By. JAWOO KOO. A THESIS PRESENTED TO THE GRADUATE SCHOOL ... technical ideas that enabled implementation of a conceptual model into this study. ...... diseases that affect tomato in the U.S. as well as in Florida. ...... LPn. kLAT /=. (2-11) where n is 8 and LP is the calculated latent period on a given day (LP ⥠n).
MODELING THE IMPACTS OF CLIMATE VARIABILITY ON TOMATO DISEASE MANAGEMENT AND PRODUCTION
By JAWOO KOO
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2002
Copyright 2002 by Jawoo Koo
To my grandfather, Dalsuh Koo, who always takes care of me from heaven.
ACKNOWLEDGMENTS This thesis would not have been completed without the help of several people to whom I wish to express my sincere appreciation. First, I wish to express my deepest gratitude to Dr. James Jones for his immense help. He is my best advisor and best mentor. He has provided excellent guidance all the time for my life as well as studies with infinite kindness and patience. I could never have imagined a better advisor in the world. Dr. Richard Berger helped me to work out all problems and questions I had about plant epidemiology. It was a big challenge to model a pathosystem two years ago, but it became an exciting experience with his knowledge and endless help. He was always friendly to me. Dr. Howard Beck opened my eyes to the exciting world of object-oriented programming. Most programming skills used in this study were inspired by his amazing and prompt help. He always encouraged me to learn more at an improved level of technical ideas that enabled implementation of a conceptual model into this study. I would also like to show my appreciation to other friendly and helpful professors: Dr. Ken Boote, Dr. Shrikant Jagtap, Dr. Ken Pernezny, Dr. Robert McGovern, Dr. Jeff Jones, Dr. Timmer Momol, Dr. Craig Stanley, and Dr. Thomas Kucharek. Dr. Boote helped me to verify the rationale of this study. He always gave me answers and academic stimulations. Dr. Jagtap’s encouragement has been always appreciated. Dr. Pernezny kindly allowed me to use his valuable data to develop and validate the model. Dr. McGovern, Dr. Jones, and Dr. Momol helped me to understand plant disease epidemics iv
in tomato farming in Florida. Dr. Stanley thankfully provided his solar radiation data when I needed it most. Dr. Kucharek helped me to understand fungicide applications in tomato disease management. To my family in Korea, I would like to express my gratitude for their dedication, support, and love. We have been apart for three years, but my heart was always with them and their thoughtful help and care were driving forces for this achievement. I would like to acknowledge my dear Korean friends in Gainesville: Kyungha and Dongsik, Kyungmin and Sangwon, and Taejoong. Ever since we met at the Atlanta airport on June 22, 1999, we have been through everything together in the U.S. My special thanks should go to colleagues in the Crop Systems Modeling Lab. To begin with, I truly appreciate Andres Ferreyra’s excellent help with my writing. Without him, I would never have finished thesis writing. And, I should name all of these friends (alphabetically): Arjan, Amy & Carlos, Ayse & Suat, Cheryl, Estela & Fred, Fiona & Joep, Helga & Ricardo, Lily & Andres, McNair, Jay, Jean & Jim, Ramkrishnan, Ravic, Shrikant, Theo, Valerie, and Wayne. Since the first day I entered the University of Florida, they were always friendly and nice to me. Thanks to them, I soon became a part of this great lab and a member of their wonderful families. Seasonal picnics with them were always joyful and refreshing. I was always stimulated by academic discussions with them. I was always thankful for their warmness and kindness. And, most of all, I would especially love to express my dearest appreciation and affection to my wife, Soonho, who has been always with me. Her endless support, friendship, encouragement, and love were a major part of this achievement. I would never feel joy and happiness without her in my life.
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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................. iv LIST OF TABLES ............................................................................................................. ix LIST OF FIGURES............................................................................................................. x ABSTRACT .................................................................................................................... xvii CHAPTERS 1 INTRODUCTION........................................................................................................... 1 2 DESCRIPTION OF FOLIAR DISEASE MODEL, FODIS ........................................... 5 Introduction ..................................................................................................................... 5 Tomato Production in Florida .................................................................................. 5 Modeling Foliar Disease Epidemics......................................................................... 6 Crop Growth Models................................................................................................ 9 Objectives ............................................................................................................... 10 Materials and Methods .................................................................................................. 10 Measured Disease Progress Data ........................................................................... 10 Systems Analysis of Pathosystem .......................................................................... 11 The Life Cycle of the Pathogen.............................................................................. 12 Conceptual Model Description............................................................................... 14 Physiological age of the plant at infection onset............................................. 17 Infection .......................................................................................................... 17 The basic infection rate, R............................................................................... 18 Lesion expansion............................................................................................. 20 Defoliation....................................................................................................... 20 Latent period ................................................................................................... 21 Infectious period.............................................................................................. 23 Distributed delay function............................................................................... 24 Effects of Weather Conditions ............................................................................... 25 Temperature effects......................................................................................... 26 Effects of leaf wetness duration ...................................................................... 28 Effects of Plant Tolerance on Disease.................................................................... 31 Effects of Fungicide ............................................................................................... 31 Effect of the Availability of Vacant Area .............................................................. 35 Calculating Proportion of Disease Severity ........................................................... 35 vi
Prediction of Daily Leaf Wetness Duration ........................................................... 36 Linkage to the CROPGRO Crop Growth Simulator .............................................. 40 Sensitivity Analysis ................................................................................................ 42 Validation with LATESPOT .................................................................................. 43 Results and Discussions ................................................................................................ 45 Simulated and Observed Tomato Pathosystem ...................................................... 45 Sensitivity Analysis ................................................................................................ 48 Validation with LATESPOT .................................................................................. 75 Conclusions ................................................................................................................... 81 3 MODELING THE IMPACTS OF CLIMATE VARIABILITY ON TOMATO DISEASE MANAGEMENT AND PRODUCTION .................................................... 83 Introduction ................................................................................................................... 83 Materials and Methods .................................................................................................. 87 Foliar Disease and Tomato Growth Model ............................................................ 87 Simulation Study .................................................................................................... 87 Computing Marketable Fruit Yield ........................................................................ 89 Statistic Method...................................................................................................... 90 Results and Discussions ................................................................................................ 90 Calibration of Coefficients ..................................................................................... 90 Duration of Tomato Growth and Early Blight Infection ........................................ 92 Disease Progress..................................................................................................... 92 Yield ....................................................................................................................... 97 Leaf Area .............................................................................................................. 103 Biomass ................................................................................................................ 104 Fungicide Applications......................................................................................... 105 Transplanting Date ............................................................................................... 107 Conclusion................................................................................................................... 109 4 CONCLUSION ........................................................................................................... 112 APPENDICES A TECHNICAL ASPECTS OF INTERCHANGING VALUE OF VARIABLE ......... 114 B TECHNICAL ASPECTS OF SENDING FEEDBACKS TO THE CROPGRO MODEL....................................................................................................................... 117 TDLA .......................................................................................................................... 117 WLIDOT ..................................................................................................................... 118 C IMPLEMENTING CROP CULTIVAR TOLERANCE ............................................ 120 The Tolerance Framework .......................................................................................... 120 Tolerance Coefficient Input File .......................................................................... 122 Experiment Input File........................................................................................... 123
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Modifications on CROPGRO Source Codes........................................................ 124 Use of tolerance framework in FODIS to implement cultivar resistance ................... 124 D FODIS FILES WRITTEN IN FORTRAN................................................................. 127 PT_TMEB.FOR .......................................................................................................... 127 PT_PNLS.FOR............................................................................................................ 142 FCIDE_CT2L.FOR ..................................................................................................... 155 PEST_TM.FOR........................................................................................................... 159 PEST_PN.FOR............................................................................................................ 161 MESSENGER.FOR .................................................................................................... 163 GetNDLA.FOR ........................................................................................................... 168 E DESCRIPTION OF VARIABLES USED IN SOURCE FILES................................ 169 PT_TMEB and PT_PNLS........................................................................................... 169 FCIDE_CT2L.............................................................................................................. 173 PEST_TM and PEST_PN ........................................................................................... 174 GetNDLA .................................................................................................................... 175 MESSENGERS........................................................................................................... 175 F INPUT DATA Files .................................................................................................... 177 Experimental Input Data File ...................................................................................... 177 FODIS-TMEB ...................................................................................................... 177 Bradenton, 1991 ............................................................................................ 177 Ft. Pierce, 1998.............................................................................................. 178 Miami, 1948-1999......................................................................................... 179 FODIS-PNLS ....................................................................................................... 180 Gainesville, 1987........................................................................................... 180 Tolerance Coefficient Input Data File......................................................................... 181 Tomato.................................................................................................................. 181 Peanut ................................................................................................................... 181 Weather Input Data File .............................................................................................. 182 Gainesville, 1987.................................................................................................. 182 Bradenton, 1991-1992 .......................................................................................... 187 Ft. Pierce, 1998..................................................................................................... 191 LIST OF REFERENCES ................................................................................................ 196 BIOGRAPHICAL SKETCH........................................................................................... 202
