Citation
Irrigation and Nitrogen Best Management Practices in Corn Production

Material Information

Title:
Irrigation and Nitrogen Best Management Practices in Corn Production
Creator:
Zamora Re, Maria Isabel
Publisher:
University of Florida
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Agricultural and Biological Engineering
Committee Chair:
Dukes,Michael D
Committee Co-Chair:
Rowland,Diane L
Committee Members:
Migliaccio,Kati White
Graham,Wendy Dimbero
Kaplan,David A
Graduation Date:
5/3/2019

Subjects

Subjects / Keywords:
corn
nitrogen
production

Notes

General Note:
Nitrogen (N) is an essential component for crop growth. In most cropping systems soil N supply limits plants yields, hence, the application of N fertilizers is required. However, excessive N fertilizers applications are threatening water quality and the environment. Therefore, irrigation and N best management practices (BMPs) were evaluated as alternatives for corn production in sandy soils in Live Oak, Florida. The experimental design consisted of a randomized complete block arranged in a split plot with four replicates. Five irrigation treatments (GROW, mimics grower irrigation practices, SWB, uses the checkbook method; SMS, uses soil moisture sensors and activates irrigation using field capacity and 50% maximum allowable depletion thresholds; Reduced, applied 60% of GROW treatment; and NON, non-irrigated) and three fertility rates (low, medium and high = 336, 247 and 157 kg N/ha) were evaluated. Soil samples and tissue samples were collected and analyzed for N. Irrigation had a positive effect on final biomass, and on final ear and stem N uptake. Significant differences were found only versus the NON treatment. Water and N balances during the corn-peanut-corn rotation were simulated using DSSAT. Mineralization in the fallow periods ranged from 31 to 58 kg N/ha. This represents a potential N contribution for the following crop, or N leached after rainfall events. Average leaching amounts were 53, 23 and 42 kg N/ha during the fallow periods 2015-16, 2016-17 and 2017-18. In 2015 corn season, the SMS low, medium and high rates resulted in 30%, 54% and 84% less leaching compared to the GROW on the same rates. In 2017 season, SMS resulted in 59%, 15% and 3% N leaching reduction compared to the GROW, respectively. Heavy rainfall events in 2017 caused similar leaching amounts, particularly in the high N rate. Although rainfall was the main driver of leaching events; GROW high frequency irrigations resulted in lower N uptake and greater N leaching; whereas the SMS allowed a greater N uptake and reduced N leaching. Significant differences in corn yield were found between the irrigated and the NON treatment during all years; except in 2015 (i.e. SWB and NON yields were not significantly different). Water savings achieved by SWB, SMS and Reduced treatments were: 42%, 53% and 34% in 2015; 39%, 43% and 37% in 2016; and 42%, 45% and 36% in 2017 compared to GROW. In the three corn seasons, the medium N rate resulted in average yields with no statistical difference than the high N rate yields; while reducing N fertilizer applications by 27%. SWB, SMS and Reduced along with the medium N rate, constitute BMPs in corn production which can help reduce irrigation and N fertilizer leaving the rootzone without impacts in yield compared to conventional practices.

Record Information

Source Institution:
UFRGP
Rights Management:
All applicable rights reserved by the source institution and holding location.
Embargo Date:
11/30/2019

Downloads

This item has the following downloads:


Full Text

PAGE 1

IRRIGATION AND NITROGEN BEST MANAGEMENT PRACTICES IN CORN PRODUCTION By MARIA ISABEL ZAMORA RE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR TH E DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 201 9

PAGE 2

© 201 9 María Isabel Zamora Re

PAGE 3

To God and to my family

PAGE 4

4 ACKNOWLEDGMENTS I foremost thank God, without Him nothing would it be possible. I woul d like to thank those people who had supported me during this journey, those who had contributed during the process, and those who had helped me to complete it ; being with me since the beginning. This project was supported by the Florida Department of Agri culture Consumer and Statistics (FDACS), without their funding, this project would not have been possible. I would like to give special thanks to Michael Boyette, Ben Broughton, Bob Hochmuth and all the staff at the North Florida Research and Education Cen ter Suwannee. The field study would not have been possible without their help and expertise. I thank the staff at the Agricultural and Biological Engineering Department (ABE) Pat Rush, James Carter and Juan Brice ño , and especial thanks to Michael Gutierr ez and Marc Thomas, who helped and encouraged me during long and hard days. As well, I thank my ABE labmates and colleagues, especially to Sagarika Rath, David Hensley , Jason Merrick, Justice Diamond and Eliza Breder . I am very thankful for all my friends from Gainesville and from around the world , especially to Adilia Blandón , Ixchel Hernández, Carlos Vaca, Kenny Trigueros, César Moreira, Mary Szoka, Bernard Cardenas, Emmanuel Torres, Marco Pazmiño, Jaime Chá vez, Patricia Moreno , Enrique Orozco and Pamel a Metadjer and many more that encourage d me to continue strong along this process. I thank the faculty members who served on my Committee: Kati Migliaccio, Diane Rowland, David Kaplan and Wendy Graham, and especially my advisor Dr. Michael Dukes for giving m e the opportunity to join this project and for his contribution in my professional life. I am thankful for their mentorship and encouragement to complete this project successfully. I would like to give special thanks to my love Aaron Goyette; his

PAGE 5

5 support, patience and unconditional love were undenia bly my force to get through the . I am very y, for their support and love during the entire program , and especially for being my family in th e United States . I am so thank ful with my fami ly, my grand parents in heaven; Payín and abuelit o Chepe and my grandmothers; Pita and abuelita Salom é, my cousins , especially with Jay and Sabri and my cousin José B who supported me since the beginning of my d octoral program . I am extremely thankful with my mom, my dad, my brothers and my little nephew; who were the strong engine that motivated me to pursue this goal. T heir amazing love and support was the light that always bright from the beginning until the e nd of this journey .

PAGE 6

6 TABLE OF CONTENTS page ACKNO WLEDGMENTS ................................ ................................ ................................ . 4 LIST OF TABLES ................................ ................................ ................................ ......... 10 LIST OF FIGURES ................................ ................................ ................................ ...... 12 ABSTRACT ................................ ................................ ................................ .................. 17 CHAPTER 1 LITERATURE REVIEW ................................ ................................ ......................... 19 Background ................................ ................................ ................................ ........... 20 Nitrate Issues at the Suwanee River Basin (SRB) ................................ ........... 20 Impacts of Nitrogen on Human Health and Environment ................................ . 24 Nitrate Concentrations in Florida Springs ................................ ........................ 27 Regulations ................................ ................................ ................................ ..... 32 Agriculture: Importance, Inputs and Consequences ................................ ............... 34 Corn Physiology and Corn Production in Florida ................................ ............. 36 Water and Nitrogen Use ................................ ................................ .................. 40 Alternative Reduced Irrigation Studies ................................ ........................... 41 Nitrogen Rate and Irrigation Rate Studies ................................ ....................... 44 Nitrogen Leaching Studies ................................ ................................ .............. 4 8 BMPs ................................ ................................ ................................ .............. 54 Water Quantity and Quality BMPs for Field Crops in Florida ........................... 54 Nutrient management ................................ ................................ ............... 54 Irrigation management ................................ ................................ .............. 59 Nitrogen and irrigation BMPs previous research ................................ ....... 67 Economics ................................ ................................ ................................ 71 BMPs improvement development ................................ ........................... 72 Goal ................................ ................................ ................................ ................ 79 Objectives ................................ ................................ ................................ ....... 79 2 DEVELOPING A METHODOLOGY FOR IRRIGATION SCHEDULING IN CORN ................................ ................................ ................................ .................... 83 Introduction ................................ ................................ ................................ ............ 83 Florida Water Withdrawals and Irrigation Practices ................................ ......... 83 Soil Water Fundamental Concepts ................................ ................................ .. 84 Irrigation Scheduling and Methods to Measure Soil Water Content ................. 86 Capacitance Probe Studies ................................ ................................ ............. 87 Objectives ................................ ................................ ................................ .............. 92 Field Experiment ................................ ................................ ................................ .... 93

PAGE 7

7 Irrigation Treatments ................................ ................................ ....................... 93 Capacitance Probes ................................ ................................ ........................ 94 Root System Water Consumption Pattern (RSWC) and Field Capacity Determination ................................ ................................ ................................ ..... 96 SMS Irrigation Scheduling Proposed Methodology ................................ ................ 97 SMS Methodology Evaluation: Soil Water Dynamics Principles (SWDP) Code ................................ ................................ ................................ ............ 98 SMS Methodology Evaluation: SMS Methodology Effectiveness Daily Drainage Simulation ................................ ................................ ................... 100 Results ................................ ................................ ................................ ................ 100 Comparison of Irrigation Scheduling Methodologies at Different Crop Growth Stages ................................ ................................ ........................... 101 Evaluation of SMS Methodology Using SWDP Code ................................ .... 107 Effectiveness of Irrigation Scheduling Methodologies Drainage Simulations ................................ ................................ ................................ 110 Water Savings and Final Grain Yields ................................ ........................... 116 Conclusions ................................ ................................ ................................ ......... 116 3 EFFECT OF IRRIGATION AND NITROGEN FERTILITY RA TES ON CORN NITROGEN UPTAKE AND YIELD ................................ ................................ ....... 141 Introduction ................................ ................................ ................................ .......... 141 Hypotheses ................................ ................................ ................................ ......... 150 Objectives ................................ ................................ ................................ ............ 150 Materials and Methods ................................ ................................ ........................ 151 Experimental Field ................................ ................................ ........................ 151 Weather ................................ ................................ ................................ ........ 151 Experimental Design and Analysis ................................ ................................ 152 Irrigation Treatments ................................ ................................ ..................... 152 Nitrogen Fertility Rates ................................ ................................ .................. 154 Biomass Sampling and N Analysis ................................ ................................ 156 Nitrogen Use Efficiency Calculations ................................ ............................. 157 Harvest ................................ ................................ ................................ ......... 158 WUE Calculations ................................ ................................ ......................... 159 Statistical Analysis ................................ ................................ ........................ 161 Results ................................ ................................ ................................ ................ 161 Weather Conditions ................................ ................................ ....................... 161 Fertility ................................ ................................ ................................ .......... 163 In Season Nitrogen Uptake ................................ ................................ ........... 163 Irrigation ................................ ................................ ................................ ........ 167 End Season Nitrogen Use Efficiency (Nue grain) ................................ ........ 172 Yield ................................ ................................ ................................ .............. 173 Irrigation vs grain yield ................................ ................................ ............ 173 Fertility rat es vs grain yield ................................ ................................ ...... 175 Water Use Efficiency and Other Indices ................................ ........................ 175 Summary and Conclusions ................................ ................................ .................. 182

PAGE 8

8 4 WATER AND NITROGEN BUDGET DYNAMICS FROM A CORN FALLOW PEANUT ROTATION ................................ ................................ .......................... 207 Introduction ................................ ................................ ................................ .......... 207 The Up per Floridan Aquifer and the Suwannee River Basin .......................... 207 Regulations ................................ ................................ ................................ ... 209 Corn production ................................ ................................ ............................. 211 Crop simulation models ................................ ................................ ................. 213 Hypotheses ................................ ................................ ................................ ......... 219 Objectives ................................ ................................ ................................ ............ 219 Materials and Methods ................................ ................................ ........................ 220 Study Site ................................ ................................ ................................ ...... 220 Irrigation Treatments ................................ ................................ ..................... 220 Nitrogen Fertility Rates ................................ ................................ .................. 221 Experimental Data for Evaluation of Model Performance .............................. 221 DSSAT crop simulati on models ................................ .............................. 223 Model inputs ................................ ................................ ........................... 223 Model calibration ................................ ................................ ..................... 225 Model perfo rmance evaluation ................................ ................................ 228 Results ................................ ................................ ................................ .......... 229 Model performance ................................ ................................ ................. 229 Water ba lance ................................ ................................ ......................... 239 Nitrogen balance ................................ ................................ ..................... 243 Conclusions ................................ ................................ ................................ ......... 250 5 EFFECT OF INCRE ASING TEMPERATURE AND RAINFALL ON SIMULATED GROWTH AND YIELD IN A CORN PEANUT ROTATION UNDER DIFFERENT MANAGEMENT PRACTICES ................................ ......................... 285 Introduction ................................ ................................ ................................ .......... 285 Objectives ................................ ................................ ................................ ............ 288 Materials and Methods ................................ ................................ ........................ 289 DSSAT Crop Simulation Models ................................ ................................ ... 289 Model Data Inputs Crop Management ................................ ........................ 291 Climate Data ................................ ................................ ................................ . 293 Baseline ................................ ................................ ................................ ........ 293 Sensitivity Analysis ................................ ................................ ........................ 294 Results ................................ ................................ ................................ ................ 295 Climate Treatment Comparison during Baseline Weather P eriod (1980 2010) ................................ ................................ ................................ ......... 295 Increasing Temperature ................................ ................................ ................ 299 Corn ................................ ................................ ................................ ........ 299 Peanut ................................ ................................ ................................ .... 302 Increasing Rainfall ................................ ................................ ......................... 303 Corn ................................ ................................ ................................ ........ 3 03 Peanut ................................ ................................ ................................ .... 304 RCPs 4.5 and 8.5 Combined Effects ................................ .......................... 304

PAGE 9

9 Corn ................................ ................................ ................................ ........ 304 Peanut ................................ ................................ ................................ .... 305 Effect of Temperature and Rainfall Increase on Irrigation, Drainage and N Leaching in Corn ................................ ................................ ........................ 305 Effect of Temperature and Rainfall Increase on Irrig ation, Drainage and N Leaching in Peanut ................................ ................................ .................... 310 Conclusions ................................ ................................ ................................ ......... 314 6 SUMMARY AND FINAL REMARKS ................................ ................................ .... 334 LIST OF REFERENCES ................................ ................................ ............................ 341 BIOGRAPHICAL SKETCH ................................ ................................ ......................... 359

PAGE 10

10 LIST OF TABLES Table page 2 1 Field capacity and 5 0% maximum allowable depletion used during the corn stages for SMS irrigation scheduling methodology. ................................ ......... 120 2 2 Evaluation of irrigation scheduling method ologies used in the GROW and SMS treatments during 2015 17 corn growing seasons. ............................... 121 3 1 Taxonomic classification of predominant soils in NFREC SV, Live Oak, Florida. ................................ ................................ ................................ ............ 185 3 2 Crop coefficient values for maize used to calculate ET C for treatments under non water stress conditions and for schedule irrigation in the SWB treatment. 186 3 3 Nitrogen fertilization rates dates, amounts and types applied during corn growing seasons 2015 2017 at NFREC SV. ................................ .............. 187 3 4 Nitrogen use efficiency calculated across low, me dium and high N rates during corn growing seasons 2015, 2016 and 2017. ................................ ....... 189 3 5 Grain yield, N applied, and NUE per N fertility rates resulted in corn growing seasons 2015 2017. ................................ ................................ ........................ 190 3 6 Grain yield , irrigation applied, rainfall and irrigation efficiency indices (IWUE, WUE i , WUE t , GRSPUE) per irrigation treatments during 2015 2017corn growing seasons. ................................ ................................ ............................ 191 3 7 Grain yield , irrigation applied, rainfall and WUE i per irrigation treatments during corn growing seasons 2015 2017. ................................ ........................ 192 4 1 Taxonomic classification of pred ominant soils in NFREC SV, Live Oak, Florida ................................ ................................ ................................ ............. 255 4 2 Soil chemical analysis. Pre planting average initial soil conditions in corn field, NFREC SV 2015. ................................ ................................ ................... 256 4 3 Nitrogen fertilizati ons rates dates, amounts and types applied during corn 2015, peanut 2016 and corn 2017 growing seasons at NFREC SV. ............. 258 4 4 Cultivar geneti c coefficients calibrated for McCurdy 84aa in CERES Maize model. ................................ ................................ ................................ ............. 259 4 5 Cultivar coefficients modified for corn production simulations in CERES Maize model. ................................ ................................ ................................ ... 260

PAGE 11

11 4 6 Methods used in the DSSAT CERES maize model for the crop rotation simulations. ................................ ................................ ................................ ..... 261 4 7 Planting and harvest dates from field experiment used in the DSSAT CER ES Maize model for the crop rotation simulations. ................................ ... 262 4 8 Initial conditions from field experiment used in the DSSAT CERES Maize model for the crop rotation simulations. ................................ ........................... 263 4 9 Model performance indices for soil moisture across GROW, SMS and NON treatments at high N rates during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation. ................................ ................................ .................. 264 4 10 Model performance indices for soil nitrate across GROW, SMS and NON treatments at high, medium and low N rates during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation. ................................ .............. 265 4 11 Model performance indices for N uptake across all treatments, irrigated only and rainfed only per crop seasons ................................ ................................ ... 266 4 12 Model performance indices for fina l aboveground biomass across all treatments, irrigated only and rainfed only per crop seasons (corn 2015, peanut 2016 and corn 2017). ................................ ................................ .......... 267 4 13 Model performance indices for grain/pod yields acr oss all treatments, irrigated only and rainfed only per crop seasons. ................................ ............ 268 4 14 Simulated water balance for GROW, SMS and NON treatments during corn 2015, peanut 2016 and corn 2017 growing seaso ns and fallow periods 2015 16 and 2016 17. ................................ ................................ .............................. 269 4 15 S imulated nitrogen balance for GROW, SMS and NON treatments during corn 2015, peanut 2016 and corn 2017 growing seasons and fallow periods 20 15 16 and 2016 17. ................................ ................................ ..................... 270 5 1 Methods used in DSSAT for long term crop rotation simulations. ................... 318 5 2 Soil characteristics and initia l conditions from field experiment used in the DSSAT for the long term crop rotation simulations. ................................ ......... 319 5 3 Planting and harvest dates from 2015 16 field experiment used in the DSSAT CERES Maize model for the long term crop rotation simulations. ................... 320

PAGE 12

1 2 LIST OF FIGURES Figure page 1 1 Suwannee River Basin ................................ ................................ ...................... 81 1 2 Median NO3 N + NO2 N concentrations obtained in fourteen main springs fr om the Suwanee River Basin monitored by the Florida Springs Initiative Monitoring Network from 2001 to 2006 ................................ .............................. 82 2 1 Field capacity determination guidelines proposed by Zotarelli et al. 2013 using volumetric water content monitored by soil moisture sensors. ................ 122 2 2 Screenshot of real ti me soil moisture data collected in the experimental field. . 123 2 3 Soil moisture content at individual depths where roo t development and drainage can be determined in the soil profile across th e growing season. ..... 124 2 4 Selected depths based on root growth development for analysis of soil moisture content during the corn growing season. ................................ .......... 125 2 5 Example of sensor based irrigation scheduling at approximate V3 V6 corn stages using a 30 cm root zone.. ................................ ................................ ..... 126 2 6 Example of calendar based irrigation scheduling at earl y corn growth stages using a 30 cm root zone. ................................ ................................ ................. 127 2 7 Example of sensor based irrigation scheduling at VT Reproductive corn stages using a 60 cm root zone.. ................................ ................................ ..... 128 2 8 Example of irrigation scheduling using calendar based irrigation scheduling method in a 60 cm root zone depth. ................................ ................................ 129 2 9 Projections of near future VWC reductio ns estimated using the average ET from previous dates . ................................ ................................ ........................ 130 2 10 Volumetric water content monitored at GROW, SMS and NON irrigation treatment s during early reproductive stages in corn 2016. ............................... 131 2 11 Evaluation of sensor based irrigation scheduling methodology for 30 cm root zone depth using SWDP code for FC determination, and root system w ater consumption pattern . ................................ ................................ ....................... 132 2 12 Evaluation of calendar based irrigation scheduling methodology for a 30 cm root zone depth using SWDP code for FC determination and root system water consumption pattern . ................................ ................................ ............. 133

PAGE 13

13 2 13 Evaluation of sensor based irrigation scheduling methodology for 60 cm root zone depth using SWDP code for FC determination and root system water consumption pattern . ................................ ................................ ....................... 134 2 14 Evaluation of calendar based irrigation scheduling methodology for 60 cm root zone depth using SWDP code for FC determination and root system water consumption pattern . ................................ ................................ ............. 135 2 15 Effect of irrigation scheduling methodology on simula ted daily drainage during 201 5 corn season . ................................ ................................ ................ 136 2 16 Effect of irrigation scheduling methodology on simulated daily drainage during c orn 2016. ................................ ................................ ............................ 137 2 17 Effect of irrigation scheduling methodolo gy on simulated daily drainage during corn 2017. ................................ ................................ ............................ 138 2 18 Cumulative i rrigation applied by GROW, SMS and NON treatments during corn growing seasons 2015 17. ................................ ................................ .... 139 2 19 Corn grain yield for calendar based and sensor based irrigation scheduling methodologies used t o trigger irrigation in the GROW and SMS treatments during corn growing seasons 2015 17. ................................ ......................... 140 3 1 Aerial view of experimental site located at North Florida Research and Education Center Suw annee Valley , near Live Oak, Florida. ........................ 193 3 2 Daily weather data at NFREC SV, Live Oak, Florida during 2015 2017. ....... 194 3 3 Monthl y temperature means compared to historical temperature (2002 2017) and cumulative growing degree days over the thre e corn growing seasons . ... 195 3 4 Monthly rainfall means and cumulative daily ra infall over the three growing seasons compared to historical rainfall (2002 2017) . ................................ ....... 196 3 5 Total N applied per growing season and average total N uptake in aboveground biomass at key corn growt h stages across N fertility rates . ........ 197 3 6 Average N uptake (kg/ha) per plant part across fertility rates (high, medium and low) during three growing seasons. ................................ .......................... 198 3 7 Final leaf dry weight and N uptake means across irrigation treatments a nd fertility rates during t he three corn seasons evaluated. ................................ .... 199 3 8 Final stem dry we ight and N uptake means across irrigation treatments during the three corn seasons eva luated . ................................ ........................ 200

PAGE 14

14 3 9 Final stem dry weight and N uptake means across fertility rates during t he three corn seaso ns evaluated . ................................ ................................ ........ 201 3 10 Final ear dry weight and N uptake means acr oss irrigation treatments and fertility rates during the three c orn seasons evaluated (2015 17). .................... 202 3 11 Total aboveground biomass means as a respons e of five irrigation treatments and three N fertility rates during the three corn growing seasons (2015 17). ................................ ................................ ................................ ....... 203 3 12 Corn grain yield as a response of five irrigation treatments and three N fertility rates during thre e corn growing seasons . ................................ ............. 204 3 13 The 100 kernel weight across irrigation treatment s during corn seasons 2015 17.. ................................ ................................ ................................ ......... 205 3 14 The 100 kernel weight across N fertility rates during corn seasons 2015 17. .. 206 4 1 Irrig ation applied in 2015 corn, 2016 peanut and 2017 corn seasons. ............. 271 4 2 In season observed and simulated biomass resulted in the SMS treatment across the high, medium and low N rates ................................ ........................ 272 4 3 Daily observed and DSSAT simulated tot al soil water in profile in the GROW High N treatment. ................................ ................................ .......................... 273 4 4 Daily observed and DSSAT simul ated tot al soil water in profile in the SMS High N treatment. ................................ ................................ ............................ 274 4 5 Daily observed and DSSAT simulated t otal soil water in the profile in the NON High N treatment. ................................ ................................ ................. 275 4 6 Total observed and DSSAT simulated nitrate N in the soil profile in the SMS, GROW and NON treatments and high N fertility rate during corn 2015 peanut 2016 corn 2017 crop rotation. ................................ ................................ ......... 276 4 7 Total observed and simulated nitrate N in the soil profile i n the SMS, GROW and NON treatments and medium N fertility rate during corn 2015 peanut 2016 corn 2017 crop rotation. ................................ ................................ ......... 277 4 8 Total observed and simulated nitrate N in the soil profile in the SMS, GROW and NON treatments and low N fertility rate during corn 2015 peanut 2016 corn 2017 crop rotation. ................................ ................................ .................. 278 4 9 Observed and simula ted final aboveground N uptake in GROW, SMS and NON treatments across N rates during corn 2015, peanut 2016 and corn 2017 growing seasons. ................................ ................................ ................... 279

PAGE 15

15 4 10 O bserved and simulated final aboveground biomass resulted in GROW, SMS and NON treatments across N rates during corn 2015, peanut 2016 and corn 2017 growing seasons. ................................ ................................ ................... 280 4 11 Observed and simula ted final grain/pod yields resulted in GROW, SMS and NON treatments across N rates during corn 2015, peanut 201 6 and corn 2017 growing seasons . ................................ ................................ ................... 281 4 12 Simulated nitrogen dynamics in the SMS, GROW and NON treatments and high N fertility rate during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation. ................................ ................................ .......................... 282 4 13 Simulated nitrogen dynamics in the SMS, GROW and NON treat ments and medium N fertility rate during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation. ................................ ................................ .................. 283 4 14 Simulated nitrogen dynamics in the SMS, GROW and NON treatments and low N fertility rate during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation. ................................ ................................ .......................... 2 84 5 1 Monthly mean rainfall and maximum and minimum temperature during 30 years base line weather per iod (1980 2010) . ................................ ................... 321 5 2 Long term simulated corn biomass and yield during the baseline weather period across the GROW high, SMS medium and NON low treatments. ........ 322 5 3 Long term simulated peanut biomass and yield during the baseline weather period across the GROW high, SMS medium and NON low treatments. ................................ ................................ ................................ ...... 323 5 4 Ef fect of rising temperatur es and increase rainfall on corn biomass an d yield in the GROW high treatment compared to the b aseline observed weather period . ................................ ................................ ................................ ............. 324 5 5 Effect of ri sing temperatures and increase rainfall on corn biomass and yield in the SMS medium treatment compared to the baseline observed weather period. ................................ ................................ ................................ ............. 325 5 6 Effect of rising temperatures and increase rainfall on co rn biomass and yield in the NON low treatment compared to the baseline observed weather period . ................................ ................................ ................................ ............ 326 5 7 Projected mean corn biomass and yield change (to baseline, 1980 2010) in the GROW hi gh, SMS medium and NON low trea tments for temperature increase, rainfall increase and combined effects . ........................ 327

PAGE 16

16 5 8 Effect of rising temperatures and increase rainfall on peanut biomass and y ield in the GROW high treatment compared to the baseline observed weather period . ................................ ................................ .............................. 328 5 9 Effect of rising temperatures and increase rainfall on peanut biomass and yield in the SMS medium treatment compared to the baseline observed weather period . ................................ ................................ .............................. 329 5 10 Effect of rising temperatures and increase rainfall on peanut biomass and yield in the NON low treatment compared to th e baseline observed weather period . ................................ ................................ ................................ ............ 330 5 11 Projected mean peanut biomass and yield change (to baseline, 1980 2010) in the GROW high, SMS medium and NON low trea tments for temperature increase, rainfall increase and combined effects . ........................ 331 5 12 Effect of temperature, rainfall increase and combined effects on irrigation, drainage and N leaching across treatments evaluated in co rn compared to the baseline weather period . ................................ ................................ .......... 332 5 13 Effect of temperature, rainfall increase and combined effects on irrigation, drainage and N leaching acr oss treatments evaluated in peanut compared to the baseline weather period . ................................ ................................ .......... 333

PAGE 17

17 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 P hilosophy IRRIGATION AND NITROGEN BEST MANAGEMENT PRACTICES IN CORN PRODUCTION By María Isabel Zamora Re May 2019 Chair: Michael D. Dukes Major: Agricultural and Biological Engineering Nitrogen (N) is essential for crop growth; ho wever, e xcessive N f ertilizer applications are threatening water quality and the environment. Therefore, irrigation and N best management practices (BMPs) were evaluated as alternatives for corn production in sandy soils in Live Oak, Florida during 2015 17 . The experimental d esign was a randomized complete block arranged in a split plot ( four replicates ) . Five irrigation treatments (GROW, method; SMS, uses soil moisture sensors to activate irrigation ; Re duced, app lied 60% of GROW treat ment; and NON, non irrigated) and three N fertility rates (low, medium and high = 157, 247 and 336 kg N/ha) were evaluated . Significant differences in final biomass and N uptake were found only versus the NON treatment. W ater and N ba lances were developed using crop simulation models . S oil N after corn and peanut residues ranged from 18 32 and from 62 70 k g N/ha, respectively; representing N potentially taken up by the following crop or leached. Simulated N leaching was 88 and 69 k g N/ha during 2015 16 and 2016 17 fallow periods . In 2015 corn season, the SMS low, medium and high rates resulted in 48 %, 5 1 % and 84% l ess leaching versus the

PAGE 18

18 GROW on the same rates . D ifferences in corn yield were found only versus the NON treatment ; ex cept in 2015. Water savings achieved by SWB, SMS and Reduced ranged from 34% to 53% compared to the GROW during 2015 17 . N o differences in yield were found between the medium and the high N rate s , while reducing N fertilizer in 26%. The SWB, SMS and Reduce d strategies along with the medium N rate, constitute BMPs which can help reduce irrigation and N fertilizer without impacts in yield compared to conventional practices. A sensitivity analysis was performed to explore crop responses to future warming tempe ratures and increase in rainfall. L ong term simulations indicate d negative impacts in growth and yield; however, reductions in N fertilizer, irrigation , and in N leaching resulted when following BMPs versus conventional practices. Thus, BMPs may contribute to reduce N leaving the rootzone, improve N uptake without impacts in

PAGE 19

19 CHAPTER 1 LITERATURE REVIEW Water is one of the most important res ources in the state of Florida. This state is comprised of 700 springs , more than 1,700 streams and rivers and about 7,800 freshwater lakes. Also, there are approximately 405,000 hectares of wetlands and several aquifers which provide water fo r human and environmental needs (Fernald and Patto n 1984) . I ncreasing population, tourism and agriculture represent the greatest water demand of these renewable but limited water resources of the s tate. Population in Florida increased 25% since 2000; reaching a total of 19.8 million people in 2015. Furth ermore, p opulation projections estimate 26.3 million residents by 2040 , a 65% incre ase since 2000 which is expected to intensify the demand for water supply significantly (U niversity of Florida, Bureau of Economic and Business Research 2015) . Likewise, a substantial groundwater demand is attributed to agriculture due to its agriculture, natura l resources and food industries accounted for $148.5 billion (Hodges et al. 2015) . The total water withdrawals in Florida were assessed to be 53.8 million m 3 /d in 2012 (USGS 2012) . Saline surface water withdrawals accounted for 55% of the total, whereas 45% corresponded to fresh water withdrawals (29% groundwater and 16% surface withdrawals, respectively) (Mare lla 2015) . Almost all the saline withdrawals are destined for power generation cooling water; however, those withdrawals are returned to the source (Marella 2014) . Fresh water is primarily used for irrigation, powe r generation and public supply.

PAGE 20

20 Florida, being one of the largest groundwater users in the U . S . (Maupin et al. 2014) , provides drinking water to over 18 million residents (93% of the state population) using groundwat er extraction for public water supply systems or private domestic wells. Near ly 8.7 million m 3 /d was extracted for public supply in 2012; however, agriculture was the largest freshwater user with withdrawals up to 9.5 million m 3 /d in that year. From the es timated harvested cropland (890,308 ha ), nearly half of it is irrigated land (USDA, NASS 2014) . Background Nitrate I ssues at the Suwanee River Basin (SRB) The Suwannee River Basin (SRB) is located within the Coastal Pl ain region of the southeastern U.S. (Figure 1 1 ). This basin covers an area of approximately 28,500 km2 containing a mixture of springs, wetlands, subtropical forests, rivers and estuarine habitats. The SRB is characterized by a highly variable hydrogeolog ical system drained by four main rivers: Suwannee, Alapaha, Withlacoochee and Santa Fe Rivers. The upper two thirds of the basin are characterized by surface drainage (e.g. streams, lakes, wetlands); while the southern third is a thin layer of highly porou s sands that overlies limestone karst and the Floridian Aquifer system (Katz and Raabe 2005) . The Floridian Aquifer is the main water supply source and it is directly recharged by rainfall in the southern region. Howev er, there is a transition from highlands to lowlands characterized by stream to sink sub basins area, thus, stream flow is abruptly caught by sinkholes and diverted into the aquifer (Katz and Raabe 2005) . The presence of springs, conduit systems and sinkholes along the river are interconnection points between surface and ground water, allowing a rapid movement of contaminants from

PAGE 21

21 the surface to the groundwater with very little attenuation or degradation (Katz et al. 2009) . The SRB is one of the largest and most ecologically exceptional systems of the southeastern U.S., which also supports an expanded economy based on agriculture, fisheries and ecotourism (Katz and Raabe 2005) . However, natural resources have been threatened in the basin due to the increased use of pesticides and fertilizers, runoff from poultry and dairy farms. Point and non point sources of contamination have impacted the surfac e and groundwater quality in recent decades resulting in concern of human health and ecological effects. Nitrogen (N) is an essential element for crop growth. Inorganic fertilizers (e.g. N sources) are applied in agricultural lands, lawns, turfgrass and nu rsery plants. The U.S. was ranked fourth top nutrient consumer in the world during the period of 2006 2009 (FAOSTAT 2015a) . Furthermore, since 2009 the consumption of nutrients, especially nitrogen (N) has steadily incr eased reaching nearly 13.2 Mt in 2012 (FAOSTAT 2015a) . A total of 2.1 Mt (~16%) of fertilizer were reported to be consumed in the state of Florida; while Suwannee County had a total of 43,620 t of fertilizer applied in t he same year (FDACS 2012) . Nitrate is the most common and soluble form of N; thus, it can easily leach from the soil profile by irrigation or rainfall into the groundwater, tile drains and streams (DeSimone 2009) . Background concentrations (i.e. natural concentrations without human activity intervention) of nitrate in rainwater, surface and groundwater is typically below laboratory detection limits (i.e. 0.05 mg/L NO 3 , as N). Howev er, nitrogen in groundwater may originate from natural sources (e.g. precipitation, aquifer materials

PAGE 22

22 and leaching from organic debris), as well as, from anthropogenic activities (e.g. fertilizer applications, wastewater, animal production) (DeSimone 2009) . Natural and anthropogenic nitrate is possibly the most widespread pollutant in groundwater and it can persist for decades (Hallberg and Keeney 1993) . The main water qualit y issues linked with agricultural production are the excess nutrients (i.e. nitrogen) entering surface waters or leaching through soils into groundwater; which results in excess algal growth in aquatic systems. Excess growth blocks sunlight to aquatic vege tation. Further, the decay of this algae decreases the available dissolved oxygen which also can cause death to other organisms (FDACS 2015a) . The Florida Department of Environmental Protection (FDEP) established threshol ds based on previous research developed on the relationship of nutrients to algal growth in springs. Therefore, in clear water streams (including spring vents), the nutrient criterion is 0.35 mg/L of nitrate nitrite (NO 3 + NO 2 ) as an annual geometric mean, not to be exceeded more than once in any three calendar year period (62 302.530(47)(b), F.A.C., (FDEP 2013) . background concentration plus an extraneous component from low level influence of human activities (Nolan and Hitt 2003) . In order to define the latter term, data from shall ow groundwater of relatively underdeveloped areas compiled for the USGS and National Water Quality Assessment (NAWQA) was analyzed. Findings showed that all groundwater sampled was likely influenced by humans, therefore the value of 1.1 mg/L

PAGE 23

23 was establishe d as an upper bound estimate of relative background concentration of nitrate in shallow groundwater in the U.S. Hence, nitrate concentration greater than 1 mg/L suggests greater anthropogenic influence and therefore, greater need for additional monitoring to protect water resources (Nolan and Hitt 2003) . Excess of nitrogen is one of the main causes of water quality impairment and ecology stressor of the SRB springs in Florida (FDACS 2015a; Harrington et al. 2010) . Annual crops or multi cropping are common in the SRB, which demand for larger amounts of nitrogen fertilizers applied on an annual basis through drip or overhead irrigation. During the last 50 years, th e combination of these practices plus the changes in agricultural land use in Alachua, Columbia, Gilchrist, Lafayette, and Suwannee Counties have contributed to variable amounts of N impacting the quality in the SRB groundwater system (FDACS 2015a; Katz et al. 1999) . The increasing nitrate concentrations in the SRB during the last decades constitutes one of the major concerns to be solved. The implicit characteristics of the SRB (i.e. karstic region wit h conduits and sinkholes) makes this region more susceptible to elevated NO 3 concentrations resulting in human health and environmental issues. The Springs Initiative Monitoring Program reported that 21 of the 49 springs in Florida (~43%) resulted in NO 3 N median concentrations greater than 1 mg/L from 2001 to 2006; thus greatly influenced by anthropogenic activities. In the SRB, fourteen main springs were evaluated and a 93% of the springs showed a median nitrate + nitrite N concentrations above the nutrie nt criterion of 0.35 mg/L [ NO 3 N + NO 2 N]. Fanning, Troy, Lafayette Blue, Manatee, and Devils Ear presented the highest nitrate concentrations of the SRB Group. Fanning spring median nitrate concentration was 5.2

PAGE 24

24 mg/L NO 3 N representing the highest concent ration of the group (Figure 1 2). In general, the springs with higher concentrations are located in areas that include agriculture or former agricultural areas undergoing urbanization (Harrington et al. 2010) . Impa cts of N itrogen on H uman H ealth and E nvironment Large fertilizer inputs are contributing to excessive nutrients to groundwater recharge impacting the quality of spring waters (Katz 2004) . Increasing nitrogen loads to wat erbodies has become a major concern around the world, especially in areas where sandy soils and shallow groundwater are predominant (Andraski et al. 2000; Ferguson et al. 1991; Gehl et al. 2005; Strebel et al. 1989; Zhang et al. 1996) . Studies conducted in Western Europe have shown high NO 3 N concentrations exceeding 2 4 times the European drinking water limit of 50 mg/L NO 3 (equiva lent to 11 mg/L NO 3 N ). These concentrations were found mainly in sandy soils with arable crops, intensively managed grazed grassland and field cropping of vegetables (Strebel et al. 1989) . Likewise, the large consump tion of N fertilizers has potentially increased the N groundwater contamination in China. Researchers evaluated 14 cities and counties for a total of 69 locations in NE China. The results showed that NO 3 in ground and drinking water exceeded the 50 mg NO 3 / L limit (11 mg/L NO 3 N ). Concentrations reached up to 300 mg/L NO 3 (66 mg/L NO 3 N) in areas of vegetable production where N applications ranged from 500 to 1900 kg N/ha and only 40% plant uptake was achieved (Zhang et al . 1996) . In the United States, about 86% of the population acquires drinking water from groundwater sources; public supply being the second largest groundwater user in the nation (Maupin et al. 2014) . However, the i ncrease of contaminants over the last 50 years had decreased the groundwater quality causing ecosystems degradation and high

PAGE 25

25 risk of health effects. Thus, contaminated shallow and deep groundwater are of major public concerns when used for drinking, since pollutants may migrate into deeper layers, reaching groundwater supplies (Nolan et al. 2002) . Usually, privately owned domestic wells are shallower than public supply wells. This makes them more susceptible to nitrate c ontamination; however, they are not typically monitored for water quality. Therefore, a national study performed by the USGS, evaluated water quality conditions for 2,167 domestic wells in the principal aquifers in the U.S. An 80% of the wells showed nitra te concentrations within the range of 0.05 to 5.79 mg/L as N with a median value of 0.55 mg/L as N. A 41% of the wells showed concentrations greater than 1 mg/L, which is considered a level resulted from anthropogenic activities (Nolan and Hitt 2003) . Areas with predominant agricultural land use resulted in nitrate concentrations greater than the MCL 10 mg/L as N in 7.1%; while underdeveloped land uses only showed 0.7% above the MCL. However, in wells that targeted agr iculture land use exceeded the MCL in 23.4%, showing higher nitrate N concentrations in areas of agricultural land use versus other land uses. Positive nitrate correlations were found with the amount of N fertilizer applied in areas surrounding the wells ( and even higher correlations in well drained soils), with concentrations of dissolved oxygen, and with the percentage of agricultural land use (exceeding the 10 mg/L NO 3 N limit) (DeSimone 2009) . The Mississippi Ri ver Basin is another susceptible area for nitrate pollution in the depleted) water zone in the Gulf of Mexico, as well as, for the degradation of the water quality and aquatic ecos ystems (Donner et al. 2004; Goolsby et al. 2000; Rabalais et al. 1996) .

PAGE 26

26 Almost 90% of the nitrate leached into the river system comes from fertilized crops (Donner et al. 2004) . Nitrate exports by the Mississippi River to the Gulf of Mexico has increased almost three fold since 1950, causing a significant increase hypoxia (Turner, R. E. and Rab alais 1991) . Regression models were used to estimate the annual N flux to the Gulf of Mexico and determine the origin of the N sources. The mean annual total flux was estimated as 1.5 Mt per year during the period 1980 1996, where 63% was nitrate as N (wh ich tripled in 30 years), 37% organic N and 2% ammonium as N. This flux increased nearly 50% in wet years and especially from agricultural lands, since greater nitrate leaching occurred from the soils and unsaturated zones in the basin (Goolsby et al. 2000) . Excess nitrate in surface waters may cause an overall degradation to the environment causing a high risk of damage and changes to t he inhabitant species living in the waterbody. As a consequence of the algal decay, depletion of oxygen and reduction in the water clarity occurs (Upchurch et al. 2007) . Besides the environmental issues, elevated n itrate concentrations in drinking water (limit 10 mg/L NO 3 N ) causes when the hemoglobin (Fe 2 + ) in an infant's red blood cells is oxidized to methemoglobin (Fe 3 + ). Cyanosis occ urs because methemoglobin is unable to carry oxygen in the blood, turning the skin into a bluish tint, causing potential illness or fatality. This syndrome is more predominant in infants who are very susceptible to low oxygen levels in the blood (Knobeloch et al. 2000) .

PAGE 27

27 Nitrate Concentrations in Florida S prings degradation and a decline in its water quality during the last decades (FDEP 2013; Katz and DeHan 1996; Katz et al. 1999; Katz 2004; Katz and Raabe 2005; Katz et al. 2009; Suwannee River Water Management District 2004) . The Upper Florida aquifer (UFA) underlays about 300,000 km 2 among Florida, southern Georgia, south Alabama and small areas of Mississippi and South Carolina. Due to its high quality water, large yiel d and shallow depth, is a great source of water supply and it is one of the most productive karst aquifers in the world. Most of the north central Florida regions underneath the aquifer are unconfined, and also are estimated as the most vulnerable regions by the Floridian Aquifer Vulnerability Assessment (FAVA) (Arthur et al. 2007) . These areas are characterized by high porosity and transmissibility allowing contaminants to travel long distances in short periods of ti me. Groundwater dynamics causes greater complexity to model mixing, solute transport, water yields and contaminant attenuation in the UFA. The Suwannee River is of great interest to the State of Florida and has been importance. However, the increase of nitrogen in this valued river is of concern and interest to water resources agencies. In the north central Florida, the Suwannee River flows through groundwater areas of high n itrate concentrations and ultimately drains in the Gulf of Mexico, where nitrate loadings can cause ecological damage. These elevated concentrations come from a mix of organic (manure and wastewater) and inorganic sources (fertilizers) (Katz et al. 1999) .

PAGE 28

28 that act as N sinks, as well as, the high vulnerability to N pollution intrinsic in karstic areas. Previous studies have shown the increas e of nitrate N in springs from NO 3 N ) to above 5 mg/L NO 3 N during the last 40 years (Katz et al. 1999; Katz 2004) . Steady increments of nitrate N have been found in several spring waters within the Suwannee River basin in Florida (e.g. Convict spring with 8 mg/L NO 3 N ) (Upchurch et al. 2007) . Continued studies in the SRB area during the period 1940 1998 reported evidence th at the greatest nitrate N pollution found in the lower basin comes from fertilizer sources (Katz et al. 2009) . A 20 year study performed from 1971 to 1991 between Georgia and Florida (including the Suwannee River Basin ) showed evaluated nutrient concentrations collected at surface water sites. The study used environmental characteristics of land (i.e. Southern Piedmont, Southern Coastal Plain, Coastal Flatwoods, and Central Florida Ridge), land use, and non point and po int source discharges within the study area. The authors investigated long term trends to determine the temporal distribution of nutrient concentrations. The U.S. Environmental Protection Agency (USEPA) guidelines were used: the maximum contaminant level i n drinking water (10 mg/L) was used for nitrate concentrations, the chronic exposure of aquatic organisms to un ionized ammonia (2.1 mg/L) was used for ammonia concentrations, the concentration in flowing water to discourage excessive growth of aquatic pla nts (0.1 mg/L) was used for total phosphorus concentrations, and no guidelines for Kjeldahl concentrations. Nutrient concentrations within the study area were low; though, long term increasing nutrient trends were found. Their results showed increasing nit rate N concentration at a rate of

PAGE 29

29 0.02 mg/L per year reaching a median NO 3 N concentration of 0.5 mg/L. It was hypothesized that the main NO 3 contributing sources were synthetic fertilizers, septic tanks and animal waste (Ham and Hatzell 1996) . In 1995, a study was conducted in the Lower and Upper Suwannee River (from Dowling Park to Branford, FL) in order to determine the effect of springs and other Nitrate concentrations (dissolved NO 2 + NO 3 N ) were evaluated on 11 springs and three river sites during a three day period during baseflow in the river. The results showed higher concentrations in springs than in the river sites. Along the study reach, ni trate loads increased from 2,300 to 6,000 kg/d (160% increase), 46% was contributed by the springs and 54% from ground water inflow. Nitrate N concentrations ranged from 1.3 to 8.2 mg/L measured in the springs. Convict Spring, a low discharge spring, resul ted in the highest concentration. This elevated concentration might be derived from septic tanks leaching at developed areas surrounding the spring or from close cropland fertilized areas (Andrews 1994) . Mainly agri cultural land use was related to the increase of NO 3 N load on groundwater, since any NO 3 N from fertilizers, animal wastes, septic tanks or any other sources could readily infiltrate into the groundwater (Pittman et al. 1997) . In 2003, the Aucilla, Econfina, Fenholloway, Steinhatchee, Suwannee, and Waccasassa Rivers transported nearly 4,165 t of nitrate N to the Gulf of Mexico (Suwannee River Water Man agement District 2004) . Due to the concern of steady increase in nitrate concentrations in first magnitude springs located in Florida, a multiple isotopic study was conducted in order to evaluate sources and timescales of nitrate

PAGE 30

30 pollution. Nitrate N conc entrations in the springs ranged from 0.5 to 4.2 mg/L and the isotopes indicated that the dominant source of nitrate contamination was coming from inorganic N fertilizers (Katz 2004) . In terms of land use, Jackson Blue S pring and Fannin g Spring contained 49% and 55% of agricultural land use (based on 1993 land cover data) resulting in high nitrate N concentrations of 3.1 and 4.2 mg/L, respectively. Also, this study showed that nitrate could remain in the aquifer for sever al decades due to a slow transport of solutes through the aquifer matrix. It was estimated that the age of water emerging from spring vents is about 20 to 40 years old, thus the nitrate N in the groundwater is likely associated with a prolonged input of N to the aquifer (Cohen et al. 2007; Katz 2004) . The estimation of the actual quantity of NO 3 N is more complex due to the mixed chemistry of groundwater influenced by past and current land use activit ies. Therefore, tracing and age dating evidence becomes more important and required for a better assessment and management. However, only a few studies have investigated temporal trends in Floridian aquifer water quality. Strong (2004) evaluated 69 springs and compare them with baseline data (pre 1977). In most of the cases, a significant enrichment was observed (higher present concentrations versus early measurements); however, a few springs showed a reduction in concentrations over time. It was also found that a strong correlation existed between pre and post data showing a natural control in the quality of groundwater that persists although anthropogenic contributions. Therefore, a great understanding of the springshed and conduits, as well as, the tempor al water and nitrate dynamics due the changes in land use are critical for (Strong 2004) .

PAGE 31

31 Large variations on NO 3 N concentrations have been found in Florida spring s. Values ranging from 1.3 to 8.2 mg/L at Royal and Convict Springs, respectively, possibly caused by septic tanks or from fertilized cropland nearby (Hornsby and Mattson 1997) . Nitrate N concentrations have substanti ally increased from <0.1 mg/L (near background concentrations) to greater than 5 mg/L during the last five six decades (Katz et al. 2009) . In Silver springs, urban and residential areas increased from 98.4 km2 to 424 .8 km2 from 1977 to 1995 causing a decline in water quality in the springs. This springshed has been mostly an agricultural area. Studies performed in 1950s showed concentrations of NO 3 N near 0.2 mg/L (Odum 1957) . In 2000 2001 another study evaluated 56 groundwater wells and the collected samples were compared with samples taken in 1989 1990. Results showed concentrations of NO 3 N from 1.04 mg/L (1989 1990) to 1.2 mg/L (2000 2001) reaching a maximum concentration from 3.6 mg/L to 12 mg/L (Phelps 2004) . The Suwannee River Water Management District established a surface water (76 sites including 22 springs) and groundwater (147 wells randomly selected and changed every 5 years) mon itoring network collectively known as Water Assessment Regional Network (WARN). The main purpose was to monitor changes in groundwater quality through time (trend detection), as well as, identify problems and develop water quality management strategies. A positive trend indicates that nitrate N concentrations are increasing, whereas a negative trend designates a decreasing nitrate N concentrations. WARN was implemented in 2000, but where data is available, it includes data from 1989 to early 2007. The resul ts of the monitored rivers and streams showed that 12% of

PAGE 32

32 the 81 sampling stations had increased nitrate concentrations and 28% decreased over ten years (1997 to 2007). The evaluated springs located in the middle and lower Suwannee River showed an increasi ng nitrate trend; however, springs of second magnitude (smaller, 0.28 2.8 m 3 /s) showed a decrease in nitrate concentrations. This response is due to the direct relationship between the spring discharge and drainage basin. Thus, in the short term greater re sponse to land use practices can be seen in smaller magnitude springs and springsheds due to the shorter groundwater flow paths. From the wells evaluated, 19% showed a positive trend; while 23% had a negative trend through time (Upchurch et al. 2007) . This analysis was initiated when nitrogen loading reduction efforts started to be implemented by the District. Thus, trends of nitrate concentration reduction were not expected to be observed in 2000. Thus, further red uctions in nitrate contamination may require several decades to reflect the changes in land use management. Therefore, a need to focus on source reduction rather than sink enhancement has been reported in previous studies (Cohen et al. 2007) . In order to reduce the nitrate flux to ground and surface waterbodies, a reduction or better management of the application of N fertilizers or N bearing materials is required (Upchurch et a l. 2007) . Hence, developing or optimizing N and irrigation best management practices (BMPs) could be a management strategy to target load reductions for water quality improvements. Regulations As a response to the increasing water demand and the excess of nutrient loading regulations have been developed through time. In 1972, the Clean Water Act (CWA)

PAGE 33

33 was established by the Congress; which establishes the basic structure for regulating discharges of pollutants into waters of the U.S. (EPA, US Environmental Protection Agency 2015a) . As part of the Clean Water Act section 303(d), the Total Maximum Daily Loads (TM DLs) were established to set the maximum amount of pollutant in a waterbody allocating the required reductions to the pollutant sources (EPA, US Environmental Protection Agency 2016a) . In 197 4, Congress enacted the Safe Drinking Water Act (SDWA), which determines the level of contaminants in drinking water at which no adverse health effects are likely to occur ensuring drinking water quality for the U.S. population. This act, authorizes EPA to set standards to public water systems for drinking water with the purpose of protecting population health against possible natural or anthropogenic contaminants (EPA, US Environmental Protecti on Agency 2015b) . For groundwater and drinking water sources, the EPA established maximum contaminant levels goals enforceable health goals, based only on possible health risks and exposure over a lifetime with an adequate margin of and nitrite are 10 mg/L and 1 mg/L, respectively (EPA, US Environmental Protection Agency 2016b) . In 1995, the Office of Agricultural Water Policy (OAWP) within the Florida Department of Agriculture and Consumer Services (FDACS) was established by the Florida Legislature. The OAWP is directly involved with statewide programs to implement the Federal Clean Water Act's Total Maximum Daily Load (TMDL) requirements f or agriculture. Afterwards, the Florida Watershed Restoration Act (FWRA) was passed in 1999 as a response to the TMDLs. Consequently, this act determined

PAGE 34

34 the Florida Department of Environmental Protection (FDEP) as the organization responsible of the imple mentation of TMDLs through water quality protection programs. The OAWP enables communications among federal, state and local agencies and the agricultural industry on water quantity and water quality issues associated with agriculture. Thus, the OAWP is ac tively involved in the development of Best Management Practices (BMPs) addressing both water quality and water conservation on a site specific, regional and watershed basis. Producers, industry groups, the FDEP, the University of Florida Institute of Food and Agricultural Sciences, the Water Management Districts, and other parties collaborate in the development and implementation of economically and technically feasible BMP programs (FDACS 2013) . Agricultural Best Managemen t Practices (BMPs) are practices based on research, field testing and expert review developed to maintain or improve surface and groundwater water quality. In addition, BMPs must be technically and economically feasible, and designed to prevent, reduce or treat pollutant discharges entering water resources, and to conserve water supply (FDACS 2015a) . Water in the U.S. is being threatened mainly by non point sources (urban and agriculture). Runoff draining into waterbodies ( i.e. lake, rivers, and groundwater) is possibly the greatest contributor to water pollution in the U.S. Thus, producers can prevent and potentially minimize the impacts to the environment by implementing BMPs while maintaining agricultural production (FDACS 2015a) . Agriculture: I mportance, I nputs and C onsequences Agriculture plays an important role in the economy of Florida and Georgia, which accounted $16,956 million total sales of agricultural products in 2012 (USDA, NASS 2012) . Maize or f ield corn ( Zea mays L. ) for grain production is one important crop for

PAGE 35

35 the state of Florida . From the tot al cropland in Florida, around 40,500 ha wer e planted with field corn in 2015 (USDA, NASS 2016a) . Among Florida and Georgia, a sum of $381.6 million were sold in this commodity (USDA, NASS 2012) . In 2012, field corn accounted for $6.2 millio n of the economy of Suwannee County (USDA, NASS 2012) . Maize production is the second largest commodity in the United States. In 2013, around 1,121 Mt were produced accounting for $67 billion in the U.S. economy (FAOSTAT 2015b) . In 2016, corn planted area was estimated as 37.9 million ha; 6% greater than 2015 and the third highest planted area since 1944 (USDA, NAS S 2016b) . Remarkably, in 2007 growers planted near 36.4 million ha of corn in the U.S for the first time in 60 years due to the high corn prices and the large ethanol demand (USDA, NASS 2007) . Similarly, prices and p lanted area increased in Florida in 2008, as well as, in 2012. equivalent to $4.2 million cash receipts. However, it increased 3 fold up to $17.3 million cash receipts in 2008. Furthe 46,500 ha producing 2.62 Mt (8.4 t/ha on average) and accounting for $46.8 million cash receipts as maximum peak. Corn prices averaged $130/t ($/t=dollars per metric ton) produced since 2000; however, in 2012 prices increased up to $295/t causing an increase in the production trend. Since 2014, corn planted area and total production have decreased following the reduction in corn prices (i.e. $150/t in 2015) (USDA, N ASS 2016a) . Due to the ethanol boom along with the high corn prices, new irrigation wells and pivots have been established for corn and peanut in Florida (Wright et al. 2003) . After a field verification performed in 2 015, a total of 1466 center pivots were

PAGE 36

36 observed accounting for a total of 37,675 irrigated hectares in the Suwannee River Water Management District (Marella et al. 2016) . Corn Physiology and Corn Production in Flor ida In Florida, corn production is important for grain and silage. It is common to grow corn in rotation with peanuts resulting in yield benefits for both crops. Implementing a good soil management can prevent soil erosion from water and wind, as well as, improve organic matter when incorporating crop residue left on the soil surface. Irrigation and fertilization are two essential components to assure profitable yields (Wright et al. 2003) . Field operations timing can be optimized when knowing the corn growth stages and the nutrient requirements at each stage. Hence, irrigation, fertilization, disease control and harvest can be properly managed to achieve high yields (McWilliam s et al. 1999) . Hanway (1963) established a growth stage system as a basis for research sampling. The stages consist of ten stages explained as following: Stage 0: plant emergence. It commonly occurs between 4 5 days after planting under warm and moist co nditions; while it can take 2 weeks under cool and dry conditions. Stage 1: collar of 4 th leaf visible. Period of tassel initiation and end of differentiation of leaf initials (all corn leaves are differentiated). The stem growing point will be very close to the soil surface. Before this stage, the radicle (i.e. primary root) provide a temporal root system of the plant. Then, nodal roots (i.e. come from stem) turn into the permanent root system of the plants. Stage 2: collar of 8 th leaf visible. This stage consists of a rapid leaf growth (stem elongation, tip of the stem grows over the surface) increasing the nutrient rate uptake. Stage 3: collar of 12 th leaf visible. Rapid dry matter accumulation (i.e. linear function until near maturity). All leaves have r eached their maximum area, increasing weight and green color. Tassel is developed but enclosed within the whorl of leaves. Accelerated root growth.

