle points, but ridge analys is suggested that N rates greater than 224 kg N ha-1 and plant populations gr eater than 8 plants m-2 might result in highest root yields. Th e same type of analysis suggested that diagnostic leaf yields might increase at low plant population densities but with increasing N rates. More evaluations on plant population densiti es should be pursued. Despite the fact that sufficiency levels of some elements were not reached at the peak yield response, leaf testing could be usef ul in diagnosis of mine ral needs. Potassium and N leaf concentration were very low comp ared with sufficiency levels reported in

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98 Table 2-1 by Mills and Jones (1996) and Hochmuth et al. (1 991), suggesting that further research should be done under Florida condi tions for revising e ither fertilization recommendations or tissue analyses for those Brassicas Data in this study questions the validity of using plant minera l sufficiency ranges in turnip and mustard plant tissue as reported by Mill and Jones (1996) and Hochmuth et al. (1991) to be applied to Floridas growing conditions. Mill and Jones (1996) do point out that numerous factors can impact plant mineral composition including genetics, plant tissue type, age and position on the plant, climate, soil properties, soil amendmen ts, and pathological effects. At the same time it appears that the only data cited by Hochmuth et al. (1991) to support their suggested mineral sufficiency ranges for turn ip was from a paper by Brantley (1961). The research by this author was conducte d in the Georgia piedmont area on a Lloyd sandy loam soil. Brantley (1961) only repor ted yield and N concentration values and gave no data on concentrations of ot her minerals in the plant tissue. Similarities between our study and that of Brantley (1961) are the use of Shogoin cultivar of turnip, both experiments were plan ted in the fall of the year and tissue was analyzed by the microKjedahl method. Differences included the following: Georgia piedmont soil vs. a Florida coastal plain so il; N applied broadcast before planting in Georgia vs. side dress with split applications in Florid a; N rates up to 101 kg ha-1 in Georgia and up to 224 kg ha-1 in Florida. Row width of 0. 25 m in Georgia and 0.75 m in Florida; for N analysis whole tops from two plants in each treatment were collected 10 d before harvest in Georgia vs. six youngest matu re leaves from each plot 7 to 9 d before harvest in Florida.

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99 As noted, there were numerous differences between the two sites but possibly the primary difference was due to soil type Soils of the Georgia piedmont are predominantly upland, well drained red in color with CECs of 6 to 12 cmol kg-1. They are low in P but higher in K, Ca, and Mg due to significant quantities of feldspar minerals compared to the Coastal Plain soils (P lank, 1978; R. N. Gallaher, personal communication, April, 2005). Soils of the Coastal Plain have sandy surfaces and CECs of less than 6 cmol kg-1. The soil test for turnip in 2003 shown in Table 2-2 reported CECs of 6.4 cmol kg-1. Soils in the Florida coastal pl ain are naturally infertile, due in part to the lack of almost no feldspar minerals and they are generally low in clay content compared to soils of the Piedmont (Plank, 1978). Figures 2-43 and 2-44 show a compar ison between the Georgia experiment (Piedmont soil) (Brantley, 1961) and our Florid a experiment (only in the range of 0 to 112 kg N ha-1) for fresh top yield and tissue N concentration. It can be noted that the yield and N concentrations in Georgia for the check plot without any N is 56 and 40 % higher, respectively for fresh top and tissue N compared to the check plots without any N applied to the Florida experiment. The Pied mont soil would have had more favorable properties for turnip growth compared to the Florida soil and this is reflected by comparing the data (Figures 2-43 and 2-44) wh ere both locations res ponded positively to N fertilizer application in the range of 0 to 100 kg ha-1.

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100 400 800 1200 1600 2000 056112 Nitrogen rate, kg ha-1Fresh top yield, g m-2 Figure 2-43. Fresh turnip top yield in the Ge orgia piedmont experiment (Brantley, 1961) at 0, 34, 68, and 102 kg N ha-1 ( ) and Florida experiment ( )at 0, 56, and 112 kg N ha-1. 20 30 40 50 056112 Nitrogen rate, kg ha-1N concentration, g kg-2 Figure 2-44. Turnip N concentration in th e Georgia piedmont experiment (Brantley, 1961) at 0, 34, 68, and 102 kg N ha-1 ( ) and Florida experiment at 0, 56, and 112 kg N ha-1 ( ).

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101 The response curve for the better soil of the piedmont was much greater than the Florida soil. Yield response differences can partially be expl ained by the wider row spacing in the Florida study which would likely have had a lower leaf area index compared to the narrow rows in Georgia. However, large difference between the two sites for tissue N concentration remains a myster y that must be related in part to soil property difference between the two sites. Hopefully, this compar ison and discussion will emphasize the fact that there is a need to have research under Florida conditions in regard to mineral sufficiency ranges in pl ant tissue of Brassicas It should also emphasize that you surely would not want to send out recommendations for Floridas sandy soil based on a single study conducted on a soil as drastically di fferent as those of the Georgia piedmont.

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102 CHAPTER 3 SWEET CORN YIELD, PLANT NUTRITI ON, AND NEMATODES AS AFFECTED BY COWPEA MULCH Introduction Corn is a crop of New World origin. It is one of the most important crops in providing human sustenance, directly or i ndirectly as feed for domestic animals (Jugenheimer, 1976, Hochmuth et al., 2002). Co rn is a major crop on cultivated land of the United States and has become an importa nt crop because of its productivity and great adaptability (Duncan, 1975; Fraz ier, 1983). Florida ranks nu mber one nationally in the production and value of fresh market sweet corn, typically accounting for approximately 25% of both national sweet corn production and of U.S. cash receipts for fresh sales. A total of 247 million kg of fresh sweet corn, valued at $122 million, was produced on 15,060 ha in Florida during the years 2000 and 2001 (FASS, 2001). Corn typically planted as a spring crop has s hown little success when planted in the fall in Florida (Bustillo and Gallaher, 1989). Several studies have been co nducted to develop genotypes adapted to specific climatic condition such as photoperiod or temper ature (Breuer et al., 1976; Hunter et al., 1977; Robe rts and Struckmeyer, 1938). Nitrogen is the nutrient that most limits corn yield. Low cost of N fertilizer and the lack of a yield loss from over application of N have lead to a management approach emphasizing generous applications (Br ouder et al., 2000; Vanotti and Bundy, 1994). Nitrogen management in corn is important because excessive N can result in contamination of the environment (NO3-N leaching) and inadequa te N can result in yield

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103 and profit losses to the grower (Doerge, 2002; Mamo et al., 2003; Scharf et al., 2002; Toth and Fox, 1998). The challenge to research scientists is to fi nd the best management practices for N use on agricultural crops. Some factors to consider in making management decisions for corn are timing of N application, deficiency levels in the plant (Scharf et al., 2002), and site-specific variati on which identifies areas in the field where corn responds to specific N rates with a minimum of NO3-N losses to the environment (Mamo et al., 2003; Katsvairo et al., 2003; Zh ao et al., 2000; Kranz and Kanwar, 1995). The accuracy of N fertilizer recommendations ha s implications for residual N in the soil profile and water quality (Lory and Scharf 2003). Sweet corn mineral sufficiency concentrations are shown in Table 3-1 as reported by Mill and Jones (1996) and Hochmuth et al. (1991). Table 3-1. Mineral sufficiency levels for m acro and micronutrients for sweet corn at late tasseling. Plant mineral Mill and Jones, 1996 Hochmuth et al., 1991 Macronutrients (g kg-1) N 25.0-35.0 15-25.0 P 2.5-4.0 2.0-5.0 K 15.0-28.0 12.0-20.0 Ca 6.0-25.0 3.0-6.0 Mg 2.0-8.0 1.5-4.0 Micronutrients (mg kg-1) Fe 50.0-350.0 30.0-100.0 Mn 20.0-300.0 20.0-100.0 Cu 5.0-25.0 4.0-10.0 Zn 20.0-150.0 20.0-40.0 Na No data No data Bustillo and Gallaher (1989) concluded that low temperatures during the grain filling period is one of the major critical lim iting factors in the fall to obtain high grain yield. Residue mulches protect the soil from wind and water erosion, but also delay soil

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104 warming in the spring (Swan et al., 1996). Cool er soil temperatures tr anslate into slower seed germination, reduced uptake of non-mob ile soil nutrients (esp ecially P), and less vigorous early crop growth (Riedell et al., 2000 ) so timing is important for those crops that prefers warmer temperatures. Rivero (1997) has shown that cation exch ange capacity (CEC), organic carbon, and available P and K in soil as well as dry matter yield, and N, P, and K concentration in the leaf tissue of corn increases with mulch applications. Statistically significant increases were observed when sorghum ( Sorghum bicolor L.) and sunn hemp ( Crotalaria juncea L.) mulches were used. Crop production was very dependent on soilresidue interaction but their effects were for a short period of time. It is important to note that a reduction in the N fertilization rate for corn is required to maximize profit in the presence of le gumes (Roberts et al., 1998). Some studies suggested that corn yield was weakly corre lated to significant changes in N fertilizer application rates from the optimum recomme nded rate (Ferguson et al., 1991; Rasse et al., 1999). Nematodes injure sweet corn by reducing co rn root growth, stalk height and stalk diameter. In most cases, plants weakened by nematodes produce smaller and fewer ears, and plants that are heavily parasitized may produce no ears, resulting in up to 100% crop loss. General symptoms of nematode inju ry include stunting, wilting, and nutrient deficiency symptoms, often in patches throughout the field due to irre gular distri bution of nematodes. Some nematodes affecting sweet corn in Florida include sting ( Belonolaimus spp.), stubby-root ( Trichodorus spp., Paratrichodorus spp.), lesion ( Pratylenchus spp.), and occasionally root-knot ( Meloidogyne spp.). Corn yiel d reduction from these

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105 nematodes is generally higher in sandier so ils (Crhistie, 1959; Noling, 1997; 1999). Ring ( Criconemella spp.) and spiral ( Helicotylenchus spp.) are commonly associated with corn as well, but cause little damage to the crop. Sting nematode has been shown to be seve rely damaging to sweet corn in Florida when the crop is grown on sandy soils. Feedi ng damage results in reduced root systems. Plants experience stunting, premature wilting and leaf chlorosis, and plant death may occur. Often, sting nematodes are found togeth er with stubbyroot nematodes. Constant moisture, such as that encount ered under irrigation, may c ontribute to higher populations of the sting nematode. Sting nematode damage to sweet corn may range from limited to total yield loss (Chris tie, 1959; Shurtleff, 1980; Rhoades, 1986). Stubbyroot nematodes are one of the most important pests of Florida sweet corn, especially in sandier soils. Thei r life cycle, spent entirely in the soil, is relatively short, and populations can build up quickly. Although rarely killed by st ubbyroot nematode attack, corn plants are highly susceptible, and yields can therefore be significantly reduced (Christie, 1959; Shurtleff, 1980). The lesion nematode is an important pest of corn, as well as numerous other crops. Corn is tolerant to low populations of th ese nematodes, but at high population levels, stunting, chlorosis, nutrient deficiency sy mptoms, and blackened roots may be evident (Christie, 1959; Shurtle ff, 1980; MacGowan, 1981). Root-knot nematodes enter the host plant root as immature larvae and settle within to feed. At the feeding site, their secretions cause the surrounding pl ant cells to enlarge, producing the characteristic ga lls associated with root-knot attack (Christie, 1959). Gall

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106 sizes on corn and other grasses are usually very small. In addition to expending the plants resources, the gall tissue is more susceptible to secondary infe ctions such as root rots. The objectives of the research reported in th is chapter were to determine the effect of five cowpea ( Vigna ungiculata (L.) Walp.) mulch rates on sweet corn yield and plant nutrition, nematode populations (sting, stubby-root, lesion, a nd root-knot), as well as the effect of nematode densities on sweet corn yield. Material and Methods A field experiment was conducted with sw eet corn in the fall of 2003 at the University of Florida Statistical Design Fi eld Teaching Lab in Ga inesville, Florida on a Millhopper fine sand soil (loamy, silice ous, semiactive, hyperthermic Grossarenic Paleudults) (USDA-NRCS, 2003). Sweet corn hybrid R was planted at 45 seeds per 7.2 m of row, thinned to 35 plants per row to obtain a fi nal population of 64,000 plants ha-1. Individual plots were four rows of 7.2 m in length and 0.76 m wide. Weeds were controlled by mechanical cultivation a nd by hand. A minimum of 30 mm water was applied each week, either from rainfall or ov erhead sprinkler irrigation. Five cowpea mulch rates were applied as treatments to the corn crop in a random ized complete block design with four replications 'Cream 40' cowpea vari ety, harvested at early pod formation, was used as fresh mulch. Before treatments were applied cowpea exploratory samples were analyzed for N concentration, (21.5 g N kg-1). Table 3-2 shows cowpea mulch rates and their equi valent N supplied. A soil sample was collected prior to initi ating the experiment and analyzed for Mehlich I extractable minera ls (Mehlich, 1953), pH, and lim e requirements (Adams and Evans, 1962; Hanlon et al., 1996) (Table 3-3). Analysis of the soil showed that K was the only element required to be corrected in the soil. Therefore, K2SO4:MgSO4 (K-Mag)

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107 was used as a broadcast application over the site at a rate of 100 kg K ha-1. The labeled rate of LannateLV {S-methyl-N-[(m ethyl-carbamoyl)oxy]thioacetimidate}, manufactured by E.I. DUPONT DE NEMOUR S and CO. (Inc.) was applied twice to control leaf-feeding insects. Table 3-2. Green Cream 40 cowpea mulch treatments. Mulch Rates Equivale nt N rate applied 0 kg m-2 0 kg N ha-1 2.2 kg m-2 67 kg N ha-1 4.4 kg m-2 134 kg N ha-1 6.6 kg m-2 201 kg N ha-1 8.8 kg m-2 268 kg N ha-1 Six recently expanded leaves ( 5th leaf from the top of the plant) were collected at late tasseling and early silking to measure leaf area us ing a Licor LI-3100 Area Meter and fresh weight. Samples were dried at 70 C, re-weighed and analyzed for essential minerals (N, P, K, Ca, Mg, Cu, Fe, Mn, and Zn) and Na. Total ear, fancy ear, stalk, and total plant yields were harvested from the two center rows of the experimental plot. Total and fancy ears were counted and weighed fresh. Ears were graded as fancy following the USDA standards, which is defined as those ea rs that are well developed, free of insects and diseases, fairly well filled with plump and milky kernels and well covered with fresh husks with the length of each cob not less than 15.24 cm (USDA, 1997). Soil samples were taken at 0, 21, 42, 63, and 84 days after planting (DAP) for nematode analysis. Each soil sample consis ted of six cores (2.54-cm diameter x 20.32cm deep) collected in a diagonal pattern fr om the two center rows of the plot and

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108 combined into a plastic bag for transport. Sample points were marked at every sampling date so that the next samples were taken in a different spot. Table 3-3. Soil test report and standard fertilization r ecommendation (University of Florida, IFAS Extension Service, 2002). Soil property pH 7.2 BpH 7.9 OM (%) 1.70 CEC, (cmol kg-1) 5.47 Macronutrients (mg kg-1) Phosphorus 64.5 Potassium 38.5 Magnesium 48.0 Calcium 822.0 Macronutrients (mg kg-1): Iron 7.8 Manganese 3.4 Copper 0.3 Zinc 0.8 Sodium 2.0 Recommendations : Lime 0.0 Nitrogen (kg N ha-1) 224.0 Phosphorus (kg P2O5 ha-1) 0.0 Potassium (kg K2O ha-1) 90.0 In the laboratory, a 100 cm3 soil sub-sample was removed for nematode extraction using a modified sieving and centrifugation procedure (Jenkins, 1964). Nematodes were identified and counted under a dissecting micr oscope, and nematode count data were logtransformed before conducting an alysis of variance (ANOVA). Statistical analysis of the data was perf ormed using SAS (SAS Institute, 2000). Analysis of variance showed that mulch rate s affected yields as well as plant mineral

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109 concentration, and suggested that a quad ratic polynomial (QP) would provide a good approximation of the true relationship between mulch and both responses. Treatment means averaged over repetitions we re used. To evaluate the association between response variables a nd treatments, variation be tween experimental units receiving the same treatment was not need ed (Gomez and Gomez, 1984). A second order polynomial regression model [1] was fitted to th e yield and plant nutrition data using the general linear model procedure, PROC GLM, included in SAS (SAS Institute, 2000). Means were estimated using the LSMEAN option in PROC GLM. 2 2 1 0X X Y [1] For the statistical model in [1], Y denotes responses (yield and plant nutrition) and X denotes mulch rates. The regression co efficient vector is represented by = ( 0, 1, 2) and stands for the error term. In order to determine economically optimal levels of N fertilization (provided by cowpea mulch) for corn, a linear-plateau (LP) model was fit to total and fancy ear yields and diagnostic l eaf weight, as well as, to N, K, Zn, and Na concentrations using the NLIN procedure in SAS. In general a LP function may be expressed as: otherwise Y X Y X Ym m, ,1 0 1 0 [2] where Y is the response variable (in this cas e, yield and plant nutrition), X is the mulch rate, is a random error term, and Ym is the maximum yield, also referred to as the plateau yield. The parameters 0 and 1 are intercept and slope, respectively. Maximum yield is often defined as

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110 m mX Y1 0 [3] where Xm is the level of mulch necessary to reach the plateau. The point (Ym, Xm) can be defined as the knot point at wh ich the response and plateau po rtions are splined. For the modeling the star ting point of Ym was arbitrarily chosen to be the yield reached at the IFAS recommendation for N (224 kg N ha-1), which corresponded to 7.4 kg cowpea m-2. Nematode populations affected by mulch rate were analyzed as repeated measures on time and response surface analysis. A quadratic model has been used for determining the impact of two factors on agronomic re sponse variables (Ga llaher et al., 1972; Gallaher et al., 1975a). However, marginal analysis showed that mulch rates and sampling date had a cubic effect on nemat ode population. The MIXED procedure in SAS was used to perform analysis of varian ces on nematode population, with mulch rates (X1), sampling dates (X2), and their interaction (X1X2) as fixed effects and block nested in sampling date as random effect. Cubic polynomial regression mode ls [4] were fitted using response surface methodology with the RSREG procedure in SAS, with Y, , and defined as in model [1]. Final models were selected using the stepwise backward selection method and examined to determin e adequacy in predicting the response. 2 1 7 3 2 6 3 1 5 2 2 4 2 1 3 2 2 1 1 0X X X X X X X X Y [4] Surface response three-dimensional plots were generated using MATLAB (MathWork, 2001). Optimum conditions for QP models were investigated through partial differentiation of [4], fi nding potential critical points as was reporte d in chapter 2. The knot point in LP models was used as th e critical point to report recommendations.

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111 Results and Discussion Yield Results Analysis of variance, in Table 3-4, s hows that the number of total ears was not affected by mulch rates. Other sweet corn vari ables such as number of fancy ears, total and fancy ear, stalk, and to tal plant yields, and area a nd weight of diagnostic leaf responded to increasing cowpea mulch rates (T ables 3-4 and 3-5). Table 3-6 shows the averaged responses for number of total and fa ncy ears, total and fancy ear yields. Stalk, and total plant yields, and area and weight of di agnostic leaf are reported in Table 3-7. Number of total ears was not impacted by mulch (Figure 3-1) compared to number of fancy ears, with an averag e of two more fancy ears per m2 when control treatment (no mulch) was compared to the highest mulch ra te (p = 0.0002). Fanc y ear yield increased steadily with mulch rates and did not reach a maximum even at the highest rate, while total ear yield increased with increases of mulch rates up to 716 g m-2 at approximately 6.6 kg cowpea m-2 where it tended to level off (Figure 3-2). Stalk and to tal plant yields followed the same pattern as total ear yield (F igure 3-3). Diagnostic leaf area and weight also responded positively to mulch rates. Figures 3-4 and 3-5 show that when the treatment control was compar ed with the highest mulch rate differences of 143 cm3 leaf-1 and 5 g leaf-1 were found for leaf area and weight, respectively. In general, the QP regression equations fit yi eld responses, with R2 value greater than 92%. The LP model was significant (p < 0.05) for total and fanc y ear yields and for diagnostic leaf weight, with R2 values greater than 97% (Tables 3-8 and 3-9).

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112 Table 3-4. Analysis of variance for sweet co rn number and yield of total and fancy ears as affected by five rates of cowpea mulch. Sweet corn ears Source of variation df Total Fancy ear m-2 g m-2 ear m-2 g m-2 Total 24 --------Replicate 4 --------Mulch 4 NS *** ** *** Error 16 --------Coefficient of Variation, % 13.1 14.7 44.3 43.2 ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant. Table 3-5. Analysis of variance for sweet co rn affected by five rates of cowpea mulch. Source of variation df Total plant weight Stalk weight Diagnostic leaf area Diagnostic leaf, weight g m-2 g m-2 cm3 leaf-1 g leaf-1 Total 24 --------Replicate 4 --------Mulch 4 *** *** *** *** Error 16 --------Coefficient of Variation, % 12.5 14.5 6.5 6.8 *** Significant at the 0.001 level Table 3-6. Sweet corn yield mean s affected by five mulch rates. Sweet corn ears Mulch rate Total Fancy kg m-2 ear m-2 g m-2 ear m-2 g m-2 0 4.6 236.6 0.10 17.6 2.2 5.5 458.0 0.76 125.2 4.4 5.4 608.2 1.44 239.2 6.6 5.7 715.6 1.98 339.6 8.8 5.4 673.8 2.10 371.2 LSD@ p=0.05: Number total ear = NS; Total ear weight= 119.0 ; Nu mber Fancy ear= 0.71; Fancy ear weight= 120.5;

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113 Table 3-7. Sweet corn yield mean s affected by five mulch rates. Mulch rate Total plant weight Stalk weight Diagnostic leaf area Diagnostic leaf weight kg m-2 g m-2 g m-2 cm3 leaf-1 g leaf-1 0 706.0 469.6 374.6 8.8 2.2 1031.0 572.6 441.4 11.0 4.4 1458.0 849.8 473.8 12.4 6.6 1719.6 1004.0 510.0 13.2 8.8 1591.2 917.4 517.4 13.7 LSD@ p=0.05: Plant = 212.3; Stalk = 148.5; Leaf area = 36.4; Fresh leaf = 1.04. Table 3-8. Quadratic models for sweet corn yields affected by mulch rates. Response Regression model R2 (p 0.05) Total ear, g m-2 230.7 + 125.4X 8.4X2 0.99 Fancy ears, number m-2 0.09 + 0.39X 0.02X2 0.99 Fancy ears, g m-2 10.6 + 63.40X 2.44X2 0.99 Total plant, g m-2 656.2 + 251.02X 15.82X2 0.97 Stalk, g m-2 425.5 + 125.5X 7.41X2 0.92 Leaf area, cm3 leaf-1 376.2 + 31.03X 1.70X2 0.99 Fresh Diag. leaf g leaf-1 8.9 + 1.05X 0.06X2 0.99 X: mulch rates Maximum stalk and plant yields, calculat ed from QP models were 957 and 1652 g m-2, respectively. These maximums were reached around 8 kg cowpea m-2, close to the IFAS recommendation for N of 224 kg N ha-1, corresponding to 7.4 kg cowpea m-2. However, for the same variables LP models estimated critical points at higher mulch

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114 rates, indicating that those vari ables may plateau at rates higher than the range tested in this study. 0 1 2 3 4 5 6 7 02.24.46.68.8 Cowpea mulch rate, kg m-2Number of ears m -2 Fancy ears Total ears Figure 3-1. Number of fanc y and total ears as affected by five cowpea mulch rates. 0 200 400 600 800 02.24.46.68.8 Cowpea mulch rate, kg m-2 Ear yield, g m -2 Fancy ears Total ears Figure 3-2. Total and fancy ear yields as affected by five cowpea mulch rates.

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115 400 700 1000 1300 1600 02.24.46.68.8 Cowpea mulch rate, kg m-2Plant and stalk yields, g m -2 Total plant stalk Figure 3-3. Total plant and stalk yields as affected by five cowpea mulch rates. 5 10 15 20 25 02.24.46.68.8 Cowpea mulch rate, kg m-2Diag. leaf yield, g leaf -1 Figure 3-4. Fresh diagnostic (diag.) leaf yields as affected by five cowpea mulch rates.

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116 200 300 400 500 600 02.24.46.68.8 Cowpea mulch rate, kg m-2Diag. leaf area, cm 3 leaf -1 Figure 3-5. Diagnostic (diag.) leaf area as affected by five cowpea mulch rates. 0 200 400 600 800 02.24.46.68.8 Cowpea mulch rates, kg m-2Total ear yield, g m -2 LP QP Figure 3-6. Quadratic and linea r-plateau models for total ear yields as affected by five cowpea mulch rates.

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117 0 100 200 300 400 500 02.24.46.68.8 Cowpea mulch rates, kg m-2Fancy ear yield, g m -2 LP QP Figure 3-7. Quadratic and linea r-plateau models for fancy ear yields as affected by five cowpea mulch rates. 5 10 15 02.24.46.68.8 Cowpea mulch rates, kg m-2Diag. leaf yield, g leaf -1 LP QP Figure 3-8. Quadratic and lin ear-plateau models for fresh diagnostic leaf yields as affected by five cowpea mulch rates. The QP and LP models showed similar f its for total and fancy ears and diagnostic leaf yields with maximums being reached at rates close to IFAS recommendation for corn growing in Florida (Figur es 3-6, 3-7, and 3-8).