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LIST OF TABLES Table page 2-1. Definition of abbreviations and terms used in Figure 2-4. ............................................ 15 2-2. Differential equations to describe the disease epidemic in a cohort. Subscripts for leaf cohorts are omitted for clarity of presentation. .................................................. 15 2-3. Definition of variables used in equations in Table 2-2. ................................................. 16 2-4. A chlorothalonil persistence model. .............................................................................. 32 2-5. List of parameters used in sensitivity analyses. ............................................................. 50 2-6. Relative sensitivities of parameters used in the FODIS-TMEB model when weekly fungicide applications were simulated. ..................................................................... 51 2-7. Relative sensitivities of parameters used in the FODIS-TMEB model when no fungicide applications were simulated. ..................................................................... 52 2-8. Parameters of FODIS-TMEB model converted to simulate the peanut late leafspot pathosystem............................................................................................................... 77 3-1. Summary of statistical test results analyzed with Duncan’s method for parameters used in Chapter 3....................................................................................................... 106 C-1. Description of steps illustrated in Figure C-1. .............................................................. 121 C-2. Description of input data used in PEST INITIAL CONDITION section to apply damages and tolerances............................................................................................. 123 C-3. Description of input data used in PEST INITIAL CONDITION section to apply damages and tolerances............................................................................................. 126 D-1. List of files written or modified in this study................................................................ 127
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LIST OF FIGURES Figure page 2-1. Proportion of disease severity for tomato early blight was assessed in Ft. Pierce, 1998........................................................................................................................... 11 2-2. Disease tetrahedron developed and its adaptation to this study..................................... 12 2-3. Development and symptoms of disease caused by Alternaria. ..................................... 13 2-4. Diagram of the FODIS model coupled with the CROPGRO model. ............................ 14 2-5. The latent period and the incubation period of A. solani in tomato leaf cohorts........... 22 2-6. Latent period of A. solani in the tomato early blight pathosystem. ............................... 23 2-7. Relationship between daily average temperature and the infectious. ............................ 24 2-8. Temperature effects on the efficiency of infection of A. solani. ................................... 26 2-9. Daily average temperatures during the tomato growth period in Ft. Pierce, Florida, 1998........................................................................................................................... 27 2-10. Daily temperature efficiency for spore germination of A. solani during the tomato growth in Ft. Pierce, Florida, 1998............................................................................ 28 2-11. Required leaf wetness durations for conidia of A. solani to germinate. ...................... 30 2-12. Estimated leaf wetness duration on a given day and the required leaf wetness duration for spore germination vs. plant age............................................................. 30 2-13. Simulated dynamics of chlorothalonil residue throughout the 1998 tomato growth period in Ft. Pierce. ................................................................................................... 34 2-14. Relationship between daily rainfall amount and rainfall duration............................... 39 2-15. Simulated disease severity progress in four cohorts. ................................................... 41 2-16. Observed (Pernezny, unpublished) and simulated proportions of disease severity in Ft. Pierce, 1998.......................................................................................................... 45 2-17. Simulated fruit weight in Ft. Pierce, 1998, with and without tomato early blight epidemic. ................................................................................................................... 46 x
2-18. Simulated fruit number in Ft. Pierce, 1998, with and without tomato early blight epidemic. ................................................................................................................... 46 2-19. Simulated LAI for healthy leaf area in Ft. Pierce, 1998, with and without tomato early blight epidemic................................................................................................. 47 2-20. Simulated tops weight as a biomass in Ft. Pierce, 1998, with and without tomato early blight epidemic................................................................................................. 47 2-21. The change in leaf area at each life stage of the pathogen as the tomato early blight progressed through the crop growth period............................................................... 48 2-22. Sensitivity of the proportion of disease severity to changes in PST_INIT without fungicides. ................................................................................................................. 53 2-23. Sensitivity of LAI to changes in PST_INIT without fungicide applications............... 53 2-24. Sensitivity of tops weight to changes in PST_INIT without fungicide applications... 53 2-25. Sensitivity of yield to changes in PST_INIT without fungicide applications. ............ 53 2-26. Sensitivity of the proportion of disease severity to changes in MIN_LP without fungicides. ................................................................................................................. 54 2-27. Sensitivity of LAI to changes in MIN_LP without fungicide applications. ................ 54 2-28. Sensitivity of tops weight to changes in MIN_LP without fungicide applications. .... 54 2-29. Sensitivity of yield to changes in MIN_LP without fungicide applications................ 54 2-30. Sensitivity of the proportion of disease severity to changes in LP without fungicides. ................................................................................................................................... 55 2-31. Sensitivity of LAI to changes in LP without fungicide applications. .......................... 55 2-32. Sensitivity of tops weight to changes in LP without fungicide applications. .............. 55 2-33. Sensitivity of yield to changes in LP without fungicide applications.......................... 55 2-34. Sensitivity of the proportion of disease severity to changes in MIN_FP without fungicides. ................................................................................................................. 56 2-35. Sensitivity of LAI to changes in MIN_FP without fungicide applications. ................ 56 2-36. Sensitivity of tops weight to changes in MIN_FP without fungicide applications...... 56 2-37. Sensitivity of yield to changes in MIN_FP without fungicide applications. ............... 56
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2-38. Sensitivity of the proportion of disease severity to changes in FP without fungicides. ................................................................................................................................... 57 2-39. Sensitivity of LAI to changes in FP without fungicide applications. .......................... 57 2-40. Sensitivity of tops weight to changes in FP without fungicide applications. .............. 57 2-41. Sensitivity of yield to changes in FP without fungicide applications.......................... 57 2-42. Sensitivity of the proportion of disease severity to changes in ONSET_AGE without fungicides. .................................................................................................... 58 2-43. Sensitivity of LAI to changes in ONSET_AGE without fungicide applications......... 58 2-44. Sensitivity of tops weight to changes in ONSET_AGE without fungicides. .............. 58 2-45. Sensitivity of yield to changes in ONSET_AGE without fungicide applications. ...... 58 2-46. Sensitivity of the proportion of disease severity to changes in RMAX without fungicides. ................................................................................................................. 59 2-47. Sensitivity of LAI to changes in RMAX without fungicide applications.................... 59 2-48. Sensitivity of tops weight to changes in RMAX without fungicides. ......................... 59 2-49. Sensitivity of yield to changes in RMAX without fungicide applications. ................. 59 2-50. Sensitivity of the proportion of disease severity to changes in FAV_LEVEL without fungicides. .................................................................................................... 60 2-51. Sensitivity of LAI to changes in FAV_LEVEL without fungicide applications. ........ 60 2-52. Sensitivity of tops weight to changes in FAV_LEVEL without fungicide applications................................................................................................................ 60 2-53. Sensitivity of yield to changes in FAV_LEVEL without fungicide applications........ 60 2-54. Sensitivity of the proportion of disease severity to changes in INT_SS without fungicides. ................................................................................................................. 61 2-55. Sensitivity of LAI to changes in INT_SS without fungicides. .................................... 61 2-56. Sensitivity of tops weight to changes in INT_SS without fungicide applications....... 61 2-57. Sensitivity of yield to changes in INT_SS without fungicide applications. ................ 61 2-58. Sensitivity of the proportion of disease severity to changes in KLEX without fungicides. ................................................................................................................. 62
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2-59. Sensitivity of LAI to changes in KLEX without fungicide applications. .................... 62 2-60. Sensitivity of tops weight to changes in KLEX without fungicide applications. ........ 62 2-61. Sensitivity of yield to changes in KLEX without fungicide applications.................... 62 2-62. Sensitivity of the proportion of disease severity to changes in PST_TOLR without fungicides. ................................................................................................................. 63 2-63. Sensitivity of LAI to changes in PST_TOLR without fungicides. .............................. 63 2-64. Sensitivity of tops weight to changes in PST_TOLR without fungicides. .................. 63 2-65. Sensitivity of yield to changes in PST_TOLR without fungicides.............................. 63 2-66. Sensitivity of the proportion of disease severity to changes in HALO without fungicides. ................................................................................................................. 64 2-67. Sensitivity of LAI to changes in HALO without fungicide applications..................... 64 2-68. Sensitivity of tops weight to changes in HALO without fungicide applications......... 64 2-69. Sensitivity of yield to changes in HALO without fungicide applications. .................. 64 2-70. Sensitivity of the proportion of disease severity to changes in WLIDISDOT without fungicides. ................................................................................................................. 65 2-71. Sensitivity of LAI to changes in WLIDISDOT without fungicide applications.......... 65 2-72. Sensitivity of tops weight to changes in WLIDISDOT without fungicide applications................................................................................................................ 65 2-73. Sensitivity of yield to changes in WLIDISDOT without fungicide applications. ....... 65 2-74. Sensitivity of the proportion of disease severity to changes in TAIRHR without fungicides. ................................................................................................................. 66 2-75. Sensitivity of LAI to changes in TAIRHR without fungicide applications................. 66 2-76. Sensitivity of tops weight to changes in TAIRHR without fungicide applications..... 66 2-77. Sensitivity of yield to changes in TAIRHR without fungicide applications. .............. 66 2-78. Sensitivity of the proportion of disease severity to changes in LWD without fungicides. ................................................................................................................. 67 2-79. Sensitivity of LAI to changes in LWD without fungicide applications....................... 67 2-80. Sensitivity of tops weight to changes in LWD without fungicide applications........... 67
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2-81. Sensitivity of yield to changes in LWD without fungicide applications. .................... 67 2-82. Sensitivity of the proportion of disease severity to changes in RLWD without fungicides. ................................................................................................................. 68 2-83. Sensitivity of LAI to changes in RLWD without fungicide applications.................... 68 2-84. Sensitivity of tops weight to changes in RLWD without fungicides........................... 68 2-85. Sensitivity of yield to changes in RLWD without fungicide applications. ................. 68 2-86. Simulated sensitivity of AUDPC to changes in the tolerance coefficient to in Ft. Pierce, 1998, under simulated weekly fungicide application.................................... 70 2-87. Simulated sensitivity of IHLAI (Integrated Healthy Leaf Area Index) to changes in the tolerance coefficient to in Ft. Pierce, 1998, under simulated weekly fungicide application. ................................................................................................................ 71 2-88. Simulated sensitivity of tops weight to changes in the tolerance coefficient to in Ft. Pierce, 1998............................................................................................................... 71 2-89. Simulated sensitivity of the fruit weight to changes in tolerance coefficient to in Ft. Pierce, 1998............................................................................................................... 72 2-90. Simulated sensitivity of AUDPC to changes in fungicide efficiency in Ft. Pierce, 1998........................................................................................................................... 73 2-91. Simulated sensitivity of integrated healthy LAI to changes in fungicide efficiency in Ft. Pierce, 1998. .................................................................................................... 73 2-92. Simulated sensitivity of tops weight to changes in fungicide efficiency in Ft. Pierce, 1998........................................................................................................................... 74 2-93. Simulated sensitivity of fruit weight to changes in fungicide efficiency in Ft. Pierce, 1998........................................................................................................................... 74 2-94. Simulated sensitivity of fruit weight (yield) to changes in fungicide efficiency and tolerance coefficient in Ft. Pierce, 1998.................................................................... 75 2-95. Comparisons of observed disease severity data with the corresponding FODISPNLS and LATESPOT simulations.......................................................................... 80 2-96. Comparisons of observed LAI with the corresponding FODIS-PNLS and LATESPOT simulations. .......................................................................................... 80 2-97. Comparisons of observed seed weight with the corresponding FODIS-PNLS and LATESPOT simulations. .......................................................................................... 81 3-1. The location of the study area in Florida. ...................................................................... 86 xiv