PAGE 37

37 Stage 4: collar of 16 th leaf visible. T he husks of the uppermost ear (at the nodes of the 12 th or 14 th leaf) start developing fast. Each node can have an ear shoot; however, most do not develop enough to be pollinated. Stage 5: 75% of plants have visible silks and pollen shedding. Full development of tassel, stem and leaves. Tassel will fully develop about 2 or 3 days before silk appearance. At this stage, vegetative growth ceases and subsequent growth occurs in the ear. Rapid growth of the cob and ear shank. Elongation of the silks continues until pollen fertilization. Stage 6: 12 days after 75% silking. Cob an d ear almost reach full development. The critical for biomass accumulation in a daily rate until maturity. Remobilization from vegetative to reproductive structures occu rs in order to develop the grain. Stage 7: 24 days after 75% silking. Kernel growth occurs rapidly and slow rate of growth of the embryo is slow. Cell division in the epidermal layer of the endosperm ends and starts a size increase of the embryonic plant. Stage 8: 36 days after 75% silking. Accelerated growth of the embryo, and the radicle and embryonic leaves become fully differentiated. Seminal roots are initiated, cover s about 25% of the plants. Stage 9: 48 days after 75% silking. The rate of dry matter accumulation starts Stage 10: 60 days after 75% silking. Plants reach physiological maturity and dry matter accumulation ceased.Senescence occurs in husks and leaves. The ear (cob and grain) will continue to lose moisture after this time based on climatic conditions and hybrid characteristics (Hanway 1963) . However , in general and in the industry, corn growth stages can be classified as vegetative and reproductive in which the presence of silks differentiates these groups (Ritchie et al. 1993) By selecting the adequate hybrid , growers can determine the yield potential, maturity, disease and insect resistance, grain quality and adaptability for each region. Growers can select among early, medium or late maturity hybrids. In general, the early maturity hybrids (115 days after em ergence) are the best adapted in Florida, because in these maturity occurs 1 2 weeks earlier, are shorter (less subject to lodging), might

PAGE 38

38 require less irrigation, and are more suitable for double cropping (Wright et al . 2003) . a high drought tolerance capacity, making it very suitable under limited or dryland conditions in Florida (DuPont 20 16) . Temperature plays an important role in corn growth and development. Temperature of 12.7 °C at the top soil 5 cm during at least three consecutive days is required for germination (i.e. from February to mid March). If freezes occur, the aboveground t issue can be damaged; however, the corn plant can survive since its growing point is below the surface until it grows to about 30 cm tall. Planting early ( i.e. early mid March , after the last freeze ) is suggested because soil moisture is usually grater at this time , lower temperatures and longer days occur during subsequent pollination, hence a higher yield potential could be achieved. In general, greater plant population is required for irrigated versus non irrigated corn. Thus, a plant population of 59,30 0 79,100 plants/ha is recommended for early and medium maturity hybrids with irrigation (Wright et al. 2003) . Soil tests and realistic yield goal are required to determine the fertilization (Waskom and Bauder 1994) . Adequate pH for corn production ranges from 5.6 to 6.2. Below 5.2 can cause nutrient fixation and a decline in soil bacterial activity reducing the availability of nutrients to the crop. In Florida, careful management of N fertilizations are recommended due to its mobility and potential leaching in sandy soils (Wright et al. 2003) . In order to increase the N recovery efficiency throughout the season, split applications (i.e. multip le applications with small amounts) are recommended to improve plant nutrient uptake and reduce N leaching . In general, these applications consist of:

PAGE 39

39 20 25% of the crop N demand is applied at planting using a starter fertilizer near the row. Then, the 75% of the N requirement can be applied side dress and/or through fertigation (i.e. injected through the center pivot) at the plant needs (Wright et al. 2003) . The UF/IFAS fertilization recommendations for non irrigated c orn with a target pH of 6.5 are: 168 kg N/ha/yr, 56 kg P 2 O 5 /ha/yr and 67 K 2 O kg/ha/yr (based on interpretation of a Mehlich potassium rates) (Mylavarapu et al. 2015) . On the other hand, recommendations for irrigated corn are: 235 kg N/ha/yr, 78 kg P 2 O 5 /ha/yr and 78 K 2 O kg/ha/yr (under medium P and K conditions) (Mylavarapu et al. 2015) pply at planting all P 2 O 5 , 30% of the K 2 O and 34 kg N/ha. Then, the remaining 70% of K 2 0 should be applied side dress; while the remaining 135 kg N/ha should be applied in two or more side also recommended to use a fertilizer containing between 17 and 22 kg of sulfur per hectare required for the crop (Mylavarapu et al. 2015) . Generally, no effects in yield (only in grain N content) are seen from N a pplications after the silk tassel period. If N is applied through fertigation, applications should be done at planting (33 45 kg/ha) and (45 56 kg/ha) side dress when corn is about 30 40 cm tall. The remainder N applications should be completed on a biweek ly basis before tassel emergence. If a rain event greater than 50 mm occurs after the N application, an additional 33 kg N/ha can be applied as a recovery application (Wright et al. 2003) . Research has shown that irrig ation significantly increases corn yield in the S.E. averaging 10.1 14.8 ton/ha; while non irrigated corn can result in 5% to 75% of irrigated

PAGE 40

40 corn grain yield, based on rainfall variability within the growing season. Irrigation demand for corn ranges from 508 to 588 mm during the crop season; however, it varies based on weather conditions, plant density, fertility, days to maturity and soil type (Wright et al. 2003) . Research has shown grain yield reductions of 25%, 50 % and 21% when water stress occurred at vegetative, silking and ear stages, respectively. Stress during vegetative stages caused an indirect effect on yield by reducing the size of the assimilatory surface at the ear development stage; whereas, stress occu rring after the ear emergence causes a more direct effect due to the reduction of assimilation in that critical stage (Denmead and Shaw 1960) . Reproductive stages from silking to grain filling are the most susceptib le growth stages for water stress (Denmead and Shaw 1960; Shanahan and Nielsen 1987) . Water and N itrogen U se In 2010, thermoelectric power and irrigation were the largest uses of water in the U.S. ( t otal water use by all uses = 1344 million m 3 /d). Total irrigation withdrawals accounted for 38% (435.2 million m 3 /d) of the total freshwater withdrawals. Surface water and groundwater withdrawals represented a 57% and 43% or total irrigation withdr awals. Irrigation accounted for 65% of the total fresh groundwater withdrawals mainly in California, Arkansas, Texas and Nebraska. These states accounted for 42% cumulative fresh water irrigation withdrawals from the national total. This amount is three ti mes greater than withdrawals for public supply; the second largest user of groundwater in the U.S. (Maupin et al. 2014) . The Florida Department of Agriculture and Consumer Services (FDACS) OAWP developed the Florida Statewide Agricultural Irrigation Demand (FSAID) geodatabase, which is a central data repository for water use projections in agriculture (FDACS

PAGE 41

41 2015b) . Based on the FSAID, the annual average state irrigation water deman d for crop irrigation during 2015 was estimated as 8.07 million m 3 /d. The future irrigation projections were developed using historical trends in the ratio of water applied vs. irrigated area. From 1978 to 2013 this historical trend showed an annual conser vation estimate about 41.2 m 3 /ha less irrigation applied per year. Therefore, using this historical trends, by 2035 the projected irrigation demand is 7.6 million m 3 /d statewide. This assumes about 1.3 million m 3 /d of water conservation through 2035 by usi ng any possible irrigation advance (e.g. improved irrigation scheduling and efficiency, sensor based automation, irrigation systems changes, among others). Nevertheless, when analyzing data by county, a total of 12,406 ha was estimated as irrigated area in Suwannee County in 2015 and it is projected to increase up to 31% reaching 16,224 ha by 2035. Irrigation projections are estimated to increase from 0.1 million m 3 /d to 0.14 million m 3 /d; a 40.4% increment in Suwannee county by 2035 (The Balmoral Group 2015) . Alternative Reduced I rrigation S tudies In many regions of the world, as well as, regions of the U.S such as the Great Plains (i.e. Ogallala Aquifer), water sources are declining and limiting crop produc tion (Klocke et al. 2007; Klocke et al. 2011; Payero et al. 2006; van Donk et al. 2013) . Thus, a new irrigation management paradigm based on the maxi mum economic net return per unit of water, rather than the biological maximum yield should be adopted (English et al. 2002) . As an alternative, irrigators can decrease water use by reducing irrigation applications w hile incurring water deficit during part or all the growing season (e.g. during vegetative stages, except to the reproductive and grain filling stages in corn). As a beneficial factor, these studies have been performed in predominantly silt loam soils,

PAGE 42

42 wit h high water holding capacities (~0.17 0.18 m 3 / m 3 ). Therefore, rainfall contributions plus low irrigation frequencies could serve as a potential water savings strategy due to greater soil water storage capacity. Due to the importance of irrigated corn in the U.S. Great Plains, 20 years of studies were performed across this region in order to determine crop production functions: yield versus irrigation. The predominate soil texture is Cozad silt loam (fluventic Haplustoll) with a plant available soil water holding capacity of 0.17 m 3 / m 3 (VWC 32% for FC and 15% for PWP over 0 to 3 m soil depth). During this period, irrigated corn contributed to the depletion of groundwater. The studies showed that yields did not decrease with the same r ate as the rate of irrigation was reduced (Klocke et al. 2011) . One of these studies was conducted from 1986 to 1998. The deficit irrigation treatment, which used only 47% of full irrigation, resulted in 90% of full irrigated yields. The study started irrigation at the beginning of the reproductive stage, when water was most critical (Klocke et al. 2007) . Similarly, the response of full and limited irrigation on maize yield was evaluated in South Central Nebraska, where irrigation is essential to reach maximum productivity. Predominant soil is Hastings silt loam (fine, montmorillonitic, mesic Udic Argiustoll) with a FC of 0.34 m 3 /m 3 , PWP of 0.14 m 3 /m 3 , and a saturation point of 0 .53 m 3 /m 3 . The effect of four irrigation regimes (fully irrigated treatment (FIT), 75% FIT, 60% FIT, and 50% FIT) and a rainfed treatment were evaluated based on plant height, leaf area index (LAI), grain yield and biomass production, actual crop evapotran spiration (ET a ), yield production functions, yield response factors (K y), and harvest index (HI). All parameters were significantly affected by the irrigation regimes. A strong relationship

PAGE 43

43 between yield and irrigation was established. However, variations resulted from the ET a This main difference showed a strong weather impact over yields and the importance of considering variations in weather conditions for yield. Am ong the irrigated regimes, no significant differences in grain yield were found between the 75% FIT and 100% FIT. Furthermore, the 75% FIT and 60% FIT treatments were very similar to the 100% FIT in terms of crop response to water, providing an opportunity reduce water use while increasing crop water productivity (Djaman et al. 2013) . Further, in order to determine the yield response to irrigation and evapotranspiration (ET) fully irrigated and deficit irrigated corn w as evaluated from 2005 2009 in southwest Kansas. Two crop rotations, sunflower corn and corn corn were evaluated. Irrigation frequency ranged from 5 to 17 days applying 25 mm of water per event. Irrigation treatments consisted of 100%, 80%, 70%, 50%, 40% a nd 25% of full irrigation (the reduction in irrigation was performed by reducing the frequency of application). Volumetric soil water content was measured biweekly to 2.4 m in 0.3 m increments using neutron attenuation techniques. Then, drainage was calcul ated (Wilcox type equation) and crop evapotranspiration (ET c ) was estimated using a soil water balance equation (with change in soil water, rainfall, net irrigation and estimated drainage). Grain yields varied across the irrigated treatments; however, no d ifferences were found in dry matter accumulation among treatments. The deficit irrigation treatments utilized greater non growing season rainfall since the previous crop extracted more water from deeper layers in the soil than the fully irrigated treatment s. This allowed greater room to store the successive precipitation. Overall, as irrigation

PAGE 44

44 decreased yield variability increased, causing a greater income risk for producers. Yield response to irrigation was a key piece of information to develop economic s tudies to determine alternative crops, evaluate deficit irrigation, as well as, income risk (Klocke et al. 2011) . N itrogen R ate and I rrigation R ate S tudies Corn yield response to nitrogen rate and irrigation rate ha s been evaluated in several regions in the U.S., especially in areas vulnerable to nitrate leaching (Al Kaisi and Yin 2003; Derby et a l. 2005; Ferguson et al. 1991; Gehl et al. 2005; He 2008) . Gehl et al. (2005) evaluated corn yield response to N rate and timing and to irrigation in sandy soils (i.e. fine sandy loam, silt loam and loam fine sand soils) along Kansas waterways. The fertil izer treatments consisted of 6 N treatments: (i) 300 kg N/ha applied at planting, (ii) 250 kg N/ha applied at planting, (iii) 250 kg N/ha split (50% applied at planting and 50% side dress at V 6 crop stage), (iv) 185 kg N/ha split (33% applied at planting and 67% side dress at V 6 crop stage), (v) 125 kg N/ha split (20% applied at planting, 40% side dress at V 6 crop stage and 40% applied at V 10 crop stage), and (vi) 0 kg N/ha. Maximum grain yield was achieved using a split application of 185 kg N/ha; neve rtheless, in most of the cases 125 kg N/ha was satisfactory to reach maximum yield. These results were on average, 40% lower of the recommended N rates in Kansas, even under the sandy soils of this region. The authors concluded that efficient use and timin g of N fertilizer (e.g. split applications), along with an optimum irrigation management tied to the crop requirement must be implemented; especially in sandy soils and vulnerable regions to nitrate leaching (Gehl et al . 2005) . In Hungary, an experiment evaluated six N fertilizer rates (0, 30, 60, 90, 120, and 150 kg/ha; total amounts applied three weeks before sowing) under irrigated and non -

PAGE 45

45 irrigated conditions in order to compare the chlorophyll (Chl) concentration o f maize leaves at different growth stages to the soil nitrate N, the N fertilizer applied and grain yield. The experiment was conducted using the short season maize hybrid Mv 277 SC under silt loam soils (FC ranging from 0.27 and 0.34 c m 3 /c m 3 soil water co ntent). Irrigation resulted in a positive effect on N uptake; however, it resulted in a lower Chl content at three growth stages (V6, V12 and R1) due to the reduction of the chlorophyll meter readings (CMR) value per unit of dry matter. The experiment was conducted during two contrasting years: 2007 was dry and 2008 was very wet. In 2007, the shortage in rainfall reduced significantly the non irrigated treatment yield across all N rates in comparison to 2008. The authors concluded that the increase in N dos es does not always result in a yield increase if the crop water requirement is not supplied or it is limiting. A significant reduction in yield was observed due to lack and also excess of water during both years of evaluation. In 2008, due to the excess of rainfall plus irrigation combined with the high crop N uptake, the N available for the plant declined resulting in a yield reduction even in the highest N rate treatment (150 kg N/ha) (Széles et al. 2012) There are different methods to determine the optimum N rate for corn production. A study performed at 79 sites in Nebraska used a recommended N application rate derived using a yield goal, soil NO 3 N levels and irrigation water NO 3 N concentration as inputs. Then a realistic yield goal was determined (i.e. mean of yields from previous 5 years plus 5%). Finally, an algorithm was used to determine final N rate. The algorithm increases the N requirement per bushel as the yield goal increases. Results showed little yield reductions when the application N rate was reduced by 56 kg/ha below the

PAGE 46

46 recommended application rate (i.e. average rate for all sites 145 kg N/ha); suggesting that the algorithm overestimated the requirement to optimize yield. This could be possible beca use it was developed from N studies under dryland and irrigated conditions. Based on the results, the N rate could be reduced by 56 kg/ha without effect on yield but reducing the potential nitrate leaching while saving money on fertilizer reductions. Modif ications to the algorithm could be applied using studies under irrigated conditions for a better N rate recommendation (Ferguson et al. 1991) . In general, N rate recommendations based on yield response represent l arge geographic regions. For example, in Kansas a formula is used for the entire state (Leikam et al. 2003) : (1 1) Where: N rec = recommended N rate (kg/ha). Y grain = expected attainable grain yield (Mg/ha). 28.6=internal N requirement of corn per unit of grain yield. N OM = Net N produced by minera lization of soil organic matter (kg/ha) determined as 2.24 x soil OM content (g/kg). N r = profile N: available pre season inorganic soil N in the surface 60 cm (kg N/ha), determined as 0.12 x sampling depth (cm) x soil NO 3 N (mg/kg). N m = inorganic N availab le from manure application (kg N/ha). N o = inorganic N from other sources (e.g. irrigation water) (kg N/ha). C= previous crop adjustments (kg N/ha). N recommendations in the Northern Great Plains excluded several factors that may affect corn yield. Therefo re, N fertility, soil, weather and irrigation interactions were included in a linear model to predict corn yield using a field study performed in southern North Dakota during 6 years (Derby et al. 2005) . The N fertili zer and irrigation management effects over groundwater quality were evaluated. Soils were generally sandy or sandy loam. The field was divided into four quadrants and irrigation was

PAGE 47

47 assigned to each of them. Irrigation scheduling methods (I) consisted of ( i) tensiometer: when readings at 0.3 m were 40 ± 10 kPa or crop water stress index (CWSI) reached 0.25 ± 0.05 irrigation was triggered; (ii) water balance algorithm (Stegman and Coe 1984) before the first irrigation , it allowed 60 70% plant extractable water depletion, then full ET replacement from 1st irrigation to blister kernel stage (R2), irrigation was applied if soil moisture depletion was greater than 60 70% from R2 stage through maturity; (iii) water balance algorithm replacing 80% of estimated ET c ; (iv) irrigation based on CERES Maize crop growth model predictions of plant extractable water in the soil. The first, second and fourth irrigation scheduling methods applied 32 mm per irrigation event; while the th ird method applied only 25 mm per event. The average seasonal (1991 1995) amounts resulted in 136, 125, 106 and 122 mm for the first through the fourth irrigation methods, respectively. The fertilizer treatments (Nt) consisted of reference regime of zero N as 0, 45, 90, 135, 180 and 225 kg N/ha (without the starter N) all randomly arranged. Post emergence N applications were applied in three splits: at the V6, at V15 and R1 rates greater than 135 kg N/ha, except for the fourth irrigation treatment (CERES) where maximum yield resulted at 180 kg N/ha. However, the highest yields were obtained using the wate r balance algorithm treatment across all N rates, suggesting that this treatment was able to deliver enough amounts of water during critical growth periods of highly increasing N uptake (Derby et al. 2005) . Different methods are used to determine N recommendation rates. However, when soil inorganic N is not considered correctly, or when yield predictions are larger

PAGE 48

48 than expected based on soil types and weather conditions, N applications may exceed crop requirements and be lost to the environment (Keeney 1986) . Thus, effective N and irrigation management recommendations should include adjustments for low productivity soils, evaluation of growing conditions and must be implemented b ased on soil climate conditions in order to reduce potential environmental risks (e.g. nitrate leaching) (Derby et al. 2005) . N itrogen Leaching S tudies In the soil solution, nitrate (NO 3 ) is the dominant form of nitr ogen and it is taken up by plants. Most of the usable form of N is in the soil solution and readily moves as the water moves. As a result, nitrate can move rapidly to the roots, it can be taken up and be depleted by plants; however, it is also subject to r apid removal from the soil by leaching (Foth and Ellis 1996) . In most of the soils, generally the concentration of ammonium (NH 4 + ) is low because it can be readily converted into NO 3 . In contrast, NO 3 is negatively cha rged and is not retained by the soils; therefore, it is the predominant form of N that leaches Nitrate N leached from agricultural lands impacting groundwater quality is an environmental problem in Florida and worldwide. N leaching under different irrigat ion and N fertilizer management regimes has been evaluated extensively under different soils and climatic conditions (Andraski et al. 2000; Gheysari et al. 2009; Jia et al. 2014; Klocke et al. 1999; Moreno et al. 1996; Schneekloth et al. 1996; Zotarelli et al. 2007) . Ideal N fertilizer and irrigation management is required in order to r educe N leaching from the rootzone. A corn wheat crop rotation study evaluated the effect of irrigation and N fertility rates on NO 3 N leaching using lysimeters in China. The study consisted of the evaluation of two irrigation rates (525 and 263 mm), two N fertilizer

PAGE 49

49 levels (100 and 200 kg N/ha) using two fertilizer types (urea and manure) and two maize varieties (Zhongnong 99 [Z99] and Lainong 14 [L14]). From 2 m depth, the maximum N leaching resulted in 77.2 kg N/ha and 47.9 kg N/ha for Z99 and L14, respe ctively when the highest irrigation and N fertilizer were applied (525 mm and 200 kg N/ha) (Jia et al. 2014) . Another study in the Southwest of Spain evaluated furrow irrigated maize and two N rates: 500 kg and 170 kg N/h a/yr. The former represents a rate that farmers apply in the region traditionally; whereas the latter is a third of that rate. The results showed that significant drainage occurred after rainfall and when soil was previously irrigated resulting in 150 and 43 kg N/ha in the high and low fertility treatments evaluated (Moreno et al. 1996) . A nitrogen management study included four cropping manure management systems: (i) continuous corn (at least 5 years) without manure ap plication (CCC), (ii) continuous corn with manure application (m CCC), (iii) second year corn following 3 yr alfalfa with no manure applied for at least 5 yr (ACC), and (iv) second year corn following 3 yr alfalfa with manure applied to first year corn. Al l management systems were representative of common maize production practices in Wisconsin. Treatments tested seven N rates (0, 34, 68, 102, 136, 170 and 204 kg N/ha), surface broadcast applied as ammonium nitrate three days before planting. The Pioneer 35 78 (105 d relative maturity) was planted in a Plano silt loam soil (fine silty, mixed, superactive, mesic Typic Argindoll, >2m depth to the water table). Soil water NO 3 N samples were collected using porous cup samplers through the season. Significant effe ct of N rates resulted on corn grain yield for all management systems in the first year and only on

PAGE 50

50 CCC and m CCC in the second year. Economic optimum N rates (EONR) for CCC, m CCC, ACC, AmCC were 130, 20,105 and 80 kg N/ha resulting in 8.7, 9.7, 11.2, 11. 2 Mg/ha grain yields in the first year. In contrast, EONR were 150, 80, 0 and 0 Kg N/ha resulting in 12, 10.5, 12.8 and 12.6 Mg/ha for CCC, m CCC, ACC, AmCC; respectively in the second year. N mineralization from legume or recent manure applications increa sed N availability resulting in lower EONR and higher grain yields. N leaching samples increased as the N applied in excess of the EONR increased. The predicted soil water NO 3 N at EONR was 18 mg/L. Concentrations were <10 mg/L for N rates >50 kg N/ha belo w EONR and >20 mg/L for N rates >50 kg N/ha above EONR. The estimated total NO 3 N leaching averaged across N rates, trials and years were: 20, 41, 34 and 56 kg N/ha for CCC, m CCC, ACC and AmCC, respectively. Authors concluded that increasing soil N availa bility due to past crop and manure management lead to: an increase in N mineralization resulting in high soil NO 3 N early in the season, a decreased economic optimum corn N rate, and an increased soil NO 3 N content and concentration at the end of the seaso n which also resulted in flux below the root zone. As well, an end of season soil NO 3 N test could be used to assess corn N management practices and the excess N fertilizer amounts applied that will potentially be leached from the root zone (Andraski et al. 2000) . The effect of irrigation and N rates over grain yield has been extensively studied in the Great Plains, as well as, potential negative impacts resulting from improper use of these variables to the environ ment, especially to the Ogallala Aquifer, the main source for irrigation. From 1998 to 2000 a study performed in Yuma, Colorado evaluated three irrigation rates (0.60, 0.80, and 1.00 estimated ET), four N fertility rates (30, 140, 250,

PAGE 51

51 and 360 kg N/ha; inc luding N in soil and irrigation water), and three plant populations (57,000, 69,000 and 81,000 plants/ha). The field experiment was conducted in well drained Haxtun sandy loam soil (fine loamy, mixed, superactive, mesic Pachic Argiustolls). Optimum corn yi eld resulted from the combination of 0.80 ET to 1.00 ET 140 to 250 kg N/ha and 57,000 to 69,000 plants/ha population. However, when using 0.80 ET across those N rates and plant populations resulted in the best management system for optimum WUE and to achie ve water savings and preserve the Ogallala Aquifer (Al Kaisi and Yin 2003) . Contamination of aquifers by nitrate N has been a growing problem in irrigated areas such as the western Corn B elt where about 95% of the population uses groundwater as the primary source of potable water. A long term study performed in western Nebraska was performed with the aim to broader understand the amount and timing of nitrate leaching losses from a continuous corn and in a soybean co rn rotation under sprinkler irrigation. Experimental site was located in Cozad silt loam (Fluventic Haplustoll) soils and leachate was collected using monolithic percolation lysimeters. Results from 1993 to 1998 showed that leaching patterns were influence d by rainfall patterns and the active water uptake periods by plants. Although irrigation was applied in a careful manner, it did not prevent leaching during the growing season. Average drainage was 218 mm/yr and it did not differ statistically between the crop rotation and continuous corn production. Average nitrate concentrations resulted in 24 mg/L and 42 mg/L for the continuous corn and for the corn soybean rotation, respectively. Thus, yearly nitrate leaching was 52 kg/ha and 91 kg/ha for the continuou s corn and the corn -

PAGE 52

52 soybean rotation, respectively. This represents an equivalent of 27% and 105% of the fertilizer applied during the 6 years of study (Klocke et al. 1999) . Over enrichment (eutrophication) caused by urban, agricultural and industrial development associated with climate change promotes toxic cyanobacteria (blue green algae) growth which is raising concerns in structure and function of freshwater ecosystems (Carmichael 1992; Paerl and Huisman 2008; Paerl and Otten 2013) . The cyanobacterial harmful blooms (CyanoHABs) are toxin producing species that have threaten ecological and human health (Huisman et al. 2005) . Ecological impacts include the scum formation which reduces light to benthic primary producers and the excessive biomass at bloom die off which may kill fish due to oxygen depletion or hypoxia. Human health may also be impa cted causing fatal human liver, digestive, neurological and skin diseases (Carmichael 1992; Huisman et al. 2005; Paerl et al. 2011) . Water management agencies face challe nges to control the development of control mainly toxin producer algal blooms and mi tigate these consequences. Due to the capacity of to fix atmospheric N2 and satisfy their N requirement, traditionally a reduction in phosphorus inputs would control some CyanoHABs since many are limited by P. However, non N2 fixing CyanoHABs are increasin g in eutrophying systems now being N and P co limited or even N limited. As N loads increase, constraints to N and P inputs will most be likely required to control CyanoHABs in the long term (Paerl et al. 2011) .

PAGE 53

53 A rec ent study was performed in Lake George, Florida in order to determine dynamics of cyanobacteria (N2 fixers and nonfixers) in respond to nutrient levels and biophysical factors. The most important results showed nonlinear relationships and critical threshol ds among cyanobacteria and environmental covariates. Results showed that due to the ability to fix N2, fixing cyanobacteria were relatively insensitive to TKN; whereas the nonfixing genera showed increasing partial dependence (i.e. predominantly non linear model relationships between the lagged explanatory variables and the genera) on TKN with increasing concentrations until a threshold of 1.5 2.5 mg/L. Thus, management practices oriented to reduce TKN could have a positive effect to reduce bloom intensitie s in nonfixer cyanobacteria. On the other hand, most cyanobacteria genera were less sensitive to changes in total phosphorus (TP) concentrations than to TKN, maybe due to their ability of buoyancy regulation. However, N2 fixing species showed a modest sens a relatively high P demand of some nitrogen fixers which gives limited evidence for the potential control of this genera. As well, they found a strong relationship between cyanobacteria biom ass and temperature; where as a negative correlation between cyanobacteria biomass and both flow and color. Hence, their findings suggested that other abiotic covariates could contribute to algal blooms despite nutrient concentrations; however, management limitations on abiotic covariates will ultimately lead to the management of nutrient variables. Therefore, a dual nutrient strategy could be more beneficial due to the variability of CyanoHABs taxas and structures of individual ecosystems (Nelson et al. 2018) .

PAGE 54

54 BMPs Due to the water use limitations and the potential environmental degradation issues associated with the use of nitrogen fertilizers and excessive water use to sustain agriculture, the implementation of N an d irrigation best management practices (BMPs), and/or new regulations has become fundamental. In Florida, the adoption and implementation of BMPs aim to assist producers in priority watersheds statewide with the purpose of protect water quality and reduce water use. These practices include upgrading irrigation systems to achieve greater efficiency, the implementation of equipment and tools to target fertilizer applications and possibly reduce losses to the environment, as well as, adopting an efficient sche duling irrigation to help protect water resources. If a BMP program is established and in place, all growers adopting agricultural BMPs will benefit since they are considered to be Thus, if water quality standards are not met, growers will be protected from liabilities due to water quality degradation (FDACS 2015a) . Water Q uantity and Q uality BMPs for Field C rops in Florida Nutrient m anagement Nutri ent management is essential to protect water quality of waterbodies. The fate and behavior of N in soil plant systems could be tracked and controlled by strategically managing the application rates, timing and method of application of N fertilization and i rrigation (Li and Yost 2000) . Fertilization and irrigation are the two main practices associated with the N cycle, since water acts as a carrier of nutrients and pollutants (e.g. N fertilizers). Excess nutrient discharg es may be related due to the excessive use, inefficient placement, or

PAGE 55

55 right rate, right time and right place is an approach that has become successfully adopted for n selecting the appropriate and balanced supply of essential nutrients in available forms to be extracted carefully chosen based on the crop demand, soil supply, nutrient loss risks and f established based on the root soil dynamics, nutrient movement and soil variability, with the aim of maximizing plant uptake and limit potential losses to the environment (Hochmuth et al. 2014) . Successful implementation of the 4Rs of nutrient management is linked with careful and adequate water management. When water is applied and it exceeds the over the surface as runoff. By contrast, inadequate water applications will prevent fertilizers to be dissolved reducing the nutrient availability to the crops. As a consequence, it may lead to an increase in the production costs, a reduction in marketable yields and potentially the increase in human health and environmental issues (FDACS 2015a) . Another important factor to take int o account is the geographic characterization of the production region. In North Florida, for example, the main agricultural areas lay in well drained sandy soils over karstic regions (i.e. unprotected limestone), making it more susceptible to nitrate leach ing to the groundwater and sinkholes connected to the aquifer (FDACS 2015a) . Therefore, more effective practices should be implemented in susceptible areas to reduce the risks.

PAGE 56

56 Nutrient and irrigation BMPs are based on pla nt nutrition principles: (i) supply the right amount of the essential macro and micronutrients; (ii) diminishing returns: the highest nutrient uptake that reflects the highest yield. Thus, greater amounts of fertilizer will decrease monetary return. Fertil izer N use efficiency: maximum percentage of fertilizer taken up versus the applied fertilizer. (iii) Fertilizer timing: fertilizer application to sandy soils are chara cterized by low organic matter, low cation exchange capacity and prone to nutrient leaching; thus, small fertilizer applications are recommended. Likewise, nutrient incorporation in the planting beds is another implemented practice to avoid nutrients leach ing (FDACS 2015a) . Total fertilization should account for soil nutrients plus the fertilizer applied in order to achieve optimum crop growth. Therefore, soil tests should be performed in order to determine the initial nutr ient supply by the soil, which could reduce the amount of fertilizer required to be applied. Furthermore, the fertilizer source and the method of application is another way to reduce potential losses to the environment. Several conventional N fertilizers a re granular and soluble; however, the method of application can be broadcast (applied over the entire field), modified broadcast (applied on raised beds) or banding (applied in bands near the root system). The banding method has been identified as the pref erred method of application (FDACS 2015a) . Some irrigation and fertilizer BMPs and strategies used by growers in Florida are: (i) N application scheduling matching the crop needs and in small amounts to avoid potential lo sses, (ii) avoid N applications in non cropped areas prone to

PAGE 57

57 erosion/leaching, (iii) minimizing large N residues at the end of the growing season since remaining N is subject to leaching (Hochmuth 2000) . N applica tions are tempted to be applied in excess due to the misconception that extra N will reduce the associated risks in crop production in order to reach the best yields. Nevertheless, when the extra N applied does not contribute to greater yields, it only mea (Hoch muth 2000) . Therefore, recommended practices are: (i) determine the crop nutrient requirement and target the crop demand stages to perform the N applications, supplemen tal N (27 36 kg/ha) should be applied under N impending conditions detected by leaf or petiole sap or when leaching rainfall events occur (76 mm in 3 days or 100 mm in 7 days); (ii) perform N applications during the most active N uptake stages (V3, V6 and V8 through before VT), avoiding applications at the end of the season when N uptake is reduced. The development of BMPs for N applications in sweet corn was evaluated using the CERES Maize Model of the Decision Support System for Agrotechnology Transfer (D SSAT). A total of six irrigation and 54 N fertilizer strategies were evaluated using 33 year continuous historical weather conditions for sandy soils in Gainesville, Florida. The best potential strategies were selected under two criteria: simulated yield a bove the acceptable value of 3,400 kg/ha and resulting in the lowest leaching values. The irrigation strategies of 5 mm applied at 20% MAD and 7.5 mm at 30% MAD resulted as the best practices. These results support the assumption that nitrogen leaching cou ld be reduced by frequent low volume applications, where less water is lost through deep percolation. In terms of N applications, the author divided the growth season in three stages: small leaf, large leaf and ear developmental stages. The best strategies for the

PAGE 58

58 application in the small stage, 1/2 of total N (except for the starter N fertilizer: 17 kg N/ha) in the larger leaf stage, and 1/2 in the ear development stage) and much N fertilizer should be applied per fertilization event) if the production cost was considered in addition to yield and N leaching. This result also streng thens the assumption that frequent and low amount N applications potentially reduce N leaching (He 2008) . Nevertheless, the selection on these best strategies mean that growers need to irrigate and fertilize more often, in creasing their production costs. Thus, a final selection was performed taking into account the production costs. After the evaluation of six potential BMPs for uncertainties of yield and cumulative N leaching caused by weather and input parameter uncertain ty, the BMPs (i) irrigation of 7.5 mm triggered at N/ha an irrigation of 7.5 mm with a MAD value of 30%, a total of 196 kg N/ha with a split pli cation amount of 30 kg N/ha , were best options for real sweet corn production, compared with other BMPs and the actual grower practice. These simulations supported the UF/IFAS N fertilizer recommendations for sweet corn production (224 kg/ha) (He 2008) . Another recommended practice for nutrient management is set realistic yield goals (Hochmuth 2000; Shapiro et al. 2008) . In Nebraska, for example, an N applicat ion is recommended based on the following equation (Shapiro et al. 2008) : (1 2)

PAGE 59

59 Where: EY = expected yield (bu/ac). NO 3 N ppm = average nitrate N concentration in the root zone (2 4 foot dept h) (ppm). OM = organic matter (%). Other N credits = N from legumes, manure, other organic materials and irrigation water. Price adj = adjustment factor for prices of corn and N. Timing adj = adjustment factor for fall, spring and split applications. important to note that N applications are sensitive to wide fertilizer and corn price fluctuations. Therefore, the price factor is determined on the reduction effect of increasing N rate on corn yields. Thus, greater N per bushel yield increase can be prof itably applied as N fertilizer becomes less expensive than corn prices (Shapiro et al. 2008) . Irrigation m anagement Irrigated agriculture has been, and it is still today, an instinctive and essential part of human c ivilization due to its contributions to food security and poverty reduction. Nevertheless, annual global water shortages have been predicted to be 640 billion cubic meters by 2050 (Spears 2003) . In an increasingly gr owing population highly demanding for food, water scarcity has become more common, especially in places where water resources are limited and highly exploited and the population increase is the greatest (Wallace 2000 ) . Several water use efficiency indices were quantified for four irrigation regimes in a long term research on irrigated corn (Irmak 2015a) . Treatments consisted of: fully irrigated treatment (FIT), limited irrigation (75% FIT, 60% FIT and 50% FIT), and rainfed as a control assessed from 2005 to 2010. FIT irrigation was triggered when soil water depletion reached 40 45% of total available water in order to avoid water stress impact on crop yield and water productivity. The results showed a linear increase of maize

PAGE 60

60 seasonal ET a (actual evapotranspiration) with increasing irrigation. The ET a values showed a strong inter annual variation (mainly influenced by precipitation), ranging from low to high irrigation treatment 468 to 654 mm, 461 to 647 mm, 528 to 646 mm, 617 to 690 mm, 509 to 637 mm, and from 596 to 654 mm for 2005 to 2010, respectively. The rainfed treatment had the lowest ET a and grain yield; while the FIT had the highest values. Irrigation had a significant effe ct on maize yield. Rainfed and FIT treatments yields ranged from 4.7 to 14.8 t/ha, 6.4 to 15.2 t/ha, 8.7 to 14.8 t/ha, 13.9 to 16.1 t/ha, 9.1 to 15.5 t/ha, and from 11.8 to 15.4 t/ha, respectively from 2005 through 2010. Average yield increase attributable to irrigation was 7.7, 4.6, 1.6, 5.8, and 2.7 t/ha as all treatment average in comparison to the rainfed treatment. No significant differences were found between FIT and 75% FIT from 2007 to 2010. Thus, under those environmental and management conditions, nearly the same yield can be achieved irrigating 75% of the crop water requirement, saving 25% of the water applied including pumping costs and reducing potential leaching. The author confirmed a curvilinear relationship of grain yield and irrigation wher e maize grain yield (15.5 t/ha) increased with up to 180 mm of irrigation, above it became excessive reducing the oxygen and nutrient uptake to the crop while increasing potential N leaching, therefore diminishing returns (Irmak 2015a) . Irrigation water use efficiency (IWUE), crop water use efficiency (CWUE), evapotranspiration water use efficiency (ETWUE), annual precipitation use efficiency (ANNPUE), growing season precipitation use efficiency (GRSPUE), and growing sea son precipitation use efficiency with respect to rainfed yield (GRSPUE rainfed ) were evaluated in maize under the same conditions and treatments previously described

PAGE 61

61 (Irmak 2015b) . The equations for each of these water u se efficiencies are described as following: The previous indices could be calculated by initially calculating the actual crop evapotranspiration (ET a ), using a soil water balance approach (Irmak 2015b) : (1 3) Where: P = precipitation (mm) I = irrigation water applied (mm) U = Upward soil moisture flux (mm) Run on = surface run on Runoff = surface runoff from individual treatments (mm) ge in soil water storage in the soil profile (mm) at the beginning and end of the season DP = deep percolation (mm) below the crop root zone. Assuming that upward flow and run on are negligible, then the equation is reduced to: (1 4) To determine the maize productivity response under different irrigation regimes, crop water use efficiency (CWUE) is computed as the ratio of grain yield to actual crop evapotranspiration (Irmak 2 015b) . (1 5) Where: CWUE = crop water use efficiency (kg/m 3 ) on a unit water volume basis or in g/kg on a unit water mass basis Y = grain yield (g/m 2 ) ET a = actual crop evapotranspiration (mm)

PAGE 62

62 In order to evaluate the effe ct of irrigation in terms of crop water productivity, irrigation water use efficiency (IWUE) and evapotranspiration water use efficiency (ETWUE) are calculated (Musick and Dusek 1980; Viets 1962) . These two indices allow to quantify the crop productivity variations and actual crop evapotranspiration (ET a ) with different irrigation regimes compared to yield obtained under rainfed conditions (Irmak 2015b) : (1 6) (1 7) Where: I WUE = irrigation water use efficiency (kg/m 3 ) ETWUE = evapotranspiration water use efficiency (kg/m 3 ) Y = dry grain yield at 15. 5% moisture content (g/m 2 ) I i = applied gross irrigation water (mm) Subscript i = irrigation level Subscript r = treatment with no seasonal irrigation (rainfed or dry land) ET ai = actual evapotranspiration for irrigation level i ET ar = actual evapotranspir ation for the associated rainfed treatment In order to quantify the efficiency of various irrigation levels in terms of increase in ETa attributable to irrigation with respect to applied irrigation, the irrigation evapotranspiration use efficiency (IRRETU E) is calculated (Irmak 2015b) . It indicates the effectiveness of irrigation management compared to crop water requirement: (1 8) Where: IRRETUE= Irrigation eva potranspiration use efficiency (%) ET ai = increase in ET a attributable to irrigation (i.e. ET a for irrigated treatment minus ET a of rainfed treatment) A 100% IRRETUE indicates that the total water applied through irrigation is used for ETa (i.e. very diff icult to accomplish). IRRETUE values greater than 100% show

PAGE 63

63 irrigation applied exceeded crop water requirement (i.e. overirrigation) whereas IRRETUE values below 100% represent irrigation applications insufficient to meet ETa (i.e. underirrigation or limit ed irrigation) (Irmak 2015b) . Precipitation water stored (dormant season) and/or during growing season can make significant contributions to effective irrigation management strategies. The PUE (t/ha/mm) is the ratio of aboveground net primary production (grain yield and/or dry matter production; t/ha) to ANNPUE (mm) or GRSPUE (mm) described as follows (Irmak 2015b) : (1 9) (1 10) Where: Growing season precipitation= total precipitation from emergence to physiological maturity. Then, GRSPUE rainfed consists of the evaluation of t he effectiveness of different irrigation treatment regimens over yield in comparison to rainfed yield, accounting for potential increase in irrigated yield in comparison to the rainfed yield under different water management practices (i.e. irrigation treat ments) (Irmak 2015b) . The results of this long term research showed that 75% FIT resulted in a maximized CWUE, with 25% less irrigation applications. Across the six year study, average CWUE values were: 2.34, 2.32 , 2.29, 2.24, and 1.73 kg/ m 3 for FIT, 75% FIT, 60% FIT, 50% FIT and rainfed treatments, resulting in a linear relationship with yield and ETa. IWUE values ranged from 1.75 kg/m 3 to 5.9 kg/m 3 for 50% FIT during 2008 (the wettest year) and 2009, respectivel y. FIT, 75% FIT, 60% FIT, and 50% FIT six year

PAGE 64

64 averages were 4.01, 4.45, 4.48, and 4.13 kg/m 3 , respectively. Limited irrigation treatments (50% FIT, 75% FIT and 60% FIT) resulted in maximized IWUE due to a reduction in soil evaporation losses, keeping tran spiration rate close to the potential rate; thus, with relatively high yields. The IRRETUE index resulted in values from 37.8% (62.2% under irrigation) and 149.6% (49.6% overirrigation) for 50% FIT in 2010 and 2008, respectively. About 56% underirrigation occurred since irrigation applications did not meet crop water requirements in 2010. By contrast, two excessive irrigation events occurred in 2008 (the wettest year) in which irrigation did not have an effect on yield due to high precipitation. Average IRR ETUE values (without 2008 overirrigations) were optimal to maintain 80 90% IRRETUE values (i.e. soil profile less than fully replenished) allowing it to capture potential p recipitation while reducing runoff and future irrigation applications (Irmak 2015b) . Previous research showed that yield increased up to 127 130 mm of irrigation and plateaued with the exception of 2010, when it was obs erved mostly a linear response (did not plateaued) versus a curvilinear (Irmak 2015a) . The effectiveness of irrigation management practices can be assessed using the IRRETUE index with respect to meeting crop ET a and ma ximum grain yield (Irmak 2015b) . Improving irrigation scheduling constitutes a potential strategy to increase irrigation water use efficiency while implementing BMPs. In soils with low water holding capacities, low amo unts but more frequent water applications have been proven to get better results in comparison to the high irrigation volumes with fewer applications (El Hendawy and Schmidhalte r 2010; He 2008; Hochmuth 2000; Locascio 2005) . As a drawback, these require more intensive labor and can be more expensive. However,

PAGE 65

65 implementing automated irrigation using soil moisture sensors could be an alternative to mana ge frequent and low volume applications for crop production in sandy soils (Munoz Carpena et al. 2005) . A field experiment on drip irrigated maize was conducted in a very arid area of Egypt (annual precipitation 20 mm). Different irrigation frequencies (F) (once every 1 (F1), 2 (F2), 3 (F3) and 4 days (F4)) and water application rates (I1=1.00, I2=0.80 and I3=0.60 of ET c ) were evaluated in the 2007 and 2008 growing seasons. Maximum yield components were obtained under the most frequent irrigation applications (F1 and F2). By contrast, on average across the two seasons, F3 and F4 resulted in 27.7 and 42.3% lower ear weight, 35.2 and 62.7% lower grain weight per plant, and 38.1 and 56.3% reductions in grain yield/ha , compared to F1. As well, irrigation application rates significantly affected yield components. The 1.00 ET treatment resulted in the highest yield, whereas the 0.80 and 0.60 ET treatments showed reductions of 13.8 and 50.9% for ear weight/plant, 20.7 and 63.8% for grain number/plant, 14.9 and 56.1% for grain weight/plant and 18.0 and 60.8% for grain yield, respectively. F2I1 (2day F and 0.8 ET application rate) resulted in the highest yields. Grain yield and seasonal crop ET resulted in a linear and posit ive relationship each year. An average of 246.1 mm were needed as crop ET to start grain yield production. Authors attributed the maximum yields and yield components (F1I2 and F2I1) to maintaining the optimal soil water content available in the root zone w ithout deep percolation (El Hendawy and Schmidhalter 2010) . The effects of irrigation over sweet corn yield and cumulative N leaching were investigated (He 2008) . A total of nine irrigation strategies were evaluated, in which

PAGE 66

66 automatic irrigation was triggered to refill the soil profile (50 cm) based on the remaining water available in the soil. A maximum dry yield of 3,867 kg/ha was obtained when irrigation was triggered at 30% m aximum allowable depletion (MAD). Yield reductions were observed within the range of 80 50% MAD, where yields were below 3 , 000 kg/ha due to water stress. However, when irrigation was triggered at 20 and 10% with small depth of irrigation (5 and 2.5 mm, res pectively), then dry yields decreased slightly that could be attributed to N leaching, thus less N available for crop uptake. Therefore, irrigation scheduling using a 30 40% MAD and with application depths of 7.5 to 10 mm was found as the optimum method to result in higher yields and lower N leaching (He 2008) . Irrigation scheduling improvements have been studied through the assessment of soil moisture sensors complemented with ET data (i.e. estimation of water lost from the soil surface and from the crop through evaporation and transpiration, respectively) (Adhikari and Penning 2016) . This method is widely used since it provides a broad scale irrigation recommendation. However, us ing real time soil moisture data monitored in situ, provides a great complement to the ET method. A study was conducted to evaluate both methods. Results showed that the combination of ET data, which provides the amount of water lost that needs to be repla ced with irrigation, and the soil moisture data, which confirms if the amounts were efficiently applied in the root zone, is an efficient methodology to schedule irrigation. As well, the use of multiple soil moisture sensors provides information about the soil variability, thus irrigation can be adjusted manually or using smart irrigation systems. This is an opportunity for the producer or for

PAGE 67

67 the irrigation manager to react accordingly with the constant changes in the field and/or weather conditions (Adhikari and Penning 2016) . N itrogen and irrigation BMPs p revious research Previous research determined the importance of integrating the N management practices with irrigation management practices in cropping systems, since failing to link them resulted in elevated concentrations of NO 3 N in the soil, as well as, in groundwater (Schepers et al. 1995) . Thus, several studies have investigated BMPs of N fertilizer and irrigation in the United States in order to reduce NO 3 N leaching below the corn root zone, as well as, to use more efficiently both resources especially when any of them is limited (Ferguson et al. 1991; He 2008; Klocke et al. 1999; Schepers et al. 1995) . Due to the specificity of the soil, crop and climate conditions N and irrigation recommendations should be site specific to reduce errors on the applications. Locat ion and year characteristics (e.g. soil water holding capacity and precipitation amounts and timing) are main factors influencing yield. Careful N and irrigation management must be implemented in irrigated corn production in order to reduce the nitrate lea ching below the root zone and avoid environmental impacts. A total of 79 sites with irrigated corn were evaluated in south central, west central and northeast Nebraska from 1984 through 1988. Several parameters were evaluated: NO 3 in the soil and in the ir rigation water, realistic yield goal selection and irrigation scheduling based on crop water use. The recommended N rate was determined using a logarithm taking into account the yield goal, soil NO 3 N levels and water NO 3 N concentrations as inputs (Univ. Nebraska, Lincoln procedure (UNL)). The average N credits from soil and irrigation water NO 3 N was 50 kg/ha. After considering these N credits, the recommended rate ranged from 40 to 270 kg/ha among

PAGE 68

68 the different regions and years evaluated. N applications of 56 kg/ha above and below the recommendation were also applied. Yearly irrigation amounts varied largely (ranged 160 1217 mm) and averaged 520.7 mm. Results showed little differences in yield among the three N rates. A regression analysis showed a relat ive insensitivity of yield to changes in the rate of N fertilizer. Yield goals (last 5 years average yield with a 5% increase) for all sites averaged 10.7 Mg/ha and yield at the recommended N rate averaged 10.9 Mg/ha; suggesting an overestimation of the al gorithm used to develop N rate recommendations to achieve yield goals under irrigated conditions (due to significant NO 3 N contributions from soil and irrigation water). Therefore, a reduction in N rate by 56 kg/ha could significantly achieve economic savi ngs and minimize potential NO 3 loss to the environment. Average irrigation was 521 mm per acre, with a range from 160 to 1217 mm per acre, which exceeded the water needed to achieve optimum yield (i.e. in most of the years < 381 mm were required to optimiz e yield). Thus, the authors recognized a need to improve water management to avoid excessive water applied and therefore, reduce soluble N leaching into the groundwater (Ferguson et al. 1991) . Cropping systems with out integration of N and irrigation management are more likely to result in larger NO 3 N concentrations in the soil and groundwater. These conditions are common in places where the main purpose of irrigation is to reduce drought effects and where N fertili zers are inexpensive. A study conducted In the Platte River Valley near Shelton, Nebraska from 1991 to 1993 evaluated three N and water management scenarios in maize under monoculture and crop rotation systems (Sch epers et al. 1995) . Four maize hybrids

PAGE 69

69 differing in yield potential, maturity and stay green characteristics were selected for the crop rotation study. The irrigation systems evaluated were: (i) conventional furrow, (ii) surge flow furrow with laser gradi ng and runoff water recovery system and (iii) center pivot sprinkler irrigation. The conventional furrow irrigation scheduling was determined by the producer; while the checkbook method was used to schedule irrigation for the other two systems. Based on th e University of Nebraska, a yield of 12.4 Mg/ha was expected with a crop requirement of 240 kg/ha N. However, the N fertilizer recommendation was lower (113 kg/ha N) after taking into account the residual soil nitrate and irrigation nitrate concentrations. There was a high variation between the years of evaluation for all treatments. In 1991, the conventional, surge flow and center pivot treatments had 106, 173 and 95 kg/ha N residual. All treatments applied 33 kg/ha as starter N fertilizer and received 75 mm of precipitation during the growing season. For the conventional, surge flow and center pivot, respectively the total N fertilizer applied was 201, 123 and 33 kg/ha, N contributions provided by irrigation were 301, 144 and 109 kg/ha N where total irriga tion applied was 940, 450 and 340 mm. Groundwater used for irrigation was a source of N (~30 mg NO 3 N /L) that could serve as fertigation providing at least 50% of crop N needs (<5 kg/ha) as well as to replace water loss through ET during the July and Augu st when ET values are near 8 mm/d. Grain yields in 1991 were 12.5, 12.3 and 12.2 Mg/ha for the treatments evaluated. In 1992, all treatments received 21 kg/ha as starter N fertilizer and 315 mm of precipitation during the growing season. Total N applied pe r treatment was 178, 73 and 47 kg/ha, irrigation N contributions were 237, 74 and 67 kg/ha N, and irrigation amounts were 740, 230 and 210 mm for the conventional, surge flow and center pivot, respectively. Grain yields

PAGE 70

70 were 13.0, 12.6 and 11.0 Mg/ha for t he respective treatments. In 1993, 30 kg/ha of starter N were applied in all treatments. The highest precipitation occurred in this year for a total of 762 mm during the growing season. Total fertilizer applied was 187, 157 and 155 kg/ha, N credits from ir rigation were 73, 43 and 28 kg/ha and irrigation applied was 200, 110 and 80 mm for the treatments, respectively. Grain yields decreased for all treatments resulting in 8.9, 8.0 and 8.2 Mg/ha due strong winds causing stalk breakage. Results showed 45 69% w ater savings using the surge flow furrow versus the conventional, and 60 72% water reductions when using the center pivots versus the conventional furrow irrigation. Even when in 1991 and 1992 the conventional treatment (producer) achieved yields comparabl e to the regional yields, the producer applied 88, 170 and 35 kg/ha over the recommended application for corn in 1991, 1992 and 1993, as well as, in addition to the excessive irrigation application associated with high nitrate levels. The surge flow furrow irrigated system applied the fertilizer (after the soil and irrigation nitrate adjustment) through a side dress N application in 1991 and fertigation in the following years. Therefore, it provided enough N for crop uptake while reducing early nitrate leac hing. The center pivot resulted in similar yield in 1991 with significant lower N fertilizer and irrigation. However, the reduction of N fertilizers and water application rates throughout the years caused a decrease in residual N before planting. However, lower residual N after harvest will reduce the potential nitrate leaching during the winter months. Thus, reductions of remainder N to the following growing season can be achieved by implementing improved N management practices such as fertigation (Schepers et al. 1995) .

PAGE 71

71 Economic s I rrigation is crucial to produce corn in Florida ; production without irrigation results risky and profitable yields become unsure (Wright et al. 2003) . Furthermore, with the aim to achieve high yields, producers apply large amounts of fertilizer and may exceed the UF/IFAS fertilizer application recommendations. Intensive agricultural production practices have increased NO 3 concentrations in surface and groundwater occasioning environmental and economic concerns (Andraski et al. 2000) . As described previously, several studies have investigated optimum N and irrigation rates; however, few refer to economic returns and impacts (Paredes et al. 2014) which is the main interest of producers, thus resulting in limitations on the applicability of these practices. From an economic perspective, producers will apply greater inputs (i.e . amounts of N and irrigation) in order to increase profit and reduce perceived risk. Inputs will continue until the revenue produced per unit input equals the cost related to purchasing and applying the last unit. Nevertheless, when the crop is near the m aximum grain yield, there is a smaller increase in grain yield per unit input. This is because grain yield follows a diminishing return to most of the inputs and the efficiency of these inputs declines although the net income continues to increase (Rudnick et al. 2016) . A previous study performed in south central Nebraska investigated the yield response to limited irrigation applying 75% of full irrigation (75% FIT). Their results showed the 75% FIT a feasible approach with minimal impact in corn grain yield (Rudnick and Irmak 2013) . Therefore, in order to provide economic information relevant to corn producers, a subsequent study was performed in order to evaluate the economic i mpacts of several N fertilizer rates (0, 84, 140, 196, and 252 kg/ha) under full irrigation,

PAGE 72

72 75% FIT, and rainfed conditions (Rudnick, D. et al. 2016) . As well as, the authors aimed to compare corn crop water producti vity (CWP) (measured as crop water use efficiency (CWUE) and irrigation water use efficiency (IWUE)) to net income for the described treatments and growing seasons in south central Nebraska. The results showed variations in gross income through the years a nd prices. An interaction between irrigation and N fertilizer rate had an effect in gross and net income. Under rainfed conditions, the optimum N rate to reach maximum net income was 140 kg/ha for a dry year, while 196 kg/ha for a wet year. Under irrigated conditions, the optimum rate was 196 kg/ha during all years; except 2012. Further, a full irrigation (under no water limited conditions) and 75% FIT (under limited water conditions) with N applications lower than 196 kg/ha are the recommendations to achie ve high economic returns under those conditions (Rudnick, D. et al. 2016) . BMPs i mprovement d evelopment Capacitance SMS probes. Understanding soil water dynamics is key for a successful irrigation management. ET, irr igation, rainfall, runoff and drainage losses below the root zone are the main factors of the water cycle in an agricultural field. In order to support optimum plant growth and yields, it is required to maintain adequate soil water content within the root zone to minimize water drainage, nutrient leaching and groundwater contamination (Fares and Alva 2000a) . A continuous monitoring system of the soil water status pre and post irrigation/rainfall is needed for irrigation and fertilizer management (Fares and Alva 2000b) . Irrigation decisions might be improved by using time domain reflectometry (TDR) tensiometers and capacitance probes. These devices measure dielectric constant of the soi l water

PAGE 73

73 molecules in an electric field). Since the dielectric constant of water (80) is larger than the die will strongly influenced the dielectric of the soil water air medium. If the capacitor (a probe surrounded by soil) contains soil as the insulator between the two elec trodes, (Fares and Alva 2000b) . Capacitance probes have been used extensively in laboratory and field conditions. Probes have been used to investigate field scale spatial and temporal characteristics of soil moisture and their importance for modeling (De Lannoy et al. 2006) , as well as, to optimize irrigation scheduling in citrus (Fares and Alva 2000a; Fares and Alva 2000b) , quantify the effect of water stress on corn yields, quantify in real time the spatial variation of soil water content under plow tillage and no tillage corn (Starr and Paltineanu 1998) , among other uses. EnviroSCAN capacitance probes were assessed to optimize citrus irrigation and to estimate a soil water balance for young citrus trees. Soil water content through the soil profile was monitored by the probes in sandy soils of Central Florida. Data from the probes located at the root zone (10, 20, 40 cm) and below it (i.e. drainage at 70 and 110 cm) was used to schedule irrigation. Available soil water (ASW=0.075 cm 3 /cm 3 ) was calcula ted using a rooting depth of 40 cm and values of field capacity (FC=0.08 0.10 cm 3 /cm 3 ) and permanent wilting point (PWP= 0.015 cm 3 /cm 3 ). ASW was allowed to be depleted 67% before replenishment, except during flowering and fruit set periods, when it was all owed to be depleted above 33%. Results showed that water content was reduced to 0.09 cm 3 /cm 3 within a day after saturation due to heavy irrigation or rainfall

PAGE 74

74 event. Water drainage from the root zone increased water content of soil layers below the root zo ne. Cumulative annual ET and drainage were 920 and 890 mm, respectively. However, most of the drainage resulted from a wet and unusual season. Authors concluded that capacitance probes are successful tools that can be used for irrigation scheduling for cit rus groves in sandy soil conditions (Fares and Alva 2000a) . A similar study evaluated Sentek capacitance probes (EnviroSCAN RT5) to schedule irrigation in orange trees grown in Candler fine sand. Based on water stress s PWP. Water was successfully maintained in the root zone within the defined irrigation points. Leaching was calculated by the variation in water storage (WS) below the root zone. Minimal increase (3 5 mm) in the WS resulted from irrigation events; whereas, after high rainfall WS was 8 10 fold higher (30 40 mm). Thus, changes in W S in the soil below the root zone were a good indication of the effectiveness of a well managed irrigation that minimized excess water leaching below the root zone (Fares and Alva 2000b) . The OPE3 interdisciplinary proj ect was developed in order to address the major environmental and economic issues facing the U.S. The impact of water stress on corn yields was quantified using 48 EnviroSCAN capacitance probes (i.e. 256 sensors). Using the water dynamics per site, stress days were identified by the slowdown in photosynthetic rate. A total of 15 days of stress resulted in 50% yield reduction. Their results showed that even only 2 days of water stress caused 10% yield reduction, which

PAGE 75

75 when combined with yield data, it quanti fied up to 40% of profits (Sentek Technologies 2002) . Invaluable information can be obtained using capacitance probes that can help growers optimize irrigation and fertilization management strategies, hence, improve yields and profit. Modeling. Traditional BMPs use field research data in order to develop effective methods to reduce potential environmental issues. Although their high effectiveness, in general are spatial and temporally specific besides being ti me consuming and highly costly. Therefore, as an alternative crop models can be used to evaluate current BMPs, as well as, to develop new crop management strategies (He et al. 2012) . for predicting the growth, development and yield of a crop, given a set of genetic coefficients and relevant environmental (Monteith 1996) . Two main types are defined as: (i) Mechanistic models which take into account physical and physiological basis for each quantified process; while (ii) empirical models use functions (often chosen arbitrarily) to fit field or laboratory measurements. Nevertheless, in practice most models represent a conciliation among ri gor and utility (Monteith 1996) . Thus, computer models can be used for better predict growth and development of a crop, determine optimum yield, as well as, to estimate environmental impacts. The Decision Support Sy stem for Agro technology Transfer (DSSAT) is a software application program that comprises simulation models for near 42 crops including maize (Hoogenboom et al. 2015) . Further, the program simulates maize yield to gether with water and N balances, transformations and uptake. Therefore, DSSAT is a tool that can

PAGE 76

76 be used for the determination of growth and yield, as well as, to estimate the N loads from non point source of pollutions to the environment (i.e. excess of NO 3 N leaching) and therefore, improve irrigation and N management practices in corn production. A research using the CERES Maize Model of DSSAT was performed to develop irrigation and N BMPs for sweet corn production on sandy soils in Florida. This study evaluated systematically 24 irrigation schedules, 21 N fertilizer levels, 30 N split applications and 20 N application rates per split in a single factor simulations. After analyzing 324 management scenarios (6 irrigation scheduling and amounts and 54 N ap plication strategies) in a multifactor analysis, the authors determined: (i) crop water stress and reductions in simulated yield if irrigation was triggered at a maximum allowable depletion (MAD) of 60% or greater; (ii) N applications greater than 168 kg/h a did not increase yield; (iii) split N applications did not have an effect on yield; however, N leaching was reduced when split N applications were performed during the small leaf stage; (iv) yield increased when N applications were reduced from 100 kg/ha to 70 kg/ha (no effect in yield was found below 70 kg/ha); (v) N leaching was reduced substantially if fertigation application rates were 70 kg N/ ha per event. From the evaluated scenarios, the selected BMPs were: 2 irrigation schedules with 5.0 and 7.5 m m with 20% and 30% MAD; two N levels of 196 and 224 kg N/ha; two split strategies (0 ¼ ¾ and 0 1/3 2/3) applied during the small leaf, large leaf and ear development stages, respectively; and two N application rates per fertigation (30 and 40 kg N/h a) (He et al. 2012) . The CERES Maize model has been also used to characterize yield variability (influenced by temporal interactions of management, soil properties, and environment)

PAGE 77

77 and corn response to N (Paz et al. 1999) . The accuracy in determining those parameters is critical to define the optimum N rate and the possibility to implement variable rate fertilization (VRF). Calibration of the model was performed using three years of dat a from 224 grids in a 16 ha field. The model provided excellent predictions on yield trends along transects of the field (i.e. 57% of variability explained). It was found a high spatial distribution of optimum N fertilizer prescription for grids in the fie ld. Optimum N rates of 141 to 160 kg/ha were ideal for 64 of 224 grids, which concords with the typical N application in Iowa. The lower amounts of fertilizer produced the higher yields and was more profitable compared to either transect or field level (si ngle rate) fertilizer application. In order to develop and evaluate management prescriptions across the field, the authors found that use of crop growth model is a viable and powerful tool (Paz et al. 1999) . Application rates, timing and methods of application are key to evaluate the fate and behavior of N in soil plants systems. In general, split applications (i.e. multiple applications of fertilizer) improve plant uptake and reduce potential leaching; however, it could increase the costs (Li and Yost 2000) . A study evaluated a model that searched for optimum N management to minimize NO 3 N leaching, as well as, maximize production and profits. Management oriented modeling (MOM) consist ed on a generator of possible management alternatives, a simulator that assessed each alternative, and an evaluator that defined the best alternative for the user weighted multiple criteria. As a result, the best alternative found by MOM for maize producti on increase profit by 64% (from $570 to $935/ha) while reducing NO 3 N leaching by 80.5% (from 36 to 7 kg N/ha) (Li and Yost 2000) .

PAGE 78

78 In order to identify major N loss pathways and quantify the N load released to the enviro nment, a four year study on potato production was conducted in the Middle Suwannee River Basin (MSRB). Using the potato model SUBSTOR and a partial N budget approach, it was found N loading rates with one standard deviation from observed data, resulting in 25 to 38% N leaching (equivalent to 85 to 138 kg/ha) of the total input (310 to 349 kg/ha). N contribution from the crop residue (after tuber harvest) resulted in 64 to 110 kg/ha. However, this residual N represents a potential N leaching during the subse quent fallow period due to the absence of succeeding cover crops and the presence of frequent rainfall events (Prasad et al. 2015) . Possible solutions . Future intensification of agriculture is foreseen due to the incre ase food demand of a rapidly growing population and the decrease in land resources. As well, double or triple N application are projected within the next 30 years, enlarging the N pollution problem. Based on the previous research and literature review, sug gestions for solving the problem, such as improving best management practices for irrigation and nitrogen fertilizer, as well as, improving environmental awareness, are the main purpose of this research. Improved understanding of irrigation and nutrient ma nagement practices associated with a potential of optimum yield grain goal are essential to reduce the demand of high quality drinking water supplies, as well as, to reduce surface and groundwater pollution resulted from nutrient runoff and leaching. There fore, the effect of precision irrigation management on leaching of soluble nitrogen beyond the root zone needs to be quantified in agricultural areas overlaying karst aquifers. In addition, there is a need to quantify tools such as embedded electrical cond uctivity measurements that

PAGE 79

79 might allow tracking of fertilizer through the soil profile and may allow for better fertilization management which would result in less loss of fertilizer to the environment and more efficient agricultural production. Delineatin g areas of high vulnerability to N loading and transport is a challenge due to the diversified soil types predominant in Florida and particular characteristics. Modeling can be used in order to determine the most prone areas for N loading, and therefore in force the corresponding management practices reducing the environmental, health and economic negative impacts. Soil data can be used into the model to evaluate vulnerability and most adequate practices to be implemented. Within the model, best management s cenarios for NO 3 N load reductions can be created and analyzed in order to reach maximum water conservation potential, minimum potential environmental risks, as well as, maximum profits for growers. Goal Research was conducted on corn irrigation practices and N fertility levels to develop real world applicable management tracking tools for growers in springsheds of the Suwannee Ri ver Water Management District. Objectives The overall objectives of this project are: 1. To compare four irrigation strategies: SMS, SWB, r educed and conventional irrigation practices and quantify their impact on corn nitrogen uptake, grain yield and potential water savings. 2. To d evelop a water and N balance to evaluate the effectiveness of the irrigation treatments and N rates within a corn fallow peanut rotation, in an effort to effectively estimate N processes and fate. 3. To develop a simple methodology for irrigation scheduling in corn production easy to be implemented by growers (or any other end users) using real time soil moisture s ensor data .

PAGE 80

80 4. To assess the long term effectiveness of BMPs to r educe N lea ching without impacts in yield, as well as, N losses and contributions from co rn fallow peanut crop rotations.

PAGE 81

81 Figure 1 1. Suwannee River Basin (Adapted from U.S. Geological Sur vey digital data, 1972).

PAGE 82

82 Figure 1 2. Median NO 3 N + NO 2 N concentrations obtained in fourteen main springs from the Suwanee River Basin (SRB) monitored by the Florida Springs Initiative Monitoring Network from 2001 to 2006 (Harrington et al. 2010)

PAGE 83

83 CHAPTER 2 DEVELOPING A METHODOLOGY FOR IRRIGATION SCHEDULING IN CORN Introduction Florida Water Withdrawals a nd Irrigation Practices Water withdrawals in Florida were estimated as 53.8 million m 3 /d in 2012 (USGS 2012) , where saline and freshwater withdrawals accounted for 55% and 45% of the total withdrawals, respectively. From the total freshwater withdrawals, a 65% corresponded to groundwater and the reminder 35% to surface withdrawals (Marella 2015) . (U.S. Census Bureau 2018) relies on groundwater for drinking wate r. Besides the amount required to be supplied for a growing population and tourism, agriculture has a large water demand that plays an important role in this (Marella 2015) . A total of 1.6 million fu ll time and direct jobs , pl us about 656,000 indirect jobs are provided by a griculture , natural resources, and food manufacturing, distribution and services . These sectors employ the largest number of people in Florida (~ 19.8% of all Florida jobs ). In 2015, the economic contributions of these sectors were $160.714 billion (Hodges et al. 2017) . From the total water freshwater withdrawals (surface and groundwater), 39% is extracted for agricultural self supply (Marella 2015) . More specifically, in Suwannee County in Florida, nearly 45,784 ha correspond to irrigated lands or have potential for irrigation. After a field verification, a total of 1466 center pivots were observed accounti ng for a nearly 37,675 irrigated ha in the Suwannee River Water Management District. From this irrigated land, nearly 13,139 ha were irrigated corn (Marella et al. 2016) .