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118 Table 3-9. Linear-Plateau models for sw eet corn yields affected by mulch rates. Response Regression model R2 (p 0.05) Total ear, g m-2 otherwise X if X Y 7 694 3 5 5 84 5 248 0.99 Fancy ear, g m-2 otherwise X if X Y 4 426 4 9 9 41 2 34 0.97 Fresh diag. leaf, g leaf-1 otherwise X if X Y 7 13 8 6 66 0 2 9 0.97 Mineral Nutrition Results One advantage of applying mulches to crops is nutrient fertilization. Results from the analysis of variance showed that N, K, Zn and Na leaf concentrations were affected by cowpea mulch (Tables 3-10 and 3-11). Nitr ogen concentration sufficiency levels reported by Mill and Jones (1996) and Hochmu th, et al. (1991) (Table 3-1) were predicted by the QP and LP models to be reached at mulch rates around 7.2 kg cowpea m-2 (Figure 3-9). In Figures 3-10, 3-11, and 3-12 it can be observed that K, Zn, and Na leaf concentrations increased with increases in mulch rates (Tables 3-12 and 3-13). However, all of these minerals were in the sufficiency level ranges shown in Table 3-1 even at rates

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119 of 0 kg cowpea m-2 (Mill and Jones, 1996 and Hochmuth et al., 1991). This increase in nutrients as mulch rate increased could be explained by the plants uptake of more nutrients from the soil solution than they need; this excess is referred to as luxury consumption (Mill and Jones, 1996) Table 3-10. Analysis of varian ce for N, P, K, Ca, and Mg leaf concentrations as affected by five rates of cowpea mulch. Source of variation df N P K Ca Mg g kg-1 g kg-1 g kg-1 g kg-1 g kg-1 Total 24 ----------Replicate 4 --------Mulch 4 *** NS ** NS NS Error 16 ----------Coefficient of Variation, % 8.7 5.0 8.4 11.2 8.5 Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant. Table 3-11. Analysis of variance for Fe, Mn, Zn Cu, and Na as affected by five rates of cowpea mulch. Source of variation df Fe Mn Zn Cu Na mg kg-1 mg kg-1 mg kg-1 mg kg-1 mg kg-1 Total 24 ----------Replicate 4 ----------Mulch 4 NS NS NS Error 16 ----------Coefficient of Variation, % 16.2 17.9 32.7 20.4 13.7 Significant at the 0.05 level. NS, no significant. Quadratic polynomial regression equations fo r N, K, Zn, and Na leaf concentration showed a good fit with R2 values over 89%. The LP mode ls were significant (p < 0.05) for N, K, and Zn with R2 values over 98% (Tables 3-14 and 3-15 and Figures 3-10, 3-11, and 3-12). Maximums for these minerals were predicted by the QP models outside the

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120 mulch rate range in this study. Similarly, LP models predicted maximums outside the range for K, and Zn. For leaf N concentrati on the LP models plateau occurred at 7.2 kg cowpea ha-1 with a maximum of 30.5 g N kg-1. Table 3-12. Sweet corn N, P, K, Ca, and Mg leaf concentrations affected by five mulch rates. Mulch rate N P K Ca Mg kg m-2 g kg-1 g kg-1 g kg-1 g kg-1 g kg-1 0 16.6 4.2 24.9 2.37 2.1 2.2 21.3 4.2 26.8 2.52 2.0 4.4 26.3 4.0 28.1 2.50 1.9 6.6 28.6 4.1 29.2 2.49 2.0 8.8 30.5 4.2 31.7 2.49 2.1 LSD@ p=0.05: N = 2.9; P = NS; K = 3.2; Ca = NS; Mg = NS. 10 20 30 40 02.24.46.68.8 Cowpea mulch rates, kg m-2Leaf N, g kg -1 LP QP Mill and Jones, 1996 N sufficiency levels (25.0 to 35.0 g kg-1). Hochmuth et al., 1991 N sufficiency levels (15.0 to 25.0 g kg-1). Figure 3-9. Quadratic and linea r-plateau models for leaf N c oncentration as affected by five cowpea mulch rates.

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121 10 20 30 02.24.46.68.8 Cowpea mulch rates, kg m-2Leaf K, g kg -1 LP QP Mill and Jones, 1996 K sufficiency levels (15.0 to 28.0 g kg-1). Hochmuth et al., 1991 K sufficiency levels (12.0 to 20.0 g kg-1). Figure 3-10. Quadratic and Linear-Plateau mode ls for leaf K concentration as affected by five cowpea mulch rates. 10 20 30 40 02.24.46.68.8 Cowpea mulch rates, kg m-2Leaf Zn, mg kg -1 LP QP Mill and Jones, 1996 Zn sufficiency levels (20.0 to 150.0 g kg-1). Hochmuth et al., 1991 Zn sufficiency levels (20.0 to 40.0 g kg-1). Figure 3-11. Quadratic and linear-plateau models for leaf Zn concentration as affected by five cowpea mulch rates.

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122 Table 3-13. Sweet corn Fe, Mn, Zn, Cu, and Na leaf concentrations affected by five mulch rates. Mulch rate Fe Mn Zn Cu Na kg m-2 mg kg-1 mg kg-1 mg kg-1 mg kg-1 mg kg-1 0 96.0 9.0 14.2 3.6 23.0 2.2 88.0 8.6 15.8 3.6 24.0 4.4 92.0 8.4 19.2 4.4 27.0 6.6 92.0 7.8 23.6 3.4 29.0 8.8 102.0 7.6 26.4 3.8 36.0 LSD@ p=0.05: Fe = NS; Mn = NS; Zn = 8.7; Cu = NS; Na = 0.05. Table 3-14. Regression models for N, K, Zn, and Na leaf concentrations as affected by mulch rates. Response Regression model R2 (p 0.05) Nitrogen, g kg-1 16.50 + 2.67X 0.12X2 0.99 Potassium, g kg-1 25.1 + 0.60X + 0.015X2 0.98 Zinc, mg kg-1 13.89 + 1.02X 0.05X2 0.98 Sodium, mg kg-1 23.17 0.02X + 0.16X2 0.98 X: mulch rate. Fresh diagnostic leaf wei ghts responded to cowpea mulch rates following the same pattern as N leaf concentrat ion, with maximum yield p eaking between 6.6 and 8.8 kg cowpea m-2. Correlation analysis in Table 3-16 showed positive and significant associations between fresh di agnostic leaf weight and tota l and fancy ear number (r = 0.76 and 0.77, respectively) and yield (r = 0. 52 and 0.87, respectively). Table 3-16 shows that N concentration was also positively corr elated with number and yield of fancy ear, total ear yield, and fresh diagnostic leaf weight (r = 0.79, 0.81, 0.84, and 0.84, respectively). The same variables were positively correlated with K, Zn, and Na leaf

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123 concentrations (Table 3-16). These results indi cated that leaf testi ng would be useful in the diagnosis of mineral needs, especially N, for sweet corn and could be used as a good predictor of corn yields. Nematode Results Repeated measurement analyses of varian ce were conducted to de termine the effect of cowpea mulch and time in DAP for ring, sting, stubby-root, spir al, lesion, and rootknot nematode populations (Table 3-17). On ly ring, stubby-root, and spiral nematode populations were impacted by cowpea mulch ra tes (Table 3-17), but the trend of the response was not clear from this study. Moreover, all nematode populations changed substantially during the crop s eason. Ring, sting, stubby-root, and spiral nematode mean populations increased with time up to 63 DAP followed by a decreased population at the end of the season (Table 3-18). These nema todes are external parasites; hence their populations would increase where large masses of roots are available. In contrast, lesion and root-knot nematode populations are distribut ed in different patterns given the fact that they enter into the root at some stage of their life cy cle. The lesion population was not important enough to cause any damage to corn plants. However, after an initial average population of 24 nematodes per 100 cm3 of soil, root-knot nematodes were reduced to 20 and 16 nematodes per 100 cm3 of soil at 21 and 42 DAP, respectively. At 63 DAP, the root -knot nematode population increased to 93 nematodes per 100 cm3 of soil, decreasing to 63 nematodes per 100 cm3 of soil when the crop season was ending (Table 3-18). Root-knot initial population (Pi), sampled at 0 DAP, and final population (Pf), sampled at 84 DAP, were positively correlated with number of total ears (Table 3-22). In general, Pi was positively correlated with Pf. Ring and root-knot Pi were positively

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124 correlated with spir al and root-knot Pf (Table 3-23). Sting and stubby-root Pf affected number of total ears but not the other respons es. Figure 3-19 shows decreases in number of total ears when sting Pf increased. Stubby-root Pf and number of total ears per m2 increased indicating that vigorous plants su stained higher nematode populations (Figure 3-20). Table 3-15. Linear-plateau models for N, K, and Zn leaf concentration as affected by mulch rates. Response Regression model R2 (p 0.05) Nitrogen, g kg-1 otherwise X if X Y 5 30 2 7 86 1 05 17 0.99 Potassium, g kg-1 otherwise X if Y 6 31 2 9 0.73X 24.94 0.98 Zinc, mg kg-1 otherwise X if X Y 6 27 7 9 46 1 4 13 0.98 Cubic polynomials were used to fit nema tode populations to marginal mulch rates and sampling dates (Tables 3-19 and 3-20). Surface response methodology was used to summarize the marginal analysis for ring, stubby-root, and root-knot nematode in multiple models including mulch rates and sampling dates (Table 3-21).

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125Table 3-16. Correlation coefficients between yield and leaf mineral concentration in sweet corn affected by five cowpea mulch rates. Number Fancy Ears Fancy ear Number total ear Total ear Fresh diag. leaf Ca Mg K N P Na Cu Fe Mn Zn Number Fancy Ears 1 Fancy ear, g m-2 0.99 *** 1 Number total ear 0.22 NS 0.22 NS 1 Total ear, g m-2 0.88 *** 0.89 *** 0.56 ** 1 Fresh diag. leaf, g m-2 0.76 *** 0.77 *** 0.52 ** 0.87 *** 1 Ca, g kg-1 0.18 NS 0.20 NS 0.28 NS 0.25 NS 0.18 NS 1 Mg, g kg-1 -0.05 NS -0.01 NS 0.23 NS -0.06 NS -0.04 NS 0.58 ** 1 K, g kg-1 0.62 *** 0.64 *** 0.21 NS 0.63 *** 0.64 *** 0.19 NS -0.03 NS 1 N, g kg-1 0.79 *** 0.81 *** 0.31 NS 0.84 *** 0.84 *** 0.19 NS 0.07 NS 0.64 ** 1 P, g kg-1 -0.18 NS -0.15 NS 0.23 NS -0.11 NS -0.23 NS 0.29 NS 0.62 ** 0.08 NS -0.18 NS 1 Na, mg kg-1 0.42 0.44 0.14 NS 0.45 0.62 ** -0.07 NS 0.14 NS 0.73 *** 0.66 ** 0.04 NS 1 Cu, mg kg-1 0.12 NS 0.14 NS 0.18 NS 0.19 NS 0.13 NS 0.27 NS 0.12 NS -0.18 NS 0.11 NS -0.00 NS -0.22 NS 1 Fe, mg kg-1 0.07 NS 0.08 NS -0.11 NS 0.00 NS -0.02 NS -0.17 NS 0.16 NS 0.25 NS 0.20 NS 0.18 NS 0.23 NS -0.02 NS 1 Mn, mg kg-1 -0.26 NS -0.26 NS 0.12 NS -0.21 NS -0.27 NS 16 NS 0.21 NS -0.38 + -0.44 0.27 NS -0.53 ** 0.04 NS -0.03 NS 1 Zn, mg kg-1 0.53 ** 0.55 ** 0.63 ** 0.69 ** 0.64 ** 0.43 0.36 0.55 0.56 ** 0.28 NS 0.54 ** 0.22 NS -0.08 NS -0.20 NS 1 +, *, **, *** Significant at the 0.1, 0.05, 0.01, and 0.001 level, respectively. NS no significant.

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126 Table 3-17. Analysis of variance for ring, st ing, stubby-root, spiral, lesion, and root-knot nematode population (nematodes cm3) affected by five rates of cowpea mulch and sampling time (days after planting). Source of variation df Ring Sting Stubbyroot Spiral Lesion Root-knot Nematodes per 100 cm3 soil Mulch rate (X1) 4 ** NS ** NS NS Sampling date (X2) 4 NS *** NS ** X1X2 16 NS NS NS NS NS NS 2 block(sampling) 34.19 0.63 10.09 218.41 0 650.65 2 296.42 1.48 57.89 201.82 2.9 3146.40 Significant at the 0.05 level. ** Significant at the 0.01, and level. *** Significant at the 0.001 level. NS = not significant. In general, maxima in nematode population levels were not reached in the factor level ranges used in this st udy. Ridge analysis obtained from PROC RSREG (SAS Institute, 2000) indicated th at ring, stubby-root, and root -knot maximum populations may increase with time at mulch rates be tween 4.4 and 6.6 kg of cowpea mulch m-2. Individual mulch rate and sa mpling date analysis for ring, stubby-root, and root-knot population are shown in Figures 3-13, 3-15, and 3-17, while surface plots are shown in Figures 3-14, 3-16, and 3-18. Figure 3-14 showed that ri ng populations were greater when 0 kg of mulch was applied, but increased steadily with time. A surface plot of the stubby-root population agreed w ith the ridge analysis with the maximum being reached later in the crop season and w ith the highest mulch rates (F igure 3-16). Maximum rootknot population was reached at about 63 DAP and 6.6 kg of cowpea mulch m-2.

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127 Table 3-18. Nematode popul ations affected by five rates of cowpea mulch. Mulch Sampling date (days after planting) Rate 0 21 42 63 84 Average kg m-2 Ring nematodes per 100 cm3 soil 0 4.4 10.4 18.2 51.2 29.0 22.6 2.2 11.2 9.2 24.6 23.2 23.6 18.4 4.4 6.2 3.8 10.2 13.0 9.4 8.5 6.6 10.0 12.6 20.2 32.6 24.4 20.0 8.8 2.4 1.6 11.8 14.0 11.6 8.3 Average 6.8 7.5 17.0 26.8 19.6 Sting nematodes per 100 cm3 soil 0 0.2 0.8 0.6 2.4 1.0 1.0 2.2 0.0 0.0 0.6 1.4 0.4 0.5 4.4 0.0 0.4 0.0 1.8 0.2 0.5 6.6 0.2 0.2 0.0 0.2 0.4 0.2 8.8 0.0 0.0 0.4 2.0 0.6 0.6 Average 0.1 0.3 0.3 1.6 0.5 Stubby-root nematodes per 100 cm3 soil 0 2.4 2.2 5.6 8.2 4.8 4.6 2.2 4.6 2.8 13.2 18.4 12.6 10.3 4.4 3.2 1.8 7.4 21.4 12.4 9.2 6.6 1.8 1.0 9.4 25.0 16.8 10.8 8.8 1.8 0.8 7.0 18.6 9.8 7.6 Average 2.8 1.7 8.5 18.3 11.3 Spiral nematodes per 100 cm3 soil 0 1.2 0.6 1.0 3.0 2.4 1.6 2.2 8.6 16.0 14.0 26.8 24.2 17.9 4.4 2.0 2.4 6.6 14.8 12.0 7.6 6.6 2.4 1.2 2.8 13.0 8.2 5.5 8.8 16.2 10.0 10.8 18.6 22.0 15.5 Average 6.1 6.0 7.0 15.2 13.8 Lesion nematodes per 100 cm3 soil 0 1.4 0.0 0.0 0.6 0.8 0.6 2.2 0.2 0.0 0.0 0.4 1.6 0.4 4.4 0.2 0.0 0.0 2.0 1.6 0.8 6.6 0.6 0.4 0.2 2.2 1.2 0.9 8.8 1.6 0.0 0.0 4.4 1.2 1.4 Average 0.8 0.1 0.04 1.9 1.3 Root-knot nematodes per 100 cm3 soil 0 24.2 2.8 4.4 87.6 60.8 36.0 2.2 21.0 4.8 11.6 102.4 70.8 42.1 4.4 25.2 1.8 10.0 62.8 51.0 30.2 6.6 32.6 6.4 11.4 136.0 89.2 55.1 8.8 17.0 3.2 2.4 74.8 44.8 28.4 Average 24.0 3.8 8.0 92.7 63.3 LSD @ p=0.05 p for Mulch rate: Ring = 9.26; Sting = NS; Stubby-root= 4.34; Spiral = 8.04; Lesion = NS; Root-knot = NS. LSD @ p=0.05 p for Sampling: Ring = 9.26; Sting = 0.75; Stubby-root= 4.34; Spiral = 8.04; Lesion =1.00; Root-knot = 32.23.

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128 3-19. Ring, stubby-root, and root-knot populations affected by mulch rates. Mulch Rate Regression equation R2 (p 0.05) kg m-2 Ring nematode 0 5.9 1.09X + 0.06X2 0.0005X3 0.89 2.2 10.7 0.27X + 0.02X2 0.0001X3 0.91 4.4 6.1 0.36X + 0.02X2 0.0001X3 0.97 6.6 10.4 0.4X + 0.03X2 0.0002X3 0.97 8.8 2.1 0.23X + 0.02X2 0.0001X3 0.95 Stubby-root nematode 0 2.41 0.18X + 0.01X2 9*10-5 X3 0.99 2.2 4.43 0.46X + 0.02X2 0.0002 X3 0.99 4.4 3.67 0.75X + 0.03X2 0.0003 X3 0.94 6.6 2.21 0.77X + 0.04X2 0.0003 X3 0.97 8.8 2.14 0.64X + 0.03X2 0.0002 X3 0.96 Root-knot nematode 0 27.8 4.75X + 0.16X2 0.001 X3 0.84 2.2 24.8 4.75X + 0.17X2 0.001 X3 0.86 4.4 26.9 3.67X + 0.12X2 0.001 X3 0.92 6.6 38.0 6.82X + 0.24X2 0.002 X3 0.84 8.8 20.4 3.89X + 0.14X2 0.001 X3 0.79

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129 Table 3-20. Ring, stubby-root and root-knot populations affected by sampling date. Sampling Date Regression equation R2 (p 0.05) DAP Ring nematode 0 5.0 + 2.5X 0.3X2 + 0.003 X3 0.57 21 11.2 5.0X + 1.5X2 0.12X3 0.54 42 19.3 1.7X + 0.4X2 0.03 X3 0.21 63 52.3 25.5X + 6.5X2 0.44 X3 0.91 84 30.4 9.6X + 2.2X2 0.15X3 0.56 Stubby-root nematode 0 2.43 + 2.02X 0.58X2 + 0.04 X3 0.99 21 2.22 + 0.69X 0.25X2 + 0.01 X3 0.98 42 6.08 + 4.47X 1.11X2 + 0.07 X3 0.54 63 8.46 + 4.57X 0.19X2 0.02 X3 0.97 84 5.21 + 2.78X 0.02X2 0.03 X3 0.85 Root-knot nematode 0 24.5 7.23X + 2.82X2 0.24 X3 0.95 21 3.2 0.44X + 0.25X2 0.02 X3 0.15 42 4.8 + 3.11X 0.27X2 0.01 X3 0.88 63 93.5 20.61X + 7.69X2 0.63 X3 0.24 84 64.0 11.22X + 4.70X2 0.41 X3 0.39 DAP = Days after planting

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130 Summary The UF-IFAS extension recommendation (Uni versity of Florida, IFAS Extension Service. 2002) called for 224 kg N ha-1; however, in this study sweet corn responded to mulch rates corresponding to at least 201 kg N ha-1. Total ear, fancy ear, stalk, and total plant yields as well as area a nd weight of diagnostic leaf responded to increasing mulch rates. Quadratic polynomials and linear-plat eau models fitted to data agreed with the critical N levels reported by Mill and Jones (1996) and Hochmuth et al. (1991) and were found to be a useful tool for sele cting proper N fertilization rates. Fresh diagnostic leaf area and weight as well as leaf concentrations of N, K, Zn, and Na were correlated (p 0.05) with total and fancy ears. These variables could be used as good predictors of corn yields. In addition, leaf testin g could be useful in diagnosis of N needs. Table 3-21. Ring, stubby-root and root-knot populations affected by mulch rates and sampling date. Nematode Regression equation R2 (p 0.05) Ring 8.98 1.1X1 0.09X1 2 0.35X2 + 0.03X2 2 0.0002X2 3 + 0.02X1X2 0.64 Stubby-root 2.27 + 1.6X1 0.22X1 2 0.62X2 + 0.03X2 2 0.0002X2 3 + 0.02X1X2 0.88 Root-knot 23.2 + 3.8X1 0.42X1 2 4.76X2 + 0.17X2 2 0.001X2 3 0.003 X1X2 0.74

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131 (a) -10 0 10 20 30 40 50 60 021426384 Days after plantingNematodes per 100 cm3 soil 0 2.2 4.4 6.6 8.8 Mulch rates, kg (b) 0 10 20 30 40 50 60 02.24.46.68.8 Cowpea mulch rate, kg m-2Nematodes per 100 cm3 soil 0 21 42 63 84 Days after planting Figure 3-12. Marginal cubic polynomials fo r ring nematode population as affected by days after planting (a) and cowpea mulch rates (b).

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132 Figure 3-13. Surface response for ring nema tode population as affected by days after planting and cowpea mulch rates.

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133 (a) -5 0 5 10 15 20 25 021426384 Days after plantingNematodes per cm3 soil 0 2.2 4.4 6.6 8.8 Mulch rates, (b) 0 5 10 15 20 25 30 02.24.46.68.8 Cowpea mulch rate, kg m-2Nematodes per 100 cm3 soil 0 21 42 63 84 Days after planting Figure 3-14. Marginal cubic polynomials for stubby-root nematode population as affected by days after planting (a) and cowpea mulch rates (b).

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134 Figure 3-15. Surface response for stubby-root nematode population as affected by days after planting and cowpea mulch rates.

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135 (a) -50 0 50 100 150 021426384 Days after plantingNematodes per cm3 100 soil 0 2.2 4.4 6.6 8.8 Mulch rates, (b) 0 50 100 150 02.24.46.68.8 Cowpea mulch rate, kg m-2Nematodes per 100 cm3 soil 0 21 42 63 84 Days after planting Figure 3-16. Marginal cubic polynomials for root-knot nematode popul ation as affected by days after planting (a) a nd cowpea mulch rates (b).

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136 Figure 3-17. Surface response for root-knot nematode population as affected by days after planting and cowpea mulch rates.

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137 Y = 5.9 -1.2Pf R2 = 0.67 0 1 2 3 4 5 6 7 0.20.40.60.81 Nematodes per 100 cm 3 (Pf)Number of ears m 2 3-18. Effect of sting nematodes fina l population (Pf) on num ber of total ears. Y = 4.3 + 0.09PfR2 = 0.87 0 1 2 3 4 5 6 7 591317 Nematodes per 100 cm 3 (Pf)Number of ears m 2 3-19. Effect of stubby-root nematodes fina l population (Pf) on nu mber of total ears.

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138Table 3-22. Correlation coefficient among yi eld, leaf mineral concentr ation, initial and final ne matode population in sweet co rn affected by five cowpea mulch rates. Number fancy ears Fancy ear g m-2 Number Total ear Total ear g m-2 Diag. leaf g m-2 Ca, g m-2 Mg g m-2 K g m-2 N g m-2 P g m-2 Na mg m-2 Cu mg m-2 Fe mg m-2 Mn mg m-2 Zn mg m-2 Initial population Ring -0.02 NS -0.02 NS 0.33 NS 0.08 NS 0.04 NS 0.09 NS -0.14 NS 0.06 NS -0.21 NS 0.03 NS -0.28 NS -0.14 NS -0.51 ** 0.09 NS 0.03 NS Sting -0.08 NS -0.05 NS 0.14 NS -0.01 NS -0.13 NS 0.08 NS 0.31 NS 0.14 NS 0.25 NS 0.11 NS 0.52 ** 0.28 NS 0.42 -0.20 NS -0.13 NS Stubby-root -0.18 NS -0.18 NS 0.03 NS -0.06 NS -0.14 NS 0.04 NS -0.10 NS -0.17 NS -0.12 NS 0.55 ** -0.11 NS 0.07 NS -0.09 NS 0.29 NS 0.08 NS Spiral -0.07 NS -0.07 NS -0.02 NS -0.03 NS 0.14 NS -0.57 ** -0.02 NS -0.17 NS -0.11 NS -0.08 NS -0.13 NS -0.18 NS -0.11 NS 0.01 NS -0.12 NS Lesion -0.02 NS 0.01 NS 0.09 NS -0.03 NS -0.09 NS 0.28 NS 0.62 ** -0.18 NS 0.01 NS 0.53 ** -0.08 NS 0.02 NS 0.00 NS 0.31 NS 0.13 NS Root-knot -0.04 NS -0.03 NS 0.37 + 0.12 NS -0.04 NS 0.02 NS -0.12 NS -0.08 NS -0.08 NS 0.03 NS -0.24 NS 0.24 NS 0.29 NS 0.20 NS 0.08 NS Final population Ring -0.17 NS -0.17 NS 0.06 NS -0.19 NS -0.23 NS -0.14 NS -0.15 NS 0.08 NS -0.40 0.02 NS -0.16 NS -0.33 NS -0.23 NS -0.11 NS -0.00 NS Sting -0.17 NS -0.13 NS -0.02 NS -0.17 NS -0.27 NS 0.32 NS 0.38 + -0.22 NS -0.23 NS 0.43 -0.39 + 0.01 NS -0.08 NS 0.46 0.03 NS Stubby-root 0.31 NS 0.29 NS 0.30 NS 0.40 0.37 + -0.10 NS -0.18 NS -0.03 NS 0.21 NS -0.07 NS -0.07 NS -0.10 NS -0.09 NS 0.19 NS 0.16 NS Spiral -0.00 NS -0.01 NS -0.11 NS -0.01 NS 0.04 NS -0.53 ** -0.10 NS 0.03 NS 0.26 NS 0.02 NS 0.36 + -0.30 NS 0.39 + -0.22 NS -0.24 NS Lesion 0.12 NS 0.15 NS 0.14 NS 0.22 S 0.21 NS 0.35 + 0.22 NS 0.15 NS 0.16 NS 0.15 NS 0.14 S 0.09 NS -0.35 + -0.13 NS 0.32 NS Root-knot -0.02 NS -0.01 NS 0.39 + 0.12 NS 0.04 NS -0.07 NS -0.21 NS -0.05 NS -0.11 NS 0.01 NS -0.24 NS 0.26 NS -0.41 0.15 NS 0.12 NS +, *, **, *** Significant at the 0.1, 0.05, 0.01, and 0.001 level, respectively. NS no significant. sampled at 0 DAP sampled at 84 DAP

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139Table 3-23. Correlation initial and fi nal nematode population in sweet corn affected by five cowpea mulch rates. Initial population Final population Ring Spiral Sting Stubbyroot Lesion Rootknot Ring Spiral Sting Stubbyroot Lesion Rootknot Initial population Ring 1 Spiral -0.32 NS 1 Sting 0.03 NS -0.12 NS 1 Stubby-root -0.07 NS 0.09 NS -0.14 NS 1 Lesion -0.25 NS -0.00 NS 0.49 -0.05 NS 1 Root-knot 0.33 NS -0.25 NS 0.51 ** 0.11 NS 0.01 NS 1 Final population Ring 0.76 *** -0.27 NS -0.11 NS -0.09 NS -0.30 NS 0.20 NS 1 Spiral 0.04 NS -0.26 NS 0.38 + -0.09 NS 0.67 ** 0.17 NS 0.04 NS 1 Sting 0.01 NS 0.02 NS -0.03 NS 0.12 NS 0.17 NS -0.13 NS -0.17 NS 0.06 NS 1 Stubby-root -0.36 + 0.86 *** -0.10 NS 0.05 NS -0.09 NS -0.35 + -0.38 + -0.32 NS 0.03 NS 1 Lesion 0.33 NS -0.20 NS 0.05 NS -0.15 NS -0.11 NS -0.13 NS -0.02 NS -0.03 NS -0.02 NS -0.04 NS 1 Root-knot 0.45 -0.21 NS 0.32 NS -0.05 NS -0.15 NS 0.83 *** 0.29 NS 0.07 NS 0.01 NS -0.31 NS -0.03 NS 1 +, *, **, *** Significant at the 0.1, 0.05, 0.01, and 0.001 level, respectively. NS no significant. sampled at 0 DAP sampled at 84 DAP

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140 In general, nematode populations reach ed maximum levels around 63 DAP and at mulch rates between 4.4 and 6.6 kg m-2. Root-knot initial population (Pi) and final population (Pf) were positively correlated with number of total ears. Sting and stubbyroot Pf affected number of total ears but not the other respon ses. Stubby-root Pf and number of total ears increased together, suggesting that vigor ous plants sustained higher stubby-root populations. Maxi mum fancy ear yield was reached around the same mulch level as maximum population levels of ring, stubby-root, and root-knot nematodes, suggesting little impact of nematodes on the cr op yield. However, it should be noted that the more highly productive corn plant could sustain greater populations of nematodes.