3-2. Simulated disease severity values in Bradenton, 1991. Fungicide application was turned on and off. ...................................................................................................... 91 3-3. Simulated disease severity values in Bradenton, 1991. Fungicide efficiency was changed ±20% from its original value. ..................................................................... 91 3-4. Simulated disease severity values in Bradenton, 1991. Tolerance coefficient was changed ±10% from its original value. ..................................................................... 92 3-5. The distribution of final PDS (Proportion of Disease Severity). ................................... 93 3-6. Probability of simulated tomato early blight PDS exceeding any value with weekly fungicide applications. .............................................................................................. 94 3-7. The distribution of final AUDPC (Area Under the Disease Progress Curve). .............. 95 3-8. Probability of simulated tomato early blight AUDPC exceeding any value with weekly fungicide applications................................................................................... 96 3-9. Simulated disease progresses in 3 years: 1996 (neutral), 1997 (El Niño), and 1998 (La Niña). .................................................................................................................. 96 3-10. The distribution of marketable yields. ......................................................................... 97 3-11. Comparison of three types of simulated yields from the seasonal analyses. ............... 98 3-12. Yield ratio of simulated potential and marketable yields and simulated actual yield. 99 3-13. The distribution of yield loss due to the tomato early blight epidemics...................... 100 3-14. Simulated differences of tomato yield and number of fruit at harvest maturity between potential value with and without tomato early blight. ................................ 101 3-15. Differences of daily net fruit mass growth between values with and without tomato early blight epidemic................................................................................................. 102 3-16. Differences of daily total number of fruit between values with and without tomato early blight epidemic................................................................................................. 102 3-17. The distribution of leaf area loss due to the tomato early blight epidemics. ............... 104 3-18. The distribution of biomass loss due to the tomato early blight epidemics................. 105 3-21. Averaged attainable yield for each ENSO phase with biweekly transplanting date varying in Miami for 51 years (1948-1998).............................................................. 108 3-22. Averaged actual yield for each ENSO phase with biweekly transplanting date varying in Miami for 51 years (1948-1998).............................................................. 108
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3-24. Averaged yield loss for each ENSO phase with biweekly transplanting date varying in Miami for 51 years (1948-1998). .......................................................................... 109 A-1. An example of top-down module structure. The main driver has two sub-modules and each of them has chained sub-sub-modules. ...................................................... 115 A-2. An example of top-down module structure with the messenger module to pass a variable. ..................................................................................................................... 116 B-1. Diagram of interacting modules to feed back simulated value of TDLA from FODIS to CROPGRO............................................................................................................ 117 B-2. Diagram of interacting modules to feed back simulated value of WLIDOT from FODIS to CROPGRO. .............................................................................................. 119 C-1. Diagram of the tolerance framework: two input files (EXP and TOL) and four new modules linked to the PEST module......................................................................... 121 C-2. An example tolerance coefficient file for soybean, SBGRO980.TOL. ........................ 122 C-3. An example experiment input data file for soybean with modified sections to implement the tolerance coefficient to CROPGRO. ................................................. 123 C-4. Tomato cultivar tolerance coefficients file used in this study, TMGRO980.TOL. ...... 125 C-5. An added section in the experiment input file to implement the tolerance coefficient to CROPGRO............................................................................................................ 125
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science MODELING THE IMPACTS OF CLIMATE VARIABILITY ON TOMATO DISEASE MANAGEMENT AND PRODUCTION By Jawoo Koo May 2002 Chair: Dr. James W. Jones Department: Agricultural and Biological Engineering Tomato early blight, caused by Alternaria solani Sorauer, is one of the major foliar diseases that affect tomato in the U.S. as well as in Florida. To understand the impacts of climate variability on tomato early blight epidemic and its interrelationship with tomato production, the foliar disease model for tomato early blight, FODIS-TMEB, was developed and coupled with the tomato growth model, CROPGRO-Tomato. Based on a systems analysis on the tomato early blight pathosystem, the linked model simulated interrelationships between disease development and its impacts on tomato growth and yield. A fungicide persistence model was also developed and linked to simulate the application of chlorothalonil and its residue persistence that directly affected the basic infection rate in each cohort. The potential of the FODIS-TMEB model can be used to implement other foliar disease pathosystems. Parts of the FODIS-TMEB model were converted to describe the peanut late leafspot pathosystem to verify the validity of this disease model structure and xvii
to test its portability to simulate another foliar pathosystem. The resulting FODIS-PNLS model predicted disease progression as accurately as the old model, LATESPOT, did. Simulated seasonal analyses with the linked model (CROPGRO-Tomato model linked with the tomato early blight model and the fungicide persistence model) were performed for Miami, Florida, with 51 years of observed weather data. Duncan's multiple range method was used to test any statistically significant impacts of climate variability on early blight development and tomato production in terms of each ENSO phase. Significances were found as followings: 1) longer tomato growth period in El Niño, 2) higher AUDPC in El Niño, 3) lower marketable yield in El Niño, 4) higher yield loss in La Niña, 5) higher biomass loss in La Niña, and 6) more fungicide applications in El Niño. Given simulated results from seasonal analyses, possible strategies to manage the early blight were discussed in terms of El Niño and La Niña. In El Niño years, 1) more tolerant tomato variety should be selected, 2) farmers may be advised to plant more acreage to compensate for lower potential yield, 3) farmers may be advised to prepare extra amounts of fungicides, and 4) use of a later transplanting date within the typical range may be helpful to increase potential yield and decrease losses due to early blight. In La Niña years, 1) farmers should be aware of higher probability of yield losses, 2) farmers should expect a shorter tomato growth season, and 3) a later transplanting date may be helpful to reduce probability of losing yield due to early blight disease.
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CHAPTER 1 INTRODUCTION Plant diseases can seriously impact the quality of horticultural crops, and are thus of great concern to vegetable growers. Grades and standards for market vegetables usually specify strict limits on the disease damage that may be present on vegetables of a designated grade. To make their product marketable under these regulations, growers must be aware of disease occurrence during all stages of crop production. The market for fresh tomato also has strict size, quality, and maturity regulations controlled by a Federal Marketing Order (Sargent, 1997; USDA-AMS, 2001); tomatoes that do not meet United States Department of Agriculture (USDA) standards due to damage, small size or maturity are culled. Florida ranks first in production value of fresh tomatoes produced in the United States. Florida tomatoes account for 95% of all U.S. grown tomatoes from October through June and for 45% of all tomatoes consumed annually in the U.S. (Aerts and Nesheim, 1999). However, tomato producers believe that more than 12% of their potential yield is lost on average to disease, insect, nematode, and weed pests (Aerts and Nesheim, 1999). Wherever farmers grow tomatoes, they face numerous kinds of diseases. Over 200 diseases have been reported to infect tomatoes (Atherton and Rudich, 1986). Among these, tomato early blight (Alternaria solani Sorauer) is one of the most economically significant (Sargent, 1997). Florida's warm and humid climate is an ideal environment for disease development in tomatoes. Among various climatic events, variability in rainfall and temperature, strongly 1
2 associated with the El Niño-Southern Oscillation (ENSO) phenomenon, may have an important impact on disease epidemics (Coakley et al., 1999). Depending on sea temperature conditions in a region of the Pacific Ocean, each year can be classified as one of three ENSO phases: El Niño, La Niña, and Neutral (Tremberth, 1997). ENSO is known to be a major cause of climate variability in Florida during fall and winter seasons. Climate prediction research performed in Florida reported that southern areas of the state historically had 20-30% more precipitation in El Niño years and 20-30% less precipitation in La Niña years during the winter season compared to normal precipitation (Hansen et al., 1999). A longer period of humid conditions in El Niño years due to more precipitation is believed to create more favorable conditions for disease occurrence than other phases of ENSO (Hildebrand et al., 1999). The variability of rainfall and temperature also affects the optimal management of fungicide applications (Coakley et al., 1999). Contemplating this aspect of tomato production is necessary in order to reach realistic conclusions about climate variability effects on disease epidemics and their impact on the tomato crop. The overall objective of this study was to determine whether, and if so, to what extent, climate variability affects the tomato early blight epidemic, and to assess the ultimate impact of this effect on crop growth and yield in South Florida. If a significant impact of climate variability is found, climate forecasts could be used to support making a longterm strategic decision for disease management such as planting a resistant variety or preparing for extra amounts of fungicide prior to the season. The analysis of disease progress in each ENSO phase required several years of disease-related data. Since a dataset of the necessary characteristics was unavailable,
3 coupling a disease simulation model with a dynamic crop growth simulation model was proposed. Scholberg (1996) developed the CROPGRO-Tomato model to describe field-tomato growth. This model was a modification of an existing model, CROPGRO (Boote et al. 1998). Messina et al. (2001) demonstrated that CROPGRO-Tomato accurately predicted tomato yields and their variations with ENSO phases in South Florida. The model has a capability to simulate yield losses due to observed pest damage (Boote et al., 1983; Batchelor et al., 1993) but does not simulate disease progress dynamically. A new foliar disease model that simulates the progress of tomato early blight disease was developed and linked with CROPGRO-Tomato to study interrelationships between disease development and crop growth and yield. Chapter 2 describes the development of the foliar disease model. Objectives of the chapter include: 1) developing a tomato early blight model, 2) implementing a fungicide application model, 3) linking the models with CROPGRO-Tomato, 4) testing the sensitivity of relevant model parameters, and 5) evaluating the generality of the combined model by converting it to simulate another pathosystem, peanut late leafspot (Cercosporidium personatum Deighton). Chapter 3 analyzes disease epidemic and crop growth simulation results to determine how climate variability impacts disease development and tomato production. The impact of changing transplanting date in each ENSO phase is also simulated. Each output is classified by ENSO phase and statistically tested to detect significant climate variability effects. Finally, the chapter discusses the potential too use the disease model together
4 with climate forecasts as a decision support tool to improve disease management and tomato production in South Florida. Finally, Chapter 4 summarizes the conclusions and new knowledge resulting from this study.
CHAPTER 2 DESCRIPTION OF FOLIAR DISEASE MODEL, FODIS Introduction Tomato Production in Florida Florida ranks first in the production value of fresh tomatoes produced in the United States. Florida tomatoes account for 95% of all the tomatoes grown in the U.S. from October through June, and 45% of all tomatoes consumed annually in the U.S. (Aerts and Nesheim, 1999). However, tomato producers believe that more than 12% of their potential yield is lost to disease, insect, nematode, and weed pests (Aerts and Nesheim, 1999). Wherever farmers grow tomatoes, they face numerous kinds of diseases. Over 200 diseases have been reported to infect tomatoes (Atherton et al., 1986). Florida's warm and humid climate is an ideal environment for disease development in tomatoes. Among various climatic events, variability in rainfall and temperature, strongly associated with the El Niño-Southern Oscillation (ENSO) phenomenon, may have an important impact on plant disease epidemics (Coakley et al., 1999). Depending on sea temperature conditions in a region of the Pacific Ocean, each year can be classified as one of three ENSO phases: El Niño, La Niña, and Neutral (Trenberth, 1997). ENSO is also known to be a major cause of climate variability in Florida during the fall and winter seasons. Climate prediction research performed in Florida reported that southern areas of the state historically had 20-30% more precipitation in El Niño years and 20-30% less precipitation in La Niña years during the winter season compared to normal precipitation (Hansen et al., 1999). The longer period of humid conditions in El Niño years due to
5
6 more precipitation is believed to create more favorable conditions for disease occurrence than other phases of ENSO (Hildebrand et al., 1999). Early blight, caused by Alternaria solani Sorauer, is economically one of the most important diseases of tomatoes in the U.S. (Jones et al., 1993). To study whether, and if so to what extent, ENSO-related climate variability affects the tomato early blight epidemic, and to assess the impact of this effect on crop growth and yield in South Florida, require a large dataset containing multiple occurrences of each ENSO phase. The annual publication, “Fungicide and Nematicide Tests (F&N Tests)” contains many such years of disease damage data. For examples, a total of 17 tomato early blight data were published in its volume 55 (Alexander and Waldenmaier, 2001; Drennan and Zitter, 2001; Hausbeck et al., 2001; Johnston and Fogg, 2001; McGovern et al., 2001a and 2001b; MacNab, 2001a, 2001b, 2001c, and 2001d; McNabb, 2001a, 2001b, and 2001c; Shoemaker et al., 2001a and 2001b; Sikora and Caylor, 2001; Zitter and Drennan, 2001). However, their dataset only reports disease severity at the end of the season, and in most cases multiple diseases affected the plants. The combination of these two factors makes the analysis of environmental influences on a specific disease of interest very difficult. Modeling Foliar Disease Epidemics A system is a collection of components and their interrelationships that have been grouped together to study some part of the real world but separated by clear boundaries (Jones and Luyten, 1998). A plant pathosystem is a system composed of plant, pathogen, and the environment affecting the plant-pathogen relationship (Zadoks and Schein, 1979). A disease is able to progress only if the conditions provided by the host and environment are favorable, and if a pathogen is present.