PAGE 84

84 ainfall distribution and low soil water holding capacity, irrigation is required to ensure economic viability for many crops (Kisekka et al. 2016; NRCS 2016a) . However, increased deep percolation ( drainage) of water below the plant root zone may result from improper irrigation and nutrient loss (Gehl et al. 2005; Sigua et al. 2017; Zotarelli et al. 2007) . Calendar base d methods are commonly used to schedule irrigation in Florida, weather conditions (Migliaccio et al. 2010) . These methods have been evaluated extensively in different crops resulting in low water and energy efficiencies since they do not consider real time soil status or environmental factors in the schedule (Dukes et al. 2012) . Soil Water Fundame ntal Concepts Plant water uptake is related to the soil water balance, which is associated with three main terms: field capacity (FC), permanent wilting point (PWP), and available water (AW) . Generally, after a large rainfall or irrigation event enough to exceed FC, soil water starts draining deeper in the soil. Typically, one or two days (based on soil type) afterwards, a nearly constant value is reached for a particular depth. This arbitrary value is defined as the FC (Kirkham 2014) . It is not known who first used th e FC term ; however, Taylor and Ashcroft (1972) recognized there was an equilibrium point after drainage significantly decreased, at which water was held by the well drained soils against gravitational fo rces, and they felt it was the upper limit of AW for plants (Kirkham 2014; Taylor and Ashcroft 1972) . This definition has changed in time, with soil scientists recogniz ing that the equilibrium point is never reached due to soil water dynamics; water reduction is continuous due to

PAGE 85

85 evaporation, drainage, transpiration, and water inputs are continuous due to rainfall, irrigation and dew and capillary rise. Therefore, there is not a single value of FC; there are a range of values that represent this term (Taylor and Ashcroft 1972) . Despite this fact, for irrigation management it is convenient to use a single value of FC. Therefore, field capacity (FC) can be de a n empirical measurement drainage has ceased. It is usually measured about two days after an infiltration event. The measured value depends on the init ial depth of wetting and on the texture and (Hillel 2004) . FC values are influenced by many factors (Hillel 1971) : 1. History of previous soil: e.g. a higher FC values are present in a soil that is saturated and then it dries (hysteresis) compared to a soil being wetted without drying periods. 2. Soil texture and structure: for example, clay soils can hold more water, thus have greater FC values compared to sandy soils. 3. Clay type: higher FC values in clayey soils with greater amounts of montmorillonite. 4. Organic matter (OM): greater OM helps to retain more water. 5. Temperature: influences the amount of water held by the soil when previously wetted. This amoun t of water held at FC decreases as temperature increases. 6. Water table: this could influence the determination of FC, since FC applies for free draining soils. 7. Depth of wetting: in general, greater FC values result from wetter soils because of greater depth s of wetting during infiltration and slower rate of redistribution. 8. Layers: FC increases due to the presence of layers (e.g. sand, clay, gravel) that may impede water redistribution. 9. cts gradients and flow direction, hence the water redistribution (Hillel 1971) . Alternatively, t he PWP is defined as the value of soil moisture in the root zone at the time plants wilt; similarly to FC , PWP is not easy to recognize (Hillel 2004) . The

PAGE 86

86 plant available water (PAW) may be defined as the difference between FC and PWP. F or many crops, yield is reduced if water in the soil approaches PWP. To relate soil water to pl ant performance , other concepts have been developed, such as the non limiting water range (NLWR) (Letey 1985) , which considers the interaction between water and other physical factors (e.g. aeration and mechanical resis tance) which affect the availability of water to plants (Kirkham 2014) . However, the available water holding capacity (AWHC) is commonly calculated by multiplying PAW by the depth at which the water extraction occur s. Nevertheless, only a fraction of the AWHC is replenished with irrigation using the maximum allowable depletion (MAD). Thus, this fraction corresponds to the readily available water (RAW). This soil water threshold, from MAD to FC, provides an ideal scen ario for irrigation management practices (Zotarelli et al. 2013) . Irrigation Scheduling a nd Methods t o Measure Soil Water Content Irrigation scheduling (IS) corresponds to the timing and amount (or depth) of irrigat ion and it could be used to improve the efficiency of irrigation management practices. Greater efficiency can be achieved if combined with a water balance and/or soil moisture based approaches using evapotranspiration (ET) or soil moisture sensor (SMS) tec hnologies, respectively (Irrigation Association 2011) . Other approach es use water deficit sensing methodologies to evaluate plant physiological responses to irrigate (i.e. measures the plant stress response directly) (Jones 2004) . Direct and indirect methods can be used to measure the moisture content in the soil; however, most of the practical techniques to monitor soil moisture rely on indirect measurements. An exampl e of direct methods are soil samples/extractions and gravimetric analysis. Other indirect methods to measure soil moisture include neutron probes, capacitance or electromagnetic sensors (e.g. time domain reflectometry (TDR),

PAGE 87

87 capacitive and resistance probe s (Jones 2007) . The two main soil moisture sensors used for irrigation scheduling measure soil water potential (tension/suction) or volumetric water content (Munoz Carpena 2 012) . Capacitance Probe S tudies Capacitance based SMS probes measure volumetric water content of the soil; however, interpretation of the soil moisture measurements is required to guarantee adequate irrigation management and avoid unde r or over irrigation (Zotarelli et al. 2013). Zotarelli et al. (2013) established guidelines to determine FC, as well as, when to irrigate in sandy soils using SMS. The water holding capacity of soils depend on texture and structure which define the upper and lower limits: FC and PWP, respectively. These guidelines use MAD as a fraction of AWHC, which defines the readily available water for the plants, as a threshold for irrigation management. Drainage occurs after a rainfall or an irrigation event saturates the soil and it is a subsequent rapid downward soil water flux. Then, the FC can be defined when the slope of drainage and extraction lines changes from rapid to slower decrease and the soil water flux stabilizes (Figure 2 1) (Zotarelli et al. 2013) . A typical irrigation sc heduling method based on ET, applies irrigation to replace water losses through ET. When ET values are combined with field estimated crop coefficients, better estimations on irrigation requirements can be calculated. Despite the benefits of this method, on e of the major limitations is the lack of soil moisture status conditions across different soil types and locations, which might lead to under or over irrigation. Using real time soil moisture data monitored in situ provides a complement to the ET method. Soil moisture based irrigation scheduling detects the moisture available

PAGE 88

88 in the soil and assists growers or irrigation managers to apply irrigation while maintaining an adequate moisture threshold in the soil. A study conducted to evaluate both methods sho wed that the combination of ET data, which provides the amount of water lost that needs to be replaced with irrigation, and the soil moisture data, which confirms if the amounts were efficiently applied in the root zone, is an efficient methodology to sche dule irrigation (Adhikari and Penning 2016) . In addition, the use of multiple soil moisture sensors provides information about the soil variability, water fluxes in the profile, as well as, crop water consumption. Through the use of soil moisture sensors, growers or irrigation managers have the opportunity to react accordingly with the constant changes in the field and/or weather conditions. Extensive research has been performed on capacitance probes in which studi es have evaluated spatial and temporal characteristics of soil moisture at the field scale (De Lannoy et al. 2006) , the optimization of citrus IS (Fares and Al va 2000a; Fares and Alva 2000b) , the water stress effect on corn yields, the spatial variation of soil water content under plow tillage and no tillage corn (Starr and Paltineanu 1998) , among others. Soil water conten t was monitored by EnviroSCAN RT5 (Sentek Pty Ltd, Kent Town, South Australia) capacitance probes to optimize citrus irrigation and to estimate a soil water balance for young citrus trees in sandy soils of Central Florida. To schedule irrigation, probe dat a from the root zone (10, 20, 40 cm) and below it (i.e. drainage at 70 and 110 cm) was used. Available soil water (ASW = 0.075 cm 3 /cm 3 ) was calculated using a rooting depth of 40 cm and values of field capacity (FC = 0.08 0.10 cm3/cm3) and permanent wiltin g point (PWP = 0.015 cm 3 /cm 3 ). A 67% ASW was depleted before

PAGE 89

89 replenishing the soil profile with irrigation. Only during flowering and fruit set periods was a 33% deficit allowed. Due to low water holding capacity and high hydraulic conductivity, soil water content sharply increased and then rapidly decreased when reaching saturation after a heavy irrigation or rainfall event. Afterwards, soil water content was reduced to 0.09 cm 3 /cm 3 within a day. Water content of deep soil layers increased due to water dra ined from the root zone. Cumulative annual ET and drainage were 920 and 890 mm, respectively. During the growth stages requiring the most irrigation, drainage from the root zone was very low; however, most resulted from an unusually wet season. Authors con cluded that capacitance probes are successful tools that can be used for citrus groves irrigation scheduling in sandy soils while minimizing drainage from the root zone (Fares and Alva 2000a) . In a companion study, Sent ek capacitance probes (EnviroSCAN RT5) were evaluated to schedule irrigation in orange trees grown in Candler fine sand. Irrigation FC and PWP, based on water stress sensitivity of the citrus growth stages. Leaching was calculated by the variation in water storage (WS) below the root zone and minimal increase (3 5 mm) in the WS resulted from irrigation events; whereas, after high rai nfall WS was 8 10 fold higher (30 40 mm). Using these irrigation points, water was successfully maintained within the root zone. Thus, results on WS changes below the root zone showed a good indication of the effectiveness of a well managed irrigation th at minimized excess water leaching below the root zone (Fares and Alva 2000b) .

PAGE 90

90 Maize plants are more sensitive to water deficit stress during the flowering stage (just before floral initiation or after pollination), res ulting in the greatest reductions in grain yields (Boyer and Westgate 2004) . Several studies evaluating crop production functions (yield versus irrigation) of full irrigated versus deficit irrigation at different corn growth stages in the U.S. Great Plains showed that yields did not decrease with the same rate as the rate of irrigation was reduced (Klocke et al. 2011) . The deficit irrigation treatment, which used only 47% of full irrigation, resulted in 90% of full irrigated yields. The study started irrigation at the beginning of the reproductive stage, when water was most critical (Klocke et al. 2007) . This study was conducted in soils wit h predominant silt loam texture (Cozad silt loam (fluventic Haplustoll) with a plant AWHC of 0.17 m 3 / m 3 for volumetric soil water contents from 32% for FC and 15% for PWP over 0 to 3 m of soil depth. The average annual precipitation is approximately 508 mm , which occurs mostly during the summer growing season (late April mid October). Along with the reduced irrigation strategy, another indicator of crop performance was rainfall occurring during the months of April, May and June, since this water accumulat ed closest to crop water needs was more effective than earlier precipitation. Thus, the soil characteristics allowed storage of more water during rainfall periods covering water needs during vegetative stages while reducing the need of irrigation without a ffecting yield (Klocke et al. 2007) . Another study evaluating the effect of moisture stress at different stages of growth development, showed a 25%, 50% and 21% reduction in yield when moisture stress was imposed pri or, at and after the silking stage respectively (Denmead and Shaw 1960) . Thus, vegetative stages are less sensitive to water stress; however, reproductive stages from silking to grain filling are the most susceptibl e growth stages

PAGE 91

91 for water stress (Boyer and Westgate 2004; Denmead and Shaw 1960; Shanahan and Nielsen 1987) . Irrigation applied adequately depends on soil moisture plant environment and it is essential in crop production (Jones 1989) . Using FC and RAW concepts in irrigation scheduling, could potentially provide an ideal scenario for plant water uptake and nutrient availability, while minimizing drainage and N leaching below the rootzone. Therefore, grower inputs such as irrigation and fertilizer amounts would be reduced while increasing their savings throughout more profitable crop seasons. Soil moisture sensors can provide soil water time series data that can allow researchers, agronomists and farmers better understand the processes between the crop water uptake and root development, and soil water dynamics at different depths in the soil profile (e.g. drainage, evaporation, infiltra tion, among others). Several hydrological parameters such as field saturation, field capacity, initiation of plant water stress and plant extraction limits might be determined from SMS data (Chandler et al. 2017) . In general, this data exhibits a characteristic cyclical pattern that reflects water flux dynamics of the observed soil volume (Bean et al. 2018) . This pattern was defined by Figueroa and Pope as the Root System Water Co nsumption (RSWC) and it is recognized in the time series data after irrigation and/or rainfall events (Figueroa and Pope 2017) .The RSWC could be used to determine drainage and other processes. However, subsections of the time series not agreeing with the pattern might be signs of may be required to analyze SMS time series data.

PAGE 92

92 Multi probe time series data (e.g. VWC, salinity and temp erature data recorded at nine depths every 30 minutes) could be difficult to analyze and understand. Therefore, training of agricultural producers would be required for the implementation of SMS based irrigation scheduling techniques to achieve a successfu l equipment installation and data understanding and knowledge acquisition; however, this process might be impractical and time consuming. Thus, a recent study evaluated several approaches for automated soil water cycles analysis; among them a Matlab code b ased on soil water dynamics principles (SWDP) (Bean et al. 2018) . This code evaluates SMS time series data without the need of training sets or pre processing data and it uses two approaches to estimate FC: (i) regressio n of exponential decay (SWDP R) and (ii) K). Best results were found when using the SWDP R approach across analyses (Bean et al. 2018) . The SWDP code to define FC was used in comparison with the methodology proposed in this chapter. Further details This chapter aimed to develop a sensor based irrigation scheduling methodology mainly to be used by corn growers. However, slight changes could be adapted in SMS based sched uling irrigation in other crops. Objectives 1. Develop a simple methodology for irrigation scheduling in corn production to be implemented by growers (or any other end users) using soil moisture sensors (SMS). 2. Evaluate the SMS treatment performance on irrigat ion scheduling using a Matlab code based on soil water dynamics principles (SWDP) for the determination of field capacity to be used in irrigation scheduling, and its effectiveness in keeping adequate moisture f or plant uptake; while avoiding drainage from the rootzone.

PAGE 93

93 Field Experiment A field experiment was conducted from 2015 to 2017 at the North Florida Research and Education Center Suwannee Valley (NFREC SV), near Live Oak, Florida (30.31353 N, 82.90122 W). The experimental design consisted of a r andomized complete block arranged in a split plot with four replicates (i.e. blocks) for each treatment. Irrigation strategies as main plots and N fertility rates as sub plots. The Sentek Drill & Drop MTS probes (Sentek Pty Ltd, Australia) consists of nine sensors placed every 10 cm starting from 5 cm to 85 cm. Probes were installed in three irrigation treatments (GROW, SMS and NON) across three fertility rates (high, medium and low N) to monitor volumetric water content during the three corn seasons (March August) ; however, only used in the SMS treatment to trigger irrigation. Irrigation Treatments Soil moisture time series data from three irrigation treatments were evaluated. The irrigated treatments used two different irrigation scheduling methods descr ibed as follows: 1. GROW: uses a calendar based irrigation scheduling. It practices. Information from local growers was collected from extension agents and Suwannee River Water Management District to develop the GROW method. The tar get irrigation rates varied based on growth stages. For the first 30 days after planting (DAP) zero irrigation was applied (unless severe windy conditions occurred). At 31 DAP, 25 mm/wk was targeted unless rainfall events equal or greater to 10 mm occurred . At 40 59 DAP, target irrigation was 38 mm/wk with irrigation events of 10 mm. If rainfall events were equal or greater than 13 19 mm one irrigation event was skipped, and two events were skipped if greater than 19 mm of rain occurred. Afterwards, the i rrigation target increased up to 51 mm/wk unless 13 25 mm of rain occurred the day prior to a scheduled irrigation. Two irrigations were skipped if 25 mm of rain occurred. Finally, at full dent stage (105 DAP), weekly irrigation targets were 41mm/wk. If rainfall events were equal or greater than 13 19 mm one irrigation event was skipped, and two events were skipped if rainfall greater than 19 mm occurred. Irrigation was terminated after physiological maturity (i.e. black layer) around 115 DAP.

PAGE 94

94 2. SMS: sens or based irrigation scheduling. Sentek Drill & Drop MTS capacitance probes were used to monitor volumetric water content (VWC) in the soil profile . Irrigation was determined using the MAD and FC thresholds to refill the soil profile with irrigation accordi ng to guidelines proposed by Zotarelli et al. ( 2013) . A total of 10 mm per irrigation event w ere applied. Soil Survey published values of FC, MAD and Permanent Wilting Point (PWP) for 0 90 cm for Florida Chipley Foxworth Albany soil were used in this study (FC = 9.1%, 50% MAD = 6.3%, AWHC = 0.05 cm/cm and PWP = 3.5%) (NRCS 2016b) . A comparison between theoretical and actual values of FC, MAD and PWP was performed for 0 90 cm depth resulting in similar values among th em. Based on root/crop development, the soil water content measured by the different sensors was adjusted through the growing season. This SMS irrigation treatment was triggered when VWC in any of the probes showed values below the 50% MAD threshold. 3. NON: non irrigated treatment. Plots used as a control. Irrigation was applied in these plots only to provide adequate moisture for germination and after granular fertilizer applications to ensure adequate fertilizer incorporation in the soil. Capacitance Probes Capacitance probes provide an indirect measurement of water content by measuring the dielectric constant of the soil water air mixture. The dielectric constant of a medium depends on the polarization of its molecules in an electric field. For example, the dielectric constant of water has value of 80, whereas, soil and air have values of <10 and 1, respectively. Therefore, the dielectric constant of the soil mixture (soil water air) is strongly influenced by changes in soil water content, which also is rela ted to the soil type and the frequency range of the probe. The Sentek Drill & Drop MTS probes use capacitance based technology. By creating a high frequency electric field around the sensor, extending to the access tube to the surrounding soil, the sensors detect dielectric constant, or permittivity, of the soil over time. At high frequency the measurement is predominantly affected by water molecules (Sentek Pty Ltd 2003) . Sentek (Drill & Drop MTS Probe) multi sensor capacitance probes (Sentek Pty Ltd 2003) consist of nine sensors located at 10 cm intervals along their length (i.e. 5 cm to 85 cm). Each sensor provides data for 5 cm

PAGE 95

95 above and below its depth (e.g. sensor at 5 cm provides data for top 0 10 cm). Sensors provide three outputs from the soil profile: volumetric water content (VWC), volumetric ion content (VIC, as a measurement of salinity) and temperature. Readings are taken every 30 minutes. Sensors are wirelessly co nnected to a network in which data is updated every 4 hours and it can be accessed remotely through a website ( http://myfarm.highyieldag.com/home ). Through this website, users can view the three measured s oil parameters (VWC, salinity and temperature) as a function of time for each depth, or as a cumulative of several depth horizons (Figure 2 2). The user can download the raw data as a csv format. Data presented in the website provide insights of root activ ity and development throughout the season (Figure 2 3, A). As well, potential drainage or leaching events could be identified when volumetric water content increases at depths below the rootzone (Figure 2 3, B). Volumetric water content was measured by Sen tek capacitance probes at nine different depths. Probes were installed in the GROW, SMS and NON irrigation treatments during 2015 17 corn growing seasons . A total of 27 probes were installed in blocks 2, 3 and 4 at the experimental field located at the NFR EC SV (further details can be found in Chapter 3). Datasets were used to analyze water dynamics and schedule irrigation on the SMS treatment. In addition, physical characteristics of the Chipley Foxworth Albany soil published at the Florida Web Soil Surv ey were used in this study. Theoretical values used were FC= 9.1%, 50% MAD= 6.3%, AWHC= 0.05 cm/cm and PWP= 3.5% (NRCS 2016b) .Collected dataset s were used to evaluate the efficiency of the calendar based and sensor bas ed irrigation scheduling methodologies.

PAGE 96

96 The sum of VWC recorded by the sensors within the desired management depth was used (e.g. sum of VWC of 5 cm, 15 cm and 25 cm was used for a 30 cm soil water management depth). Root System Water Consumption Pattern ( RSWC ) a nd F ield C apacity Determination Soil texture and structure are properties influencing the capacity to hold water and therefore influence FC and PWP. After each irrigation or rainfall event, the soil moisture time series data collected by the sensors provided a cyclical pattern of water dynamics throughout the soil profile. This pattern is composed of the interactions of soil, water and crop water consumption and it is referred as the root system water consumption pattern (RSWC) (Figueroa and Pope 2017) . The RSWC pattern can be divided in different sections following Figueroa and Pope ( 2017 ) (Figueroa and Pope 2017) descriptions, that can help determine FC, using Zotarelli et al. (2013) guidelines: 1. Irrigation or rainfall: this section corresponds to the increase in volumetric water content. The duration of this segment depends on soil properties and magnitude of the event (e.g. application rate or rainfall amounts). Generall y, irrigation and or rainfall this segment reaches peak VWC. 2. Decrease or drainage: following an irrigation or rainfall event that saturates the soil a continuous downward movement (i.e. drainage) occurs rapidly due to gravitational forces. In the time ser ies data, it is defined as the decrease or drainage segment in which the decrease in VWC occurs relatively rapidly (greater than in the consumption rate). The rate of the drainage is related to the hydraulic conductivity of the soil. Generally, this is a s hort duration segment and it is not always present, only when volumetric water content exceeds FC. In sandy soils, field capacity can be assumed when the slope of drainage rate and extraction lines changes from rapid to slower decrease in soil water conten t (Zotarelli et al. 2013). 3. Consumption: in this section, the volumetric water content in the soil decreases in a slower rate than the drainage time series. Reductions in water content are mainly due to evapotranspiration losses.

PAGE 97

97 SMS I rrigation S cheduling P roposed M ethodology During the 2015 17 crop seasons the SMS or sensor based irrigation sch eduling was performed using Zotarelli et al. (2013) and Figueroa and Pope (2017) guidelines following the methodology described below : 1. Root depth was modified based on crop development (i.e. increased with crop growth). Root depth was considered the water storage to be replenished with irrigation after a 50% maximum allowable depletion (MAD). Thus, according to the corn growth stage and root development, the sensor d epth representing the root zone for irrigation management was selected. Three main depths were used for this analysis: 25 cm, 35 cm and 55 cm. The former depth represents the initial vegetative growth stages in which growth occurs at a slow rate, approxima tely V3 to V6 corn growth stages. The 35 cm depth is selected when the peak of growth in vegetative stages occurs. During this phase, growth rate is linear resulting in rapid canopy and root development; approximately the V7 to VT corn growth stages. At ta sseling (VT) reproductive stages start; thus, under optimum growth conditions all vegetative and roots are mostly developed. At this stage, sensor depth was switched to 55 cm (Figure 2 4). For management irrigation practices, no deeper sensors were conside red because most of the crop root activity was predominantly in the top 55 cm soil layers. In addition, due to the low water holding capacity of sandy soils, considering deeper layers for irri gation management can incur an increased risk of water stress an d/or leaching if large irrigation applications were performed. 2. Soil physical characteristics of the Chipley Foxworth Albany published at the Florida Web Soil Survey were used (FC= 9.1%, 50% MAD= 6.3%, AWHC= 0.05 cm/cm and PWP= 3.5% (NRCS 2016b) . This was the predominant soil with highest drainage rates in the field experiment. Those values were compared with FC values determined using Zotarelli et al. (2013) proposed guidelines (Figure 2 1) for each crop growth stage an d the corresponding root zone use for irrigation purposes (Table 2 1). 3. Diurnal fluctuations in soil moisture at the root zone were used to determine water losses (e.g. ET and drainage) from the soil to be refilled with irrigation. The average ET from previ ous 3 5 days was used to estimate near future ET values (i.e. VWC reductions in following 1 2 days). 4. Finally, an irrigation event for the SMS treatment was scheduled when VWC approached 50% MAD in any of the probes installed for this treatment. Therefore , using the above guidelines, Figures 2 5 and 2 7 show examples of the SMS irrigation scheduling methodology followed by the SMS treatment.

PAGE 98

98 SMS M ethodology E valuation : Soil W ater Dynamics P rinciples (SWDP) C ode Soil moisture time series data collected in t he GROW and SMS treatments in the experimental field were evaluated using the SWDP code. This code was developed in Matlab with a combination of numerical methods and principles of science to evaluate automated soil water cycles. These cycles have two phas es: (i) the wetting phase when water enters the soil profile and VWC increases; (ii) and the drying phase when water leaves the soil profile and VWC decreases (Bean et al. 2018) . The code labels the VWC cycles with three points: the initiation of a wetting event (A), the peak water content from the wetting event (B), and the end of the soil drying period (C) and initiating the next wetting event. The local maxima (B) point was determined as values exceeding both: prior an 0.006) using Equation 1. ( 2 1 ) The initiation of a wetting event (A) was identified as the local minima that preceded each peak (B), this point (Ai) was also ide ntified as the end of the preceding cycle (Ci 1). Therefore, the peak wetting and the drying duration criteria were used to filter out invalid VWC cycles. The first criteria ensured that valid cycles had enough wetting to exceed minimum threshold to elimin ate noise in the VWC time series data; whereas the second criteria ensured enough drying occurred after the wetting event that allowed determination of FC (Bean et al. 2018) . Field Capacity estimation: SWDP K (the knee approach). Estimations of FC

PAGE 99

99 continuously decrease through the day and overnight until ET rat es increase during the be FC (Bean et al. 2018) . Field c apacity estimation: SWDP R (exponential decay). Estimations of FC were made using a second regression approach under the principle that without ET to be FC. In this case, the overnight time series follows a decay curve until ET starts to d distribution is negligible overnight. Therefore, authors isolated the overnight times series subset of a cycle following a peak using data from 11 pm to 6 am. Then, the ra te of water content decrease in soil was assumed to be proportional to the water content exceeding FC (Hillel, 1998). ( 2 2 ) Where: t i = time since B Then, as time tends to infin ite, i approaches FC Therefore, estimations of FC were performed using the inverse times (t i 1 ) and the corresponding water content values were then regressed, with the intercept estimating the water content at an infinite time since the peak Through the use of the SWDP code, valid SMS cyclical patterns can be identified, and FC values can be determined (Bean et al. 2018) . Furthermore, by analyzing time series data, it can be used for outlier detection , and to determine malfunctioning of SMS probes . Using the SWDP code, estimated FC (FC SWDP) was compared with previously determined FC value using Zotarelli et al. (2013) guideline s while monitoring

PAGE 100

100 SMS in the field experiment (FC field) . Furthermore, FC field and 50% MAD theoretical values were evaluated by comparing with the new estimated SWDP FC values at the main crop growth stages defined. The ability to keep VWC among thresholds was evaluated. SMS Methodology Evaluation: SMS M ethodology E ffectiven ess D aily Drainage S imulation The effecti veness of the irrigation scheduling methodologies was evaluated by their effect on keeping adequate moisture for plant uptake; while avoiding drainage from the rootzone. Therefore, daily drainage during the three corn seasons was simulated using the crop s imulation model CERES Maize within DSSAT . This model was calib rated and evaluated in Chapter 4 providing simulation results within the range of the observed values in the experimental field. Results The result section shows: 1. A comparison between the calend ar based and the sensor based irrigation scheduling methodologies performed in the field experiment (GROW and SMS treatments), 2. An evaluation of the proposed SMS methodology and the determination of FC using the SWDP code. 3. Effectiveness evaluation of the p roposed SMS methodology vs. a calendar based irrigation scheduling methodology by simulating drainage using DSSAT. For illustration purposes, and knowing that FC is a dynamic value, corn early vegetative stages (V3 V6) and reproductive stages (starting with VT); corresponding to 30 cm and 60 cm root zones depths for irrigation management purposes were selected for evaluation.

PAGE 101

101 Comparison of Irrigation Scheduling Methodologies at Different Crop Growth Stages This section provides examples of the SMS metho dology proposed applied at 30 and 60 cm root zone depths. The former depth corresponding to early vegetative stages of the crop in which the root system is continuously growing within this period; however, it is not fully developed. The latter depth corres ponds to the corn growth stages after tasseling. At this growth stage, the root system is mostly developed and although the roots might reach deeper soil layers, irrigation management was set to 60 cm to avoid potential water stress or leaching. SMS irriga tion scheduling methodology 30 cm root zone example. The 50% MAD threshold for a 30 cm root zone irrigation management corresponds to 27 mm, according to theoretical values for the Chipley Foxworth Albany soil type. Figure 2 5 represents an example of ho w irrigation was scheduled following the SMS methodology for a 30 cm root zone depth. Following the guidelines proposed by Zotarelli et al. (2013), FC was determined on April 28 as the inflection point when drainage is reduced to a slower rate (Figure 2 5, dotted line). After this moment, soil moisture was monitored using theoretical values of FC and 50% MAD (9.1% and 6.3%, respectively) to trigger irrigation. Within this period, n of moisture (i.e. step down) represents diurnal fluctuations corresponding to the crop evapotranspiration (ET c ) and drainage (D) in the soil profile. Afterwards, potential and crop evapotranspiration ( ET c and ET o , respectively) values are negligible at n ight ; thus, moisture is relativel y constant (i.e. flat) (Figure 2 5 ). As soil water content ( ) decreases, soil water potential ( ) becomes more negative resulting in lower plant soil

PAGE 102

102 water uptake ; thus , lower ET c /ET o ra tios (Morgan et al. 2006) , as well as , in a greater uptake is also reduced (i.e. lower ET c /ET o ratio); thus, water should be replaced with irrigation when it approaches th e MAD threshold. Using this visualization, an irrigation event (10 mm) was scheduled for 4 May 2016. After this event, soil moisture increased up to the FC 50% MAD range; however, to keep moisture content within this threshold, a second irrigation event was scheduled for 6 May. In the experimental field, irrigation was performed only during week days due to personnel availability. Therefore, soil moisture decreased during the following days (weekend) falling below the 50% MAD threshold. Nevertheless, an irrigation event was scheduled on the following Monday keeping moisture within adequate FC 50% MAD range (Figure 2 5). It is important to note that this moisture reduction below the FC MAD range occurred during early vegetative stages of the crop. Earl y in the season, reductions in water can help develop the root system when searching for water uptake. However, corn plants are more sensitive to water deficit stress during the flowering stage ( just before floral initiation or after pollination ), resultin g in the greatest re ductions in grain yields (Boyer and Westgate 2004) . Studies have shown that corn exposed to low water potential during pollination for five days resulted in abortion of embryos and a significant re duction in kernel (Zinselmeier et al. 1999) . L imited irrigation (i.e. lower water applications than full crop demand), dryland and full irrigation in corn grown in crop rotations was evaluated in deep silt loam so ils. Limited yields were 80 90% of full irrigated yields while applying approximately only half the applied water. This study

PAGE 103

103 started irrigation at the beginning of the reproductive stage, when water was most critical (Klocke et al. 2007). Thus, during rep roductive stages, careful irrigation management is recommended to avoid VWC reductions below the threshold that might incur into potential reductions in yield. Therefore, during reproductive stages, irrigation was applied more frequently intentionally to r educe potential yield negative impacts. Calendar based irrigation scheduling methodology 30 cm root zone example. Soil water content was monitored in the GROW treatment, which uses a calendar based irrigation scheduling methodology and it was compared wi th the proposed SMS irrigation scheduling methodology. The period from April 28 to 11 May and the sensor depths up to 25 cm (i.e. total 30 cm root depth) were selected for comparison. The calendar based methodology applies irrigation as the crop grows. Thu s, at these growth stages, a total of 38 mm per week are applied. A total of 7 irrigation events were applied from 28 April to 6 May 2016. However, irrigation was applied when VWC was above theoretical FC for 30 cm root zone depth. Only after the 7 May irr igation was VWC reduced to below the FC threshold. The calendar based methodology starts increasing the water budget at this stage, assuming the water uptake is greater due to greater crop growth. However, it does not consider the actual moisture in the so il, nor the actual transpiration or drainage occurring with each following irrigation event. Figure 2 6 shows the VWC dynamics after these events. It is important to note that within this period of the crop season, growth is occurring at a high rate; thus, the root zone depth can be increased accordingly with the growth of the root system.

PAGE 104

104 SMS irrigation scheduling methodology 60 cm root zone example . Later in the season, the root zone to be managed for irrigation will increase as the root system fully de velops. At the beginning of reproductive stages (i.e. tasseling), the root system is mostly developed; however, a total depth of 60 cm was used for irrigation management purposes (i.e. 550 mm sensor depth). Therefore, sensor depths were selected from 50 to 550 mm to plot the total soil moisture content within the 60 cm soil profile. After tasseling, a 60 cm root zone was used for irrigation management purposes. Theoretical FC and 50% MAD values for this depth correspond to VWC of 55 mm and 38 mm, respective ly (black solid lines, Figure 2 7). Therefore, VWC was kept within this range, triggering irrigation when VWC was reduced to the 50% MAD low boundary threshold. On 10 June 2016 a 20 mm rainfall occurred resulting in a sharp increase in VWC exceeding FC. A rapid decrease in VWC occurred slightly after the rainfall event. This rainfall event caused an increase in VWC in almost all soil layers with lower magnitude as it approached the deepest sensor depth at 850 mm. However, an increase in VWC at this depth wa s observed, which represents a possible drainage event (Figure 2 7 bottom, A). This is an important factor to consider when scheduling irrigation. It is difficult for producers to know the effect of the rainfall or irrigation in the soil profile; however, this visualization provides valid information in terms of infiltration (flux of water in the soil profile) and the effectiveness of the rainfall based on its amount and duration. A rainfall event might increase moisture in the rootzone but might also surpa ss the water holding capacity of the soil resulting in drainage from the rootzone. If enough moisture for plant water uptake is present in the rootzone, no irrigation

PAGE 105

105 wa s observed during the following days, representing the diurnal VWC losses due to ET each individual sensor depth are reduced in magnitude, starting from top to bottom soil layers (Figure 2 7, bottom). However, the more active roots, which are located deeper in the profile, continue to uptake water as VWC is reduced. This is an indication that although upper layers have less water available for uptake, the deepest roots conti nue water uptake. Finally, when the total VWC in the 60 cm root zone reaches 50% MAD, irrigation was applied. This VWC can be predicted using the average of ET values Near future estimation of VWC reductions Calendar based irrigation scheduling methodology 60 cm root zone example. The same period and depths (from 6 to 27 June and sensor depths from 50 550mm) were selected for comparison (Figure 2 8). After the 20 mm rainfall event on 10 June 20 16, total VWC increased to more than 110 mm within the soil profile. Then, VWC decreased sharply during the following day mostly as drainage, which can be observed as the VWC increment in the deepest soil layer (850 mm) ( Figure 2 9 , A ). This rainfall event caused an increase in VWC in all layers with a delay in time; reaching the deepest soil layer about 24 hours after the initial VWC peak caused by rainfall (Figure 2 8, A). At this growth stage, the GROW treatment applies a total budget of 20 mm per week w ith 10 mm per event. A total of 12 irrigation events were applied in 21 days. The continuous irrigation applications resulted in increments of VWC that remained above FC during the entire period. During the irrigation applications performed during 16 18 June, all soil layers increased in VWC through time, reaching the deepest layer on 18

PAGE 106

106 June as potential water leaving the rootzone as drainage (Figure 2 8, A). The two main events described as potential drainage events are also related to the salinity in t he soil profile (Figure 2 9, bottom). After the drainage events, a delayed increment in salinity was observed in deeper layers (Figure 2 8, B). This could represent potential N leaching from the rootzone caused by the frequent irrigation events followed by a calendar based irrigation scheduling methodology. These visualizations can help understand the effect of rainfall or irrigation events in the soil profile, as well as, the potential loss of water and/or nutrients from the rootzone. Near future estimatio n of VWC reductions. The graphs showing daily water use provide the reduction in moisture that occurred per day. This value can also be used for future estimations of VWC losses (ET and drainage). Furthermore, using the weather forecast and the average ET from previous 3 5 days, an approximation of the future VWC reductions can be made; thus, an irrigation event can be scheduled when moisture is estimated to reach 50% MAD (Figure 2 9). VWC behavior at different irrigation scheduling methods . Differences b etween the irrigation scheduling methods can be seen when VWC among the treatments is monitored through time. Figure 2 1 0 compares the VWC among irrigation treat ments monitored at and after tasseling; when water is critical for corn production. Based on th e GROW methodology, after tasseling the irrigation target increased up 38 mm/wk unless 13 19 mm of rain occurred the day prior to a scheduled irrigation or two irrigations were skipped if 25 mm of rain occurred. As a result, without rainfall GROW VWC star ted cumulatively increasing after every irrigation event remaining above FC. It did not decline until a rainfall event occurred and irrigation was skipped. In comparison,

PAGE 107

107 using SMS methodology allowed VWC to be reduced and be kept within the FC 50% MAD t hreshold, since daily monitoring of the soil moisture was performed before irrigation was triggered. SMS VWC increased above the threshold only when rainfall events occurred; however, water depletion was allowed until VWC decreased to the FC 50% MAD thre shold to trigger irrigation. The VWC at different irrigation treatments was evaluated during early reproductive stages (25 May 10 June 2016) after irrigation and or rainfall events (Figure 2 10). GROW VWC remained above FC during this period of reproduct ive stages. This reflects how the calendar based irrigation scheduling (i.e. GROW treatment), which uses a fixed target irrigation budget per crop stages, overirrigates keeping soil moisture above FC; thus, potentially causing a greater drainage especially after rainfall events. The SMS VWC was successfully kept between FC and MAD range within this period ; however, heavy rainfall events increased VWC above this range (i.e. June 6 ) . T he NON VWC was reduced below the MAD threshold in this period: end of tasse ling (June 1 to June 6) is one the most water stress sensitive stages in corn production (Figure 2 10 ). Evaluation of SMS Methodology Using SWDP Code The performance of the proposed sensor based irrigation scheduling methodology was evaluated by determining the SWDP FC values during the corn growing seasons (Figure s 2 11 2 14 , orange dots). As previously described, t h e S WDP code uses a regression approach to calculate the inflection point when drainage rate decreases to a slower rate, commonly used as the FC point. In addition, this model can identif y irrigation and/or rainfall cycles by determining the start of an irr igation/rain

PAGE 108

108 event (A), the peak point (B) of the irrigation/rainfall event and the FC point (FC) ; characteristic point s within the root system water consumption pattern (RWCP) . The efficiency of the SMS methodology proposed was evaluated cons idering the n umber of times an irrigation event was scheduled to refill the soil profile when VWC approached 50% MAD based the FC value estimated using the SWDP code . Two main examples are provided for early vegetative and reproductive stages for the soil moisture sens or scheduling technique (Figures 2 11 and 2 13 , respectively). The calendar based irrigation scheduling methodology was also evaluated using the SWDP code during those growing stages (Figures 2 12 and 2 14, respectively). Sensor based methodology e valuatio n 30 cm. In the experimental field, VWC was monitored and following Zotarelli et al. (2013) guidelines in conjunction with theoretical FC and 50% MAD values ( 27 and 19 mm, respectively) (Figure 2 11, solid lines), it was determined when to irrigate at the different growth stages. To evaluate the effectiveness of this proposed method, SWDP FC values were calculated and compared with theoretical values. Estimated SWDP FC values (Figure 2 11, orange dots) ranged from 26 to 28 mm and corresponding 50% MAD values were 18 mm and 19 mm. Calculate SWDP FC value (avg = 27 mm) was very close to theoretical values used in the field experiment (FC field = 27 mm) to trigger irrigation in the SMS treatment. Figure 2 11 shows how irrigation was effectively applied w hen VWC was approaching the lower 50% MAD boundary based on calculated SWDP FC value. Calendar based methodology e valuation 30 cm. The effectiveness of the calendar based methodology was evaluated d uring the same period and root zone depth as the sensor based method. The calculated SWDP FC values were 24 mm for

PAGE 109

109 the irrigation events applied on 19 and 21 April 2017; however, SWDP FC increased to 31 mm for the irrigation of 28 April. Prior this event, two other irrigation applications were performed on 24 and 26 April. Thus, a fter several irrigation events applied, the estimated SWDP FC value increased from 24 to 31 mm. Using this method, it can be observed how the value of FC is dynamic and it could have been influenced by the effect of previous and c ontinuous applications of water building up moisture content in the soil; thus, increasing the inflection point of FC. Additionally, as the crop grows, the calendar based methodology increases the frequency of irrigation events. Thus, when more frequent ir rigation applications are performed, VWC increases above the FC threshold (Figure 2 12). Sensor based methodology e valuation 60 cm. The SMS methodology called for 12 irrigation events, which corresponds to 55% less times than the calendar based irrigation scheduling methodology. At reproductive stages, corn water uptake rate is high and irrigation is essential for optimum grain yield. Thus, irrigation events occurred more frequently intentionally to avoid any water stress that could negatively impact final yield. During these stages, VWC remained within the FC 50% MAD range ; except when a combination of rainfall and irrigation events were performed, in which VWC exceeded theoretical values of FC. Irrigation events were applied from 16 to 24 June; however, no SWDP FC values were calculated , since irrigation amounts did not result in a sufficient increase in VWC . However, VWC was kept above the 50% MAD threshold. Then, afte r rainfall events occurring on late June , estimated SWDP FC values were higher (FC = 59 mm). This is an example of how FC values can vary after continuous water applications and exceeding the irrigation management thresholds (FC

PAGE 110

110 and % 50 MAD). After these rainfall events, VWC was monitored and irrigation was triggered when it decreased reaching values within the FC 50% MAD threshold. Afterwards, the combination of rainfall and irrigation resulted in an increased value of SWDP FC (i.e. 59 and 63 mm, respectively). Overall, the SMS methodology kept VWC within the proposed irrigation ma nagement thresholds without incurring into potential water stress (Figure 2 13). Calendar based methodology e valuation 60 cm. The calendar based irrigation scheduling method continuously applies irrigation during reproductive stages unless rainfall amounts exceed the irrigation application rate . During the reproductive stages, a total of 27 irrigation events were applied. Immediately before heavy rainfall events occurred on 5 and 10 June, the GROW irrigation applied four irrigation events ( cum. irrigation 4 3 mm), which cumulatively increased moisture in the soil . T herefore, after the rainfall events , VWC increased up to 120 mm, surpassing the theoretical FC level more than double. Using the SWDP code , SWDP FC was determined as the inflection point after d rainage occurred. However, this does not necessary represent FC in the soil, since soils were already saturated and were continuously draining. Although the decline in VWC was reduced to a smaller rate, this point only corresponds to a reduction in the dra inage rate but might be incorrectly assigned to the FC point. Effectiveness of Irrigation Scheduling Methodologies Drainage Simulations These two methodologies were compared in terms of water applied, water saved and number of irrigation events during di fferent growth stages (Table 2 2). For the 2015 17 corn growing seasons, the SMS treatment applied 7, 13 and 18 irrigation events during vegetative stages; whereas the GROW treatment applied 9, 18 and 25 events. On the other hand, during reproductive sta ges the SMS treatment applied 5, 15 and 12

PAGE 111

111 events; whereas, the GROW treatment applied 21, 29 and 29 events. Total water savings achieved by the SMS was 53%, 43% and 45% resulting in 54%, 59% and 37% less drainage than in conventional practices. Additional ly, the effectiveness of the irrigation scheduling methodologies was evaluated by their effect on keeping adequate moisture for plant uptake; while avoiding drainage from the rootzone. Therefore, t he crop simulation model CERES Maize within DSSAT was also used to determine the effectiveness of the SMS in terms of potential drainage within each corn growing season. D aily drainage was calculated per season for both methodologies. Figures 2 15 2 17 show the irrigation applied by the GROW and SMS treatments, daily rainfall and simulated daily drainage per treatment during the 2015 17 corn growing seasons. 2015 season . During the 2015 corn season, simulated daily drainage in the GROW treatment started on 3 June and continued until the end of the season. Drai nage amounts varied based on the previous irrigation applied and rainfall events. Small drainage amounts (<10 mm) resulted after continuous and frequent irrigation events; whereas, larger drainage amounts resulted after heavy rainfall events and/or the com bination of previous irrigation applied and rainfall events. For example, irrigation applied from 31 May to 2 June (cum. irrigation = 41 mm) was followed by a 19 mm rainfall event; resulting in a 24 mm cumulative drainage during the following days (4 6 J une). Afterwards, on 9 June a heavy rainfall event (34 mm) occurred in combination with previous irrigation events (cum. irrigation = 31 mm) caused a 44 mm cumulative drainage. Afterwards, this same pattern was repeated: a low magnitude drainage amounts oc curred almost daily after irrigation was applied. However, when irrigation

PAGE 112

112 was applied and rainfall occurred the following day, then higher magnitude drainage amounts occurred. Examples of these occurred on 26 June (cum. drainage = 19 mm after 20 mm irriga tion applied and 15 mm rainfall event), 16 17 July and 24 July (cum. drainage = 89 mm after 109 mm cum. heavy rainfall events and one irrigation event 10 mm) and on 7 8 August (cum. drainage 51 mm after 65 mm prior rainfall events). During the 2015 cor n season, cumulative rainfall was 545 mm, cumulative GROW irrigation was 331 mm and cumulative simulated drainage was 328 mm in this treatment. Most of the drainage occurred after continuous and frequent irrigation applications throughout the season (from June until August); however, larger drainage amounts resulted due to the combination of irrigation and rainfall events (Figure 2 15). In comparison, the SMS treatment did not result in drainage events during vegetative stages and only low magnitude (<10 mm ) daily drainage events occurred throughout the season, except after the 5 6 August heavy rainfall events (cum. rainfall = 65 mm). In addition, in the SMS treatment, all drainage events occurred after rainfall events, but not after irrigation applied. Th e first drainage event occurred after the 9 June heavy rainfall events (cum. drainage = 6 mmm after cum. rainfall = 34 mm). Then, cumulative drainage resulted in 23 mm after the 40 mm rainfall event on 29 June. During the period of 16 24 July, cumulative rainfall summed up to 109 mm distributed in three heavy rainfall events resulting in 56 mm cumulative drainage. The last drainage events occurred after heavy rainfall events on 5 6 August 2015 (cum. drainage = 51 mm after 65 mm cum. rainfall). Corn phys iological maturity had occurred at this time; thus, crop water uptake was reduced. Therefore, any large amounts of water are most likely lost from the rootzone. Total cumulative rainfall was 545 mm, cumulative SMS

PAGE 113

113 irrigation was 151 mm and cumulative simul ated SMS drainage was 150 mm. Most of the drainage occurred after heavy rainfall events occurred during the season, not after irrigation events (Figure 2 15). 2016 season . In 2016, frequent and heavy rainfall events occurred early in the season affecting d rainage in all treatments equally. The first events occurred on 23 26 March (cum. rainfall 53 mm), resulting in a cumulative drainage of 42 mm on subsequent days. Afterwards, on 1 April, a 76 mm rainfall occurred resulting in 64 mm cumulative drainage di stributed on the following three days. These drainage events were the largest ones during the season (Figure 2 16). The GROW treatment resulted in low magnitude but consistent periods of drainage events as a result of the high frequency of irrigation event s, except during three drainage events occurred after heavy rain (16 May 4 5 June and 9 June). On several occasions, drainage occurred when irrigation was applied and rainfall occurred afterwards. When irrigation and rainfall were combined, then greater drainage amounts occurred during the season. Total rainfall, total irrigation and cumulative drainage in the GROW treatment was 384 mm, 508 mm and 374 mm, respectively (Figure 2 16). In 2016, rainfall amounts were approximately 30% less than in 2015. Due t o the uneven rainfall amounts and distribution throughout the season, greater irrigation was required. The simulated drainage on the SMS treatment occurred only after heavy rainfall events. The first one occurred on 4 5 June (cum. rainfall = 43 mm and cu m. drainage = 15 mm) and the second occurred on 9 June (cum. rainfall = 20 mm and cum. drainage = 12 mm). A small drainage event (5 mm) occurred on 3 July after irrigation was applied and rainfall occurred the following day. Using the SMS irrigation schedu ling

PAGE 114

114 methodology proposed, drainage amounts caused by irrigation applications were less likely to occur (Figure 2 16). 2017 season . An initial irrigation application was performed after planting to provide enough moisture for germination. This application resulted in a small drainage event in all treatments (cum. drainage = 6 mm). At this stage in the crop season, the root system for water uptake is not developed yet; thus, irrigation amounts should be carefully applied to avoid water losses from the rootzo ne (Figure 2 17). S imilar to the 2016 season, heavy rainfall events occurred early in the 2017 corn season resulting in large drainage amounts in all irrigation treatments. The first heavy rainfall events were on 2 3 April (cum. rainfall = 108 mm) result ing in a cumulative drainage of 92 mm. The GROW treatment showed small and continuous drainage events during the period of 3 22 May after frequent irrigation events. In comparison, the SMS treatment resulted in no drainage events during this period. Mid season heavy rainfall events occurred on 23 May (cum. rainfall = 59 mm), 2 6 June (cum. rainfall = 170 mm) and 16 18 June (cum. rainfall = 56 mm) resulting in large drainage amounts in all irrigation treatments. Cumulative drainage simulations in the G ROW treatment after those heavy rainfall events resulted in 67 mm, 154 mm and 42 mm, respectively. Similarly, the SMS treatment cumulative drainage was 54 mm, 137 mm and 18 mm, respectively (Figure 2 17). Thus, most of the drainage occurred in both treatme nts was due to rainfall; however, slightly higher drainage amounts occurred in the GROW treatment due to irrigation applied just prior rainfall events. After these mid season rainfall events, rainfall occurred more sporadically. During reproductive stages , irrigation applications are intentionally performed more

PAGE 115

115 frequently to avoid water stress that might negatively impact grain yield. The GROW treatment, which used a calendar based irrigation method, applied 27 irrigation events after 1 June during reprod uctive stages; whereas the SMS applied 12 irrigation events; a 55% less events than the GROW treatment. Rainfall uncertainty makes this decision more challenging; since if it rains, over irrigation might cause drainage below the rootzone; however, if it do es not rain, yield might be compromised (Figure 2 17). Even when careful monitoring of VWC is performed to determine when to irrigate in the SMS treatment, on some occasions, irrigation was applied and rainfall occurred the following day (s). This most lik ely results in small drainage events. For example, after five days without rainfall, SMS irrigation was applied on 25 June and 27 June; however, rainfall occurred on 27 June (16 mm) and 29 June (11 mm), resulting in small drainage events during the followi ng days (cum. drainage = 19 mm). In contrast, the GROW treatment applied five irrigation events from 25 29 June and therefore a cumulative drainage of 49 mm (i.e. 157% increase in drainage). These two different patterns of drainage amounts were followed by the two scheduling methodologies until the end of the growing season (Figure 2 17). Late in the season, heavy rainfall events occurred on 20 July, 25 July, 29 July and 1 August (cum. rainfall = 21 mm, 25 mm, 26 mm and 39 mm). These events resulted in cu mulative drainage of 40 mm, 16 mm, 10 mm and 33 mm in the GROW treatment; and in cumulative drainage of 0 mm, 8 mm, 10 mm and 33 mm during the following days. Total cumulative rainfall, irrigation and cumulative drainage in the GROW treatment was: 653 mm, 558 mm and 677 mm, respectively; whereas in the SMS treatment irrigation and drainage totaled 303 mm and 429 mm (Figure 2 17).

PAGE 116

116 The GROW treatment resulted in low magnitude but consistent periods of drainage events because of the frequency of irrigation eve nts characteristic of the calendar based irrigation scheduling method. The calendar based irrigation scheduling methodology resulted in greater cumulative irrigation applied and therefore greater cumulative drainage during all three seasons. In contrast, t he sensor based methodology achieved 53%, 43% and 45% water savings compared to the calendar based method. In addition, this proposed methodology resulted in 54%, 59%, and 37% less drainage compared to the calendar based during the 2015 17 corn seasons. Reduction in cost of irrigation applications (e.g. fuel, time, water, fertilizer) can be achieved when using soil moisture sensors to schedule irrigation, as well as, a reduction in nutrients leaching (i.e. nutrients dissolved within the drainage amounts l eaving the rootzone, e.g. N leaching). Water Savings and F ina l Grain Y ields The sensor based methodology resulted in 53, 43% and 45% water savings compared to the calendar based irrigation scheduling methodology during 2015, 2016 and 2017 corn growing seas ons, respectively (Figure 2 18). In addition, no statistical difference was found in final corn grain yield across the two irrigation scheduling methodologies during the three corn growing seasons evaluated (Figure 2 19). Conclusions Through the use of soi l moisture sensors, it is possible to observe the water behavior within the rootzone before and after irrigation or rainfall events. In addition, following the root system water consumption pattern, the plant water uptake, the water interaction with soil a nd roots and drainage can be determined using these sensors.

PAGE 117

117 rainfall and/or irrigation events. Continuous monitoring of soil moisture using multisensor capacitance probes allows determination of the effective rainfall and/or irrigation that has entered the soil profile, in addition to drainage occurring between soil layers (i.e. downward water flux across layers) or leaving the soil profile (i.e. increments in VWC detected by sens ors located in depths deeper than root zone). Thus, during the growing season, an optimal irrigation management can be achieved throughout the use and continuous monitoring of these sensors It is important to recognize that even when using capacitance prob es to measure VWC in the soil profile, a fundamental difficulty encountered in experimental fields is the inherently complicate d space time relationships involved in the soil water uptake process by plants. Microscopic gradients and fluxes of water in the immediate proximity of roots might not be possible to accurately measure, besides the fact that roots grow in different directions and spacing. Conventional methods intend to sense the content of water in the soil of a relatively large volume; thus, smalle r gradients might not be considered or possible to measure (Hillel 1971) . Temporal and spatial rainfall variability is the main challenge for any irrigation scheduling methodology in Florida. Rainfall uncertainty m ay cause over irrigation in crops when an irrigation event is scheduled and rainfall events occur subsequently; increasing the moisture in the soil profile above FC. Thus, an increase in drainage can occur. On the other hand, rainfall uncertainty also can cause under irrigation. Inadequate soil moisture conditions may occur if irrigation is not scheduled because

PAGE 118

118 forecasts indicate high probability of rainfall, but rainfall does not occur or the amounts are lower than expected. The sensor based irrigation scheduling methodology proposed consists of five different steps: 1. Select r oot depth based on crop developmen t and modify it as crop grows. Three main root depths were proposed: 25 cm, 35 cm and 55 cm (i.e. representing approximately V3 V6, V7 VT and r eproductive stages, respectively. 2. Use physical characteristic values of field soil type published at the Florida Web Soil Survey as irrigation thresholds (FC and 50% MAD). 3. Follow Zotarelli et al. (2013) guidelines to determine FC at selected root zone de pth (i.e. FC: the slope of drainage and extraction lines changes from rapid to slower decrease and the soil water flux stabilizes). 4. iurnal fluctuations in soil moisture at the root zone to determine water losses (e.g. ET and drainage) from the soil to be refilled with irrigation . Near future ET values (i.e. VWC reductions in following 1 2 days) can be estimated using average ET from previous 3 5 days. 5. Scheduled an irrigation event when V WC approaches 50% MAD. Using this simple irrigation scheduling methodology, cumulative drainage was reduced by 54%, 59% and 37% during the 2015 17 corn growing seasons. In addition, water savings ranging from 43% to 53% were achieved by using this method ology in comparison to the calendar based irrigation scheduling method during the three years of field evaluation. Furthermore, no statistical differences in final grain yield were found across the methods. Using this methodology, producers can maintain a dequate moisture within the managed root zone, as well as, reduce the loss of water via drainage. Better understanding the dynamics of water in the soil after rainfall or irrigation events, can provide insights of crop water uptake/consumption, root develo pment, and drainage. Determining how to manage irrigation within the proposed thresholds (i.e. FC and % 50

PAGE 119

119 MAD), production costs (e.g. fuel, time, labor, fertilizer) and negative environmental impact (e.g. N leaching) can be reduced.

PAGE 120

120 Table 2 1. Field ca pacity (FC) and 50% maximum allowable depletion ( % 50 MAD ) used during the corn stages for SMS irrigation scheduling methodology. Corn growth stage Depth (mm) Threshold % Equivalent (mm) V3 V6 300 FC 9.1% 27 300 50% MAD 6.3% 19 V7 V11 400 FC 9.1% 36 400 50% MAD 6.3% 25 VT Reproductive 600 FC 9.1% 55 600 50% MAD 6.3% 38

PAGE 121

121 Table 2 2. Evaluation of irrigation scheduling methodologies (calendar based and sensor based) used in the GROW and SMS treatments during 2015 17 corn growing seasons. C orn season Parameter evaluated Calendar based Sensor based 2015 Cumulative rainfall (mm) 545 545 Cumulative irrigation (mm) 320 151 Number of irrigation events in vegetative stages 9 7 Number of irrigation events in reproductive stages 21 5 Cumu lative ET c (mm) 435 437 Cumulative drainage (mm) 1 328 150 Water savings per season (%) 53 2016 Cumulative rainfall (mm) 384 384 Cumulative irrigation 508 291 Number of irrigation events per season 47 28 Number of irrigation events i n vegetative stages 18 13 Number of irrigation events in reproductive stages 29 15 Cumulative ET c (mm) 443 448 Cumulative drainage (mm) 1 374 153 Water savings per season (%) 43 2017 Cumulative rainfall (mm) 673 673 Number of irri gation events per season 54 30 Cumulative irrigation 546 302 Number of irrigation events in vegetative stages 25 18 Number of irrigation events in reproductive stages 29 12 Cumulative ET c (mm) 438 438 Cumulative drainage (mm) 1 677 429 Wate r savings per season (%) 45 1 Cumulative ET c (i.e. transpiration) simulated in DSSAT. Simulated potential ET o for all seasons was: 580 mm, 650 mm and 630 mm 2 Cumulative drainage (due to rainfall and/or irrigation) simulated during the corn 2015 17 sea sons using CERES Maize model within DSSAT .

PAGE 122

122 Figure 2 1. Field capacity (FC) determination guidelines proposed by Zotarelli et al. 2013 using volumetric water content monitored by soil moisture sensors. Example of FC determination (left) and an adequate irrigation management example for sandy soils (Zotarelli et al. 2013).

PAGE 123

123 Figure 2 2 . Screenshot of real time soil moisture data collected in the experimental field. From top to bottom: s oil moisture content (VWC) at individual depths in soil profile, soil salinity (volumetric ion content) at individual depths , cumulative soil moisture (mm, based on selected sensor depth), daily water use (mm), and sensor depth (mm). Cumulative Soil Moisture Content (mm) Soil Moisture Content per depth Soil Salinity (VIC) per depth (mm) Depth

PAGE 124

124 Figure 2 3 . Soil moisture content at individual depths where r oot development (A) and drainage (B) can be determined in the soil profile across the growing season. B A

PAGE 125

125 Figure 2 4 . Selected depths (25, 35 and 55 cm) based on root growth development for analysis of s oil moisture content during the c orn growing season. 35 cm 55 cm 25 cm 35 cm May 23 May 30 55 cm 25 cm Irrigation terminate d SMS High N 2015 corn season Apr 15 Tasseling

PAGE 126

126 FC Th eoretical FC field ET+D air effect Soil moisture content (mm) Soil moisture content (mm) Figure 2 5. Example of sensor based irrigation scheduling at approximate V3 V6 corn stages using a 30 cm root zone. Top: total VWC in soil profile (blue line), irrigation applied (red dots) and theor etical FC and 50% MAD values for Chipley soil (black solid lines) (NRCS 2016b) . FC field = value of FC using of moisture due to ET occurr ing during the day and the constant moisture during the night (i.e. negligible VWC losses). VWC reduced below MAD threshold during the weekend. Experimental field operations were dependent on personnel availability; thus, irrigation was scheduled for the f ollowing Monday to keep VWC within the proposed thresholds. Bottom: sensor depths 50 250 mm were selected for the 30 cm irrigation management purposes following root water uptake (bottom, orange arrows). Weekend 50% MAD

PAGE 127

127 FC Th eoretic al ET+ D air effect Soil moisture content (mm) Figure 2 6. Example of calendar based irrigation scheduling at early corn growth stages using a 30 cm root zone. Total VWC in soil profile (blue line), irrigation applied (red dots) and theoretical FC and 50% MAD values for Chipley soil (black solid lines) (NRCS 2016b) . FC field = value of FC using Zotarelli et al. (2013) occurring during the day and the constant moisture during the night (i.e. negligible VWC losse s). VWC reduced below MAD threshold during the weekend. Experimental field operations were dependent on personnel availability; thus, irrigation was scheduled for the following Monday to keep VWC within the proposed thresholds. Weeke nd

PAGE 128

128 FC theoretical ET+ D effect Soil moisture content (mm) Soil moisture content (mm) Figure 2 7. Example of sensor based irrigation scheduling at VT Reproductive corn stages using a 60 cm root zone. Top: total VWC in soil profile (blue line), irrigation applied (red dots) and theoretical FC and 50% MAD values for Chipl ey soil (black solid lines) (NRCS 2016b) . Bottom: soil moisture content at individual sensor depths. Potential drainage events evidenced by VWC increments at the deepest 850 mm soil layer (A). Stair step effect as diu rnal fluctuations due to ET and drainage. Reduction in VWC in top soil layers, but continuous water uptake in deeper layers in time as potential evidence of root search for water uptake deeper in the soil profile (B). A B 50% MAD

PAGE 129

129 Figure 2 8. Example of irrigation scheduling using calendar based irrigation scheduling method in a 60 cm root zone depth. Top: total VWC in soil profile (blue line) remaining above theoretical FC value (>FC) and irrigation applied (red dots). Stair ste p effect as diurnal fluctuations occurred because of ET and drainage not observed in VWC data. Center: soil moisture content at individual sensor depths. Potential drainage events evidenced by VWC increments at the deepest 850 mm soil layer (A). Effect of frequent irrigation events scheduled with a calendar based method on salinity increments at deep layers in time, as evidence of potential leaching (B). >FC ET+ D NO air effect Soil moisture content (mm) Soil moisture content (mm) B Salinity (VIC) A A

PAGE 130

130 Figure 2 9. Projections of near future VWC reductions estimated using the average ET from prev ious dates (red values). Blue line represents total VWC in 60 cm soil depth. Black solid lines represent theoretical FC and 50% MAD values for Chipley soil (NRCS 2016b) . Irrigation applied (Irr) when VWC estimated pro jections were approaching the 50% MAD threshold. 50% MAD FC

PAGE 131

131 Figure 2 10 . Volumetric water content monitored at GROW, SMS and NON irrigation treatments (lines) during early reproductive stages in corn 2016. Rainfall and irrigation applied (bars). Dash lines show FC and 50% MAD threshold.