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141 CHAPTER 4 CORN GROWTH MODELING FOR TOTAL PLANT DRY MATTER Introduction Field corn ( Zea mays L.) production occurs mostly in central and north Florida. Corn is used for both silage and grain. Planted area reported in 2000 was 69,160 ha (FASS, 2000). Temperate corn hybrids tend to yield better for ear ly plantings, while tropical corn hybrids should be selected over temperate varieties for late planting. Summer planted corn will partition dry matter to vegetative and reproductive tissues in a different manner compared to spring plante d corn. Tropical corn hybrids are more resistant to insects and diseases than temper ate hybrids. Therefore tropical corn hybrids should be used if planting is delayed until late April or May. With the long growing season in Florida, double cropping of corn is possible. The study conducted by Overman and Gallaher (1989) evaluated the proper mana gement required for temperate, tropical and subtropical field corn growing in Florida. Growth and development are continuous dur ing the corn plant life cycle. Growth depends on the supply of inputs required such as internal or genetic factors and external or environmental factors. In crop producti on, growth rate and yield need to be maximized through those factors. Dry weight accumulation measured over time, often is used to define dry matter increases and partitioning of nutrients, which determine plant growth (Duncan, 1975; Hanway, 1963; Thomison, 1995). Growth models in agricultural processes ar e complex and nonlinea r as a function of time (Coelho and Dale, 1979; Frasier, 1983; Thompson, 1969). The pattern of corn

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142 growth is often represented as a sigmoid curve with time. This S shaped curve of growth is a result of the differential rates of grow th during its life cycle. As young seedlings expand leaf area, accumulation of crop bi omass per unit area increases exponentially until the canopy develops sufficient leaf ar ea to intercept approximately 100 % of available radiation. Once a complete leaf canopy is developed, the accumulation of biomass is quite linear for most of the grow th period until approximately grain maturity. Biomass accumulation then slows as leaf senescence occurs late in the season. Longitudinal data analysis is frequent in agricultural studies. Longitudinal data or growth curve data consist of repeated observations of a gi ven continuous characteristic over time (Zimmerman and Nuez-Anton, 2001). The mixed methodology approach, for longitudinal analysis, includes modeling the co variance structure and mean or treatment effects. Modeling those struct ures allows one to find a f unctional relationship between the covariance and mean of any two observati ons and the times of their measurement and possibly other covariates. The objective of this research was to develop dry matter accumulation growth models over time, for total corn plant, as affected by genotypes and planting dates. Material and Methods Data presented in this chapter were co llected in a study designed to identify genotypes and planting dates suitable for Flor ida field corn production (Overman, 1991). The full data set contains measurements of ot her response variables, but the main analysis in this chapter was done on total plant dry matter (DM). A field experiment was conducted in 1988 at the Green Acres Agronomy Field Research Lab near Gainesville, Florida. So il at the experimental site was an Arredondo fine sand (loamy, siliceous, semiactive, hype rthermic Grossareni c Paleudults) (USDA-

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143 NRCS, 2003). After conventional tillage, pl ots were mechanically over planted and thinned to the desired po pulation of 85,000 plants ha-1. Fertilizers were applied on the basis of soil samples collected from the site pr ior to planting (Univers ity of Florida, IFAS Extension Service, 2002). Anhydrous ammonia (100 kg N ha-1) was injected 0.25 m under the row during the planting operat ion. Ammonium nitrate (75 kg N ha-1), muriate of potash (112 kg K ha-1), triple super phosphate (23 kg P ha-1) sulfate of potash magnesia (12 kg Mg ha-1, 25 kg K ha-1, 26 kg S ha-1), and perk (28 kg ha-1 containing 5% S, 5% Mg, 0.02% B, 0.5% Cu, 9% Fe, 2% Mn, 0.003% Mo, and 1% Zn) was broadcast immediately after planting. Sidedress app lications of ammonium nitrate (35 kg N ha-1 each time) were applied at 40 and 60 d after planting (DAP). Water was applied by overhead sprinkler in addition to rainfall to insure at least 2.5 cm per 4 to 7 d until early tassel, increasing to a maximum of 3.8 cm per 4 d during rapid seed fill and decreasing to 2.5 cm per 4 to 7d during late seed fill. Counter (terbufos), LannateLV (methomyl), Dual (metolachlor), and Gramoxone (paraquat) were applied to control insects and weeds as needed Additional weed control was by hand. The experiment was set up as a randomi zed complete block design with eight replications with genotypes a nd planting dates arranged as a 3 x 3 factorial. Pioneer Brand 3320, a temperate hybrid and Pioneer Brand X304C, a tropical hybrid and FLOPUP, an open pollinated s ubtropical experimental variet y from Florida were tested at three planting dates: 25 March, 25 May, and 9 August. Destructive above ground plant samples we re collected 12 times at 10-d intervals beginning at 35 DAP. Eight replications of approximately 24 plants in a 2.75 m2 area were sampled each time. During early vege tative growth, early reproductive stage,

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144 reproductive, and later stages samples were separated in top and bottom leaves, top and bottom stems, tassel, ear, s huck, cob, and grain accordi ng to Overman (1991). Samples were washed, weighed, dried at 70C in a forced air oven and reweighed. The fixed effect of genotypes, planting date s, sampling time, and their interactions on corn growth were tested using linear mi xed models for repeated measurements in PROC MIXED-SAS (Littell, 1996). This methodology is based on maximum likelihood (ML) and restricted maximum likelihood (RML ) tests, leading to more parsimonious effect models. Deviation from the expected agronomic growth prof ile in the raw data was also checked. Possible outliers were estimated using plots in Figure 4-1 and comparing the averages and standard deviati ons of one observation with the previous and the next to it in order to targ et observations out of the genera l data profile. The observed data for each replicate were co mpared with their mean and median to find the individual observation causing the change in the profile of the genotype in any given planting date. Observations with values out of the genera l trend at all sampling times were removed from the database because they were considered outliers. The longitudinal aspect of repeated measur es occurs when observations of response variables are obtained at different occasions through time for each subject under investigation. Measures in the same replicat e or experimental unit are correlated because they share the same contributi on to the response. Observations taken close in time are more correlated than those taken apart. This phenomenon generates a covariance structure in the data set. The assumption of equal variances would not be adequate and was tested. Ignoring or a voiding modeling this structur e may result in incorrect conclusions or inefficient analysis. The general linear mixed model (GLMM)

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145 implemented in the MIXED procedure of the SAS system (SAS Institute, 2000) was used to model this covariance stru cture (Littell, et al.1998; 2000). The GLMM in matrix notation is Y = X + ZU + e, where Y is a vector of data at different sampling times, X is a matrix of fixed effects parameters, is the vector of fixed effects, Z is a matrix of random effects parameters, U is the vector of random effects, and e is the vector of errors. Variance s for the random part of the model (ZU and e) are V(U) = G and V(e) = R, with U and e independent. The response variable variance is V(Y) = ZGZ + R. When modeling the covariance structures, V(Y), matrix R and G are used for the mixed model. X ZU, and e, should be specified as pa rt of the GLMM in PROC MIXED (Littell, 2002). Diggle (1988), Wolfinger (1993), and Litte ll et al. (1998) among others suggested the exploratory analysis of th e covariance structure as the first stage of the modeling. This was done using a general saturated model including genotype, planting date, sampling time, and their interactions. As Zimmerman and Nuez-Antn (2001) stated, this saturated model is flexible and does not impose a parametric form of the mean. However, it can be considered as a general st age. Alternatives do exist, which can be considered because previous experiences suggested a more specific mean model, needs for a more parsimonious model or it may be pos sible to infer mean responses at times out of the range of measurements. At first, the goals were to investigate th e covariance structure to be modeled, as well as, to assure unbiasedne ss of the fixed effect estimates (genotypes and planting dates). An unstructured co rrelation structure on a saturated model, where covariance

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146 structure is estimated without restriction, was proposed to investigate if these fixed effects depended upon time. Averaged values in each diagonal of the covariance matrix from the previous unstructured model were used in order to te st the variances hom ogeneity hypothesis using an IML program (SAS Institute, 2000). This averaged correlation was plotted as a function of time interval (lag). Using th e same IML program variances were plotted against sampling times. Those plots (Figure 4-4) help to recognize covariance structures of the data, which can be modeled mathem atically (Littell et al., 1998). When the assumption of equal variances was rejected, several heterogeneous covariance structures were proposed among those available in the MIXED procedure in SAS. Assuming the same model for each genotype by sampling date combination, heterogeneous covariance structures were ev aluated. Models were selected based on Schwarzs bayesian criterion, BIC (Schawrz, 1978), Akaikes information criterion, AIC (Akaike, 1973), and corrected Akaikes info rmation criterion for small samples, AICC (Hurvich and Tsai, 1989) and the restricted log likelihood cr iteria (REMLlogL). Smaller values from the above criteria provided by the MIXED procedure (SAS Institute, 2000) imply better fitting models. After selecting the covariance structure for genotype by sampling date combination the model was fu rther analyzed to check if covariance structure varied marginally for each genotype or planting date. The selection criteria level in this stage should be high e nough to justify an increment in the number of parameters in the model. Once the covariance structure was selected as second stage in the analysis, the mean model was fitted using generalized leas t square methods. Predicted means from the

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147 saturated model and mean for each replicate were plotted. Both graphs can check if total plant DM accumulation agrees with the typical corn growth profile. Total corn plant growth is known to increase over tim e reaching a plateau and a final decay. A graph of observed data means showed two concavities, one positive at early plant growth stages and another that was negativ e when growth reaches its maximum (Figure 4-1(a), (b), and (c)). This graphical anal ysis was corroborated by fitting a cubic model with the mean and the heterogeneous covari ance(s) selected on the previous stage and checking the significance of high-order factors. The sigmoid profile in corn growth and its posterior decay is plausible to be approximated with the followi ng third-order polynomial. 3 3 2 2 1 0t t t Y [1] where Y is the response variable, t is time as DAP and = ( 0, 1, 2, 3) is the vector of parameters related to genotypes, pl anting dates, and their interaction. A more parsimonious fixed effect model wa s selected in the third stage of the analysis, in order to reduce the number of parameter in the model. A third-order polynomial was proposed to model growth as affected by genotypes, planting dates and interaction as a function of time. This se lection model procedure was done for genotype by planting date models as well as for indi vidual genotype and planting dates models. Factors were selected through a stepwise backward procedur e with factors entering into the model at the 0.05 level of probability (Rawling et al., 1998). Global tests available in PROC MIXED (S AS Institute, 2000) were used to test nested models through restricted likelihood ra tio tests (RLRT) and likelihood ratio test (LRT). The RLRT and LRT are statistical tests of the goodness-of-fit between two

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148 nested models and provided an objective cr iterion for selecting among possible models. The LRT compares a complex model to a simpler model to see if it fits a data set significantly better. The LRT statistic approxima tely follows a chi-sq uare distribution and can be compared with critical values from sta ndard statistical tables (Searle et al., 1992). The LRT (or difference in log-likelihoods ) approach consists in comparing the maximized log-likelihood for a parametric model (L1) and the maximized log-likelihood for a sub-model (L0) of that model obtained by imposing p independent constraints on the parameters. The null hypothesis stating that th e sub-models is better and can be tested by comparing 2(L1-L0) to the chi-square distribution with p degrees of freedom (df). The restricted likelihood ratio te st (RLRT) is based on the same comparison but with L1 and (L0) replaced, respectively, by the maximized residual log-likelihoods for a parametric model and a sub-model of it (Zimmerman and Nuez-Anton, 2001). Observations taken on the last sampling tim es (after 125 DAP) presented an errant behavior. Increases in measurements when a decline in growth is expected would be the product of taking a small sample by chance at one sampling time and a big one at the following sampling time. Also, this behavi or could be associat ed with erroneous sampling. This fact may interfere when fitting models. The cubic model would be affected in that maximum values will be offs et from their true value in the model. In order to assess this problem, cubic models were compared with spline models.

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149 (a) (b) (c) Figure 4-1. Observed field corn total plant dry matter growth profile of Pioneer Brand 3320 planted in March (a), May (b), and August (c).

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150 The cubic spline method, combines two c ubic polynomials joined at approximately 115 DAP. The spline model compared with the cubic model shown in [1] was the following: F(t) = 0 + 1t + 2t2 + + 3t3 + U(t-c) [ 4 (t-c)2 + 5(t-c)3] [2] 0 0 0 1 ) ( c t c t c t U With the first part of the model described as in [1], U(t-c) is a indicator function, t is a function of time as DAP and c is the ti me where the maximum is reached. The spline model is more flexible for further investigation and it is more adequate than fitting one model for the accumulation growth period (35 to 115 DAP) and another to model for the decay period (>115 DAP). Also, spline models guarantee mathematical continuity and differentiation in the knot or joint point. Because the cubic model in [1] did not contain the same fixed effects as the spline model in [2], it is not possible to us e the RLRT. A regular LRT provided by the METHOD = ML option on the PROC MIXED (SAS Institute, 2000) statement was used to compare both models (Littell et al., 1996). The final model was selected comparing fi xed effects for the final spline models with and without random coefficients. After se lecting the final model, inferences about fixed effects (genotypes, planting dates and interaction) were pe rformed. Hypothesis tests for fixed effects were estimated usi ng the ESTIMATES statements in SAS (SAS, 2000).

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151 Results and Discussion Observed data from individual experimental replicates of the hybrid Pioneer Brand 3320 were plotted against sampling time for a ll three planting dates (March, May, and August). Figures 4-1 (a), (b), and (c) show an early increase in DM for that hybrid in all three planting dates, which reached a maximum followed by a final decrease in agreement with the typical corn growth curv e. The other genotype s presented the same growth profile. In general, replicates perf ormed similarly, with measures taken close in time more correlated than those taken dist ant in time. Variance among replicates increased as total plant DM increased over time. From the previous exploratory analysis, it seemed that the mixed model methodology was the best approach to analy ze the data given its obvious longitudinal aspect. Genotype means plots for each plan ting date, confirmed that a cubic model was adequate to represent the si gmoid growth profile (Figures 4-2(a), (b), and (c)). A saturated model included genotypes (X1), planting dates (X2), and their interaction (X1X2), as well as DAP (t), under an uns tructured correlation structure was fitted as initial model. Table 4-1 shows that over time, all the effects were important (p < 0.1). However, residual analysis from this model (Figure 4-3) indicated heterogeneity in the covariance structure; hence the hypothesis of e qual variance was rejected. Using an IML program in SAS (SAS Institut e, 2000) values in each diagonal of the covariance matrix from the prev ious unstructured model were averaged in order to test the variances homogeneity hypothesis. Figur e 4-4(a) plots the averaged correlation against time interval (l ag) and shows a steep and monotone decrease in correlation up to the 7th lag. This exponential decline with lag of time indicated that an autoregressive model looked appropriate for this data. The heterogeneous nature of the variance can be

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152 observed in Figure 4-4(b), wh ere it can be observed that have a steady increase of variances with time up to 105 DAP and an er ratic behavior after that date. As a consequence, heterogeneous autoregressi ve first-order, ARH(1); heterogeneous compound symmetric, CSH; heterogeneous To eplitz, TOPH; heterogeneous first-order Toeplitz, TOPH(1); and anti-dependence (AD) st ructures were used to select an initial covariance saturated model. All the cova riance structures tested assumed common replicate covariance. Table 4-2 shows the number of covarian ce parameters, REMLlogL, AIC, AICC, BIC criteria, and if the model converged for f our covariances among the structures tested. The TOPH structure did not converge but repr esents a potential structure for further analysis. All criteria agreed to select ARH(1) and AD as the best models. However, with AIC = 10777 and BIC = 10803 and a more parsimonious model with only 13 unknown parameters (twelve variances and one corr elation parameters) ARH(1) structure was selected at this stage (Table 4-2). Heter ogeneous autoregressive first-order structure, ARH(1), has heterogeneous variances and corr elations that declin e exponentially with time (Littell et al., 1996). After selecting the previous covariance structure the model was further analyzed to check if the covariance struct ure varies marginally for each genotype or planting date. The analysis was done using the SUBJECT = REP(X1X2) and GROUP = X1 or GROUP = X2 options in the REPEATED statement (SAS Institute, 2000). There was no improvement in the model when co variance was specified by genotype (X1).

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153 Table 4-1. Analysis of variance for the fiel d corn total plant dry matter general saturated model. Source of variation df DM Total 830 --Replication 7 --Genotype (X1) 2 Planting date (X2) 2 *** X1X2 4 *** Sampling time (t) 11 *** X1t 22 + X2t 22 *** X1X2t 44 *** Error 716 --CV 21.7% + Significant at the 0.1 level. Significant at the 0.05 level. *** Significant at the 0.001 level. A LRT shows significant improvement (139 and 26 df, p < 0.0001) when covariance were differen tiated by planting dates (X2). Under the covariance structures selected above and through a stepwise backward procedure the cubic saturated model was reduced to a more parsimonious model with f actors entered into the model at the 0.05 level of probability. The saturated model 3 3 2 2 1 0t t t Y was decomposed in terms of X1, X2, and X1X2, as they affected DM. Both models are shown below:

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154 (a) (b) (c) Figure 4-2. Pioneer Brand 3320, Pioneer Brand X304C, and FLOPUP averaged field corn total plant dry matter yields planted in March (a), May (b), and August (c).

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155 Figure 4-3. Residual analysis for the dry ma tter (DM) general saturated model including genotype, planting date, sampling date, and their interactions.

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156 (a) (b) Figure 4-4. Covariance struct ure analysis for the dry ma tter general saturated model including genotype, planting date, sampling date, and their interactions. (a) Averaged correlation against time interv al (lag) and (b) variance against days after planting.

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157 Table 4-2. Model selection criteria for four covariance structures (smaller-is-better). Covariance Structure Number of Parameters REML logL AIC BIC AICC Converge TOPH(1) 12 11251112751127611303 Yes CSH 13 11213112391123911268 Yes ARH(1) 13 10777108031080310832 Yes AD 23 10748107941079610847 Yes -2 Res Log Likelihood Akaikes Information criterion, (Akaike, 1973) Schwarzs bayesian cr iterion, (Schawrz, 1978), Corrected Akaikes information criterion, (Hurvich and Tsai, 1989) Saturated model: Y = (X1+X2+X1X2) + (X1+X2+X1X2)t + (X1+X2+X1X2)t2 + (X1+X2+X1X2)t3 [3] Final model: Y = (X1+X2+X1X2) + (X1+X2+X1X2)t + (X2)t2 + (X2)t3 [4] A LRT of 183.2 and 26 df indicat ed that the model in [4] fit this set of data better (p < 0.0001). In summary, analysis suggested that the cubic polynomial in [4] with an ARH(1) error covariance structure (R) with structure being di fferentiated by planting dates is a suitable model. Predicted and observed means, as well as, residual analysis s uggested that model [4] fit is not perfect and c ould be improved. For example, for the hybrid Pioneer 3320 planted in March the cubic model underestimated the real data tre nd in the period from 95 to 105 DAP and overestimated it in the period from 115 to 145 DAP. Residuals ranged from -400 to 400 g m-2 around zero. This would be the product of data which does not present the typical decay period af ter 125 DAP. The cubic model compensated

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158 this fact moving the maximum DM to the latest sampling dates, around 135 DAP (Figures 4-5 and 4-6). Spline smoothing polynomials were proposed in an attempt to improve model fitting. A mixed spline model containing tw o cubic polynomials [5] with joint knot at 115 DAP was implemented with the MIXED procedure. Spline model: Y = (X1+X2+X1X2) + (X1+X2+X1X2)t +(X2)t2 + (X2)t3 + H(t-115) [X2(t-115)2 + X2 (t-115)3] [5] The model in [5] considered the first accumulation growth profile and then the typical decay. A better fit for the spline cubic polynomial is show n when predicted and observed means (Figure 4-7) and residua ls were analyzed (Figure 4-8). Models in [4] and [5] do not contain the same set of fixed effects, hence cannot be statistically compared using a RLRT. A regul ar LRT, constructed through results from METHOD = ML option in MIXED PROC, gave a LRT = 63.2 and 6 df showing that the spline model fit the data better th an the cubic model (p < 0.0001). Parameters for the model in [5] are shown in Table 4-3. In Table 4-3 the parameter vector was decomposed in the X1, X2, and X1X2 effects over time. Table 4-4 shows the final spline model (R2 = 0.86). Using this model with the ESTIMATES option in SAS, inferences about genotype and planting dates were done (Figures 4-9, 4-10, and 4-11).

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159 Figure 4-5. Predicted and observed field corn total plant dry matter for Pioneer Brand 3320 planted in March (cubic model). Figure 4-6. Residual analysis for the cubic model for thr ee genotypes planted in March.

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160 In general, temperate hybrids have been se lected for cooler weather, longer days, and lower humidity. The tropi cal corn hybrids have been se lected for shorter days, high temperatures, and high humidity as well as fo r disease and insect resistance. At early planting (March), Pioneer 3320, a temperate hyb rid, had greater yield than the tropical hybrid and subtropical variety (Figure 4-9). Temperate corn DM at early planting was 137 g m-2 more than FLOPUP (p < 0.05), an op en pollinated subtropical variety from Florida (Table 4-5). In Table 4-4 it can be observed that delaying planting until May favored DM for Pioneer X304C, a tr opical hybrid, reaching 316 and 197 g m-2 more (p < 0.001) than temperate and subtropical, respec tively (Figure 4-10). In Florida, lateplanted corn is plagued with high incidence of diseases and insects. FLOPUP, a variety selected for late planting condi tions in Florida, yielded 207 g m-2 more (P = 0.001) total plant DM than the temperate hybrid when planted in August (Figure 4-11). All three corn types gave high yield at early plantings. Maximum total plant DM obtained for temperate, tropical, and subt ropical genotypes in March were 1242, 1028, and 897 g m-2 more (p < 0.0001) than those reached in August (Figure 4-12). Maximum DM generally was reached between 105 and 125 DAP. Results from this study agree with those reported by Ov erman and Gallaher (1989).

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161 Table 4-3. Decomposed splin e model including genotype (X1), planting date (X2), and interaction (X1X2) effects over time. Planting date (X1) Genotype (X2) 0 = (X1 + X2 + X1X2) 1 = (X1 + X2 + X1X2) 2 = (X2) 3 = (X2) 4 = (X2) Spline 5 = (X2) Spline 3320 32.3 + 133.7 + 31.5 105.6 363.2 + 45.5 118.4 -8.0 -132.4 64.8 March X304C 13.4 + 133.7 20.4 128.0 -363.2 + 21.3 118.4 -8.0 -132.4 64.8 FLOPUP 133.7 135.2 -363.2 118.4 -8.0 -132.4 64.8 3320 32.3 70.4 + 55.6 105.6 + 17.8 + 5.0 30.8 -3.4 136.0 -22.6 May X304C 13.4 70.4 + 5.5 128.0 + 17.8 + 25.6 30.8 -3.4 136.0 -22.6 FLOPUP 70.4 135.2 + 17.8 30.8 -3.4 136.0 -22.6 3320 32.3 117.8 105.6 16.0 -2.12 170.4 -30.5 August X304C 13.4 117.8 128.0 16.0 -2.12 170.4 -30.5 FLOPUP 117.8 135.2 16.0 -2.12 170.4 -30.5

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162 Table 4-4. Final spline mode l for field corn total plant dry matter affected by genotype and planting date over time (t). Planting date (X1) Genotype (X2) Dry matter model DM = 0 + 1t + 2t2 + 3t3 + H(t-115) [ 4(t-115)2 + 5(t-115)3] 3320 197.5 212.1t + 118.4t2 8.0t3 + H(t-115)[-132.4(t-115)2 + 64.8(t-115)3] March X304C 126.7 213.9t + 118.4t2 8.0t3 + H(t-115)[-132.4(t-115)2 + 64.8(t-115)3] FLOPUP 133.7 228.0t + 118.4t2 8.0t3 + H(t-115)[-132.4(t-115)2 + 64.8(t-115)3] 3320 17.5 128.4t + 30.8t2 3.4t3 + H(t-115)[136.0(t-115)2 22.6(t-115)3] May X304C -51.5 171.4t + 30.8t2 3.4t3 + H(t-115)[136.0(t-115)2 22.6(t-115)3] FLOPUP -70.4 153.0t + 30.8t2 3.4t3 + H(t-115)[136.0(t-115)2 22.6(t-115)3] 3320 85.5 105.6t + 16.0t2 2.1t3 + H(t-115)[170.4(t-115)2 30.5(t-115)3] August X304C -104.4 128.0t + 16.0t2 2.1t3 + H(t-115)[170.4(t-115)2 30.5(t-115)3] FLOPUP -117.8 135.2t + 16.0t2 2.1t3 + H(t-115)[170.4(t-115)2 30.5(t-115)3] R2 = 0.86

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163 Table 4-5. Estimates of total plant dry ma tter (DM) means at maximum growth of field corn. l Planting date (X1) Genotype (X2) DM 3320 1930 March X304C 1988 FLOPUP 1793 3320 1217 May X304C 1533 FLOPUP 1336 3320 689 August X304C 860 FLOPUP 896 Summary A heterogeneous first-order autoregressive covariance structure was adjusted with the matrices differentiated by planting date. After the variance-covariance structure was modeled, results showed an interaction between genotypes and planting dates (p < 0.0001). Sampling time effects suggested that DM accumulation could be described by a cubic polynomial curve. However, the spline model fit the data better than the cubic model, with significant LRT (p < 0.0001). Th e intercept and the linear effect of time depend on genotypes, planting dates, and their in teraction. The changes in curvature of the model were associated with planting date; moreover this was the most important effect on DM.

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164 Figure 4-7. Predicted and observed field corn total plant dry matter for Pioneer Brand 3320 planted in March (Spline Model). Figure 4-8. Residual analysis for the spline model for three genotypes planted in March.

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165 Figure 4-9. Field corn tota l plant dry matter affected by genotype and sampling date when planted in March. Figure 4-10. Field corn total plant dry ma tter affected by genotype and sampling date when planted in May.

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166 Figure 4-11. Field corn total plant dry ma tter affected by genotype and sampling date when planted in August. Figure 4-12. Maximum field corn total plant dry matter affected by genotype and planting date.

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167 At early planting (March) Pioneer 3320, a temperate hybrid, had greater total plant biomass than the tropical hybrid and the subt ropical variety. Delaying planting until May favored Pioneer X304C, a tropical hybrid over the temperate hybrid and open-pollinated subtropical variety. FLOPUP, a variety selected for late planting conditions in Florida was selected to withstand the high incidence of diseases and insects, which is common when corn is planted late in the summer. Modeling the effect of genotypes, planting dates, and their inte raction through time allowed a bett er understanding of how genetics and planting dates are important factors wh en corn is grown under Florida conditions.