7 The environment is assumed as a driving force among the three components of the pathosystem (Hardwick, 1998). Weather and climate are its most important and dynamic aspects. Environmental changes, especially in temperature and leaf wetness, influence the rate of progress at any stage of plant disease development, including germination, penetration, colonization, sporulation, and dissemination of a fungal spore (Agrios, 1997). For example, temperature unusually higher than normal may lead to an increased number of plant disease infection cycles and therefore, more crop losses and the rapid evolution of aggressive pathogen races (Chakraborty et al., unpublished data by way of Coakley's review, 1999). A disease simulator is a computer program based on a complex system analysis, which mimics the progress of a disease epidemic. According to Berger (1999), as of 1998 approximately 75 disease simulators had been developed for various pathosystems, but only one tomato early blight simulator was published. EPIDEM (Waggoner and Horsfall, 1969), one of the earliest disease simulators, described the tomato early blight epidemic by dividing the infection process into detailed stages. However, according to Rotem (1994) the published model suffered from somewhat unnatural construction and parameters; it required accurate information that was not readily available when it was written. For example, the incubation period of tomato early blight was found to be dependent on the physiological age of leaf tissue in later field research (Johnson & Teng, 1990; Rotem, 1994) but EPIDEM did not include effects of changes in plant development on disease progress. Given the economic impact of tomato early blight, a number of models were developed to forecast the occurrence of its epidemic (Gleason et al., 1995). Some
8 noteworthy efforts were FAST (Madden et al., 1975) and TOMCAST (Jasinski, 1999). Disease occurrence forecast models are relatively simple empirical relationships that indicate likelihood of disease appearance and recommend timing of fungicide application. Unless explicit relationships between disease forecasts and disease occurrences are associated quantitatively, it is difficult to utilize disease forecasts in modeling disease progress (Jasinski, personal communication). The effect of fungicide applications on disease progress is an important component of the disease model. In Florida, tomato farmers are advised to spray fungicide to control early blight every 5-14 days throughout the growing season (Kucharek, 1994). Chlorothalonil, also referred to by the brand name Bravo, has been used widely against many diseases include early blight of vegetables and field crops (Agrios, 1997). Persistence of chlorothalonil has been modeled on the foliage of tomato (Lukens and Ou, 1976), peanut (Nokes and Young, 1992), and potato (Bruhn and Fry, 1982). Bruhn and Fry (1982) modeled redistribution of sprayed chlorothalonil residues within four canopy layers considering the impacts of rainfall and temperature. Patterson and Nokes (2000) adopted Bruhn and Fry’s model and calibrated it to improve functionality of the disease forecasting system for tomato early blight in Ohio. They divided the tomato canopy into two layers and calibrated parameters with the new product, Bravo Weather Stik, which has the same active ingredient, chlorothalonil, but improved stickiness. This new product has also been used in Florida tomato farming to control early and late blights, gray leafspot, target spot, Botrytis, and Rhizoctonia fruit rot, with a maximum application rate of 3.51 liters per ha and a higher rate at fruit set (Kucharek, 2000).
9 Crop Growth Models A disease epidemic is an integrated process of the crop, the environment, and the pathogen. Therefore, a disease model must consider crop responses. For example, estimating yield loss due to a foliar disease requires quantifying the disease-induced defoliation, loss of light interception, and consequent loss of photosynthesis. To simulate tomato growth and yield response to a disease epidemic, a new framework that links the disease simulator to a dynamic crop growth simulator is required. Many efforts were made to link pest and crop models in the 1980s with computer simulation models, databases, and decision algorithms (Kropff et al., 1995). For foliar diseases, plant pathologists usually examined the relationship between crop yield and the disease severity, but the relationships were often disappointing since the effects of a disease epidemic differ throughout the growing season. Waggoner and Berger (1987) found a strong relationship between the absorption of solar radiation in healthy leaf area and yield in potatoes. They proposed to model yield loss by subtracting diseased leaf area from total leaf area, thus obtaining the healthy leaf area to use in calculation of solar radiation interception. Bourgeois (1989) applied the same approach to model late leafspot disease (Cercosporidium personatum Deighton) on peanut. CROPGRO is a generic crop model that originated from the soybean, peanut, and drybean models. It models eleven crops in version 3.7 (Porter and Jones, 2000). The CROPGRO model is process-oriented and includes carbon balance, crop and soil nitrogen balance, and soil water balance models. It has one set of FORTRAN code, and all species attributes related to each crop are input from external parameter files (Boote et al., 1998). A generic framework to extend the use of crop growth models to predict yield loss caused by pest damage was developed and incorporated into the CROPGRO model
10 (Batchelor et al., 1993). Pest damage was categorized according to its effects on plant growth and crop response (Boote et al., 1983). In this approach, pest damage was an input based on scouting, not dynamically simulated based on environmental conditions. This approach allowed the CROPGRO model to simulate crop growth under certain damaging conditions but did not allow the simulation of pest dynamics given its exclusive dependence on input data scouted in the past. Objectives Specific objectives in this study is 1) to develop a tomato early blight disease model as influenced by weather, 2) to implement a fungicide application and persistence model, 3) to link the developed disease and fungicide models with the CROPGRO-Tomato model, and 4) to evaluate the generality of this model by converting it with a disease model to simulate another pathosystem. Materials and Methods A generic foliar disease model (FODIS) was developed in this study. This generic model was implemented for two specific pathosystems, FODIS-TMEB for the tomato early blight, and FODIS-PNLS for the peanut late leafspot. Measured Disease Progress Data Pernezny (1998, unpublished) measured the disease progress used to develop the model in Ft. Pierce, Florida. He transplanted tomatoes on March 9 and harvested on June 11, 1998. The purpose of his experiment was to test the effectiveness of fungicide spraying. His field had a tomato early blight epidemic throughout the season and he measured proportion of disease severity, defined as the fraction of original leaf tissue lost by defoliation or damaged as a result of the disease (Figure 2-1). These data were used to:
11 1) estimate the physiological age of onset of infection, 2) calculate the basic infection rate, and 3) compare the simulated output with the measured data. 1.0
Severity
0.8 0.6 0.4 0.2 0.0 0
20
40
60
80
100
120
Days after transplanting
Figure 2-1. Proportion of disease severity for tomato early blight was assessed in Ft. Pierce, 1998 (Pernezny, unpublished). As his data did not include measurements of tomato plant growth, such as leaf area index or biomass, these were simulated using measured weather data. Input data for the simulation are included in Appendix F. Systems Analysis of Pathosystem A plant pathosystem is composed of plant and pathogen components as well as the environment that affects plant-pathogen relationships (Zadoks and Schein, 1979). The interactions of the components of disease have been often visualized as a triangle (Agrios, 1997). Zadoks and Schein (1979) also included humans as a component, considering human effects on plants, pathogens, and their environment. Zadoks and Schein represented the system as a disease tetrahedron rather than a triangle (Figure 2-2).
12 The pathosystem described and simulated in my study also considers four major components: tomato as the host plant, A. solani as the pathogen, temperature and leaf wetness duration as the environment, and fungicide applications as the effects of humans. A generic modeling approach was used so the model could be easily adapted to simulate other pathosystems. This generality was evaluated by adopting FODIS to simulate Cercosporidium leafspot on peanut and to compare results with field measurements and a model developed by Bourgeois (1989). Below, I first describe the tomato early blight model (FODIS-TMEB) and then the peanut late leafspot model (FODIS-PNLS).
human being Fungicide application
pathogen
host
Alternaria solani
tomato
environment Temperature & leaf wetness duration
Figure 2-2. Disease tetrahedron developed by Zadoks and Schein (1979), and its adaptation to this study. The Life Cycle of the Pathogen The life cycle of the plant pathogen, A. solani, begins with a spore landing on a leaf surface of a susceptible host plant. When environmental conditions of temperature and available moisture are favorable, the spore germinates, its hyphae elongate and penetrate
13 into the leaf cuticle, stomata, or wounds (Figure 2-3). As soon as hyphae enter the leaf, the infection process begins. At first, the affected portion of the healthy leaf is latently infected. During the latent stage, the affected leaf area does not show any symptom until a length of time known as the incubation period has elapsed. The affected leaf area then develops symptoms and the pathogen begins to sporulate during the infectious stage. After this infectious stage, disease symptoms remain on the leaf without sporulation; this is known as the removal stage.
Figure 2-3. Development and symptoms of disease caused by Alternaria (Agrios, 1997) Zadoks (1969) describes the above sequence using four state variables that represent the leaf area affected by each stage (vacant, latent, infectious, and removed area) and three rate variables (occupation, apparition, and removal). Bourgeois (1989) applied this concept to his peanut late leafspot model with five state variables of vacant, latent, preinfectious, infectious, and post-infectious levels. Similarly, the FODIS model has four
14 state variables: vacant (AVAC), latent (ALAT), infectious (AINF), and post-infectious (APOS) leaf area. Thus, disease is represented as leaf area that shows disease symptoms and defoliation. Conceptual Model Description A conceptual model was developed to describe disease progress on the crop and effects of the disease on plant growth and yield (Figure 2-4). Disease progress is modeled for each leaf cohort, the leaf area that is produced in one day by the crop. Initially, this leaf area is infection-free (AVAC), but is subjected to infection as time progresses. Disease progress of the entire plant canopy is computed by summing up disease progress on each leaf cohort. Figure 2-4 shows the four disease state variables (AVAC, ALAT, AINF, and APOS) for one leaf cohort as well as the main development processes and links with the tomato growth and yield model. Descriptions of variables used in Figure 2-4 are given in Table 2-1.
Figure 2-4. Diagram of the FODIS model coupled with the CROPGRO model. Descriptions of abbreviations are given in Table 2-1.
15
Table 2-1. Definition of abbreviations and terms used in Figure 2-4. Unit Term Description AVAC Vacant leaf area. cm2(leaf)/m2(ground) cm2(leaf)/m2(ground) cm2(leaf)/m2(ground) cm2(leaf)/m2(ground) day-1
E
Latently infected leaf area. Infectious leaf area. Post-infectious leaf area. Rate of area transition from vacant to latent stage. Rate of area transition from latent to infectious stage. Rate of area transition from infectious to post-infectious stage. Rate of expanded lesion area
NS Defol. LWD
Naturally occurred senescence Defoliation due to foliar diseases Leaf wetness duration
cm2(leaf)/m2(ground) cm2(leaf)/m2(ground) Hours
ALAT AINF APOS L I P
day-1 day-1 day-1
Differential equations to describe the rates of change of state variables for each leaf cohort are presented in Table 2-2. A list of variables and their descriptions are shown in Table 2-3. This conceptual model was developed and refined by first reviewing published foliar disease models and subsequently analyzing existing tomato early blight data and performing sensitivity analysis. Details are provided in the following sections on the relationships used to model each process of FODIS-TMEB.