PAGE 132

132 Figure 2 11. Evaluation of sensor based irrigation scheduling methodology for 30 cm root zone depth using SWDP code for FC determination (SWDP FC, orange dots) and r oot system water consumption pattern (RSWC ): A represen ts the start point before irrigation or rainfall events and B the peak in VWC after those events. Theoretical FC and 50% MAD values for 30 cm Chipley soil are represented by the black solid lines. FC 50% MAD

PAGE 133

133 Figure 2 12. Evaluation of calendar based irrigation s cheduling methodology for a 30 cm root zone depth using SWDP code for FC determination (SWDP FC, orange dots) and r oot system water consumption pattern (RSWC ): A represents the start point before irrigation or rainfall events and B the peak in VWC after those events. Theoretical FC and 50% MAD values for Chipley soil are represented by the black solid lines. FC 50% MAD

PAGE 134

134 Figure 2 13. Evaluation of sensor based irrigation scheduling methodology for 60 cm root zone depth using SWDP code for FC determination (SWDP FC, orange dots) and r oot system water consumption pattern (RSWC ), where A represents the start point before irrigation or rainfall events and B the peak in VWC after those events. Theoretical FC and 50% MAD values for 30 cm Chipley soil are represented by the black solid lines. FC 50% MAD

PAGE 135

135 Figure 2 14. Evaluation of calendar based irrigation scheduling methodology for 60 cm root zone depth using SWDP code for FC determination (SWDP FC, orange dots) and r oot system water consumption pattern (RSWC ): A represent s the start point before irrigation or rainfall events and B the peak in VWC after those events. Theoretical FC and 50% MAD values for Chipley soil are represented by the black solid lines. FC 50% MAD

PAGE 136

136 Figure 2 15. Effect of irrigation scheduling methodology on si mulated daily drainage (bottom bars) during 2015 corn season. Irrigation applied per treatment (GROW and SMS) (top light blue and orange bars) and rainfall (dark blue bars) that occurred.

PAGE 137

137 Figure 2 16. Effect of irrigation scheduling methodology on sim ulated daily drainage (bottom bars) during corn 2016. Irrigation applied per treatment (GROW and SMS, top light blue and orange bars) and rainfall (dark blue bars) that occurred.

PAGE 138

138 Figure 2 17. Effect of irrigation scheduling methodology on simulated daily drainage (bottom bars) during corn 2017. Irrigation applied per treatment (GROW and SMS, top light blue and orange bars) and rainfall (dark blue bars) that occurred.

PAGE 139

139 Figure 2 18. Cumulative irrigation app lied by GROW, SMS and NON treatments during corn growing seasons 2015 17. 53% 4 3% 45 % Water savings

PAGE 140

140 Figure 2 19. Corn grain yield (kg/ha) for calendar based and sensor based irrigation scheduling methodologies used to trigger irrigation in the GROW and SMS treatments during corn growing seasons 2015 17.

PAGE 141

141 CHAPTER 3 EFFECT OF IRRIGATION AND NITROGEN FERTILITY RATES ON CORN NITROGEN UPTAKE AND YIELD Introduction Maize ( Zea mays L. ) is the second largest commodity in the United States. In 2013, around 1,121 Mg were produced ac counting for $67 billion in the U.S. economy (FAOSTAT 2015b) . In the state of Florida, maize (corn) production for grain and silage is important and it is commonly grown in rotation with peanuts resulting in yield benefi ts for both crops (Wright et al. 2003) . The average area planted with corn in Florida from 2015 17 was 31,700 ha and total corn sales accounted for $43.7 million for this state according to the 2012 census data (USDA, NASS 2018) . Water is essential for crop growth. It is a source of transport and solvent of nutrients from the soil to the roots and throughout the plants, it is the main medium for chemical processes involved in pla nt metabolism and it could serve as a cooling mechanism to maintain adequate temperature in the plant (Haman and Izuno 2003) . Water required for plant growth is supplied by rainfall and/or irrigation, but due to the l successful crop production during the dry periods (Kisekka et al. 2016) . The total water withdrawals (55% saline and 45% freshw ater) in Florida were 53.8 million m 3 /d in 2012 (USGS 2012) . Agriculture was the largest freshwater user with withdrawals up to 9.5 million m 3 /d in that year. Nearly half of the estimated harvested cropland (890,308 ha) is irrigated (USDA, NASS 2014) , where water applied in irrigated corn fields is about 2134 m 3 /ha (USDA, NASS 2018) . Irrigation, fertilization, dis ease control and harvest should be properly managed to achieve high yields (McWilliams et al. 1999) . By selecting the adequate hybrid,

PAGE 142

142 growers can determine the yield potential, maturity, disease and insect resis tance, grain quality and adaptability for each region. Pioneer 1498 YHR/Bt is considered appropriate under limited or dryland conditions in Florida (DuPont 2016) . Temperature plays an important role in corn growth and development. Mean cardinal temperatures for corn (whole plant) are 6.1°C, 30.8°C and 42°C for minimum, optimum and maximum temperatures for growth and developm ent, respectively. Outside these thresholds, growth rate stops or is slower than optimum. Cardinal temperature thresholds vary at the different phenological processes occurring at the plant growth stages (i.e. emergence, tassel initiation, anthesis and gra in filling). Grain filling is the most sensitive stage to high temperatures, where minimum, optimum and maximum temperatures are 8.0°C, 26.4°C and 36°C. Corn minimum and maximum lethal temperatures causing irreversible damage are 1.8°C and 46°C, respectiv ely (Sanchez et al. 2014) . In general for corn , a s igmoid curve characterizes growth and contains four phases: exponential, linear, dampened exponential and steady state (as it reaches physiological maturity (Hammond and Kirkham 1949) . During early crop growth stages, N uptake is slow, and it increases as the plant grows and develops (Hanway 1963) . For this study a t planting, a 34 kg/ha (N P K) fertilization was performed with subsequent ro ot development initiated at germination . After planting (i.e. early during the exponential stage) , the root system is relatively small and the soil is generally cool. Although the fertilizer concentration s stimulate root growth; the plant requirement is very small. Fertilizer should be placed where the primary roots are for successful and

PAGE 143

143 effective uptake when root development starts. During this stage, rainfall represents a risk for N leaching. If a heavy rainfall occurs during this period, the N placed for future uptake can be leached from the rootzone, as occurred in 2016 and 2017 early seasons. Further in the growing season, after the root system and aboveground biomass (e.g. leaf, stack formation) are being developed, the N uptake is close to linear. During this phase, most of the vegetative tissues are being developed and fertilizers contribute the most to the large N demand. During this phase, most of the N fertilizations are applied. Later in the sea son, there is a slow rate of N uptake by the plant as it reaches maturity. However, during this period, no fertilizations were performed (all were applied previously). In North central Florida, soils are characterized by coarse texture, with low water hold ing capacities, low organic matter and low cation exchange capacity, but with high infiltration rates increasing the potential of nitrate leaching (NRCS 2016a) . Typically, the field corn growing season spans from mid March or early April to August. Planting is performed after potential freezes sinc e a minimum temperature of 12.7 °C in the top 5 cm during at least three consecutive days is required for corn germination and root growth. Corn is known to be susceptible to frost damage (aboveground tissue); however, the apical meristem is below the surface, thus the plant may overcome a freeze until it grows about 30 cm tall. Planting early is suggested because greater moisture is stored in the soil, lower temperatures and l onger days occur during pollination, hence a higher yield potential could be achieved. In general, a plant population of 59,300 7 9,100 plants/ha is recommended for early and medium maturity hybrids with irrigation (Sanchez et al. 2014; Wright et al. 2003) .

PAGE 144

144 The potential yield depends on the interaction of several factors: (i) sum of the photosynthetic active radiation (PAR) intercepted by green tissue which depends on the interception of solar rad iation and the length of the growing period, (ii) radiation use efficiency (RUE), and (iii) the harvest index (HI) which is the fraction of accumulated biomass allocated to the grain. Therefore, potential yield is set by the genetics; while the actual yiel d is set by the management and environment. The interaction of these factors will determine final yields (Fischer et al. 2014) . Therefore, the ability of the plants to uptake N early in the season leads to a rapid d evelopment of optimal leaf area index (LAI) which res ults in greater interception of PAR. As well, it would improve RUE through an increased leaf maximum photosynthetic rate. Final grain yield also depends on the successful reallocation of compounds to the grain during the gra i n filling period. Plants can transport photosynthates from where they are produced to places in the plant that are needed in a process called translocation. A long distance transport is used to move the glucose (i.e. source) produced during the process of photosynthesis in the chloroplast, to the grain (i.e. sink). Therefore, an important factor determining final yield would be the ability of the plant to translocate the sugars and nutrients into the grain later in the season. The proc ess of loading sugars at the source and unloading at the sink, as the driving force, provides the pressure gradient that generates movement of phloem sap in a long distance (Taiz et al. 2015; van B el 2003) . Understanding these concepts can provide insights to the technology to enhance productivity and the increase of photosynthates in the grain. Remobilization corresponds to the movement of compounds from an area where they were previously deposite d in the plant to an area

PAGE 145

145 where they can be re utilized. Photosynthesis is the main process involved in grain filling; however, remobilization has a particular importance in grain filling during stress conditions (Fi scher et al. 2014) . Studies have evaluated the effect of irrigation and water stress at different developmental stages on field corn growth and grain yield (Cakir 2004; Den mead and Shaw 1960; Shanahan and Nielsen 1987) . The results of one of these studies determined that both vegetative and reproductive stages were affected due to soil water shortages. Short term water deficits occurring during vegetative and tasseling stag es caused reduction in plant height with a 28 32% reduction in final biomass; whereas, up to 40% yield losses occurred when water deficit occurred during sensitive growth stages (i.e. tasseling and ear formation). Thus, during vegetative stages, water stre ss generally reduces plant height; however, during reproductive stages the crop can recover if adequate water is applied (Cakir 2004; NeSmith and Ritchie 1992; Robins and Domi ngo 1953) . Nitrogen (N) is an essential element for crop growth and it is supplied by organic yield; processes that require large N amounts for the formation of p roteins, nucleic acids, chlorophyll, and growth regulators, which are key components of plant growth and development (Below 2002; Taiz et al. 2015) . Since potential reduction in yield can be observed due to N deficiencies, addition of N fertilizer is generally required to maximize yields. Cost estimates show that N fertilizers account for 80% of all fertilization costs and 30% of energy costs in crop production (H auck 1984) .

PAGE 146

146 Most of the N is taken up by the root system, process in which inorganic N (NO 3 and NH 4 ) is absorbed, transported across membranes and stored within the plant for assimilation into organic compounds (Pe ssarakli 2014) . N accumulation by the plants can be separated into three main phases: (i) initial: slow N accumulation due to limited crop biomass, (ii): linear: period of rapid accumulation (near linear) that coincides with rapid plant growth, (iii) cess ation: occurring with advance maturity (Pessarakli 2014) . Most plant N accumulation in cereal s occurs during vegetative growth; however, it can be affected by the availability of N, planting date, irrigation and cl imate at that time . Studies on corn have shown cases in which nearly 75% of total N accumulation have occurred by anthesis ; a lthough, continuing accumulation during grain filling has been shown to be beneficial for high yielding genotypes (Pessarakli 2014; Swank et al. 1982) . The N in the grain results from N uptake during grain filling and N remobilized from vegetative tissues (Pan et al. 1986) . The relative f low and remobilization of carbon (C) and N to the grain depends on the particular source/sink ratio of the crop, which further depends on the genotype and the environment, and can be altered by management (i.e. planting density, irrigation, fertilization, and others) (Ciampitti et al. 2013; Ciampitti and Vyn 2013) . Although most of the N uptake occurs before anthesis, any application exceeding the potential N uptake will be lost. Therefore, it is important to consider amount, timing and placement of the fertilizer to provide adequate uptake by the plants. Nitrate (NO 3 ) is the dominant form of N in soil solution that can move rapidly be taken up by plant roots. Nitrate is a negatively charge d molecule, thus it is not retained by the soil and therefore is the dominant form of N (Di and Cameron 2002) . Careful

PAGE 147

147 management of N fertilization is recommended due to its mobility and potential leaching in sandy soil s (Wright et al. 2003) . To increase the N recovery efficiency throughout the season, split applications (i.e. multiple app lications with small amounts) are recommended to improve plant nutrient uptake and reduce N leac hing (Li and Yost 2000) . In general, these applications consist of: 20 25% of the crop N demand applied at planting using a starter fertilizer (i.e. 16 16 0 N P K ) near the row. Subsequently , 75% of the N requirement can be applied side dress and/or through fertigation (i.e. injected through the center pivot) (Wright et al. 2003) . The UF/IFAS fertilization recommendations for irrigated corn are: 235 kg N/ha/yr, 78 kg P 2 O 5 /ha/yr and 78 K 2 O kg/ha/yr (under medium P and K conditions) (Mylavarapu et al. 2015) . Irrigation and fertilization are two main components in field corn production. Careful and effective management of both components is requir ed to keep nutrients within the rootzone for optimum plant uptake and lower potential environmental risks (e.g. nitrate leaching). Corn yield response to nitrogen rate and irrigation rate has been evaluated in several regions in the U.S., especially in san dy soils or in areas vulnerable to nitrate leaching (Al Kaisi and Yin 2003; Derby et al. 2005; Ferguson et al. 1991; Gehl et al. 2005; He 2008) . A 6 year field study performed in sandy or sandy loam soils in southern North Dakota, evaluated the effect of four irrigation scheduling strategies and six N rates on corn grain yield and N uptake. Results showed that yield increased significan tly with N rates up to 135 kg N/ha; however, average yield reductions of 1.25 Mg/ha resulted in the second year of evaluation due to cool climatic conditions. An irrigation scheduling method using a soil water balance algorithm as a strategy to

PAGE 148

148 replenish w ater depleted by ET resulted in higher yields in comparison to the other methods (Derby et al. 2005) . Gehl et al. (2005) evaluated different fertilizer rates and timing for irrigated corn in sandy soils (Carr fine sa ndy loam and Pratt loamy fine sand soils) along Kansas waterways. Treatments consisted of: (i) 300 kg N/ha applied at planting, (ii) 250 kg N/ha applied at planting, (iii) 250 kg N/ha split (50% applied at planting and 50% side dress at V 6 crop stage), (i v) 185 kg N/ha split (33% applied at planting and 67% side dress at V 6 crop stage), (v) 125 kg N/ha split (20% applied at planting, 40% side dress at V 6 crop stage and 40% applied at V 10 crop stage), and (vi) 0 kg N/ha. Maximum grain yield was achieved using a split application of 185 kg N/ha; however, in most of the cases 125 kg N/ha was satisfactory to reach maximum yield. Thus, efficient use and timing of N fertilizer (e.g. split applications), along with optimum irrigation management tied to the crop requirement must be implemented; especially in sandy soils and vulnerable regions to nitrate leaching (Gehl et al. 2005) . A study was performed in the Great Plains to determine the effect of irrigation and N fertility levels on corn growth and N use efficiency (NUE). Three irrigation levels were assessed: precipitation plus irrigation equal to one, two, or three times the calculated ET rate. A 15N enriched fertilizer applied at 100 and 200 kg N/ha was used to evaluate NUE. Grain and dry matter yields, N content, and NUE were impacted mostly by yearly weather parameters (i.e. temperature). No differences in yield or N content was found among the two fertility rates during the years in which temperature was below the hist orical 30 year average. In contrast, during years with temperatures favoring corn growth, the high N fertility treatment (200 kg N/ha) resulted in a 60% yield

PAGE 149

149 increase, 75% greater N content, and 60% greater percent of N derived from fertilizer in comparis on to the low N treatment (100 kg N/ha) evaluated (Wienhold et al. 1995) . Plant growth is mainly limited by N and water, and both are extensively used to enhance crop yields. Improving irrigation scheduling constit utes a potential strategy to increase irrigation water use efficiency while implementing BMPs. In soils with low water holding capacities, low amounts but more frequent water applications have been proven to get better results in comparison to the high irr igation volumes with fewer applications (El Hendawy and Schmidhalter 2010; He 2008; Hochmuth 2000; Locascio 2005) . As a drawback, the more frequent applications require more intensive labor and can be more expensive. However, implementing automated irrigation using soil moisture sensors could be an alternative to manage frequent and low volume applications for crop production in sandy soils (Munoz Carpena et al. 2005) . The water requirement of plants can be determined from a soil water balance (SWB) of water inputs and outputs to the root zone. ET based irrigation scheduling uses evapotranspiration (ET) loss es to determine when and how much water needs to be replaced in the rootzone to fulfill plant requirements. The main inputs are the effective rainfall (fraction of rainfall that contributes to water requirements, net irrigation (amount of water required fo r optimum crop growth), and capillary contributions (i.e. water contributions from shallow groundwater tables). On the other hand, outputs correspond to the losses from the rootzone: ET and deep percolation or drainage (i.e. downward movement of water leav ing the rootzone). A SWB is an alternative for irrigation scheduling that can improve irrigation efficiency.

PAGE 150

150 Irrigation scheduling (i.e. timing and depth of irrigation) is more efficient when based on ET or soil moisture sensors (SMS) (Irrigation Association 2011) . The use of real time soil moisture data (SMS) complemented with ET data has been studied to improve irrigation scheduling (Adhikari and Penning 2016) . A s tudy conducted to evaluate both methods showed that the combination of ET data, which provides the amount of water lost that needs to be replaced with irrigation, and the soil moisture data, which confirms if the amounts were efficiently applied in the roo t zone, is an efficient methodology to schedule irrigation (Adhikari and Penning 2016) . As well, the use of multiple soil moisture sensors provides information about the soil variability, thus irrigation can be adj usted manually or using smart irrigation systems. This is an opportunity for producers or for irrigation managers to react and make decisions according to the constant changes in the field and/or weather conditions. Hypotheses 1. The use of irrigation schedul ing alternatives (daily soil water balance, SWB; real time soil moisture sensor, SMS; and a reduced conventional practice) reduce irrigation amounts while maintaining yield in comparison to conventional practices (i.e. calendar based irrigation scheduling) . 2. N itrogen uptake and final grain yield obtained using l ower N rates (i.e. 157 and 247 kg N/ha ) does not differ from conventional practices (336 kg N/ha ) . Objectives 1. To evaluate the use of irrigation scheduling alternatives: (i) daily soil water balance ( SWB); real time soil moisture sensor (SMS); and a reduced conventional practice (Reduced) in their effectiveness to reduce irrigation amounts while maintaining yield in comparison to calendar based irrigation scheduling practice (GROW). 2. To determine the co rn grai n yield and N uptake response to four irrigation treatments ( SWB, SMS, Reduced and NON) and two N fertility rates ( medium and low) in comparison to conventional practices (GROW treatment and high N rate).

PAGE 151

151 Materials and Methods Experimental Field T he three year research study (2015 17) was conducted at the North Florida Research and Education Center Suwannee Valley (NFREC SV), near Live Oak, Florida (30.31353 N, 82.90122 W). Predominant soils were identified as: Blanton Foxworth Alpin complex ( 48.7%), Chipley Foxworth Albany (31.6%) and Hurricane, Albany and Chipley soils (19.6%) (USDA, NRCS 2013) . The taxonomic classification for these soils is shown in Table 3 1. Based on the published Soil Survey from Flo rida theoretical values for Chipley Foxworth Albany soil were used in this study : f ield c apacity (FC) = 9.1% (by volume) , 50% m aximum allowable depletion (MAD) = 6.3%, Available water holding capacity (AWHC) = 5% and p ermanent w ilting point (PWP) = 3.5% (N RCSS 2016b). Typically, corn growing seasons span from late March to early August. During the three years of this study, corn was planted at 76.2 cm row spacing, and 16.5 cm among plants for a total density of approximately 80 , 000 plants per hectare. Weath er Weather parameters such as daily rainfall, max, min and average temperature and ET were collected from the on site FAWN weather station located in Live Oak, FL (FAWN 2017) . Growing degree days (GDD) or units were calcul ated to quantify crop growth rate based on annual temperature variation using the following equation (Angel et al. 2017) : ( 3 1)

PAGE 152

152 Where T max and T min are the daily maximum a nd minimum temperatures ( ° C), respectively. However, corn growth was limited by 30 and 10 max and min temperatures. Thus, if T max was >30 °C, a value of 30 was used and when T min was <10°C, a value of 10 was used. Experimental Design and Analysis The experi mental design consisted of a randomized complete block arranged in a split plot with four replicates (i.e. blocks) for each treatment. Irrigation strategies as main plots and N fertility rates as sub plots. Research plots were 12.2 m long and 6.1 m wide se parated by 6.1 m alleys. A 12.2 m alley was included between the blocks to let the irrigation system achieve adequate pressure to switch irrigation rates among treatments (Figure 3 1). Irrigation Treatments The irrigation treatments evaluated consisted of: 1. collected from extension agents and Suwannee River Water Management District to develop the GROW method. The target irrigation rates varied based on growth stages. For the first 30 days after planting (DAP) zero irrigation was applied (unless severe windy conditions occurred). At 31 DAP, 25 mm/wk was targeted unless rainfall events equal or greater to 10 mm occurred. At 40 59 DAP, target irrigation was 38 mm/wk with irrigati on events of 10 mm. If rainfall events were equal or greater than 13 19 mm one irrigation event was skipped, and two events were skipped if greater than 19 mm of rain occurred. Afterwards, the irrigation target increased up to 51 mm/wk unless 13 25 mm of r ain occurred the day prior to a scheduled irrigation. Two irrigations were skipped if 25 mm of rain occurred. Finally, at full dent stage (105 DAP), weekly irrigation targets were 41mm/wk. If rainfall events were equal or greater than 13 19 mm one irrigati on event was skipped, and two events were skipped if rainfall greater than 19 mm occurred. Irrigation was terminated after physiological maturity (i.e. black layer) around 115 DAP. 2. I2, SWB: soil water balance treatment. Irrigation was determined using a th eoretical SWB equation. It simulates soil water storage in corn root zone due to changes in effective rainfall (R), effective irrigation (I), run off (RO), crop evapotranspiration (ET c ), and deep drainage (D). It assumes negligible rates for RO, and D unle ss

PAGE 153

153 water exceeds water holding capacity in the rootzone. All excess water is assumed to leave the soil each day. The simplified (without RO and D) daily SWB equation used to call for irrigation in this treatment is described as following: ( 3 2) Soil water content of the previous day (SWC t 1 ) is a function of root depth and water holding capacity (WHC=0.07 mm/mm). The allowable depletion was a function of the root depth, maximum allowable depletion (MAD) and WHC. MAD values of 50% and 33% were used during vegetative and reproductive stages, respectively. Effective water holding capacities. Weather data (i .e. rainfall, ET, temperature) was obtained from the on site FAWN weather station located in Live Oak, FL (FAWN 2017) . Crop evapotranspiration (ET c ) was calculated using phenologically based crop coefficients (Kc) (K State Research & Extension Mobile Irrigation Lab 2014) (Table 3 2) and reference evapotranspiration (ET o ) as follows: ( 3 3) 3. I3, SMS: capacitance probes monitor ed volumetric water content. The Sentek probes consist of nine sensors placed every 10 cm from 5 cm to 85 cm. Irrigation was determined using the MAD as 50% of the difference between FC and PWP to refill the soil profile with irrigation according to guidel ines proposed by Zotarelli et al. (2013). A total of 10 mm per irrigation event was applied. A comparison between theoretical and actual values of FC, MAD and PWP was performed for 0 90 cm depth resulting in close values among them (e.g. average FC = 9. 3 % (±0.2 % ) in a 30 cm root zone depth vs. 9.1% FC theoretical value). Based on root/crop development, the soil water content measured by the different sensors was adjusted through the growing season. This SMS irrigation treatment was triggered when VWC in any of the probes showed values below the 50% MAD threshold. 4. I4, Reduced: applies only a 60% of GROW treatment but using the same frequency with fixed application rates of 6 mm, representing a lower irrigation treatment scenario. 5. I5, NON: non irrigated/rainfe d plots. These plots were used as a control for comparison. These plots along with all other plots received irrigation after granular fertilizer applications to ensure that granular fertilizer was incorporated into the soil. Sentek 90 cm MTS multi sensor c apacitance probes (Sentek Pty Ltd 2003) were installed in I1, I3 and I5 plots located in blocks 2, 3 and 4 for monitoring. However, only the SMS method used the volumetric water content to schedule irrigation.

PAGE 154

154 The i rrigation system consisted of a two span Valley Linear End feed 8000 (Valmont Industries 2015) , Valley, NE) with a Variable Rate (VRI) package. This system has the capacity of irrigating a field area of 6. 7 ha using a flow of 68 m 3 /hr (10 m 3 /hr/ha). Senninger (Senninger Irrigation, Inc., Clermont, FL) LDN UP3 Flat Medium Grove ¾ M NPT nozzles were attached to drops at a 3 m sprinkler spacing. Valley 69 kPa pressure regulators (PSR 2 10 10(PSI) ¾ F NPT) were installed on each drop to maintain a constant flowrate. The VRI system was used to irrigate different amounts to the crop based on the corresponding treatments. All treatments received irrigation after granular fertilizer applications to provide adequate moisture conditions for nutrient uptake. N itrogen F ertility R ates Three N fertility rates were evaluated: F1 = 336 kg/ha; a high scenario commonly applied in corn production, F2 = 247 kg/ha; a medium rate slightly above the UF/IFAS recommended N rate (235 kg N/ha) (Mylavarapu et al. 2015) , and F3 = 157 kg/ha; a low N scenario. The low and high N rates deviated ±36.4% from the medium rate. Table 3 3 summarizes all fertilizer application across the three growing seaso ns. The application of N fertilizer was performed following GDDs during the three growing seasons and following the BMP protocol (FDACS 2015a) . Generally, fertilization consisted of a starter, two granular and four liquid sidedress applications. Ideally applied before tasseling, for optimum uptake. Following the BMP manual, depending upon the stage of crop development, a single N and or K application might be applied if rainfall exceeds 76 mm in three days or 102 mm in seve n days (FDACS 2015a) .

PAGE 155

155 A pre plant soil sampling analysis was performed to determine initial soil conditions each year. Corn fertilization for phosphorus, potassium and micronutrients was adjusted based on soil testing resu lts performed prior each crop season. At planting an initial liquid application of 34 kg/ha of 16 16 0 was applied in the row across all treatments. The N fertility rates started 14 DAP with the first granular application (at 347, 262 and 359 cumulative GD Ds, at V3 corn growth stage across each respective year). Total N applied on the low, medium and high rates was: 9, 25 and 34 kg N/ha, respectively using a 33 0 0 fertilizer (16.49% ammoniacal N and 16.51% Nitrate N). The second granular application (33 0 0) took place near V6 corn growth stage at 651, 663 and 617 GDDs in respective 2015 17 growing seasons. A total of 11, 27 and 45 kg N/ha was applied on the low, medium and high rates, respectively. Afterwards, split liquid sidedress applications (28 0 0) w ere applied between V8 and VT corn growth stages. At each liquid sidedress application a total of 26, 40 and 56 kg N/ha were applied on the low, medium and high rates, respectively (Table 3 3). Heavy rainfall events occurred 2 April 2016 (totaling 76 mm), thus, following the BMP protocol (FDACS 2015a) , an application of 34 kg N/ha (21 0 0 24S, ammonium sulfate) was performed on 19 April 2016. In 2017, a few days after the starter application a heavy rainfall occurred on Apr il 4 (95 mm), hence a supplemental application of 17 kg N/ha was performed to compensate for possible N leaching. Although extra N was applied, the fertility rates were consistent as high, medium and low (F1, F2 and F3 N rates) (Table 3 3). Phosphorus and potassium applications were performed based on soil analysis results and equally applied across all fertility rates as required. In all years, 34 kg P/ha

PAGE 156

156 was applied at planting (16 16 0). In 2015, based on analysis results, 84 kg P/ha of 0 46 0 (Triple Su perphosphate) was applied during the first granular application; whereas, no phosphorous was required in 2016 nor in 2017. In terms of potassium, 110 kg K/ha and 86 kg K/ha of 0 0 60 was applied during the first and second granular applications in 2015. In 2016, 40 kg K/ha of 0 0 60 was applied in both granular applications; whereas, 98 and 73 kg K/ha of 0 0 60 was applied in the first and second granular applications in 2017. In addition, a supplemental application of 24 kg K/ha of K Mag (0 0 22) was added to the second granular K applications to address sulfur and magnesium concerns in the crop in 2017 (Table 3 3). Biomass S ampling and N A nalysis Tissue samples were collected during key corn growth periods (i.e. two sampling during vegetative stages in the early season, then at 80% tasseling, at dough stage, and mature stage close to harvest. In 2015, tissue samplings took place on 15 April, 11 May, 12 June, 10 July, and 19 August. In 2016, samplings were performed on 8 April, 10 June, 5 July, and 2 August; whereas in 2017, samplings were done on 4 April, 5 May, 8 June, 28 June, and 7 August. On average across the seasons, samplings were performed at 314, 920, 1,843, 2,617 and 3,786 GDDs (i.e. on average at 14, 42, 76, 101 and 137 DAP). Tissue samples were collected from a 1 meter linear section within a row , representative of the plot . The total number of plants were counted. Afterwards, plants were sectioned into stalks, leaves, and ears (when present). Additional parameters measured included: number of le aves per plant and number of ears. All samples were placed in ovens and dried in 60°C for 72 hours. Dry weight was recorded. Dry corn samples (from plant sections) were chopped with a chipper machine prior to grinding.

PAGE 157

157 Afterwards, samples were ground in a Wiley mill using 2 mm screen and mixed well before taking a subsample for the lab analysis. Samples were analyzed for Total Kjeldahl Nitrogen (TKN). For nitrogen analysis, samples were digested using a modification of the aluminum block digestion procedure of (Gallaher et al. 1975) Sample weight was 0.25 g, catalyst used was 1.5 g of 9:1 K 2 SO 4 :CuSO 4 , and digestion was conducted for at least 4h at 375°C using 6 ml of H 2 SO 4 and 2 ml H 2 O 2 . Nitrogen in the digestate wa s determined by semi automated colorimetry (Hambleton 1977) . Nitrogen uptake (kg/ha) of the different plant tissues was calculated using the % N concentrations obtained from TKN laboratory analysis. During the thr ee years of evaluation, samples were taken on GROW, SMS and NON treatments across all fertility rates during the first biomass sampling. However, in season biomass was collected only on the SMS irrigation treatment at subsequent sampling events. At harve st, the final sampling was performed in all plots (i.e. all irrigation and fertility treatments). Nitrogen Use Efficiency C alculations Additional knowledge about N use by crop plants is a strategy to help improve N fertilizer management (Below 2002) . The measurement of how N is utilized by the plants is commonly called nitrogen use efficiency (NUE) (Garnett et al. 2015) . However, several definitions and formulas have been used to describe nutrient use efficiency in plants (Good et al. 2004) . For the analysis of the N rates evaluated in the field experiment, NUE was calculated by using two main approaches: (i) NUptE: the efficiency of absorption (i.e. uptake) and (ii) NUEgrain: the efficiency of utilization (i.e. the efficiency with which the N absorbed is utilized to produce grain). These two NUE approaches were selected because data was collected in season and end season,

PAGE 158

158 allowing the estimatio n of both NUE indices. The NUptE corresponded to the total aboveground N uptake by the plants per sampling event over the total N applied by a particular sampling date . This approach provide d an estimation of the N uptake from the soil as the fertilizer wa s applied . This calculation was performed only in the SMS treatment across all fertility rates (low, medium and high) during the growing season. (3 4 ) Where: NUptE = N uptake efficiency (in season). This index measures efficiency of uptake of nitrogen into plant (Moll et al. 1982) . Nuptake i = total N uptake collected in sampling event (i) (kg N/ha). Napplied i = total N applied by sampling event (i) (kg N/ha). i = sampling event number during the growing season. NUEgrain corresponds final corn grain weight over total N supplied per season. This approach allow ed the estimation of the final N in the corn grain at the end of the season ( while evaluating the efficiency of all other processes : N uptake, N partitioning and N remobilization). This index was calculated in all irrigation and N rate treat ments. (3 5 ) Where: NUEgrain = nitrogen use efficiency (grain). Reflects increased yield per unit applied nitrogen (Moll et al. 1982) . Shelled corn weight = shelled corn yield (adj. 10.5% standar d marketable moisture, kg/ha). Total N applied = total N applied during corn season (kg N/ha). Harvest Corn harvest took place on 18 August 2015, 3 August 2016 and 16 August 2017 at NFREC SV , Live Oak, FL. Yield determination was performed mostly on the 6th and the 7th planting rows starting three meters inside each plot to avoid border effects.

PAGE 159

159 Representative rows were selected for yield determination when rows 6th and 7th were impacted by l ow seed density at planting (2017). A total length of six meters in each row was harvested for data analysis. Before harvesting, all plants within the 6.1 m on the 6th and 7th rows were counted. Immediately after, ears were hand harvested and placed in lab eled bags. Bags were taken into a storage facility for processing. Ears were counted and after removing the husk, total ear weight was recorded. Total ears per plot were shelled using a manual grinder and shelled corn was placed in sacks. Finally, three re plicate grain moisture measurements were taken from each plot for final average moisture calculation. The moisture content percentage was determined using a moisture meter (John Deere Grain Moisture Tester SW08120). Final corn yield was calculated to meet the standards of 15.5% market moisture. WUE Calcul ations Water use efficiencies can be calculated in different terms depending on the factors to be evaluated. From a production standpoint , water use efficiency can be generally defined as (Viets 1962) : (3 6 ) WUE has units of ( kg/m 3 ) in a unit water volume basis if crop yield is expressed in g/m 2 a nd water used is expressed in mm, or (g/kg) when expressed on a unit water mass basis. Although this equation is useful in several analysis, it does not consider irrigation. Therefore, other water efficiency indices have been developed (Bos 1980; Bos 1985) . To evaluate the effect of irrigation in terms of crop water productivity, irrigation water use efficiency (IWUE) can be calculated (Bos 1980; Musick and Dusek 1980; Viets 1962) . This index allows to quantify the crop productivity variations with

PAGE 160

160 different irrigation regimes compared to yield obtained under rainfed conditions (Bos 1980; Irmak 2015b) : (3 7 ) Where: IWUE = irrigation water use efficiency (kg/m 3 ) Y = dry grain yield at 15.5% moisture content (g/m 2 ) I i = applied gross irrigation water (mm) Subscript i = irrigation level Subscript r = treatment wi th no seasonal irrigation (rainfed or dry land) Other calculations include the ratio of final grain yield over irrigation applied (WUE i ) and final grain yield over total water applied (WUE t ) (3 8 ) (3 9 ) Where: WUE i = irrigation water use efficiency (kg/m 3 ) . Y = dry grain yield at 15.5% moisture content (g/m 2 ) . I i = applied gross irrigation water (mm) . Subscript i = irrigation level . P growing season = cumulative precipitation occurred during growing season (mm) . In non irrigated or rainfed systems, seasonal precipitation plays a critical role to investigate water use, therefore a precipitation (i.e. rain) use efficiency (PUE) can be calculated. Howev er, this index could be used to quantify WUE of irrigated systems in terms of the irrigated conditions. Growing season precipitation (GRSPUE) corresponds to the total precipitation from emergence to physiological maturity, GRSPUE rainfed consists of the eva luation of the effectiveness of different irrigation treatment regimens over yield in comparison to rainfed yield, accounting for potential increase in irrigated

PAGE 161

161 yield in comparison to the rainfed yield under different water management practices (i.e. irri gation treatments) (Irmak 2015b) . (3 10 ) Another index is the crop water use efficiency. It is the ratio of final grain yield to actual crop evapotranspiration (3 11 ) Where: CWUE = crop water u se efficiency (kg/m 3 ) on a unit water volume basis or in g/kg on a unit water mass basis. Y= grain yield (g/m 2 ) ET a = actual crop evapotranspiration (mm). This was assumed equal for all irrigated treatments (i.e. no water stress) Statistical A nalysis Data was analyzed using the SAS GLIMMIX proc procedure (SAS Institute Inc. 2013) , with irrigation and N fertility rates as main effects while treating the replication and its interactions with class variables as rand om effects. Normality assumptions were met; thus, no data transformation was required. Covariance structures were selected for each response variable using the corrected Akaike information criterion (AICC). Covariance structure (CS) used for response varia bles in this chapter was CHS (Heterogeneous CS). Analysis of Variance (ANOVA) and least squared means (LSM) differences with normal p values were used for multiple comparison with significant differences at the 95% confidence level. Results Weather C ondit ions Corn growing seasons spanned (planting to harvest) April 4 to August 18, March 22 to August 3, and March 21 to August 16 in 2015 17, respectively. Climatic conditions

PAGE 162

162 at the experimental field varied during the three growing seasons under evaluation ( Figure 3 2). In 2016 and 2017, corn growth rate was slower due to early season low temperatures (minimum temperature 1.8 ° C and 5.4 ° C, respectively), causing a delay in biomass formation. In 2015 17 seasons, the cumulative growing degree days were 3934, 368 5 and 3647 GDDs, respectively. Early in the 2015 season , minimum temperatures were higher (9.5 ° C) than subsequent years which resulted in a positive effect on corn growth rate and in GDD accumulation, and therefore in biomass production compared to 2016 an d 2017 seasons (Figure s 3 2 and 3 3 ). In this area, annual precipitation is variable in both magnitude and timing. Cumulative rainfall was 531, 370 and 688 mm during the 2015 17 corn growing seasons, respectively. The 2016 season received about 30% and 46% lower rainfall compared to the 2015 and 2017 seasons, respectively. Nevertheless, rainfall distribution varied significantly during all three growing seasons. In 2015, more frequent and constant rainfall events occurred across the growing season reducing the need for irrigation. In contrast, the magnitude of rainfall varied drastically in 2016 and 2017. Early in the 2016 season, heavy rainfall events (April 2, 76 mm) resulted in N leaching and rainfall contribution was minimal until a few sporadic rainfall events occurred in May and June. In 2017, another high magnitude leaching rainfall event occurred early in the season (April 4, 95 mm). Afterwards, rainfall events were minimal until May. However, during late May (cumulative rainfall 58 mm) and early June (cumulative rainfall 120 mm) heavy rainfall events occurred. Afterwards, more frequent rainfall events occurred in the season (Figure 3 4 ).

PAGE 163

163 Fertil ity In all three growing seasons, fertilizations consisted of one starter, two granular and four liquid sided ress applications performed before tasseling for optimum N uptake and nutrient remobilization. Supplemental fertilizations we re required in 2016 and 2017 (34 and 17 kg N/ha, respectively) due to heavy leaching rainfall events occurring early in the season. Following the BMP manual, depending upon the stage of crop development, a single N and or K application might be applied if rainfall exceeds 76 mm in three days or 102 mm in seven days (FDACS 2015a) . In S eason N itrogen U p take Estimated N uptake was calculated as the total N within the biomass accumulated (i.e. %N * d ry weight). In season N uptake was estimated for the SMS treatment across the N rates during the three corn growing seasons ( Figure 3 5) . In 2015, the l ow, med ium and high N fertility rates applied 157, 247 and 336 kg N/ha , respectively . Early in the growing season , the trend of N uptake was similar across all rates; however, as the growing season continued, this trend diverged especially between the high and lo w N rates. The greatest N uptake in the total aboveground (AG) biomass was observed in the third tissue sampling, which was performed around ~80% tasseling, after all fertilizations were already performed in the field. At this point, about 78%, 70% and 7 4% of the final N uptake in the season was taken up in the low, medium and high rates, respectively ( 159 , 195 , and 215 kg N/ha). However, these values correspond to NuptE of 1.01, 0.79 and 0.64 ratios respect to the N fertilizer applied in the low, medium an d high N rates. Thus, in terms o f NUE, the greater efficiency was achieved by the lowest rate and vice versa. Final N taken up in

PAGE 164

164 the high rate (290 kg/ha) was 4% greater than in the medium rate (278 kg/ha ), and 30% more than the low rate (low = 203 kg/ha) (Figure 3 5). In 2016, d ue to the initial low temperatures at planting and leaching rain early in the season, biomass accumulation and N uptake were slow. D uring the sampling performed at ~80% tasseling, the medium N rate resulted in an 87% and 45% N upta ke increase compared to the low and high rates (low = 93 kg N/ha, medium = 174 and high = 120 kg/ha). A t this sampling date about 38%, 72% and 45% of the total N uptake had occurred, on average by the low, medium, and high N rates, respectively. In 2016, 3 4 kg N/ha additional fertilizer was applied due to early season leaching rain. The highest N uptake interval was observed after all fertilizations were applied at the fourth sampling date (5 July 2016). After this sampling, the uptake rate was largely redu ced across all fertility levels. Final N uptake means were 244, 244 and 267 kg/ha for the low, medium and high rates, respectively; the high rate resulting in 9% greater uptake (Figure 3 5). In 2017, a supplemental fertilizer application added 17 kg N/ha t o each of the evaluated N rates due to leaching rainfall events occurring early in the growing season. The low temperatures at planting plus the leaching event, caused a delay in overall growth. Thus, in general, N uptake showed a linear increase throughou t the season. By the ~80% tasseling sampling, about 55%, 42% and 26% of the total N was taken up by the low, medium, and high rates, respectively. However, as temperature increased, crop growth increased, and final N uptake resulted in 217, 241 and 261 kg/ ha for the low high N rates, respectively. The high rate resulted in 8% and 17% greater N uptake than medium and low rates (Figure 3 5).

PAGE 165

165 In season NUptE ratio s (N removed by the crop and the amount of fertilizer applied) were calculated per sampling ev ent (Table 3 4). Similar NUptE ratios resulted in early crop growth stages across the three N rates (i.e. first sampling event per year). Early in the corn season, most of the vegetative tissues (i.e. leaves and stems) contribute to the total biomass N upt ake. In general, after the third sampling (i.e. ~80% tasseling), the total amount of N fertilizer at each rate has already been applied. Therefore, after this sampling date, any increase in NUE will be determined by a combination of the crop N uptake from the soil, as well as, N partitioning (i.e. deposition of assimilates from photosynthesis to various plant part) and N remobilization (i.e. transport of assimilates which were previously deposited in other plant parts where they can be re utilize d from vege tative tissues. For example, N uptake by the sampling of 12 June 2015 was 159, 195 and 215 kg N/ha for the low, medium and high N rates. By this sampling date, all fertilizer was already applied in all N rates (i.e. 157, 247 and 336 kg N/ha). Therefore, N UptE ratios resulted in 1.01, 0.79, and 0.74 kg N uptake/kg N fertilizer applied, respectively; indicating a higher efficiency in the low N rate compared to the medium and high rates (Table 3 4). In subsequent samplings towards the end of the season, repro ductive structures were present and ha d a large contribution to the total N uptake (i.e. grain N uptake). During the grain filling period, the photosynthate/assimilate (i.e. products of photosynthesis) that is deposited in the grain can be obtained from cu rrent leaf photosynthesis, current non leaf photosynthesis (i.e. stems, panicle parts, tassels) or from the remobilization of assimilates where they can be re utilize d . When partitioning and remobilization rates are high, greater N concentrations were pres ent in the grain.

PAGE 166

166 These processes were observed during mid and late season samplings as a decrease in N concentration in the leaf and stems; whereas, ear N concentration i ncreased. Therefore, NUptE values increase as N partitioning to the grain increases. Subsequent samplings showed increments in N uptake in which the low N rate was constantly higher than the other rates until the end of the season. The highest NUptE ratios were obtained at the final sampling during the three corn seasons. Corresponding NUp tE ratios for the low N rate during 2015 2017 seasons were 1.30, 1.28, 1.25; for the medium N rate were 1.13, 0.87 and 0.90; and for the high N rate were 0.86, 0.72 and 0.74 kg N uptake/kg N fertilizer applied, respectively. Thus, at the low N rate the N u ptake was higher compared to the total N applied through the fertilizations, and vice versa, the high N rate resulted in the lowest NUptE ratio, or the lowest ratio of N uptake versus N fertilizer applied (Table 3 4). These ratios indicate the efficiency o f uptake of N into plant; the higher the value the greater N uptake efficiency and lower the N losses, respect to the amount of N fertilizer applied. (e.g., in general, crops are more efficient at recovering N when the fertilizer N rate is relatively low) (Burns 2004) . Figure 3 6 shows two analyses of total aboveground biomass N uptake: (i) N uptake in vegetative tissues (i.e. stems and leaves) and in reproductive tissues (i.e. grain); and (ii) N uptake through the crop season at different growth stages (V3, V6, VT (tasseling) until final accumulated N uptake at harvest. N uptake was analyzed across low , medium and high N rates during 2015 17 corn growing seasons. The N uptake and partitioning dynamics within the plant ti ssues can be observed in Figure 3 6. The N uptake increases early in the season; increasing N concentration s in vegetative tissues (i.e. leaves and stems), and as reproductive structures are formed,

PAGE 167

167 the greatest increase in N concentration is observed in t he grain, as it is also remobilized from vegetative to reproductive tissues (i.e. N transport from leaves and stems as grain filling stage is reached; reducing N in vegetative tissues). Irrigation Final AG biomass dry weight and N uptake collected from cor n plant sections (i.e. leaf, stem and ear) across irrigation treatments were analyzed at the end of the three corn seasons. Irrigation had a significant effect on final leaf, stem and ear dry weight. By contrast, N fertility rates did not have a significan t effect on the dry weight of any of the corn sections analyzed. Irrigation had a significant effect in final ear and stem N uptake, but not on leaf N uptake. By contrast, N rates resulted in significant effect on final ear and leaf N uptake, but not on fi nal stem N uptake. Significant differences were found across the years of evaluation. Leaf dry weight and N uptake. Irrigation had a significant effect on final leaf dry weight; in contrast, fertility rates did not have a n effect on final dry weight. No in teractions on final dry weight were found. Irrigated treatment leaf dry weight means were higher compared to the non irrigated treatment in all years (Figure 3 7). Final leaf dry weight for irrigated treatments ranged from 3,268 to 3,010 kg/ha, from 2,656 to 2,514 kg/ha and from 2,635 to 2,343 kg/ha during 2015, 2016 and 2017 corn seasons, respectively. In comparison, final lower leaf dry weights occurred in the non irrigated treatment: 2,462 kg/ha, 1,953 kg/ha and 1,734 kg/ha in 2015 17 seasons, respective ly. Differences were found among the years of evaluation, in which leaf dry weight means in 2015 were higher (3,002 kg/ha) than in 2016 and in 2017 (2,476 and 2,372 kg/ha), respectively.

PAGE 168

168 Fertility rates and year resulted in effects on leaf N uptake. In con trast, the irrigation treatments had no effect on final leaf N uptake ; n o interactions between what? were found. D ifferences in leaf N uptake were present across fertility rates between years; however, within each year, leaf N uptake did not differ among N fertility rates. Leaf N uptake means within each corn season ranged from 21.0 to 22.6 kg/ha in 2015, from 25.5 to 30.3 kg/ha in 2016; and from 20.8 to 26.0 kg/ha in 2017. Across the years of evaluation, leaf N uptake was higher in F1 2016 compared to F3 2 015 and 2017 (30.3 kg/ha vs. 21.0 and 20.8 kg/ha, respectively) (Figure 3 7). Stem dry weight and N uptake. A n interaction by irrigation and year was found in final stem dry weight. Fertility rates had no effect on final stem dry weight. Irrigated treatmen ts resulted in higher dry stem weight compared to the non irrigated (NON) treatment in all years; however, differences were not found between the irrigated treatments within crop seasons. Stem dry weight means ranged from 4,940 4,202 kg/ha, 4,104 3,635 kg/ ha and 4,152 3,684 kg/ha in 2015, 2016 and 2017, respectively. In contrast, the I5 stem dry weight means were 3,182, 2,325 and 1,272 kg/ha in 2015 17 seasons, respectively (Figure 3 8). In 2017, the NON irrigated treatment stem N uptake was the lowest amon g seasons and treatments (Figure 3 8). Irrigation had an effect on stem N uptake in 2017 only, where the SWB and SMS N uptake was higher than the N uptake in the NON treatment (26.7 and 27.8 kg/ha vs 12.6 kg/ha). No differences were found in stem N uptake across irrigation treatments or the non irrigated treatment in 2015 and 2016 seasons. N fertility rates did not have a n effect on final N uptake in stems in any of the corn growing seasons (Figure 3 9).

PAGE 169

169 Ear dry weight and N uptake. Irrigation had a n effect on final ear dry weight in each corn growing season; however, differences were found only between the irrigation treatments versus the non irrigated, which resulted in lower ear dry weight during all years of evaluation. Fertility rates did not result in effects on final ear dry weight. No interactions among effects were found. Irrigated treatments final ear dry weight ranged from 15,942 18,183 kg/ha in 2015. D ifferences in ear dry weight means were found between the SMS and Reduced treatments (17,488 and 18,182 kg/ha, respectively) compared to the NON irrigated (12,417 kg/ha) only. In 2016, final ear dry weight across irrigated treatments ranged from 16,312 to 16,749 kg/ha, without differences among them, only compared to the NON treatment, which resulted in a lower ear dry weight (10,921 kg/ha). Similar results occurred in 2017 with the NON irrigated treatment resulting in lower ear dry weight in comparison to the irrigated treatments means (NON = 7,360 kg/ha vs. irrigated treatments range: 14,513 15,518 k g/ha). In contrast, N fertility rates did not result in a n effect on final ear dry weight within corn growing seasons. Final ear dry weight means across N fertility rates ranged from 15,631 to 16,560 kg/ha in 2015, from 15,091 to 15,831 kg/ha in 2016; and from 12,846 to 13,976 kg/ha in 2017 (Figure 3 10). Irrigation treatments and year had a n effect on ear N uptake. Irrigated treatments resulted in higher N uptake compared to the NON treatment in all corn seasons. GROW, SWB, SMS and Reduced, ranging from 17 9 207 kg/ha. No differences were found across irrigated treatments; however, the SMS and Reduced treatments resulted in higher N uptake (207 and 205 kg/ha, respectively) compared to the NON treatment (154 kg/ha). In 2016, no differences were found in N upt ake across irrigated treatments,

PAGE 170

170 which ranged from 178 to 195 kg/ha. Differences in N uptake were found in comparison to the non irrigated treatment only (136 kg/ha). In 2017, similar results were observed, only the NON treatment (97 kg/ha) resulted in sta tistically lower N uptake compared to the irrigated treatments (ranging from 174 190 kg/ha) (Figure 3 10). There was no effect of the N fertility rates evaluated on final N uptake within each corn growing season. In 2015, ear N uptake means were 179, 187 a nd 195 kg/ha for the low, medium and high N rates, respectively. In 2016 ear N uptake means were 175, 174 and 186 kg/ha, respectively, and in 2017, these means were 152, 167 and 176 kg/ha, respectively. No differences were found across fertility rates with in each corn season (Figure 3 10). Overall higher N uptake was found in plant tissues receiving irrigation during all growing seasons. Thus, the non irrigated treatment resulted in lower final dry weight in all the different plant tissues evaluated, except in 2015, year in which NON stem dry weight did not differ from the GROW treatment. Total aboveground final biomass. Final aboveground (AG) biomass across all irrigation treatments and fertility rates was collected 19 August 2015, 2 August 2016 and 7 Augus t 2017 (i.e. last tissue sampling performed at physiological maturity, close to harvest). The results showed no interactions among main effects; therefore, results are shown separately by irrigation and by N fertility rates (Figure 3 11). Total AG biomass was composed of the sum of corn leaves, stems and ears dry biomass. During the three corn growing seasons, only irrigation had a n effect on final biomass. Fertility rates did not have a n effect on final biomass. No differences in final biomass were found b etween irrigated treatments, only versus the NON treatment

PAGE 171

171 during the three seasons under evaluation. Final AG biomass means for the GROW, SWB, SMS, Reduced and NON were: 23,342, 23,604, 25,696, 26,077 and 18,061 kg/ha in 2015 respectively; 23,277, 22,931, 22,770, 23,307 and 15,199 kg/ha, respectively in 2016; and 21,300, 22,156, 21,213, 20,768 and 10,366 kg/ha respectively in 2017 (Figure 3 11). O nly in 2015, the GROW treatment final biomass did not differ from the accumulated biomass in the NON treatment (GROW = 23,342 and NON = 18,061 kg/ha). During the 2017 season, the NON irrigated treatment resulted in the lowest mean biomass compared to all treatments and years (NON = 10,366 kg/ha). There were no d ifferences between the N fertility rates evaluated wi thin each corn season. Final biomass means for the low, medium and high N rates were: 23,007, 23,093, 23,968 kg/ha in 2015; 21,456, 21,106, 21,928 kg/ha in 2016 and 18,374, 19,209, 19,898 in 2017. D ifferences were found between years, where N rates applied in 2015 resulted in higher mean AG biomass than corresponding rates in 2017 (Figure 3 11). Total aboveground N uptake. Irrigation and fertility rates resulted in effects on total N uptake means (i.e. N uptake from aboveground plant sections) ; n o interacti ons were found among main effects. During 2015, no differences were found between irrigation treatments. Total N uptake means were 216, 236, 257, 246 and 190 kg N/ha in GROW, SWB, SMS, Reduced and NON treatments, respectively. In 2016, only the NON treatme nt resulted in lower N uptake (181 kg N/ha) than SMS (252 kg N/ha) and Reduced (255 kg N/ha); however, it did not differ from the N uptake in the GROW and SWB treatments (231 and 246 kg N/ha, respectively). In 2017, final N uptake means

PAGE 172

172 resulted in 226, 24 2, 235, 221 and 129 kg N/ha for the GROW, SWB, SMS, Reduced and NON, respectively. D ifferences were found only between the NON irrigated compared to the irrigated treatments (Figure 3 11). End Season N itrogen Use E fficiency (Nuegrain) The NUEgrain index reflects increased yield per unit applied N (Moll et al. 1982) . This index calculates the ratio of final grain yield (kg/ha) by the total N in fertilizer applied (kg N/ha) during the growing season (Table 3 5). Final N UEgrain resulted in a n interaction between irrigation and fertility rates, as well as an interaction between irrigation treatments and years of evaluation. For the interaction between irrigation and fertility rates, the low N rate across all irrigated trea tments (GROW Reduced) resulted in the highest NUEgrain values (NUEgrain = 70.2, 65.1, 65.5 and 65.9 for I1 I4 low N rate, respectively). The medium N rate was lower than the low N rate in GROW, SMS and Reduced treatments (NUEgrain = 46.1, 46.1, 48.1, acc ordingly). However, the SWB medium N rate resulted in no differences in NUEgrain compared to the GROW, SMS and Reduced high N rates, nor to the NUE obtained in NON low N rate. The lowest NUEgrain resulted in the NON medium and high N rates (Table 3 5) . A n interaction between irrigation and year was found. In all years of evaluation, the NON treatment resulted in a lower NUEgrain compared to irrigated treatments (GROW Reduced). Except in 2015, where SWB NUEgrain values did not differ from the NON trea tment (NUEgrain SWB = 49.6 and NUEgrain NON = 39.1) Average NUEgrain values resulted in the irrigated treatments were 52.6, 45.7 and 50.7 in 2015, 2016 and 2017, respectively; whereas the NON treatment NUEgrain values were 39.0, 32.3 and 23.8 during 2015 17 seasons, respectively.

PAGE 173

173 Yield During the three growing seasons evaluated, there were no interactions between irrigation treatments and fertility rates for final grain yield. Therefore, final results are described separately by irrigation and fertility. The effect of irrigation strategies and N fertility rates over corn grain yield during 2015 2017 corn growing seasons are shown in Figure 3 12. A 100 kernel weight was measured as second response variable for final yield (Figures 3 13 and 3 14). Irrigation vs g rain y ield Final grain yields did not differ across irrigated treatments , with differences only between irrigated treatments and the non irrigated treatment. An exception was in 2015 when the SWB treatment resulted in lower yield (11,201 kg/ha) compared t o the other irrigated treatments, but not different than the NON irrigated treatment (8,993 kg/ha) (Figure 3 12) . In the 2015 season, mean grain yields resulted in 12,105, 11, 20 1, 12,011, 12,638 and 8, 9 93 kg/ha for the GROW, SWB, SMS, Reduced and NON treat ments, respectively. In comparison, cumulative irrigation was 320, 185, 151, 211 and 15 mm for the GROW, SWB, SMS, Reduced and NON treatments, respectively. The GROW treatment cumulative irrigation was 42%, 53%, 34% and 95% higher than the SWB, SMS, Reduce d and NON treatments. Irrigation applications during corn reproductive stages are essential for final grain yield; however, rainfall played an important role during these stages susceptible to water stress. Due to rainfall contribution (cum ulative rainfall = 556 mm), the NON irrigated treatment was less severely impacted; however, it resulted in lower yield compared to the irrigated treatments , except for SWB, which yield

PAGE 174

174 did not differ. Total water savings achieved were: 42%, 53% and 34% by SWB, SMS and Re duced irrigation strategies compared to GROW, respectively (Figure 3 12) . The 2016 corn season mean yields were: 12,705, 11,554, 11,818, 11,964 and 7,973 kg/ha for the GROW, SWB, SMS, Reduced and NON treatment, respectively. Whereas, cumulative irrigation resulted in: 508, 310, 291, 321 and 25 mm, respectively. GROW treatment irrigation was 39%, 43%, 37% and 95% greater SWB, SMS, Reduced and NON treatment, respectively. As well, this calendar based treatment resulted in 63% greater cumulative irrigation com pared to the amount applied in 2015 mainly due to the low and non uniform distribution rainfall amounts in 2016 (cum rainfall = 371 mm). However, even when rainfall contributions were low, water savings of 39%, 43% and 37% were achieved using the SWB, SMS and Reduced irrigation strategies compared to the GROW treatment (Figure 3 12) . GROW, SWB, SMS, Reduced and NON treatment mean yields in 2017 were: 12,566, 12,740, 12,203, 12,190 and 5,779 kg/ha, respectively. Cumulative irrigation was: 546, 315, 302, 347 and 48 mm. Water savings (compared to GROW treatment) were: 42%, 45%, 36% and 91% achieved by SWB, SMS, Reduced and NON treatment, respectively. Cumulative rainfall totaled 680 mm; however, it was sporadically distributed mostly in large magnitude events ( i.e. rainfall amounts greater than soil water holding capacity) resulting in drainage and potential N leaching. Therefore, there was a high demand for irrigation in 2017 corn growing season (Figure 3 12) . In summary, irrigation ha d a positive effect on fin al corn grain yield. All irrigated treatments resulted in higher mean yield s than the non irrigated treatment, except in 2015, in which the NON treatment did not differ from SWB final yield (Figure 3 12) .

PAGE 175

175 Fertility rates vs g rain y ield The effect of N rate s over final grain yield is shown in Figure 3 10. In 2015, differences in yield were not found between high and medium fertility rates (336 and 247 kg N/ha), or medium and low rates (247 and 157 kg N/ha); however, the high N rate mean yield (12,314 kg/ha) was higher than the mean yield at the low N rate (10,534 kg/ha) (Figure 3 12). Final mean corn yields in 2016 resulted in 11,487, 11,285 and 10,839 kg/ha for the high, medium and low N rates (370, 280 and 191 kg N/ha). No difference in yield was found betw een fertility treatments (Figure 3 12). In the 2017 growing season, mean yields resulted in 11,603, 10,979 and 10,706 kg/ha for the high, medium and low N rates (353, 263 and 174 kg N/ha). Only the low N rate mean yield was lower than the high and medium N rates mean yields (Figure 3 12). The 100 kernel weight showed effects of irrigation and year. Therefore, data is shown separate d by corresponding main effects. In terms of irrigation, t he NON treatment resulted in statistically lower 100 kernel weight mea n (28.7 g) compared to the irrigated treatments (33.6, 32.4, 32.5, 33.1 g for GROW Reduced, respectively) (Figure 3 13 ). Fertility rate means for low, medium and high were: 31. 3 , 33. 2 and 3 3 . 9 g respectively in 2015, 32. 8, 33. 6 and 34. 5 g in 2016 and, 29 .4 , 29.8 and 30.1 g in 2017 (Figure 3 14). L ower 100 kernel weight resulted only in 2017 compared to 2015 and 2016 (Figure s 3 13 and 3 14). Water Use Efficiency and O ther I ndices Although many definitions of water use efficiency (WUE) have been used in the past (Bos 1980; Howell 2001) , this section describes a few indices that evaluate the irrigation effectiveness in terms of final grain yield. Rainfall occurring during the growing

PAGE 176

176 season strongly i nfluences WUE and potential yields across different crops (Turner 2004) . As observed in Figures 3 2 and 3 4, daily rainfall amounts and distribution varied across all three corn growing seasons; however, overall most of the rainfall occurred during mid or late season (June August) (Figure 3 2). Cumulative actual crop evapotranspiration (ET a ) during 2015 17 corn seasons was: 495, 494 and 479 mm, assumed to be equal for all irrigated treatments (i.e. I1 I4 no water str ess). ET a was used in the calculation of crop water use efficiency (CWUE), assuming no water stress in all irrigated treatments. Corn usually reaches physiological maturity (i.e. crop growth stage at which maximum kernel weight is achieved (Daynard and Duncan 1969) by the end of July/early August, therefore rainfall amounts occurring after this growth stage can be considered inefficient, since most of the water uptake required for final grain yield occurs prior physi ological maturity. The effectiveness of irrigation strategies were evaluated through IWUE, WUE i , WUE t , GRSPUE and CWUE indices described as following. IWUE . This index allows to quantify the crop productivity variations with different irrigation regimes co mpared to yield obtained under rainfed conditions (Bos 1980; Irmak 2015). Therefore, is the ratio of irrigated yield compared to yield obtained under rainfed conditions (i.e. NON irrigated treatment yield) divided by the irrigation supply. Irrigation and y ear resulted as significant effects on IWUE. However, no statistical differences were found across irrigated treatments (I1 I4) in 2015 nor 2016 (IWUE mean values = 1.47 and 1.01 kg/m 3 in 2015 and 2016, respectively). In 2017, the highest IWUE resulted in the SWB and SMS treatments (IWUE = 2.21 and 2.13 kg/m 3 , respectively).