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168 CHAPTER 5 CONCLUSION In this dissertation, modeling was used in the statistical analysis of experimental data, in which mathematical equations give very precise and concise descriptions (models) of data and how depe ndent variables are related to independent variables. Several statistical techniques were used to i llustrate growth, development, and yield of some crops growing in Florida. In the second chapter growth of turnip and mustard was analyzed as split-plot designs, with plant populations of 2, 4, and 6 plants m-2 in 2002 and 2, 4, 6, and 8 plants m-2 in 2003 as main plot treatments. Five N rates (0, 56, 112, 168, and 224 kg N ha-1) were used as sub-treatments. Differences among means were tested, at the 5% level of probability, using the least significant differen ce (LSD) test. Analyses indicated that N rates and plant population densities are important factors in predicting mustard and turnip yields, with expected improvements in growth at their optimum le vels. Response surface methodology and multiple regression models were important techniques to optimize and model growth in the Brassicas Those techniques were used to find the relationship between population density and N rates affecting mustard and turnip yields a nd to establish a set of recommendations in order to maximize yields. Under the previous premises, the selected models represented fresh and dry yields for both crops and their results can be used to make recommendations to maximize yields. Models indicated that for 2002, turnip maximum yields were predicted with the combination of 168 kg N ha-1 and either 4 or 6 plants m-2

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169 depending on whether the final marketing product will be top or roots. The highest mustard yields were also found at 168 kg N ha-1 and 6 plants m-2. Fresh and dry diagnostic leaf yields for both crops reached maximum values at 168 kg N ha-1 and 2 plants m-2. Turnip responses in 2003 were affected by the incidence of crown rot. Maximum fresh yields were predicted to be outside our treatment leve l ranges. In general for 2003 data, N rates greater than 224 kg N ha-1 and plant populations gr eater than 8 plants m-2 will produce the highest top and total plant yi elds. In order to reach maximum root and diagnostic leaf yields, low plant populati on densities and high N rates should be researched further. Mustard fresh top a nd total plant reached their maximum around 8 plants m-2 and 160 kg N ha-1. Fresh root and diagnostic leaf yield analysis produced saddle points, but ridge analysis sugges ted that N rates greater than 224 kg N ha-1 and plant populations greater than 8 plants m-2 might result in highest root yields. The same type of analysis suggested that diagnostic leaf yields might increase at low plant population densities but with increasing N rates. In chapter 3, five cowpea mulch rates were the treatments in a randomized complete block design. The effect of mulch ra tes was tested using the LSD test at the 5% level of probability. Quadratic polynomials and linear-plateau models were fitted to the data to estimate optimum N supply provided by cowpea mulch rates. The recommendation called for 224 kg N ha-1 (University of Florida, IFAS Extension Service, 2002); however, in this study sweet corn res ponded to mulch rates corresponding to at least 201 kg N ha-1. Total ear, fancy ear, stalk, and total plant yields responded to increasing mulch rates. Quad ratic polynomials and linear-pla teau models agreed to N

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170 critical levels reported in th e literature and were found to be useful tools for selecting proper N fertilization rates. Despite the fact that sufficiency levels fo r some elements were not reached at the peak yield response, leaf testing could be usef ul in diagnosis of mine ral needs. Potassium and N leaf concentration was very low compar ed with those reported in the literature, suggesting that further research is needed in the fertilizer reco mmendations or tissue analyses under Florida conditions. Diagnosti c leaf area and weight as well as leaf concentrations of N, K, Zn, and Na were posit ively correlated with total and fancy ears. These variables could be used as good predictors of corn yields. In addition, leaf testing could be useful in diagnosis of N needs for sweet corn. In general, nematodes reached maximu m population levels around 63 DAP and at cowpea mulch rates between 4.4 and 6.6 kg m-2. Root-knot initial populations (Pi) and final populations (Pf) were positively correlated with number of total ears. Sting and stubby-root Pf affected number of total ears but not the other responses. Stubby-root Pf and number of total ears increased together, suggesting that vigorous plants sustained a higher stubby-root nematode population. Optim um fancy ear yield was reached at about the same cowpea mulch level as maximum nematode populations, suggesting little impact of nematodes on the crop, but it shoul d be noted that the more highly productive corn plants could sustain great er populations of nematodes. In the fourth chapter, the fixed effect of hybrids, planting dates, sampling time, and their interactions on corn growth was tested using longitudinal analysis. A linear mixed model for repeated measurements was used to adjust a heterogeneous first-order autoregressive covariance structure with th e matrices differentiated by planting date.

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171 After the variance-covariance structure was modeled, results showed an interaction between hybrids and planting dates (p < 0.0001). Sampling time effect suggested that dry matter (DM) accumulation could be described by a cubic polynomial curve. However, the spline model fit the data better than the cu bic model, with a significant likelihood ratio test (p < 0.0001). The intercept and the linear effect of time were dependent on hybrid, planting date, and their in teraction. The changes in curvature of the model were associated with planting date; moreover it was the most important effect on DM. The estimated means suggested that early planting (March) of Pioneer 3320, a temperate hybrid, resulted in greater yield than the tropical h ybrid and subtropical variety. Delaying planting until May favored Pioneer X304C, a tropical hybrid, over the temperate hybrid and open-pollinated subtropi cal variety. FLOPUP, a variety selected for late planting conditions in Florida, was selected to withstand the high incidence of diseases and insects, which are common when corn is planted late in the summer. Modeling the effect of genotype planting date, and their in teraction through time allowed a better understanding of how genetics and plan ting date are important factors when corn is grown under Florida conditions. The use of statistical modeling techniques shown in this work will help in developing a theoretical model to understa nd the biological proce ss involved in crop growth, development, and yield. Classical anal ysis of variance, regression and correlation analysis, surface response methodology, non-linear regression, and longitudinal analysis are important tools to determine maximu m or optimum yields by determining the optimum factors affecting those yields.

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172 LIST OF REFERENCES Abramowitz, M., and I.A. Stegun (ed.). 1972. Handbook of mathematical functions with formulas, graph, and mathematical tables. 9th edition. New York, NY. Adams, F., and C.E. Evans. 1962. A rapid method for measuring lime requirement of red-yellow podzolic soils. Soil Sci. Soc. Amer. Proc. 26:355-357. Aikman D. P., and A. Scaife. 1993. Modeling plant growth under varying environment conditions in a uniform canopy. Ann. Bot. 72:485-492. Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. p. 267-281. In 2nd International symposium on information theory. B.N. Petrov and F. Csaki (ed.). Budapest: Akademia Kiado. Alt, C., H. Kage, and H Sttzel. 2000a. M odelling nitrogen conten t and distribution in cauliflower ( Brassica oleracea L. botrytis ). Ann. Bot. 86(5):963-973. Alt, C., H. Sttzel, and H Kage. 2000b. Op timal nitrogen content and photosynthesis in cauliflower ( Brassica oleracea L. botrytis ). Scaling up from a leaf to the whole plant. Ann. Bot. 85(6):779-787. Brantley, B. B. 1961. Effect of source a nd level of nitrogen on the yield and nitrogen content on turnip greens. Proc. Amer. Soc. Hort. Sci. 77:500-502. Breidt F., and H. P. Fleming. 1998. Modeling of the competitive growth of Listeria monocytogenes and Lactococcus lactis in vegetable broth. Appl. Environ. Microbiol. 64:3156-3165. Breuer, C.M., R. B. Hunter, and L.W. Kannenberg. 1976. Effects of 10 and 20-hour photoperiod treatments at 20 and 30 C on ra te of development of a single-cross maize hybrid. Can. J. Plant Sci. 56:795-798. Brouder, S. M., D. B. Mengel, and B.S. Hofmann. 2000. Diagnostic efficiency of the blacklayer stalk nitrate and grain nitrogen test for corn. Agron. J. 92:1236-1247. Burkill, I.H. 1966. A dictionary of econom ic products of the Malay peninsula. Art Printing Works, Kuala Lumpur. Malaysia.

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173 Bustillo, J.J., and R.N. Gallaher. 1989. Dry matter partitioning in notillage tropical corn in Florida. p. 40-42. In I.D. Teare, (ed.). Proc. 1989 Southern Conservation Tillage Conference. Tallahassee, FL 12-13 July 1989. Inst. of Food and Agri. Sci. Special Bulletin 89-1. Univ. of Florida, Gainesville, FL. Christie, J. 1959. Plant nematodes. Their bionomics and control. H. and W. B. Drew Company. Jacksonville, FL. Coelho, D.T., and R.F. Dale. 1979. An en ergy-crop growth variab le and temperature fucntion for predicting corn growth and deve lopment: Planting to silking. Journal paper No. 7791. Agric. Exp. Stn. Purdue Univ. West Lafayette. Diggle, 1988. An approach to the analysis of repeated measurement. Biometrics 44:959971. Doerge, T.A. 2002. Variable-rate nitroge n management creates opportunities and challenges for corn producers. Plant Mana gement Network [Online]. Available at http://www.plantmanagementnetwork .org/sub/cm/review/variable-n/ (Verified 5 March 2005). Duncan, W.G. 1975. Maize. p. 23-50. In L.T. Evans (ed.) Crop physiology: some case histories. Cambridge Univ. Press. London. Duke, J.A. 1978. The quest for tolerant germplams. p. 1-61. In : ASA Special Symposium 32, Crop toleran ce to suboptimal land conditions. American Society of Agronomic. Madison, WI. Duke, J.A. 1983. Handbook of energy crops [Online]. Avalaible at http://www.hort.purdue.edu/newcrop/ duke_energy/Brassica_juncea.html (Verified 5 March 2005). Fergurson, R.B., C.A. Shapiro, G.W. Herg ert, W. L. Kranz, N.L. Klocke, and D.H. Krull. 1991. Nitrogen and irrigation management-pract ices to minimize nitrate leaching from irrigated corn. J. Prod. Agric. 4:186-192. Florida Agricultural Statistics Service [FASS]. 2000. Field crops summary. Florida Agricultural Statistics Service, Or lando, FL [Online]. Available at http://www.nass.usda.gov/fl/rtoc0.htm (Verified 8 April 2005). Florida Agricultural Statistics Service [FASS]. 2001. Ve getable acreage, production, and value. Florida Agricultural Statistics Service, Orlando, FL [Online]. Available at http://www.nass.usda.gov/fl/rtoc0.htm (Verified 5 March 2005). Fraiser, D.M. 1983. Effects of N and K applied to two maize/soybean no-tillage cropping systems on yields, profitability, gr owth, and soil acidity M.S. Thesis. University of Florida. Gainesville. FL.

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174 Gallaher, R.N., W.L. Parks, and L.M. Josephson. 1972. Effect of levels of soil potassium, fertilizer potassium, and season on yield and ear leaf potassium content of corn inbreds and hybrids. Agron. J. 64:645-647. Gallaher, R.N., H.B. Harris, O.E. Anderson, and J.W. Dobson, Jr. 1975a. Hybrid grain sorghum response to magnesium fertilization. Agron. J. 67:297-300. Gallaher, R.N., C.O. Weldon, and J.G. Futral 1975b. Aluminum block digester for plant and soil analysis. Soil Sci. Soc. Amer. Proc. 39:803-806. Gallaher, R.N. 1978. Multiple cropping -value of mulch. p. 9-16. In J.T.Touchton and D.G. Cummins (ed.). Proc. First Annual S outheastern No-Till Systems Conference. Experiment, Georgia Exp. Sta. Special P ub. No. 5 Univ. of Georgia, Agri. Exp. Stn., Experiment, GA. Gomez, K.A., and A.A. Gomez. 1984. Statis tical procedures for agricultural research. 2nd edition. John Wiley and Sons, Inc. New York, NY. Hanlon, E.A., J.M. Gonzalez, and J.Bartos. 1996. IFAS extension soil testing laboratory chemical procedures and training manua l. Circular 812. Univ. of Florida, Gainesville, FL. Hanway, J. J. 1963. Growth stages of corn ( Zea mays L.). Agron. J. 55:487-492. Hartwell, J. L. 1982. Plants used agains t cancer. Quarterman Publications, Inc. Lawrence, MA. Hay, R.K.M., and A.J. Walker. 1989. An in troduction to the physio logy of crop yield. Longman Scientific and Technical Group. UK. Hochmuth, G.J., D.N. Maynard, C.S Vavrin a, and E.A. Hanlon. 1991. Plant tissue analysis and interpretation for vegetable crops in Florida. SS-VEC-42. Soil and Water Science Department, IFAS, Univers ity of Florida, Gainesville, Fl. Hochmuth, G.J., D.N. Maynard, C.S. Vavrina, W.M. Stall, T.A. Kucharek, S.E. Webb, T.G. Taylor, and S.A. Smith. 2002. Vegetable production guide for Florida 20022003. S. Olson and D.N. Maynard (ed.). IFAS Extension-University of Florida and Citrus&Vegetable magazine. Florida. Hunter, J.S. 1959. Determination of optim um operating conditions by experimental methods. Ind. Qual. Control 15:6-12. Hunter, R.B., M. Tollenar, and C. M. Br euer. 1977. Effects of photoperiod and temperature on vegetative and reproductive gr owth of a maize hybrid. Can. J. Plant Sci. 57:1127-1133. Hurvich, C.M., S. S. Jeffrey, and C.Tsai 1989. Regression and time series models election in small samples. Biometrika 76:297-307.

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175 Jugenheimer, R. W. 1976. Corn. Improve ment, seed production, and uses. John Wiley and Sons, Inc. New York, NY. Jenkins, W.R. 1964. A rapid centrifugal-flota tion technique for separating nematodes from soil. Plant Dis. Reptr. 48:692. Katsvairo, T.W., W.J. Cox, H.M. Van Es, a nd M. Glos. 2003. Spatial yield response of two corn hybrids at two nitrogen levels. Agron. J. 95:1012-1022. Kranz, W.L., and R.S. Kanwar. 1995. Spa tial distribution of leachates loses due to preplant tillage methods. In clean wate r-clean environment-21st century. Vol 2 Proc. Conf. Working Group in Water quality Kansas city, MO. 5-8 Mar. 1995. Am. Soc. Of Agric. Eng. St Joseph, MI. Littell, R.C., G.A. Milliken, W.W.Stroup, and R.D. Wolfinger. 1996. SAS systems for mixed models. SAS Institute, Inc. Cary, NC. Littell, R.C., P.R. Henry, and C.B.Ammerman. 1998. Statistical analysis of repeated measures data using SAS procedures J. Animal Science 76:1216-1231. Littell, R.C., J. Pendergast, and R. Natarajan. 2000. Modeling covariance structure in repeated measures data Stat. Med. 19:1793-819. Littell, R. C. 2002. Analysis of unbalanced mixed model data: A case study comparison of ANOVA versus REML/GLS. J. Ag ric. Biol. Envir on. Stat. 7:472-490. Little, T.M., and F.J. Hills. 1978. Agricultura l experimentation. Design and analysis. John Wiley and Sons, Inc. New York, NY. Lory, J.A., and P.C. Scharf. 2003. Yield goal versus delta yield for predicting fertilizer nitrogen need in corn. Agron. J. 95:994-999. MacGowan, J.B. 1981. A lesion nematode, Pratylenchus zeae Nematology Circular No. 72. Florida Department of Agriculture a nd Consumer Services Division of Plant Industry, Gainesville, FL. Mamo, M., G.L. Malzer, D.J. Mulla, D.R. H uggins, and J. Strock. 2003. Spatial and temporal variation in economically optimum nitrogen rate for corn. Agron. J. 95:958-964. MathWorks, 2001, MATLAB [CD-ROM], ver 6, rel 12, [Computer programme]. Available Distributor: The MathWorks Inc., 24 Prime Park Way, Natick, MA, 01760-1500, USA McSorley, R., and R.N. Gallaher. 1993. P opulation densities of root-knot nematodes following corn and sorghum in cropping systems. p. 26-29. In. P.K. Bollich (ed.). Proc. 1993 Southern Conserv. Tillage Conf. for Sustainable Agriculture. Louisiana Agric. Exp. Stn. Ms. No. 93-86-7122, Monroe, LA.

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178 Shurtleff, M.C. 1980. Compendium of Corn Diseases, 2nd ed., American Phytopathological Societ y, St. Paul, MN. Singh, M., and M.J. Jones. 2000. Statistical estimation of time trends in two-course crop rotations. J. A ppl. Stat. 27:589-597. Stewart J. 1995. Calculus. Early transcende ntals. 3rd edition. Brooks/Cole Publishing Company. Pacific Grove, CA. Swan J.B., T.C. Kaspar, and D.C. Erbach 1996. Seed-row residue management for corn establishment in the northern U.S. corn belt. Soil Tillage Res. 40:55-72. Thomison, P. 1995. Identifying vegetative gr owth stages in corn Agronomy facts # AGF-127-95. Ohio State University Extens ion. Department of Horticulture and Crop Science [Online]. Available at http://ohioline.osu.edu/agf-fact/0127.html (verified 5 March. 2005). Thompson, L. M. 1969. Weather and technology in prediction of corn in the corn belt. Agron. J. 61:453-456. Toth, J. D., and R.H. Fox. 1998. Nitrate loss es from a corn-alfalfa rotation: Lysimeter measurement of nitrate leaching. J. Environ. Qual. 27:1027-1033. United States Department of Agriculture [U SDA]. 1997. United States standards for grades of sweet corn [Online]. Available at http://www.ams.usda.gov/standards/cornswt.pdf (verified 5 March. 2005). United States Department of Agriculture – Natural Resources Conservation Service [USDA-NRCS]. 2003. Of ficial soil series desc riptions. USDA-NCRS. Washington, DC [Online]. Available at http://soils.usda.gov/ technical/classification/osd/index.html (verified 5 March. 2005). University of Florida, IFAS Extension Se rvice. 2002. Soil test analysis. Wallace building 631 P.O. Box 110740. Gainesville, FL 32611-6740. Vagts, T. 2004. Northwest Iowa crop growth simulation page. Iowa State University Extension [Online]. Available at http://www.extension.iast ate.edu/nwcrops/cropmodeling.htm (verified on 4 Feb. 2005). Vanotti, M.B., and L.G. Bundy. 1994. Corn nitrogen recommendations based on yield response data. J. Prod. Agric. 7:249-256. Wann, R. 1984. A dynamic model for plan t growth: validation study under changing temperatures. Ann. Bot. 53:45-52. Wolfinger, R 1993. Laplace’s approximation fo r non-lineal mixed models. Biometrika 80:791-795.

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179 Zhao, S.L., S.C. Gupta, D.R. Huggins, and J. F. Moncrief. 2000. Predicting subsurface drainage, corn yield, and nitrate nitrogen losses with DRAINMOD-N. J. Environ. Qual. 29:817-825. Zimmerman D., and V. Nuez-Antn. 2001. Pa rametric modeling of growth curve data. An overview. Tests. 10:111-999.

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180 BIOGRAPHICAL SKETCH Belkys Y. Bracho B. was born November 1, 1965, in Maracaibo, Venezuela. In 1986 her son Jhonatan E. Bracho was born in the same city. She studied agronomic engineering at the Facultad de Agronomia, Universidad del Zulia, graduating in 1988. From 1988 to 1992 she worked as computer as sistant on the Project “Reubicacion de la comunidad Curva del Pato” and taught Statistics at Colegio Universitario Pedro E. Coll. In 1992 she won a permanent position at the Facultad de Agronomia, Universidad del Zulia, to teach statistics. In August 1999, sh e was admitted to the Graduate School of the University of Florida and graduated in the fall of 2002 with a master’s degree from the Department of Statistics. In the spring of 2003 Belkys began her PhD program in the University of Florida Agronomy Department. Sh e is expected to graduate with a PhD in agronomy in spring 2005.


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Permanent Link: http://ufdc.ufl.edu/UFE0010109/00001

Material Information

Title: Application of Statistical Techniques to Modeling Crop Growth
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0010109:00001

Permanent Link: http://ufdc.ufl.edu/UFE0010109/00001

Material Information

Title: Application of Statistical Techniques to Modeling Crop Growth
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0010109:00001


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Full Text











APPLICATION OF STATISTICAL TECHNIQUES TO MODELING CROP GROWTH


By

BELKYS YASMIN BRACHO BRAVO

















A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2005
































Copyright 2005

by

Belkys Yasmin Bracho Bravo



































This dissertation is dedicated to my father Ivan E. Bracho, In memoriamn, 1931-2004.
















ACKNOWLEDGMENTS

I would like to express my gratitude to my maj or professor, Dr. Raymond N.

Gallaher, for giving me the necessary guidance and support to reach my goals and for his

caring attitude during the course of my program. I would like to thank my committee,

Dr. Robert McSorley for all his advice and useful comments not only about nematodes

but also about agriculture research methods in general, Dr. Jerry Bennett and Dr. Kenneth

Boote for their guidance, support and for serving on my committee. Special thanks go to

Dr. Ramon Littell, who was my maj or professor for my master' s degree, for making my

arrival in the USA more straightforward and helping me from the first moment by giving

me his support and invaluable advice.

Thanks go to Howard Palmer, Jim Chichester, and J.J. Frederick for their technical

support in the field and laboratory. I appreciate those involved with the fall 2002 and fall

2003 Field Plot Techniques class for assistance with much of the field work. I would

also like to acknowledge Deanye Overman for some of the data used in this work.

I would like to thank the Universidad del Zulia, Venezuela, and the University of

Florida for providing financial support. I am grateful for receiving financial assistance

through awards from the College of Agriculture and Life Sciences (IFAS travel Grants)

and the Family of Paul Robin Harris. I thank Dr. Tim White and Dr. Robert Schmidt for

all their encouragement.

I wish to express my gratefulness to Salvador Pintos for encouraging me to come to

this country, for all his statistical advice, and for his belief in me more than I believed in









myself. Many thanks go to my friends and fellow graduate students Veronica Emhart,

Gabriela Luciani, and Kimberly Seaman for their daily companionship and lovely

support. Special thanks go to Salvador Gezan for his helpful and loyal friendship.

Most importantly, I thank the Lord for helping me to withstand all worries and

difficulties. I would like to thank my family for their continuous encouragement and

love. Finally, I am devoted to my Son Jhonatan Bracho for his help in the field, and for

typing, plotting, and editing of this dissertation, and for his unconditional love and

presence in my life.





















TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .............. .................... iv


LI ST OF T ABLE S ............._. ...._... .............._ viii..


LIST OF FIGURES .............. ....................xii


AB STRAC T ................ .............. xvii


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


2 OPTIMIZING TURNIP AND MUSTARD YIELDS INT RESPONSE TO PLANT
POPULATION AND NITROGEN FERTILIZER USING RESPONSE
SURFACE METHODOLOGY ................. ......... ...............8.......


Introducti on ................. ...............8.................
M materials and M ethods .............. ...............10....
Results and Discussion ................ ...............15........... ....
Yield Results for 2002 ................. ...............15................
Yield Results for 2003 ................ ...............31................
Mineral Concentration Results ................. ...............53........... ....
Sum m ary ................. ...............82.......... ......


3 SWEET CORN YIELD, PLANT NUTRITION, AND NEMATODES AS
AFFECTED BY COWPEA MULCH .........._.._.......... .....__ ...........10


Introducti on ....__ ................. ...............102......
Material and Method s ......._................. .........._.........10
Results and Discussion .....__ ................ .........._.........11
Yield Results .............. ... ...............111..............
Mineral Nutrition Results .....__ ................ .........._ ......... 11
Nematode Results ........._..... ...._... ...............123....
Summary ........._..... ...._... ...............130....













4 CORN GROWTH MODELING FOR TOTAL PLANT DRY MATTER ...............141


Introducti on ................. ...............141................
M material and M ethod s ................. ................. 142........ ....
Results and Discussion ................ ...............151................

Summary ................. ...............163................


5 CONCLUSION................ ..............16


LIST OF REFERENCES ................. ...............172................


BIOGRAPHICAL SKETCH ................. ...............180......... ......

















LIST OF TABLES


Table pg

2-1 Mineral sufficiency levels for turnips and mustard. ............. .....................1

2-2 Soil test report and standard fertilization recommendation (University of
Florida, IFAS Extension Service, 2002). ................ ...............................16

2-3 Analysis of variance for fresh and dry turnip yield affected by three
population densities and five rates of nitrogen (2002). ................ .............. .....17

2-4 Analysis of variance for fresh and dry turnip yield affected by four population
densities and five rates of nitrogen (2003) ................. .............. ........ .....17

2-5 Analysis of variance for fresh and dry mustard yield affected by three
population densities and five rates of nitrogen (2002). ................ .............. .....18

2-6 Analysis of variance for fresh and dry mustard yield affected by four
population densities and five rates of nitrogen (2003). ................ .............. .....18

2-7 Analysis of variance for turnip mineral concentration affected by three
population densities and five rates of nitrogen (2002). ................ .............. .....19

2-8 Analysis of variance for turnip mineral concentration affected by four
population densities and five rates of nitrogen (2003). ................ .............. .....19

2-9 Analysis of variance for mustard mineral concentration affected by three
population densities and five rates of nitrogen (2002). ................ .............. .....20

2-10 Analysis of variance for mustard mineral concentration affected by four
population densities and five rates of nitrogen (2003). ................ .............. .....20

2-11 Yield of fresh turnip plant and its parts for three (2002) and four (2003)
population densities and five rates of nitrogen. ................ ......... ...............23

2-12 Yield of dry turnip plant and its parts for three population densities and five
rates of nitrogen (2002) ......... ......... ......... ................ ...............24

2-13 Fresh and dry weight of turnip diagnostic leaf for three (2002) and four (2003)
population densities and five rates of nitrogen. ................ ......... ...............25










2-14 Yield of fresh mustard plant and its parts for three (2002) and four (2003)
population densities and five rates of nitrogen. ................ ......... ...............26

2-15 Yield of dry mustard plant and its parts for three (2002) and four (2003)
population densities and five rates of nitrogen. ................ ......... ...............27

2-16 Fresh and dry weight of mustard diagnostic leaf for three (2002) and four
(2003) population densities and five rates of nitrogen ................. ............... ....28

2-17 Multiple regression models for turnip yield, 2002-2003. ............. ....................29

2-18 Multiple regression models for mustard yield, 2002-2003 ................ ................ .30

2-19 ANOVA F-values of the effects of nitrogen rate and population densities on
fresh turnip yields, 2002-2003 .......... ................ ..............................33

2-20 ANOVA F-values of the effects of nitrogen rate and population densities on
dry turnip yields, 2002-2003 ................ ...............34................

2-21 ANOVA F-values of the effects of nitrogen rate and population densities on
fresh mustard yields, 2002-2003 ................ ...............35................

2-22 ANOVA F-values of the effects of nitrogen rate and population densities on
dry mustard yields, 2002-2003 ......... ................ .............. ...............36

2-23 Mineral concentrations (Ca, Mg, and P) in turnip leaf for three (2002) and
four (2003) population densities and five rates of nitrogen ................. ...............72

2-24 Mineral concentrations (N, P, and Na) in turnip leaf for three (2002) and four
(2003) population densities and five rates of nitrogen ................. ............... ....73

2-25 Mineral concentrations (Cu, Fe, and Mn) in turnip leaf for three (2002) and
four (2003) population densities and five rates of nitrogen ................. ...............74

2-26 Mineral concentration (Zn) in turnip leaf for three (2002) and four (2003)
population densities and five rates of nitrogen. ................ ......... ...............75

2-27 Mineral concentrations (Ca, Mg, and K) in mustard leaf for three (2002) and
four (2003) population densities and five rates of nitrogen ................. ...............76

2-28 Mineral concentrations (N, P, and Na) in mustard leaf for three (2002) and
four (2003) population densities and five rates of nitrogen ................. ...............77

2-29 Mineral concentrations (Cu, Fe, and Mn) in mustard leaf for three (2002) and
four (2003) population densities and five rates of nitrogen ................. ...............78

2-30 Mineral concentration (Zn) in mustard leaf for three (2002) and four (2003)
population densities and five rates of nitrogen. ................ ...._ ..............79











2-3 1 Multiple regression models for turnip leaf mineral concentration at 2 plants m
2, 2002-2003 ................ ......... ........ ......... ........ ................80

2-32 Multiple regression models for mustard leaf mineral concentration at 2 plants
m -2, 2002-2003. ........._.._.._ ...............8 1...._.._....