Table 2-2. Differential equations to describe the disease epidemic in a cohort. Subscripts for leaf cohorts are omitted for clarity of presentation. Vacant Leaf Area dAVAC = − R ⋅ AINF − ILAT − LEXP − DLVAC dt
16
Latently Infected Leaf Area dALAT (1) = + R ⋅ AINF + ILAT + LEXP − kLAT ⋅ ALAT (1) − DLLAT (1) dt dALAT (i ) = + kLAT ⋅ ALAT ( i − 1) − kLAT ⋅ ALAT (i ) − DLLAT (i ) dt Infectious Leaf Area dAINF (1) = + kLAT ⋅ ALAT ( nl ) − kINF ⋅ AINF (1) − DLINF (1) dt dAINF (i ) = + kINF ⋅ AINF (i − 1) − kINF ⋅ AINF ( i ) − DLINF ( i ) dt Post - infectious Leaf Area dAPOS = + kINF ⋅ AINF ( ni ) − DLPOS dt Table 2-3. Definition of variables used in equations in Table 2-2. Variable Description Unit AINF Total infectious area for a given cm2(leaf)/m2(ground) cohort AINF(i) Infectious area in ith sub-stage for a cm2(leaf)/m2(ground) given cohort ALAT Total latent area for a given cohort cm2(leaf)/m2(ground) ALAT(i) Latent area in ith sub-stage for a cm2(leaf)/m2(ground) given cohort APOS Post-infectious area for a given cm2(leaf)/m2(ground) cohort AVAC Vacant area for a given cohort cm2(leaf)/m2(ground) DLINF(j) Area loss due to defoliation in ith cm2(leaf)/m2(ground) infectious area DLLAT(i) Area loss due to defoliation in ith cm2(leaf)/m2(ground) latent area DLPOS Area loss due to defoliation in post- cm2(leaf)/m2(ground) infectious area DLVAC Area loss due to defoliation in cm2(leaf)/m2(ground) vacant area i Index to specify a cell within an array variable of latent or infectious stage ILAT Initial area of latent infection cm2(leaf)/m2(ground) kINF Transition rate for infectious area. 1/day See Equation 2-3
17
kLAT LEXP ni nl R
Transition rate for latent area. See Equation 2-2 Area of lesion expanded Index for the last cell in an array variable for infectious stage Index for the last cell in an array variable for latent stage Basic infection rate
1/day cm2(leaf)/m2(ground)
1/day
Physiological age of the plant at infection onset Early blight primarily affects mature tomato crops. The effects of physiological age on the tomato crop’s susceptibility have been extensively discussed in the literature and reviewed by Rotem (1994). To implement this effect, the onset of the early blight epidemic is suspended until the plant’s accumulated physiological age surpasses a specific threshold. This threshold is estimated as a value that made the measured and the simulated disease progresses start on the same date in the 1998 data set (Figure 2-1). Infection Once the tomato plant reaches a physiological age at which it is susceptible to A.
solani, simulation of the infection process can begin. In this study, based on the assumption that there are spores in the atmosphere all the time, two types of inoculum sources were conceptualized: 1) one type was spores coming from the environment (outside of a cohort) and 2) the other type was sporulations from infectious diseased area within a cohort. For the first case, a certain amount of initial infection must be set. However, the amount of initially infected leaf area is unknown, so its value must be estimated. Van der Plank (1992) set this value at a very small constant level. In this study, the amount of infection was set to 0.25%, a proportion of 0.0025 of the leaf area in the vacant stage at
18 the time of infection (AVac). Then, the initially infected area was multiplied by the basic infection rate (R) (Equation 2-1a). An infection initiates from the latent stage when the effectiveness of inoculum (f(environment) in Equation 2-3) reaches a certain level (0.50). Further infections can begin whenever environmental conditions are favorable. For the second case, further infection from existing infectious area in each cohort was calculated as Equation 2-1b, where R is the basic infection rate and AINF is the leaf area in the infectious stage. Details to calculate R are given in the following section.
a) Further infection from environments = R × AVAC × 0.001
(2-1)
b) Further infection from an infectious area = R × AINF Based on simulated disease progress in Ft. Pierce in 1998, relative sensitivities of four model outputs (the proportion of disease severity, leaf area index, biomass, and yield) to parameters that involved the infection process were analyzed. The basic infection rate, R The basic infection rate (R) was defined by Van der Plank (1963) as the ratio of reproduction of secondary to primary pustules per day. The value of R includes visible disease as well as the invisible latent infection. The R is different from the epidemic rate (r), which accounts for visible lesions only. The R parameter can be estimated from observed disease progress data using r and the latent period (Van der Plank, 1963). When the proportion of disease at a given time t is yt, R is:
R = r×
yt
(2-2)
y (t − Latent Period)
where y(t-Latent Period) is the infectious area during the epidemic period.
19 Each pathosystem has a maximum value of R, Rmax, which is the end result of interactions among the factors that influence the infection process i.e. temperature, leaf wetness duration, fungicide efficiency, etc.
Rmax has been used to calculate R in many disease epidemic simulators (Berger, personal communication). Usually, Rmax is calculated from the disease progress curve of whole crop growth season for use in disease simulations. However, this approach is inappropriate for the tomato early blight pathosystem. First, tomato shows different levels of susceptibility to infection at different physiological ages of the plant. Second, once the plant is susceptible, the disease develops very rapidly. Therefore, the calculated Rmax from Equation 2-2 was not appropriate and failed to simulate the disease severity observed in Ft. Pierce in 1998 (Figure 2-1). To overcome this problem, an Rmax such that can fit a simulated disease progress into the observed one most closely was obtained from a calibration process and used in this study. The actual R on any given day was expressed as the following function of Rmax and other factors that affect R:
R = Rmax x f(environment) x f(fungicide efficiency) x f(tolerance) x f(vacant area availability)
(2-3)
The f(environment) function represents effects of temperature and leaf wetness duration;
f(fungicide efficiency) represents the effectiveness of applied fungicide; f(tolerance) represents the effect of cultivar tolerance; f(vacant area availability) represents the availability of vacant leaf area that can be infected. Details on these functions are given in the following sections. The calculated R was used to increase the latently infected leaf area on a daily basis (Table 2-2; Equation 2-1). Based on the simulated disease progress
20 in Ft. Pierce in 1998, relative sensitivities of four model outputs (the proportion of disease severity, leaf area index, biomass, and yield) to R were analyzed. Lesion expansion In many pathosystems, lesions in infected leaves continue to grow radially or linearly after their initial appearance, until much or the entire host unit is symptomatic (Berger et al., 1997). Johnson and Teng (1990) measured the rate of early blight lesion expansion on potato as 0.2 units per day, which Berger et al. (1997) converted to a rate of area increase of 0.012 cm2 per day per cm2 of leaf area. This rate, limited by non-diseased leaf area, was used here to expand the infected leaf cohort area, as shown in Equation 2-4. TLA − DLA TLA
(2-4)
DLA = ALAT ( symptom ) + AINF + APOS
(2-5)
LEXP = 0.012 × DLA ×
where LEXP is the expanded leaf area, DLA is the diseased leaf area showing symptoms, and TLA is the total leaf area in a cohort. The expanded lesion area (LEXP) thus calculated was added to the latently infected leaf area (Table 2-2). Equation 2-5 defines a diseased area to be used in Equation 2-4 as a summation of: 1) area in latent stage but shows symptom (ALAT(symptom), 2) area in infectious stage (AINF), and 3) area in postinfectious stage (APOS). Defoliation Defoliation often follows the appearance of symptoms on leaves. However, it is difficult to measure defoliated area or mass because the defoliated leaf portion is no longer attached to the plant and may be displaced, removed, etc. Nevertheless, defoliation must be included in disease assessment, and thus needs to be estimated. Plaut and Berger (1981) developed the following equation to include defoliation:
21 ytotal = (1 − ydefoliated ) × yvisible + ydefoliated
(2-6)
where ytotal is the total fraction of diseased leaf area, ydefoliated is the defoliated fraction no longer on the plant, and yvisible is the fraction of visible lesions at a given time. The ytotal was used as the proportion of disease severity in the FODIS model. Vloutoglou and Kalogerakis (2000) reported a mathematical relationship between yvisible and ydefoliated in tomato early blight given by the following equation: ydefoliated = −1.53 + 0.80 × yvisible
(2-7)
This equation is used to estimate the defoliated area from the simulated diseased area in each cohort. The defoliated leaf area is used to reduce the cohort leaf area in each stage (DLVAC, DLLAT, DLINF, and DLPOS in Table 2-2). Latent period The latent period is the time between the pathogen’s entry and production of the first infectious propagules (Van der Plank, 1963). For each leaf cohort, a different latent period was needed to implement a distributed delay function for the flow of infection depending on a cohort’s physiological age (Equation 2-11). However, there was no available information in the literature to calculate this value. Thus, an indirect method was adopted. That is, the incubation period is used to estimate the latent period. The incubation period is the time between the pathogen’s entry and the apparition of disease symptoms (Van der Plank, 1963). Johnson and Teng (1990) reported the relationship between physiological age of leaf tissue and the incubation period in tagged leaf cohorts for potato early blight pathosystem (A. solani). To estimate the latent period from the incubation period, the interval between the appearance of symptoms and sporulation needed to be defined (Figure 2-5).
22
Figure 2-5. The latent period and the incubation period of A. solani in tomato leaf cohorts. To estimate the latent period (LP), the incubation period (IP) was calculated and the interval between symptom apparition and sporulation (INT_SS) was obtained from the literature. For tomato plants, the incubation period was measured as two to three days (Walker, 1952), and the latent period was eight days (O’Leary, 1985). Based on these reports, a delay of five days was assumed between apparition of disease symptoms and sporulation in a cohort. For potato early blight (A. solani), Johnson and Teng (1990) calculated an incubation period (IP) in the tissue of each tagged leaf as a function of its physiological age (PALeaf) as follows:
IP = −0.0078 × PALeaf + 8.8
(2-8)
By adding the interval between symptom appearance and sporulation (INT_SS) to the incubation period (IP), the latent period (LP) was calculated with the following function: LP = −0.0078 × PA[Leaf] + 8.8 + INT _ SS
(2-9)
Since INT_SS is a constant value and older leaf cohorts have shorter incubation periods (Johnson and Teng, 1990), the oldest leaf cohort had the shortest latent period (Figure 2-6). Based on the simulated disease progress in Ft. Pierce in 1998, relative sensitivities of four model outputs (the proportion of disease severity, leaf area index,
23 biomass, and yield) to the latent period and the interval between symptom appearance and sporulation were analyzed.
13
Latent period
11
9
7
5 20
40
60
80
100
Physiological age of leaf tissue Figure 2-6. Latent period of A. solani in the tomato early blight pathosystem, expressed as a linear function of the physiological age of leaf tissue. Infectious period Information on the infectious period of A. solani in tomato was scarce in the literature although this is a critical component of a plant disease development model. Available data for A. macrospora in cotton was used instead. Rotem et al (1989) reported that spore production lasted for 14 days at high temperature (a minimum of 20 °C at night and a maximum of 30 °C during the day), for 35 days at medium temperature (15 °C and 25 °C), and for 40 days at low temperature (10 °C and 20 °C). The maximum and minimum temperatures for sporulation of A. solani were found to be 10 °C and 35 °C, respectively (Rotem, 1994). Based on this information, simple linear regression was used to estimate the duration of the infectious period as a function of temperature (Figure 2-7).