PAGE 177

177 Therefore, rainfall contributions played an important factor in this index. Cumulative rainfall amounts during the 2015 17 growing were 556, 370 and 673 mm (Table 3 6). During 2015, th e amounts and distribution were optimum for growth and development, reducing the water stress in the non irrigated plants and achieving high yields. Therefore, overall the increase in yield due to irrigation efficiency (i.e. yield above the obtained by the non irrigated treatment), was low, especially in treatments applying large irrigation amounts. Although the results showed no statistical differences across the irrigation treatments, it is evident that the GROW irrigation efficiency in 2015 was lower (0. 95 kg/m 3 ) compared to SWB, SMS and Reduced (IWUE = 1.19, 2.00 and 1.73 kg/m 3 ). The greatest difference was observed versus the SMS treatment. Water use efficiency increase almost double in the SMS (IWUE SMS = 2.00 vs. IWUE GROW = 0.95) applying ~53% less wat er than the GROW in 2015 while achieving same statistical yields (Table 3 6). In comparison, cumulative rainfall during the 2016 growing season was 370 mm (33% and 45% less than 2015 and 2017 rainfall). Therefore, irrigation requirement was almost double t han in 2015 and 2017 to achieve similar yields. No significant differences in IWUE were found between irrigated treatments (I1 I4) (Table 3 6). During the 2017 growing season, cumulative rainfall was 673 mm; however, temporal and spatial rainfall distribut ion caused an increase in irrigation requirement across all treatments. Nevertheless, the GROW treatment resulted in statistically lower IWUE index compared to SWB, SMS and Reduced treatments (1.25 kg/m 3 vs. 2.21, 2.13, 1.85 kg/m 3 , respectively). These irr igation strategies resulted in 42%, 45% and

PAGE 178

178 36% water savings, respectively compared to the GROW treatment without statistical reductions in yield (Table 3 6). WUE i . Considers the efficiency of irrigation, as the total irrigation applied over the final gra in yield obtained at each irrigation treatment. The evaluation of this index resulted in significant interaction between irrigation and year, as well as, a significant effect of fertility rates (Tables 3 6 and 3 7). In 2015, the SMS WUE i index was signific antly highest (7.95 kg/m 3 ) compared to all other irrigation treatments. The SWB and Reduced treatments resulted in WUE i of 6.05 and 5.99 kg/m 3 , respectively; statistically higher than the GROW but lower than SMS indices. The GROW treatment resulted in sign ificantly lower WUE i compared to all irrigated treatments (WUE i = 3.78 kg/m 3 ). Thus, greater irrigation applications did not increase final grain yields. In 2016, the GROW treatment WUE i was significantly lower (2.50 kg/m 3 ) than all irrigated treatments ev aluated (SWB, SMS and Reduced WUE i = 3.73, 4.06 and 3.73 kg/m 3 , respectively). However, no significant differences were found between SWB, SMS nor Reduced irrigation strategies. During the 2017 corn season, the lowest WUE i value resulted in the GROW treatm ent (2.30 kg/m 3 ), followed by the Reduced treatment (3.51 kg/m 3 ). The SWB and SMS treatments resulted in significantly higher WUE i values (4.04 for both). Statistical differences were found between all treatments except SWB and SMS. As well, this index ref lects the irrigation contribution to final grain yield. The SWB, SMS and Reduced treatments resulted in no significant differences in yield; however, achieved 42%, 45% and 36% water savings in comparison to the GROW treatment (Table 3 6).

PAGE 179

179 Fertility rates h ad a significant effect on the WUE i index . During the 2015 season, the high fertility rate resulted in a statistically higher WUE i compared to the low N fertility rate (WUE i = 6.34 vs 5.49 kg/m 3 ). The medium rate did not differ from the high nor the low ra tes (WUE i = 6.01 kg/m 3 ). No significant differences were found across fertility rates in 2016 (mean WUE i = 3.50 kg/m 3 ). Same pattern as in 2015 resulted in 2017 season. Only the high N rate resulted in a statistically higher WUE i compared to the low rate. The medium rate did not differ statistically from the high nor the low rates (WUE i low , medium and high = 3. 33 , 3.47 and 3. 62 kg/m 3 , respectively) (Table 3 7). WUE t . Considers the efficiency of total water (i.e. sum of irrigation and rainfall) during the g rowing season over the final grain yield. This is calculated across all treatments evaluated (i.e. including the NON irrigated). All main effects (irrigation, fertility rates and year) were statistically significant. No statistical interactions were found. In 2015, no statistical differences were found across all irrigation treatments (I1 I5 mean WUE t = 1.56 kg/m 3 ). Due to rainfall contributions in 2015, differences across irrigation treatments were not found. Even the NON treatment resulted in WUE t = 1.57 kg/m 3 not statistically different than the irrigated treatments. In 2016, the GROW treatment resulted in a significant lower WUE t compared to the NON treatment (WUE t = 1.45 vs 2.04 kg/m 3 , respectively). All other irrigated treatments did not differ statist ically among them. In 2017, the GROW treatment WUE t (1.43 kg/m 3 ) was significantly lower compared to the SWB treatment WUE t (1.87 kg/m 3 ). No significant differences were found between the other irrigation treatments. A separate analysis was performed using only irrigated treatments (I1 I4, data not shown) to evaluate the performance across irrigation strategies. During 2015 and 2016, only the GROW treatment resulted in a

PAGE 180

180 significantly lower WUE t compared to the SMS treatment (WUE t = 1.38 vs 1.70 kg/m 3 in 2 015 and 1.45 vs 1.79 kg/m 3 in 2016, respectively). In 2017, the GROW treatment resulted in a significantly lower WUE t compared to all irrigated treatments (WUE t GROW = 1.43 kg/m 3 vs SWB, SMS and Reduced 1.87, 1.85 and 1.76 kg/m 3 ) (Table 3 6). GRSPUE . GRSPU E considers the efficiency of irrigation treatments in terms of the use of total growing season rainfall relative to rainfed yield. Therefore, this index evaluates the effectiveness of the irrigation strategies in terms of precipitation water use. No signi ficant differences were found among irrigation treatments across all three corn seasons. However, in general greater values were found in the wettest year (2017 rainfall = 673 mm) compared to 2015 and 2016 (Table 3 6). CWUE . T his index estimates the crop w ater use efficiency as the ratio of final grain yield to actual crop evapotranspiration (ET a ). Cumulative ET a per growing season was assumed to be equal across all irrigated treatments (i.e. non water stressed). Only irrigated treatments (GROW Reduced) w ere analyzed. No statistical differences were found in CWUE between irrigation treatments, and no interactions were found. Only significant differences were seen across the years (Table 3 6). Similar studies evaluated the inter annual variation in various crop productivity indices to irrigation and evapotranspiration and climatic conditions (Irmak 2015a) . The focus was to measure those indices (e.g. CWUE, IWUE, GRSPUE), as well as, to quantify and evaluate the inter ann ual variation of these indices. The project evaluated four irrigation treatments: fully irrigated treatment (FIT), and limited irrigation treatments (75% FIT, 60% FIT and 50% FIT) in silt loam soils. During the six years of study, CWUE values ranged from 1 .21 kg/m 3 for a ra infed treatment to 2.51 kg/m 3 for 60% FIT. The

PAGE 181

181 maximum values obtained by Irmak (2015), are similar to the CWUE values in the experimental field (CWUE means 2015 17 = 2.42, 2.43 and 2.59 kg/m 3 , respectively). Similar values were found by (Payero et al. 2006) ; where CWUE values ranged from 0.91 kg/m 3 under rainfed conditions (dry year) to 2.26 kg/m 3 under well irrigated conditions (dry year). Irmak (2015b) found out that CWUE values were optimized (m aximum values) under 75% FIT, which was most likely attributable to a reduction in soil evaporation while keeping transpiration at a potential rate. This is also comparable with the Reduced treatment, which applies 60% of the GROW treatment. High CWUE valu es were observed during the three seasons. However, all other irrigation treatments resulted in values with no significant differences. Thus, the irrigation applied was providing enough water while maintaining potential transpiration rate. The six year IW UE mean values were 4.01, 4.45, 4.48, and 4.13 kg/m 3 for FIT, 75% FIT, 60% FIT, and 50% FIT, respectively, with SD values of 0.82, 0.84, 0.83, and 1.88 kg/m 3 for the same treatments. The total amounts of irrigation applied in the study described above diff ers from the observations in the field experiment of this chapter. Silt loam soils have greater water holding capacity; thus, lower irrigation amounts are required compared to sandy soils. Total irrigation applied during the years of evaluation ranged from 51 mm to 280 mm; which represent in many years half (or less) of the irrigation applied in the experimental field. Thus, IWUE indices were higher (almost double) compared to the observed values in NFREC SV. In terms of GRSPUE, Irmak (2015b) found the gr eatest values in the wettest year, as well, rainfed yields were mostly benefited precipitation occurring during the growing season. These results coincide with 2015 NON treatment yields, which had no significant differences

PAGE 182

182 compared with SWB final yields, due to rainfall contributions (amounts and distribution) along the growing season (Irmak 2015a) . Ideally, crop water use efficiency should be calculated using crop water use as total transpiration of the plant at each irrigation treatment, using ET a field measurements for the calculation. This calculation would consider other factors from the soil water balance equation (e.g. water losses in drainage/deep percolation) after irrigation applications events, providing a be tter estimation of the efficiency of the irrigation treatments. Summary and Conclusions In 2015, the irrigation strategies evaluated (i.e. SWB, SMS and Reduced) resulted in total water savings of 42%, 53%, 34% compared to the GROW treatment, which mimics t ypical irrigation practices in corn. Reducing the irrigation amounts did not have a negative impact on yield. No significant differences in yield were found between the irrigated treatments, only the SWB treatment resulted in statistically lower yields. I n 2016, the SWB, SMS and Reduced treatment achieved 39%, 43% and 37% water savings vs. GROW treatment. The difference between years was attributed to the variation in rainfall amounts and distribution patterns between 2015 and 2016. Greater amounts of irri gation were required in 2016; however, none of the irrigation alternatives resulted in negative impacts on yield. Average yields did not differ statistically compared to the GROW treatment. Following a rate close to the UF/IFAS corn N fertilization recomme ndation (F2 = 247 kg N/ha) resulted in average yields with no statistical difference than average yield obtained with high N applications (F1 = 336 kg N/ha) in the 2015 and 2017 corn growing seasons. Thus, same yield could be achieved following the medium rate while reducing

PAGE 183

183 N fertilizer applications by 27%. In 2016, no statistical differences were found between the three N fertility rates evaluated. The implementation of BMPs focused on different irrigation strategies (i.e. SWB, SMS and Reduced) resulted in yields without statistical differences than the conventional irrigation practices and with substantial water savings in the three years of evaluation. The implementation of a soil water balance (SWB), soil moisture sensors (SMS), and or, a reduced strat egy (Reduced) achieved a total water savings of: 42%, 53% and 34% in 2015, 39%, 43% and 37% in 2016; and 42%, 45% and 36%, respectively in 2017 compared to conventional practices (GROW).Therefore, growers could potentially save water and achieve same yield s as conventional practices, when implementing these irrigation scheduling strategies and tools. Irrigation had a significant effect on the final biomass and N uptake in corn stems and ears during the years of evaluation. No significant differences were fo und across irrigated treatments (GROW Reduced) only versus the non irrigated treatment, which resulted in a significantly lower biomass and N uptake. In contrast, fertility N rates did not have a significant effect on final biomass nor N uptake. Thus, lowe r applications (e.g. medium rate = 247 kg N/ha) resulted in no differences in biomass, N uptake nor final grain yield, when using any of the irrigation strategies proposed (SWB, SMS or Reduced) compared to conventional practices. The total ear N uptake ove r total N biomass ratio was on average 0.81, 0.77 and 0.78 in 2015 17 corn seasons. At harvest, only the ear N is removed from the field. Stems, leaves and roots are chopped and left aboveground after harvest. The N contained in these plant sections corres ponds to about 20%. This is an important

PAGE 184

184 amount of N that generally is not considered in the fertilization applications of the following crop season and is left in the fields. During the fallow period (off season), this N is potentially leached by excess p recipitation. Knowing that irrigation has the major effect on final biomass and yield, but no differences were found among the irrigated treatments, leads an important consideration on the total inputs applied for crop production. Final results showed that high irrigation and N amounts eventually leave the rootzone, which is further intensified after heavy rainfall events. Thus, careful irrigation and N management must be considered to avoid N leaching. Due to the high spatial and temporal rainfall variabi lity in Florida, irrigation scheduling is a difficult task for growers. Nevertheless, the proposed irrigation strategies can serve as tools to achieve water savings. If these are applied on the right timing and amounts, reductions on N leaching could also be achieved. As well, main results showed that a reduction in 27% on conventional fertilization practices can be implemented without impacting corn yield when following close to UF/IFAS fertilizer recommendations. Thus, further reductions in potential N le aching can be achieved. No statistical differences in N uptake across N fertility rates provides information about the potential N uptake in the different plant tissues as it reaches a plateau. Thus, applications that result in higher rates than the N upta ke, will result in N losses. If potential losses are avoided, these will be converted into economic savings, allowing

PAGE 185

185 Table 3 1. Taxonomic classification of predominant soils in NFREC SV, Live Oak, Florid a (Soil Survey Staff 1999; Soil Survey Staff 2006) . Soil name Family or higher taxonomic classification Albany Loamy, siliceous, subactive, thermic Grossarenic Paleudults Alpin Thermi c, coated Lamellic Quartzipsamments Blanton Loamy, siliceous, semiactive, thermic Grossarenic Paleudults Chipley Thermic, coated Aquic Quartzipsamments Foxworth Thermic, coated Typic Quartzipsamments Hurricane Sandy, siliceous, thermic Oxyaquic Alortho ds

PAGE 186

186 Table 3 2. Crop coefficient (K c ) values for maize used to calculate ET C for treatments under non water stress conditions and for schedule irrigation in the SWB treatment. DAP Root Depth K c .1 (mm) 2 99 0.25 6 150 0.25 10 201 0.41 14 249 0. 57 18 300 0.73 22 351 0.89 26 399 1.05 30 450 1.05 34 500 1.05 38 551 1.05 42 600 1.05

PAGE 187

187 Table 3 3. Nitrogen fertilization rates (high, medium and low) dates, amounts and types applied during corn growing seasons 2015 2017 at NFREC SV. Year Date Fertilizer Composition Nutrient N fertility rates (kg/ha) 1 Low Medium High 2015 03 Apr Planting day (16 16 0) N 34 34 34 P 34 34 34 K 0 0 0 17 Apr 1 st Granular (33 0 0) N 9 25 34 (0 46 0) P 84 84 84 (0 0 60) K 110 110 110 01 May 2 nd Granular (33 0 0) N 11 27 45 (0 46 0) P 5 0 50 50 (0 0 60) K 86 86 86 08 May 1 st Liquid Sidress (28 0 0) N 26 40 56 15 May 2 nd Liquid Sidress (28 0 0) N 26 40 56 22 May 3 rd Liquid Sidress (28 0 0) N 26 40 56 29 May 4 th Liquid Sidress (28 0 0) N 26 40 56 TOTAL N applied N 157 247 336 2016 22 Mar Planting day (16 16 0) N 34 34 34 P 34 34 34 K 0 0 0 05 Apr 1 st Granular (33 0 0) N 9 25 34 (0 46 0) P 0 0 0 (0 0 60) K 39 39 39 19 Apr Supplemental Granular 2 (21 0 0 24S) N 30 30 30 27 Apr 2 nd Granular (33 0 0) N 11 27 45 (0 46 0) P 0 0 0 (0 0 60) K 39 39 39 03 May 1 st Liquid Sidress (28 0 0) N 26 40 56 09 May 2 nd Liquid Sidress (28 0 0) N 26 40 56 13 May 3 rd Liquid Sidress (28 0 0) N 26 40 56 19 May 4 th Liquid Sidre ss (28 0 0) N 26 40 56

PAGE 188

188 Table 3 3. Continued Year Date Fertilizer Composition Nutrient N fertility rates (kg/ha) 1 Low Medium High 2017 21 Mar Planting day (16 16 0) N 34 34 34 P 34 34 34 K 0 0 0 06 Apr 1 st Granular (33 0 0) N 9 2 5 34 (0 46 0) P 0 0 0 (0 0 60) K 98 98 98 06 Apr Supplemental Granular 2 (21 0 0) N 17 17 17 20 Apr 2 nd Granular (33 0 0) N 11 27 45 (0 46 0) P 0 0 0 (0 0 60) K 73 73 73 20 Apr K Mag Application (0 0 22) K Mag 25 25 25 27 Apr 1 st Liquid Sid ed ress (28 0 0) N 26 40 56 03 May 2 nd Liquid Sid ed ress (28 0 0) N 26 40 56 09 May 3 rd Liquid Sid ed ress (28 0 0) N 26 40 56 15 May 4 th Liquid Sid ed ress (28 0 0) N 26 40 56 Total N applied N 174 263 353 1 N rates: high=336 kg N/ha; medium: 247 kg N/ha; low 157 kg N/ha. 2,3 Supplemental N application (34 and 17 kg N/ha) due to leaching rain events modified all N fertility rates in 2016 and 2017 gr owing seasons, respectively.

PAGE 189

189 Table 3 4. Nitrogen use efficiency (NUE) calcul ated across low, medium and high N rates during corn growing seasons 2015, 2016 and 2017. Sampling date Total aboveground N uptake (kg/ha) NU pt E 1 (kg N uptake/kg N applied) 2015 Low Medium High Low Medium High 04/15/2015 0.7 0.7 0.8 0.02 0.02 0.02 05/1 1/2015 21.5 32.7 39.2 0.27 0.26 0.23 06/12/2015 158.9 194.6 214.7 1.01 0.79 0.64 07/10/2015 175.9 200.3 259.0 1.12 0.81 0.77 08/19/2015 * 203.3 278.3 290.1 1.30 1.13 0.86 2016 04/08/2016 0.7 0.8 0.6 0.02 0.01 0.01 06/10/2016 92.5 174.5 119.6 0.4 9 0.62 0.32 07/05/2016 224.0 261.7 233.0 1.18 0.93 0.63 08/02/2016 * 244.3 243.7 267.4 1.28 0.87 0.72 2017 04/04/2017 1.0 1.3 1.3 0.03 0.04 0.04 05/05/2017 51.3 61.2 59.3 0.42 0.34 0.25 06/08/2017 119.9 100.9 67.2 0.69 0.38 0.19 06/28/2017 152. 6 166.4 188.1 0.88 0.63 0.53 08/07/2017 * 216.8 241.1 261.4 1.25 0.92 0.74 1 NU pt E calculated as the ratio of N uptake in total aboveground biomass (leaves, stems and ears) and N in fertilizer applied by each sampling date through the growing season. * Co rresponds to the final NUptE calculation, representing the efficiency of N uptake vs. N applied at the end of the corn season. Note: fertilizer applications dates and amounts are shown in Table 3 3.

PAGE 190

190 Table 3 5. Grain yield, N applied, and NUE per N fertil ity rates resulted in corn growing seasons 2015 2017. Grain yield 1 NUE grain 3 Irrigation N fertility rate (kg/ha) (kg grain/kg N applied) GROW Low 12,167 a 70.2 a GROW Medium 12,159 a 46.1 b GROW High 13,052 a 37.0 cde SWB Low 11,261 a 65.1 a SWB Medium 11,853 a 45.0 bc SWB High 12,382 a 35.1e SMS Low 11,343 a 65.5 a SMS Medium 12,129 a 46.1 b SMS High 12,562 a 35.7 de Reduced Low 11,402 a 65.9 a Reduced Medium 12,644 a 48.1 b Reduced High 12,747 a 36.2 cde NON Low 7,551 bc 44.0 bcd NON Medium 6,913 c 26.6 f NON High 8,578 b 24.5 f 1 Average grain yield per N fertility rate adjusted to 15.5% standard moisture content. 2 N applied in low, medium and high rates: 147, 247 and 336 kg/ha, respectively. Note: in 2016 and 2017, N rates differed due to supplemental fertilization applied after leaching rains. Following the BMP protocol, additional 34 kg N/ha and 17 kg N /ha were applied in 2016 and 2017, respectively. 3 NUE= nitrogen use efficiency calculated as a ratio of grain yi eld (kg/ha) and total N fertilizer (kg/ha) applied during the growing season. Different letters indicate differences at the 95% CI for N fertility means across all years of evaluation.

PAGE 191

191 Table 3 6. Grain yield (kg/ha), irrigation applied, rainfall and irri gation efficiency indices (IWUE, WUE i , WUE t , GRSPUE) per irrigation treatments during 2015 2017corn growing seasons. Year Grain yield 1 Irrigation Rainfall IWUE 2 WUE i 3 WUE t 4 GRSPUE 5 CWUE 6 Water savings 7 (kg/ha) (mm) (mm) (kg/m 3 ) (kg/m 3 ) (kg/m 3 ) (kg/m 3 ) (kg/m 3 ) (%) 2015 556 GROW 12,105 a 320 0.95 bc 3.78 cd 1.38 d 0.55 ab 2.45 ab 58 SWB 11,201 ab 185 1.19 abc 6.05 b 1.51 bcd 0.40 b 2.26 b 47 SMS 12,011 a 151 2.00 abc 7.95 a 1.70 abcd 0.54 ab 2.43 ab 66 Reduced 12,638 a 211 1.73 abc 5.9 9 b 1.65 abcd 0.66 ab 2.55 ab NON 8,993 b 15 1.57 bcd 61 2016 370 57 GROW 12,705 a 508 0.83 abc 2.50 e 1.45 bcd 1.14 ab 2.57 ab 63 SWB 11,554 a 310 0.99 abc 3.73 cd 1.70 abcd 0.83 ab 2.34 ab SMS 11,819 a 291 1.14 abc 4.06 cd 1.79 a bcd 0.90 ab 2.39 ab 58 Reduced 11,964 a 321 1.08 abc 3.73 cd 1.73 abcd 0.94 ab 2.42 ab 55 NON 7,974 b 25 2.04 a 64 2017 673 58 GROW 12,567 a 546 1.25 c 2.30 e 1.43 d 1.01 a 2.62 ab 47 SWB 12,740 a 315 2.21 a 4.04 c 1.87 ab 1.03 a 2.6 6 a 66 SMS 12,203 a 302 2.13 ab 4.04 c 1.85 abc 0.95 a 2.55 ab Reduced 12,191 a 347 1.85 ab 3.51 d 1.76 abcd 0.95 a 2.54 ab 61 NON 5,779 b 48 1.46 cd 1 Grain yield standardized for 15.5% market moisture content. 2 IWUE= calculated as the ra tio of the difference between irrigated and rainfed yield (g/m 2 ) by the irrigation applied (mm), multiplied by 100. 3 WUE i = calculated as the ratio of irrigated yield (g/m 2 ) by total irrigation applied (mm), multiplied by 100. 4 WUE t = calculated as the ra tio of final grain yield (g/m 2 ) by total water received during the growing season (sum of irrigation and rainfall). 5 GRSPUE = calculated as the ratio of the difference between irrigated and rainfed yield (g/m 2 ) by cumulative growing season rainfall (mm), multiplied by 100 6 CWUE = calculated as the ratio of final grain yield (g/m 2 ) by the actual crop evapotranspiration (ET a , mm). 7 Water savings compared to the irrigation applied by the GROW treatment. Different letters indicate differences at the 95% CI f or irrigation means across all years of evaluation.

PAGE 192

192 Table 3 7. Grain yield (kg/ha), irrigation applied, rainfall and WUE i per irrigation treatments during corn growing seasons 2015 2017. Year Grain yield 1 N applied Rainfall WUE i (kg/ha) (kg/ha) (mm) (k g/m 3 ) 2015 556 Low 10,534 a 157 5.49 b Medium 11,322 ab 247 6.01 ab High 12,314 a 336 6.34 a 2016 370 Low 10,839 a 191 3.37 cd Medium 11,284 a 280 3.56 cd High 11,486 a 370 3.59 cd 2017 673 Low 10,706 b 174 3.33 d Medium 10,979 a b 263 3.47 cd High 11,603 a 353 3.62 c

PAGE 193

193 Figure 3 1. Aerial view of experimental site located at North Florida Research and Education Center Suwannee Valley (NFREC SV), near Live Oak, Florida (30.31353 N, 82.90122 W).

PAGE 194

194 Figure 3 2. Daily weathe r data: maximum, minimum and average air temperature (°C) (lines), rainfall (mm) (bars) and fertilizer applications (dots) performed at NFREC SV, Live Oak, Florida during 2015 2017 from top to bottom.

PAGE 195

195 Figure 3 3. Monthly temperature means (bars) comp ared to historical temperature (2002 2017) and cumulative growing degree days (lines) over the three corn growing seasons (2015 17).

PAGE 196

196 Figure 3 4. Monthly rainfall (R) means (bars) and cumulative daily rainfall (lines) over the three growing seasons (20 15 17) compared to historical rainfall (2002 2017). Error bars represent the standard deviation of historical rainfall across years (2002 2017).

PAGE 197

197 Figure 3 5. Total N applied per growing season (dots) and average total N uptake in aboveground biomass (li nes TB) at key corn growth stages across low (F3), medium (F2) and high (F1) N fertility rates. Samples collected in the SMS (I3) treatment across the three N fertility rates .

PAGE 198

198 Figure 3 6. Average N uptake (kg/ha) per plant part across fertility rates ( high, medium and low) during three growing seasons (2015 17). N uptake in TB = total aboveground biomass (grain, leaves and stems), G = grain, L = leaves and S = stems. Symbols (X) represent fertilization events: starter, two granular applications (large s ymbols), four liquid sidedress applications and supplemental applications when required. Note: N rates were modified in 2016 and 2017 due to leaching rain; thus, additional 34 and 17 kg N/ha were applied to all rates in 2016 and 2017 , respectively. High Medium Low 2015 2016 2017

PAGE 199

199 Fig ure 3 7. Final leaf dry weight (bars) and N uptake (dots) means across irrigation treatments (top) and fertility rates (bottom) during the three corn seasons evaluated (2015 17). Error bars show SE of irrigation and N fertility rate means. Different letter s indicate differences at the 95% CI for irrigation means (I1 I5 = GROW, SWB, SMS, Reduced and NON) and N fertility rate means (low, medium and high = 157, 147 and 336 kg N/ha). Note: N rates were modified in 2016 and 2017 due to leaching rain; thus, addi tional 34 and 17 kg N/ha were applied to all rates, respectively.

PAGE 200

200 Figure 3 8. Final stem dry weight (bars) and N uptake (dots) means across irrigation treatments during the three corn seasons evaluated (2015 17). Error bars show SE of irrigation means. Different letters indicate differences at the 95% CI for irrigation treatment means (I1 I5 = GROW, SWB, SMS, Reduced and NON treatments ).

PAGE 201

201 Figure 3 9. Final stem dry weight (bars) and N uptake (dots) means acros s fertility rates during the three corn seasons evaluated (2015 17). Error bars show SE of N fertility rate means. Different letters indicate differences at the 95% CI for N fertility rate means (low, medium and high = 157, 147 and 336 kg N/ha). Note: N r ates were modified in 2016 and 2017 due to leaching rain; thus, additional 34 and 17 kg N/ha were applied to all rates, respectively.

PAGE 202

202 Figure 3 10. Final ear dry weight (bars) and N uptake (dots) means across irrigation treatments (top) and fertility rat es (bottom) during the three corn seasons evaluated (2015 17). Error bars show SE of irrigation and N fertility rate means. Different letters indicate differences at the 95% CI for irrigation means (I1 I5 = GROW, SWB, SMS, Reduced and NON) and N fertility rate means (low, medium and high = 157, 147 and 336 kg N/ha). Note: N rates were modified in 2016 and 2017 due to leaching rain; thus, additional 34 and 17 kg N/ha were applied to all rates, respectively.

PAGE 203

203 Figure 3 11. Total aboveground (AG) biomass mea ns as a response of five irrigation treatments (I1 I5 = GROW, SWB, SMS, Reduced and NON) (top) and three N fertility rates (low, medium and high) (bottom) during three corn growing seasons (2015 17). Note: N rates were modified in 2016 and 2017 due to le aching rain; thus, additional 34 and 17 kg N/ha were applied to all rates, respectively.

PAGE 204

204 Figure 3 12. Corn grain yield (kg/ha) as a response of five irrigation treatments (GROW, SWB, SMS, Reduced and NON) (left) and three N fertility rates (high = 336 kg/ha, medium = 247 kg/ha and low = 157 kg/ha) (right) during three corn growing seasons (2015 17). Data standardized for 15.5% market moisture. Boxplots (n=4) lower boundary of the box indicates the 25th percentile, a l ine within the box marks the median, and the upper boundary of the box indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles. Note: N rates were modified in 2016 and 2017 due to leaching rain; t hus, additional 34 and 17 kg N/ha were applied to all rates, respectively. Irrigation treatments N fertility rates

PAGE 205

205 Figure 3 13. The 100 kernel weight means across irrigation treatments during corn seasons 2015 17. Different letters indicate difference s at the 95% CI for irrigation means (I1 I5 = GROW, SWB, SMS, Reduced and NON) per season. Error bars show SE of kernel weight across means.

PAGE 206

206 Figure 3 14. The 100 kernel weight across N fertility rates during cor n seasons 2015 17. Different letters indicate differences at the 95% CI for fertility rate means per season. Error bars show SE of kernel weight across means.

PAGE 207

207 CHAPTER 4 WATER AND NITROGEN BUDGET DYNAMICS FROM A CORN FALLOW PEANUT ROTATION Introduction Inc reasing nitrogen loads to waterbodies has become a major concern around the world, especially in areas where sandy soils and shallow groundwater are predominant (Andraski et al. 2000; Ferguson et al. 1991; Gehl et al. 2005; Strebel et al. 1989; Zhang et al. 1996) . A decline in water quality, the degradation of aquatic ecosystems and the growth of a seasonal hypoxic zone s are some of the major consequences seen worldwide due to these N loads (Donner et al. 2004; Howarth et al. 201 1; Rabalais et al. 1996; Turner a nd Rabalais 1991; Zhang et al. 1996) . The Upper Floridan Aquifer and the Suwannee River Basin The Upper Florid an aquifer (UFA) underlays an area of about 300,000 km 2 , it is a source of water supply , and it is one of the most productive karst aquifers in t he world (Bush and Johnston 1988) . Most of the North central Florida regions underneath the aquifer are unconfined (i.e. water in an aquifer that has a water table that is exposed to the atmosphere ) ; characterized by h igh porosity and transmissibility allowing contaminants to travel long distances in short periods of time (Arthur et al. 2007) . The UFA is the primary source f or industrial, agricultural, and municipal use in the Suw annee River basin (SRB) in Florida (Katz et al. 1997) . The SRB is located in North central Florida, and it is characterized by the presence of springs and other karst features (e.g. sinkholes, caves, conduits, siphons and disappearing streams). In most parts of the basin, these features cause interactions between groundwater and surface water , constituting a single dynamic system . Therefore, the SRB has high vulnerability to N pollution characteristic of karstic areas.

PAGE 208

208 The Suwannee River is of great interest to the State of Florida due to its historical and recreational importance; however, the increase of nitrogen (N) is of concern and interest to water resources agencies. In north central Florida, the Suwannee River f lows through groundwater areas of high nitrate concentrations and ultimately drains in the Gulf of Mexico, where nitrate loadings can cause ecological damage. These elevated concentrations come from a mix of organic (manure and wastewater) and inorganic so urces (i.e. fertilizers) (Katz et al. 1999) . Fertilizer inputs are contributing to excessive nutrients to groundwater recharge impacting the quality of spring waters (Katz 2004) . Pre vious studies have shown the increase of nitrate N in springs from 3 N) to above 5 mg/L NO 3 N during the last 40 years (Katz et al. 1999; Katz 2004) . Continued s tudies in the SRB area during the period 1940 1998 reported evidence that the greatest nitrate N pollution found in the lower basin comes from fertilizer sources (Katz et al. 2009) . Large variations and i ncrements of n itrate N have been found in the SRB Florida springs ; ranging from 1.3 to 8.2 mg/L at Royal and Convict Springs, respectively (Upchurch et al. 2007) ; possibly caused by septic tanks or from fertilized cropland nearb y (Hornsby and Mattson 1997) . Concerns have risen over the structure and function of freshwater ecosystems due eutrophication (i.e. excessive richness of nutrients in body of water) derived from urban, agricultural a nd industrial development associated with climate change that have promoted toxic cyanobacteria growth (blue green algae) (Paerl and Huisman 2008; Paerl and Otten 2013) . The cyanobacterial harmful blooms (CyanoHABs) are toxin producing species that have threaten ecological and human health (Huisman et al. 2005) , though the toxin production, residue formation which reduces light to benthic

PAGE 209

209 primary producers, an d the excessive biomass at bloom die off which may kill fish due to oxygen depletion or hypoxia. T hus, nitrate over enrichment in surface waters may liver, digestive, neuro logical and skin diseases are the most important human consequences of these toxic CyanoHABs (Carmichael 1992; Huisman et al. 2005; Paerl et al. 2011) . Regulations With th have been developed in response to the increasing water demand and excess nutrient loading to waterbodies. In 1972, the Clean Water Act (CWA) established the basic structure for regul ating discharge of pollutants into waters of the U.S. (EPA, US Environmental Protection Agency 2015a) . Within this act (section 303(d)), the Total Maximum Daily Loads (TMDLs) were established to set the maximum amount of pollutant in a waterbody allocating the required reductions to the pollutant sources (EPA, US Environmental Protection Agency 2016a) . In 1974, the Safe Drinking Wate r Act (SDWA) determined the level of contaminants in drinking water ensuring drinking water quality for the U.S. population. The EPA established maximum contaminant level enforceable h ealth goals, based only on possible health risks and exposure over a lifetime with an respectively (EPA, US Environmental Protection Agency 2016b) . In 1995, the Office of Agricultural Water Policy (OAWP) within the Florida Department of Agriculture and Consumer Services (FDACS) was established. The OAWP is directly involved with statewide programs to implement the Federal Clean Water Act's Total Maximum Daily

PAGE 210

210 Load (TMDL) requirements for agriculture. Afterwards, the Florida Watershed Restoration Act (FWRA) was passed in 1999 as a response to the TMDLs. Consequently, this act determined the Florida Department of Environmental Protection (FDEP) as the organization responsible of the implementation of TMDLs through water quality protection programs. FDEP established thresholds based on previous research developed on the relationship of nutrients to algal growth in s prings. Therefore, in clear water streams (including spring vents), the nutrient criterion is 0.35 mg/L of nitrate nitrite (NO 3 + NO 2 ) as an annual geometric mean (62 302.530 (47) (b), F.A.C., (FDEP 2013) . The OAWP enables communication among federal, state and local agencies and the agricultural industry on water quantity and water quality issues associated with agriculture. Thus, the OAWP is actively involved in the development of Best Management Practices (BMPs) addressin g both water quality and water conservation on a site specific, regional and watershed basis. Producers, industry groups, the FDEP, the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS), the Water Management Districts, and other p arties collaborate in the development and implementation of economically and technically feasible BMP programs (FDACS 2013) . Agricultural Best Management Practices (BMPs) are practices based on research, field testing and expert review developed to maintain or improve surface and groundwater water quality. In addition, BMPs must be technically and economically feasible, and designed to prevent, reduce or treat pollutant discharges entering water resources, and to conserve w ater supply (FDACS 2015a) . Water in the U.S. is being threatened mainly by non point sources (urban and agriculture). Runoff draining into waterbodies (i.e. lake, rivers, and groundwater) is possibly the greatest contribut or to

PAGE 211

211 water pollution in the U.S. Thus, producers can prevent and potentially minimize the impacts to the environment by implementing BMPs while maintaining agricultural production (FDACS 2015a) . Corn production Field corn (maize) production is an important crop for north Florida and southern Georgia. In 2012 field corn in Florida and Georgia, had a value of $381.6 million (USDA, NASS 2012) . The expansio n of corn production in the SRB requires irrigation in addition to N fertilizer (UF/IFAS recommendation for irrigated corn is 235 kg N/ha, Mylavarapu et al. 2015) to optimize economic return. Unpredictable rain combined with high irrigation and fertilizati on in sandy soils makes this region susceptible to nitrogen leaching. Hence, understanding the water and N balances (inputs and outputs) in this system could improve current management practices in corn production and reduce negative impacts to the environ ment. Due to the high mobility of N in the soil, irrigation and fertilization management should be managed simultaneously to reduce potential environmental risks caused by nitrate leaching. Several research studies have been conducted to evaluate the yield responses to irrigation regimes focused on the reduction of irrigation as a reduction in the frequency of applications and/or the timing of application in loamy soils. (Klocke et al. 2007; Klocke et al. 2011; Payero et al. 2006; van Donk et al. 2013) . Klocke et al. (2007) evaluated the application of limited irrigation (i.e. lower water applications than full crop demand), dryland and full irrigation in corn grown in crop rotations in deep silt loam soils. Limited irrigation yields were 80 90% of full irrigated yields while applying approximately half the applied water. This study started irrigation at the beginning of the reproductive stage, when wate r was most critical (Klocke et al. 2007). Djaman et al.

PAGE 212

2 12 (2013) examined the effect of four irrigation regimes (fully irrigated treatment (FIT), 75% FIT, 60% FIT, and 50% FIT) and a rainfed treatment on several response parameters. In terms of grain yield, no significant differences were found between the 75% FIT and 100% FIT irrigation regimes evaluated; and the 75% FIT and 60% FIT treatments were very similar to the 100% FIT in terms of crop response to water, providing an opportunity reduce water use whil e increasing crop water productivity (Djaman et al. 2013) . Other studies evaluated corn yield responses to N rates and irrigation rates, particularly in areas vulnerable to nitrate leaching (Al Kaisi and Yin 2003; Derby et al. 2005; Ferguson et al. 1991; Gehl et al. 2005; He 2008) . Gehl et al. (2005) evaluated corn yield response to six N treatments in sandy soils: (i) 300 kg N/ha applied at planting, (ii) 250 kg N/ha applied at planting, (iii) 250 kg N/ha split (50% applied at planting and 50% side dress at V 6 crop stage), (iv) 185 kg N/ha split (33% applied at planting and 67% side dress at V 6 cr op stage), (v) 125 kg N/ha split (20% applied at planting, 40% side dress at V 6 crop stage and 40% applied at V 10 crop stage), and (vi) 0 kg N/ha. Maximum grain yield was achieved using a split application of 185 kg N/ha; however, in most cases 125 kg N/ ha was satisfactory to reach maximum yield. Authors emphasized in the importance of an efficient use and timing of N fertilizer (i.e. split applications) with an optimum irrigation management in regions vulnerable to nitrate leaching. In Florida, careful management of N fertilization is recommended due to its mobility and potential leaching in sandy soils (Wright et al. 2003) . Split applications (i.e. multiple applications with small amounts) are recommended to improve plant nutrient

PAGE 213

213 uptake and reduce N leaching (Li and Yost 2000) . In general, these applications consist of a starter fertilizer applied at planting near the row fulfilling 20 25% of the crop N demand; whereas the 75% of the N requirement can be applied side dress and/or through fertigation throughout the season (Wright et al. 2003) . e, right rate, right time and right place approach), adequate water management is intrinsically linked with nutrient management for maximizing plant uptake and limit potential losses to the environment (Hochmuth et al . 2014) . In soils with low water holding capacities, low amounts but more frequent water applications have been proven to result in better results compared to high irrigation volumes with fewer applications (El Hendawy and Schmidhalte r 2010; He 2008; Hochmuth 2000; Locascio 2005) . Irrigation scheduling (i.e. timing and depth of irrigation) is more efficient when based on ET or soil moisture sensors ( SMS) (Irrigation Association 2011) . Implementing automated irrigation using soil moisture sensors could be an alternative to manage frequent and low volume applications for crop production in sandy soils (Munoz Carpena et al. 2005) . Crop simulation models Computer models can be used to predict growth and development of a crop, determine optimum yield, and to estimate environmental impacts. The Decision Support Sys tem for Agro technology Transfer (DSSAT) is a computer program that comprises simulation models for 42 crops including maize (Hoogenboom et al. 2015) . Further, the CERES Maize model within the program simulates yie ld together with water and N transformations and uptake (Jones et al. 1986) . Therefore, DSSAT is a tool that can be used for the determination of growth and yield, as well as, to estimate the N loads from

PAGE 214

214 non point so urces to the environment (i.e. excess NO 3 N leaching) and therefore, improve irrigation and N management practices in crop production. The CERES Maize model divides the crop cycle into several phases and determines the rate of growth development using ther mal time, or growing degree days (GDDs), which is calculated using daily maximum and minimum temperatures. The GDDs required to progress from one growth stage to another are either defined as a user input or are computed internally based on user inputs and assumptions about duration of intermediate stages. The main purpose of the CERES crop simulation models is to predict crop growth duration, development (i.e. average growth rate), as well as, the partitioning (i.e. distribution of assimilates into variou s plant parts). These models estimate biomass growth using the radiation use efficiency (RUE) approach and then the biomass produced is partitioned into the different organs (e.g. leaves, stems, roots, ears and between temperature and growing stages (Ritchie, J. T. et al. 1998) . Thermal time (td) can be defined as: (3 1) Where: T a = daily mean air temperature T b =base temperature at which development stops n= number of days of temperature observations used in the summation. According to (Monteith 1977) , biomass production was linearly related with cumula tive light interception along with adequate water and nutrients. Therefore, the RUE method is used in the CERES models to estimate biomass production using a

PAGE 215

215 crop specific RUE parameter. In the model it is assumed that the photosynthetic active radiation ( i.e. light quantity available for plant interception) is half the input for daily solar radiation. Light interception is calculated as a function of leaf area index (LAI), plant population and row spacing. However, the new amount of dry matter available fo r growth per day is modified by the most limiting of water, N, temperature and/or CO2 concentration. Therefore, aboveground biomass has priority for carbohydrates (the unused carbohydrates, are allocated to the roots) (Jones et al. 2003) . The stage of development and the growing conditions (i.e. management and concept and are modified due to water and nutrient deficiencies. Based on temp erature the potential kernel growth rate at optimal temperature, which is negatively impacted due to unusual low or hot temperatures. The daily biomass production and the not directly affected by water or nutrient deficits; however, a low supply of assimilate (i.e. products from photosynthesis such as sucrose) production during grain filling is the major cause for a reduction in yield and grain weight. Yield is determined as the grain number per plant times the average kernel weight at physiological maturity. Grain number is determined using the average photosynthesis per plant during a period close carbohydrate accumulation during flowering, along with temperature, water and N stresses. Potential kernel number is a user defined input for some c ultivars (e.g. the genetic coefficient G3 can be calibrated). Afterwards, the model calculates the daily

PAGE 216

216 grain growth rate based on user specified cultivar input (i.e. optimum kernel growth rate) multiplied by the duration of grain filling period. Thus, te mperature and assimilate availability modify growth daily rate. However, a fraction of carbon can be remobilized from vegetative to reproductive sinks, if the daily pool of carbon is insufficient to allow growth at potential rate (Jones et al. 2003; Ritchie et al. 1998) . This model simulates a daily soil water balance (SWB) and it uses soil inputs to calculate: drained upper limit (DUL, equivalent to field capacity with water potentials in the ra nge of 0.1 to 0.33 bar), the lower limit of plant water availability (LL, equivalent to permanent wilting point and to water potential of 15 bars), and saturation point (SAT, equivalent to field saturation). These three inputs are required for all layer s defined. Other inputs required, but not for particular depths are: the soil surface albedo (SALB), the limit of first stage soil evaporation (U), the runoff curve number (CN2) and the drainage coefficient (SWCON). Runoff is calculated by a modification o f the USDA Soil Conservation Service (SCS) curve number method (Williams 1991) , which empirically derived the approximate volume of runoff when only daily rainfall is known ( Ritchie 1998) . A cascading approach is used for soil water redistribution during infiltration processes (i.e. downward movement of water from the surface to lower layers). When the soil water content of a layer (SW (L)) is between the saturation point (SA T (L)) and the drained upper limit (DUL (L)), drainage occurs. After calculating the movement of water among soil layers, the drainage at the bottom layer represents the total outflow from the lowest layer in the soil profile. This water flow rate is also used for the nitrate

PAGE 217

217 leaching calculation (Ritchie 1998) . The model uses a daily time step for the simulations. Plant growth can be affected by N supply. Nitrogen is an essential element required for the synthesis o f chlorophyll, protein and enzymes, and it is vital for the utilization of carbohydrates (Below 2002; Godwin and Singh 1998; Taiz et al. 2015) . Earlier in the growing season, hi gher N concentrations are typical in young plants due to the formation of organic N compounds; whereas, as the plant grows, lower amounts are required; lowering whole plant N concentration. The presence of both, NH 4 + and NO 3 provides better growth due to the cation anion balance within the plant (Bloom 1994) . The CERES Maize model simulates N uptake by evaluating the potential N supply from the soil and the demand of the crop. The crop N demand has two main components : (i) deficiency demand, which is the N demand to reach critical N concentration (i.e. the lowest concentration at which maximum growth occurs); and (ii) demand for new growth, which is usually a lower component of N demand. During grain filling, the grain N requirement is moved from vegetative and root pools to a grain pool. This reduction in vegetative pools might cause an increase in the N demand. The total plant N demand (ANDEM) is calculated as the sum of the deficiency demand and the demand for new gr owth (Godwin and Singh 1998) . To calculate the potential N from the soil, the model calculates availability factors for nitrate and ammonium from soil concentrations. Then, a soil water factor (calculated using the r elative availability of soil water) is used to determine N uptake. This factor is reduced as soil moisture reaches saturation to account for anaerobiosis and reductions in root function. Then the maximum N uptake per layer is dependent of the coefficient o f

PAGE 218

218 maximum daily N uptake and the total root present in each soil layer. Thus, the potential N uptake from the soil profiles an integration value dependent on root density, nitrate and ammonium concentrations present in the soil, as well as, soil moisture. However, if the potential supply is greater than the crop demand, an N uptake factor is calculated, and it will reduce the N uptake per layer to reach the N level of the demand (Godwin and Singh 1998) . As part of the N cycle, N mineralization corresponds to the decay of crop residues and soil organic matter, which can lead to a net release of mineral N. Immobilization is also related; however, it occurs when inorganic N compounds are converted into organic and are tem porarily unavailable for plant uptake. In DSSAT, the model simulates the decay of fresh organic matter (FOM) (i.e. crop residue or green manure) and the associated turnover of the fresh organic N (FON). The inputs in the model are: the amount of this mater ial, the C:N ratio and its depth of incorporation into the soil. Using these inputs, as well as, an estimated root residue remaining from the previous crop, FOM and respective FON amounts are ascribed to the soil layers. The three main pools comprising the FOM pool are: carbohydrate, cellulose and lignin. Each of these pools have a different decay rate (under non limiting conditions, a constant decay rate of 0.20, 0.05 and 0.0095 is used, respectively for those pools). The three main factors influencing dec ay rate are: moisture, temperature and C:N ratio (Godwin and Singh 1998) . The mineralization of the humic fraction (more stable fraction of soil organic matter) occurs at a slower rate. The oxidation (i.e. under aero bic conditions) of NH 4 + to NO 3 is referred as nitrification. This process is limited by oxygen, soil pH, availability of ammonium,

PAGE 219

219 temperature and soil water. In DSSAT, nitrate and urea are capable of moving across layers, whereas ammonium is not. This mo vement is highly dependent on water movement. Therefore, first the volume of water moving across layers is calculated (i.e. flux) then using the water present per layer and this flux of water draining per layer, the nitrate lost per layer is calculated. Us ing the cascading approach (from upper to lower layer), a fraction of the mass nitrate from each layer moves with each drainage event to the following layer. No further leaching occurs for concentrations lower than 1 µg NO 3 (g 1 soil) (Godwin and Singh 1998) . Due to the inherent relationship between irrigation and fertilization, an adequate management of both are required for successful crop production in sandy soils minimizing N leaching and groundwater pollution. Fi eld experiments associated with cropping simulations systems are tools that can help understand several processes occurring in the soil, crop and environment and therefore evaluate the effectiveness of best management practices. Hypotheses 1. Irrigation and N rate B est Management Practices (B MPs ) reduce water use and N leaching in corn production. 2. University of Florida/ Institute of Food and Agricultural Sciences ( UF/IFAS ) recommended N rates produce similar corn grain yields and N uptake as higher N fertiliza tion rates, but less leaching. 3. Peanut N fixation contributes to N fertilization in corn as a subsequent crop. Objectives 1. Develop a water and N balance using DSSAT crop simulation models to better estimate N processes and N fate within a corn fallow peanut fallow corn rotation. 2. Evaluate the effectiveness of three irrigation treatments and three N fertility rates on N uptake and N leaching in corn fallow peanut fallow corn crop rotation in sandy soils using DSSAT crop simulation models .

PAGE 220

220 3. Evaluate the effect of peanut N contribution to the N requirement in subsequent corn season using DSSAT crop simulation models. 4. Estimate N mineralization from crop residue and potential contributions for subsequent crop production using DSSAT crop simulation models. Materials and Methods Study S ite A three year research study (2015 2017) was conducted at the North Florida Research and Education Center Suwannee Valley (NFREC SV), near Live Oak, FL (30.31353N, 82.90122W). Predominant soils were identified as: Blanton Foxworth Alpin complex (48.7%), Chipley Foxworth Albany (31.6%) and Hurricane, Albany and Chipley soils (19.6%) (USDA, NRCS 2013) . The taxonomic classification for these soils is shown in Table 4 1. This chapter will focus on t he crop rotation planted in the Chipley Foxworth Albany soil predominantly. Irrigation T reatments The corn and peanut irrigation treatments evaluated in this section were: 1. pract ices was collected from extension agents and the Suwannee River Water Management District (SRWMD) to develop the GROW method. The target irrigation rates vary based on growth stages. For the first 30 days after planting (DAP) zero irrigation was applied (u nless severe windy conditions occurred). At 31 DAP, 25 mm/wk was targeted unless rainfall events equal or greater to 10 mm occurred. At 40 59 DAP, target irrigation was 38 mm/wk with irrigation events of 10 mm. If rainfall events were equal or greater than 13 19 mm one irrigation event was skipped, and two events were skipped if greater than 19 mm of rain occurred. Afterwards, the irrigation target increased up to 51 mm/wk unless 13 25 mm of rain occurred the day prior to a scheduled irrigation. Two irrigat ions were skipped if 25 mm of rain occurred. Finally, at full dent stage (105 DAP), weekly irrigation targets were 41mm/wk. If rainfall events were equal or greater than 13 19 mm one irrigation event was skipped, and two events were skipped if greater than 19 mm of rain occurred. Irrigation was terminated after physiological maturity (i.e. black layer) around 115 DAP. 2. SMS: capacitance probes monitored volumetric water content. The Sentek probes (Sentek Pty Ltd, Australia) consists of nine sensors placed eve ry 10 cm starting from

PAGE 221

221 5 cm to 85 cm. Irrigation was determined using the maximum allowable depletion (MAD as 50% from FC an PWP) and field capacity (FC) points to refill the soil profile with irrigation according to guidelines proposed by Zotarelli et al. (2013). A total of 10 mm per irrigation event was applied. A comparison between theoretical and actual values of FC, MAD and PWP was performed for different root depths resulting in close values among them (e.g . average FC = 9.3% (±0.2) in a 30 cm root zo ne depth vs. 9.1% FC theoretical value). Therefore, based on the published Soil Survey from Florida theoretical values for Chipley Foxworth Albany soil were used in this study (FC = 9.1%, 50% MAD = 6.3%, AWHC = 0.05 cm/cm and PWP = 3.5%) (NRCS 2016). Based on root/crop development, the soil water content measured by the different sensors was adjusted through the growing season. The irrigation for SMS treatment was triggered when VWC in any of the probes showed values below the 50% MAD threshold. 3. NON: non ir rigated/rainfed plots. These plots were used as a control for comparison. These plots received irrigation after granular fertilizer applications to provide adequate moisture conditions for nutrient uptake. N itrogen F ertility R ates 1. High: 336 kg N/ha. It rep resents fertilization amounts commonly applied in corn production in Florida. 2. Medium: 247 kg N/ha. This N rate is slightly above the UF/IFAS recommendation for irrigated corn (235 kg N/ha , Mylavarapu et al. 2015 ). 3. Low: 157 kg N/ha. It represents a low N sc enario (due to low water holding capacity, low organic matter and low cations exchange capacity in sandy soils, a minimum N is required for growth). Note: following the BMP manual (FDACS 2015a) , during the 2017 corn season , an additional fertilization of 17 kg N/ha was applied on 6 April 2017 to all N rates due to leaching rain events occurred on 4 and 5 April (cumulative rainfall = 108 mm) . Experimental D ata for E valuation of M odel P erformance Data collected during the ro fallow peanut fallow experimental field at the NFREC SV, was analyzed and compared to simulations using CERES Maize crop model within DSSAT. Observed data consisted of: Final total aboveground biomass : tissue sampling was perfo rmed in season and at the end season (end of the growing period of both crops ) . In season samplings were performed in SMS treatment only; whereas the end season samples were collected

PAGE 222

222 across the three irrigation and three N fertility rates (high, medium an d low) . Plant sections were separated into leaves, stems, and ears /pods, were dried (60°C for 72 hours) and analyzed for Total Kjeldahl Nitrogen (TKN) digestion. For nitrogen analysis, samples were digested using a modification of the aluminum block digest ion procedure of (Gallaher et al. 1975) . Sample weight was 0.25 g, catalyst used was 1.5 g of 9:1 K 2 SO 4 :CuSO 4 , and digestion was conducted for at least 4h at 375°C using 6 ml of H 2 SO 4 and 2 ml H 2 O 2 . Nitrogen in th e digestate was determined by semi automated colorimetry (Hambleton 1977) . Nitrogen uptake (kg/ha) of the different plant tissues was calculated using the % N concentrations obtained from TKN laboratory analysis. The final dry weight of aboveground biomass (i.e. sum of leaves, stems, ears/pods in kg/ha) was compared with DSSAT simulations. N uptake: N (%) concentration obtained from the tissue samples was multiplied by the corresponding biomass dry weight to obtain total N uptake (aboveground). This was compared with DSSAT simulations. More details on biomass and N uptake can be found in Chapter 2. Soil N ( NO 3 N and NH 4 N): Pre plant soil samples were taken on GROW, SMS and NON treatments across all N fertility rate s to determine initial soil conditions in corn (pre plant/fertilization) (Table 4 2 ). To determine N levels throughout the soil profile, soil samples were taken at 0 15, 15 30 , 30 60 and 60 90 cm biweekly during the crop growing season and monthly after harvest . Following a protocol, soil samples were collected using a hand or a power auger. Each depth sample was well mixed and a sub sample was collected, sealed in plastic bags, kept refrigerated and transported in coolers for processing. Field samp les fresh weight was recorded and samples were divided in two sub samples of at least 100 g placed in aluminum plates: a) A nalytical Research L aboratory (ARL) sub sample: fresh weight was measured. Then , samples were air dried for 48 hours, sieved using a 2 m m sieve, placed in paper bags and delivered to the ARL lab (UF/IFAS Anserv Labs 2011) for NO 3 N and NH 4 N analysis following the EPA 353.2 and the EPA 350.1 procedures, respectively. b) Agricultural and Biological Engineering ( ABE ) sub sample: fresh weight was recorded. Samples were oven dried at 105 °C for 48 hours. Dry weight was recorded to determine gravimetric water content. Volumetric water content was calculated based on field bulk density measurements (bulk density values per soil type a nd depth can be found in Table 4 2). Soil moisture: Sentek probes (Drill & Drop MTS Probe) consist of the three main associated sensors (i.e. moisture, salinity and temperature) all encapsulated in one probe. The sensors are s paced at 10 cm intervals, with the first sensor at 5 cm from the top of the probe, and a total probe length of 90 cm. Sensor numbers increase with sensor depth (Sentek Pty Ltd 2003) . To monitor soil moisture and sal inity at different depths in the soil profile, 54 Sentek probes were installed in treatments GROW, SMS and NON irrigation treatments across all fertility rates (high, medium

PAGE 223

223 and low) in both crop fields. Sensors collected data every 30 minutes during the 2 015 17 crop growing seasons. The sensor provides three outputs: soil moisture content, salinity and temperature (Sentek Pty Ltd 2003) : a) The first output is a signal of dimensionless frequency (raw count) that is con verted into volumetric water content (Vol %) or millimeters of water per 100 mm of soil depth. b) The second output is a dimensionless frequency (raw count) that in conjunction with the first output signal, is proportional to changes in soil water content and salinity. The output of the data model is nominal Volumetric Ion Content (VIC). These measurements can be quantitatively related to the soil Electric conductivity (EC) through site specific soil sampling and analysis (Sentek Pty Ltd 2003) . DSSAT crop simulation m odel s The CERES Maize model and the CROPGRO models within DSSAT 4.7 (Jones et al. 1986) were used for estimating drainage amounts and N leaching, as well as, other pro cesses involved in the cycles that improve the understanding of water and N transport and fate in a corn and peanut rotation. Model i nputs DSSAT CERES Maize Four main input files created in DSSAT for model calibration are described as follows: Soil . Requi red soil inputs for all layers are: drain upper limit (DUL, which could be equivalent to FC), the lower limit of plant water availability (LL, equivalent to PWP), and the field saturation (SAT). Other inputs used are: the soil surface albedo (SALB), the li mit of first stage soil evaporation (U), the runoff curve number (CN2) and the drainage coefficient (SWCON). A modification of the USDA Soil Conservation Service (SCS) curve number method (Williams 1991) is used to estimate runoff (RO).

PAGE 224

224 A cascading approach is used for soil water redistribution during infiltration processes. When soil water content of a layer (SW (L)) is between the saturation point (SAT (L)) and the drained upper limit (DUL (L)), drainage occurs. I n nitrate leaching calculations, this water flow rate is also calculated. For the model calibration, average soil data (pH, TKN (%), initial N ( NO 3 N and NH 4 N ) , bulk density (Mg/m 3 ), sand (%), clay (%), loam (%), DUL, LL and calculated total organic C (%) ,) was obtained from pre planting soil samples taken prior to fertilizer applications. These values were included as inputs in the model. For Chipley Foxworth Albany (the predominant soil type identified) soil taxonomic characteristics, drainage and slope were obtained from the Soil Web Survey (NRCS 2016b) . Weather . Weather data was obtained from the on site FAWN weather station located in Live Oak, FL (30.305 N, 82.8988 W, at an elevation of 165 ft.) (FAWN 2017) . The main weather parameters collected were: minimum and maximum air temperature, solar radiation, and rainfall on a daily basis . Historical weather data was collected from 2002 to 2017 and it was used as inputs for model testi ng. Cultivar Genetic Coefficients . In the field experiment located in Live Oak, FL, the corn hybrid Pioneer 1498 YHR/Bt was used for evaluation. However, this cultivar was not found in the DSSAT database; therefore, the crop growth and yield parameters of 84aa closer to the measured values following the systematic approach described by Hunt and ev used for simulations.

PAGE 225

225 Crop management . The model followed field experiment planting specifications: row spacing of 76.2 cm with a 16.5 cm seed spacing for a total plant po pulation of 80,300 plants/ha for corn. Whereas a row spacing of 76.2 cm and seed spacing of 6 cm or a total plant population of 222,300 seeds/ha in peanut. Table 4 3 shows the fertilizer applications performed at each N fertility rate during 2015 corn, 201 6 peanut and 2017corn seasons. Figure 4 1 shows the irrigation applied during those seasons, respectively. Field management information was used as inputs for model testing (Table 4 3). Model c alibration The process of estimating model parameters by compar ing model predictions for a given set of assumed conditions with observed data is called model calibration. Model calibration is an important step to ensure adequate constants and response functions for the species with which the model was developed, as we ll as, to ensure that the model works well for the cultivars used in a particular region (Hunt and Boote 1998) . When calibrating a model, following a systematic approach has been recommended, in which the parameters ar e evaluated in a logical sequence (Hunt and Boote 1998) . The cultivar file for maize has a total of six genetic coefficients: P1, P2, P5, G2, G3 and PHINT. P1: Thermal time from seedling emergence to the end of the juv enile phase (expressed in degree days above a base temperature of 8 ° C) during which the plant is not responsive to changes in photoperiod. P2: Extent to which development (expressed as days) is delayed for each hour increase in photoperiod above the longes t photoperiod at which development proceeds at a maximum rate (which is 12.5 hours). P5: Thermal time from silking to physiological maturity (expressed in degree days above a base temperature of 8 ° C).