2-33 Correlations coefficients between yield and leaf mineral concentration for
turnip (2002). ............. ...............85.....

2-34 Correlations coefficients between yield and leaf mineral concentration for
turnip (2003). ............. ...............86.....

2-35 Correlations coefficients between yield and leaf mineral concentration for
mustard (2002). .............. ...............87....

2-36 Correlations coefficients between yield and leaf mineral concentration for
mustard (2003). .............. ...............88....

3-1 Mineral sufficiency levels for macro and micronutrients for sweet corn at late
tasseling............... ...............10

3-2 Green 'Cream 40' cowpea mulch treatments............... ..............10

3-3 Soil test report and standard fertilization recommendation (University of
Florida, IFAS Extension Service, 2002). ................ ..............................108

3-4 Analysis of variance for sweet corn number and yield of total and fancy ears
as affected by fiye rates of cowpea mulch. ................ ................ ......... .1 12

3-5 Analysis of variance for sweet corn affected by fiye rates of cowpea mulch......1 12

3-6 Sweet corn yield means affected by fiye mulch rates ................. ................ ...112

3-7 Sweet corn yield means affected by fiye mulch rates ................. ................ ...113

3-8 Quadratic models for sweet corn yields affected by mulch rates. ................... ....113

3-9 Linear-Plateau models for sweet corn yields affected by mulch rates. ................118

3-10 Analysis of variance for N, P, K, Ca, and Mg leaf concentrations as affected
by fiye rates of cowpea mulch. ................ ...............119.....__._...

3-11 Analysis of variance for Fe, Mn, Zn, Cu, and Na as affected by fiye rates of
cowpea mulch. ........... ..... ._ ...............119..

3-12 Sweet corn N, P, K, Ca, and Mg leaf concentrations affected by fiye mulch
rates. ............ ..................120.











3-13 Sweet corn Fe, Mn, Zn, Cu, and Na leaf concentrations affected by five mulch
rates. .............. ...............122....

3-14 Regression models for N, K, Zn, and Na leaf concentrations as affected by
mul ch rates. ........._ ...... .. ............... 122...

3-15 Linear-plateau models for N, K, and Zn leaf concentration as affected by
mul ch rates. .....__ ................ ........._..........12

3-16 Correlation coefficients between yield and leaf mineral concentration in sweet
corn affected by five cowpea mulch rates............... ...............125.

3-17 Analysis of variance for ring, sting, stubby-root, spiral, lesion, and root-knot
nematode population (nematodes cm3) affected by five rates of cowpea mulch
and sampling time (days after planting) ................. .............. ......... .....126

3-18 Nematode populations affected by five rates of cowpea mulch. ......................... 127

3-19 Ring, stubby-root, and root-knot populations affected by mulch rates................128

3-20 Ring, stubby-root, and root-knot populations affected by sampling date............129

3-21 Ring, stubby-root, and root-knot populations affected by mulch rates and
sampling d ate. ............. ...............13 0....

3 -22 Correlation coefficient among yield, leaf mineral concentration, initial and
final nematode population in sweet corn affected by five cowpea mulch rates. .138

3-23 Correlation initial and final nematode population in sweet corn affected by
five cowpea mulch rates............... ...............139.

4-1 Analysis of variance for the field corn total plant dry matter general saturated
m odel ................ ...............153....... ......

4-2 Model selection criteria for four covariance structures (smaller-is-better). ........157

4-3 Decomposed spline model including genotype, planting date, and interaction
effects over time ................. ...............161................

4-4 Final spline model for field corn total plant dry matter affected by genotype
and planting date over time (t). .............. ...............162....

4-5 Estimates of total plant dry matter (DM) means at maximum growth of field
corn. ............. ...............163....

















LIST OF FIGURES


Figure pg

2-1 Marginal quadratic polynomials for fresh turnip top yield as affected by N rate
and plant population (2002). ............. ...............37.....

2-2 Surface response for fresh turnip top yield as affected by N rate and plant
population (2002). ............. ...............38.....

2-3 Marginal quadratic polynomials for fresh turnip root yield as affected by N rate
and plant population (2002). ............. ...............39.....

2-4 Surface response for fresh turnip root yield as affected by N rate and plant
popul ation (2002). ............. ...............40.....

2-5 Marginal quadratic polynomials for fresh turnip total plant yield as affected by
N rate and plant population (2002). ............. ...............41.....

2-6 Surface response for fresh turnip total plant yield as affected by N rate and plant
popul ation (2002). ............. ...............42.....

2-7 Marginal quadratic polynomials for fresh turnip diagnostic (diag.) leaf yield as
affected by N rate and plant population (2002)............... ...............43.

2-8 Surface response for fresh turnip diagnostic (diag.) leaf yield as affected by N
rate and plant population (2002). ............. ...............44.....

2-9 Marginal quadratic polynomials for fresh mustard top yield as affected by N rate
and plant population (2002). ............. ...............45.....

2-10 Surface response for fresh mustard top yield as affected by N rate and plant
popul ation (2002). ............. ...............46.....

2-11 Marginal quadratic polynomials for fresh mustard root yield as affected by N
rate and plant population (2002). ............. ...............47.....

2-12 Surface response for fresh mustard root yield as affected by N rate and plant
popul ation (2002). ............. ...............48.....

2-13 Marginal quadratic polynomials for fresh mustard total plant yield as affected by
N rate and plant population (2002). ............. ...............49.....










2-14 Surface response for fresh mustard total plant yield as affected by N rate and
plant population (2002). ............. ...............50.....

2-15 Marginal quadratic polynomials for fresh mustard diagnostic (diag.) leaf yield as
affected by N rate and plant population (2002)............... ...............51.

2-16 Surface response for fresh mustard diagnostic (diag.) leaf yield as affected by N
rate and plant population (2002). ............. ...............52.....

2-17 Marginal quadratic polynomials for fresh turnip top yield as affected by N rate
and plant population (2003). ............. ...............56.....

2-18 Surface response for fresh turnip top yield as affected by N rate and plant
popul ation (2003). ............. ...............57.....

2-19 Marginal quadratic polynomials for fresh turnip root yield as affected by N rate
and plant population (2003). ............. ...............58.....

2-20 Surface response for fresh turnip root yield as affected by N rate and plant
popul ation (2003). ............. ...............59.....

2-21 Marginal quadratic polynomials for fresh turnip total plant yield as affected by
N rate and plant population (2003). ............. ...............60.....

2-22 Surface response for fresh turnip total plant yield as affected by N rate and plant
popul ation (2003). ............. ...............61.....

2-23 Marginal quadratic polynomials for fresh turnip diagnostic (diag.) leaf yield as
affected by N rate and plant population (2003)............... ...............62.

2-24 Surface response for fresh turnip diagnostic (diag.) leaf yield as affected by N
rate and plant population (2003). ............. ...............63.....

2-25 Marginal quadratic polynomials for fresh mustard top yield as affected by N rate
and plant population (2003). ............. ...............64.....

2-26 Surface response for fresh mustard top yield as affected by N rate and plant
popul ation (2003). ............. ...............65.....

2-27 Marginal quadratic polynomials for fresh mustard root yield as affected by N
rate and plant population (2003). ............. ...............66.....

2-28 Surface response for fresh mustard root yield as affected by N rate and plant
popul ation (2003). ............. ...............67.....

2-29 Marginal quadratic polynomials for fresh mustard total plant yield as affected by
N rate and plant population (2003). ............. ...............68.....










2-30 Surface response for fresh mustard total plant yield as affected by N rate and
plant population (2003). ............. ...............69.....

2-3 1 Marginal quadratic polynomials for fresh mustard diagnostic (diag.) leaf yield as
affected by N rate and plant population (2003)............... ...............70.

2-32 Surface response for fresh mustard diagnostic (diag.) leaf yield as affected by N
rate and plant population (2003). ............. ...............71.....

2-33 Leaf N concentration in turnip and mustard planted at 2 plants m-2 as affected by
N rates (2002-2003). ............. ...............83.....

2-34 Leaf P concentration in turnip and mustard planted at 2 plants m-2 as affected by
N rates (2002-2003). ............. ...............84.....

2-35 Leaf K concentration in turnip and mustard planted at 2 plants m-2 as affected by
N rates (2002-2003). ............. ...............89.....

2-36 Leaf Ca concentration in turnip and mustard planted at 2 plants m-2 as affected
by N rates (2002-2003). ............. ...............90.....

2-37 Leaf Mg concentration in turnip and mustard planted at 2 plants m-2 as affected
by N rates (2002-2003). ............. ...............91.....

2-38 Leaf Fe concentration in turnip and mustard planted at 2 plants m-2 as affected
by N rates (2002-2003). ............. ...............92.....

2-39 Leaf Mn concentration in turnip and mustard planted at 2 plants m-2 as affected
by N rates (2002-2003). ............. ...............93.....

2-40 Leaf Cu concentration in turnip and mustard planted at 2 plants m-2 as affected
by N rates (2002-2003). ............. ...............94.....

2-41 Leaf Zn concentration in turnip and mustard planted at 2 plants m-2 as affected
by N rates (2002-2003). ............. ...............95.....

2-42 Leaf Na concentration in turnip and mustard planted at 2 plants m-2 as affected
by N rates (2002-2003). ............. ...............96.....

2-43 Fresh turnip top yield in the Georgia piedmont experiment (Brantley, 1961) at 0,
34, 68, and 102 kg N ha-l and Florida experiment at 0, 56, and 112 kg N ha- ....100

2-44 Tumnip N concentration in the Georgia piedmont experiment (Brantley, 1961) at
0, 34, 68, and 102 kg N ha-l and Florida experiment at 0, 56, and 112 kg N ha-l .100

3-1 Number of fancy and total ears as affected by five cowpea mulch rates. ..............1 14

3-2 Total and fancy ear yields as affected by five cowpea mulch rates. ................... ...114










3-3 Total plant and stalk yields as affected by five cowpea mulch rates. ................... .115

3-4 Fresh diagnostic (diag.) leaf yields as affected by five cowpea mulch rates. ........1 15

3-5 Diagnostic (diag.) leaf area as affected by five cowpea mulch rates. ................... .1 16

3-6 Quadratic and linear-plateau models for total ear yields as affected by five
cowpea mulch rates. ................ ...............116................

3-7 Quadratic and linear-plateau models for fancy ear yields as affected by five
cowpea mulch rates. ................ ...............117................

3-8 Quadratic and linear-plateau models for fresh diagnostic leaf yields as affected
by five cowpea mulch rates ................. ...............117........... ...

3-9 Quadratic and linear-plateau models for leaf N concentration as affected by five
cowpea mulch rates. ............. ...............120....

3-10 Quadratic and Linear-Plateau models for leaf K concentration as affected by five
cowpea mulch rates. ............. ...............121....

3-11 Quadratic and linear-plateau models for leaf Zn concentration as affected by five
cowpea mulch rates. ............. ...............121....

3-12 Marginal cubic polynomials for ring nematode population as affected by days
after planting and cowpea mulch rates ................. ...............131..............

3-13 Surface response for ring nematode population as affected by days after planting
and cowpea mulch rates. ............. ...............132....

3-14 Marginal cubic polynomials for stubby-root nematode population as affected by
days after planting and cowpea mulch rates ................. .............................133

3-15 Surface response for stubby-root nematode population as affected by days after
planting and cowpea mulch rates. ............. ...............134....

3-16 Marginal cubic polynomials for root-knot nematode population as affected by
days after planting and cowpea mulch rates .............. ...............135....

3-17 Surface response for root-knot nematode population as affected by days after
planting and cowpea mulch rates. ............. ...............136....

3-18 Effect of sting nematodes final population (Pf) on number of total ears. .............. 137

3-19 Effect of stubby-root nematodes final population (Pf) on number of total ears. ...137

4-1 Observed field corn total plant dry matter growth profile of 'Pioneer Brand
3320' planted in March, May, and August .............. ...............149....










4-2 'Pioneer Brand 3320', 'Pioneer Brand X304C', and 'FLOPUP' averaged Hield
corn total plant dry matter yields planted in March, May, and August ........._......154

4-3 Residual analysis for the dry matter (DM) general saturated model including
genotype, planting date, sampling date, and their interactions. ............. ................155

4-4 Covariance structure analysis for the dry matter general saturated model
including genotype, planting date, sampling date, and their interactions.
Averaged correlation against time interval (lag) and variance against days after
planting ................. ...............156................

4-5 Predicted and observed Hield comn total plant dry matter for 'Pioneer Brand
3320' planted in March (cubic model). ....._ .....___ ............__........15

4-6 Residual analysis for the cubic model for three genotypes planted in March........159

4-7 Predicted and observed Hield comn total plant dry matter for 'Pioneer Brand
3320' planted in March (spline model). ............. ...............164....

4-8 Residual analysis for the spline model for three genotypes planted in March.......164

4-9 Field comn total plant dry matter affected by genotype and sampling date when
planted in M arch ................. ...............165................

4-10 Field comn total plant dry matter affected by genotype and sampling date when
planted in M ay ................. ...............165................

4-11 Field comn total plant dry matter affected by genotype and sampling date when
planted in August. ............. ...............166....

4-12 Maximum Hield corn total plant dry matter affected by genotype and planting
date. ............. ...............166....
















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

APPLICATION OF STATISTICAL TECHNIQUES TO MODELING CROP GROWTH

By

Belkys Yasmin Bracho Bravo

May 2005

Chair: Raymond N. Gallaher
Major Department: Agronomy

Crop models that incorporated genetics, climate, and management operations may

be useful tools to make better decisions throughout the growing season for turnip

(Bra~ssica ra-pa L.), mustard (Bra~ssica juncea L.), sweet and field corn (Zea mays L.).

Experiments were conducted near Gainesville, Florida, to test plant populations and N

fertilizer on turnip and mustard; to determine the effect of cowpea (Vigna unguiculata

(L.) Walp.) mulch on sweet corn yield, plant nutrition, and nematode populations, as well

as the effect of nematodes on yield; and to develop dry matter growth models over time

for field corn as affected by genotypes and planting dates. Variance, correlation, linear

and non-linear regression, surface response, ridge, and longitudinal analysis were used to

optimize management decisions.

Ridge analysis predicted that a combination of 168 kg N ha-l and more than 6

plants m-2 Will prOduce maximum mustard and turnip shoot as well as total biomass

yields. Sweet corn yield responded to increasing cowpea mulch, peaking at rates

corresponding to at least 201 kg N ha- Quadratic and linear-plateau models agreed with









the critical N levels reported by others and could be used as tools to select N fertilizer

requirements. Diagnostic leaf area and weight as well as N, K, Zn, and Na

concentrations of the leaf increased as total and fancy ears increased and all could be

good predictors of sweet corn yield. Fancy ear yield reached maximum levels around 63

days after planting (DAP) and between 4.4 and 6.6 kg of cowpea mulch m-2. Nematode

population also peaked at 63 DAP but there was little impact of nematodes on ear yield.

Analysis showed that the N-sufficient corn plants were able to sustain greater nematode

densities compared to N-deficient plants.

For field corn, genotype and planting date affected total dry matter accumulation

over the life cycle of the field corn. At early planting (March) 'Pioneer 3320', a

temperate hybrid, had greater yield than 'Pioneer X304C' and FLOPUP. Delaying

planting until May favored 'Pioneer X304C', a tropical hybrid. When planted in August

FLOPUP, an open pollinated variety developed under Florida conditions, performed

better than the other corn hybrids. FLOPUP had more resistance to late season diseases,

especially compared to Pioneer 3320.


XV111















CHAPTER 1
INTTRODUCTION

Agricultural producers, researchers, consultants, and industry representatives are

faced with crop management and decisions throughout the growing season. The need to

manage and predict crop behavior over a wide range of plant densities, planting dates,

mineral fertilization, climate, pest control, among others has become increasingly

important. Crop growth models have traditionally been used to address many of these

research problems by increasing our understanding of crop growth, development and

yield. The emphasis in production agriculture is to obtain the maximum yield possible,

or at least to obtain the most economical yield. Final crop yield depends mainly on the

management decisions made throughout the growing season. The use of crop models,

incorporating climatic conditions and management operations, may assist in making more

timely and better management decisions (Aikman and Scaife, 1993; Singh and Jones,

2000).

The term "model" has many meanings in agronomy. Representative plants are

commonly referred to as model plants; qualitative hypotheses are referred to as models,

and the mathematical simulation of crop growth, development, and yield is referred to as

a crop simulation model. This dissertation considered the statistical analysis of

experimental data, in which mathematical equations give very precise and concise

descriptions of data and how dependent variables are related to independent variables. In

particular, the goals were to find model parameters with useful interpretations relative to

the growth of crops studied. Models can be used to calculate and estimate relative









maximums, predicted or out-of-range values that would be useful for the optimization of

management decisions, such as fertilization, plant population, genetics, or planting dates,

providing a framework for comparison.

A crop model is usually more accurate in predicting crop growth than in predicting

final yields. This is because that most plants can be effectively characterized for growth

based on accumulated growing time. Crop yield is an accumulation and integration of

physiological processes through time. Numerous errors are associated with yield

predictions and these errors are cumulative through time (Vagts, 2004). Crop modeling

is important to enhance the understanding of plants because they are complex and

dynamic, and plant growth is difficult to address only by empirical approaches.

Modeling is a useful tool for understanding the developmental mechanisms of plants as

they interact with environmental variations (Wann, 1984).

In this dissertation, statistical modeling was used to examine the effect of different

management inputs on the growth of four important crops in Florida. A goal of this

research was to develop a theoretical model to obtain maximum or optimum yields by

determining the factors affecting those yields. A number of models have been developed

to predict plant growth and yields. These models may be classified as empirical models;

they describe sigmoid functions that approximate plant growth curves versus time or

optimize crop responses (Hanway, 1963). However, the usefulness of empirical models

is limited, frequently providing parameters with no agronomic or biologic meaning.

Mechanistic models may be developed from theoretical or can be experimentally

determined from data describing the cause or mechanism behind the dynamic changes

observed in an experimental system (Breidt and Fleming, 1998). Models in this research










may be classified as partially mechanistic, based on fertilization, plant population,

genetics, and planting and sampling dates as variables that affect crop growth. In

general, N fertilization, plant population densities, cowpea (Vigna unguiculat (L.)

Walp.) mulches, genetics, planting and sampling dates were important in the growth of

turnip (Bra~ssica rapa L.), mustard (Bra~ssica juncea L.), and sweet and field corn (Zea

mays L.). By incorporating these factors as parameters into models, values for these

parameters could be estimated to gain insight into their relative importance in the growth

process, and ultimately on the final yields of those crops.

Turnip is a biennial cool season crop, resistant to frost and mild freezes (Duke,

1983). This plant is native to Westemn Asia and is grown today in the United States, Asia

and Europe. In Florida it is grown commercially and by home gardeners. Its roots,

swollen white, pink or yellow-fleshed tubers, are eaten raw or cooked. Its thin, light to

dark green leaves are also eaten raw or cooked like spinach (Spinacia oleracea L.).

Turnip may be intercropped with comn or used as a catch crop after vegetables. In

Florida, turnip is planted from August to February, with a growing period of 40 to 50 d

(Maynard et al., 2002). Mustard is an annual cool season vegetable, native to central

Asia (Northwest India), and widely grown for its seed. However, its glabrous young

leaves are extensively eaten raw or cooked like spinach or turnip (Duke, 1983).

More than half of the total leaf N is in components associated with photosynthesis,

which is positively associated with leaf N concentration (Alt et al., 2000b). Increases in

green leaf yields and hence in rates of photosynthesis are often expected with the addition

of N fertilizer. Quantifying the interaction between N concentration in leaf tissue and

plant productivity under specific conditions will result in optimum N fertilizer









recommendations, avoiding both environmental concerns and also N deficiency (Alt et

al., 2000a).

Although biomass on a land area basis can be manipulated through varying

population density; in general, when density is increased, competition for nutrients, light,

and water also increases. At low densities an adequate economical yield may not be

reached. On the other hand, high densities may increase production cost without

significant increases in yield (Hay and Walker, 1989).

Response surface methodology is a statistical technique used for optimization

studies when two or more independent variables have a combined effect on the desired

response (Hunter, 1959). A three-dimensional surface response graph can be obtained by

use of a multiple regression polynomial equation to describe two or more independent

variables that have a combined effect on the desired response (Little and Hills, 1978).

The obj ectives of the second chapter of this dissertation were to use this methodology to

establish the relationship between population density and N rates affecting mustard and

turnip yields and to establish a set of optimum recommendations of those variables for

obtaining maximum yields.

Corn is a crop of New World origin. It is one of the most important crops in

providing human sustenance, directly or indirectly as feed for domestic animals

(Jugenheimer, 1976; Hochmuth et al., 2002). Corn is a major crop on cultivated land of

the United States and has become an important crop because of its productivity and great

adaptability (Duncan, 1975; Frazier, 1983). Florida ranks number one nationally in the

production and value of fresh market sweet corn, typically accounting for approximately

25% of both national sweet corn production and of U. S. cash receipts for fresh sales










(FASS, 2000). Field corn is typically planted as a spring crop and has shown little

success when planted in the fall in Florida (Bustillo and Gallaher, 1989).

Nitrogen is the nutrient that most limits comn yield. Low cost of N fertilizer and the

lack of a yield loss from over-application of N has led to a management approach

emphasizing generous applications (Brouder et al., 2000; Vanotti and Bundy, 1994).

Nitrogen management in comn is important because excessive N can result in

contamination of the environment (NO3-N leaching) and inadequate N can result in yield

and profit losses to the grower (Doerge, 2002; Mamo et al., 2003; Scharf et al., 2002;

Toth and Fox, 1998).

Residue mulches protect the soil from wind and water erosion, conserve moisture,

control weeds, often increase crop production, but also delay soil warming in the spring

(Swan et al. 1996; Gallaher, 1978). Rivero (1997) has shown that cation exchange

capacity (CEC), organic carbon, and available P and K in soil as well as dry matter yield,

and N, P, and K concentration in the leaf tissue of comn increase with mulch applications.

Statistically significant increases were observed when sorghum (Sorghum bicolor L.) and

sunn hemp (Crotalaria juncea L.) mulches were used. Crop production was very

dependent on soil-residue interaction but effects were for a short period of time.

Nematodes injure sweet corn by reducing comn root growth, stalk height, and stalk

diameter. In most cases, plants weakened by nematodes produce smaller and fewer ears,

and plants that are heavily parasitized may produce no ears, resulting in up to 100% crop

loss. General symptoms of nematode injury include stunting, wilting, and nutrient

deficiency symptoms, often in patches throughout the Hield due to irregular distribution of

nematodes. Some nematodes affecting sweet comn in Florida include sting (Belonolaimus










spp.), stubby root (Trichodorus spp., Paratrichodorus~PP~~P~~PP~~P spp.), lesion (Pratylenchus spp.),

and occasionally root-knot (M~eloidogyne spp.). Corn yield reduction from those

nematodes is generally higher in sandier soils (Christie, 1959; Noling, 1997; 1999). The

obj ectives addressed in the third chapter were to determine the effect of five cowpea

mulch rates on sweet comn yield and plant nutrition, sting, stubby-root, lesion, and root-

knot nematode populations, as well as the effect of nematode densities on sweet corn

yield.

Field comn production occurs mostly in central and north Florida. Corn is used for

both silage and grain. Tropical comn hybrids are more resistant to insects and diseases

than temperate hybrids. Therefore tropical corn hybrids should be used if planting is

delayed until late April or May. With the long growing season in Florida, double

cropping of corn is possible. Temperate comn hybrids tend to yield higher in early

plantings, while tropical corn hybrids should be selected over temperate varieties for late

planting. Summer-planted corn will partition dry matter to vegetative and reproductive

tissues in a different manner compared to spring-planted comn. A study conducted by

Overman and Gallaher (1989) evaluated the proper management required for temperate,

tropical and subtropical field corn growing in Florida.

The pattern of corn growth is often represented as a sigmoid curve with time. This

S shaped curve of growth is a result of the differential rates of growth during its life

cycle. As young seedlings expand leaf area, accumulation of crop biomass per unit area

increases exponentially until the canopy develops sufficient leaf area to intercept

approximately 100 % of available radiation. Once a complete leaf canopy is developed,

the accumulation of biomass is quite linear for most of the growth period until










approximately grain maturity. Biomass accumulation then slows as leaf senescence

occurs late in the season.

Longitudinal data analysis is frequently used in agricultural studies. Longitudinal

data consists of repeated observations of a given continuous characteristic over time

(Zimmerman and Nufiez-Anton, 2001). Growth curve data over time would be such

longitudinal data. The mixed methodology approach for longitudinal analysis includes

modeling the covariance structure and mean or treatment effects. Modeling those

structures allows one to find a functional relationship between the covariance and mean

of any two observations and the times of their measurement and possibly other

covariates. The general objective presented in the fourth chapter was to develop dry

matter accumulation growth models over time, for total biomass provided by field corn,

as affected by genotypes and planting dates.















CHAPTER 2
OPTIMIZING TURNIP AND MUSTARD YIELDS IN RESPONSE TO PLANT
POPULATION AND NITROGEN FERTILIZER USING RESPONSE SURFACE
METHODOLOGY

Introduction

Turnip (Bra~ssica urapa L.) is a biennial cool season crop, resistant to frost and mild

freezes (Duke, 1983). This plant is native to Western Asia and is grown today in the

United States, Asia and Europe. In Florida it is grown commercially and by home

gardeners. Its roots, swollen white, pink or yellow-fleshed tubers, are eaten raw or

cooked. Its thin, light to dark green leaves are also eaten raw or cooked like spinach

(Spinacia oleracea L.). Turnip roots and leaves have been used as a folk remedy for

cancer (Hartwell, 1982). Turnip may be intercropped with comn (Zea mays L.) or used as

a catch crop after vegetables. In Florida, turnip is planted from August to February, with

a growing period of 40 to 50 d (Maynard et al., 2002). Turnip tops are harvested when

plants are young and tender. Roots may be harvested in 40 to 60 d for bunching when

they are 5 cm in diameter and for topping when they are 7.5 cm in diameter (Duke,

1983). Fresh yield range between 12.5 to 25 MT ha l, depending upon if harvested as

bunched or topped roots.

Mustard (Bra~ssica juncea L.) is an annual cool season vegetable, native to central

Asia (Northwest India) and widely grown for its seed. However, its glabrous young

leaves are extensively eaten raw or cooked like spinach or turnip. Mustard has been

reported as a natural remedy to relieve headache, inflammations and hemorrhages, and

their ingestion may impart a body odor to repel mosquitoes (Duke, 1983; Burkill, 1966,









Hartwell, 1967). The growing period is 40 to 50 d (Maynard et al., 2002) and its leaves

are harvested tender when they are 15 to 30 cm long. In the United States fresh yields of

mustard greens average about 12 MT ha-l (Duke, 1983).

More than half of the leaf N is in components associated with photosynthesis,

which is positively associated with leaf N concentration (Alt et al., 2000b). High leaf N

also increases respiration, which may partially compensate for the increase in net C

accumulation rates (Penning de Vries et al., 1974). Increases in green leaf yields and

hence in rates of photosynthesis are expected with increases in N fertilization. The study

of the interaction between N concentration in the diagnostic leaf and plant yield not only

helps determine crop N fertilizer demands under specific environment conditions but also

helps avoid environmental concerns and N deficiency (Alt et al., 2000a). The N

fertilization recommendation for turnip and mustard is 13 5 kg ha-l (University of Florida,

IFAS Extension Service, 2002). The N sufficiency ranges in the youngest mature leaves

as reported Mills and Jones (1996) and in the whole plant tops as reported by Hochmuth

et al. (1991) are shown in Table 2-1.