24
Infectious period, FP (days)
50 y = -1.3x + 62.167 40 30 20 10 0 10
15
20
25
30
35
Daily average temperature (C) Figure 2-7. Relationship between daily average temperature and the infectious period was assumed as a linear function of daily average temperature between minimum and maximum temperatures for spore production. Three measured infectious periods in relationship with daily average temperature (Rotem, 1994) were used. When the daily average temperature (TAVG) is between 10 °C and 35 °C, the duration of the infectious period (FP) can be estimated as follows: FP = −1.3 × TAVG + 62.167
(2-10)
No sporulation was assumed when the average temperature on a given day was either above the maximum or below the minimum temperature for sporulation. Based on the simulated disease progress in Ft. Pierce in 1998, relative sensitivities of four model outputs (the proportion of disease severity, leaf area index, biomass, and yield) to the infectious period were analyzed. Distributed delay function A distributed delay function was used to generate a distribution of stage completion times (Berger and Jones, 1985; Manetsch, 1976). With a distributed delay function, the infections flow through a series of substages, and they emerge as a distribution of stage
25 completion times. The number of substages (n) in the delay chain is equivalent to the minimum days to complete each infection stage. The latent stage has eight substages (n=8) because the minimum incubation period was found to be three days (Johnson and Teng, 1990) and the interval between symptom apparition and sporulation was assumed as five days. The infectious stage has fourteen substages (n=14) since the minimum infectious period was reported to be fourteen days in the literature (Rotem, 1994). All development rates between substages is the same within each stage. The rate for area transition among successive latent substages and between the last such substage and the first infectious substage was defined as kLAT (Table 2-2) with units of 1/day, computed by: kLAT = n/LP
(2-11)
where n is 8 and LP is the calculated latent period on a given day (LP ≥ n). Likewise, the rate for area transition within the infectious stage was defined as kINF (Table 2-2) with units of 1/day, computed by:
kInf = n/FP
(2-12)
where n is 14 and FP is the calculated infectious period on a given day. Effects of Weather Conditions To apply effects of weather conditions on the calculation of R, the effects of temperature (F_TMP) and the effects of leaf wetness duration (F_LW) were combined as a factor of environmental conditions and used as f(environment) in Equation 2-3. f (environment ) = F_TMP × F_LW
(2-13)
26 Temperature effects The effect of temperature on effectiveness of inoculum (F_TMP) was modeled with the pathogen’s minimum, optimum, and maximum temperatures for spore germination. sp Rotem (1994) reported 5, 27, and 35 °C as, respectively, the minimum ( TMin ), optimum
sp sp ( TOpt ) and maximum ( TMax ) temperatures for germinating spores of A. solani. A cosine
function was used to interpolate between these three known temperatures (Figure 2-8). Bourgeois (1989) used similar method to estimate the effect of temperature on peanut late leafspot disease development; as shown in Figure 2-10 the curve is divided into two sp sp (corresponding to (1 − cos(0)) ) and TOpt cosine sections: one between TMin
sp (corresponding to cos(0) ), and the other between TOpt (corresponding to cos(0) ) and
sp TMax (corresponding to cos( π2 ) ). The value of R is scaled accordingly.
Factor value (F_TMP)
1.0 0.8
Minimum, optimum and maximum temperature for infection
0.6
Interpolation with cosine function
0.4 0.2 0.0 0
10
20
30
Daily average temperature (°C) Figure 2-8. To apply temperature effects on the efficiency of infection of A. solani to the calculation of R, a cosine function was used to interpolate values between observations.
27 However, A. solani has wide range of temperatures over which its spores can germinate. The minimum temperature for spore germination is known as 5 °C (Rotem, 1994), but the minimum daily average temperature during the spring season in Ft. Pierce was 12.5 °C in 1998.
Temperature (°C)
40
30 Temperature Optimum Maximum Minimum
20
10
0 0
20
40
60
80
100
Days after transplanting (day) Figure 2-9. Daily average temperatures during the tomato growth period (3/18-7/6) in Ft. Pierce, Florida, 1998. During the period, temperatures were always within the range of maximum and minimum temperature for spore germination.
28
Temperature efficiency
1.0 0.9 0.8 0.7
Temperature efficiency Average Maximum Minimum
0.6 0.5 0
20
40
60
80
100
Days after transplanting (day)
Figure 2-10. Daily temperature efficiency (F_TMP) for spore germination of A. solani during the tomato growth period (3/18 – 7/6) in Ft. Pierce, Florida, 1998, was calculated with the interpolation shown in Figure 2-8. Temperatures were always within the range of maximum and minimum germinating temperature during the whole growth period (Figure 2-9). Temperature efficiency of spore germination throughout the season averaged 0.93 and its minimum was 0.51 (Figure 2-10). Hence, temperature may not be a limiting factor to the pathogen’s spore germination in the mild climate of South Florida. Effects of leaf wetness duration Leaf wetness duration affects inoculum effectiveness based on the plant’s physiological age-conditioned susceptibility. Rotem (unpublished data, 1994) measured the minimum leaf wetness duration necessary to develop potato early blight and found that 24 hours were required to infect 2-week-old plants, but only 4 hours were required for more susceptible 2-month-old plants. He did not include data about the physiological age of tomato plants, however. More detailed data to develop a model of the impact of
29 leaf wetness duration on infection were not available in the literature. Thus, simple rules were used to decide if the estimated leaf wetness duration (LWD) satisfied the minimum wetness duration (RLWD), which is a function of plant age, based on Rotem’s measurements as follows:
LWD ≥ RLWD LWD < RLWD
1 F_LW = 0
(2-14)
where F_LW is used as an effect of leaf wetness duration in Equation 2-13. Estimated leaf wetness duration was compared to a threshold (RLWD) obtained by interpolating Rotem’s data (Equation 2-15, Figure 2-11). Germination was allowed only if the estimated duration exceeded the threshold. This calculation was performed on a daily basis.
24 RLWD = − 0.4348 × age + 30.087 4
(2-15)
age < 14 14 ≤ age < 60 age ≥ 60
24 Measured
Required LWD
20
Interpolated
16 12 8 4 0 0
20
40
60
Actual age of tomato plant
80
100
30 Figure 2-11. Required leaf wetness durations for conidia of A. solani to germinate. Linear regression was used to interpolate between observations. The implemented greater susceptibility to A. solani infection in older plants was illustrated in our data; simulated leaf wetness duration in Ft. Pierce in 1998 was insufficient for the pathogen to germinate (F_LW=0) until the plant was 31days old (Figure 2-12). An overlaid chart of rainfall amount illustrated the effect of rainfall on estimated leaf wetness duration. After the plant matured enough to become susceptible, every rainfall event provided favorable conditions for spore germination. Based on the simulated disease progress in Ft. Pierce in 1998, relative sensitivities of four model outputs (the proportion of disease severity, leaf area index, biomass, and yield) to hourly temperature and daily leaf wetness duration were analyzed.
70
Leaf wetness duration Required leaf wetness duration Rainfall
16 14
60 50
Hours
12 10
40
8
30
6
20
4 2
10
0
0 1
21
41
61
81
Rainfall (mm/day)
18
101
Days after transplanting date Figure 2-12. Estimated leaf wetness duration on a given day and the required leaf wetness duration for spore germination vs. plant age. Daily rainfall amounts were overlaid to illustrate the effects of precipitation on estimated leaf wetness duration.
31 Effects of Plant Tolerance on Disease Tolerance is the ability of plants to produce a good crop even when they are infected with a pathogen, and it is a cultivar specific characteristic (Agrios, 1997). Tolerant plants are susceptible to the plant pathogen but they are not killed by it and they generally show little damage. Since tolerance of the cultivar used in simulation analysis was not known quantitatively, a parameter to represent this effect was calibrated so the final proportion of disease severity without fungicide application was approximately 0.70, the corresponding final proportion of disease severity observed by McGovern (1993). The calibrated cultivar tolerance coefficient was used in f(tolerance) (Equation 2-16) to calculate the basic infection rate (Equation 2-3).
f(tolerance) = 1 – tolerance coefficient
(2-16)
Effects of Fungicide The chlorothalonil persistence model developed by Patterson and Nokes (2000) was implemented as a separate module (Table 2-4) and linked to the FODIS-TMEB model. This module determines if the leaves are protected. It splits the canopy into two layers (Patterson and Nokes, 2000) and estimates chlorothalonil residues on each of the layers, and checks if the residue on any layer is lower than the threshold level of 1.5µg/cm2 (Elliot and Spurr, 1993) on a given day. The fungicide persistence model dynamically simulates changes in chlorothalonil residue on each leaf layer based on rainfall amount and temperature. Fungicide application rules were assumed based on the chemical control guide for vegetable diseases (Kucharek, 2000). The rules were: 1) apply the fungicide every seven days with a dose of 1.68 kg a.i./ha (two pints per acre), 2) apply 2.52 kg a.i./ha (three
32 pints per acre) once, when fruits set, 3) in the case of rainfall, don’t apply and delay one day, and 4) don’t apply fungicide when harvest is expected within two days. Patterson and Nokes (2000) measured the amount of chlorothalonil residue deposited on tomato foliages as 11.4 and 4.4 µg/cm2 for the upper and lower canopies, respectively, with an application rate of 2.52 kg a.i./ha. With scaled accordingly, 7.6 and 2.9 µg/cm2 were used with an application of 1.68 kg a.i./ha in this study. When an application is simulated on a given day based on the rules, the model adds the corresponding amount of residues to each of upper and lower layer. Determining whether a cohort is in upper or lower layer used an output variable of sunlit LAI (LAISL), which is calculated in CROPGRO-Tomato. FODIS-TMEB assumed the youngest cohort is on the top canopy, and the oldest is in the bottom. Based on LAISL, FODIS-TMEB determined which cohorts are in the sunlit canopy, and those cohorts were assumed to be in the upper canopy. Therefore, the rests of cohorts were considered to be in the lower canopy.
Table 2-4. A chlorothalonil persistence model was developed by Bruhn and Fry (1982) and adopted by Patterson and Nokes (2000). The model simulates chlorothalonil residues in two canopy layers and effects of temperature and rainfall. Equations rt (1) = [ gt ⋅ dt ]rt − 1(1)
rt (2) = [ gt ⋅ dt ] s rt − 1(2) + [1 − gt ⋅ dt ]zrt − 1(1)
exp[a ⋅ pt 1 / 3 + b( pt (tapp − 1))1 / 3 for pt ≥ 0.1cm gt = 1.0 for pt < 0.1cm
exp[c ⋅ (T − T ' )] for T > T ' dt = 1.0 for T ≤ T '
Variable Definition Residue on upper canopy at time t (µg/cm2) rt (1)
33 r t ( 2) gt dt s z pt
T T’ c
Residue on lower canopy at time t (µg/cm2) Reduction factor for effects of rainfall Reduction factor for effects of temperature Parameter describing chlorothalonil weathering (0.15) Parameter defining proportion of fungicide removed from upper canopy and re-deposited onto lower canopy (0.05) Precipitation on day t (cm) Daily average temperature (˚C) Parameter (16.5 ˚C) Parameter (-0.01)
A fungicide efficiency parameter (FC_EFF) was calibrated so the final proportion of disease severity for the simulation period was approximately 0.25, which was observed by McGovern (1993) when the fungicide was applied. If the fungicide residue was more than 1.5 µg/cm2, which is the threshold level of chlorothalonil (Elliot and Spurr, 1993) in a given canopy layer, this FC_EFF value was used as the fungicide efficiency. Otherwise, if the fungicide residue was less than the threshold level, the fungicide efficiency was assumed as none (Equation 2-17). This fungicide efficiency is used in f(fungicide
efficiency) to calculate R (Equation 2-3). FC_EFF fungicide efficiency = 0
RSD > 1.5µg / cm 2
(2-17)
RSD ≤ 1.5µg / cm 2
f(fungicide efficiency) = 1 – fungicide efficiency
(2-18)
Chlorothalonil is a protectant that must be present on leaf surfaces to provide protection; its stickiness is thus an important factor. It is known that sprays do not stick properly when applied during rain (Agrios, 1997), so fungicide application was avoided i.e. not simulated, on rainy days. Leaf cohorts that emerged between applications were assumed not protected. The model simulated thirteen weekly applications throughout the
34 tomato growth season in Ft. Pierce in the 1998 simulation but the optimality of this scheme was not validated. The dynamics of fungicide residue were plotted daily and the plant was well protected during the period (Figure 2-13).