PAGE 226

226 G2: Maximum possible number of kernels per plant. G3: Kernel filling rate during the linear grain filling stage and under optimum conditions (mg/day). PHINT: Phylochron interval; the interval in thermal time (degree days) between successive leaf tip appearances. The systematic approach proposed by Hunt and Bo ote (1998) was followed to calibrate maize genetic coefficient parameters using the cultivar (already in the DSSAT cultivar database) suitable for Florida conditions. The SMS high N rate treatment was selected for calibration, under the as sumption of being the non stressed treatment capable of achieving maximum growth and yield potential. Calibration steps were as follows: 1. Crop life cycle. The first step in the calibration focused in two main growth parameters: flowering date (P1) and matur to start the calibration (assuming the critical day length and photoperiod sensitivity values are correct). Then, using observ ed data, the duration of the period between germination/emergence and flower appearance until the flowering date was adjusted until it was simulated correctly. Afterwards, the period between first seed and physiological maturity was adjusted until simulate d maturity date was correct. Based on field records, flowering date occurred on 57 DAP and physiological maturity occurred on 115 DAP. Thus, P1 decreased since flowering date was occurring earlier than the original cultivar; whereas, P5 increased since mat urity date needed to be delayed compared to original cultivar. 2. Dry matter accumulation. Afterwards, a comparison between simulated and measured total aboveground biomass data was performed (Figure 4 2). Simulated biomass was within the range of variability of the observed data. Thus, no further adjustment of the parameters that affect leaf and canopy photosynthesis was needed. Seed size and seed dry weight. Finally, a comparison with simulated and observed final grain weight (i.e. yield) was performed. The maximum number of kernels per plant (G2) and the kernel filling rate (G3) were adjusted (i.e. G2 decreased and G3 increased) until simulated and observed final seed number and weight matched. Therefore, to calibrate these coefficients, a range of parameter values were selected and compared with observed biomass and yield. For G2, a range from 920 to 800 with reductions of 10 units were evaluated until biomass resulted in the lowest RMSE compared with observed data. Then, for G3, a range from 8 to 10 with

PAGE 227

227 in crements of 0.25 were evaluated. The combination of genetic coefficients were used to simulate biomass and yield for a total of 21 iterations. Calculated RMSE values for biomass and yield were 1,744 and 62 kg/ha compared to the average data across blocks. Table 4 4 shows the comparison of simulated and observed biomass and yield across a range of combined genetic coefficient parameters. Final calibration results (G2 = 800, G3 = 10) were selected based on lowest RMSE compared to biomass a nd yield average obs erved data . Table 4 5 shows calibrated cultivar used for simulations. The model outputs were compared with observed data from the south field crop rotation growing season (corn 2015, fallow 2015 16, peanut 2016, fallow 2016 17, corn 2017 and fallow 2017 18 ). Data included as inputs of the model w ere : biomass (leaf, stem, and ear), N concentration (leaf, stem, and ear), soil parameters (soil physical characteristics (sand, silt, clay) and chemical characteristics, NO 3 N and NH 4 N at 4 depths through the seas on) and final grain yield data. The m odel was evaluated for N concentrations (e.g. calculations for plant N uptake), biomass (leaf, stem and ear weight in season), final yield and potential N leaching. Soil samples were taken before planting/fertilizing an d the results were used to determine main soil parameters and initial soil conditions for model inputs (see DSSAT CERES Maize section). Fertilizer applications are summarized in Table 4 3. Total N applied across seasons were: 336, 247 and 157 kg N/ha in co rn 2015 season, 17 kg N/ha in peanut 2016 (minimal N requirement), and 353, 264 and 174 kg N/ha in corn 2017. The DSSAT CERES Maize model simulations were performed using the methods described in Table 4 6, the planting and harvest dates described in Tab le 4 7, and initial conditions input values defined in Table 4 8. The DSSAT Century method was used for the soil organic matter module. Using observed organic matter values from initial sampling, total organic C was estimated for the model initiation (Tota l Organic C = Organic Matter/1.72). All simulations were

PAGE 228

228 which was developed in DSSAT using observed data. This soil file was constr ucted using four depths (0 15, 15 30 , 30 60 and 60 90 cm), same as th e soil N data collected in the experimental field. The simulation start date was 2 April 2015 (i.e. one day prior corn planting date in 2015). represent a mix of grasses during the fallow period prior to planting corn in the model output was evaluated and compared to measured field values. M odel p erformance e valuation The DSSAT CERES Mai ze model has been extensively used in corn production for crop growth, development, as well as, other interactive processes (Li et al. 2015; Liu et al. 2011; Ma et al. 2006; O'Neal et al. 2002; Paredes et al. 2014; Paz et al. 1999) . Crop m odel performance was quantified by calculating the root mean square error (RMSE) on soil moisture, soil nitrate, final N uptake, final bioma ss, and yield per season (i.e. corn 2015, peanut 2016 and corn 2017) over the irrigation treatments and fertility rates evaluated. This is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. It represents the square root of the variance of the differences between predicted and observed values. The RMSE serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of p redictive power. RMSE is scale dependent; thus, it is a measure of accuracy used to compare forecasting errors of different models for a particular dataset and not between datasets (Hyndman and Koehler 2006) . In add ition, the coefficient of determination (R 2 ) was calculated for irrigated treatments, rainfed treatments and combined treatments (irrigated and rainfed

PAGE 229

229 treatments) for all seasons and variables evaluated (i.e. soil moisture, soil nitrate, final N uptake, f inal biomass, and yield). Percent bias (PBIAS) measures the average tendency of the simulated data to be larger or smaller than their observed counterparts. The optimal value is zero; positive values indicate model bias towards underestimation, whereas neg ative values indicates overestimation compared to observed data (Gupta et al. 1999) . PBIAS was calculated for total soil moisture. Results Based on the calibrated parameters, soil moisture and soil nitrate in the tota l soil profile (90 cm), final aboveground biomass, final N uptake and final grain yields were compared with observed data taken from GROW, SMS and NON irrigated treatments. Comparison was performed using the high N rate (336 kg N/ha), medium (247 kg N/ha) and low (157 kg N/ha) rates. The additional 17 kg N/ha fertilization applied on 6 April 2017 across all N rates as consequence of leaching rainfall events ( 4 and 5 April 2017) was included in the model. Model p erformance In season biomass . To evaluate the crop growth and development based on calibrated parameters, the aboveground biomass from the non stressed treatment (i.e. SMS high) was compared with model simulations. The model performance was also evaluated comparing estimations under lower fertility rates (SMS medium and SMS low). Biomass RMSE values were 1,579, 1,275 and 1,408 kg/ha for SMS high, medium and low treatments. RMSE values were lower than the variation within the field trial (STD across four blocks = 4,879, 5,356 and 1,780 kg/ha in the SMS high, medium and low treatments, respectively). Thus, these results showed high agreement between observed and simulated across all rates (Figure 4 2).

PAGE 230

230 Soil m oisture . Daily soil moisture in the soil profile (90 cm) during the growing seasons (co rn 2015 peanut 2016 and corn 2017) was compared with crop simulations performed in DSSAT (Figures 4 3 4 5). During all crop seasons, calculated RMSE values for the GROW, SMS and NON at high N rate were 13, 12 and 18 mm, with R2 values of 0.59, 0.68 and 0.49, respectively (Table 4 9). Soil moisture simulation during corn 2015 was slightly higher than the observed data in the irrigated treatments (GROW and SMS). Calculated PBIAS for the GROW, SMS and NON rotation resulted in 7.7, 6.7 and 6.3; indicating mo del bias towards overestimation (Table 4 9). However, the variation in soil moisture in the measured data across the field was very high (standard deviation (STD) ranged from 2 to 31 mm in GROW, and from 1 to 25 mm in SMS treatment). Simulated soil moistur e was within the range of variability in most of the crop rotation for GROW and SMS. The variation in the observations was reduced after corn 2015 and simulations followed the soil moisture pattern across the irrigated treatments within the range of variat ion (Figures 4 3 and 4 4). Disagreement between observed and simulated data is more pronounced from mid through late June 2016, and during the month of April 2017. The former period represents early growth stages of peanut; whereas, the latter represents early stages of corn. The model assumes slower water uptake and/or low evaporation losses from the soil profile compared to observed values, resulting in lower moisture content during both periods. The NON irrigated treatment had the highest RMSE across s easons and throughout the entire crop rotation (RMSE = 17, 20, 17 and 18 mm during corn 2015, peanut 2016, corn 2017 and during the entire crop rotation) (Table 4 9). The model

PAGE 231

231 under predicted soil moisture from 6 June to 16 July 2015. Although rainfall ev ents occurred, simulated total moisture was lower than the observed. Similarly, during 8 July to 30 July 2017, simulations were lower than observed. Nevertheless, simulated soil moisture in the NON treatment followed the trend of the observed data remained within the field soil moisture variability (Figure 4 5). A study evaluated the performance of CERES Maize model to simulate soil moisture at four depths of 0 20, 20 40, 40 60 and 60 80 for irrigated experiment; agreement of simulations and observed data. In comparison, simulated seasonal variation of soil moisture in the rainfed experiment showed larger variations compared to the irrigated experiment (Tojo Soler et al. 2 007) . Another study evaluated the model simulations versus observed soil moisture measured in three different sites (Bushland and Prairie View, Texas, and Powder Mill Maryland) during four years. Estimated RMSE values ranged from 0.03 to 0.11 cm/cm in Pra irie View and RMSE ranged from 0.05 to 0.14 cm/cm in Bushland, indicating a fair skill in simulating soil moisture in the upper 50 cm. Authors indicated that model performance varied year to year, but more accurate soil moisture simulations were observed d uring the growing seasons compared to simulations off seasons (Meng and Quiring 2008) Soil n itrate N . Due to the daily changes in weather, crop growth, soil microbial activities and soil water movement, soil mineral N is dynamic and varies over time. Measured soil nitrate in the profile (0 90 cm) was compared with DSSAT CERES Maize and CROPGRO simulations (Figures 4 6 4 8). Soil sampling events were scheduled to track the movement of nitrate before and after the fertili zer applications during all crop

PAGE 232

232 season (i.e. nitrate present in the soil, N uptake and N leaching); however, changes to the schedule were subject to personnel availability and weather conditions. RMSE was calculated to quantify the crop model performance. The RMSE values calculated for the high N rate resulted in 10.1, 14.1 and 20.3 mg/kg in the GROW, SMS and NON treatments. Calculated RMSE values were higher to the standard deviation calculated across all samplings (STD all = 7.7, 11.1 and 9.3 mg/kg). The highest RMSE was seen in the NON high treatment, reflected by the disagreement of the observed versus simulated data particularly during corn 2017. In general, the model overestimates nitrate in the soil after all fertilizer applications were performed i n corn 2015 and 2017. As well, high mineral N resulted from the decay of peanut residue. Although the observed data showed a significant increment in soil nitrate after fertilization events and organic matter decomposition, it occurred in a lower magnitude compared to the simulations. After this peak, the model continues to simulate the trend of the observed data (Table 4 10, Figure 4 6). Soil nitrate RMSE values calculated for the medium N rate resulted in 8.3, 9.3 and 11.7 mg/kg for the GROW, SMS and NON, respectively. These values were close to the standard deviation measured across all samplings (STD all = 6. 6, 4. 8 and 7. 3 mg/kg). In general, simulated soil nitrate followed the trend of the measured data and it was within the range of soil nitrate variabi lity within each sampling event, except during the fallow period 2016 17 after peanut biomass was left in the field and soil nitrate simulations were higher than observed, as well as, after all fertilizations in corn 2017 (Table 4 10, Figure 4 7). Due to t he sampling frequency (i.e. monthly after harvest), it is possible that higher soil nitrate values were not captured during subsequent sampling in the fallow

PAGE 233

233 2016 17 when peanut highest soil nitrate values were simulated. At the low N rate, the model simul ations overall were within the range of variability of sampling events, except for the high peak of nitrate simulated during the fallow 2016 17 due to the mineralization of peanut residue. Soil nitrate RMSE values were: 7.1, 6.4 and 8.0 mg/kg; these RMSE v alues were close to the total variability of the observed data during the crop rotation (STD all = 6.6, 4.2 and 5.5 mg/kg for the GROW, SMS and NON, respectively) (Table 4 10, Figure 4 8). Most of the disparities between simulated and observed data resulte d in the period of fallow 2015 2016, as well as, during the fallow 2016 2017. Thus, during the first fallow the observed data in the SMS high, medium and low N rates and the in the GROW medium and low N rates reflects higher soil nitrate due to the incorporation of corn residue through different agronomic practices performed in the field (i.e. disk, harrow, and plow). However, the model shows a lower agreement with the observed data, since it simulates an increase in soil nitrate of much lower magni tude during these periods. In contrast, soil nitrate simulations were over predicted in the second fallow period (2016 17). Simulated soil nitrate values were higher than the observed data in all treatments due to high mineralization of peanut biomass post harvest. Nevertheless, RMSE values were generally within the range of field observed soil nitrate measured standard deviations across different irrigation strategies and N rates. A study evaluated the applicability of DSSAT simulation models for soil N an d decomposition of soil organic matter in low input systems (Gijsman et al. 2002) . Authors used the CROPGRO crop simulation model and the Century method to simulate soil organic matter residue decomposition using a Brazilian experiment with seven

PAGE 234

234 leguminous residue types. Mineral N concentration was evaluated at 0 15 and 15 30 cm soil layers, resulting in RMSE ranging from 2.5 to 7.7 mg/kg for the top layer, and from 3.3 to 12.5 mg/kg in the 15 30 cm soil layer (Gijsman et al. 2002) . Thus , RMSE values increased with soil depth. These performance indices are lower than the ones found in the experimental field in NFREC SV; however, authors only considered the top 30 cm soil lay ers. Greater disagreement could be found in deeper layers, resulting in higher RMSE, as in the experimental field. Nevertheless, the higher mean differences were largely due to the peanut residue decomposition, increasing soil N in the profile for the subs equent corn growing season. N uptake . During the 2015 corn season, the model followed the trend of N uptake under the different irrigation strategies and N fertilizer rates. The RMSE for N uptake in the irrigated corn was 34 kg N/ha (individual RMSE rangin g from 3 to 46 kg N/ha). However, these values were lower than the variation within the observed data (STD ranged from 14 84 kg N/ha). In contrast, the RMSE for rainfed corn was 69 kg N/ha (individual RMSE per irrigation and fertility rates ranged from 5 2 to 83 kg N/ha). Higher RMSE occurred in the rainfed treatment, which also was higher than the variation within the observed data (STD = 35 kg N/ha). The model over predicted N uptake under rainfed conditions. The combined RMSE (i.e. irrigated and rainfed treatments) was 43 kg N/ha. Overall, RMSE indicated reasonable results to represent the observed N uptake; however, over predicted N uptake under rainfed conditions. The R 2 values for the irrigated, rainfed and combined treatments were 0.59, 0.78 and 0.80 , respectively (Table 4 11 and Figure 4 9).

PAGE 235

235 During the 2016 peanut season the N uptake was simulated as the total N uptake from the soil plus the N fixed by the crop during the growing season. The N uptake RMSE values were 36, 30 and 45 kg N/ha for the irr igated, rainfed and combined treatments, respectively. All RMSE values were below the variation in the observed data (STD = 86, 71 and 87 kg N/ha for the same treatments, respectively). The R 2 values for the irrigated, rainfed and combined were 0.83, 0.00 and 0.38, respectively (Table 4 11, Figure 4 9). Model was validated using 2017 field data . Most of the simulations were within the range of observed values. However, the model predicted lower N uptake values in the GROW low treatment (i.e. calendar based irrigation and low N) and overall, over predicted N uptake in the NON treatment across all N fertility rates compared to the observed values. During the 2017 corn season, RMSE for the irrigated, rainfed and combined treatments was 21, 82 and 50 kg N/ha; on ly the rainfed was higher than the variation within the observed data (STD = 45, 28 and 63 kg N/ha for the same treatments, respectively) (Table 4 11, Figure 4 9). Like in 2015, N uptake simulations in the NON treatment across all N rates were higher than observed data. Biomass . Aboveground biomass RMSE values for the irrigated, rainfed and combined treatments was 1,680, 1,236 and 1,547 kg/ha. Nevertheless, observed biomass variation was higher than the RMSE values (STD = 4,305, 3,499 and 5,056 kg/ha, respe ctively). The coefficient of determination (R 2 values) for the irrigated, rainfed and combined was 0.58, 0.98 and 0.86, respectively (Table 4 12, Figure 4 10). In the 2016 peanut growing season, aboveground peanut biomass RMSE values were 1,331, 1,396 and 1,353 kg N/ha for the irrigated, rainfed and combined treatments,

PAGE 236

236 respectively. The variation in the observed corresponding treatments was 2,668, 2,568 and 2,778 kg/ha. All RMSE indices resulted in lower values in comparison to variation of field experimen t. In general, the model simulations follow the biomass trend observed across irrigation treatments; thus, RMSE values support a good representation for biomass accumulation in peanut (Table 4 12, Figure 4 10). For the 2017 corn season, most of the simulat ions were within the range of observed values for the irrigated treatments; however, the model over predicted aboveground final biomass in the NON treatment across all N fertility rates. The RMSE for the irrigated, rainfed and combined treatments was 2,207 , 8,051 and 4,985 kg N/ha. These high RMSE values show the over prediction of the model compared to observed data, particularly in the rainfed treatment. During this year, a large variation in the measured data was observed (STD = 3,783, 2,233 and 7,399 kg /ha for the irrigated, rainfed and combined treatments, respectively). The model provided much lower error when simulating irrigated corn biomass versus under rainfed conditions. RMSE values obtained in the irrigated treatments across the three corn seaso ns were comparable with the ones reported by López Cedrón et al. (2008) after testing the CERES Maize model to accurately predict maize biomass and yield under water limiting conditions. Biomass RMSE across means of seven irrigation treatments was 2,020 kg /ha (López Cedrón et al. 2008) . As well, similar biomass normalized RMSE values (nRMSE = RMSE/mean of observed data, expressed as percentage) were found by Tojo Soler et al. (2007) across four maize hybrids gro wn in Brazil. Normalized RMSE values ranged from 23.6% to 32.9% for the irrigated corn; whereas for the rainfed corn ranged from 10.1% to 24.7% across the hybrids evaluated. For comparison, nRMSE

PAGE 237

237 were calculated for biomass obtained in the field experiment in NFREC SV resulting in values of 7% equally for the irrigated, rainfed and combined treatments during corn 2015; 9%, 10% and 9%, respectively for peanut 2016 biomass; and 11%, 87% and 32%, respectively for corn 2017 season. Based on the model simulati on evaluation agreement for biomass in 2015 corn and 2016 peanut for all treatments; whereas a ombined treatments during the corn 2017 season. Yield . During the 2015 corn season, final yield RMSE for the irrigated, rainfed and combined treatments was 1,139 kg/ha, 1,194 and 1,158 kg/ha, respectively. Individual estimations of RMSE values across all i rrigation and fertility rates ranged from 399 kg/ha to 1,448 kg/ha. Nevertheless, all calculated RMSE indices were lower than the variation within the observed data (STD = 1,765, 3,031 and 2,662 kg/ha). Corresponding R 2 were 0.65, 0.74 and 0.82. Estimated RMSE values and yield trend among irrigation and fertility treatments showed reasonable results to represent the observed yields (Table 4 13, Figure 4 11). Peanut pod yield RMSE values across irrigated, rainfed and combined treatments were 507, 870 and 651 kg/ha. Peanut yield varied between 94 and 876 kg/ha for irrigated peanut and between 757 and 1,044 kg/ha in rainfed peanut. The highest estimated RMSE values were similar to the variation in the observed data (STD = 753 and 818 kg/ha for irrigated and rai nfed treatments, respectively). The R 2 values for the irrigated, rainfed and combined were 0.43, 0.12 and 0.95 respectively. Based on

PAGE 238

238 the results of the performance evaluation indices, the model provided a good representation of the observed peanut yield d ata (Table 4 15, Figure 4 11). In the 2017 corn season, yield RMSE values ranged between 134 and 3799 kg/ha in irrigated corn; the highest RMSE found in the GROW Low treatment. In contrast, higher RMSE values were found in the rainfed treatment ranging fro m 4,330 to 5,826 kg/ha, reflecting a lower agreement with observed data and a yield over estimation under rainfed conditions. RMSE values for the irrigated, rainfed and combined treatments were 1,661, 5,328 and 3,362 kg/ha, respectively. In contrast, varia tion of observed data was lower in the same corresponding treatments (STD = 942, 1,362 and 3,338 kg/ha, respectively). RMSE values indicate a better representation of yield obtained under irrigated conditions; whereas a yield overestimation under rainfed c onditions (Table 4 13, Figure 4 11). Similar RMSE values have been reported for corn yield after the evaluation of the model across seven irrigated treatments (López Cedrón et al. 2008) . Authors reported RMSE v alues of 1,127 kg/ha for irrigated corn grain yield and RMSE values of 2,954 kg/ha for rainfed corn after model improvements for simulating water deficit. Another study conducted by Yang, et al. (2013) evaluated the CERES Maize model performance over long term continuous maize growth across three N rates: (i) no N (N0), (ii) 165 kg N/ha from synthetic fertilizer (N165), and (iii) 50 kg N/ha from synthetic fertilizer plus 115 kg N/ha from manure (N165M). Authors used nRMSE and arbitrary 15% agreed with measured yield data; however large disagreements were found in a few

PAGE 239

239 years of evaluation, thus, RMSE values we re 1,146, 1,482 and 1,749 kg/ha for the N0, N165 and N165M treatments. The nRMSE were 26%, 32% and 23%, respectively; N165 and N165M treatments between the simulated and the measured maize yields (Yang et al. 2013) . These RMSE reported values were similar to the RMSE values for irrigated yields obtained in NFREC SV experimental field. Calculated nRMSE values for the irrigated, rainfe d and combined treatments were 9%, 13% and 10% in 2015; and 13%, 92% and 33% in 2017 season, respectively. Thus, according to the classification 2015 season; whereas in 2017, the irrigated, rainfed and combined treatments resulted yields, respectively. As well, similar nRMSE values were found by Tojo Soler et al. (2007) across four maize hybrids gr own in Brazil (nRMSE < 10%). W ater b alance Table 4 14 shows the DSSAT simulated water balance for the crop rotation. Inputs correspond to irrigation, precipitation, water added with new mulch and initial soil moisture; whereas the outputs in the model cor respond to drainage, tile drain flow, runoff, mulch evaporation, soil evaporation, transpiration and potential ET. Simulations showed negligible values for water added with new mulch (i.e. no mulch added), RO (except during excessive and heavy rainfall ev ents; especially influenced by Hurricane Irma), tile drain flow and mulch evaporation. In addition, during the fallow periods, no transpiration or irrigation occurred. Generally, drainage occurring in irrigated treatments is higher than the NON treatment, since soil moisture is kept at high levels (i.e. close to FC) allowing less

PAGE 240

240 storage for rainfall events. Despite this fact, to evaluate irrigation efficiency on the GROW and SMS treatments in this section, cumulative simulated drainage in the NON irrigated was assumed as drainage due to rainfall across all treatments. Then, it was subtracted from cumulative drainage due to irrigation to compare the efficiency of irrigated treatments on keeping moisture within the soil profile. Corn 2015 . Irrigation applied in the GROW, SMS and NON treatments during the corn 2015 season was 321, 151 and 15 mm, respectively. The SMS treatment resulted in 53% water savings compared to the GROW; whereas, in the rainfed treatment 95% water savings was achieved. During the corn 2 015 season, 54 precipitation events occurred totaling 545 mm. In general, these events did not exceed 35 mm per event and were uniformly distributed during the growing season. Most of the precipitation occurred during reproductive stages; stages in which c orn is highly susceptible to water stress. Rainfall distribution and intensity provided favorable soil moisture conditions for the NON irrigated treatment. In fact, no significant differences in yield were found between the SWB treatment and the NON irriga ted treatment during that year (see Chapter 2). Simulated drainage corresponds to water leaving the rootzone as a result of rainfall and/or irrigation. Cumulative drainage in the GROW, SMS and NON irrigated treatments was 326, 158 and 87 mm for the GROW, S MS and NON treatments averaged across fertility rates. Thus, a reduction in final drainage of 51% and 73% was obtained by the SMS and NON treatments, respectively. Drainage resulted from irrigation was 239 and 71 mm in the GROW and SMS treatments, respecti vely.

PAGE 241

241 Therefore, a sensor based irrigation scheduling method can reduce cumulative drainage by 70% compared to a calendar based irrigation scheduling method. During the growing season, transpiration amounts differed between irrigated (GROW and SMS) and the rainfed (NON) treatments; resulting in a 15% reduction in transpiration in the NON treatment compared to the irrigated. Slight variations in transpiration occurred between N rates, where a 3% reduction in transpiration occurred in the low N rate compared to the high and medium rates. Fallow 2015 2016 . The fallow period 2015 16 covered from late August after corn harvest, until early May before the peanut season. Precipitation totaled 617 mm from which 60% resulted in drainage (average 370 mm across all tre atments) . A pproximate ly 40% resulted in soil evaporation (average 252 mm across all treatments) . Due to the intrinsic relationship between water and nitrate leaching, it is important to note that during this period about 60% of the total rainfall was lost as drainage, and consequently contributed to nitrate leaching. Peanut 2016 . During the peanut growing season, total irrigation applied was 544, 193 and 18 mm in the GROW, SMS and NON treatments, respectively. Thus, 64% and 97% water savings resulted in the SMS and NON treatments compared to the GROW treatment. Precipitation totaled 659 mm during the season, almost equal to the potential ET for this period (673 mm); however, the distribution and excessive amounts resulted in losses from the soil profile. Due to high intensity rainfall events, even in the NON irrigated treatment, most of the rainfall was not effective, resulting in high drainage amounts (i.e. drainage due to rainfall = 306 mm).

PAGE 242

242 Total cumulative drainage was 673, 366 and 306 mm in the GROW, SMS and NON treatments, respectively. Thus, the SMS treatment reduced total drainage by 46%; whereas, the NON treatment by 55% when compared to conventional practices (GROW treatment). In contrast, drainage occurring due to irrigation was 367 and 60 mm in the GROW and SMS treatments, respectively. Thus, drainage amounts can be reduced in 84% when using soil moisture sensors to schedule irrigation compared to a calendar based method. The model simulated runoff (average cum. runoff = 11 mm) due to the high inten sity rainfall events occurring during this season. Simulated transpiration resulted in a 22% reduction in the NON treatment compared to the irrigated ones. Fallow 2016 2017 . The fallow 2016 17 period encompasses the late October after peanut harvest until early March 2017 time period. Precipitation during this period totaled 242 mm; which was 60% lower than in the fallow period in 2015 16. On average across all treatments, 98 mm was lost as drainage and 94 mm as soil evaporation. Corn 2017 . During the corn 2017 season, irrigation applied in the GROW, SMS and NON treatments was 548, 303 and 48 mm, respectively. The SMS treatment resulted in 45% water savings compared to GROW; whereas, in a rainfed treatment, 91% water savings were achieved. However, in this c orn season, the GROW and SMS applied 70% and 100% more irrigation than in the 2015 season. During the corn 2017 season, 45 precipitation events occurred totaling 650 mm. Unlike the 2015 rainfall, in 2017 some rainfall events exceeded 35 mm and occurred in a sequence (i.e. heavy rainfall events consecutively one after the other), as well as, after fertilizer applications. The distribution and variation in rainfall magnitude, caused a higher requirement for irrigation across the treatments.

PAGE 243

243 Total drainage dur ing corn 2017 resulted in 685, 438 and 259 mm for the GROW, SMS and NON treatments averaged across fertility rates. Although heavy rainfall events were the main cause of drainage (259 mm), still a reduction in final drainage of 36% and 62% was obtained by the SMS and NON treatments, respectively. Then, drainage due to irrigation was 426 and 179 mm in the GROW and SMS treatments, respectively. Particularly during the 2017 corn season, due to the variability and uncertainty of rainfall, irrigation was schedul ed and in certain occasions it was followed by an unexpected rainfall event; resulting in drainage from the rootzone. Nevertheless, drainage reductions of 58% were achieved when using a sensor based compared to a calendar based irrigation scheduling method (i.e. SMS vs. GROW treatment). During the growing season, transpiration amounts differed between irrigated (GROW and SMS) and the rainfed (NON) treatments; averaging a 17% reduction in transpiration in the NON treatment compared to the irrigated ones. Tra nspiration in the GROW and SMS treatments receiving the medium and high N rates resulted in similar values (average 422 mm); whereas the low rate transpiration was 14% and 7% lower (362 and 394 mm for GROW and SMS, respectively). N itrogen b alance The DSSAT simulated N balance is presented in Table 4 15. In the simulated N balance, the model considers: initial final soil N (total NH 4 N and NO 3 N), mineralized N, and N applied through fertilizer as inputs. Whereas, the outputs of the simulated N balance cor respond to : N uptake from the soil, nitrate leaching, N denitrified, ammonia volatilization and N immobilized. Only significant (i.e. not negligible values) inputs and outputs are reported in this chapter.

PAGE 244

244 This section shows the N balance simulations perfo rmed in DSSAT during the corn 2015 fallow peanut 2016 fallow corn 2017 rotation (Table 4 15). Dynamics of components in the N balance (i.e. soil N, N uptake, N mineralized and N leached) are shown in Figures 4 12 4 14. Corn 2015. During corn in 2 015, simulated initial N was high (NO 3 N + NH 4 N = 39 N kg/ha) due to the incorporation and posterior mineralization of previous grasses planted in the experimental field . After corn planting, three N rates were evaluated: low, medium and high rates applyi ng 157, 247 and 336 kg N/ha, respectively. Simulated total aboveground N uptake varied across fertility rates and irrigated treatments. In general, greater N uptake was found in the highest N rates. However, as N rates increased , a greater variation in N u ptake was found across irrigation treatments; whereas, similar N uptake values were found in the low N rate. The low N rate (157 kg N/ha) resulted in similar N uptake values across irrigated treatments: 2 32 , 2 50 and 2 60 kg N/ha in GROW, SMS and NON, respe ctively. Increasing N applications to 247 kg N/ha (medium N rate), resulted in an N uptake of 2 50 , 2 90 and 27 3 kg N/ha in GROW, SMS and NON, respectively. In contrast, N uptake in the high rate resulted in: 2 6 0, 3 30 and 27 4 kg N/ha, respectively . Thus, inc reasing to N applications from the medium to the high N rate (i.e. from 247 to 336 kg N/ha) resulted in only 4 %, 1 4 % and 0.2 % greater N uptake in the GROW, SMS and NON treatments, respectively. Whereas, increasing N rates from the low to the high N rate, r esulted in 12 %, 38 % and 21% greater N uptake for the GROW, SMS and NON treatments, respectively. Therefore, under high fertilizer applications, careful irrigation

PAGE 245

245 management is required to maintain N within the rootzone promoting its uptake by the crops. Ratios of N uptake vs. N applied for the GROW, SMS and NON treatments were: 1. 48 , 1. 52 and 1. 44 , respectively at the low N rate; 1.02 , 1.1 8 and 1.1 2 , respectively at the medium N rate; and 0.7 7 , 0.9 8 and 0.82, respectively at high N rate. Thus, these ratio s provide insights of the use efficiency of the fertilizer application rates across irrigation strategies, where the highest ratio values were achieved using the lowest N applications and vice versa. The initial N (39 kg N/ha before fertilization ) and the rainfall amounts and distribution could have contributed to the total aboveground N uptake during the season. Th e initial N was the result of previous fallow periods in which a combination of grasses was grown and incorporated into the soil prior to planti ng. However, based on previously described model performance evaluation, it is important to note that the model overestimates corn N uptake under rainfed conditions. The SMS low, medium and high N rates resulted in 3%, 1 6 % and 2 7 % greater N uptake than t he GROW low, medium and high N rates, respectively. Similarly, the NON irrigated treatment resulted in 3 % lower and , 9 % and 5 % greater simulated N uptake compared to the GROW treatment. Rainfall contributions during the 2015 growing season allowed N from the fertilizer to be in solution for uptake; however, excessive irrigation resulted in the opposite effect. Nitrate leaching was 16 , 8 3 and 1 61 kg N/ha in the GROW low, medium and high rates, respectively. In comparison, SMS NO 3 N leaching was 8 , 4 9 and 89 kg N/ha in the low high rates (i.e. 48 %, 51 % and 84% less leaching compared to the GROW across the same rates ) . N leaching in the

PAGE 246

246 NON treatment was 5 , 37 and 108 kg N/ha, which represents 65 %, 5 5% and 33 % lower N leaching compared to the GROW receivi ng the same N rates. The N contribution from previous crops should be considered in the following crop fertilization program. Is evident that 39 kg N/ha resulted in a large contribution for the uptake of N during initial stages . T he high frequency irrigati on in the GROW treatment resulted in lower N uptake and larger N leaching amounts (i.e. GROW h igh N); whereas, keeping adequate moisture for plant growth, allow ed greater N uptake and reduce d N leaching (i.e. SMS high and medium rates). Fallow 2015 16 . During this period the initial N ranged from 8 32 kg N/ha; the highest value in the NON high N rate treatment. Soil N from inorganic fertilizer or as result of mineralization processes remains in the soil profile if crop is unable to uptake it. Thus, in the NON irrigated treatment, the excess of N not taken up by the crop was available for the following season. Furthermore, after harvest, corn residue (i.e. all biomass except ears) was left in the field; allowing mineralization processes to occur. Duri ng the fallow 2015 16 period, the potential cumulative mineralize N was 198, 220 and 236 kg N/ha on average on the GROW, SMS and NON irrigation treatments across N rates, respectively. However, simultaneous processes are happening in the model; thus, not a ll this amount is available for plant uptake. Cumulative N immobilized averaged 115 kg N/ha across all treatments ; whereas average N leaching was 66, 79 and 120 kg N/ha for the GROW, SMS and NON irrigated treatments across N rates. Particularly in the high N rate, simulated N leaching during this fallow period (after corn fertilizers applied in 2015) resulted in high values across all irrigated treatments (68, 96 and 149 kg N/ha in the GROW, SMS and NON high, respectively). The cumulative

PAGE 247

247 mineralization v alues were reduced on average to 26 kg N/ha as mineral N available at the end of the season after all soil processes occurred. N leaching could potentially be reduced if a winter crop is planted during this fallow period, while it can actively uptake N as mineralization processes occur. However, the field remained as fallow until May 2016. Thus, most of the N that potentially could be absorbed by the crops from mineralization processes was lost as leaching. Peanut 2016 . After the winter fallow period, the field was prepared for peanut production. Initial mineral N was on average 26 kg N/ha. A minimum N application (17 kg N/ha) was performed during peanut planting . At the end of the peanut season, cumulative mineralized N was 109, 94 and 75 kg N /ha on the GR OW, SMS and NON treatments, on average across fertility rates applied in the previous 2015 corn season. Cumulative simulated l eaching during the peanut growing season resulted in 53 , 38 and 37 kg N/ha for the GROW, SMS and NON across all N rates. Under fre quent irrigation events, greater N leaching occurred; thus, using SMS to trigger irrigation reduced N leaching by 3 4 % , 22% and 27% compared to GROW low, medium and high N rates ; whereas in a rainfed system (NON treatment), simulations resulted in 30 % , 28 % and 33% nitrate leaching reduction compared to the GROW in the same N rates, respectively . Peanut is a legume capable of fix ing atmospheric N. Th us, simulated N uptake corres ponds to the N uptake from the soil without considering the total N fixed . Avera ge peanut N uptake from the soil averaged 61, 74 and 50 kg N/ha for the GROW, SMS and NON treatments across N rates. F allow 2016 17 . The fallow period 2016 17 covered from mid October (after peanut harvest) until late March of the following year (before c orn planting). During this

PAGE 248

248 period, after peanut biomass was left in the ground, simulated mineralization processes occurred in a rapid rate. Cumulative mineral N averaged 192 kg N/ha for all treatments during this fallow period; however, average immobilize d N averaged 64 kg N/ha (lower than in the fallow 2015 16 when corn residue was left aboveground). Therefore, greater organic matter from peanut residue started mineralization processes simulating large N available for uptake and less N immobilization. The field remained fallow during approximately five months, most N was lost as nitrate leaching by the end of this period. On average, simulated N leaching was 69 kg N/ha across irrigated treatments and N rates during the fallow 2016 17. Based on these result s, it is important to recognize that N potentially available for plants it can be leached if not taken up or hold for the following crop season. Although field data and model simulations presented the highest discrepancies in soil nitrate during this fallo w period, still field soil samplings showed an increase in soil N after the decay of peanut residue. This increment is representative for N uptake and/or N leaching, regardless the model over predictions. Corn 2017 . Initial soil N after the fallow period r esulted in 64 kg N/ha, averaged across all treatments. Due to leaching rainfall events occurring early in the season after fertilizer applications, an additional N fertilizer application of 17 kg N/ha was applied equally across all treatme nts. During this period, N uptake in the GROW low, medium and high rates was 1 63 , 2 51 and 2 5 5 kg N/ha, respectively. These amounts represent a ratio of 0. 94 , 0.9 6 and 0. 72 kg N uptake/kg N applied, respectively. The N uptake in the GROW h igh N rate was 57 % greater than GROW low rate; however, only 2 % greater than the GROW m edium rate. In comparison, the SMS N uptake was 236 , 2 56 and 2 61 kg N/ha for the

PAGE 249

249 low, medium and high rates, representing a 1. 3 5, 0.9 8 and 0.7 4 ratios kg N uptake per kg of N applied. The high rate re sulted in 1 1% higher N uptake than the low rate, but only in 2% greater N uptake compared to the medium rate. In contrast, simulated N uptake in the NON irrigated treatment was generally high across all rates: 1 89 , 211 and 2 26 kg N/ha; corresponding to a 1 .09 , 0. 80 and 0. 64 kg N uptake/ kg N applied ratios , low medium and high, respectively . The high rate was 2 0 % and 7 % higher than the low and medium rates, respectively. In all treatments, the high N rate resulted in the lowest efficiency ratio (kg N uptake /kg N applied) . However, in the GROW treatment the highest ratio was achieved by the medium rate; whereas in the SMS and NON treatments, the low rate resulted in the highest ratio. N itrate leaching during this period was greater than in 2015, mostly due to the rainfall intensity and distribution during the season. Total N leaching amounts for low, medium and high rates were: 124 , 118 and 204 kg N/ha in the GROW treatment, 56 , 119 and 200 kg/ha in the SMS treatment and 89 , 156 and 227 kg/ha in the NON treatm ent, respectively. Therefore, the high N rate resulted in the largest N leaching amounts between N rates across all irrigation treatments . The NON high treatment resulted in the highest nitrate leaching ( 227 kg/ha) . Similar leaching amounts were found in the GROW and the SMS treatments, except in the low rate. The SMS low resulted in a 5 5 %, N leaching reduction compared to the GROW treatment at the same rate. In contrast, N leaching in the NON treatment resulted in a 29 % reduction under a low N rate app lication, but on a 32 % and 11 % N leaching increase in the medium and high N rates, respectively compared to the GROW treatment in the same N rates .

PAGE 250

250 Rainfall amounts and distribution play an important role in N leaching events. Most of the simulated nitrate leaching events occurred after the fertilizer applications, as well as, after heavy rainfall events. Conclusions The evaluation of the DSSAT crop growth models (CERES Maize and CROPGRO) with field measurements provided good performance on the irrigated tr eatments; however, lower model performance was found when compared to the NON irrigated treatment. Simulated nitrogen uptake, final aboveground biomass and yields in the irrigated treatments resulted in RMSE values within the variation of the observed valu es and PBIAS values lower than the coefficient of variation of the observed data. By contrast, simulations under rainfed conditions, resulted in larger RMSE and PBIAS values towards overestimation in comparison to observed data; particularly in the 2017 co rn season. Nitrogen uptake resulted in RMSE values of 34, 69 and 48 kg N/ha and PBIAS values of 12.6%, 35.5% and 19.2% towards overestimation for irrigated, rainfed and combined treatments during corn 2015, compared to observed data. During peanut 2016, N uptake RMSE values were 30, 45 and 36 kg N/ha and PBIAS values were 0.9%, 6.6% and 2.6% towards underestimation for the irrigated, rainfed and combine treatments, respectively. During corn 2017 season, performance indices showed higher discrepancies in rainfed corn between the model and the observed data. RMSE values were 29, 64 and 44 kg N/ha; leading to PBIAS of 2.8%, 62.2% and 15.8% for the irrigated, rainfed and combined treatments, respectively. In terms of corn aboveground biomass, RMSE values we re 1,680, 1,236 and 1,547 kg/ha; whereas PBIAS were 1.5%, 6.4% and 2.8% for the irrigated, rainfed and

PAGE 251

251 combined treatments, respectively during 2015 corn season. In peanut 2016, RMSE values for the same treatments were 1,331, 1,396 and 1,353 kg/ha; whereas PBIAS estimations were 6.1%, 7.4% and 9.1% in comparison to the observed data. In corn 2017, RMSE values for the irrigated, rainfed and combined treatments were 2,428, 9,051 and 5,589 kg/ha, respectively and PBIAS estimations of 9.1%, 86.7% and 24.3%. By the large numbers, both indices reflect an overestimation of the model in the rainfed treatment compared to observed values. Irrigated, rainfed and combined corn yield RMSE values were 1,139, 1,194 and 1,158 kg/ha; resulting in PBIAS values of 8.4%, 7.4% and 8.1% towards overestimation during corn 2015. Peanut yields resulted in RMSE values of 507, 870 and 651 kg/ha for the same treatments and in calculated PBIAS of 4.3%, 16.8% and 7.3%, respectively; indicating an underestimation of simulated yields. C alculated RMSE values for corn yields in 2017 resulted in 1,661, 5,328 and 3,362 kg/ha for the irrigated, rainfed and combined treatments, respectively. The larger RMSE values in the rainfed corn indicating a larger deviation between simulated and observed data. Corresponding calculated PBIAS values were 2.2%, 91.4% and 15.5%, respectively; indicating a model large overestimation on rainfed corn yields. The use of this crop simulation models allowed a better understanding of the N processes and N fate not quantified in field conditions during the corn fallow peanut fallow corn rotation. In terms of soil nitrate, model overestimations were found in soil nitrate concentrations in the total soil profile during the fallow period 2016 17 after peanut residue wa s left in the field, as well as, after all fertilizer applications were performed in corn. In contrast, model underestimations occurred when mineralization

PAGE 252

252 processes occurred. However, soil nitrate simulations followed the soil nitrate trend across irrigat ion strategies and N fertility rates of the experimental data. In addition, important contributions from mineralization process could benefit following crops growth and development. Cumulative N mineralization during the fallow 2015 16 (i.e. during decay o f corn residue) ranged from 195 to 245 kg N/ha, whereas during fallow 2016 17 (i.e. during decay of peanut residue) ranged from 179 to 198 kg/ha. Simultaneously, N immobilization processes occurred according to the C:N ratio, ranging from 105 128 kg N/ha i n the fallow 2015 16 and from 45 74 kg N/ha. Due to the intrinsic dynamics of the processes of the N balance (i.e. mineralization, immobilization, uptake, leaching), at the end of those fallow periods, soil N resulted in 26 kg N/ha and 64 kg N/ha, which co nstitute the initial soil N available for the subsequent crop. These amounts are readily available N for crop uptake; otherwise are potentially leached due to the low water holding capacity of sandy soils combined with high intensity and magnitude of rainf all events in Florida. Based on these results, initial N concentrations per crop season could potentially be incorporated in the fertilization program of the following crop. If initial N is incorporated in the fertilizer application schedule, N fertilizer N fertilizer. Pre plant soil N analysis in the top soil layer is recommended to determine initial N conditions; avoiding excessive fertilizer applications if initial N requirements are fulfilled. Through the water and N balances, t he effectiveness of the irrigation treatments and N fertility rates on N uptake and N leaching during the crop rotation in sandy soils was evaluated. Major differences in N uptake were found between the low and the high

PAGE 253

253 rate, but not between the medium and the high rates. During the first corn season, i ncreasing N applications by 26% from the medium to the high N rate (i.e. from 247 to 336 kg N/ha) resulted in only 4 %, 1 4 % and 0.2 % greater N uptake in the GROW, SMS and NON treatments, respectively. Th us , under high fertilizer applications, careful irrigation management is required to maintain N within the root zone promoting its uptake by the crops . In contrast, N uptake in the low treatment r esulted in 11%, 28% and 18% lower N uptake compared to the high N rate. During 2017 corn season, similar N uptake values resulted in the medium and high rates, where the high N rate was only 2%, 2% and 7% higher than the medium N rate in the GROW, SMS and NON treatments, respectively. In contrast, N uptake in the high N rate was 57%, 11% and 20% higher than in the low N rate. During the 2017 season, heavy rainfall events reduced the availability of N for uptake. T he use of SMS resulted in 3 4 %, 22 % and 27 % l ower leaching compared to the GROW across the low, medium and high N rates during corn 2015; whereas in 2017, resulted in a 5 5 %, 0% and 2 % N leaching reduction compared to the GROW treatment at the same rates. Similar N leaching values resulted in the medi um and high rates in both treatments in 2017. Th e difference in N leaching across seasons was mainly due to the heavy rainfall events occurred in 2017, which resulted in greater N leaching than in 2015 . In 2015 corn season, t he NON irrigated treatment resu lted in 65 %, 55 % and 33 % lower N leaching compared to the GROW low, medium and high N rates. During 2017 corn season, N leaching in the NON low treatment resulted in 29 % less leaching compared to the GROW low rate, but in 32% and 11% higher N leaching than the

PAGE 254

254 GROW treatment at the medium and high N rates, respectively. In 2017, the N uptake in the NON treatment was affected by the lower soil moisture during the season, which caused a reduction in N availability through the season. Consequently, lower N uptake resulted in high N leaching across the fertility rates, particularly in the medium and high rates. Nevertheless, due to discrepancies between model simulations and observed data in the NON irrigated treatment, these results might need further model improvements to be representative of the observations under rainfed conditions. During the fallow periods 2015 16 and 2016 17, average N leaching was 88 and 69 kg N/ha, respectively. Potential leaching in these periods might be reduced by planting cover c rops, which can cut fertilizer costs, reduce the need of herbicides or pesticides, T he high frequency irrigation applications in the GROW treatment resulted in lower N uptake and larger N leaching amounts (i.e. GROW h igh N); whereas, keeping adequate moisture for plant growth, allow ed a greater N uptake and reduce N leaching (i.e. SMS high and medium rates). Simulated results showed that the use of SMS can potentially incre ase N uptake due to an increase in soil nitrate, whereas reducing N leaching. Similar yields were found between the GROW and SMS treatments at the high and medium N rates. Thus, reductions in water use, drainage and N leaching can be achieved when using a sensor based methodology for irrigation scheduling and following N rates close to the UF/IFAS recommendations. Nevertheless, even careful irrigation and fertility management may result in N leaching if rainfall amounts and distribution exceed the potential holding capacity of soil layers in sandy soils.

PAGE 255

255 Table 4 1. Taxonomic classification of predominant soils in NFREC SV, Live Oak, Florida (Soil Survey Staff 1999; Soil Survey Staff 200 6) . Soil name Family or higher taxonomic classification Albany Loamy, siliceous, subactive, thermic Grossarenic Paleudults Alpin Thermic, coated Lamellic Quartzipsamments Blanton Loamy, siliceous, semiactive, thermic Grossarenic Paleudults Chipley Th ermic, coated Aquic Quartzipsamments Foxworth Thermic, coated Typic Quartzipsamments Hurricane Sandy, siliceous, thermic Oxyaquic Alorthods

PAGE 256

256 Table 4 2. Soil chemical analysis. Pre planting average initial soil conditions in corn field, NFREC SV 2015. Block Map unit Depth Bulk Density Organic Matter TKN NO 3 N NH 4 N pH Mehlich 1 P Mehlich1 K Mehlich 1 P Mehlich 1 K ( cm ) (Mg/m 3 ) (%) ----(mg/kg ) --------(mg/kg ) --------(ppm) ----1 43 0 15 1.45 1.2 419.3 2.6 2.1 6.3 45.4 22.6 11.3 5.7 1 4 3 15 30 1.31 0.8 405.8 2.1 1.3 6.6 39.8 23.7 9.9 5.9 1 43 30 60 1.53 0.3 231.0 1.6 1.5 6.3 35.5 8.2 8.9 2.0 1 43 60 90 1.54 0.3 94.0 0.8 0.7 6.0 32.0 5.9 8.0 1.5 1 43 0 15 1.43 1.2 219.6 1.6 1.1 6.2 37.1 15.0 9.3 3.7 1 43 15 30 1.70 1.1 213.4 1.1 1.4 6 .0 31.7 9.6 7.9 2.4 1 43 30 60 1.61 0.8 112.3 0.7 1.0 5.6 24.4 7.5 6.1 1.9 1 43 60 90 1.50 0.2 90.5 0.4 1.4 5.4 19.1 6.9 4.8 1.7 1 43 0 15 1.51 2.1 544.5 3.7 2.0 5.8 109.5 41.1 27.4 10.3 1 43 15 30 1.29 1.3 335.0 3.3 2.5 5.6 85.6 23.9 21.4 6.0 1 43 30 60 1.47 1.0 209.4 2.4 1.9 5.7 60.8 6.9 15.2 1.7 1 43 60 90 0.2 128.5 2.0 1.1 5.5 46.6 4.6 11.6 1.1 2 45 0 15 1.64 1.8 542.1 1.7 2.4 5.8 47.5 18.7 11.9 4.7 2 45 15 30 1.60 1.4 396.6 0.3 1.9 5.8 38.6 26.0 9.7 6.5 2 45 30 60 1.47 0.5 109.7 0.3 3.2 5.6 1 4.6 8.2 3.6 2.1 2 45 60 90 1.33 1.2 410.8 2.7 1.3 6.2 70.5 33.4 17.6 8.3 2 26 0 15 1.47 1.2 434.5 3.1 1.6 6.2 72.9 33.6 18.2 8.4 2 26 15 30 1.49 1.0 325.5 1.5 1.1 6.0 42.3 15.2 10.6 3.8 2 26 30 60 1.50 0.9 173.5 0.9 1.1 5.5 36.6 6.5 9.2 1.6 2 26 60 90 0.5 109.0 1.5 1.3 5.2 37.1 5.3 9.3 1.3 3 45 0 15 1.40 1.6 624.1 3.1 3.3 5.7 53.4 37.2 13.4 9.3 3 45 15 30 1.53 1.7 452.9 1.1 2.3 5.9 33.0 28.4 8.2 7.1 3 45 30 60 1.48 0.8 202.9 0.7 1.2 5.7 26.2 16.1 6.5 4.0 3 45 60 90 1.34 0.3 98.9 0.6 3.9 5.5 16.5 6 .2 4.1 1.6 3 26 0 15 1.32 1.2 731.2 2.2 1.5 5.2 45.8 18.6 11.4 4.6 3 26 15 30 1.46 0.8 343.0 0.6 1.5 5.5 42.4 22.8 10.6 5.7 3 26 30 60 1.51 0.2 174.5 0.7 5.4 5.6 30.1 12.3 7.5 3.1 3 26 60 90 1.63 0.5 127.8 0.4 3.8 5.2 19.7 15.8 4.9 3.9

PAGE 257

257 Table 4 2. Cont inuation Block Map unit Depth Bulk Density Organic Matter TKN NO 3 N NH 4 N pH Mehlich 1 P Mehlich1 K Mehlich 1 P Mehlich 1 K ( cm ) (Mg/m 3 ) (%) ----(mg/kg ) --------(mg/kg ) --------(ppm) ----4 45 0 15 1.39 1.5 596.2 4.0 1.3 5.4 51.5 17.7 12.9 4.4 4 45 15 30 1.56 0.8 468.2 2.5 2.0 5.6 39.9 28.3 10.0 7.1 4 45 30 60 1.50 0.5 186.8 1.3 1.7 5.5 30.6 15.5 7.6 3.9 4 45 60 90 1.61 0.1 105.8 0.5 3.9 5.3 17.9 12.2 4.5 3.0 4 26 0 15 1.46 1.2 489.0 3.6 1.8 5.7 68.5 29.3 17.1 7.3 4 26 15 30 1.60 0.8 5 11.3 3.3 2.4 5.7 65.7 28.5 16.4 7.1 4 26 30 60 1.47 0.5 241.3 1.3 1.1 5.6 42.7 5.3 10.7 1.3 4 26 60 90 1.45 0.8 160.4 1.1 1.3 5.3 31.0 3.6 7.7 0.9 Average 0.9 306.1 1.7 2.0 5.7 42.8 17.2 10.7 4.3 STD 0.5 179.8 1.1 1.0 0.3 20.4 10.5 5.1 2.6 Max 2.1 731.2 4.0 5.4 6.6 109.5 41.1 27.4 10.3 Min 0.1 90.5 0.3 0.7 5.2 14.6 3.6 3.6 0.9

PAGE 258

258 Table 4 3. Nitrogen fertilizations rates (high, medium and low) dates, amounts and types applied during corn 2015, peanut 2016 and corn 2017 growing seasons at N FREC SV. Year Date Fertilizer Composition Nutrient N fertility rates (kg/ha) 1 Low Medium High Corn 2015 03 Apr Planting day (16 16 0) N 34 34 34 P 34 34 34 K 0 0 0 17 Apr 1 st Granular (33 0 0) N 9 25 34 (0 46 0) P 84 84 84 (0 0 60) K 110 110 110 01 May 2 nd Granular (33 0 0) N 11 27 45 (0 46 0) P 50 50 50 (0 0 60) K 86 86 86 08 May 1 st Liquid Sidedress (28 0 0) N 26 40 56 15 May 2 nd Liquid Sidedress (28 0 0) N 26 40 56 22 May 3 rd Liquid Sidedress (28 0 0) N 26 40 56 29 May 4 th Liquid Sidedress (28 0 0) N 26 40 56 TOTAL N applied N 157 247 336 Peanu t 2016 13 May Planting day 23 Jun Gypsum application Ca 493 493 493 S 359 359 359 24 Jun Granular application N 17 17 17 P 39 39 39 K 157 157 157 TOTAL N applied N 17 17 17 Corn 2017 21 Mar Planting day (16 16 0) N 34 34 34 P 34 34 34 K 0 0 0 06 Apr 1 st Granular (33 0 0) N 9 25 34 (0 46 0) P 0 0 0 (0 0 60) K 98 98 98 06 Apr Supplem. Granular 2 (21 0 0) N 17 17 17 20 Apr 2 nd Granular (33 0 0) N 11 27 45 (0 46 0) P 0 0 0 (0 0 60) K 73 73 73 20 Apr K Mag Application (0 0 22) K Mag 25 25 25 27 Apr 1 st Liquid Sidedress (28 0 0) N 26 40 56 03 May 2 nd Liquid Sidedress (28 0 0) N 26 40 56 09 May 3 rd Liquid Sidedress (28 0 0) N 26 40 56 15 May 4 th Liquid Sidedress (28 0 0) N 26 40 56 TOTAL N applied N 174 263 353 1 N rates: high = 336 kg N/ha; medium = 247 kg N/ha; low = 157 kg N/ha. 2 Supplemental N application (17 kg N/ha ) due to leaching rain events modified all N fertility rates in 2017. 1 N fertility rates in peanut correspond to the rates applied in pre vious corn 2015 growing season. Fertilizer applications performed in peanut 2016 were equally applied in all plots.

PAGE 259

259 T able 4 4 . Cul tivar genetic coefficients calibrated for McCurdy 84aa in CERES Maize model. Cultivar genetic coefficient Obs. Biomass Obs. Yield Sim. Biomass Sim. Yield RMSE Biomass RMSE Yield G2 G3 ------------------------------(kg/ha) ----------------------------920 8 27526 13066 26090 13465 1436 399 910 8 27526 13066 24945 12214 2581 852 900 8 27526 13066 24834 12094 2692 972 890 8 27526 13066 24724 11974 2802 1092 880 8 27526 13066 24614 11854 2912 1212 870 8 27526 13066 24503 11734 3023 1 332 860 8 27526 13066 24393 11613 3133 1453 850 8 27526 13066 24282 11493 3244 1573 840 8 27526 13066 24172 11373 3354 1693 830 8 27526 13066 24062 11253 3464 1813 820 8 27526 13066 23951 11133 3575 1933 810 8 27526 13066 23840 11012 3686 2054 800 8 27526 13066 23724 10892 3802 2174 800 8.25 27526 13066 23609 10772 3917 2294 800 8.5 27526 13066 23929 11109 3597 1957 800 8.75 27526 13066 24238 11445 3288 1621 800 9 27526 13066 24548 11782 2978 1284 800 9.25 27526 13066 24857 12118 2669 948 800 9 .5 27526 13066 25165 12455 2361 611 800 9.75 27526 13066 25474 12792 2052 274 *800 10 27526 13066 25782 13128 1744 62 *Final combination of genetic coefficients (G2 = 800, G3 = 10) selected based on lowest RMSE compared to biomass and yield average obse rved data across four blocks.

PAGE 260

260 Table 4 5. Cultivar coefficients modified for corn production simulations in CERES Maize model. Var Var name ECO# P1 P2 P5 G2 G3 PHINT IB0035 McCurdy 84aaMod . IB0001 260 0.3 1100 800 10 43 VAR#: Identification code or n umber for a specific cultivar VAR NAME: Name of cultivar ECO#: Ecotype code of this cultivar, points to the Ecotype in the ECO file (currently not used). P1: Thermal time from seedling emergence to the end of the juvenile. Phase (expressed in degree days a bove a base temperature of 8°C) during which the plant is not responsive to changes in photoperiod. P2: Extent to which development (expressed as days) is delayed for each hour increase in photoperiod above the longest photoperiod at which development proc eeds at a maximum rate (considered to be 12.5 hours). P5: Thermal time from silking to physiological maturity (expressed in degree days above a base temperature of 8 °C). G2: Maximum possible number of kernels per plant. G3: Kernel filling rate during the linear grain filling stage and under optimum conditions (mg/day). PHINT: Phylochron interval; the interval in thermal time (degree days) between successive leaf tip appearances.

PAGE 261

261 Table 4 6. Methods used in the DSSAT CERES maize model for the crop rotation simulations. Processes Simulation Method Evapotranspiration FAO 56 Infiltration Soil Conservation Service Photosynthesis Leaf photosynthesis response curve Hydrology Ritchie water balance Soil Organic Matter (SOM) Century method (Parton) Soil evapo ration method Suleiman Ritchie Soil layer distribution Unmodified soil profile 1 1 Soil profile created using 4 depths: 0 15, 15 30, 30 60 and 60 90 cm.

PAGE 262

262 Table 4 7 . Planting and harvest dates from field experiment used in the DSSAT CERES Maize m odel for the crop rotation simulations. Activity Crop Date Planting date Corn 2015 4/3/2015 Harvest date 8/18/2015 Harvest date Fallow 2015 16 5/12/2016 Planting date Peanut 2016 5/13/2016 Harvest date 10/4/2016 Harvest date Fallow 2016 17 3/20/20 17 Planting date Corn 2017 3/21/2017 Harvest date 8/16/2017 The fallow periods were harvested one day before the following crop planting date. The model assumes the day after harvest as the fallow planting date (input not required).

PAGE 263

263 Table 4 8 . Initi al conditions from field experiment used in the DSSAT CERES Maize model for the crop rotation simulations. Depth, base of layer Bulk Density, moist Organic Carbon Total Nitrogen pH in water Phosphorus extractable Volumetric water content Ammonium (NH 4 ) Ni trate (NO 3 ) (cm) g/cm 3 % % mg/kg cm 3 /cm 3 g(N)/Mg (soil) g(N)/Mg (soil) 15 1.5 0.76 0.05 6 61.9 0.1 1.8 2.5 30 1.5 0.67 0.04 6 49.8 0.1 1.7 1.7 60 1.5 0.66 0.02 5.9 39 0.1 1 1.6 90 1.5 0.34 0.01 5.7 33.5 0.1 0.9 1.4

PAGE 264

264 Table 4 9 . Model performance i ndices (RMSE , R 2 and PBIAS ) for soil moisture (mm) across GROW, SMS and NON treatments at high N rates during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation. Treatment Index Corn 2015 Peanut 2016 Corn 2017 All crop rotation GROW Hig h STD max (mm) 28 16 31 STD min (mm) 7 3 2 Median (mm) 16 8 8 RMSE (mm) 16 13 11 13 R 2 0.61 0.52 0.59 0.59 PBIAS (%) 7.7 SMS High STD max (mm) 20 17 25 STD min (mm) 3 5 1 Median (mm) 12 10 4 RMSE (mm) 12 13 10 12 R 2 0. 54 0.75 0.71 0.68 PBIAS (%) 6.7 NON High STD max (mm) 21 45 14 STD min (mm) 2 2 2 Median (mm) 14 12 6 RMSE (mm) 17 20 17 18 R 2 0.54 0.87 0.53 0.49 PBIAS (%) 6.3 STD Max : Max standard deviation during crop rotation. STD Min : Min standard deviation during crop rotation. RMSE: Quantifies the model performance (model performance index). Calculated using all crop rotation observations vs simulated values. R 2 : coefficient of determination (model performance index).

PAGE 265

265 Table 4 10 . Model performance indices (RMSE and R 2 ) for soil nitrate (mg/kg) across GROW, SMS and NON treatments at high, medium and low N rates during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation. High Medium Low GROW SMS NON GROW SMS NON GROW SMS NON STD all 7.72 11.12 9.32 6.59 4.76 7.26 6.59 4.24 5.52 STD MaxS 21.82 68.23 29.53 23.58 14.24 25.45 23.58 9.38 23.19 STD MinS 0.15 0.55 0.23 0.11 0.24 0.24 0.11 0.19 0.15 RMSE 7.68 11.71 17.52 5.98 6.18 8.83 5.97 5.25 5.09 R 2 0.67 0.3 5 0.38 0.48 0.44 0.40 0.21 0.32 0.38 STD all : Standard deviation of mean soil nitrate values across all rotation. STD MaxS : Max standard deviation from individual samplings during crop rotation. STD MinS : Min standard deviation from individual samplings duri ng crop rotation. RMSE: Quantifies the model performance (model performance index). Calculated using all crop rotation observations vs simulated values. R 2 : coefficient of determination (model performance index).