Although biomass can be manipulated through population density; in general, when

density is increased, competition for nutrients, light, and water also increases. At low

densities an adequate economical yield may not be reached. On the other hand high

densities may increase production cost without significant increases in yield (Hay and

Walker, 1989). Maynard et al. (2002) recommended row widths of 0.30 to 0.90 m and

plant spacing in the row of 0. 13 to 0.26 m for turnip and 0.05 to 0. 15 m for mustard.

Given the importance of these two vegetables as possible cool season crops in Florida,










optimum plant population and N fertilizer requirements are important in order to

maximize their yields.

Table 2-1. Mineral sufficiency levels for turnips and mustard.
Turnip Mustard
Plant mineral Mills and Jones, Hochmuth et al., Mills and Jones,
1996 1991 1996
Macronutrients (g kg- )
N 35.0 50.0 30.0 50.0 29.7 38.5
P 3.3 6.0 2.5 8.0 4.1 6.4
K 35.0 50.0 25.0 40.0 31.8 43.9
Ca 15.0 40.0 8.0 15.0 15.2 25.1
Mg 3.0 10.0 2.5 6.0 2.1 3.6
Micronutrients (mg kg- )
Fe 40.0 300.0 30.0 100.0 76.0 209.0
Mn 40.0 250.0 30.0 100.0 40.0 52.0
Cu 6.0 25.0 5.0 10.0 3.0 5.0
Zn 20.0 250.0 20.0 40.0 20.0 36.0
Na 361.0 No data 193.0 417.0



Response surface methodology is a statistical technique used for optimization

studies when two or more independent variables have a combined effect on the desired

response (Hunter, 1959). A three-dimensional surface response graph can be obtained by

use of a multiple regression polynomial equation to describe two or more independent

variables that have a combined effect on the desired response (Little and Hills, 1978).

The obj ective of this study was to use this methodology to establish the relationship

between population density and N rates affecting turnip and mustard yields.

Materials and Methods

Two independent field experiments with turnip cy. Shogoin' and mustard cy.

'Florida Broadleaf' were conducted as split-plot designs at the University of Florida

Statistical Design Field Teaching Lab in Gainesville, Florida. The experimental site is

characterized by USDA-NRCS (2003) as a Millhopper fine sand (loamy, siliceous,









semiactive, hyperthermic Grossarenic Paleudults). Plant populations of 2, 4, and 6 plants

m-2 in 2002 and 2, 4, 6, and 8 plants m-2 in 2003 were main plot treatments. Five N rates

(0, 56, 112, 168, and 224 kg N ha- ) were sub-treatments. Sub-plots were 2 rows 1.5 m

wide and 2 m long. Experiments were replicated 5 times in 2002 and 4 times in 2003.

For both years the experiments were mechanically over-planted after conventional tillage

and thinned to establish population treatments. Ammonium nitrate (34% N) was applied

in two equal splits as the N source. Weeds were controlled by mechanical cultivation and

by hand. A minimum of 30 mm water was applied each week, either from rainfall or

overhead sprinkler irrigation. A soil sample was collected prior to initiating the

experiment and analyzed for Mehlich I extractable minerals (Mehlich, 1953), pH, and

lime requirements (Adams and Evans, 1962; Hanlon, 1996) (Table 2-2). In order to

enhance soil fertility, K was applied according to soil test results. The labeled rate of
Lan n ate @LV { S -m eth yl N -[(m eth yl -car r b am oyrl~ oxy~th i o acetim id ate},\ m anufac tur r ed b y


E.I. DUPONT DE NEMOURS and CO. (Inc.) was applied twice to control leaf feeding

mnsects.

Six most recently fully expanded leaves were collected on 28 October in 2002 and

on 27 October in 2003, from each treatment, to test for essential minerals (N, P, K, Ca,

Mg, Cu, Fe, Mn, and Zn) and Na. Leaves were weighed fresh, dried at 700C and

reweighed. Dry leaf samples were ground in a Wiley mill to pass through a 2 mm

stainless steel screen. The ground samples were re-dried at 700C in a convection oven for

2 h to standardize tissue moisture content among all samples and stored in sterile

containers until lab analysis.









LeafN analysis was by modified micro-Kjedahl procedure. A mixture of 100 mg

of tissue sample, 3.2 g salt-catalyst (9: 1 K2SO4:CuSO4), 2 to 3 Pyrex beads and 10 ml of

H2SO4 WeTO VOrtexed in a 100 ml Pyrex test tube. To reduce frothing, 2 ml 30% H202

was added in 1 ml increments and tubes were digested in an aluminum block digester

(Gallaher et. al., 1975b) at 3700C for 3.5 hours. Tubes were capped with small Pyrex

funnels that allowed evolving gases to escape while preserving refluxing action. Cool

digested solutions were vortexed with approximately 30 ml of de-ionized water, allowed

to cool to room temperature, brought to 75 ml volume, transferred to storage bottles

(glass beads were filtered out), sealed, mixed and stored. Nitrogen trapped as (NH4)2SO4

was analyzed on an automatic solution sampler and a proportioning pump. A plant

standard with a long history of recorded N concentration values was subj ected to the

same procedure and used as a check.

Plant material was prepared for mineral analysis by dry ashing procedure. A 1.0 g

sample of each cultivar was placed in a beaker and ashed in a muffle furnace for 4 hours

at 4800C. After removal from the furnace, 5 ml of concentrated HCI and 15 to 20 ml of

deionized water was added to each beaker. Samples were boiled to dryness on a hot plate

to precipitate Si. Each beaker was rehydrated with 5 ml of concentrated HCI and 15 to 20

ml of deionized water, covered with a watch glass and heated until boiling. After cooling

samples were brought to volume with deionized water and a portion was transferred to a

vial. Potassium and Na were determined by flame emission spectrophotometry, while

Ca, Mg, Fe, Mn, Cu, and Zn were determined by flame abortion spectrophotometry.

Phosphorus was determined by colorimetry procedure.









Turnip experiment was harvested from 3.0 m2 plOts on 4 November 2002 and 2003.

Mustard experiment was harvested from 3.0 m2 plOts on 14 November 2002 and 6

November 2003. After weighing plots for fresh top and root yields, they were dried at

700C in a forced air oven to determine dry matter yield.

Statistical analyses of the data were performed using GLM procedure in SAS (SAS

Institute, 2000). Analysis of variance showed that plant population density and N rates

affected yields in both crops, and suggested that a quadratic polynomial would provide a

good approximation of the true relationship between yields and both factors.

A second order polynomial regression model, shown in [1], was fitted to the data

using the response surface regression procedure, PROC RSREG, included in SAS (SAS

Institute, 2000). Multivariate second-order models have been used successfully in the

past for determining the impact of three variables on crop yield (Gallaher et al., 1972;

1975a).

Y a, + PlXl + P2X2 + 3X12 +4X22 +5X1X2 1

For the general statistical model in [1], Y denotes yield, X1 and X2 denote N rate

and plant density, respectively, while P' = (Po, P1, P2, P3, P4) TepreSents the regression

coefficient vector and a stands for the error term. Treatment means averaged over

repetitions were used, because, as Gomez and Gomez (1984) stated, variation between

experimental units receiving the same treatment is not needed to evaluate the association

between yields and treatments (plant population and N rates).

Final models were selected using the stepwise backward method and examined to

determine adequacy in predicting the response. Three-dimensional plots were generated









using MATLAB (MathWorks, 2001). Optimum conditions were investigated through

partial differentiation of [1], finding potential critical points:

(2Be p2 3 1
X, = p 4~, [2]

52ax 4a p3
X2 -24,-# [3]


aY
= P, + 2 3X, + PX 2
8X,

aY
= P2 + 2P4X2 5PX,


82Y
= 2P3


82Y
= 2P4


82Y
8X, 8X 2


The second partial derivatives test, (Abramowitz and Stegun, 1972; Stewart, 1995)

was used to discern if the critical points Xj, j=1 or 2 in equations [2] and [3] are

maximum or minimum. The discriminant D is defined as:



8X 2 8X X C32Y ZY d2Y
8= 2Y 2Y 8X 2X2 XX



For our model [1] the discriminant is:


D, = r2~ 13 5_ a (4 3 4/ 5 2
Ps 204









The second derivatives test uses D and classifies the point Xj as a maximum or

minimum according to the following statements:

82Y
(a) If D>0 and < 0, then it is a local maximum.
8X 2

82Y
(b) If D>0 and > 0, then it is a local minimum.
8Xz

(c) If D < 0, then it is a saddle point.

(d) If D = 0, higher order test must be used.



Results and Discussion

Yield production and mineral concentration in turnip and mustard harvested in

2002 differed from those harvested at 2003 (p < 0.0001). Therefore, yield and plant

nutrition data were analyzed and will be discussed separately for each year. Analysis of

variance with the corresponding df breakdown and significance level for treatments are

included in Tables 2-3 to 2-10. Data from soil tests (Table 2-2) resulted in fertilizer

recommendations only for N and K for both crops.

Yield Results for 2002

Fresh top, root, total plant (Table 2-1 1), and fresh diagnostic leaf yield (Table 2-13)

increased with increases in N rates and population density for turnip. Fresh top, root, and

total plant yield at 4 and 6 plants m-2 were significantly different (p < 0.05) from those at

2 plants m-2. However, data suggested that turnip growing at densities as high as 6 plants

m-2 Still might have not achieved optimum yield. Fresh diagnostic leaf yield decreased as

plant population increased, weighing an average of 1.5 g leaf less at 6 plants m-2 than at

2 or 4 plants m-2. These results illustrated the impact of plant competition on the size of









the leaf. Dry diagnostic leaf yields responded inversely to population density, with yield

at 6 plants m-2 Of 0.2 g leaf less than for the other two densities (Table 2-13).

Significant differences (p < 0.05) were found in dry yields among turnip growing at

higher densities compared with turnip growing at 2 plants m-2



Table 2-2. Soil test report and standard fertilization recommendation (University of
Florida, IFAS Extension Service, 2002).
2002t 20038
Soil property Turnip Mustard Turnip Mustard

pH 7.1 7.1 7.2 7.3
BpH --- --- 7.9 7.9
OM (%) 1.4 1.4 1.5 1.4
CEC (Cmol kg- ) --- --- 6.4 5.8

Macronutrients (ma kg- )
Phosphorus 143 143 >164 84
Potassium 33 33 48 39
Magnesium 52 52 56 48
Calcium 872 872 >1136 924

Macronutrients (mn kng- 1
Iron 18.8 18.8 18.2 13.8
Manganese 5.68 5.68 5.00 4.36
Copper 0.28 0.28 0.22 0.20
Zinc 5.64 5.64 1.40 0.86
Sodium 13.6 13.6 4.8 3.2


Recommendations:
Lime 0 0 0 0
Nitrogen (kg N ha-") 135 135 135 135
Phosphorus (kg P20s ha- ) 0 0 0 0
Potassium (kg K20 ha- ) 135 135 112 112
Jf One composite sample over experimental site
8 Average of two composite sample over experimental site










Table 2-3. Analysis of variance for fresh and dry turnip yield affected by three
population densities and five rates of nitrogen (2002).


Fresh Fresh Fresh Fresh
top root total diag.


leaf


Dry
top


Dry
root


Dry
total


Dry
diag.
leaf


Source of Variation df
Total 74 ---
Replicate 4 ---
Plant Population (X1) 2 +
Error(a) 8 ---
Nitrogen (X2) 4 **
X1X2 8 NSt
Error(b) 48 ---
Coefficient of
Variation, % 26.5 2
+ Significant at the 0.10 level.
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Signifieant at the 0.001 level.
Jf NS = not significant.


* + ** **


NS


NS NS NS NS NS NS


:6.0


25.0


13.7 26.2 27.0 25.3 11.6


Table 2-4. Analysis of variance for fresh and dry turnip yield affected
densities and Hyve rates of nitrogen (2003).
Fresh Fresh Fresh
top root total
Source of Variation df


by four population

Fresh
diag.
leaf


Total 79 --
Replicate 3 --
Plant Population (X1) 3 *
Error(a) 9 --
Nitrogen (X2) 4 ***
X1X2 12 NStf
Error(b) 48 --
Coefficient of
Variation, % 25.6
+ Significant at the 0.10 level.
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Signifieant at the 0.001 level.
Jf NS = not significant.


22.0


22.0


23.0




































Table 2-6. Analysis of variance for fresh and dry mustard yield affected by four
population densities and five rates of nitrogen (2003).
Fresh Fresh Fresh Fresh Dry Dry Dry Dry
top root total diag. top root total diag.
Source of Variation df leaf leaf
Total 79 -- ------ -- -- -
Replicate 3 -- ------ -- -- -- --
Plant Population (X1) 3 ** + + + *
Error(a) 9 -- ------ -- -- -- --
Nitrogen (X2) 4 *** + *** *** *** + *** ***
X1X2 12 NStf NS NS NS NS NS NS NS
Error(b) 48 --- --- --- --- --- --- --- ---


Table 2-5. Analysis of variance for fresh and dry mustard yield affected by three
population densities and five rates of nitrogen (2002).


Fresh Fresh


Fresh


Fresh
diag.
leaf


Dry
top


Dry
root


Dry
total


Dry
diag.
leaf


top root total


Source of Variation df
Total 74 ---
Replicate 4 ---
Plant Population (X1) 2 *
Error(a) 8 ---
Nitrogen (X2) 4 ***
X1X2 8 NSf P
Error(b) 48 ---
Coefficient of
Variation, % 35.3 4
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Significant at the 0.001 level.
Jf NS = not significant.


* * ***

** *** *** *** *** *** ***
NTS NS NS NS NS NS NS


0.0


34.2 15.5 13.3 33.1 27.3 30.7


Coefficient of
Variation, % 25.4 4
+ Significant at the 0.10 level.
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Significant at the 0.001 level.
Jf NS = not significant.


1.5


25.0 15.1 26.1 41.4 25.5 16.4





Table 2-7. Analysis of variance for turnip mineral concentration affected by three
population densities and five rates of nitrogen (2002).


Source of
Variation
Total


df Ca Mg K
74 --- --


N P Na Cu Fe Mn Zn


Replicate 4 --- --
Plant 2 NStf NS
Population (X1)
Error(a) 8 --- --
Nitrogen (X2) 4 NS *
X1X2 8 NS NS
Error(b) 48 --- ---
Coefficient of
Variation, % 16.5 9.2
+ Significant at the 0.10 level.
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Significant at the 0.001 level.
Jf NS = not significant.


+ NS NS NS NS + NS NS


*** ** *** NS ** + ***
NS NS NS NS NS NS NS NS


11.7 16.9 7.8 25.6 36.7 24.2 17.4 19.3


Table 2-8. Analysis of variance for turnip mineral concentration affected by four
population densities and five rates of nitrogen (2003).
Source of
Variation df Ca Mg K N P Na Cu Fe Mn Zn
Total 79 --- -- -- -- -- -- -- -- --
Replicate 3 --- -- -- -- -- -- -- -- -- --
Plant 3 NStf + + NS NS NS NS + NS NS
Population (X1)
Error(a) 9 --- -- -- -- -- -- -- -- -- --
Nitrogen (X2) 4 NS *** *** ** *** + NS NS **
X1X2 12 NS NS NS NS NS NS NS NS NS NS
Error(b) 48 --- -- -- -- -- -- -- -- -- --
Coefficient of
Variation, % 21.7 12.0 12.6 12.8 6.1 19.6 44.9 26.1 22.9 17.1
+ Significant at the 0.10 level.
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Significant at the 0.001 level.
Jf NS = not significant.





















NS + + *** ** *** *** +
NS NS NS NS NS NS NS NS


14.5 90.4 8.1 20.0 15.4 35.4 12.6 15.4


Table 2-9. Analysis of variance for mustard mineral concentration affected by three
population densities and five rates of nitrogen (2002).


Source of
Variation
Total


df Ca Mg K
74 --- --


N P Na Cu Fe Mn Zn


Replicate 4 --- --
Plant 2 NStf +
Population (X1)
Error(a) 8 --- --
Nitrogen (X2) 4 ** NS
X1X2 8 NS NS
Error(b) 48 --- ---
Coefficient of
Variation, % 11.1 9.0
+ Significant at the 0.10 level.
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Significant at the 0.001 level.
Jf NS = not significant.


+ NS


** NS + NS +


Table 2-10. Analysis of variance for mustard mineral concentration affected by four
population densities and five rates of nitrogen (2003).


Source of
Variation


df Ca Mg K


N P Na Cu Fe Mn Zn


Total 79 --- -- -- -- -- -- -- -- --
Replicate 3 --- -- -- -- -- -- -- -- -- --
Plant 3 + + NStf + NS NS NS + NS NS
Population (X1)
Error(a) 9 --- -- -- -- -- -- -- -- -- --
Nitrogen (X2) 4 ** NS *** + + *** NS + *** NS
X1X2 12 NS NS NS NS NS NS NS NS NS NS
Error(b) 48 --- -- -- -- -- -- -- -- -- --
Coefficient of
Variation, % 20.7 72.4 17.0 16.2 8.2 31.8 26.1 35.5 20.0 15.7
+ Significant at the 0.10 level.
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Significant at the 0.001 level.
Jf NS = not significant.









Dry top, root, and total plant yields at 2 plants m-2 were 72.7, 51.8 and 124.4 g m-2,

respectively, indicating larger plants at that population density (Table 2-12). Fresh yields

of mustard at 6 plants m-2 preSented significant increases (p < 0.05) of 1090.3 and 783.5 g

m-2 for top weight; and 1112.5 and 792.5 g m-2 for total plant compared to fresh yields at

2 and 4 plants m-2, TOSpectively (Table 2-14). Root yields of 58. 1 g m-2 at 2 plants m-2

was significantly smaller than root yields obtained at higher densities. Dry top, root, and

total mustard plant weights at 6 plants m-2 were 321.6, 30.2, and 351.4 g m-2,

respectively. These yields were significantly greater (p < 0.05) than yields at the lower

population densities (Table 2-15). Table 2-16 shows that fresh diagnostic leaf weight

decreased more than 3 g leaf when density increased from 2 plants m-2 to 4 or 6 plants

m-2. Dry diagnostic leaf yield per leaf decreased as population densities increased,

indicating a reduction of leaf size at higher densities.

In Tables 2-11 to 2-16 it can be observed that as N fertilization increased, fresh and

dry yields increased for both crops. These data fit quadratic polynomial regression

models with coefficients of determination (R2) greater than 86% for turnip and 89% for

mustard.

Multiple regression models were selected through a stepwise backward procedure

with factors entering into the model at the 0.05 level of probability (Rawling et al., 1998).

Tables 2-17 and 2-18 show the final equations for turnip and mustard, respectively.

Table 2-19 shows that N rates linear and quadratic factors affected turnip diagnostic

leaf yields (p < 0. 1), with little or no plant population linear effect. In mustard (Table 2-

19), linear and quadratic effects were important in explaining the differences in

diagnostic leaf yields (p < 0. 1), with models accounting for 93, 86, 91, and 94% of the









variation of fresh and dry diagnostic leaf yields for both crops, respectively (Tables 2-17

and 2-18).

The examination of F-values from regression analysis of variance was done as

reported by Gallaher et al. (1972; 1975a). Table 2-19 shows that both linear and

quadratic effects of N rates and plant population are important (p < 0.05) on influencing

turnip yields, while the interaction between linear effects occurred at the 0.05 level of

probability for fresh top yields but not for yields of other variables.

Interaction of quadratic effects was not significant. The models accounted for 94,

90, and 92% of the variation in top, root, and total fresh yields, respectively. For dry top,

root, and total yields models explained 89, 91, and 92%, respectively of the variation in

turnip response (Table 2-20).

Table 2-21 indicate that N rates linear, N rates and plant population quadratic, and

the interaction effects were significant (p < 0.05) factors in explaining differences in

mustard top and total plant yields, accounting for 96% of the variation in both fresh and

dry yields. For root yields, the most important factors were the N rates and plant

population linear and the N rates quadratic effects (p < 0.01), explaining 89 and 91% of

the variation for fresh and dry yields, respectively. The plant population linear effect did

not influence mustard root yields as it did in turnip root yields, suggesting that for the

latter, root competition must be considered (Table 2-20).

Response surface methodology was used to optimize population densities and N

rates (Figures 2-1 to 2-16). For mustard top, total, and diagnostic leaf yields canonical

analysis gave saddle-points indicating that for those variables the predicted response

surface did not have a unique optimum.













Table 2-11. Yield of fresh turnip plant and its parts for three (2002) and four (2003) population densities and five rates of nitrogen.
Year
2002 2003

Nitrogen Plants m-2 ~ Plants m-2~
Rate 2 4 6 Average 2 4 6 8 Average
- kg ha- Fresh turnip tops, g m-
0 300.2 773.6 691.2 588.38 285.8 200.0 390.0 430.0 326.58
56 531.6 988.0 915.4 811.7 403.3 485.8 746.67 700.0 584.0
112 750.4 923.2 1180.8 951.5 513.3 706.7 813.3 753.3 696.7
168 596.6 977.2 1278.0 950.6 603.3 688.3 800.0 941.7 758.3
224 542.8 989.4 1237.6 923.3 739.2 700.0 753.3 708.3 725.2
Average 544.3"f 930.3 1060.6 509.0"f 556.2 700.7 706.7
SFresh turnip roots, g m-2
0 415.6 972.0 770.2 719.38 503.3 350.0 440.8 325.0 404.88
56 841.8 1315.2 1226.2 1127.7 560.0 510.8 686.7 661.7 604.8
112 1129.6 1303.6 1209.0 1214.1 608.3 823.3 725.8 701.9
168 990.2 1353.4 1483.0 1275.5 840.0 510.8 776.7 753.3 769.7
224 853.2 1449.4 1177.4 1160.0 955.8 746.7 768.3 550.0 755.2
Average 846.1"f 1278.7 1173.2 693.5"f 593.2 699.2 603.2


Fresh turnip total plant, g m2
789.2 550.0
963.3 996.7
1122.7 1356.7
1443.3 1396.7
1695.0 1446.7
1202.5"f 1149.3


0
56
112
168
224
Average
"fLSD @ p
"fLSD @ p
8LSD @ p
8LSD @ p


715.4
1373.2
1880.0
1587.2
1396.0
1390.4J


1745.6
2303.2
2227.0
2330.4
2438.6
2208.9


1461.4
2141.8
2389.8
2761.0
2414.6
2233.7


1307.5%
1939.4
2165.6
2226.2
2083.1

= 146.07;
= 103.42;
188.58; 1
115.62; 1


830.84
1433.3
1636.7
1576.7
1521.7
1399.8


755.0
1361.7
1479.2
1695.0
1258.3
1309.8


731.38
1188.8
1398.5
1527.9
1480.4


0.05 for population (2002): top
0.05 for population (2003): top
0.05~~~~~~~ f ntoe 20) p
0.05 for nitrogen (2002): top -


S182.32; total
S97.84; total
235.38; total=
109.39; total=


= 315.71
179.16
407.58
200.31


root
root
root
root













Table 2-12 Yield of dry turnip plant and its parts for three population densities and five rates of nitrogen (2002).
Year
2002

Nitrogen ~~Plant m-2
Rate 2 4 6 Average
kg ha Dry turnip tops g m-
0 40.4 103.8 92.6 78.9 $
56 74.6 138.8 128.8 114.1
112 94.4 116.4 148.6 119.8
168 84.4 138.0 180.6 134.3
224 69.6 123.0 158.8 117.1
Average 72.7"f 124.0 141.9
~Dry turnip roots, g m-2
0 30.8 71.6 56.4 52.98
56 55.4 86.4 80.2 74.0
112 49.4 86.6 80.0 72.0
168 66.6 90.8 99.6 85.7
224 56.8 96.2 78.2 77.1
Average 51.8"f 86.3 78.9


- Dry turnip total plant, g m2
149.4
209.2
228.8
280.2
236.8
220.9
: 12.4; total = 40.0
NS; total = NS
= 16.3; total = 40.0
NS; total = NS


0
56
112
168
224
Average
"fLSD @ p
"fLSD @ p
8LSD @ p
8LSD @ p


70.8
130.0
144.0
150.8
126.2
124.4J
0.05 for population (2002): top
0.05 for population (2003): top
0.05 for nitrogen (2002): top
0.05 for nitrogen (2003): top


175.4
225.2
202.6
229.0
219.0
210.2
19.4; root
NS; root =
25.0; root
NS; root =


131.98
188.1
191.8
220.0
194.0





V


Table 2-13. Fresh and dry weight of turnip diagnostic leaf for three (2002) and four (2003)
mitrogen.
Year


population densities and five rates of


2002


2003


Plant m-2


Plant m-2


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average


Average 2 4
- Fresh turnip diagnostic leaf, g leaf
8.28 11.5 10.6
9.7 15.0 8.7
10.2 13.9 12.8
10.2 15.5 11.9
10.1 13.9 13.4
14.0"f 11.5
- Dry turnip diagnostic leaf, g 1 leaf
1.01) 1.53 1.18
1.14 1.96 1.31
1.16 1.66 1.53
1.13 1.85 1.50
1.14 2.14 1.58
1.83"f 1.42


6 8


Average


8.5
10.7
11.0
10.8
9.9
10.2J


8.9
10.4
10.4
10.7
10.5
10.2


9.4
9.7
11.7
10.7
12.7
10.8


9.8
10.8
11.2
12.7
11.3
11.2


10.38
11.1
12.4
12.7
12.8


0
56
112
168
224
Average
"fLSD @ p
"fLSD @ p
8LSD @ p
8LSD @ p


1.10
1.27
1.28
1.25
1.16
1.21J


1.08
1.18
1.20
1.14
1.17
1.16


0.85
0.96
1.02
0.99
1.10
0.98


1.15
1.16
1.36
1.28
1.54
1.30
= 0.07
= 0.17
= 0.09
= 0.19


1.13
1.11
1.43
1.52
1.25
1.29


1.258
1.39
1.50
1.54
1.63


0.05 for population (2002): Fresh diagn. leaf
0.05 for population (2003): Fresh diagn. leaf
0.05 for nitrogen (2002): Fresh diagn. leaf
0.05 for nitrogen (2003): Fresh diagn. leaf


0.77;
1.65;
0.99;
1.85;


Dry diagn. leaf
Dry diagn. leaf
Dry diagn. leaf
Dry diagn. leaf













Table 2-14. Yield of fresh mustard plant and its parts for three (2002) and four (2003) population densities and five rates of nitrogen.
Year


2002


2003


Plant m-2


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average

0
56
112
168
224
Average

0
56
112
168
224
Average
"fLSD~ @
"fLSD @ p -
8LSD~ @
8LSD @ p


Average 2
- Fresh mustard tops, g m-
743.18 586.7
1474.6 918.3
1975.0 905.0
2051.3 1193.3
2206.5 980.0
916.7J


8 Average


665.4
987.0
1452.8
1600.0
1416.8
1224.4J

27.4
54.6
59.4
73.4
75.6
58.1J

692.8
1041.2
1511.8
1673.4
1492.4
1282.3J


Plant m-2
4

564.8
1436.0
1956.2
1876.0
1823.2
1531.2

37.6
79.0
85.6
86.8
66.6
71.1

602.4
1515.0
2041.6
1962.6
1890.0
1602.3


999.2
2000.8
2516.0
2678.0
3379.6
2314.7

49.0
66.0
96.2
98.0
91.6
80.2

1048.4
2066.8
2612.2
2776.0
3471.0
2394.8


770.0
1220.0
1328.3
1461.7
973.3
1150.7


843.3
1000.0
1486.7
1370.0
1596.6
1259.3

69.2
69.3
91.4
115.5
112.4
91.6

912.5
1069.3
1578.1
1485.5
1709.1
1350.9


866.7
1291.7
1478.3
1428.3
1343.3
1281.6

98.2
70.1
104.9
123.3
105.5
100.4

964.9
1361.7
1583.2
1551.6
1448.9
1382.1


766.78
1107.5
1299.6
1363.3
1223.3



72.08
72.5
92.3
98.9
94.7


839.68
1180.0
1391.9
1462.3
1318.0


- Fresh mustard roots, g m-2
38.08 61.4 62.9
66.5 79.7 70.7
80.4 63.6 109.3
86.1 55.7 101.3
77.9 86.6 74.2
69.4"f 83.7
- Fresh mustard total plant, g m-2


781.2)
1541.0
2055.2
2137.3
2284.5


648.0
998.1
968.6
1249.0
1066.6
986.1J
=15.6; total
= 22.2; total
20.1; total=
= 24.8; total-


832.88
1290.7
1437.7
1562.9
1047.6
1234.4
:392.9
S186.7
507.2
= 208.8


0.05 for population (2002):
0.05 for population (2003):
0.05 for nitrogen (2002):
0.05 for nitrogen (2003):


387.1; root
:175.8; root
500.5; root
196.5; root













Table 2-15. Yield of dry mustard plant and its parts for three (2002) and four (2003) population densities and five rates of nitrogen.
Year


2002


2003


Plant m-2
4


Plant m-2
6 8


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average

0
56
112
168
224
Average

0
56
112
168
224
Average
"fLSD~ @
"fLSD @ p -
8LSD~ @
8LSD @ p


Average 2
- Dry mustard tops, g m-
108.38 41.4
232.0 78.8
274.9 85.2
283.7 110.9
278.3 81.7
79.6J
- Dry mustard roots, g m-2


Average


97.0
155.2
202.2
221.2
178.8
170.9J

10.60
24.0
24.0
29.0
22.4
22.0?