Threshold
Higher canopy
Lower canopy
micro-g/cm2
20 15 10 5 0 0
20
40
60
80
100
Days after transplanting Figure 2-13. Simulated dynamics of chlorothalonil residue throughout the 1998 tomato growth period in Ft. Pierce. Days in which either the upper or lower layer of the canopy is not protected by the fungicide are defined as risky days. This condition occurs before the first fungicide application or when frequent rainfall occurs and the residues are consequently washed off. In the 1998 Ft. Pierce simulation, 12 days were defined as risky out of a total of 115 days of tomato growth. In simulations, the fungicide adhered relatively well to the leaves even under rainfall events. Leaves in the lower canopy, corresponding to older cohorts, were especially well protected because they received additional residues washed out by rainfall from the upper layers.
35 Effect of the Availability of Vacant Area As more leaf area is colonized by the disease epidemic, less vacant leaf area is available for infection. Therefore, lower values of R should be used for the infection as disease colonizes more leaf area in a cohort. To implement this effect of less vacant area,
f(vacant area availability), ratio of vacant leaf area (AVAC) to total leaf area (ATOTAL) in a cohort (Equation 2-19), is calculated and used to calculate R in Equation 2-3.
f(vacant area availability) =
AVAC ATOTAL
(2-19)
Calculating Proportion of Disease Severity The proportion of disease severity (PDS) was used as an important output to describe disease progress in this study. As described in Equation 2-6, PDS is a function of: 1) the proportion of visible symptom (yvisible) and 2) the proportion of defoliation (ydefoliated). These two proportions were calculated with following equations:
yvisible = Diseased leaf area* / Total leaf area
(2-20)
ydefoliated = 1-(Total leaf area / Non-diseased total leaf area)
(2-21)
To calculate ydefoliated, the non-diseased total leaf area, which is the leaf area of healthy plant grown in same environment, was needed to be introduced (Equation 2-21). This value was obtained from another run of simulation without tomato early blight infections. Thus, PDS for tomato plant was calculated as following Equation 2-22:
PDS = (1 − ydefoliated ) × yvisible + ydefoliated
(2-22)
TLA DLA TLA = 1 − 1 − + 1 − × NDLA TLA NDLA
*
The diseased leaf area is a sum of: a) area in latent stage but shows symptom, b) area in infectious stage, and c) area in post-infectious stage (Equation 2-5).
36 where TLA is the total leaf area, DLA is the total diseased leaf area, and NDLA is the total leaf area in healthy plant. Prediction of Daily Leaf Wetness Duration Leaf wetness duration is an important aspect of a plant disease epidemic and a fundamental input for many disease simulators, because most pathogenic spores need a film of water over the leaf tissue to infect the plant host and sporulate (Huber and Gillespie, 1992). However, leaf wetness duration is difficult to define because various portions of leaves and canopies are wet and dry at different times during a day. Many attempts have been made to design sensors for leaf wetness duration, but their use is not feasible in most places due to the cost and unfamiliarity of leaf wetness measuring equipment and the labor required for continuous monitoring (Campbell and Madden, 1990). Hourly meteorological data have often been used to develop leaf wetness duration estimation models (Rao et al, 1998; Thompson, 1981; Pedro & Gillespie, 1981). Especially when leaf wetness sensors are not available, the threshold model that were hours of relative humidity (RH) ≥ 90% has been traditionally used as an estimate of leaf wetness duration (Campbell and Madden, 1990). This indirect estimate is appealing because standard weather stations measure RH. Rao et al. (1997) examined the RH threshold approach and concluded its estimates were as good as those from other complex physically based models. However, Thompson (1981) showed that this method was not accurate for dense canopies such as those of grain crops, but concluded that prediction was reasonable for the sparser canopies more common in many fruit and vegetable crops. Since observed leaf wetness duration data
37 were not available in this research, the RH based empirical model was used as modified by Berger (personal communication) and Bourgeois (1989), who adapted it to Florida climatic conditions using a RH threshold of 93% instead of 90%. The CROPGRO model uses daily weather inputs. Hourly values of temperature and RH are estimated from daily data. Hourly temperature estimation is based on Parton and Logan’s function (1981), and the estimated hourly temperatures are used in hourly RH estimation, assuming that the daily minimum temperature is the dew point. The accuracy of the hourly RH estimations was verified with 4 months of hourly weather data (December through March), for 4 years (1998 – 2001), measured in Homestead, Florida. Hourly temperatures were predicted well (R2=0.88; RMSE=1.88 °C). However, correlation between measured and observed values of RH was lower and the prediction error was considerable (R2=0.57; RMSE=12.92 %). The discrepancy was probably caused by poor predictions of hourly dew point temperature (R2= 0.50; RMSE=3.11 °C). Also, RH is difficult to measure accurately, and errors are likely to be greater in measured RH than in temperature values. Two methods to improve the accuracy of the dew point temperature were tried: i) assuming that hourly dew point temperatures lie on the line connecting the daily minimum temperatures, which were assumed to occur at 8 AM (R2=0.61; RMSE=2.97 °C), and ii) the previous method to predict dew point temperature, but shifted 2 °C higher through sensitivity analyses (R2=0.61; RMSE=2.31 °C). When the latter was used, the accuracy of RH prediction was improved by 2.8% (R2=0.56; RMSE=10.12%). However, prediction of the number of hours having RH ≥ 93% was ineffective (R2=0.0046,
38 RMSE=5.43hrs) even with this improved method. Therefore, both methods were not incorporated in FODIS-TMEB. An alternative regression based approach was tested. A polynomial regression model having inputs of daily meteorological data such as solar radiation, rainfall amount, maximum and minimum temperature, temperature depression (difference between daily maximum and minimum temperatures), day length, and their squared values was tested. However, it failed to improve the leaf wetness duration predictions (RMSE=5.28 hours) and the results did not show any significant relationship between the input variables and daily hours with RH ≥ 93%. The quality of RH measurement was also considered as another potential source of error. Due to the very humid climate in the Homestead area, dew would be expected to collect on leaf surfaces at the hour of minimum temperature. However, hourly observed RH records did not reach 100% RH on many days (average=94.53%, standard deviation=7.21). Bourgeois (1989) also found that accurate measurements of hourly RH were not available. Although we may admit a certain degree of error in the estimation of hourly RH, the methods described above do not provide sufficiently good estimates for the purpose of this study, simulating disease progresses under climate variability effects, because none of the aforementioned methods considers the effects of rainfall on RH. We extended the RH-based leaf wetness duration model to take rainfall into account. A regression approach was used to explore the relationship between rainfall amount and duration. When Gillespie and Sutton (1979) developed a disease forecasting system to control Alternaria leaf blight in carrots, they added hours to their estimated leaf
39 wetness duration based on the occurrence of rainfall. A similar concept was adopted to introduce rainfall effects into the leaf wetness duration (LWD in Equation 2-14) by adding hours of rainfall (RainHrs) to the estimated leaf wetness hours (RH93Hrs) (Equation 2-23).
LWD = RH93Hrs + RainHrs
(2-23)
A non-linear regression model was derived to estimate the duration of rainfall hours as a function of daily rainfall amount. Three years of hourly precipitation data spanning from December to March in each year (January 1998 – March 1998, December 1998 – March 1999, and December 2000 – March 2001) were used to develop the model (Figure 2-14), and one year of independent data (December 1999 – March 2000) was used for validation (R2=0.51, RMSE=1.16 hr).
14 12
Hours
10 8 6 4 Observed Predicted
2 0 0
10
20
30
40
50
60
Rainfall amount (mm)
Figure 2-14. Relationship between daily rainfall amount and rainfall duration. Three years of winter season weather data (January 1998 – March 1998, December 1998 – March 1999, and December 2000 – March 2001) observed in Homestead, Florida were used to develop a model by non-linear regression.
40 The regressed model equation is a function of daily rainfall amount in mm/day, as shown in Equation 2-24: - rainfall amount × 0.4402 rainy hours = 6.3997 × 1 − exp 2.0
(2-24)
With this equation, estimated rainfall duration was added to the predicted RH ≥ 93% hours on each day and compared with observed RH ≥ 93% hours. The comparison still showed poor prediction (R2=0.0008, RMSE=5.37 hr) as the error in the hourly RH estimations did. However, this was the method adopted in this study to consider precipitation as a factor that affects RH and leaf wetness duration. Linkage to the CROPGRO Crop Growth Simulator When the leaf area of the whole canopy is modeled as a complete unit, an averaged infection rate R tends to under predict infections for older and heavily infected tissue and over predict infections for newly emerged leaves as plants grow (Berger, personal communication). To overcome this problem, Berger defined the leaf canopy as an aggregation of daily cohorts and achieved realistic values of R for each cohort. Each daily leaf growth cohort has its own basic infection rate depending on the infected leaf area of the cohort. The same approach was implemented in later models (Bourgeois, 1989; Johnson and Teng, 1990) and in this study. The number of cohorts is equal to the number of days the tomato plant has grown. As designed, each cohort has its own disease epidemics (Figure 2-15). The FODIS-TMEB model manages each epidemic, calculates the total diseased leaf area, and defoliated leaf mass, and feeds these data back to the CROPGRO-Tomato model to affect plant growth.
41
100
Cohort No.53
Disease Severity (%)
Cohort No.63 80
Cohort No.74 Cohort No.83
60 40 20 0 40
60
80
100
120
Days after transplanting
Figure 2-15. Simulated disease severity progress in four cohorts. Each cohort number stands for the days after transplanting date when the cohort has emerged. As the plant naturally senesces, special attention is paid to keep the total area of the cohorts equal to the total leaf area calculated by CROPGRO-Tomato. The FODIS calculates defoliated leaf area with a function of PDS in each cohort (Equation 2-7) (Vloutoglou and Kalogerakis, 2000) and that function is applied to all cohorts proportionally. In addition, the natural senescence is applied starting from the oldest cohort. CROPGRO-Tomato simulates naturally occurring leaf abscission with senescence. For example, if the leaf area that naturally senesced is larger than the oldest cohort area but smaller than the oldest plus the second oldest one, FODIS-TMEB abscises the oldest cohort and subtracts the remainder from the second oldest cohort. The FODIS-TMEB module requires various input parameters that are produced by other modules within CROPGRO-Tomato, related to crop growth, senescence, and environmental conditions. The technical aspects of interchanging parameter values between the two modules are discussed in Appendix A.