PAGE 266

266 Table 4 1 1 . Model performance indices (RM SE , R 2 and PBIAS) for N uptake across all treatments, irrigated only and rainfed only per crop seasons (corn 2015, peanut 2016 and corn 2017). Season Treatment Mean Obs. values Simulated values STD Obs CV RMSE per treatment RMSE (irrigated, rainfed or combined) R 2 PBIAS -----------(kg N/ha) ----------(%) ---(kg N/ha) ---(%) Corn GROW Low 229 232 84 0.36 3 2015 GROW Med 204 250 49 0.24 46 GROW High 217 260 32 0.15 43 SMS Low 203 238 60 0.29 35 SMS Med 278 290 49 0.1 8 12 SMS High 290 330 14 0.05 40 Irrigated 59 34 0.78 12.6 NON Low 174 226 9 0.05 52 NON Med 205 273 38 0.19 68 NON High 191 274 49 0.25 83 Rainfed 35 69 0.80 35.5 All treatments 56 48 0.59 19.2 Peanut GROW Low 482 474 53 0.11 8 2016 GROW Med 488 474 133 0.27 14 GROW High 426 474 54 0.13 48 SMS Low 443 452 78 0.18 9 SMS Med 458 452 115 0.25 6 SMS High 506 452 91 0.18 54 Irrigated 86 30 0.00 0.9 NON Low 442 367 39 0.09 75 NON Med 356 368 37 0.10 12 NON High 386 371 102 0.26 15 Rainfed 71 45 0.16 6.6 All treatments 88 36 0.52 2.6 Corn GROW Low 200 163 24 0.12 37 2017 GROW Med 233 251 5 0.02 18 GROW High 247 255 31 0.12 8 SMS Low 217 236 75 0.35 19 SMS Med 240 256 29 0.12 16 SMS High 247 261 72 0.29 14 Irrigated 45 29 0.83 2.8 NON Low 134 189 30 0.22 55 NON Med 116 211 27 0.24 95 NON High 137 226 30 0.22 89 Rainfed 28 64 0.00 62.2 All treatments 63 44 0.38 15.8

PAGE 267

267 Table 4 1 2 . Model performance indices (RMSE , R 2 and PBIAS ) for final aboveground biomass across all treatments, irrigated only and rainfed only per crop seasons (corn 2015, peanut 2016 and corn 2017). Season Trea tment Mean Obs. values Simulated values STD Obs CV RMSE per treatment RMSE (irrigated, rainfed or combined) R 2 PBIAS ------(kg/ha) ----(%) ----(kg/ha) -----(%) Corn GROW Low 24711 23819 5520 0.22 892 2015 GROW Med 21529 24459 3 175 0.15 2930 GROW High 23786 24994 2777 0.12 1208 SMS Low 22553 24132 4879 0.22 1579 SMS Med 27008 25733 5356 0.20 1275 SMS High 27526 26118 1780 0.06 1408 Irrigated 4305 1680 0.58 1.5 NON Low 17389 19111 1072 0.06 1722 NON Med 18212 19263 4649 0.26 1051 NON High 18583 19298 4596 0.25 715 Rainfed 3499 1236 0.98 6.4 All treatments 5056 1547 0.86 2.8 Peanut GROW Low 15374 16892 2822 0.18 1518 2016 GROW Med 16493 16895 4144 0.25 402 GROW High 14652 16893 1300 0.09 2241 SMS Low 14974 16303 2234 0.15 1329 SMS Med 15358 16312 2892 0.19 954 SMS High 16972 16293 2995 0.18 679 Irrigated 2668 1331 0.03 6.1 NON Low 14823 12574 2079 0.14 2249 NON Med 12451 12577 1857 0.15 126 NON High 13520 12641 3583 0.27 879 Rainfed 2578 1396 0.01 7.4 All treatments 2778 1353 0.52 2.1 Corn GROW Low 19747 18667 2490 0.13 1080 2017 GROW Med 21148 24288 514 0.02 3140 GROW High 23005 24491 2792 0.12 1486 SMS Low 20581 23235 7220 0.35 2654 SMS Med 22000 24170 2724 0.12 2170 SMS High 21060 24280 5231 0.25 3220 Irrigated 3783 2428 0.55 9.1 NON Low 10699 18336 2710 0.25 7637 NON Med 9272 19565 2364 0.25 10293 NON High 11129 20155 1681 0.15 9026 Rainfed 2233 9051 0.00 86.7 All treatments 7621 5589 0.62 24.3

PAGE 268

268 Table 4 1 3 . Model performance indices (RMSE , R 2 and PBIAS ) for grain/pod yields across all treatments, irrigated only and rainfed only pe r crop seasons (corn 2015, peanut 2016 and corn 2017). Season Treatment Mean Obs. values Simulated values STD Obs CV RMSE per treatment RMSE (irrigated, rainfed or combined) R 2 PBIAS --------(kg/ha) -------(%) ---(kg/ha) ---(%) Corn GROW Low 11602 12356 1233 0.11 754 2015 GROW Med 11767 12976 1436 0.12 1209 GROW High 12947 13465 1039 0.08 518 SMS Low 10685 12667 1717 0.16 1982 SMS Med 12282 13465 2141 0.17 1183 SMS High 13066 13465 2518 0.19 399 Irrigated 1766 1139 0.65 8.4 NON Low 8178 9626 1147 0.14 1448 NON Med 8381 9658 4854 0.58 1277 NON High 10421 9681 2168 0.21 740 Rainfed 3031 1194 0.74 7.4 All treatments 2662 1158 0.82 8.1 Peanut GROW Low 7663 7903 635 0.08 240 2016 GROW Med 8399 7909 507 0.06 490 GROW High 8399 7904 507 0.06 495 SMS Low 7961 7085 754 0.09 876 SMS Med 7556 7083 594 0.08 473 SMS High 6995 7089 743 0.11 94 Irrigated 753 507 0.43 4.3 NON Lo w 4983 4202 396 0.08 781 NON Med 5315 4271 1143 0.21 1044 NON High 5092 4335 957 0.19 757 Rainfed 818 870 0.12 16.8 All treatments 1499 651 0.95 7.3 Corn GROW Low 12244 8445 534 0.04 3799 2017 GROW Med 12135 1279 9 1564 0.13 664 GROW High 13321 12937 767 0.06 384 SMS Low 12144 12278 511 0.04 134 SMS Med 12130 13098 910 0.08 968 SMS High 12336 13099 973 0.08 763 Irrigated 942 1661 0.03 2.2 NON Low 5837 10167 1002 0.17 4330 NON Me d 5403 11229 1413 0.26 5826 NON High 6097 11795 1862 0.31 5698 Rainfed 1362 5328 0.04 91.4 All treatments 3338 3362 0.12 15.5

PAGE 269

269 Table 4 1 4 . Simulated water balance for GROW, SMS and NON treatments during corn 2015, peanut 2016 and corn 2017 growing seasons and fallow periods 2015 16 and 2016 17. Period GROW Low GROW Med GROW High SMS Low SMS Med SMS High NON Low NON Med NON High Corn Irrigation 321 321 321 151 151 151 15 15 15 2015 Precipitation 545 545 545 545 545 545 545 545 545 Drainage 326 326 326 159 158 158 87 88 86 Soil Evaporation 81 71 68 77 65 64 77 70 71 Transpiration 422 433 435 424 437 437 361 366 366 Potential ET 585 585 585 585 585 585 585 585 585 Fallow Precipitation 617 617 617 617 617 617 617 617 617 2015 Drainage 371 370 370 371 370 371 370 370 370 20 16 Runoff 1 1 1 1 1 1 1 1 1 Soil Evaporation 252 252 252 252 252 252 252 252 252 Potential ET 837 837 837 837 837 837 837 837 837 Peanut Irrigation 544 544 544 193 193 193 18 18 18 2016 Preci pitation 659 659 659 659 659 659 659 659 659 Drainage 673 672 673 366 366 366 306 306 306 Runoff 13 13 13 10 10 10 9 9 9 Soil Evaporation 120 119 120 88 88 88 80 80 80 Transpiration 442 442 442 435 435 435 339 339 339 Potential ET 673 673 67 3 673 673 673 673 673 673 Fallow Precipitation 242 242 242 242 242 242 242 242 242 2016 Drainage 103 103 103 100 100 100 91 91 91 20 17 Soil Evaporation 94 94 94 94 94 94 93 93 93 Potential ET 427 427 427 427 427 427 426 426 426 Corn Irrigation 548 548 548 303 303 303 48 48 48 2017 Precipitation 688 650 650 650 650 650 650 650 650 Drainage 706 675 675 440 437 436 268 258 251 Runoff 12 12 12 12 12 12 11 11 11 Soil Evaporation 155 88 86 105 80 79 85 76 72 Transpiration 362 420 423 394 42 3 424 332 351 362 Potential ET 658 600 600 600 600 600 600 600 600 Fallow Precipitation 584 622 622 622 622 622 622 622 622 2017 Drainage 393 415 416 415 415 415 415 416 416 20 18 Runoff 32 32 32 32 32 32 32 32 32 Soil Evaporation 161 178 176 178 178 178 178 176 176 Potential ET 547 600 601 600 600 600 600 600 600

PAGE 270

270 Table 4 1 5 . Simulated nitrogen balance for GROW, SMS and NON treatments during corn 2015, peanut 2016 and corn 2017 growing seasons and fallow periods 2015 16 and 2016 17. Period GROW Low GROW Med GROW High SMS Low SMS Med SMS High NON Low NON Med NON High CORN Soil NO 3 initial 23 23 23 23 23 23 23 23 23 2015 Soil NH 4 initial 16 16 16 16 16 16 16 16 16 Fertilizer N 157 245 336 157 245 336 157 245 336 Mineralized N 74 74 76 72 73 75 57 59 60 Leached N 16 83 161 8 41 89 5 37 108 N uptake 232 250 260 238 290 330 226 273 274 N immobilized 8 10 12 8 11 15 7 10 13 FALLOW Soil NO 3 initial 8 8 9 9 9 10 9 15 32 2015 16 Soil NH 4 initial 1 1 1 1 1 1 1 1 1 Fertil izer N 0 0 0 0 0 0 0 0 0 Mineralized N 195 202 199 195 217 247 214 246 249 Leached N 66 65 68 67 74 96 81 130 149 N uptake 110 126 112 110 121 128 114 105 105 PEANUT Soil NO 3 initial 25 18 26 25 29 32 26 25 25 2016 Soil NH 4 initial 1 1 1 1 1 1 1 1 1 Fertilizer N 17 17 17 17 17 17 0 0 17 Mineralized N 104 119 104 100 97 85 86 69 70 Leached N 54 50 55 35 39 40 38 36 37 N uptake 60 65 60 74 75 72 50 41 58 N immobilized 25 31 24 24 22 14 15 7 7 FALLOW Soil NO 3 initial 8 8 8 8 8 8 9 9 9 2016 17 Soil NH 4 initial 1 1 1 1 1 1 1 1 1 Fertilizer N 0 0 0 0 0 0 0 0 0 Mineralized N 190 179 190 198 198 193 187 195 195 Leached N 63 75 63 65 65 65 85 71 71 N uptake 70 54 70 74 73 69 45 60 60 CORN Soil NO 3 initial 62 56 62 65 65 64 63 70 70 2017 Soil NH 4 initial 2 2 2 2 2 2 2 2 2 Fertilizer N 174 262 353 174 262 353 174 262 353 Mineralized N 87 80 86 82 82 81 66 66 66 Leached N 124 118 204 56 119 200 89 156 227 N uptake 163 251 255 236 256 261 189 211 226 N immo bilized 19 16 22 19 21 22 13 15 16 1 N uptake in peanut corresponds to simulations of N uptake from the soil, without N fixed by the crop.

PAGE 271

271 Figure 4 1. Irrigation applied in 2015 corn, 2016 peanut and 2017 co rn seasons. CORN 2015 PEANUT 2016 CORN 2017

PAGE 272

272 Figure 4 2 . In season observed (dots) and simulated (line) biomass resulted in the SMS treatment across the high, medium and low N rates (top to bottom). Observed data averaged across four replicates (i.e. blocks ). Error bars are one standard deviation of observed values across blocks . CORN 201 7 PEANUT 2016 CORN 2015

PAGE 273

273 Figure 4 3 . Daily observed (dots) and DSSAT simulated (line) total soil water in profile (90 cm) in the GROW High N treatment. Observed data corresponds to the average of thre e Sentek probes in different blocks. Error bars are a standard deviation across the three blocks. CORN 2015 CORN 201 7 PEANUT 201 6

PAGE 274

274 Figure 4 4 . Daily observed (dots) and DSSAT simulated (line) total soil water in profile (90 cm) in the SMS High N treatment. Observed data correspond s to the average of three Sentek probes in different blocks. Error bars are a standard deviation across the three blocks . CORN 2015 CORN 201 7 PEANUT 201 6

PAGE 275

275 Figure 4 5 . Daily observed (dots) and DSSAT simulated (line) total soil water in the profile (90 cm) in the NON High N treatmen t. Observed data corresponds to the average of three Sentek probes in different blocks. Error bars are a standard deviation across the three blocks. CORN 2015 CORN 2017 PEANUT 2016

PAGE 276

276 Figure 4 6 . Total observed (circles , n=4 ) and DSSAT simulated (line) nitrate N in the soil profile (0 90 cm) in the SMS, GROW and NON treatments and high N fertility rate during corn 2015 peanut 2016 corn 2017 crop rotation. Observed data corresponds to the average across four blocks. Error bars are the standard deviation across the replicates.

PAGE 277

277 Figure 4 7 . Total observed (circles , n=4 ) and simulated (line) nitrate N in the soil profile (0 90 cm) in the SMS, GROW and NON treatments and medium N fertility rate during corn 2015 peanut 2016 corn 2017 crop rotation. Observed data corresponds to the average acr oss four blocks. Error bars are the standard deviation across the replicates.

PAGE 278

278 Figure 4 8 . Total observed (circles , n=4 ) and simulated (line) nitrate N in the soil profile (0 90 cm) in the SMS, GROW and NON treatments and low N fertility rate during cor n 2015 peanut 2016 corn 2017 crop rotation. Observed data corresponds to the average across four blocks. Error bars are the standard deviation across the replicates

PAGE 279

279 Figure 4 9 . Observed (boxplots , n=4 ) and simulated (dots) final aboveground (AG) N uptak e in GROW, SMS and NON treatments across N rates (low, medium and high) during corn 2015, peanut 2016 and corn 2017 growing seasons. Boxplots: lower boundary of the box indicates the 25th percentile, a line within the box marks the median, and the upper bo undary of the box indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles. Note , N rates were modified in 2017 due to leaching rain; additional 17 kg N/ha were applied to a l l rates.

PAGE 280

280 Figure 4 10 . Observed (boxplots , n=4 ) and simulated (dots) final aboveground (AG) biomass resulted in GROW, SMS and NON treatments across N rates (low, medium and high) during corn 2015, peanut 2016 and corn 2017 growing seasons. Boxplots: lower boundary of the box i ndicates the 25th percentile, a line within the box marks the median, and the upper boundary of the box indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles. Note , N rates were modified in 2017 due to leaching rain; additional 17 kg N/ha were applied to all rates.

PAGE 281

281 Figure 4 11. Observed (boxplots , n=4 ) and simulated (dots) final grain/pod yields resulted in GROW, SMS and NON treatments across N rates (low, medium and high) during corn 2015, pe anut 2016 and corn 2017 growing seasons. Final corn grain yield adjusted to 15.5% standard moisture and peanut pod yield adjusted to 10.5% standard moisture. Boxplots: lower boundary of the box indicates the 25th percentile, a line within the box marks the median, and the upper boundary of the box indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles. Note, N rates were modified in 2017 due to leaching rain; additional 17 kg N/ha were applied to all rates.

PAGE 282

282 Figure 4 12. Simulated nitrogen dynamics (total soil N (0 90 cm), N uptake, N applied and N leached) in the SMS, GROW and NON treatments and high N fertility rate during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation.

PAGE 283

283 Figure 4 13. Simulated nitrogen dynamics (total soil N (0 90 cm), N uptake, N applied and N leached) in the SMS, GROW and NON treatments and medium N fertility rate during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation.

PAGE 284

284 Figure 4 1 4 . Simulated nitrogen dynamics (total soil N (0 90 cm), N uptake, N applied and N leached) in the SMS, GROW and NON treatments and low N fertility rate during corn 2015 fallow peanut 2016 fallow corn 2017 crop rotation.

PAGE 285

285 CHAPTER 5 EFFECT OF INCREAS ING TEMPERATURE AND RAINFALL ON SIMULATED GROWTH AND YIELD IN A CORN PEANUT ROTATION UNDER DIFFERENT MANAGEMENT PRACTICES Introduction Agriculture is one of the major contributors of the economy in the Suwannee County. According to the 2012 Census of Agr iculture, the number of farms and the land in farms increased in 18% and 15%, respectively from 2007 to 2012. Then in 2012, the land in farms was distributed in 43.5% cropland, 26.1% woodland, 24.6% pastureland and 5.8% other uses. Hence, the market value of products sold (i.e. crop and livestock sales) was $296,320,000, representing a 50% increase since 2007 (NASS 2018) . Excessive fertilizer applications leaving the rootzone are one of the major causes of eutrophicatio n and water quality degradation. Research studies have shown the increase of nitrate N) to above 5 mg/L NO3 N during the last 40 years (Katz e t al. 1999; Katz 2004) . Steady increments of nitrate N have been found in several spring waters within the Suwannee River Basin (SRB) in Florida (e.g. Convict spring with 8 mg/L NO3 N) (Upchurch et al. 2007) . Evid ence that the greatest nitrate N pollution found in the lower basin comes from fertilizer sources was reported by continued studies in the SRB area during the period 1940 1998 (Katz et al. 2009) . Therefore, developing solutions to reduce N loads agricultural fields. the increasing water demand and the excess of nutrient loading to waterbodies, several regulations have been developed through time. Agricultural Best Management Practices

PAGE 286

286 (BMPs) are practices based on research, field testing and expert review developed to maintain or improve surface and groundwater water quality. Additionally, BMPs must be technically and economically feasible, and designed to prevent, reduce or treat pollutant discharges entering water resources, and to conserve water supply (FDACS 2015a) . Due to t he water use limitations and the potential environmental degradation issues associated with the use of nitrogen fertilizers and excessive water use to sustain agriculture, the implementation of N and irrigation best management practices (BMPs), and/or new regulations has become fundamental. In Florida, the adoption and implementation of BMPs aim to assist producers in priority watersheds statewide with the purpose of protecting water quality and reducing water use. These practices include upgrading irrigati on systems to achieve greater efficiency, the implementation of equipment and tools to target fertilizer applications and possibly reduce losses to the environment, as well as, adopting efficient irrigation scheduling to help protect water resources. If a BMP program is established and in place, all growers adopting agricultural BMPs will benefit since they are considered to be operating under a standards are not met, growers w ill be protected from liabilities due to water quality degradation (FDACS 2015a) . Cereal production (e.g. corn) plays an important role in a world with an increasing population. Analysis of temporal patterns of historical crop yields might provide insights for future production variability and food security (Kucharik and Ramankutty 2005) . Understanding the interaction of climate variables and their effect on crop growth for food p roduction is key for making decisions on adaptation strategies to cope with those

PAGE 287

287 impacts (Godfray et al. 2010) Thus, the quantification of the effect of separate climate variables on crop yields should be performed ; since different strategies might be required based on the climate variable impacts (Reynolds et al. 2010) . Although most cereals, such as maize are already well adapted to high temperature environments, further productivity challenges are posed due to warmer the world (Reynolds et al. 2010) . Heat stress and drought cause a similar res ponse in crops: acceleration in the life cycle while reducing photosynthetic capacity via restricted leaf area and duration. When temperatures fall outside the range of optimum temperatures for growth, metabolism is inhibited. Furthermore, seed set is redu ced if stress occurs during critical developmental stages (e.g. reproductive stages in corn) (Reynolds et al. 2010) . Most of the crop yield variations in the world are explained by rainfall and temperature fluctuat ions, which will further increase as climate changes (Jarvis et al. 2010) . However, further detriment in yields are more likely to be observed in scenarios with lack of irrigation to compensate lower rainfall or to mit igate the effects of high temperature via evaporative cooling (Reynolds et al. 2010) . On the other hand, excessive rainfall might contribute to further increments in N leaching from the rootzone. Nevertheless, prov iding optimum management to maximize genetic potential might cope with future scenarios of climate change. Furthermore, the effects of excessive N fertilizer applications leaving the rootzone, irrigation demand along with potential biomass and yield impact s of future climate change (e.g. temperature and rainfall increases) are not known. Therefore, long -

PAGE 288

288 term analysis of the N and irrigation BMPs must be performed to further evaluate their effectiveness under different climatic conditions. As reported in pre vious studies, there is a need to focus on source reduction rather than sink enhancement, (Cohen et al. 2007) . To reduce the nitrate flux to ground and surface waterbodies, a reduction or better management of the appl ication of N fertilizers or N bearing materials is required (Upchurch et al. 2007) . Hence, developing or optimizing N and irrigation BMPs could be a management strategy to target load reductions for water quality i mprovements. Focusing on a conventional corn peanut crop rotation, two main BMPs are proposed: (i) a reduction in N fertilizer from conventional practices (336 kg N/ha) to a medium N rate (247 kg N/ha) close to the University of Florida/Institute of Food a nd Agricultural Sciences (UF/IFAS) recommendations for corn production (235 kg N/ ha) and; (ii) the use of soil moisture sensors (SMS) as tools for irrigation scheduling. The study of the interaction of crops and environmental variations to understand crop responses under future climate conditions can be performed using crop simulation models. The main goal of this chapter was to evaluate the long term BMPs effectiveness to reduce N leaching from the root zone while optimizing crop yields when compared to c onventional production practices using the crop simulation models within DSSAT. Objectives 1. To evaluate the effect of rising temperatures and increase rainfall on yield and biomass in a conventional corn peanut crop rotation. 2. To evaluate the effectiveness o f using SMS to schedule and reduce irrigation without impacts in yield compared to conventional practices.

PAGE 289

289 Materials and Methods DSSAT Crop Simulation Model s The total biomass (B T ) of a crop is the product of the average growth rate (g) and the growth dura tion (d), whereas the economic yield is the fraction of the B T that is partitioned to the grain. Both, B T and economic yield, are affected by weather, genetics, management and environmental factors; which are considered in the models to simulate crop growt h, development, and final yields (Ritchie, J. T. et al. 1998) . Temperature is the main environmental factor affecting the duration of plant growth. It plays an important role in changes in stages of growth and bioma ss partitioning patterns; as well as, in the timing of development of various plant organs within the plant life cycle. The radiation use efficiency (RUE) method is used to calculate total biomass production. Then, calculated photosynthetic active radiatio n (PAR) is assumed to be half the input of solar radiation and is used to calculate the fraction intercepted by the plants (IPAR) which is a function of leaf area index (LAI). The actual daily biomass production is influenced by non optimal temperature or deficits of water or nitrogen. The models use thermal time to predict plant development, quantify the physiological age and growth stages, which is affected if temperature is outside optimum ranges for crop growth. Grain numbers per unit area are usually the most critical determinant of crop yield. The genetic coefficient G2 along with the average rate of photosynthesis during the silking period are used to determine grain number in the model. The source sink reserve procedure (i.e. model evaluates the sin k capacity of the aboveground biomass to determine if the crop is sink limited or source limited) is used to calculate the filling rate per kernel. A sink capacity is determined based on temperature and the cultivar genetic coefficient G3 (potential single kernel growth rate

PAGE 290

290 at optimal temperature. Unusual cool or hot temperatures will reduce the rate of kernel growth. During the grain filling period, the daily biomass production plus the assimilates in the stem are the source for grain filling. In this per iod, the filling rate is almost constant if average temperatures are relatively constant and within optimum ranges. However, the rate decreases if a shortage of assimilate, N, or stored carbohydrate ends before grain filling (Ritchie, J. T. et al. 1998) . In the CERES Maize model, air temperature affects several processes (e.g. leaf area growth, photosynthesis, grain filling rate, senescence and phenology). For maize, the optimum temperature for photosynthesis (i.e. t he highest temperature at which development rate is still at its maximum) is 33°C during vegetative stages, and on average 28°C during grain filling period. Then, maximum temperature (i.e. at which development rate is zero) corresponds to 44°C and 36°C for those stages, respectively. Increases in air temperature accelerates phenological development, resulting in a shorter grower period and senescence occurs as the main stress response (Ritchie, J. T. et al. 1998) . Wa ter stress is calculated as a fraction of the available soil water in the roo t zone and it affects leaf growth, photosynthesis and other crop growth parameters. If this fraction falls to 0.05, leaf senescence is enhanced. Similarly occurs in the N crop sim ulations, where crop N demand is estimated as the difference between critical and actual N content, plus the N amount required for new growth per day. An N stress factor is calculated when N concentration falls below the critical concentration, causing sim ilar effects as the water stress. Potential N uptake is a function of the root length density distribution, NO 3 and NH 4 concentrations and soil water content.

PAGE 291

291 This study was conducted using the CERES ( Crop Environment Resource Synthesis ) Maize an d the CROPGRO simulation model s within the platform of the Decision Support Systems for Agrotechnology Transfer, DSSAT v4.7 (Jones et al., 2003) . Th e model s use simplified functions to predict corn growth as influenced by weather, genetic, soils and manageme nt factors that might affect final yield. Thus, it not limited by pests, diseases, weeds and lodging, but limited by temperature, solar radiation, water and nitrogen su pply. The se model s have been used in a wide range of environments (Li, Z. T. et al. 2015; Paredes et al. 2014; Paz et al. 1999; Yang et al. 2013) . Minimum data sets of the major factors influencing yield were used (i.e. weather, genetic, soils and management). The methods used in DSSAT for long term crop rotation simulations are described in Table 5 1 . The Century model has been found to be an effective model fo r soil carbon (SOC) simulations (Gijsman et al. 2002; Yang et al. 2013) . This model keeps three humic pools (i.e. microbial, active and stable) and two fresh organic matter pools (i.e. structural and metabolic). For successful modeling decomposition of the organic matter, along with the inorganic nutrient release or immobilization to the soil; accurate estimations of initial proportions in each pool are crit ical (Porter et al. 2010) . Calculated SOC values from measured soil organic matter were used to adjust pools required in the Century model. Model D ata I nputs C rop M anagement This study was conducted using observed data from the three year (2015 2017 ) z crop rotation (corn fallow peanut fallow corn) field experiment located in the North Florida Research and Education Center Suwannee Valley (NFREC S V ),

PAGE 292

292 Live Oak, Florida. Details about data used in the model, results compared to observed data an d model performance can be found in Chapter 3. Soil data and initial conditions. All simulations were performed using the soil, which was developed in DSSAT using observed data. This soil file was constructed using four depths (0 15, 1 5 30, 30 60 and 60 90 cm), same as the soil N data collected in the experimental field. Measured soil characteristics and initial conditions from the experimental field located in the NFREC S V were used for simulations (Table 5 2 ) . Cultivar. Calibrated m aize cultivar and were used for corn and peanut simulations (see Chapter 3 for calibration details). Crop management. Selected planting dates were April 3 and May 19 for corn and peanut, respectively. Plant density was 8 plan ts/m 2 at 5 cm planting depth with a row spacing of 76 cm. In addition, corn and peanut planting and harvest dates from 2015 experimental field were used for long term simulations (Table 5 3 ). Three irrigation N rate treatments from the experimental field were selected for long term simulations. Treatments evaluated were: 1. GROW high: mimics conventional irrigation and fertility practices in corn and peanut. To simulate these practices in DSSAT, an automatic irrigation simulation option was selected using a fixed amount of 1 3 mm per event, a 40 cm irrigation management depth and 90% maximum allowable depletion (MAD). This option would apply enough water to avoid water stress during the crop season, mimicking conventional production practices in corn and pe anut . In terms of N fertilization, the high N rate corresponds to a total of 336 kg N/ha; representing conventional N fertilizer applications performed in corn. 2. SMS medium : mimics an irrigation scheduling methodology using soil moisture sensors. An auto matic simulation option with a fixed irrigation application rate of 10 mm, a 50% MAD for a 30 cm irrigation management depth and with the previously described features was used.

PAGE 293

293 The med ium N rate corresponds to 247 kg N /ha, close to the University of Flor ida/Institute of Food and Agricultural Sciences recommend rate (235 kg N/ha) (Mylavarapu et al. 2015) . 3. NON low: this treatment was selected as a representation of low inputs in which no irrigation and 157 kg N/ha were applied throughout the growing season. One irrigation event was applied one day before planting to provide adequate moisture conditions to ensure germination in the simulations. Fertilizer applications in the model were performed using 2015 17 obse rved data from experimental field. The total amount applied per N rates was distributed in seven fertilizer applications (one application at planting, two granular and four liquid sidedress applications. See details in Chapter 2). For peanut N, a minimal N application of 17 kg N/ha was applied following experimental and common practices in Florida. This amount and timing was equal for all treatments within the rotations. For model simulations, several sources of N were applied mimicking observed data. Fert ilizer was applied banded and incorporated at 5 cm beneath the surface. Fer following observed data. Climate D ata 2010) and a sens itivity analysis for future climate change regarding projections to changes of weather parameters (i.e. additive change s in temperature and percent change in rainfall) obtained from two Representative Concentration Pathways (RCPs 4.5 and RCPs 8.5) (IPCC 2014) . Baseline The historical baseline consisted of weather data from 1980 through 2010 and it was used to compare with future climate change impacts on corn and peanut biomass

PAGE 294

294 and yield. This baseline period was selected us ing the AgMERRA and AgCFSR Climate Forcing Datasets for Agricultural Modeling (Rienecker et al. 2011; Ruane et al. 2015) . To provide minimal required data for agricultural models, these datase ts were created as an element of the Agricultural Model Intercomparison and Improvement Project (AgMIP), to provide daily time series with global coverage of climate variables required for the models. Thus, this climate period is considered satisfactory to perform a climatological analysis (Guttman 1989; Rosenzweig et al. 2013; WMO 1989) . The baseline and future climate data were extracted from the grid cell where the experimenta l field is located in NFREC SV, Live Oak, Florida (30.306057, 82.900348). Sensitivity A nalysis Due to the absence of measured data sets for full factorial scenarios of climate change impacts combined with irrigation and N supply, a sensitivity study w as performed with the crop model to analyze interactive effects. Therefore, water deficit, N, individual climatic variables (temperature and rainfall) and their interactions were included in simulations. The Representative Concentration Pathways (RCPs) are greenhouse gas concentration trajectories for future climate adopted by the Intergovernmental Pane l on Climate Change (IPCC 2014) . The RCP 4.5 and RCP 8.5 were selected for mid century (2050s) impact assessment for the fut ure scenarios. These two RCPs provide regional weather changes for the selected period. The RCP 4.5 projected an air temperature increase of 2.1 °C (T+2.1°C) and a 5% increase in rainfall amounts (R+5%); whereas the RCP 8.5 projected a 2.8 °C air temperatu re increase (T+2.8°C) and a 7 % rainfall increase (R+7%) (IPCC 2014) . Therefore, each Irrigation/N treatment was evaluated

PAGE 295

295 under individual weather variable (temperature or rainfall increase) modifications per RCP; and a fi nal interaction of the two main variables was tested across treatments (i.e. T+2.1°C * +5%R] and [T+2.8°C * +7% R]). Long term simulations were performed across the seven scenarios using the same cultivars and soil type. Results Climate T reatment Compari son during Baseline Weather Period (1980 2010) Temperature and rainfall for the baseline (1980 2010) showed temporal variation, where the highest mean maximum temperatures are reached during the months of June August, and simultaneously, the greatest mea n rainfall occurred compared to the other months of the year. These months correspond to the start and end of reproductive stages in corn, which are sensitive to high temperatures and water stress. On the other hand, rainfall contributions during the repro ductive stages can reduced water stress and offset yield declines, particularly in the NON low treatment (Figure 5 1). Corn peanut ro tations were simulated across th e GROW high, SMS medium and NON low treatments using the baseline period (1980 201 0 observed weather period) as a benchmark for comparison . In addition, treatments r esults on final biomass, yield, irrigation applied , drainage and N leaching per crop wer e compared against the GROW h igh treatment , which represents conventional irri gation and fertility practices. Corn seasons. During the corn seasons (i.e. March August) within the baseline period, average maximum temperature ranged from 25.8 ° C ( ± 4.3 ° C) to 35.3 ° C ( ±2.6 ° C), reaching maximum temperatures in the month of July. Corn is sensit ive to high temperatures during reproductive stages, particularly during pollination, fertilization and the grain filling period. Thus, yield reductions were observed across all treatments during the years showing the highest temperatures occurring during reproductive

PAGE 296

296 stages. For example, a total of 34 days reached maximum temperatures above 36 ° C during reproductive stages in 1998, resulting in yield declines across all treatments, particularly in the NON low treatment (11,326, 11,326 and 2,988 kg/ha for the GROW high, SMS medium and NON low treatments , respectively). The 25% lowest irrigated yields observed during the baseline period occurred in 1982, 1986 and 1998. During 1982 corn season, heavy rainfall events caused large amounts of N leaching (170, 84 and 35 kg N/ha in the GROW high, SMS medium and NON low treatments , respectively) resulting in N stress and thus, lower yields (12,547, 12,522 and 11,076 kg/ha, for the same treatments, respectively). In 1986 and 1998, corn resulted in N str ess at the end of the season that resulted in lower yields. Nevertheless, in 1998, the high temperatures were the main cause of yield reductions. Similar trends in biomass and yield were found across the irrigated treatments due to weather conditions prese nt during the baseline period. In contrast, the NON treatment biomass and yield were drastically affected due to low rainfall and high temperature years (Figure 5 2). The average corn biomass during the baseline period was 26,026, 25,388 and 16,552 kg/ha f or the GROW high, SMS medium and NON low, respectively (STD = 1,933, 1,539 and 3,576 kg/ha, respectively). On average during this period, the SMS medium treatment resulted in a 2% lower biomass compared to the GROW h igh treatment. In comparison, th e NON low treatment mean biomass was on average 36% lower than conventional practices (9% 61% lower biomass range vs. the GROW treatment across the evaluated years) (Figure 5 2).

PAGE 297

29 7 During the baseline period, corn yield means were 14,053, 13,585 and 8,4 40 kg/ha for GROW high, SMS medium and NON low treatments , respectively. The SMS medium yield was 3% lower than the GROW high treatment ( STD = ranged from 0% to 14% reduction in yield). In contrast , the NON low treatment mean yield was 40% lower than in the GROW treatment. Reductions in yield ranged from 12% to 74% compared to the GROW h igh treatment during the baseline period (Figure 5 1). Based on field experiment results conducted in NFREC SV, average reductions in yield of 3% resulted in n o significant differences between irrigated treatments; whereas differences greater than 24% between irrigated and rainfed treatments resulted in significant differences (see details in Chapter 2). Although the GROW treatment resulted in slightly higher me an biomass and yield than in the SMS during the baseline period, it resulted in greater irrigation, drainage and N leaching compared to the SMS treatment . On average during the 15 corn seasons, the GROW irrigation applied 229 mm, drainage was 216 mm and N leaching was 99 kg N/ha. In contrast, the SMS treatment applied 107 mm, resulted in 125 mm of drainage and 29 kg N/ha leached on average per crop season . Thus, this treatment achieved on average 53% water savings and 71% lower N leaching amounts than conve ntional practices during the baseline period. As a comparison with previously treatments evaluated at NFREC SV field experiment conducted during 2015 17, corn mean yields were 12,105, 12,705 and 12,567 kg/ha in the GROW treatment; 12,011, 11,819 and 12, 203 in the SMS treatment; whereas, 8,993, 7974 and 5,779 kg/ha in the NON treatment, respectively during 2015 17 corn seasons. Significant differences were only found between the irrigated

PAGE 298

298 treatments compared to the non irrigated treatment during the three corn seasons. Irrigation applied in the GROW, SMS and NON was 320, 151 and 48 mm, respectively in 2015; 508, 291 and 25 mm in 2016 and; 546, 302 and 48 mm, respectively in 2017 (further details in Chapter 2). Simulated N leaching for GROW high, SMS m edium and NON low treatments was 161, 41 and 5 kg N/ha in 2015, and 204, 119 and 89 kg N/ha in 2017 corn season, respectively. Thus, the SMS medium resulted in 43% and 45% average water savings and in 75% and 42% N leaching reductions during 2015 and 2 017 corn seasons, respectively in comparison to the GROW high treatment (more details in Chapters 2 and 3). Peanut seasons. During the baseline period (1980 2010 observed weather), average peanut biomass and yield were less affected by weather changes ac ross the years compared to corn . Mean biomass a cross treatments were 16,257, 15,249 and 14,660 kg/ha GROW high, SMS medium and NON low, respectively (STD = 614, 697 and 1,044 kg/ha). The corresponding mean pod yields for these treatments were 7,650, 7,424 and 6,836 kg/ha, respectively (STD = 365, 334 and 599 kg/ha, respectively) (Figure 5 4). Therefore, the SMS mean biomass and yield were on average 6% and 3% lower than in the GROW treatment during 1980 2010 baseline weather period. Based on results o f previously evaluated treatments in field conditions, these percentages did not result in statistical differences across irrigated treatments. However, major differences were found on irrigation applied across irrigation treatments , which also was observe d in field experiment . In the baseline period, the GROW treatment applied 186 mm on average per peanut season ; whereas the SMS treatment applied 48 mm; representing a

PAGE 299

299 74% average water savings by the SMS treatment compared to conventional practices. A mini mal N application (17 kg N/ha) was equally performed across treatments during all peanut seasons. N leaching resulted in similar values across all treatments (39, 31 and 29 kg N/ha for the GROW high, SMS medium and NON low, respectively) . Similar res ults were observed in peanut 2016 at the NFREC SV field experiment. GROW high and SMS medium mean biomass were 14 , 652 and 15,358 kg/ha, whereas mean pod yield was 8,399 and 7,556 kg/ha, respectively. No statistical differences in biomass nor yield we re found between irrigated treatments. Likewise, SMS achieved 63% water savings compared to conventional practices. Despite variations in some of the peanut seasons, the NON low treatment average biomass and pod yield were 14,660 and 6,830 kg/ha, which r epresents only a 7% and 9% reductions compared to irrigated treatments. During the baseline, the highest rainfall amounts occurred during the months of June through August. Similar as corn, peanut is sensitive to water stress during reproductive stages, pa rticularly during the pod filling period. Thus, rainfall amounts contributed and avoided reductions in yield when occurred during sensitive stages of the crop, resulting in similar yields as in the irrigated treatments. Increasing T emperature Corn Effect of 2.1 ° C t emperature increase in corn . Increasing average air temperature by 2.1 ° C resulted in an accelerated anthesis date by three days and in maturity date by five days, on average across corn seasons. T his acceleration in the growing season ranged fro m one to five days for the anthesis date, and from three to eight days for the maturity date. Consequences of this shortened period to flowering

PAGE 300

300 and to reach physiological maturity has a direct impact on final biomass accumulation during the season, which will further decrease the glucose accumulation during grain filling; hence decrease final grain yields . Thus, increases in temperature by 2.1 ° C reduced corn biomass and yield by 5% and 12% in the GROW high treatment on average across all seasons (Figure 5 4) . Similar results were found in the SMS treatment, where average reductions in biomass and yield were 4% and 10% due to this increment in temperature (Figure 5 5) . However, yield reductions up to 28% were found in both treatments during 1998 (GROW = 8,1 81 kg/ha and SMS = 8,130 kg/ha) (Figures 5 4 and 5 5). Particularly, during 1998 mean maximum temperatures reported in the baseline reached up to 33.2 ° C and 36.8 ° C for May and June, respectively. During corn reproductive stages (June August), a total of 34 days reached maximum temperatures above 36 ° C. June maximum mean temperature (36.8 ° C ) surpassed the maximum temperature in the model at which the developmental rate at these stages is zero. Therefore, reductions in the crop growth cycle, grain filling r ate and senescence were simulated, resulting in low yields in all treatments. Simulations in the NON low irrigated showed the greatest impact in biomass and yield due to increases in temperature. Biomass reductions averaged 9% (STD = 7%) across all seaso ns; whereas, yield reductions averaged 15% (STD = 13%). In 1998, biomass and yield resulted in 27% and 47% reductions compared to the baseline, due to the 2. 1 ° C increase in temperature (Figure 5 6). Effect of 2.8 ° C t emperature increase in corn . This chang e in temperature caused the largest negative impact in all treatments. Increasing average air temperature

PAGE 301

301 by 2.8 ° C resulted in further acceleration of the growing periods (i.e. anthesis and maturity dates) and further reductions in biomass and yield . The average anthesis date was accelerated by four days; whereas the maturity date by seven days compared to the baseline period . The range of accelerated days was two to seven days for anthesis date and four to ten days for corn maturity date. Corn biomass and yield were negatively impacted when air temperature increased by 2.8 ° C across all treatments. The GROW average biomass and yield decreased by 9% and 19%, respectively (STD = 5% and 10% reductions respectively) (Figure 5 4). Similarly, the SMS treatment s howed a 7% and 17% reduction in biomass and yield on average across all corn seasons due to this increase in air temperature (STD = 5% and 10% reductions, respectively) (Figure 5 5). The effect of temperature increase of 2.8 ° C on the N ON low treatment r esulted in 12% and 21% average reduction in biomass and yield, respectively compared to the baseline period (Figure 5 6). Simulations of higher temperature in the 1998 corn season resulted in the lowest yield across all treatments. Reductions in yield up t o 40%, 41% and 41% in the GROW high, SMS medium and NON low resulted due to a temperature rise of 2.8 ° C (Figures 5 4 5 6). Anthesis and maturity dates and therefore biomass and yield were negatively impacted when air temperature increased 2.1 ° C and 2.8 ° C. The greater the temperature increase, the shorter the growing periods; thus, the lower th e biomass and final grain yield (Figures 5 4 5 6).

PAGE 302

302 Figure 5 7 shows the projected corn mean yield and biomass change to baseline in the GROW high, SMS me dium and NON low tre a tment s for rising temperatures (T+2.1 ° C and T+2.8 ° C). Peanut Effect of 2.1 ° C t emperature increase in peanut . Peanut phenology was insensitive to increases of temperature. No changes in anthesis nor maturity dates were found a cross al l treatments. On average across the peanut seasons, the biomass in the GROW high treatment increased in 1%, but average yield decreased in 6% due to the 2.1°C air temperature increase (Figure 5 8). The effect of SMS irrigation on peanut biomass in some years was positive (+2.7%) whereas in other years was negative ( 2.2%) due to increments in temperature. Therefore, the average effect evened out across all years. Nevertheless, pod yield was negatively impacted resulting in an average 7% reduction compar ed to the baseline period (Figure 5 9). Similarly, the 2.1°C increase in temperature resulted in a positive effect up to 2.1% increase in biomass in the NON treatment, whereas in others, it resulted in a negative impact up to 1.3% biomass reduction. Thus, on average, the 2.1°C increase in temperature had no effect in biomass across the peanut seasons in comparison to the baseline weather period. However, average peanut pod yield was 7% lower than in the baseline due to this increment in temperature (Figure 5 10). Effect of 2.8 ° C t emperature increase in peanut In the GROW high treatment, an increase in air temperature of 2.8 ° C resulted in a small increase in peanut biomass of 2% averaged across all seasons relative to the baseline period. However, it had a n egative impact in pod yield, resulting in 9% average reduction across all seasons (STD

PAGE 303

303 = 4% reduction in yield) (Figure 5 8). The SMS medium treatment resulted in 1% increase in biomass and 11% reduction in pod yield (STD = 4%), on average across all pea nut seasons (Figure 5 9). By contrast, the NON low treatment resulted in no average effect in biomass, but on an average reduction in pod yield of 10% (STD = 4%) (Figures 5 10). In general, despite the negative impact on biomass due to temperature increase , peanut pod yield was less sensitive to raising temperatures compared to the baseline. Figure 5 11 shows the projected peanut mean yield and biomass change to the baseline in the evaluated tre a tment s for rising temperatures (T+2.1 ° C and T+2.8 ° C). Increasi ng R ainfall Increasing rainfall by 5% or 7% had no effect on the corn or peanut phenology. Anthesis nor maturity date were impacted by rainfall increases . In general, the effect of rainfall increase in corn and peanut was lower compared to the effect of te mperature increase. Corn Effect of 5% and 7% r ainfall increase in corn . The simulations of 5% higher rainfall amounts across all corn seasons resulted in similar biomass and yield than the baseline on the GROW high and SMS medium treatments (Figures 5 4 and 5 5). On average, biomass did not change and yield was reduced by 1% on both treatments (Figure 5 7). In general, higher rainfall amounts resulted in small increases in N leaching which resulted in slightly lower yields. In contrast, the NON low t reatment was positively impacted by the 5% and the 7% increase in rainfall during most of the seasons, resulting in very small and almost negligible (2% and 3%) average increases in biomass and yield (STD = 2% and 4%, respectively) compared to the baseline

PAGE 304

304 weather period (Figures 5 9 5 11). Despite these positive effects, simulated rainfed corn yields were the lowest compared to the irrigated treatments yields. Peanut Effect of 5% and 7% r ainfall increase in peanut . The increase in rainfall resulted in no effect on biomass nor yield in the GROW high and SMS medium treatments compared to the baseline period (Figures 5 8 and 5 9). In comparison, 5% and 7% rainfall increase had small positive effects in the NON low treatment, resulting in a 1% yield inc rease on average across all seasons compared to the baseline (Figure 5 10). RCPs 4.5 and 8.5 Combined E ffects Corn Overall, the combined effect of projected temperature and rainfall increase resulted in similar negative impacts in simulated corn biomass and yield as the effects of temperature increase alone in the irrigated treatments (GROW high and SMS medium). Therefore, rainfall increments did not compensate for the negative impacts of temperature increase. In general, the models suggested negative corn biomass and yield impacts as temperatures increased. Irrigated biomass and yield simulations suggest declines of 5% and 12% (STD = 4% and 8%), respectively for the projected temperature and rainfall increase in the RCP 4.5; and declines of 8% and 18% (STD = 5% and 10%), respectively for the RCP 8.5. Thus, the more pessimistic impact resulted in the RCP 8.5 (Figure 5 7). In contrast, in the NON low treatment, simulated biomass and yield resulted in 7% and 12% declines (STD = 8% and 13%, respectively) due to the combined effect of projected temperature and rainfall changes in the RCP 4.5 compared to the baseline. Slightly higher reductions of 9% and 17% in biomass and yield resulted in the projected changes in the RCP 8.5 (STD = 10% and 17%,

PAGE 305

305 respective ly) (Figure 5 7). Simulated rainfed corn biomass and yield impacts were highly variable due to the combined effect of projected temperature and rainfall changes, suggesting mostly negative impacts , but of a lower magnitude than rising air temperatures as a n isolated effect. Thus, rainfall increments may contribute to offset the effects of temperature under rainfed conditions. Figure 5 7 shows simulated irrigated and rainfed corn biomass and yield changes due to projected changes for 2050. Peanut In peanut, the projected increments in temperature and rainfall of the RCPs 4.5 and 8.5 had a major negative impact on yield, whereas, biomass simulations remained almost insensitive to the projected changes. Similar reductions in yield were projected for all treatme nts evaluated. On average, pod yield reductions of 6% and 9% were projected for the RCPs 4.5 and 8.5, respectively same as projected reductions caused by 2.1 ° C and 2.8 ° C temperature increase, respectively. Figure 5 11 shows simulated irrigated and rainfe d peanut biomass and yield changes due to projected changes for 2050. Effect of Temperature and Rainfall Increase on Irrigation, Drainage and N Leaching i n Corn Irrigation applied, drainage from the rootzone and N leaching across all treatments were simul ated during the baseline weather period and compared to potential future changes on climatic variables (i.e. temperature increases, rainfall increases and the combination of both as stated in RCPs 4.5 and 8.5) (Figure 5 12). As well, treatments evaluated w ere compared against the GROW high treatment to evaluate the effectiveness of management on water savings and potential environmental risks (e.g. N leaching)

PAGE 306

306 The GROW treatment mean irrigation was 229 mm during the baseline period. Projected increases in irrigation of 1% resulted from temperature rising 2.1 °C and 2.8 °C; whereas reductions of 2% and 3% for rainfall increases of 5% and 7%, respectively. The combined effect of increases in temperature of 2.1°C and 5% rainfall resulted in mean irrigation re ductions of 2%; whereas the combined effect of temperature increase by 2.8 °C and 7% in rainfall had no effect compared to the baseline (Figure 5 12). Overall, low increases in irrigation resulted across all climatic scenarios in the GROW treatment. The SM S treatment applied 107 mm on average during the baseline period. In comparison, projected irrigation applied increased in 9% and 7% due to temperature rising by 2.1 °C and 2.8 °C, respectively. In contrast, projected mean SMS irrigation decrease in 5% for rainfall projections increases of 5% and 7%. Then, for the RCPs 4.5 (T+2.1 °C * +5%R) and 8.5 (T+2.8 °C * +7% R), SMS irrigation resulted in 4% and 3% increase in irrigation applied, respectively compared to the baseline. In general, increments in tempera ture will also increment transpiration and soil evaporation, resulting in a greater depletion in available water in the soil for plant uptake. Therefore, it will increase irrigation amounts, since depletion might occur more frequently. The automatic functi on in the model will replenish the soil profile when reaching 50% MAD. The opposite occurs when more water is available due to rainfall contributions; less irrigation events will be required. When comparing irrigation amounts among treatments, the SMS achi eved water savings ranging from 49% to 54% compared to the GROW high treatment across all scenarios (i.e. baseline, T+2.1 °C, T+2.8 °C, R+5%, R+7%, ([T+2.1 °C * +5% rainfall]

PAGE 307

307 and [T+2.8 °C * +7% rainfall]). Previously shown field experiment results condu cted at NFREC SV showed 53% and 45% water savings achieved in the SMS treatment compared to the GROW in 2015 and 2017 corn seasons, respectively. Main differences were due to rainfall amounts and distribution through the growing season. Despite that rain fall occurred during the 2017 corn season was 21% greater than in 2015, the SMS treatment applied significantly less irrigation than the GROW treatment (Chapter 2). During the baseline period, average drainage in the GROW high and SMS medium treatments was 216 mm and 125 mm, respectively. Irrigated corn simulations suggest drainage reductions due to projected warmer temperatures and greater drainage due to projected increase in rainfall. In the GROW treatment, simulated drainage was reduced in 4% and 6% due to projected increase in air temperature of 2.1 °C and 2.8 °C, respectively compared to the baseline period. In contrast, GROW drainage increased by 9% and 12% due to rainfall increase of 5% and 7%, respectively. For the RCPs 4.5 and 8.5, simulated dr ainage increased in 5% and 8%, respectively compared to the baseline, indicating a dominant effect of projected rainfall increase over raising temperatures. Similar trends were found in the SMS medium treatment simulations, in which average drainage decr eased in 4% and 7% due to projected rising temperatures of 2.1 °C and 2.8 °C, respectively. However, simulated SMS drainage increased in 14% in both scenarios of projected rainfall increase (5 and 7%) compared to the baseline. Similarly, simulations in the SMS treatment shows consistent increase in drainage in both RCPs. The interaction between climatic variables projected in the RCPs 4.5 and 8.5 resulted in 11% and 13% increase in average drainage across the corn seasons,

PAGE 308

308 respectively. Since the SMS treatm ent is already optimizing irrigation, increases in rainfall can contribute in reductions in irrigation; however, due to the intrinsic variability of rainfall amounts and distribution, it can also exceed the adequate moisture in the soil and result in great er drainage and N leaching. Nevertheless, drainage occurring in the SMS medium treatment ranged from 114 to 145 mm across all evaluated scenarios, whereas the GROW high ranged from 205 to 247 mm. Thus, the SMS average drainage resulted in a range of 49 % 55% reduction in drainage amounts compared to the GROW high treatment, across all scenarios. The NON low treatment average drainage during the baseline period was 101 mm. Rainfed corn simulations suggest drainage declines due to projected warmed temper atures and increases due to projected rainfall increases. The effects of rising temperatures by 2.1 °C and 2.8 °C resulted in a 6% and 9% lower projected average drainage in the NON low treatment. In contrast, the effect of 5% and 7% increase in rainfall resulted in a projected drainage increase of 21% and 16% compared to the baseline. For the interaction of climatic variables projected in the RCPs 4.5 (+2.1 °C * +5%R) and 8.5 (T+2.8 °C * +7% R), simulated drainage in the NON low treatment increased on average by 12% and 15%, respectively. In terms of N leaching, irrigated corn simulations suggest small increases in N leaching due to warmer temperatures and rainfall increase. Simulated N leaching in the GROW high treatment resulted in similar mean val ues across all projected scenarios, ranging from 99 to 109 kg N/ha. Simulated N leaching during the baseline was on average 99 kg N/ha. Increases in 3% and 5% N leaching resulted due to projected temperatures (2.1 °C and 2.8 °C) as well as due to rainfall increases (5% and 7%),

PAGE 309

309 respectively. The combined effect of temperature and rainfall changes projected in RCP 4.5 and in RCP 8.5 resulted in 6% and 9% increase N leaching, respectively on average compared to the baseline period. Average N leaching in the SMS medium treatment was 29 kg N/ha during the baseline period. Rising temperatures had a small effect on simulated N leaching in this treatment. Average increments in N leaching of 1% and 2% resulted due to projected temperature increase of 2.1 °C and 2 .8 °C, respectively. Projected 5% and 7% rainfall increase resulted in a 6% increase simulated N leaching on average across all seasons. Similarly, a 6% and 10% increase in N leaching resulted from the combined effect of temperature and rainfall changes pr ojected in RCP 4.5 and in RCP 8.5, respectively. Rainfed low N corn simulations suggested larger N leaching across all projected scenarios. In the NON low treatment, average N leaching was 19 kg N/ha during the baseline period. Increments of 7% in N le aching resulted from the four scenarios of temperature and rainfall increase compared to the baseline. However, average N leaching increased by 12% and 15% for the temperature and rainfall changes projected in the RCP 4.5 and 8.5, respectively. Temperature increases causes additional stress to this treatment, which already has water and N stress. Soil N availability in this treatment depends on rainfall. Thus, due to spatial and temporal rainfall distribution, high uncertainty on adequate moisture condition s is predominant in this treatment. Also, if N is not in a soluble form, then plants cannot take up, resulting in N leaching from the rootzone when heavy rainfall events occur. Based on previously reported results from field experiment conducted in NFREC SV (Chapter 3), simulated N leaching in the GROW high, SMS medium and NON

PAGE 310

310 low treatments was 161, 41 and 5 kg N/ha, respectively in the 2015 corn season; whereas N leaching increased up to 204, 119 and 89 kg N/ha, respectively. The main difference among years was due to heavy rainfall events and non uniform rainfall distribution occurred in the 2017 corn season (i.e. 21% greater rainfall than in 2015) that resulted in higher N leaching amounts across the treatments evaluated. Effect of Temperature a nd Rainfall Increase on Irrigation, Drainage and N Leaching in Peanut Figure 5 13 shows the effect of projected rising temperatures (T+2.1 ° C, T+2.8 ° C), rainfall increase (R+5%, R+7%), and combined effect of temperature and rainfall increase projected by the RCPs 4.5 ( T+2.1 ° C * +5% rainfall) and 8.5 ( T+2.8 ° C * +7% rainfall) on irrigation applied, drainage from the rootzone and N leaching across all treatments. The management effectiveness in terms of water savings and potential reductions in N leaching i n peanut production was evaluated across treatments. A verage irrigation applied in the GROW treatment was 186 mm durin g the baseline period. Simulations showed potential increases in irrigation of 5% and 6% due to projected temperature increase of 2.1 ° C and 2.8 ° C, respectively . In contrast, projected 5% and 7% increase in rainfall had a small or no effect on average irrigation applied in the GROW treatment (average 0 1% increase respect to the baseline ). Similarly, the combined effect of climatic vari ables projected in the RCP 4.5 and 8.5 , resulted in 1% and 3% increase d irrigation simulations, respectively (Figure 5 13 ). Average SMS irrigation applied during the baseline period was 48 mm. Projected temperature rise of 2.1 ° C had no effect on the SMS ir rigation simulations ; however, projected rise of 2.8 ° C resulted in 4 % increase average irrigation. On the other hand, reductions in average SMS irrigation resulted due to projected 5% and 7% rainfall

PAGE 311

311 increase; whereas the combined effect of climatic variab les projected in the RCP 4.5 and 8.5 , resulted in 3% and 4% less average irrigation applied in the SMS treatment, respectively. Thus, the SMS medium achieved water savings ranging from 74% to 76% compared to the GROW high treatment across all scenarios ( i.e. baseline, T+2.1 ° C, T+2.8 ° C, R+5%, R+7%, RCP 4.5 and RCP 8.5 ) (Figure 5 13 ). Average drainage in the GROW high and SMS medium treatments was 347 mm and 238 mm, respectively during the baseline period. The peanut growing seasons encompass the mont hs in which greatest rainfall amounts occurred; thus, most of the drainage is due to rainfall and not due to irrigation applied. No major changes resulted in average simulated drainage due to projected temperature increases of 2.1 ° C and 2.8 ° C. In contras t, average simulated drainage in the GROW treatment increased 9% and 11% due to projected 5% and 7% rainfall increase, respectively. In contrast, 32% and 36% drainage reductions resulted due to the combined climatic effect projected in the RPCs 4.5 and 8.5 , respectively. Similarly, average drainage in the SMS medium treatment decreased in 4% and 6% due to projected rising temperatures of 2.1 ° C and 2.8 ° C, respectively. However, SMS drainage simulations increased 10% due to projected 5% and 7% rainfall i ncrease compared to the baseline. The combined effect of the climatic variables interaction projected in the RCPs 4.5 and 8.5 , showed a 7% and 10% increase in average SMS drainage across the peanut seasons, respectively. In comparison to drainage occurring in the GROW high treatment, the SMS average drainage ranged from 225 to 265 mm across all evaluated scenarios, whereas the GROW high ranged

PAGE 312

312 from 255 to 380 mm. Hence, the SMS average drainage represents a range up to 35% less drainage compared to the GR OW high treatment, across all scenarios. The average drainage occurred in the NON low treatment was 224 mm during the baseline period. This large amount indicates high average drainage occurring during peanut growing seasons due to rainfall events. Mos t likely, higher drainage amounts occurred in the irrigated treatments due to lower storage for rainfall, since soil moisture is kept close to field capacity. However, from the total drainage, large drainage corresponds to rainfall events and not t o irriga tion. Projected increases in temperature by 2.1 ° C and 2.8 ° C resulted in 3% and 5% lower average drainage in the NON low treatment; whereas projected 5% and 7% rainfall, increased simulated drainage on average by 13% and 11%. The combined effects of cli matic variables changes effect projected in the RPCs 4.5 and 8.5 increased simulated drainage in 8% and 11%, respectively. In terms of N leaching, the GROW high treatment (i.e. high N rate applied in previous corn rotation) resulted in similar average v alues across all scenarios (N leaching means ranging from 39 to 45 kg N/ha across all projected scenarios). In comparison to the baseline (average N leaching = 39 kg N/ha), increases in average N leaching of 8% and 9% resulted due to projected temperature increase of 2.1 ° C and 2.8 ° C . As well, increases of 4% and 5% N leaching resulted due to projected 5% and 7% increase rainfall, respectively. The combined effect of temperature and rainfall changes projected in RCP 4.5 and in RCP 8.5 resulted in 10% and 1 3% increase in average N leaching compared to the baseline period.

PAGE 313

313 Average N leaching in the SMS medium treatment (i.e. medium N rate applied in previous corn rotation) was 31 kg N/ha during the baseline period. Rising temperatures of 2.1 ° C and 2.8 ° C caused an increase of 7% and 9% compared to the baseline. Rainfall increase of 5% and 7% resulted in a 4% increase N leaching on average across all seasons compared to the baseline; whereas average N leaching increased 11% and 15% in the [T+2.1 ° C * +5% ra infall] and [T+2.8 ° C * +7% rainfall] scenarios, respectively. Although most of the N leaching is caused by rainfall drainage, the SMS treatment N leaching was on average 21% lower than in the GROW treatment across all scenarios evaluated. In the NON low treatment, average N leaching was 29 kg N/ha during the baseline period. Temperature increases had a small effect on N leaching; whereas rainfall increases resulted in 5% N leaching compared to the baseline. Similarly, average N leaching increased 4% and 5% due to projections in the RCP 4.5 ( T+2.1 ° C * +5% rainfall ) and RCP 8.5 ( T+2.8 ° C * +7% rainfall) , respectively.

PAGE 314

314 Conclusions Irrigated corn simulations indicated biomass and yield declines due to projected warmer temperatures, and almost null effect due to projected rainfall increase. The combined effect of climatic variables changes projected in the RCPs 4.5 and 8.5 resulted in similar negative impacts in biomass and yield as in warmer temperatures. In contrast, rainfed corn biomass and yield impacts were highly variable and more negatively impacted due to warmer temperatures. A small positive effect was found due to projected rainfall increase, which may contribute to of fset the effects of temperature. Projected air temperature increase of 2.1 °C resu lted in an accelerated anthesis date (i.e. earlier flowering) by three days and in maturity date (i.e. shorter grain filling period) by five days on average durin g the 15 corn seasons within crop rotations. This acceleration of the crop life cycle had a di rect negative impact on biomass and yield. It reduced average corn biomass in 5%, 4% and 9% and reduced yield in 12%, 10% and 15%, respectively in the GROW high, SMS medium and NON low treatments. Moreover, projected air temperature increase of 2.8°C reduced corn biomass in 9%, 7% and 19% and corn yield in 19%, 17% and 21% in the GROW high, SMS medium and NON low treatments, respectively on average compared to the baseline weather period. Therefore, simulations indicate more negative effects in corn biomass and yield as temperature increased. In contrast, projected increases in rainfall of 5% and 7% resulted in negligible effects on irrigated corn biomass and yield and i n small positive effects on rainfed corn ( 2% and 3% increase, respectively). Simulated corn biomass and yield suggested yield declines of 6% and 12% due to increases in temperature of 2.1°C and 5% increase rainfall, whereas simulated declines of 8% and 18%, respectively resulted due temperature increase by 2.8°C and 7% rainfall inc rease on

PAGE 315

315 average across all treatments evaluated. The combined effect of temperature increase by 2.8°C and 7% rainfall increase resulted in the pessimist declines in irrigated and rainfed corn growth and yield . On the other hand, irrigated and rainfed pean ut simulations showed negligible effects in biomass due to projected te mperature increases of 2.1°C and 2.8 ° ; whereas pod yield declines of 6% and 10% , respectively across all treatments evaluated. S imilar results resulted in biomass and yield due to temp erature and rainfall changes on average across all treatments evaluated. T he long term effectiveness of using SMS to schedule irrigation and reduce N leaching without impacts in yield compared to conventional practices was evaluated using a baseline weathe r period (weather data from 1980 to 2010). On average across all corn seasons, results showed water savings of 53% and 71% lower N leaching amounts t han conventional practices ( average ir rigation applied 229 and 107 mm a nd N leaching 99 and 29 kg N/ha in t he GROW high and SMS medium , respectively). A further sensitivity analysis was performed across various projected changes in climatic variables (scena rios of raising temperatures: 2.1 °C and 2.8 °C, increase in rainfall: 5% and 7%, and the interaction of both ( T+2.1 °C * +5% rainfall) and ( T+2.8 °C * +7% rainfall). This analysis also demonstrated water savings achieved by the SMS ranging from 49% to 54% compared to the GROW high treatment across all scenarios. Furthermore, similar average biomass and yields were found in the GROW high and SMS medium across all simulated corn seasons within the baseline period. Average reductions of 2% and 3% on biomass and yield, respectively resulted in the SMS medium versus the GROW high during this period. H owever, reductions in

PAGE 316

316 26% fertilizer applied and in 71% N leaching were achieved on average in the SMS medium treatment compared to conventional practices during the baseline period and across all climatic scenarios evaluated. When evaluating irrigation applied, drainage and N leaching across various climatic scenarios, small differences were seen across scenarios within the same treatment. However, major differences were observed when comparing the treatments evaluated across these variables. Overall, th e SMS showed average reductions of 52% irrigation applied, 41% drainage and 71% N leaching compared to the GROW high with low impacts in yield across all scenarios. In comparison, the NON low treatment resulted in average reductions of 52% in drainage a nd 81% in N leaching compared to the GROW high treatment, but with negative impacts in yield. Over the long term analysis, the NON low treatment showed the highest variability and the most negative impacts on yield essentially due to the large variabil ity in rainfall amounts and distribution within the corn seasons. Furthermore, in a scenario of rising temperatures (2.1°C or 2.8°C), higher water stress will impact corn biomass and yield particularly in the NON low scenario; whereas, irrigated treatmen ts might offset temperature stress and result in lower reductions, on average. On the other hand, peanut biomass and yield were less severely impacted by the changes in the climatic variables across all treatments evaluated. Thus, this crop migh t be more tolerant to temperature and rainfall changes. Sensitivity analysis performed throughout crop simulation models is a useful tool that can provide insights of the interaction of crop growth and yield formation processes that are sensitive to climate variable s ; however, c rop responses may differ from using climate change projections (i.e. monthly

PAGE 317

317 temperature changes for a 30 year future period) obtained from General Circulation Models or GCMs , which are considered the most advanced tools currently available fo r simulating the response of the global climate system to increasing greenhouse gas concentrations.

PAGE 318

318 Table 5 1 . Methods used in DSSAT for long term crop rotation simulations. Processes Simulation Method Evapotranspiration FAO 56 Infiltration Soil Conse rvation Service Photosynthesis Leaf photosynthesis response curve Hydrology Ritchie water balance Soil Organic Matter (SOM) Century method (Parton) Soil evaporation method Suleiman Ritchie Soil layer distribution Unmodified soil profile 1 1 Soil profi le created using 4 depths: 0 15, 15 30, 30 60 and 60 90 cm.