107.6
179.2
226.0
250.4
200.8
192.8J


82.2
226.0
272.2
259.4
229.8
213.9

12.2
30.6
29.8
34.4
24.0
26.2

94.8
256.4
302.0
293.8
254.2
240.2


145.8
314.8
350.4
370.6
426.2
321.6

19.2
29.4
33.8
34.8
33.6
30.2

164.8
344.0
383.8
405.2
459.4
351.4


88.0
96.7
118.9
141.4
79.3
104.9


62.8
97.7
105.3
126.4
144.8
107.4

13.6
15.4
16.0
20.5
20.6
17.2

76.4
113.1
121.4
146.9
165.4
124.6


98.0
143.5
126.1
145.1
103.7
123.3

18.1
14.7
19.1
23.4
18.0
18.7

116.1
158.2
145.2
168.5
121.7
141.9


72.48
104.2
108.9
131.0
102.4



13.48
15.4
16.7
18.8
16.2



86.08
119.6
125.6
149.7
118.5


14.0 8 11.9 10.2
28.0 17.7 13.7
29.2 12.8 19.1
32.7 11.0 20.1
26.6 12.8 13.4
13.3"f 15.3
-Dry mustard total plant, g m-2
122.48 53.3 98.2
259.9 96.5 110.4
303.9 98.0 137.9
304.8 121.9 161.5
316.5 94.5 92.6
92.8"f 120.1


0.05 for population (2002): top
0.05 for population (2003): top
0.05~~~~~~~ f ntoe 20) p
0.05 for nitrogen (2002): top -


= 51.3; root
= 16.3; root
66.2; root
18.2; root


=4.2; total
= 4.2; total
5.4; total
4.6; total


= 53.2
=18.6
68.6
20.8





V


Table 2-16. Fresh and dry weight of mustard diagnostic leaf for three (2002) and four (2003) population densities and five rates of
mitrogen.
Year


2002


2003


Plant m-2


Plant m-2
6 8


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average


Average


Average


- Fresh mustard diagnostic leaf,glef
13.2 8 13.4 12.9 10.5
19.0 23.0 14.4 12.8
20.2 20.0 18.3 14.9
21.6 23.1 20.0 19.4
19.6 22.4 20.1 17.3
20.4"f 17.2 15.0
- Dry mustard diagnostic leaf, g leaf
2.038 2.58 1.99 1.83
2.71 4.05 2.61 2.22
2.91 4.13 2.90 2.48
2.91 3.72 3.49 3.00
2.73 3.75 3.44 2.76
3.65"f 2.90 2.46


16.5
21.0
23.3
23.0
21.5
21.1J


11.1
18.7
19.4
21.0
16.7
17.7


11.9
17.3
18.0
20.9
20.5
17.4


12.0
14.7
17.9
17.5
18.8
16.2


12.28
16.2
17.8
19.9
19.6


0
56
112
168
224
Average
"fLSD @ p
"fLSD @ p
8LSD @ p
8LSD @ p


2.56
3.21
3.45
3.25
3.21
3.13J


1.80
2.65
2.93
2.90
2.43
2.54


1.72
2.26
2.36
2.57
2.56
2.29


1.96
2.23
2.95
2.83
2.95
2.58


2.098
2.78
3.11
3.26
3.23


0.05 for population (2002): Fresh diagn. leaf
0.05 for population (2003): Fresh diagn. leaf
0.05 for nitrogen (2002): Fresh diagn. leaf
0.05 for nitrogen (2003): Fresh diagn. leaf


1.73;
1.70;
2.23;
1.89;


Dry diagn. leaf
Dry diagn. leaf
Dry diagn. leaf
Dry diagn. leaf


0.20
0.30
0.25
0.34












Table 2-17. Multiple regression models for turnip yield, 2002-2003.

Response Model


R2
(p 5 0.05)


2002
-169.7 + 3.1X, + 339.5X2 0.01X,2 32.0 X22 + 0.4X X2

-483.9 + 7.3X1 + 620.0X2 0.02X12 67.3 X22

-834.8 + 12.0X1 + 1004.7X2 0.04X12 99.2 X22

-45.1 + 0.7X1 + 50.7X2 0.002X12 4.2 X22

-42.9 + 0.3X1 + 48.7X2 0.001X12 5.2 X22

-88.0 + 1.0X1 + 99.4X2 0.003X12 9.4 X22

7.8 + 0.03X1 + 1.1X2 0.0001X12 0.2 X22 + 0.000001X1X2

1.1 + 0.002X1 + 0.1X2 0.000007Xi2 0.01 X22


Fresh tops, g m-2

Fresh root, g m-2

Fresh plant, g m-2

Dry top, g m-2

Dry root, g nf2

Dry plant, g nf2

Fresh diagn. leaf g leaf

Dry diagn. leaf g leaf'


0.94

0.90

0.92

0.89

0.91

0.92

0.93

0.86


2003
Fresh tops, g nf2 57.7 +5.0X1 + 62.6 X2 0.01X12 -- 1.2 X22- 0.0001X12X22 0.89

Fresh root, g nf2 408.7 +3.9X, -0.01X2 0.70

Fresh plant, g nf2 776.7 +3.9X1 58.6X2 +1.0 X1X2 0.01X12 + 8.6 X22- 0.001X12X22 0.89

Fresh diagn. leaf g leaf' 97.7 + 0.07X1 13.2X2 + 1.05X22 0.73

X1: N rates (0, 56, 112, 168, and 224 kg ha- )
X2: Plant population densities: 2002 (2, 4, and 6 plants m-2)
2003 (2, 4, 6, and 8 plants m-2)

Maximum yield may have not been achieved even at the highest plant population

and N rate. A ridge analysis confirmed that maximum yields could be obtained with a

combination of higher population densities and higher N rates. The functional form of

the model given in [1] was solved using partial differentiation, in order to find the

combination of factors that would provide highest yields.

The combination of 168 kg N ha-l and 6 plants m-2 prOduced maximum fresh and

dry turnip top and total yields, while maximum root yields could be achieved at 168 kg N

ha-l and 4 plants m-2. The highest mustard fresh and dry top, root, and total yields were











also found at 168 kg N ha-l and 6 plants m-2. Fresh and dry diagnostic leaf yields for both


crops reached maximum values at 168 kg N ha-l and 2 plants m-2

Table 2-18. Multiple regression models for mustard yield, 2002-2003.

Response Model (p 5 0.05)


2002
1132.7 + 8.4X1 370.2X2 0.04X + 59.6X,2 + 1.5XX,

16.2 + 0.6X1 + 5.5X2 0.002Xi-

1142.1 + 8.9X, ;1;0 l*T 0.04X, + 59.1 X,2 + 1.5X1X,

149.7 + 1.5X1 46.3X2 0.007X12 + 8.1 X22 + 0.2X1X,

6.5 + 0.2X1 + 2.0X2 0.0009X1

156.3 + 1.7X1 43.7X2 0.007X1 + 7.9 X22 +0.2X1X,

23.4 + 0.1X1 4.8X2 0.004X12 + 0.5 X2

3.5 + 0.01X1 0.6X2 0.00004X12 + 0.04 X 2


Fresh tops, g nt-

Fresh root, g nt-

Fresh plant, g nt-

Dry top, g nt-

Dry root, g nt-

Dry plant, g nt-

Fresh diagn. leaf, g leaf

Dry diagn. leaf, g leaf'


0

0

0

0

0

0

0

0


.96

.89

.97

.96

.91

.96

.91

.94



.83

.53

.80

.69

.44

.69

.87

.89


2003
Fresh tops, g nt- 295.6 +11.5X1 +288.7X2 0.04X -- 19.8X 0

Fresh root, g nt- 23.5 +0.1X, 2.5X, 0

Fresh plant, g nt- 252.9 +3.9X1 + 32.6X2 0.01X1 0

Dry top, g nt- 38.4 + 0.7X1 6.7X2 0.002X12 0

Dry root, g nt- 9.8 + 0.02X, 0.9X, 0

Dry plant, g nt- 47.0 + 0.7X1 7.6X2 0.003X12 0

Fresh diagn. leaf, g leaf' 21.5 + 0.1X1 3.5X2 0.0002X1 + 0.3 X22 0

Dry diagn. leaf, g leaf' 4.1 + 0.01X1 0.7X2 0.00004X1 + 0.06 X2 0

X1: N rates (0, 56, 112, 168, and 224 kg ha- )
X2: Plant population densities: 2002 (2, 4, and 6 plants m-2)
2003 (2, 4, 6, and 8 plants m-2)



Individual regression analysis for N rates and plant population, as well as their


correspondent plots were performed on the variables described above as a preliminary









analysis. Based on those analyses and using the models provided by the optimization,

surface responses for fresh and dry tops, roots, total plant, and diagnostic leaf were

plotted for turnip and mustard (Figures 2-1 to 2-16).

Analyzing the marginal N effect on turnip and mustard, increases in dry diagnostic

leaf matter were obtained as N fertilization increased, fitting a quadratic polynomial with

R2 ValUeS of 0.98 and 0.84, respectively. Maximum dry diagnostic leaf yield was reached

at 145 and 147 kg N ha-l for turnip and mustard, respectively.

Yield Results for 2003

Fresh yields for 2003 presented a similar trend to those for 2002 with increases as

N rate and population densities increased. However, yields for both crops in 2003 were

reduced (p < 0.001) when compared to the previous year. This reduction was probably

due to disease and nematode build up from the previous crop season. Turnip had a high

incidence of crown rot caused either by Erwinia carotovora or Phytophthora spp. This

disease attacks the roots and lower parts of turnip leaf stems (Shattuck and Mayberry,

1998). Turnip yields were more affected the second year than mustard which did not

show the disease.

Table 2-11 show differences in fresh turnip top and total yields for 6 and 8 plants

m-2 COmpared to those for 4 and 6 plants m-2 (p < 0.0001). Turnip grown at the 8 plants

m-2 density produces more tops and total plant yield by 197.7 and 107.3 g m-2 when

compared to the 2 plants m-2 density. Fresh turnip root yields were seriously reduced by

the crown rot disease attack reaching a maximum of only 699.2 g m-2 at 6 plants m-2

Fresh yield maximum seemed to be reached at about 168 kg N ha- Increases of fresh

turnip yields were steady up to this rate. Fresh top, root, and total plant yield decreases of









33.1, 14.5, and 47.5 g m-2 were found when comparing yields at 168 kg N ha-l with 224

kg N ha- .

Turnip dry top, root, and total plant yield did not show statistical treatment effects

(Table 2-12). Fresh and dry diagnostic leaf weight (Table 2-13), also increased as N rate

increased as much as 19.9 and 3.26 g leaf respectively at 168 kg N ha- However,

diagnostic leaf weight was greater when turnip was grown at 2 plants m-2

Table 2-14 shows how mustard fresh yields responded to N rates and plant

population density. Top, root, and total plant yield increased as density increased,

weighing 364.9, 31.0, and 396 g m-2 mOre at 8 plants m-2 than at 2 plants m-2. The

highest fresh yields were reached at a rate of 168 kg N ha- with differences of 596.6,

26.9, and 622.7 g m-2, when compared with yields at 0 kg N ha- .

In 2003, as plant population density increased mustard dry top, root, and total plant

yields also increased, reaching 123.3, 18.7, and 141.9 g m-2 when planted at 8 plants m-2

(Table 2-15). Dry yields of 131.0, 18.8, and 149.7 g m-2 for top, root, and total plant were

reached at 168 kg N ha- Diagnostic leaf weighed more when planted at 2 plants m-2 than

at 8 plants m-2, with differences of 4.2 and 1.07 g leaf for fresh and dry yield,

respectively (Table 2-16). Dry weight followed the same pattern as fresh yields.

Tables 2-17 and 2-18 show the final regression models for turnip and mustard in

2003. F-values from the analysis of variance in Table 2-19 show that the linear effect of

X1 was important for turnip fresh top and diagnostic leaf yields while its quadratic effect

affected only fresh top yield (p < 0.001).





















Table 2-19. ANOVA F-values of the effects of nitrogen rate (X1) and population densities (X2) On fresh turnip yields, 2002-2003.
2002 2003


Source of Variation
Linear Xi
X2
Quadr. X12
X22
Lin .Inter X1X2
Quad.Inter X12X22


plat
** a
34.10
25.63 *
21.39 *
20.94 *


diag
35.04
6.61
26.17 *
15.02 *


root Plant
31.72** 1.73 N~
-- 0.35 Ns
6.35* 0.95 Ns
-- 0.70 Ns
-- 3.45
--6.74*


dia
15.57
13.19*

8.58


top
6.95
12.78 *
12.49 *
7.70
5.79


root
351**
35.10
27.21 *
21.39 *
20.94 *


top
44.77
2.19Ns
13.13 *
0.09 Ns


-- 8.39


-- 3.17


* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Signifieant at the 0.001 level.
Jf NS = not significant.




















Table 2-20. ANOVA F-values of the effects of nitrogen rate (X1) and population densities (X2) On dry turnip yields, 2002-2003.
2002 2003


Source of Vaiton df tp
Linear X1 1 15.04
X2 1 9.54
Quadr. X12 1 8.94
X22 1 4.23
Lin .Inter X1X2 1-
Quad.Inter X12X22
+ Significant at the 0.10 level.
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Signifieant at the 0.001 level.
Jf NS = not significant.


plat diag
21.45 9.82
23.16* 0.85Nst
12.07* 6.23*
13.55* 3.61


dia
20.12
21.54*

12.18*


root
16.80 *
39.55 *
8.34
29.93 *




















Table 2-21. ANOVA F-values of the effects of nitrogen rate (X1) and population densities (X2) On fresh mustard yields, 2002-2003.
2002 2003
Source of Variation df top root plant diag. top root plant dia
Linear X1 1 10.8 9 44.3 7 12. 10' 50.65 33.11' 8.48' 28.72' 23.83
X2 1 3.27Nst 20.28* 3.01Ns 11.33* 6.44* 10.98* 19.49* 16.02*
Quadr. X12 1 16.22* 23.71* 17.39* 30.49* 19.03* --- 16.15* 7.96*
X21 5.77* --- 5.50* 7.79* 3.14* --- --- 10.31*
Lin .Inter X1X2 1 16.81~ --- 16.31
Quad.Inter X12X22
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Signifieant at the 0.001 level.
Jf NS = not significant.




















Table 2-22. ANOVA F-values of the effects of nitrogen rate (X1) and population densities (X2) On dry mustard yields, 2002-2003.
2002 2003


Source of Vaiton df tp
Linear X1 1 17.82
X2 1 2.61Nst
Quadr. X12 1 28.42 *
X22 1 5.39
Lin .Inter X1X2 1 11.62 *
Quad.Inter X12X22
* Significant at the 0.05 level.
** Significant at the 0.01, and level.
*** Signifieant at the 0.001 level.
Jf NS = not significant.


root plant
65.31 21.56
23.63* 2.11Ns
43.46* 32.74 *
-- 4.76
--10.84*


diag
55.57
11.55 *
36.08 *
4.56


plant
15.25
15.96
9.83


diag
26.88
25.30*
11.58*


to
14.37
13.73
9.34


root
3.68
9.71


-- 14.81


































16000

E
S1200





> 00


0 56 11 68 224

Nitrogen rate, kg hal
Plants m-2 2 4 A 6

(b)


16000


1200


800


400


0


I


Plant population, plants m-2
N rate, kg ha l' + 0 56 A 112 168 )K 224


Figure 2-1. Marginal quadratic polynomials for fresh turnip top yield as affected by (a) N
rate and (b) plant population (2002).

















2000-
1800
1600

E1400
S1200

0. 1000

.c 800-

400
200



224
4 168











Figure 2-2. Surface response for fresh turnip top yield as affected by N rate and plant
population (2002).







39




1600




S100
80

f 400



0 56 11 68 224

Nitrogen rate, kg ha-1
Plants m-2, + 2 5 4 A 6



(b)

1600


a 1200


800


f 400



2 4 6

Plant population, plants m-2

N rate, kg ha 0 56 A 112 168 m 224

Figure 2-3. Marginal quadratic polynomials for fresh turnip root yield as affected by (a)
N rate and (b) plant population (2002).







40


2000-
1800
1600
1400
1200
1000
800-
600-
400
200-
0
6


4 168
112


P/a~l~00~


~atek9 ha'
FJi~i04er\


2 O


Figure 2-4. Surface response for fresh turnip root yield as affected by N rate and plant
population (2002).


























0 56 11 68 224

Nitrogen rate, kg hal
Plants m-2, + 2 5 4 AE6



(b)


2800
2400
2000
1000

1200
800
400
0


Plant population, plants m-2
N rate, kg ha 6 0 56 A 112 168 m 224


Figure 2-5. Marginal quadratic polynomials for fresh turnip total plant yield as affected
by (a) N rate and (b) plant population (2002).


2800
S2400


1 2000



S800
S400
M. O




















X


168
1 2

56o


P/al .


2 0


Figure 2-6. Surface response for fresh turnip total plant yield as affected by N rate and
plant population (2002).





~20



10






5


168


112


224


Nitrogen rate, kg hal


Plants m-2


+ 2 54 A6


r
20
r
m
1
a

3 15
>I
r
m

d, ~o
m


u,
s
u


Plant population, plants m-2

N rate, kg ha l, 4 0 56 A 112 168 )K 224



Figure 2-7. Marginal quadratic polynomials for fresh turnip diagnostic (diag.) leaf yield
as affected by (a) N rate and (b) plant population (2002).







44










20-
18
16
14

.c- 12-1--;
rj 10
8


4
LI.
2-



224


o~~ 112 18





Figure 2-8. Surface response for fresh turnip diagnostic (diag.) leaf yield as affected by
N rate and plant population (2002).












3600



1200
800,,
2400

100


0 56 11 68 224

Nitrogen rate, kg hal
Plants m-2, 2 4 A 6



(b)

36000
S3200
E2800
S2400



1200
800
S400


2 4 6

Plant population, plants m-2
N rate, kg ha 0 56 A 112 168 m 224


Figure 2-9. Marginal quadratic polynomials for fresh mustard top yield as affected by (a)
N rate and (b) plant population (2002).







46










3500-

3000

o, 2500
En
-o2000



[1 500
00
6




224
I4 168







Figure 2-10. Surface response for fresh mustard top yield as affected by N rate and plant
population (2002).






























































Plant population, plants m-2

N rate, kg ha'", 0 56 A 112 168 m 224



Figure 2-11. Marginal quadratic polynomials for fresh mustard root yield as affected by
(a) N rate and (b) plant population (2002).


I


'


200


150


100


50


0


112 168

Nitrogen rate, kg ha''
Plants m-


224



* 2 54 A6


-


-


200


150


100


50


O







48


168
112

gwoe.K6


op o


Figure 2-12. Surface response for fresh mustard root yield as affected by N rate and plant
population (2002).


























0 56 11 68 224

Nitrogen rate, kg hal
Plants m-2, 2 4 AE6


(b)


36000
3200
2800
2400
2000



800
400
0


Plant population, plants m-2
N rate, kg ha l, 0 56 A 112 168 m 224



Figure 2-13. Marginal quadratic polynomials for fresh mustard total plant yield as
affected by (a) N rate and (b) plant population (2002).


S3600
E
3200

S2400

S2000



S800
u, 400
u. O







50










3500-

3000
E.db
on 2500

2000

1500

S1000-
LL
500-




224
474 168







Figure 2-14. Surface response for fresh mustard total plant yield as affected by N rate
and plant population (2002).








































2o










2 4 6

Plant population, plants m-2
N rate, kg ha 0 56 A 112 168 m 224



Figure 2-15. Marginal quadratic polynomials for fresh mustard diagnostic (diag.) leaf
yield as affected by N rate and plant population (2002).


Nitrogen rate, kg hal


168


224


Plants m- ,


+ 2 54 A6







52








25-







S10-






224







Figure 2-16. Surface response for fresh mustard diagnostic (diag.) leaf yield as affected
by N rate and plant population (2002).

The linear and quadratic effect of plant population affected only fresh diagnostic

leaf weight (p < 0.05) in 2003. Fresh root and total plant did not respond to linear or

quadratic effects in both variables but the variables interacted with each other (p = 0.05).

Similarly, the quadratic interaction between N rates and plant population was important

for fresh top yield. The models accounted for 89, 70, and 89% of the variation in turnip

top, root, and total fresh yields, respectively (Table 2-17) in 2003.

Table 2-21 shows the F-value for mustard yields. Fresh top and diagnostic leaf

weights were affected by the linear and quadratic effects of N rates and plant population

(p < 0.05) accounting for 83 and 87% of the variation. Fresh root and total plant were









affected by the linear effect of N rates and plant population but total plant yield reflected

the quadratic effect of N (p < 0.05). Interaction effects were not observed in mustard

yields planted in 2003. Models for fresh root and total plant explained 53 and 80% of the

variation in yields. Dry weight models followed the same trend as fresh yield models,

accounting for 69, 44, 69, and 89% for dry top, root, total plant, and diagnostic leaf,

respectively in 2003.

Turnip responses in 2003 were affected by the incidence of crown rot. Maximum

fresh yields were predicted to be outside our treatment level ranges. In general, ridge

analysis suggested that high plant population density and high N rates will produce the

highest top and total plant yields. In order to reach maximum root and diagnostic leaf

yields low plant population densities and high N rates should be researched further

(Figures 2-17 to 2-24).

Mustard fresh top and total reached their maximum around 8 plants m-2 and 160 kg

N ha- Fresh root and diagnostic leaf yield analysis produced saddle points, but ridge

analysis suggested that high plant population densities and high N rates might result in

highest root yields. The same type of analysis suggested that diagnostic leaf yields might

increase at low plant population densities but with increasing N rates (Figures 2-25 to 2-

32).

Mineral Concentration Results

Nitrogen leaf concentrations indicated that both crops responded to 56 kg N ha-l

but still did not reach sufficiency levels (Table 2-1) for that mineral in any treatment.

Potassium leaf concentration was also very low. Tables 2-23 to 2-30 show mineral

concentrations in turnip and mustard diagnostic leaves over the 2 yr of the study.

Nitrogen concentration in diagnostic leaves for both crops increased as N rates increased,









reaching maximum values when the Bra~ssica~s were planted at 2 plants m-2. Cubic and

quadratic models presented a good fit for the data in 2002 and 2003 at this plant

population density (Tables 2-31 and 2-32). The R2 ValUeS for models in both years were

86 and 97% for turnip and 87 and 92 for mustard, respectively. Nitrogen sufficiency

levels were not reached in both years at any N rate for either of the crops (Table 2-1).

In 2002, turnip leaf N concentration was positively correlated with leaf Mg, P, Na,

Cu, Mn, and Zn concentrations (Table 2-33). For 2003, N was positively correlated with

fresh top and total yields, P, Cu, Fe, Mn, and Zn (Table 2-34) but in both years N

correlated negatively with Ca. For 2002 and 2003, N in mustard leaf was positively

correlated with fresh top, root, total plant yields and fresh and dry diagnostic weights.

Leaf N concentration increased when Ca, Mg, Na, Cu, Mn, and Zn leaf concentration

increased in 2002, but decreased when Fe concentration increased (Table 2-35). In 2003,

N concentration was positively correlated with Na and Mn concentrations but was

negatively correlated with Ca, K, P, and Fe leaf concentrations (Table 2-36).

In Figure 2-33 it can be observed that predicted turnip leaf N concentration

increased with N fertilization (p < 0.001) reaching a maximum around 56 to 112 kg N

ha-l in 2002 and outside the range of our experiment in 2003. Figure 2-33 shows

maximum mustard leaf N concentration was reached in both years around 168 kg N ha- .

At the peak response, leaf N concentration in the diagnostic leaf was lower than the

sufficiency levels for these crops (Tables 2-24 and 2-28) as reported by Mills and Jones

(1996) and Hochmuth et al. (1991) (Table 2-1).

Turnip and mustard P leaf concentrations reached their maximum at the same N

rates as N leaf concentration (Figure 2-34) showing responses inside the range of the









sufficiency range reported for this element in both years (Table 2-1). Cubic models fit (p

< 0.05) P concentration in mustard leaves for both years with R2 ValUeS OVer 78%.

Turnip results in 2002 and 2003 also fit a cubic model (p < 0.05) with R2 ValUeS of 85

and 53%, respectively (Tables 2-31 and 2-32).

In Tables 2-33 and 2-34, it can be observed that P in turnip was positively

correlated with K, N, Mn, and Zn in both years, but showed a negative correlation with

Fe in 2002 and with Na in 2003. Mustard P leaf concentration increased as K, Cu, and

Zn increased in both years. However, in 2003 P concentration increased as Fe

concentrations decreased (Table 2-35 and 2-36). As Mill and Jones (1996) reported

interaction among mineral are expected and some of them [NH4:K, N: S or K:(Ca+Mg)]

may be speculated to be possible causes of turnip and mustard yield decreases at highest

N rates.

Figure 2-3 5 shows that maximum K concentration in diagnostic leaf was reached

by N rates as low as 56 kg hal but even those values were still outside the sufficiency

ranges (Table 2-1). The study site was fertilized following soil test recommendations

(Table 2-2); therefore sufficiency ranges (Table 2-1) indicated by Mill and Jones (1996)

for these crops may be higher than requirements for greens growing under Florida' s

sandy soil conditions.





1400





>1600


200


-2004


56


112


168


224


Nitrogen rate, kg hal
Plants m-2, 4 2 4 A 6 8


(b)


Plant population, plants m-2
N rate, kg ha- 0 56 A 112 168 m 224


Figure 2-17. Marginal quadratic polynomials for fresh turnip top yield as affected by (a)
N rate and (b) plant population (2003).


S1200
E




O 0







57


1500 -




1000




S500-
LL


0J
8


6


168
112

4ttge fateWs


DA 2 oo


PI;


2 0


Figure 2-18. Surface response for fresh turnip top yield as affected by N rate and plant
population (2003).












16000

E
a 1200


8000


u,400


0


m


Nitrogen rate, kg ha~


168


224


Plants m-2, + 2 4 A 6 8


16000

E
1200


S800(


400


0


r


Plant population, plants m-2
N rate, kg ha ', 0 56 A 112 168 m 224



Figure 2-19. Marginal quadratic polynomials for fresh turnip root yield as affected by (a)
N rate and (b) plant population (2003).





















































Figure 2-20. Surface response for fresh turnip root yield as affected by N rate and plant
population (2003).


1500


1000-





500 -


X
X
X
X X
X


6


168


224


"Y ~a/a~,,ri


L


112


ha~'
.eW






















































Plant population, plants m-2

N rate, kg ha l, & 0 56 A 112 168 m 224


Figure 2-21. Marginal quadratic polynomials for fresh turnip total plant yield as affected
by (a) N rate and (b) plant population (2003).


5


2800

2400

2000


1600


400

0


168


224



+ 2 54 A6 *8


Nitrogen rate, kg ha '
Plants m-2s


2800

2400

2000

16000

1200

800

400

0


















X )r
x


Z


112
rate >9h


lo43aDg 4


56


2 O


Figure 2-22. Surface response for fresh turnip total plant yield as affected by N rate and
plant population (2003).


























0 56 11218 224


5


Nitrogen rate, kg hal
Plants m-2,


4 2 54 A6 *


112


168


224


Nitrogen rate, kg ha"l
Plants m-2,


* 2 54 A6 *


Figure 2-23. Marginal quadratic polynomials for fresh turnip diagnostic (diag.) leaf yield
as affected by (a) N rate and (b) plant population (2003).







63









90-





75

70

~m 65 -

m 60-

55-

50

224

DoW 4 5112

#/47 2 2 0 Fatrogen t




Figure 2-24. Surface response for fresh turnip diagnostic (diag.) leaf yield as affected by
N rate and plant population (2003).




















~=f~:


\I/


j


3600
3200
2800
2400
2000
1600


4001
0


168


224


Nitrogen rate, kg ha '
Plants m-2, + 2 4 A 6 8


3600
3200
2800
2400
2000
1600
1200
800
400
0


Plant population, plants m-2
N rate, kg ha~1 6 0 56 A 112 168 m 224


Figure 2-25. Marginal quadratic polynomials for fresh mustard top yield as affected by
(a) N rate and (b) plant population (2003).






















X 3~~~
X
x
x


168


4


04/o sb kgl@a
#/4/s2 0 Nit'ogen yete,





Figure 2-26. Surface response for fresh mustard top yield as affected by N rate and plant
population (2003).



















-r


200


150


100


50


224


Nitrogen rate, kg ha"l
Plants m-2,


+ 2 54 A6 *


200

E
a 150





S50


Plant population, plants m-2


N rate, kg hal,


* 0 56 A 112 168 m 224


Figure 2-27. Marginal quadratic polynomials for fresh mustard root yield as affected by
N rate and plant population (2003).





































6

3r)t 4
iDopo _


168
112

56 k4hsl
rate
NitroSe"


'13/a'7's~h-~


Pli


2 O


Figure 2-28. Surface response for fresh mustard root yield as affected by N rate and plant
population (2003).





3600
3200
2800
2400
2000




400
0


~4=~:


168


224


Nitrogen rate, kg ha '
Plants m-2, + 2 4 A 6 8


3600
3200
2800
2400
2000

I000

400
0


Plant population, plants m-2
N rate, kg ha ', & 0 56 A 112 168 m 224


Figure 2-29. Marginal quadratic polynomials for fresh mustard total plant yield as
affected by (a) N rate and (b) plant population (2003).

















12,
i


168
112

rat" Mha~
NitroSe"


6


%R"Yn, '
~Ola~S Ih-z


2 O


Figure 2-30. Surface response for fresh mustard total plant yield as affected by N rate
and plant population (2003).



























0 56 11218 224


Nitrogen rate, kg hal

Plants m-2, 4 2 4 A 6 8


25





*a


F 5


56 12168


224


Nitrogen rate, kg ha"l

Plants m-2,


* 2 54 A6 *


Figure 2-31. Marginal quadratic polynomials for fresh mustard diagnostic (diag.) leaf
yield as affected by N rate and plant population (2003).
































0J
8


6

1E0p/ 4


168
112


~~'"'ioo~
~O/a~ lh


Pla/;


2 O


Figure 2-32. Surface response for fresh mustard diagnostic (diag.) leaf yield as affected
by N rate and plant population (2003).






























1


Table 2-23. Mineral concentrations (Ca, Mg, and P) in turnip leaf for three (2002) and four (2003) population densities and five rates
of nitrogen.
Year


2002


2003


Plant m-2
4


Plant m-2


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average


6

15.2
12.4
12.5
13.8
13.7
13.5


Average 2
- Calcium, g kg
15.1) 17.8
13.9 14.9
13.7 14.6
13.7 18.1
14.0 14.1
17.0?


4

20.8
14.6
16.0
17.4
16.7
17.0


6 8


Average


15.0
14.4
14.0
13.7
14.5
14.3J


15.2
14.7
14.6
12.4
13.7
14.1


20.5
16.7
17.0
20.0
15.8
18.0


20.4
14.2
14.1
17.9
19.0
17.1


19.83
15.1
15.4
18.3
16.3


- Magnessium, g kg
2.368 2.13
2.32 1.92
2.24 1.94
2.14 2.12
2.17 1.86
1.99J
- Potassium, g kgl-
17.18 30.0
19.3 24.5
19.1 22.4
15.3 24.6
15.6 19.7
23.8J


0
56
112
168
224
Average

0
56
112
168
224
Average
"fLSD @=
8LSD @p


2.34
2.36
2.38
2.23
2.22
2.31J

17.2
21.1
20.0
15.2
17.2
18.1?


2.45
2.41
2.16
2.04
2.13
2.24

16.1
18.4
19.4
15.6
15.1
16.9


2.29
2.21
2.19
2.14
2.15
2.20

17.9
18.4
18.1
15.3
14.5
16.8


2.40
2.24
2.16
2.26
2.24
2.26

28.6
24.7
23.9
19.7
22.8
23.9


2.32
2.23
2.20
2.52
2.27
2.31

26.0
22.3
22.7
22.1
20.9
22.8


2.06
2.00
2.33
2.12
2.38
2.18

33.1
23.9
23.1
24.3
23.1
25.5


2.368
2.10
2.16
2.25
2.19



28.98
23.8
23.0
22.7
21.6


=0.05 for population (2002): Ca=NS; Mg=NS; K=
=0.05 for nitrogen (2002): Ca=NS; Mg=0.15; K=


1.27. "fLSD @p=0.05 for population (2003): Ca=NS; Mg=0. 17; K=2. 14
1.63. 8LSD @p=0.05 for nitrogen (2003): Ca=2.78; Mg=NS; K=2.39






























1


Table 2-24. Mineral concentrations (N, P, and Na) in turnip leaf for three (2002) and four (2003) population densities and five rates of
mitrogen.
Year


2002


2003


Plant m-2
4


Plant m-2
6

19.6
25.6
22.7
28.2
32.0
25.5


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average


6

26.3
24.9
30.1
23.0
23.4
25.6


Average 2
- Nitrogen, g kg
25.18 22.2
26.9 22.1
29.3 24.6
23.4 25.6
23.9 28.8
24.7J


4

21.4
24.9
28.4
29.4
28.0
26.4


Average


25.3
27.5
28.1
22.6
23.0
25.3J


23.8
28.2
29.7
24.6
25.2
26.3


22.1
24.9
26.9
27.8
31.0
26.6


21.38
24.4
25.6
27.8
29.9


- Phosphorus, g kg-'
3.688 3.59
4.17 3.58
4.37 3.50
3.95 3.87
4.13 3.59
3.62J
- Sodium, g kg-
0.748 0.59
0.65 0.95
0.61 0.85
0.72 0.99
0.80 0.77
0.83J


0
56
112
168
224
Average

0
56
112
168
224
Average
"fLSD @=
8LSD @p


3.74
4.31
4.54
4.01
4.31
4.18?

0.60
0.64
0.50
0.80
0.69
0.65J


3.58
4.08
4.37
4.94
4.16
4.03

0.77
0.70
0.63
0.65
0.99
0.75


3.71
4.11
4.28
3.90
3.92
3.98

0.87
0.61
0.71
0.72
0.73
0.73


3.45
3.74
3.77
3.79
4.16
3.78

0.54
0.83
0.83
0.95
0.91
0.81


3.48
3.68
3.66
3.94
3.92
3.74

0.55
0.80
0.94
0.97
0.99
0.85


3.69
3.74
3.78
3.86
3.89
3.79

0.62
0.83
0.83
0.86
1.01
0.83


3.558
3.68
3.67
3.86
3.89



0.578
0.85
0.86
0.94
0.92


=0.05 for population (2002): N=NS; P=NS; Na=NS. "fLSD @p=0.05 for population (2003): N=NS; P=NS; Na=NS
=0.05 for nitrogen (2002): N=3.58; P=0.25; Na=0.14. 8LSD @p=0.05 for nitrogen (2003): N=2.33; P=0.17; Na=0.12






























1


Table 2-25. Mineral concentrations (Cu, Fe, and Mn) in turnip leaf for three (2002) and four (2003) population densities and five rates
of nitrogen.
Year


2002


2003


Plant m-2
4


Plant m-2
6 8


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average


6

3.60
3.20
3.60
3.20
2.80
3.28


Average 2
-
- Copper, mg kg
3.278 3.50
3.80 3.75
3.80 3.50
3.67 4.00
3.13 4.00
3.75J


4

3.75
4.00
3.75
4.00
4.00
3.85


Average


3.40
3.80
4.00
3.20
3.20
3.52J


2.80
4.40
3.80
4.60
3.40
3.80


3.50
3.75
4.00
4.00
4.00
3.85


3.50
4.00
3.75
3.75
4.25
3.85


3.568
3.88
3.75
3.94
4.06


- Iron, mg kgl--
144.78 100.0
167.3 97.5
133.3 97.0
138.0 92.5
108.7 90.0
95.0J


0
56
112
168
224
Average

0
56
112
168
224
Average
"fLSD @=
8LSD @p=


140.0
162.0
144.0
156.0
110.0
142.4J

15.6
15.0
14.2
14.2
15.6
14.9J


158.0
200.0
140.0
138.0
106.0
148.4

13.6
16.4
16.6
15.0
15.4
15.4


136.0
140.0
116.0
120.0
110.0
124.4

15.0
14.2
13.2
12.6
17.0
14.4


125.0
110.0
115.0
142.5
110.0
120.5

13.5
15.8
15.3
23.5
22.3
18.1


100.0
97.5
87.5
112.5
100.0
101.5

11.3
12.5
15.3
21.8
22.5
16.7


110.0
87.5
95.0
105.0
82.5
96.0

11.0
15.5
17.8
21.3
21.5
17.4


111.38
98.1
98.1
113.1
95.6



11.58
14.6
15.6
22.9
22.7


Manganese, mg kg-
10.2
14.8
14.3
25.0
24.5
17.8J


14.7)
15.2
14.7
13.9
16.0


=0.05 for population (2002): Cu=NS; Fe=20.77; Mn=NS. "fLSD @p=0.05 for population (2003): Cu=NS; Fe=17.46; Mn=NS
=0.05 for nitrogen (2002): Cu=NS; Fe=26.81; Mn=2.41. 8LSD @p=0.05 for nitrogen (2003): Cu=0.35; Fe=NS; Mn=NS













Table 2-26. Mineral concentration (Zn) in turnip leaf for three (2002) and four (2003) population densities and five rates of nitrogen.
Year


2002


2003


Plant m-2
4


Plant m-2
Average 2 4 6
- Zinc, mg kg-
30.88 29.5 26.8 25.3
32.5 24.8 35.3 29.3
43.1 27.3 29.5 26.5
35.0 28.8 34.0 34.0
33.9 26.8 34.5 34.5
29.1T 31.4 29.9
= NS. "fLSD @ p = 0.05 for population (2003):
5.22. 8LSD @ p = 0.05 for nitrogen (2003):


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average
"fLSD~ @
8LSD @ p


Average


31.0 28.8 32.6
34.6 30.6 32.4
41.4 43.8 44.2
33.0 35.6 36.4
32.8 33.6 35.4
34.6"f 34.5 36.2
0.05 for population (2002): Zn
0.05 for nitrogen (2002): Zn


25.3
28.8
32.5
32.8
37.0
31.3
Zn = NS
Zn = 4.99


26.78
28.4
30.4
32.9
33.7






























1


Table 2-27. Mineral concentrations (Ca, Mg, and K) in mustard leaf for three (2002) and four (2003) population densities and five
rates of nitrogen.
Year


2002


2003


Plant m-2
4


Plant m-2


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average


6

17.8
14.7
15.6
13.4
14.9
15.3


Average 2
- Calcium, g kg
16.48 15.3
14.9 19.9
14.5 16.6
13.6 16.2
14.0 15.6
16.7J


4

21.3
21.2
15.9
13.6
14.5
17.3


6 8


Average


15.6
15.2
14.0
13.9
13.0
14.4J


16.0
14.7
14.0
13.5
14.2
14.5


16.8
16.8
16.1
17.8
16.4
16.8


25.2
19.7
20.4
18.2
14.9
19.7


19.68
19.4
17.2
16.5
15.4


- Magnesium, g kg~l-
1.968 1.76
2.01 2.14
2.07 1.98
2.10 2.04
2.12 2.07
2.00J
-Potassium, g kg-
18.93 20.5
17.3 21.3
17.2 18.8
17.7 18.8
17.3 14.9
18.8J


0
56
112
168
224
Average

0
56
112
168
224
Average
"fLSD @=
8LSD @p=


1.87
2.01
1.94
2.11
2.07
2.00?

19.3
16.6
17.0
18.2
17.4
17.7J


1.91
2.06
1.99
2.07
2.06
2.02

17.3
17.1
17.2
16.4
16.1
16.8


2.09
1.94
2.26
2.11
2.21
2.13

20.3
18.1
17.3
18.3
18.6
18.5


2.28
2.47
1.99
1.87
5.60
2.84

25.5
21.1
20.2
18.0
13.4
19.6


1.92
2.19
2.10
2.15
2.30
2.13

22.5
18.6
19.5
17.3
17.8
19.1


2.50
2.34
2.31
2.28
1.87
2.26

28.0
19.6
19.0
16.6
16.1
19.8


2.118
2.28
2.10
2.08
2.96



24.18
20.1
19.3
17.7
15.5


=0.05 for population (2002): Ca=NS; Mg=0.10; K=1.54. "fLSD @ p=0.05 for population (2003): Ca=1.24; Mg=0.13; K=NS
=0.05 for nitrogen (2002): Ca=2.36; Mg=1.06; K=NS. 8LSD @ p = 0.05 for nitrogen (2003): Ca=2.63; Mg=NS; K=2.36























0 16.9 15.0 16.2 16.08 14.4 19.3 18.2 17.5 17.38
56 19.8 55.8 19.5 31.7 15.8 15.8 15.7 17.0 16.0
112 20.1 17.5 23.8 20.4 17.4 20.1 17.0 16.1 17.6
168 24.8 22.8 26.6 24.7 16.8 15.5 19.0 18.9 17.6
224 22.8 22.8 26.6 24.4 17.0 18.0 21.1 18.2 18.6
Average 20.9"f 26.8 22.7 16.3"f 17.7 18.2 17.5


Table 2-28. Mineral concentrations (N, P, and Na) in mustard leaf for three (2002) and four (2003) population densities and five rates
of nitrogen.
Year


2002


2003


Plant m-2
4


Plant m-2
6 8


Nitrogen
Rate
-- kgha- -


Average 2
- Nitrogen, g kg


Average


-Phosphorus, g kg'-
4.718 3.51
4.62 4.12
4.59 3.82
4.88 3.86
4.76 3.80
3.82J
-

0.508 0.44
0.60 0.49
0.68 0.62
0.73 0.88
0.74 1.00
0.69J


0
56
112
168
224
Average

0
56
112
168
224
Average
"fLSD @=
8LSD @p=


5.03
4.70
4.77
5.17
4.72
4.89J

0.51
0.57
0.60
0.82
0.76
0.65J


4.34
4.58
4.42
4.69
4.76
4.96

0.45
0.57
0.66
0.61
0.67
0.59


4.75
4.54
4.58
4.79
4.79
4.56

0.53
0.67
0.78
0.77
0.78
0.71


4.05
3.98
3.98
3.68
3.39
3.81

0.62
0.54
0.66
0.75
0.95
0.70


3.66
3.60
3.69
3.85
3.76
3.71

0.47
0.85
0.61
0.85
1.02
0.76


4.30
3.99
3.75
3.88
3.50
3.89

0.42
0.70
0.78
1.04
0.89
0.77


3.888
3.92
3.81
3.82
3.61



0.498
0.65
0.67
0.88
0.96


=0.05 for population (2002): N=
=0.05 for nitrogen (2002): N=


NS; P= 0.21; Na= 0.07. "fLSD @p =
15.50; P= 0.27; Na= 0.09. 8LSD@ p


0.05 for population (2003): N = 1.89; P
= 0.05 for nitrogen (2003): N= 2.12; P=


= NS; Na
0.27; Na=


=NS
0.16













Table 2-29. Mineral concentrations (Cu, Fe, and Mn) in mustard leaf for three (2002) and four (2003) population densities and five
rates of nitrogen.
Year
2002 2003

Nitrogen Plant m-2 --~ Plant m-2
Rate 2 4 6 Average 2 4 6 8 Average
-- kg ha- Copper, mg kg-
0 2.40 2.40 2.40 2.408 3.00 3.25 3.00 3.25 3.138
56 3.00 2.40 2.60 2.67 3.00 3.00 2.75 2.75 2.88
112 2.80 2.40 2.80 2.67 3.00 3.25 3.50 3.00 3.19
168 3.40 3.00 3.00 3.13 3.00 2.75 3.00 2.50 2.81
224 2.60 2.80 2.80 2.73 2.50 2.50 3.50 2.50 2.75
Average 2.84t 2.60 2.72 2.90- 2.95 3.15 2.80
Iron, mg kg-
0 298.0 298.0 216.0 270.78 410.0 237.5 247.5 225.0 280.08 t
56 136.0 106.0 100.0 114.0 280.0 257.5 205.0 182.5 231.3
112 118.0 92.0 90.0 100.0 227.5 215.0 210.0 197.5 212.5
168 100.0 102.0 84.0 95.3 195.0 240.0 207.5 162.5 201.3
224 128.0 112.0 88.0 109.3 225.0 282.5 192.5 162.5 215.6
Average 156.0"f 142.0 115.6 267.5"f 246.5 212.5 186.0
Manganese, mg kg-l
0 16.6 16.2 17. 4 16.78 18.0 18.3 15.8 16.0 17.08
56 15.0 15.4 13.8 14.7 19.5 20.3 17.8 17.5 18.8
112 15.4 14.8 16.4 15.5 19.0 20.0 19.3 23.0 20.3
168 18.2 18.2 17.4 17.9 19.3 19.3 22.3 23.0 20.9
224 20.6 19.0 17.2 18.9 28.5 26.8 28.0 19.0 25.6
Average 17.2"f 16.7 16.4 20.9"f 20.9 20.6 19.7
"fLSD @p=0.05 for population (2002): Cu=NS; Fe=29.01; Mn=NS. "fLSD @p=0.05 for population (2003): Cu=NS; Fe=56.47; Mn=NS
8LSD @p=0.05 for nitrogen (2002): Cu= 0.35; Fe=34.45; Mn=1.71. 8LSD @p=0.05 for nitrogen (2003): Cu=NS; Fe=64.25; Mn=3.04




















Average

26.18
24.4
25.2
24.1
23.9


Table 2-30. Mineral concentration (Zn) in mustard leaf for three (2002) and four (2003) population densities and five rates of
mitrogen.
Year


2002


2003


-Plants m-2
4


Plants m-2
6 Average 2 4 6 8
~ Zinc, mg kg-
4 26.88 23.8 27.0 26.0 27.5
0 25.7 27.5 26.0 21.3 23.0
6 26.5 23.5 25.8 23.8 27.8
4 29.1 25.8 24.0 24.0 22.5
4 29.5 26.0 23.5 25.5 20.5
6 25.3"f 25.3 24.1 24.3
i): Zn = 2.55. "fLSD @ p = 0.05 for population (2003): Zn = NS
Zn = 3.29. 8LSD @ p = 0.05 for nitrogen (2003): Zn = NS


Nitrogen
Rate
- kg ha- -

56
112
168
224
Average
"fLSD~ @
8LSD @ p


28.6 26.4 25.
28.0 25.2 24.(
30.8 24.2 24.(
31.8 27.2 28.
30.6 27.4 30.
30.0"f 26.1 26.(
0.05 for population (2002
0.05 for nitrogen (2002):










Table 2-31i. Multiple regression models for turnip leaf mineral concentration at 2 plants
m-2, 2002-2003.
R2
Response Model (p I 0.05)


2002
25.1 + 0.13X, 0.0014X, +4 410- X

3.7 + 0.02X1 0.0002X1 + 6*10 7X1

17.1 + 0.18X1 0.002X 2+ 610-6X13

15.1 0.02X1 + 7*10 5X1

2.3 + 0.002X1 3*10 5X1 + 7*10 8X1

142.3 + 0.19X1 + 0.0005X1 9*10-6X1

15.6 0.005X1 0.0001X1 + 8*10- X1

3.4 + 0.02X1 0.0002X12 + 5*10 7X1

30.4 + 0.18X1 0.0013X1 + 2*10-6X1

0.62 0.003X1 0.0002X1 + 5*10 7X1


2003
22.5 + 0.004X, + 0.0001X,

3.6 0.006X1 + 9*105X12 3*10 7X1

28.8 0.08X1 + 0.0002X1

18.0 0.14X1 + 0.002X12 5*10-6X1

2.14 0.009X1 + 0.001X1 3*10- X1

99.8 0.02X1 9*10 5X1

10.8 0.01X1 + 0.001X12 3*10-6X13

3.53 + 0.001X1 + 6*10-6X1

29.4 0.16X1 + 0.0018X1 5*10-6X1

0.61 + 0.007X1 -4*105 X1 + 5*10 8X1


Nitrogen, g kg-1

Phosphurus, g kg-1

Potassium, g kg-1

Calcium, g kg-1

Magnesium, g kg-1

Iron, mg kg-1

Manganese, mg kg-

Copper, mg kg-1

Zinc, mg kg-1

Sodium, g kg-1



Nitrogen, g kg-1

Phosphurus, g kg-1

Potassium, g kg-1

Calcium, g kg-1

Magnesium, g kg-1

Iron, mg kg-1

Manganese, mg kg-1

Copper, mg kg-1

Zinc, mg kg-1

Sodium, g kg-1


X1: N rates (0, 56, 112, 168, 224 kg ha- )










Table 2-32. Multiple regression models for mustard leaf mineral concentration at 2
plants m-2, 2002-2003.
R2
Response Model (p I 0.05)


2002
17.2 + 0.005X1 + 0.0005Xi2 2*10-6XI3

5.05 0.017X1 + 0.002X12 6*10 7X13

19.3 0.09X1 + 0.0009X12 2*10-6X13

15.7 0.012X1 + 2*10-6X12

1.88 + 0.002X1 3*10-6X12

295.3 3.9X1 + 0.02X12 5*10 5X 3

16.7 0.06X1 + 0.0006X12 1*10-6X13

2.45 + 0.004X1 + 5*10 "X12 3*10 7X13

28.5 0.04X1 + 0.0008X12 3*10-6X13

0.52 0.002X1 + 4*10 5X12 1*10 7X13


2003
14.4 + 0.03X1 0.0001X12

3.5 + 0.02X1 0.0002X12 + 4*10 7X13

20.6 + 0.01X1 0.0002X12

15.5 + 0.13X1 0.001 X12+ 4*10-6X12

1.8 + 0.01X1 96*10 5X12 + 2*10 7X13

407.1 2.54X1 + 0.008X12

17.9 + 0.09X1 0.0014X12 + 5*10-6X13

2.9 + 0.003X1 2*10 "X12

24.1 + 0.08X1 0.0009X12 + 3*10-6X13

0.4 0.002X1 + 4*10 "X12 1*10 7X13


Nitrogen, g kg-1

Phosphurus, g kg-1

Potassium, g kg-1

Calcium, g kg-1

Magnesium, g kg-1

Iron, mg kg-1

Manganese, mg kg-

Copper, mg kg-1

Zinc, mg kg-1

Sodium, g kg-1



Nitrogen, g kg-1

Phosphurus, g kg-1

Potassium, g kg-1

Calcium, g kg-1

Magnesium, g kg-1

Iron, mg kg-1

Manganese, mg kg-1

Copper, mg kg-1

Zinc, mg kg-1

Sodium, g kg-1


X1: N rates (0, 56, 112, 168, 224 kg ha- )









In general, K concentration increased with increases in P leaf concentration and

decreases in Na concentrations (Tables 2-33, 2-34, 2-35, and 2-36). Figures 2-36, 2-38

and 2-41 show that Ca, Fe, and Zn leaf concentrations in general were affected by N rates

in both 2002 and 2003, reaching sufficiency levels reported by Mills and Jones (1996)

and Hochmuth et al. (1991) (Table 2-1) for both crops.

Magnesium and Cu leaf concentrations did not reach the sufficiency levels (Table

2-1) but are close to them (Figure 2-37 and 2-40). Manganese leaf concentration for both

turnip an mustard (Figure 2-39) differed from the sufficiency levels reported by Mill and

Jones (1996) and Hochmuth, et al. (1991). Figure 2-42 shows that turnip and mustard Na

leaf concentrations were higher than those suggested by Mills and Jones (1996); and

Hochmuth et al. (1991) (Table 2-1).

Summary

Analyses indicated that N rates and plant population densities are important factors

in predicting turnip and mustard yields, with expected improvements in growth at their

optimum levels. These results are consistent with data reported by others in the literature

where individual effects of N rates and plant population densities have been reported to

affect yields (Alt et al., 2000a; 2000b; Maynard et al., 2002; Moore and Guy, 1997;

Momoh and Zhou, 2001).

Response surface methodology and multiple regression models are important

techniques to model growth in vegetables. Those techniques were used in this study to

find the relationship between population density and N rates affecting turnip and mustard

yields and to establish a set of recommendations in order to maximize yields.