PAGE 319

319 Table 5 2 . Soil characteristics and initial conditions from field experiment used in the DSSAT for the long term crop rotation simulations. Depth, base of layer Bulk Density, moist Org anic Carbon Total Nitrogen pH in water Phosphorus extractable Stable Organi c Carbon Volumetric water content Ammonium (NH 4 ) Nitrate (NO 3 ) (cm) g/cm 3 % % mg/kg % cm 3 /cm 3 g(N)/Mg (soil) g(N)/Mg (soil) 15 1.5 0.76 0.05 6 61.9 0.68 0.1 1.8 2.5 30 1.5 0.67 0.04 6 49.8 0.55 0.1 1.7 1.7 60 1.5 0.66 0.02 5.9 39 0.53 0.1 1 1.6 90 1.5 0.34 0.01 5.7 33.5 0.3 0.1 0.9 1.4

PAGE 320

320 Table 5 3 . Planting and harvest dates from 2015 16 field experiment used in the DSSAT CERES Maize model for the long term crop rotation si mulations. Activity Crop Date Harvest date Fallow 2015 4/1/2015 Planting date Corn 2015 4/3/2015 Harvest date Corn 2015 8/18/2015 Harvest date Fallow 2015 16 5/12/2016 Planting date Peanut 2016 5/13/2016 Harvest date Peanut 2016 10/4/2016 The fallo w periods were harvested one or two days before the following crop planting date. The model assumes the day after harvest as the fallow planting date (input not required).

PAGE 321

321 Figure 5 1. Monthly mean rainfall (bars) and monthly mean maximum and minimum t emperature (lines) during 30 years baseline weather period (1980 2010). Error bars are one standard deviation above and below the mean rainfall and temperature value.

PAGE 322

322 Figure 5 2. Long term simulated corn biomass (top) and yield (bottom) during the bas eline weather period (1980 2010) across the GROW high, SMS medium and NON low treatments.

PAGE 323

323 Figure 5 3. Long term simulated peanut biomass and yield during the baseline weather period (1980 2010) across the GROW high, SMS medium and NON low treat ments.

PAGE 324

324 Figure 5 4. Effect of rising temperatures (2.1 °C and 2.8 °C, red lines) and increase rainfall (+5% and +7%, blue lines) on corn biomass (top) and yield (bottom) in the GROW high treatment compared to the baseline observed weather period (19 80 2010).

PAGE 325

325 Figure 5 5 . Effect of rising temperatures (2.1 ° C and 2.8 ° C, red lines) and increase rainfall (+5% and +7%, blue lines) on corn biomass (top) and yield (bottom) in the SMS medium treatment compared to the baseline observed weather period (1980 2010).

PAGE 326

326 Figure 5 6 . Effect of rising temperatures (2.1 ° C and 2.8 ° C, red lines) and increase rainfall (+5% and +7%, blue lines) on corn biomass (top) and yield (bottom) in the NON low treatment compared to the baseline observed weather period ( 1980 2010).

PAGE 327

327 Figure 5 7. Projected mean corn biomass (left) and yield (right) change ( to baseline , 1980 2010) in the GROW high, SMS medium and NON lo w treatments for temperature increase ( T +2.1 ° C and T+2.8 ° C ), r ainfall increase (R+5% and R+7%) an d combined effects (T +2.1 ° C * R+ 5% and T +2.8 ° C * + R+ 7 % ) .

PAGE 328

328 Figure 5 8 . Effect of rising temperatures (2.1 ° C and 2.8 ° C, red lines) and increase rainfall (+5% and +7%, blue lines) on peanut biomass (top) and yield (bottom) in the GROW high treatme nt compared to the baseline observed weather period (1980 2010).

PAGE 329

329 Figure 5 9 . Effect of rising temperatures (2.1 ° C and 2.8 ° C, red lines) and increase rainfall (+5% and +7%, blue lines) on peanut biomass (top) and yield (bottom) in the SMS medium tr eatment compared to the baseline observed weather period (1980 2010).

PAGE 330

330 Figure 5 10 . Effect of rising temperatures (2.1 ° C and 2.8 ° C, red lines) and increase rainfall (+5% and +7%, blue lines) on peanut biomass (top) and yield (bottom) in the NON low treatment compared to the baseline observed weather period (1980 2010).

PAGE 331

331 Figure 5 11. Projected mean peanut biomass (left) and yield (right) change ( to baseline , 1980 2010) in the GROW high, SMS medium and NON low treatments for temperature incre ase ( T +2.1 ° C and T+2.8 ° C ), rainfall increase (R+5% and R+7%) and combined effects (T +2.1 ° C * R+ 5% and T +2.8 ° C * + R+ 7 %) .

PAGE 332

332 Figure 5 12 . Effect of temperature, rainfall increase and combined effects for the RCP 4.5 (T+2.1 °C * +5% rainfall) and RCP 8.5 ( T+2.8 °C * +7% rainfall) on irrigation, drainage and N leaching across treatments evaluated in corn compared to the baseline weather period (1980 2010). Boxplots: lower boundary of the box indicates the 25th percentile, a line within the box marks the medi an across 30 years, and the upper boundary of the box indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles.

PAGE 333

333 Figure 5 13 . Effect of temperature, rainfall increase and combined effects for th e RCP 4.5 (T+2.1 °C * +5% rainfall) and RCP 8.5 (T+2.8 °C * +7% rainfall) on irrigation, drainage and N leaching across treatments evaluated in peanut compared to the baseline weather period (1980 2010). Boxplots: lower boundary of the box indicates the 25 th percentile, a line within the box marks the median across 30 years, and the upper boundary of the box indicates the 75th percentile. Whiskers (error bars) above and below the box indicate the 90th and 10th percentiles.

PAGE 334

334 CHAPTER 6 SUMMARY AND FINAL REMAR KS Best Management Practices have been proposed to reduce inputs (i.e. irrigation and N fertilizer rates) in corn production to maintain N in the rootzone for crop uptake; hence reduce N leaching, one of the main causes of water quality and environment im pairment in the Suwannee River Basin. The proposed irrigation scheduling strategies sensors with irrigation scheduled using field capacity and 50% maximum allowable depleti on thresholds; Reduced, applied 60% of GROW treatment. Two lower N fertilizer rates (i.e. low = 157 kg N/ha and medium = 247 kg N/ha) were evaluated in comparison to conventional practices (i.e. GROW, uses a calendar based irrigation scheduling method and high N rate = 336 kg N/ha; mimicking growers irrigation practices). Irrigation had a significant effect on the final biomass and N uptake in corn stems and ears during the three years of evaluation. Irrigated treatments (GROW Reduced) did not differ sig nificantly; however, significant differences were found versus the NON treatment, which resulted in significantly lower biomass and N uptake. In contrast, N fertility rates did not have a significant effect on final biomass nor N uptake (the low, medium an d high N rates biomass means were 23,007, 23,047, 24,014 kg/ha in 2015; 21,456, 21,106 and 21,928 kg/ha in 2016; and 18,374, 19,228 and 19,920 kg/ha in 2017, respectively. Final N uptake means in the low, medium and high rates were 219, 231 and 238 kg N/ha in 2015, 224, 230 and 245 kg N/ha in 2016, 194, 214 and 224 kg N/ha in 2017, respectively). Furthermore, the medium rate resulted in no differences in final grain yield (mean yield during 2015 17 ranged from 10,979 to 11,322 kg/ha), when using any of the irrigation strategies proposed (SWB, SMS or Reduced) compared to

PAGE 335

335 the high N rate (mean yield 2015 17 = 11,486 12,314 kg/ha). The low rate did not differed than the medium rate during the three corn seasons (mean yields were 10,534, 10,839, 10,716 kg/ha i n 2015 17). In 2016, no statistical differences were found across the three evaluated N rates. The total ear N uptake and total N biomass ratio was on average 0.81, 0.77 and 0.78 in 2015 17 corn seasons. At harvest, only the grain N is removed from the field and stems, leaves and roots are left aboveground. Approximately 20% of N is contained in these plant sections, which after mineralization processes can be released in the top soil layers. This important amount of N is generally not considered in the fertilization program of the following crop and it is left in the fields. Thus, during the fallow period (off season), this N is potentially leached by excess precipitation. The main results comparing N fertility rates showed that a reduction in 26% on con ventional fertilization practices could be implemented without impacting corn yield when following rates similar to UF/IFAS fertilizer recommendations (medium N rate = 247 kg N/ha). During the three corn growing seasons, the use of BMPs with different irri gation strategies (i.e. SWB, SMS and Reduced) resulted in yields without statistical differences than the conventional irrigation practices. However, substantial water savings were achieved. The implementation of a soil water balance (SWB), soil moisture s ensors (SMS), and or, a reduced strategy (Reduced) achieved a total water savings of: 42%, 53% and 34% in 2015, 39%, 43% and 37% in 2016; and 42%, 45% and 36%, respectively in 2017 compared to conventional practices (GROW), without negative impacts on yiel d.

PAGE 336

336 Crop simulation models within the DSSAT platform were used to simulate water and N balances in a corn peanut conventional rotation and to evaluate the effectiveness of the irrigation treatments on final yield and N leaching. In 2015 and 2017 corn season s, the GROW high rate resulted in the largest N leaching (161 and 204 kg N/ha, respectively). In comparison, N leaching in the SMS low, medium and high rates in 2015 corn was 48%, 51% and 45% lower than the GROW across the same rates, respectively. In 2017 corn, heavy rainfall events resulted in greater N leaching across all treatments; thus, SMS low, medium and high rates simulated N leaching was 55%, 0% and 2% lower than the GROW across the same rates, respectively. Magnitude and distribution of rai nfall events can impact crop growth and final yields in corn peanut rotation systems. Rainfall was the main driver of N leaching; however, frequent irrigation applications could result in the same effect. The simulated N balance provided a better under standing of processes and fate of this element. During harvest, only grain N is removed from the field; leaving the rest of the biomass aboveground in open fields. This biomass mineralizes soon after its incorporation in the top soil layers. Therefore, dur ing fallow periods, important N contributions resulted after mineralization processes occurred; however, during these available is not taken up, is potentially leached af ter excessive precipitation. Due to the intrinsic dynamics of the processes of the N balance (i.e. mineralization, immobilization, uptake, leaching), at the end of the fallow periods, simulated initial soil N resulted in 26 kg N/ha and 64 kg N/ha, on avera ge across the treatments, which constitute the initial soil N available for the subsequent crop. Despite model over estimations after peanut

PAGE 337

337 residue, existing soil N should be considered in the fertilizer program of the subsequent crop season, further redu Due to high mobility of N in the soil and the risk of N leaching, a sensor based irrigation scheduling methodology was proposed for corn producers. Besides scheduling irrigation, the use of soil moisture senso rs provide insights of soil water plant dynamics. and/or irrigation events. Continuous monitoring of soil moisture using multisensor capacitance probes allows determinati on of the effective rainfall and/or irrigation that has entered the soil profile, in addition to drainage occurring between soil layers (i.e. downward water flux across layers) or leaving the soil profile (i.e. increments in VWC detected by sensors located in depths deeper than root zone). Thus, during the growing season, optimal irrigation management can be achieved throughout the use and continuous monitoring of these sensors. It is important to recognize some challenges for scheduling irrigation using so il moisture sensors. Soil moisture in experimental fields is influenced by the inherently complicated space time relationships involved in the soil water uptake process by plants, as well as, soil variability (i.e. texture, depth), microclimates among othe r variables. Moreover, the temporal and spatial rainfall variability is the main challenge for any irrigation scheduling methodology in Florida. Rainfall uncertainty may result in under or over irrigation and thus, it might incur in water stress or drainag e leaving the rootzone. Nevertheless, using the proposed SMS irrigation scheduling methodology, cumulative drainage during the 2015 17 corn growing seasons was reduced by 54%,

PAGE 338

338 59% and 37%; whereas water savings of 43%, 53% and 45% were achieved with no s tatistical differences in final grain yield in comparison to the calendar based irrigation scheduling method during the three years of field evaluation. Using this methodology, producers can maintain adequate moisture the managed root zone, as well as, red uce the loss of water via drainage. A better understanding of soil water dynamics after rainfall or irrigation events can provide insights of crop water uptake/consumption, root development and drainage. Determining how to manage irrigation within the prop osed thresholds (i.e. FC and % 50 MAD) could reduce production costs (e.g. fuel, time, labor, fertilizer) and negative environmental impact (e.g. N leaching).

PAGE 339

339 Furthermore, due to projected warming temperatures and increase in rainfall, challenges in crop production are expected. Thus, a sensitivity analysis was performed using crop simulation models to explore crop responses under future climate projections. Overall, simulations indicated negative impacts in corn peanut rotation biomass and yield under pr ojected changes in temperature and rainfall. Irrigated corn simulations showed biomass and yield declines due to projected warmer temperatures, and almost no effect due to rainfall increase. The combined effect of temperature and rainfall increase was simi lar as of warmer temperatures alone. Rainfed corn simulations suggested high variability in biomass and yield, as well as, greater negative impacts than in irrigated corn. However, a positive effect in biomass and yield resulted due to projected increase i n rainfall which may contribute to offset the effects of temperature, according to the sensitivity analysis performed. Similarly, simulations in peanut showed overall declines in yields and no effect in biomass; however, of a lower magnitude on average. Se nsitivity analysis performed throughout crop simulation models is a useful tool that can provide insights of the interaction of crop growth and yield formation processes that are sensitive to climate variables; however, crop responses may differ from using climate change projections (i.e. monthly temperature changes for a 30 year future period) obtained from General Circulation Models or GCMs, which are considered the most advanced tools currently available for simulating the response of the global climate system to increasing greenhouse gas concentrations. Nevertheless, long term simulations also showed significant reductions in N and irrigation applied, and in N leaching, when following proposed BMPs compared to conventional practices. Thus, the

PAGE 340

340 implementa tion of BMPs may contribute to the reduction of N leaving the rootzone, Future work might focus on the long term effectiveness evaluation of BMPs using GCMs for better predictions, as well as, considering the incorporation of cover crops as an alternative to reduce N leaching during fallow periods and rotations with alternative crops. In addition, N fertilization programs should be adjusted considering the N contributions from previ ous mineralization processes. Reductions in the N fertilizer applications can potentially reduce N leaching.

PAGE 341

341 LIST OF REFERENCES Adhikari, D. D., and Penning, T. (2016). "Real time monitoring of soil moisture sensors: A tool to maximize wate r use efficiency." Proc., Improving Irrigation Water Management Latest Methods in Evapotranspiration and Supporting Technologies, U.S. Committee on Irrigation and Drainage, Fort Collins, Colorado, 1 4. Al Kaisi, M. M., and Yin, X. (2003). "Effects of Nit rogen Rate, Irrigation Rate, and Plant Population on Corn Yield and Water use Efficiency." Agron. J., 95(6), 1475 1482. Andraski, T. W., Bundy, L. G., Brye, K. R. (2000). "Crop Management and Corn Nitrogen Rate Effects on Nitrate Leaching." J. Environ. Qua l., 29(4), 1095 1103. Andrews, W. J. (1994). Nitrate in Ground Water and Spring Water Near Four Dairy Farms in North Florida, 1990 93, U.S. Geological Survey ; U.S.G.S. Earth Science Information Center, Open File Reports Section [distributor], Tallahassee, Florida. Angel, J. R., Widhalm, M., Todey, D., Massey, R., Biehl, L. (2017). "The U2U Corn Growing Degree Day Tool: Tracking Corn Growth Across the US Corn Belt." Climate Risk Management, 15, 73 81. Arthur, J. D., Wood, A. R., Baker, A. E., Cichon, J. R., Raines, G. L. (2007). "Development and Implementation of a Bayesian Based Aquifer Vulnerability Assessment in Florida." Nat Resour Res, 16(2), 93 107. Bean, E., Huffaker, R., Migliaccio, K. W. (2018). "Estimating Field Capacity from Volumetric Soil Water Content Time Series using Automated Processing Algorithms." Vadoze Zone Journal, , 1 74. Below, F. E. (2002). "Nitrogen Metabolism and Crop Productivity." Handbook of Plant and Crop Physiology , 2nd Ed., Marcel Dekker Inc, New York, 385 406. Bloom, A. J. (1 994). "Crop Acquisition of Ammonium and Nitrate." Physiology and Determination of Crop Yield, (physiologyandde), 303 309. Bos, M. G. (1985). "Summary of ICID Definitions of Irrigation Efficiency." ICID Bull, 34(1), 28 31. Bos, M. G. (1980). "Irrigation Eff iciencies at Crop Production Level." International Commission on Irrigation and Drainage, Bulletin, 29(2), 18 60. Boyer, J. S., and Westgate, M. E. (2004). "Grain Yields with Limited Water." J. Exp. Bot., 55(407), 2385 2394.

PAGE 342

342 Burns, L. G. (2004). "Assessing N fertiliser requirements and the reliability of different recommendation systems." Proc., International Symposium Towards Ecologically Sound Fertilisation Strategies for Field Vegetable Production 700, , 35 48. Bush, P. W., and Johnston, R. H. (1988). Gr ound Water Hydraulics, Regional Flow, and Ground Water Development of the Floridan Aquifer System in Florida and in Parts of Georgia, South Carolina, and Alabama, 1403 C Ed., US Government Printing Office, Washington, D.C. Cakir, R. (2004). "Effect of Wate r Stress at Different Development Stages on Vegetative and Reproductive Growth of Corn." Field Crops Res., 89(1), 1 16. Carmichael, W. W. (1992). "Cyanobacteria Secondary Metabolites the Cyanotoxins." J. Appl. Bacteriol., 72(6), 445 459. Chandler, D. G., S eyfried, M. S., McNamara, J. P., Hwang, K. (2017). "Inference of Soil Hydrologic Parameters from Electronic Soil Moisture Records." Frontiers in Earth Science, 5, 25. Ciampitti, I. A., Murrell, S. T., Camberato, J. J., Tuinstra, M., Xia, Y., Friedemann, P. , Vyn, T. J. (2013). "Physiological Dynamics of Maize Nitrogen Uptake and Partitioning in Response to Plant Density and Nitrogen Stress Factors: II. Reproductive Phase." Crop Sci., 53(6), 2588 2602. Ciampitti, I. A., and Vyn, T. J. (2013). "Grain Nitrogen Source Changes Over Time in Maize: A Review." 53(2), 366 377. Cohen, M. J., Lamsal, S., Kohrnak, L. V. (2007). Sources, Transport and Transformation of Nitrate N in the Florida Environment, Final Report Ed., St.Johns River Water Management District, Gaines ville, Florida. Daynard, T. B., and Duncan, W. G. (1969). "The Black Layer and Grain Maturity in Corn." Crop Sci., 9(4), 473 476. De Lannoy, G. J., Verhoest, N. E., Houser, P. R., Gish, T. J., Van Meirvenne, M. (2006). "Spatial and Temporal Characteristics of Soil Moisture in an Intensively Monitored Agricultural Field (OPE 3)." Journal of Hydrology, 331(3), 719 730. Denmead, O. T., and Shaw, R. H. (1960). "The Effects of Soil Moisture Stress at Different Stages of Growth on the Development and Yield of Cor n." Agronomy Journal, 52(5), 272 274. Derby, N. E., Steele, D. D., Terpstra, J., Knighton, R. E., Casey, F. X. M. (2005). "Interactions of Nitrogen, Weather, Soil, and Irrigation on Corn Yield." Agron. J., 97(5), 1342 1351.

PAGE 343

343 DeSimone, L. A. (2009). Quality of Water from Domestic Wells in Principal Aquifers of the United States, 1991 2004, 2008 5227 Ed., U.S. Geological Survey Scientific Investigations, U.S. Geological Survey, Reston, Virginia. Di, H. J., and Cameron, K. C. (2002). "Nitrate Leaching in Temper ate Agroecosystems: Sources, Factors and Mitigating Strategies." Nutr. Cycling Agroecosyst., 64(3), 237 256. Djaman, K., Irmak, S., Rathje, W. R., Martin, D. L., Eisenhauer, D. E. (2013). "Maize Evapotranspiration, Yield Production Functions, Biomass, Grai n Yield, Harvest Index, and Yield Response Factors Under Full and Limited Irrigation." Trans. ASABE, 56(2), 373 393. Donner, S. D., Kucharik, C. J., Foley, J. A. (2004). "Impact of Changing Land use Practices on Nitrate Export by the Mississippi River." Gl obal Biogeochem. Cycles, 18(1), GB1028. Dukes, M. D., Zotarelli, L., Liu, G. D., Simonne, E. H. (2012). "Principles and Practices of Irrigation Management for Vegetables." Vegetable Production Handbook, Horticultural Sciences Dept., UF/IFAS, Fla. Coop. Ext . Serv, 17 27. DuPont, P. (2016). "Corn grain: P1498YHR ." < https://www.pioneer.com/home/site/us/produ cts/profile perf?smo=UDD%20&productLine=010&productCode=P1498YHR&ts=null&langu age=01 > (05/01, 2016). El Hendawy, S. E., and Schmidhalter, U. (2010). "Optimal Coupling Combinations between Irrigation Frequency and Rate for Drip Irrigated Maize Grown on San dy Soil." Agric. Water Manage., 97(3), 439 448. English, M. J., Solomon, K. H., Hoffman, G. J. (2002). "A Paradigm Shift in Irrigation Management." J. Irrig. Drainage Eng ASCE, 128(5), 267 277. EPA, US Environmental Protection Agency. (2016a). "Implementin g clean water act section 303(d): Impaired waters and total maximum daily loads (TMDLs)." < https://www.epa.gov/tmdl > (06/20, 2016). EPA, US Environmental Protection Agency. (2016b). "Ground water and dri nking water: Table of regulated drinking water contaminants." < https://www.epa.gov/ground water and drinking water/tab le regulated drinking water contaminants#Inorganic > (03/20, 2016). EPA, US Environmental Protection Agency. (2015a). "Summary of the clean water act." < https://www.epa .gov/laws regulations/summary clean water act > (06/20, 2016).

PAGE 344

344 EPA, US Environmental Protection Agency. (2015b). "Background on drinking water standards in the safe drinking water act (SDWA)." < https://www.epa.gov/dwstandardsregulations/background drinking water standards safe drinking water act sdwa > (4/15, 2016). FAOSTAT. (2015a). "Inputs. fertilizers." < http://faostat3.fao.org/browse/R/RF/E > (04/16, 2016). FAOSTAT. (2015b). "Food and agricultural commodities production / commodities by regions." < http://faostat3.fao.org/browse/rankings/commodities_by_regions/E > (03/01, 2016). Fares, A., and Alva, A. K. (2000a). "Soil Water Components Based on Capacitance Probes in a Sandy Soil." Soil Sci. Soc. Am. J., 64(1), 311 318. Fares, A., and Alva, A. K. (2000b). "Evaluation of Capacitance Probes for Optimal Irrigation of Citrus through Soil Moisture Monitoring in an Entisol Profile." Irrig. Sci., 19(2), 57 64. FAWN. (2017). "Florida automated weather network: Data access." < http://fawn.ifas.ufl.edu/data/reports/ > . FDACS. (2015a). Water Quality/Quantity Best Management Practices for Florida Vegetable and Agronomic Crops, 2015th Ed., Florida Department of Agricult ure and Consumer Services, Tallahassee, Florida. FDACS. (2015b). "Agricultural water supply planning: Florida statewide agricultural irrigation demand (FSAID) report." < http://ageconsearch.umn.edu/record/235306/files/Fountain%20upload.pdf > (7/1, 2016). FDACS. (2013). "Office of agricultural water policy." < http://www.freshfromflorida.com/Divisions Offices/Agricultural Water Policy > (03/11, 2015). FDACS. (2012). "Detail fertilizer summary by county." < http://www.freshfromflorida.com/content/download/3526/22077/Detail_Fert_Su m_by_County_Jul11_June12.pdf > (03/18, 2016). FDEP. (2013). "Surface Water Quality Standards." Florida Administrative Code , 62 302(62 302), 530 531. Ferguson, R. B., Shapiro, C. A., Hergert, G. W., Kranz, W. L., Klocke, N. L., Krull, D. H. (1991). "Nitrogen and Irrigation Management Practices to Minimize Nitrate Leaching from Irrigated Corn." J. Prod. Agric., 4(2), 186 192.

PAGE 345

345 F ernald, E. A., and Patton, D. J. (1984). Water Resource Atlas of Florida, Florida State University, Institute of Science and Public Affairs, Tallahassee, Fla. Figueroa, M., and Pope, C. (2017). "Root System Water Consumption Pattern Identification on Time Series Data." Sensors, 17(6). Fischer, R. A., Byerlee, D., Edmeades, G. (2014). "Crop Yields and Global Food Security: Will Yield Increase Continue to Feed the World?" Aciar, (158), 634 11. Foth, H., and Ellis, B. (1996). Soil Fertility, 2nd ed. Ed., CRC P ress, Boca Raton, FL. Gallaher, R. N., Weldon, C. O., Futral, J. G. (1975). "An Aluminum Block Digester for Plant and Soil Analysis 1." Soil Sci. Soc. Am. J., 39(4), 803 806. Garnett, T., Plett, D., Heuer, S., Okamoto, M. (2015). "Genetic Approaches to Enh ancing Nitrogen use Efficiency (NUE) in Cereals: Challenges and Future Directions." Functional Plant Biol., 42(10), 921 941. Gehl, R. J., Schmidt, J. P., Maddux, L. D., Gordon, W. B. (2005). "Corn Yield Response to Nitrogen Rate and Timing in Sandy Irrigat ed Soils." Agron. J., 97(4), 1230 1238. Gheysari, M., Mirlatifi, S. M., Homaee, M., Asadi, M. E., Hoogenboom, G. (2009). "Nitrate Leaching in a Silage Maize Field Under Different Irrigation and Nitrogen Fertilizer Rates." Agricultural Water Management, 96( 6), 946 954. Gijsman, A. J., Hoogenboom, G., Parton, W. J., Kerridge, P. C. (2002). "Modifying DSSAT Crop Models for Low Input Agricultural Systems using a Soil Organic Matter residue Module from CENTURY." Agron. J., 94(3), 462 474. Godfray, H. C., Bedding ton, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., Toulmin, C. (2010). "Food Security: The Challenge of Feeding 9 Billion People." Science, 327(5967), 812 818. Godwin, D. C., and Singh, U. (1998). "Nit rogen Balance and Crop Response to Nitrogen in Upland and Lowland Cropping Systems." Understanding Options for Agricultural Production, Springer, 55 77. Good, A. G., Shrawat, A. K., Muench, D. G. (2004). "Can Less Yield More? is Reducing Nutrient Input int o the Environment Compatible with Maintaining Crop Production?" Trends Plant Sci., 9(12), 597 605. Goolsby, D. A., Battaglin, W. A., Aulenbach, B. T., Hooper, R. P. (2000). "Nitrogen Flux and Sources in the Mississippi River Basin." Sci. Total Environ., 24 8(2 3), 75 86.

PAGE 346

346 Gupta, H. V., Sorooshian, S., Yapo, P. O. (1999). "Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration." J. Hydrol. Eng., 4(2), 135 143. Guttman, N. B. (1989). "Statistical Descriptors of Clim ate." Bull. Am. Meteorol. Soc., 70(6), 602 607. Hallberg, G. R., and Keeney, D. R. (1993). "Nitrate." Regional Ground Water Quality. Van Nostrand Reinhold, USA, 297 322. Ham, L. K., and Hatzell, H. H. (1996). Analysis of Nutrients in the Surface Waters of the Georgia Florida Coastal Plain Study Unit, 1970 91, 96 4037 Ed., U.S. Geological Survey Water Resources Investigations, Tallahassee, Florida. Haman, D. A., and Izuno, F. T. (2003). "Soil Plant Water Relationships." Florida Coop. Ext. Serv., (Circ. 1085) , 4/17/2018 5. < http://edis.ifas.ufl.edu/AE021 > (5/19/2009). Hambleton, L. G. (1977). "Semiautomated Method for Simultaneous Determination of Phosphorus, Calcium, and Crude Protein in Animal Feeds. " Journal of the Association of Official Analytical Chemists, . Hammond, L. C., and Kirkham, D. (1949). "Growth Curves of Soybeans and Corn." Agron. J., (J 1590), 23 29. Hanway, J. J. (1963). "Growth Stages of Corn (Zea Mays, L.)." Agron. J., 55, 487 492. Harrington, D., Maddox, G., Hicks, R. (2010). Florida Springs Initiative Monitoring Network Report and Recognized Sources of Nitrate , February 2010 Ed., Florida Department of Environmental Protection. Ground Water Protection Section, Tallahassee, FL. Hauc k, R. D. (1984). Nitrogen in Crop Production, ASA CSSA SSSA, Madison, WI. He, J. (2008). "Best Management Practice Development with the Ceres Maize Model for Sweet Corn Production in North Florida." University of Florida, , 1 329. He, J., Dukes, M. D., Hoc hmuth, G. J., Jones, J. W., Graham, W. D. (2012). "Identifying Irrigation and Nitrogen Best Management Practices for Sweet Corn Production on Sandy Soils using CERES Maize Model." Agric. Water Manage., 109, 61 70. Hillel, D. (1971). Soil and Water: Physica l Principles and Processes, Elsevier, New York, USA. Hillel, D. (2004). Introduction to Environmental Soil Physics, Elsevier Academic Press, Amsterdam ; Boston.

PAGE 347

347 Hochmuth, G., Mylavarapu, R., Hanlon, E. (2014). "The Four Rs of Fertilizer Management." Soil a nd Water Science Department, UF/IFAS Extension, SL411, 1 4. Hochmuth, G. J. (2000). "Nitrogen Management Practices for Vegetable Production in Florida." Florida Cooperative Extension Service, IFAS, UF, (Circular 1222), 1 9. Hodges, A., Mohammad, R., Court, C. (2017). "Economic Contributions of Agriculture, Natural Resources, and Food Industries in Florida in 2015." Food and Resource Economics Department, UF/IFAS Extension, (FE1020), 1 3. Hodges, A. W., Rahmani, M., Stevens, T. (2015). "Economic Contribution s of Agriculture, Natural Resources, and Food Industries in Florida in 2013." Food and Resource Economics Department, UF/IFAS Extension, (FE969), 10/01 5. < http://edis.ifas.ufl.edu/fe969 > . Hoogenb oom, G., Jones, J. W., Wilkens, P. W., Porter, C. H., Boote, K. J., Hunt, L. A., Singh, U., Lizaso, J. I., White, J. W., Uryasev, O., Ogoshi, R., Koo, J., Shelia, V., Tsuji, G. Y. (2015). "Decision support system for agrotechnology transfer (DSSAT) version 4.6 ." < http://dssat.net > (05/16, 2016). Hornsby, D., and Mattson, R. (1997). Surface Water Quality and Biological Monitoring Network Annual Report , 1996th Ed., Suwannee River Water Management District, Live Oak, FL. Howarth, R., Chan, F., Conley, D. J., Garnier, J., Doney, S. C., Marino, R., Billen, G. (2011). "Coupled Biogeochemical Cycles: Eutrophication and Hypoxia in Temperate Estuaries and Coastal Marine Ecosystems." Frontiers in Ecology and the Environ ment, 9(1), 18 26. Howell, T. A. (2001). "Enhancing Water use Efficiency in Irrigated Agriculture." Agron. J., 93(2), 281 289. Huisman, J., Matthijs, H. C. P., Visser, P. M. (2005). Aquatic Ecology Series: Harmful Cyanobacteria, 1st Ed., Springer, Dordrech t, The Netherlands. Hunt, L. A., and Boote, K. J. (1998). "Data for Model Operation, Calibration, and Evaluation." Understanding Options for Agricultural Production, Springer, 9 39. Hyndman, R. J., and Koehler, A. B. (2006). "Another Look at Measures of Fo recast Accuracy." Int. J. Forecast., 22(4), 679 688. IPCC. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerlan d.

PAGE 348

348 Irmak, S. (2015a). "Interannual Variation in Long Term Center Pivot Irrigated Maize Evapotranspiration and various Water Productivity Response Indices. I: Grain Yield, Actual and Basal Evapotranspiration, Irrigation Yield Production Functions, Evapotran spiration Yield Production Functions, and Yield Response Factors." J. Irrig. Drain. Eng., 141(5). Irmak, S. (2015b). "Interannual Variation in Long Term Center Pivot Irrigated Maize Evapotranspiration and various Water Productivity Response Indices. II: Ir rigation Water use Efficiency, Crop WUE, Evapotranspiration WUE, Irrigation Evapotranspiration use Efficiency, and Precipitation use Efficiency." J. Irrig. Drain. Eng., 141(5). Irrigation Association. (2011). Irrigation, 6th Ed., Irrigation Association, Fa lls Church, VA. Jarvis, A., Ramirez, J., Anderson, B., Leibing, C., Aggarwal, P., Reynolds, M. P. (2010). "Scenarios of Climate Change within the Context of Agriculture." Climate Change and Crop Production, 13th Ed., CABI, Chippenham, UK, 9 37. Jia, X., Sh ao, L., Liu, P., Zhao, B., Gu, L., Dong, S., Bing, S. H., Zhang, J., Zhao, B. (2014). "Effect of Different Nitrogen and Irrigation Treatments on Yield and Nitrate Leaching of Summer Maize (Zea Mays L.) Under Lysimeter Conditions." Agricultural Water Manage ment, 137, 92 103. Jones, C. A., Kiniry, J. R., Dyke, P. T. (1986). CERES Maize: A Simulation Model of Maize Growth and Development, Texas A&M University Press, . Jones, H. G. (2007). "Monitoring Plant and Soil Water Status: Established and Novel Methods R evisited and their Relevance to Studies of Drought Tolerance." Journal of Experimental Botany, 58(2), 119 130. Jones, H. G. (2004). "Irrigation Scheduling: Advantages and Pitfalls of Plant Based Methods." J. Exp. Bot., 55(407), 2427 2436. Jones, H. G. (198 9). "Plant water relations and implications for irrigation scheduling." Proc., Symposium on Scheduling of Irrigation for Vegetable Crops Under Field Condition 278, , 67 76. Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., Ritchie, J. T. (2003). "The DSSAT Cropping System Model." Eur. J. Agron., 18(3 4), 235 265. Katz, B. G., DeHan, R. S., Hirten, J. J., Catches, J. S. (1997). "Interactions between Ground Water and Surface Wa ter in the Suwannee River Basin, Florida." Jawra, 33(6), 1237 1254.

PAGE 349

349 Katz, B. G., and Raabe, E. A. (2005). Suwannee River Basin and Estuary: An Integrated Science Program White Paper, Open File Report 2005 1210 Ed., U.S. Department of the Interior and U.S. Geological Survey, . Katz, B. G. (2004). "Sources of Nitrate Contamination and Age of Water in Large Karstic Springs of Florida." Environ. Geol., 46(6 7), 689 706. Katz, B. G., Hornsby, H. D., Bohlke, J. F., Mokray, M. F. (1999). Sources and Chronology of Nitrate Contamination in Spring Waters, Suwannee River Basin, Florida, Report 99 4252 Ed., U.S. Geological Survey, Tallahassee, Florida. Katz, B. G., and DeHan, R. S. (1996). The Suwannee River Basin Pilot Study: Issues for Watershed Management in Florida , FS 080 96 Ed., U.S. Department of the Interior. U.S. Geological Survey, Tallahassee, FL. Katz, B. G., Sepulveda, A. A., Verdi, R. J. (2009). "Estimating Nitrogen Loading to Ground Water and Assessing Vulnerability to Nitrate Contamination in a Large Kars tic Springs Basin, Florida." J. Am. Water Resour. Assoc., 45(3), 607 627. Keeney, D. R. (1986). "Nitrate in Ground Water Agricultural Contribution and Control." Agricultural Impacts on Ground Water, , 229 351. Kirkham, M. B. (2014). Principles of Soil an d Plant Water Relations, 2nd edition Ed., Academic Press, Manhattan, USA. Kisekka, I., Migliaccio, K. W., Dukes, M. D., Schaffer, B., Crane, J. H. (2016). "Evapotranspiration Based Irrigation Scheduling for Agriculture." Florida Coop. Ext. Serv., (AE457), 4/14/2018 5. < http://edis.ifas.ufl.edu/ae457 > (January 2010). Klocke, N. L., Payero, J. O., Schneekloth, J. P. (2007). "Long Term Response of Corn to Limited Irrigation and Crop Rotations." Trans. ASABE, 50(6), 2117 2124. Klocke, N. L., Currie, R. S., Tomsicek, D. J., Koehn, J. (2011). "Corn Yield Response to Deficit Irrigation." Trans. ASABE, 54(3), 931 940. Klocke, N. L., Watts, D. G., Schneekloth, J. P., Davison, D. R., Todd, R. W., Parkhurst, A. M. (1999). "Nitrate Leaching in Irrigated Corn and Soybean in a Semi Arid Climate." Trans. ASAE, 42(6), 1621 1630. Knobeloch, L., Salna, B., Hogan, A., Postle, J., Anderson, H. (2000). "Blue Babies and Nitrate Contaminated Well Water." Environ. Health Per spect., 108(7), 675 678. K State Research & Extension Mobile Irrigation Lab. (2014). "KanSched." (v3.1.5), K State Research & Extension Mobile Irrigation Lab, Kansas.

PAGE 350

350 Kucharik, C. J., and Ramankutty, N. (2005). "Trends and Variability in US Corn Yields Ove r the Twentieth Century." Earth Interact., 9(1), 1 29. Leikam, D. F., Lamond, R. E., Mengel, D. B. (2003). "Soil Test Interpretations and Fertilizer Recommendations." Kansas State Univ. Agric. Exp. Stn. and Coop. Ext. Service, (Ext. Publ. MF 2586), 1 20. L etey, J. (1985). "Relationship between Soil Physical Properties and Crop Production. ." Adv. Soil Sci., 1, 277 294. Li, M. B., and Yost, R. S. (2000). "Management Oriented Modeling: Optimizing Nitrogen Management with Artificial Intelligence." Agric. Syst. , 65(1), 1 27. Li, Z. T., Yang, J. Y., Drury, C. F., Hoogenboom, G. (2015). "Evaluation of the DSSAT CSM for Simulating Yield and Soil Organic C and N of a Long Term Maize and Wheat Rotation Experiment in the Loess Plateau of Northwestern China." Agric. Sy st., 135, 90 104. Liu, H. L., Yang, J. Y., Drury, C. F. a., Reynolds, W. D., Tan, C. S., Bai, Y. L., He, P., Jin, J., Hoogenboom, G. (2011). "Using the DSSAT CERES Maize Model to Simulate Crop Yield and Nitrogen Cycling in Fields Under Long Term Continuous Maize Production." Nutr. Cycling Agroecosyst., 89(3), 313 328. Locascio, S. J. (2005). "Management of Irrigation for Vegetables: Past, Present, and Future." HortTechnology, 15(3), 482 485. López Cedrón, F. X., Boote, K. J., Piñeiro, J., Sau, F. (2008). "I mproving the CERES Maize Model Ability to Simulate Water Deficit Impact on Maize Production and Yield Components." Agron. J., 100(2), 296 307. Ma, L., Hoogenboom, G., Ahuja, L. R., Ascough, J. C., Saseendran, S. A. (2006). "Evaluation of the RZWQM CERES Ma ize Hybrid Model for Maize Production." Agric. Syst., 87(3), 274 295. Marella, R. L., Dixon, J. F., Berry, D. R. (2016). Agricultural Irrigated Land use Inventory for the Counties in the Suwannee River Water Management District in Florida, 2015, U.S. Geolo gical Survey, Reston, VA. Marella, R. L. (2015). Water Withdrawals in Florida, 2012, 2015 1156 Ed., U.S. Geological Survey, Reston, VA. Marella, R. L. (2014). "Water Withdrawals, use, and Trends in Florida, 2010: U.S. Geological Survey Scientific Investiga tions Report 2014 5088." U. S. Geological Survey, (5088), 1 59.

PAGE 351

351 Maupin, M. A., Kenny, J. F., Hutson, S. S., Lovelace, J. K., Barber, N.,L., LinseY, K. S. (2014). Estimated use of Water in the United States in 2010, Circular 1405 Ed., U.S. Geological Survey , Reston, Virginia. McWilliams, D. A., Berglund, D. R., Endres, G. J. (1999). Corn Growth and Management Quick Guide, A 1173 Ed., North Dakota State University and University of Minnesota, North Dakota. Meng, L., and Quiring, S. M. (2008). "A Comparison of Soil Moisture Models using Soil Climate Analysis Network Observations." J. Hydrometeorol., 9(4), 641 659. Migliaccio, K. W., Schaffer, B., Crane, J. H., Davies, F. S. (2010). "Plant Response to Evapotranspiration and Soil Water Sensor Irrigation Schedulin g Methods for Papaya Production in South Florida." Agricultural Water Management, 97(10), 1452 1460. Moll, R. H., Kamprath, E. J., Jackson, W. A. (1982). "Analysis and Interpretation of Factors which Contribute to Efficiency of Nitrogen Utilization 1." Agr on. J., 74(3), 562 564. Monteith, J. L. (1977). "Climate and the Efficiency of Crop Production in Britain." Phil.Trans.R.Soc.Lond.B, 281(980), 277 294. Monteith, J. (1996). "The Quest for Balance in Crop Modeling." Agron. J., 88(5), 695 697. Moreno, F., Ca yuela, J. A., Fernández, J. E., Fernández Boy, E., Murillo, J. M., Cabrera, F. (1996). "Water Balance and Nitrate Leaching in an Irrigated Maize Crop in SW Spain." Agricultural Water Management, 32(1), 71 83. Morgan, K. T., Obreza, T. A., Scholberg, J. M. S., Parsons, L. R., Wheaton, T. A. (2006). "Citrus Water Uptake Dynamics on a Sandy Florida Entisol." Soil Sci. Soc. Am. J., 70(1), 90 97. Munoz Carpena, R. (2012). "Field Devices for Monitoring Soil Water Content." Agricultural and Biological Engineering Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, BUL343, 11/01/2014 16. < http://edis.ifas.ufl.edu/ae266 > (June 2004). Munoz Car pena, R., Dukes, M. D., Li, Y. C. C., Klassen, W. (2005). "Field Comparison of Tensiometer and Granular Matrix Sensor Automatic Drip Irrigation on Tomato." HortTechnology, 15(3), 584 590. Musick, J. T., and Dusek, D. A. (1980). "Irrigated Corn Yield Respon se to Water." Trans. ASAE, 23(1), 92 &.

PAGE 352

352 Mylavarapu, R., Wright, D., Kidder, G. (2015). "UF/IFAS Standardized Fertilization Recommendations for Agronomic Crops." Soil and Water Science Department, UF/IFAS Extension, (SL129), 10/1/2015 8. < https://edis.ifas.ufl.edu/pdffiles/SS/SS16300.pdf > . NASS, U. (2018). "2012 census of agriculture. county profile. suwanne county florida." < https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Co unty_Profiles/Florida/cp12121.pdf > (10/01, 2018). Nelson, N. G., Muñoz Carpena, R., Phlips, E. J., Kaplan , D., Sucsy, P., Hendrickson, J. (2018). "Revealing Biotic and Abiotic Controls of Harmful Algal Blooms in a Shallow Subtropical Lake through Statistical Machine Learning." Environ. Sci. Technol., 52(6), 3527 3535. NeSmith, D. S., and Ritchie, J. T. (1992) . "Short Term and Long Term Responses of Corn to a Preanthesis Soil Water Deficit." Agron. J., 84(1), 107 113. Nolan, B. T., and Hitt, K. J. (2003). Nutrients in Shallow Ground Waters Beneath Relatively Undeveloped Areas in the Conterminous United States, 02 4289 Ed., U.S. Geological Survey, Denver, CO. Nolan, B. T., Hitt, K. J., Ruddy, B. C. (2002). "Probability of Nitrate Contamination of Recently Recharged Groundwaters in the Conterminous United States." Environ. Sci. Technol., 36(10), 2138 2145. NRCS, U . (2016a). "Published soil surveys for florida." < http://www.nrcs.usda.gov/wps/portal/nrcs/surveylist/soils/survey/state/?stateId= FL > (06/01, 201 6). NRCS, U. (2016b). "Web soil service." < http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx > (06/01, 2016). Odum, H. T. (1957). "Trophic Structure and Productivi ty of Silver Springs, Florida. E." Ecol. Monogr., 27, 112 55. O'Neal, M. R., Frankenberger, J. R., Ess, D. R. (2002). "Use of CERES Maize to Study Effect of Spatial Precipitation Variability on Yield." Agricultural Systems, 73(2), 205 225. Paerl, H. W., Ha ll, N. S., Calandrino, E. S. (2011). "Controlling Harmful Cyanobacterial Blooms in a World Experiencing Anthropogenic and Climatic Induced Change." Science of the Total Environment, 409(10), 1739 1745. Paerl, H. W., and Otten, T. G. (2013). "Blooms Bite th e Hand that Feeds Them." Science, 342(6157), 433 434.

PAGE 353

353 Paerl, H. W., and Huisman, J. (2008). "Blooms Like it Hot." Science, 320(5872), 57 58. Pan, W., Camberato, J. J., Jackson, W. A., Moll, R. H. (1986). "Utilization of Previously Accumulated and Concurren tly Absorbed Nitrogen during Reproductive Growth in Maize." Plant Physiol., 82, 247 253. Paredes, P., Rodrigues, G. C., Alves, I., Pereira, L. S. (2014). "Partitioning Evapotranspiration, Yield Prediction and Economic Returns of Maize Under various Irrigat ion Management Strategies." Agric. Water Manage., 135, 27 39. Payero, J. O., Klocke, N. L., Schneekloth, J. P., Davison, D. R. (2006). "Comparison of Irrigation Strategies for Surface Irrigated Corn in West Central Nebraska." Irrig. Sci., 24(4), 257 265. P az, J. O., Batchelor, W. D., Babcock, B. A., Colvin, T. S., Logsdon, S. D., Kaspar, T. C., Karlen, D. L. (1999). "Model Based Technique to Determine Variable Rate Nitrogen for Corn." Agricultural System, 61(1), 69 75. Pessarakli, M. (2014). Handbook of Pla nt and Crop Physiology, 2nd Ed., CRC Press, Tucson, Arizona. Phelps, G. G. (2004). Chemistry of Ground Water in the Silver Springs Basin, Florida, with an Emphasis on Nitrate. , Report 2004 5144 Ed., U. S. Geological Society Scientific Investigations, Alta monte Springs, FL. Pittman, J. R., Hatzell, H. H., Oaksford, E. T. (1997). Spring Contributions to Water Quantity and Nitrate Loads in the Suwannee River during Base Flow in July 1995, 97 4152 Ed., U.S. Geological Survey Water Resources Investigations, Tal lahassee, Florida. Porter, C. H., Jones, J. W., Adiku, S. G. K., Gijsman, A. J., Gargiulo, O., Singh, U. (2010). DSSAT Century Soil Organic Matter Module Data Requirements and Initialization Procedures, . Prasad, R., Hochmuth, G. J., Boote, K. J. (2015). " Estimation of Nitrogen Pools in Irrigated Potato Production on Sandy Soil using the Model SUBSTOR." PLoS One, 10(1), e0117891. Rabalais, N. N., Turner, R. E., Justic, D., Dortch, Q., Wiseman, W. J., SenGupta, B. K. (1996). "Nutrient Changes in the Mississi ppi River and System Responses on the Adjacent Continental Shelf." Estuaries, 19(2B), 386 407. Reynolds, M. P., Hays, D., Chapman, S. (2010). "Breeding for Adaptation to Heat and Drought Stress." Climate Change and Crop Production, CABI, London, UK, 71 91.

PAGE 354

354 Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., Woollen, J. (2011). "MERRA: Era Retrospective Analysis for Research and Applications." J. Climate, 24(14), 3624 3648. Ritch ie, J. T. (1998). "Soil Water Balance and Plant Water Stress." Understanding Options for Agricultural Production, Springer, Dordrecht, The Netherlands, 41 54. Ritchie, J. T., Singh, U., Godwin, D. C., Bowen, W. T. (1998). "Cereal Growth, Development and Yi eld." Understanding Options for Agricultural Production, Springer, 79 98. Ritchie, S. W., Hanway, J. J., Benson, G. O. (1993). "How a Corn Plant Develops." Iowa State Univ. of Sc. and Technol. Coop. Ext. Serv. , (48). Robins, J. S., and Domingo, C. E. (195 3). "Some Effects of Severe Soil Moisture Deficits at Specific Growth Stages in Corn." Agron. J., 45(12), 618 621. Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S. (2013). "The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies." Agric. for. Meteorol., 170, 166 182. Ruane, A. C., Goldberg, R., Chryssanthacopoulos, J. (2015). "Climate Forcing Datasets for Agricultural Mode ling: Merged Products for Gap Filling and Historical Climate Series Estimation." Agr. Forest Meteorol., (200), 233 248. Rudnick, D., Irmak, S., Ferguson, R., Shaver, T., Djaman, K., Slater, G., Bereuter, A., Ward, N., Francis, D., Schmer, M., Wienhold, B., Van Donk, S. (2016). "Economic Return Versus Crop Water Productivity of Maize for various Nitrogen Rates Under Full Irrigation, Limited Irrigation, and Rainfed Settings in South Central Nebraska." J. Irrig. Drain. Eng., 142(6), 04016017. Rudnick, D. R., a nd Irmak, S. (2013). "Impact of Water and Nitrogen Management Strategies on Maize Yield and Water Productivity Indices Under Linear Move Sprinkler Irrigation." Trans. ASABE, 56(5), 1769 1783. Sanchez, B., Rasmussen, A., Porter, J. R. (2014). "Temperatures and the Growth and Development of Maize and Rice: A Review." Global Change Biol., 20(2), 408 417. SAS Institute Inc. (2013). "SAS for Windows." (SAS 9.4), SAS Institute Inc., Cary, NC, USA.

PAGE 355

355 Schepers, J. S., Varvel, G. E., Watts, D. G. (1995). "Nitrogen and Water Management Strategies to Reduce Nitrate Leaching Under Irrigated Maize." J. Contam. Hydrol., 20(3 4), 227 239. Schneekloth, J. P., Klocke, N. L., Clark, R. T., Norton, N. A. (1996). Irrigation Management Strategies to Reduce Leaching. American Socie ty of Agricultural Engineers (ASAE), St Joseph, USA. Sentek Pty Ltd. (2003). TriSCAN Manual Version 1.2a, 1.2a Ed., Sentek Pty Ltd, Stepney, South Australia. Sentek Technologies. (2002). "Case studies: Plant water stress index in corn ." < http://www.sentek.com.au/casestudies/corn.asp > (5/1, 2016). Shanahan, J. F., and Nielsen, D. C. (1987). "Influence of Growth Retardants (Anti Gibberellins) on Corn Vegetative Growth, Water use, and Grain Yield Under Different Levels of Water Stress." Agron. J., 79(1), 103 109. Shapiro, C. A., Ferguson, R. B., Hergert, G. W., Wortman, C. S., Walters, D. (2008). "Fertilizer Suggestions for Corn." Univ. of Nebraska Coop. Ext. , Lincoln, NE., (EC117), 1 6. Sigua, G. C., Stone, K. C., Bauer, P. J., Szogi, A. A., Shumaker, P. D. (2017). "Impacts of Irrigation Scheduling on Pore Water Nitrate and Phosphate in Coastal Plain Region of the United States." Agric. Water Manage., 186, 75 85. Soil Survey Staff. (20 06). Keys to Soil Taxonomy, 10th edition Ed., U.S. Department of Agriculture, Natural Resources Conservation Service, Washington, DC. Soil Survey Staff. (1999). "Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys. " Handbook 436, 2nd edition Ed., Natural Resources Conservation Service. U.S. Department of Agriculture, . Spears, T. D. (2003). "Irrigating efficiently to feed the world in 2050." Proc., Irrigation Association Conference Proceedings, Irrigation Associatio n, Falls Church, Va, 411 422. Starr, J. L., and Paltineanu, I. C. (1998). "Soil Water Dynamics using Multisensor Capacitance Probes in Nontraffic Interrows of Corn." Soil Sci. Soc. Am. J., 62(1), 114 122. Stegman, E. C., and Coe, D. A. (1984). "Water Balan ce Irrigation Scheduling Based on Jensen Haise Equation: Software for Apple 2,2+, and 2E Computer." Agr. Exp. Sta. , N. Dak. State Univ., Rpt. 100(Rpt. 100), 26.

PAGE 356

356 Strebel, O., Duynisveld, W. H. M., Bottcher, J. (1989). "Nitrate Pollution of Groundwater in W estern Europe." Agric. Ecosyst. Environ., 26(3 4), 189 214. Strong, W. A. (2004). "Temporal Water Chemistry Trends within Individual Springs within a Population of Florida Springs." University of Florida, MS Thesis, 1 54. Suwannee River Water Management Di strict. (2004). Surfacewater Quality and Biological Annual Report 2003, WR03/04 03 Ed., Suwannee River Water Management District, Live Oak, FL. Swank, J. C., Below, F. E., Lambert, R. J., Hageman, R. H. (1982). "Interaction of Carbon and N itrogen Metabolism in the Productivity of Maize." Plant Physiol., 70(4), 1185 1190. Széles, A. V., Megyes, A., Nagy, J. (2012). "Irrigation and Nitrogen Effects on the Leaf Chlorophyll Content and Grain Yield of Maize in Different Crop Years." Agricultural Water Management, 107, 133 144. Taiz, L., Zeiger, E., Miler, I. M. I., Murphy, A. S. (2015). Plant Physiology and Development, Sixth edition. Ed., Sinauer Associates, Inc., Publishers, Sunderland, Massachusetts, U.S.A. Taylor, S. A., and Ashcroft, G. L. ( 1972). Physical Edaphology. the Physics of Irrigated and Nonirrigated Soils. Freeman, San Francisco. The Balmoral Group, L. (2015). Florida Statewide Agricultural Irrigation Demand (FSAID), PO No. POEC1121 Ed., Office of Agricultural Water Policy, Tallahas see, Florida. Tojo Soler, C. M., Sentelhas, P. C., Hoogenboom, G. (2007). "Application of the CSM CERES Maize Model for Planting Date Evaluation and Yield Forecasting for Maize Grown Off Season in a Subtropical Environment." Eur. J. Agron., 27(2 4), 165 17 7. Turner, N. C. (2004). "Agronomic Options for Improving Rainfall use Efficiency of Crops in Dryland Farming Systems." J. Exp. Bot., 55(407), 2413 2425. Turner, R. E., and Rabalais, N. N. (1991). "Changes in Mississippi River Water Quality this Century." Bioscience, 41(3), 140 147. U.S. Census Bureau. (2018). "Florida population." < https:// www.census.gov/search results.html?page=1&stateGeo=&searchtype=web&cssp=SERP&q=population+fl orida&search.x=0&search.y=0&search=submit > (07/12, 2018). UF/IFAS Anserv Labs. (2011). "Analytical research laboratory (ARL)." < http://arl.ifas.ufl.edu/ARL%20Analysis.asp > (2/15, 2015).

PAGE 357

357 University of Florida, Bureau of Economic and Business Research. (2015). "Population program, florida population studies. population estimates and projections." < https://www.bebr.ufl.edu/data/9127/state/12000 state florida > (06/21, 2016). Upchurch, S. B., Chen, J., Cain, C. R. (2007). Trends of Nitrate Concentrations in Waters of the Suwannee River Water Management District, 2007, SDII Global Corporation Project Number 3017076 Ed., Suwannee River Water Management District, Live Oak, Florida. USDA, N. (2018). "QuickStats." < https://quickstats.nass.usda.gov/results/2317A971 4BB4 371F 834D 38BA07217142 > (4/17, 2018). USDA, N. (2016a). "Quick stats." < https://quickstats.nass.usda.gov/#C1F5F23A 335E 3BA9 82E1 C25134AEEEE4 > (5/16, 2016). USDA, N. (2016b). Prospective Plantings, March 2016 Ed., National Agricultural Statistics Service, Agricultural Statistics Board, U.S. Department of Agiculture, Washington, D.C. USDA, N. (2014). 2012 Census of Agriculture. United States. Summary and State Data. Table 1. State Summary Highlights: 2012, Vol 1, Chapter 2 Ed., USDA, National Agricultural Statistics Service, Washington, D.C. USDA, N. ( 2012). 2012 Census of Agriculture State Data, 2012 Census Ed., USDA, National Agricultural Statistics Service, . USDA, N. (2007). Prospective Plantings, March 2007 Ed., National Agricultural Statistics Service, Agricultural Statistics Board, U.S. Departm ent of Agriculture, Washington, D.C. USDA, N. (2013). "Web soil survey." < http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx > (3/31, 2015). USGS, C. W. S. C. (2012). Water use in Florida, 2015 1156 Ed., U.S. Geological Survey, Orlando, FL. Valmont Industries, I. (2015). "Valley variable rate irrigation." < http://www.valleyirrigation.com/valley irrigation/us/control technology/variable rate irrigation %28vri%29 > (03/02, 2015). van Bel, A. J. E. (2003). "The Phloem, a Miracle of Ingenuity." Plant, Cell Environ., 26(1), 125 149. va n Donk, S. J., Petersen, J. L., Davison, D. R. (2013). "Effect of Amount and Timing of Subsurface Drip Irrigation on Corn Yield." Irrig. Sci., 31(4), 599 609.

PAGE 358

358 Viets, F. G. (1962). "Fertilizers and the Efficient use of Water." Adv. Agron., 14, 223 264. Wall ace, J. S. (2000). "Increasing Agricultural Water use Efficiency to Meet Future Food Production." Agric. Ecosyst. Environ., 82(1 3), 105 119. Waskom, R. M., and Bauder, T. A. (1994). "Best Management Practices for Nitrogen Fertilization to Protect Water Qu ality." Colorado State University Extension, Bulletin #XCM 172, 9/15 14. < http://extension.colostate.edu/docs/pubs/crops/xcm172.pdf > . Wienhold, B. J., Trooien, T. P., Re ichman, G. A. (1995). "Yield and Nitrogen use Efficiency of Irrigated Corn in the Northern Great Plains." Agron. J., 87(5), 842 846. Williams, J. R. (1991). "Runoff and Water Erosion." Modeling Plant and Soil Systems, (modelingplantan), 439 455. WMO. (1989 ). "Calculation of Monthly and Annual 30 Year Standard Normals." World Meteorological Organization, WCDP928 No.10, WMO TD/No.341. Wright, D., Marois, J., Rich, J., Rowland, D. (2003). "Field Corn Production Guide." Agronomy Department, UF/IFAS Extension, ( SS AGR 85), 1 12. Yang, J. M., Yang, J. Y., Dou, S., Yang, X. M., Hoogenboom, G. (2013). "Simulating the Effect of Long Term Fertilization on Maize Yield and Soil C/N Dynamics in Northeastern China using DSSAT and CENTURY Based Soil Model." Nutr. Cycling A groecosyst., 95(3), 287 303. Zhang, W., Tian, Z., Zhang, N., Li, X. (1996). "Nitrate Pollution of Groundwater in Northern China." Agric. Ecosyst. Environ., 59(3), 223 231. Zinselmeier, C., Jeong, B. R., Boyer, J. S. (1999). "Starch and the Control of Kerne l Number in Maize at Low Water Potentials." Plant Physiol., 121(1), 25 36. Zotarelli, L., Dukes, M. D., Morgan, K. T. (2013). "Interpretation of Soil Moisture Content to Determine Soil Field Capacity and Avoid Over Irrigating Sandy Soils using Soil Moistur e Sensors." Agricultural and Biological Engineering Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, AE460, 1 4. < https://edis. ifas.ufl.edu/ae460 > (December 2009). Zotarelli, L., Scholberg, J. M., Dukes, M. D., Munoz Carpena, R. (2007). "Monitoring of Nitrate Leaching in Sandy Soils: Comparison of Three Methods." J. Environ. Qual., 36(4), 953 962.

PAGE 359

359 BIOGRAPHICAL SKETCH María Isa bel Zamora Re is originally from Costa Ric a, where she obtained her University and learned through a dual educational system through experimental learning while following th program, she developed and form part of entrepreneurial projects providing solutions to society. The diversity among the participants, as well as the opportunity to lead as a general manage r of two projects, allowed her to improve her negotiation skills , manage conflicts and develop strategies and solutions to the on going projects. Maria obtained from EARTH in 2008. Then, in 2009, she started her first job developing a nine This project helped her improved her leadership, communication, organization and delegation skills under high pressure w orking in a team . T he greater lessons emerged from the experiences wo rking with local producers and personnel as part of a team comprised of experts from different disciplines. T heir wisdom shared throughout the years provided us a better understanding of the world as a whole, since there is a strong dependency among societ y, economy and the environment. During 2011 to 2013, Maria Zamora obtained her m degree in a gricultural operations m anagement. She worked i n a project of irrigation for cold protection in strawberries. The main objective was to improve water conserv ation throughout sprinkler irrigation management (different pressures and sprinkler spacing) achieving uniformity coverage during freeze events. The main r esults showed water savings up to 22% when reducing the irrigation system pressure without impacts in strawberry yields compared to conventional practices. Thus, billions of gallons of water could be saved

PAGE 360

360 every strawberry season in Florida. Previously in 2014, she collaborated in a project with Orange County Utilities and the University of Florida. It co nsisted in the evaluation of smart water application technologies (SWAT) on their efficiency and water conservation in residential landscapes. Water savings were achieved when using SWAT technologies In 2015, Maria Isabel Zamora Re started a Ph.D. program in Agricultural Operations Management (AOM) in the Department of Agricultural and Biological Engineering at the University of Florida. The project was located in North Central Florida, region in which nitrogen (N) leaching to the waterbodies is causing maj or water quality degradation and environmental damages. The main g oal was to provide best management practices (BMPs) to growers in order to address the excess of N leaving the agricultural fields to nearby waterbodies. The outstanding results showed that the irrigation strategies proposed can provide the same yield as conventional practices, but achieving between 43 53% water savings and near 26% reduction in fertilizer applications. This project was the baseline for a continuation project in which three U niversities are working together (University of Georgia, University of Alabama and University of Florida) integrating all disciplines (economic, social and environmental) to ensure economic sustainability of agriculture and silviculture in North Florida an d South Georgia while protecting water quantity, quality, and habitat in the Upper Floridian Aquifer and the springs and rivers it feeds. Mar ía re ceived her doctorate degree in agricultural operations m anagement in the spring of 2019 . All the above have be en among the most exciting and challenging experiences she has had . E ach of them have contributed in her personal and professional growth.

PAGE 361

361 María would like to share the knowledge and experiences acquired for a prosperous society that ensures economic susta inability and conserv ation of our natural resources.