42 A coupling point is a crop model state variable selected for application of pest damage (Boote et al., 1983; Batchelor et al., 1993). To send feedback to CROPGRO-Tomato from FODIS-TMEB, two coupling points already implemented in CROPGRO were used,
TDLA and WLIDOT. The TDLA coupling point was used to feed back the diseased leaf area to crop growth related variables. WLIDOT was used to feed back the defoliated leaf mass at the end of a day to the next day’s calculations. Both coupling points were necessary because calculating TDLA on a given day did not account for the defoliated area; thus the use of WLIDOT. The technical aspects of feedback in CROPGRO-Tomato are discussed in Appendix B. Sensitivity Analysis Sensitivity analysis is a useful way to evaluate a simulation model. When the value of each parameter is changed, the effect of the change is observed on output variables. This analysis can provide powerful insight on the behavior of a simulated system. Simulations of tomato early blight epidemic in Ft. Pierce, 1998 were used for sensitivity analyses on parameters that are involved in the infection process, plant growth, and environmental conditions with and without fungicide applications. For analysis with fungicide applications, weekly application was assumed to test parameter sensitivity under good fungicide control conditions. Cultivar tolerance and fungicide efficiency were obtained with a calibration process of the proportion of disease severity. First, cultivar tolerance was calibrated so that the final proportion of disease severity was approximately 0.70 without fungicide applications. Then the fungicide efficiency was calibrated so that the final proportion of disease severity with fungicide was approximately 0.25 (McGovern, 1993).
43 The sensitivity of each parameter with respect to each output was graphically plotted and quantitatively described with relative sensitivity (explained below). The graphical descriptions were prepared by plotting the proportion of disease severity (PDS), the healthy LAI, the tops weight (biomass), and the fruit weight (yield) over the growth period. Each parameter was changed by ±20% in the case of real variables and ±20%, ±33%, ±50%, or ±80% for integer variables, while keeping all the other parameters constant. Relative sensitivity was calculated for the final PDS, the AUDPC (Area Under the Disease Progress Curve), the integrated value of healthy LAI over the growth period, the biomass at maturity, and the final yield, with the same parameter variation scheme used for the graphic display. Relative sensitivity was calculated with the following function:
δy y (k + ∆k / 2) − y (k − ∆k / 2) ≈ ∆k δk k δy / y σr ( y | k ) = = σ ( y | k) δk / k y
σ ( y | k) =
(2-25)
where y represents the output variable of interest, k represents the parameter being tested,
σ(y|k) is the function of absolute sensitivity, and σr(y|k) is the relative sensitivity. The relative sensitivity can be interpreted as the relative change in y compared to the change in k so that it can be used to provide a normalized measure to compare the sensitivity of a model to several variables (Jones and Luyten, 1998). Validation with LATESPOT The FODIS-TMEB model describes one specific pathosystem, tomato early blight. However, the generic foliar disease model structure may be useful to develop and couple multiple disease epidemics. Since crops are often subject to multiple disease epidemics, a
44 disease model that can describe multiple disease epidemics and their interrelationships with crop growth may be useful as a practical decision-support tool. Complicated and arbitrarily assumed input data and the implemented relationships among components in a pathosystem potentially limit the usage of a model to the particular pathosystem for which it was developed. Bourgeois (1989) developed the LATESPOT model and coupled it with PNUTGRO, a peanut-specific predecessor of CROPGRO-Peanut, to describe the disease cycle and its interrelationship with crop growth. However, his disease model was not included in later versions of PNUTGRO / CROPGRO due to its complexity and lack of modularity (Jones, personal communication). The FODIS-TMEB model was implemented as a CROPGRO-Tomato module following the modular structure described by Porter and Jones (2000) so that new foliar disease models can be added, modified and maintained with minimal effort. To test the portability of the implemented approach to the simulation of other epidemics, FODIS-TMEB was restructured to simulate peanut late leafspot (Cercosporidium personatum Deighton). The necessary functions and other information required to implement the FODIS approach were obtained from the LATESPOT documentation (Bourgeois, 1989), while the functions to simulate inoculum sources and the spore dissemination process were replaced with the infectious area based approach that is used in FODIS-TMEB. This replacement was done to avoid incorporating the somewhat arbitrary assumptions on the sporulation and dissemination processes implemented in LATESPOT. Disease progress and peanut growth as simulated by both models were compared.
45 Results and Discussion Simulated and Observed Tomato Pathosystem During model development, disease progress data assessed in Ft. Pierce, Florida, in 1998 (Figure 2-1) were used to calculate Rmax and to see how accurately FODIS-TMEB model could predict the observed PDS. Usually the model development process culminates in a validation with an independent data set that was not used in model development. The scant availability of measured disease data limited validation in this study. However, the simulated proportion of disease severity from the FODIS-TMEB model was very similar to the observed disease progress (RMSE=0.0225) (Figure 2-16). The available disease progress dataset did not include observed tomato growth data. Simulated values of healthy LAI (Figure 2-17), fruit weight (Figure 2-18), fruit number (Figure 2-19), and biomass (Figure 2-20) are shown for two cases: with and without a tomato early blight epidemic.
Proportion of Disease Severity
1.0 Simulated
Observed
0.8 0.6 0.4 0.2 0.0 0
20
40
60
80
100
120
Days after transplanting Figure 2-16. Observed (Pernezny, unpublished) and simulated proportions of disease severity in Ft. Pierce, 1998.
46
7000
w/ early blight w/o early blight
Fruit weight (kg/ha)
6000
w/o early blight
5000 4000 3000
w/ early blight
2000 1000 0 0
20
40
60
80
100
120
Days after transplanting Figure 2-17. Simulated fruit weight in Ft. Pierce, 1998, with and without tomato early blight epidemic.
120
w/ early blight w/o early blight
w/o early blight
2
Fruit number (no/m)
100 80 60 40
w/ early blight
20 0 0
20
40
60
80
100
120
Days after transplanting Figure 2-18. Simulated fruit number in Ft. Pierce, 1998, with and without tomato early blight epidemic.
47
6.00
w/ early blight w/o early blight
Healthy LAI
5.00
w/o early blight
4.00 3.00 2.00 1.00
w/ early blight
0.00 0
20
40
60
80
100
120
Days after transplanting Figure 2-19. Simulated LAI for healthy leaf area in Ft. Pierce, 1998, with and without tomato early blight epidemic.
14000
w/ early blight w/o early blight
Top weight (kg/ha)
12000 10000
w/o early blight
8000 6000 4000
w/ early blight
2000 0 0
20
40
60
80
100
120
Days after transplanting Figure 2-20. Simulated tops weight as a biomass in Ft. Pierce, 1998, with and without tomato early blight epidemic.
48 Four life stages of vacant, latent, infectious, and post-infectious were defined to describe the life cycle of tomato early blight pathogen, A. solani. The leaf areas at each stage dynamically change depending on leaf area growth, natural senescence, and defoliation due to diseases as well as the disease epidemic process (Figure 2-21).
Vacant Latent Infectious Post-Infectious Total
40
2
2
Leaf area (1000 cm /m )
50
30
Total
Vacant
20 Latent
10 Infectious P-Infectious
0 0
20
40
60
80
100
Days after transplanting Figure 2-21. The change in leaf area at each life stage of the pathogen as the tomato early blight progressed through the crop growth period. The outputs were obtained from a simulation with the environmental conditions in Ft. Pierce, Florida, 1998. Sensitivity Analysis The sensitivity of seventeen disease model parameters was tested, and four model outputs for fifteen parameters without fungicide applications were examined for graphical sensitivity analyses. Changes in hourly temperature caused the greatest impact on all outputs (Figure 2-84, 2-85, 2-86, and 2-87). Unlike all the other parameters that affected outputs only after the infection process had begun, hourly temperature changes affected tomato growth and the early blight epidemic throughout the tomato growth season. Proportion of disease severity (PDS) and leaf area index (LAI) were impacted to various
49 degrees by changes in the following parameters: the initial amount of infection (Figure 222 and 2-23), the latent period (Figure 2-30 and 2-31), the physiological age at infection onset (Figure 2-42 and 2-43), the maximum basic infection rate (Figure 2-46 and 2-47), the interval between symptom and sporulation (Figure 2-54 and 2-55), the tomato cultivar tolerance coefficient (Figure 2-62 and 2-63), the halo factor (Figure 2-66 and 2-67), the leaf wetness duration (Figure 2-78 and 2-79), and the required leaf wetness duration (Figure 2-82 and 2-83). Aside from hourly temperature, the following parameters made noticeable impact on biomass and yield: latent period (Figure 2-30 and 2-31), maximum basic infection rate (Figure 2-44 and 2-45), tolerance coefficient (Figure 2-60 and 2-61), halo factor (Figure 2-64 and 2-65), leaf wetness duration (Figure 2-76 and 2-77), and required leaf wetness duration (Figure 2-80 and 2-81).
Table 2-5. List of parameters used in sensitivity analyses. Variable name for each parameter is used in the text and source codes (Appendix D). Parameters
Variable
UNIT
Baseline value
Changes
PST_INIT
%
0.25
±20%
Minimum latent period
MIN_LP
day
3
±33%
Latent period
LP
day
(calculated)
±20%
Minimum infectious period
MIN_FP
day
14
±50%
Infectious period
FP
day
(calculated)
±20%
Infection onset physiological age
ONSET_AGE
physiological age
50.5
±20%
Maximum basic infection rate
RMAX
-1
9.85
±20%
Infection favorable level
0.50
±80%
Interval between symptom and sporulation
FAV_LEVEL INT_SS
DAY
5
±20%
Lesion expansion rate
KLEX
cm2[leaf]/cm2[ground]/day
0.012
±20%
Tolerance coefficient
TOLCFF
0.79
±20%
Halo factor
PST_TOLR
day-1
2.0
±20%
g[leaf]/m [ground]/day
(calculated)
±20%
0.80
±20%
day
2
Defoliated leaf mass due to disease
WLIDISDOT
Fungicide efficiency
FE_EFF
Fungicide application interval
INTAPP
day
7
±30%
Hourly temperature
TAIRHR
°C
(calculated)
±20%
Leaf wetness duration
LWD
hours
(calculated)
±20%
Required leaf wetness duration
RLWD
hours
(calculated)
±20%
50
Initial amount of infection
51
Table 2-6. Relative sensitivities of parameters used in the FODIS-TMEB model to the proportion of disease severity (PDS), the area under the disease progress curve (AUDPC), the integrated leaf area index (LAI), biomass at maturity, and yield when weekly fungicide applications were simulated. Descriptions of variable are given in Table 2-5. Variable
Outputs PDS AUDPC
LAI Biomass
Yield
PST_INIT
0.6105
0.8740
-0.0857
-0.0402
-0.0109
MIN_LP
-0.3084
-0.3467
0.0339
0.0151
0.0045
LP
-1.3229
-1.4910
0.1450
0.0782
0.0187
MIN_FP
0.0000
0.0103
-0.0012
-0.0009
-0.0002
FP
0.0000
0.0450
-0.0041
-0.0033
-0.0004
ONSET_AGE
0.0000
-0.2699
0.0219
0.0059
0.0039
RMAX
1.2211
1.5231
-0.1487
-0.0776
-0.0187
FAV_LEVEL INT_SS
-0.0509
-0.0787
0.0076
0.0034
0.0011
-0.4070
-0.3728
0.0362
0.0227
0.0039
0.1018 22.9177
-0.0073
-0.0044
-0.0009
KLEX TOLCFF
-4.4774
-5.1285
0.5005
0.2694
0.0799
PST_TOLR
0.1018
0.0578
-0.0791
-0.0223
-0.0096
WLIDISDOT
0.2035
0.2699
-0.0132
-0.0234
-0.0017
FC_EFF
-3.2563
-3.7918
0.3646
0.1954
0.0491
INTAPP
0.0678
0.1457
-0.0140 14.5622
-0.0017
TAIRHR
-8.2424 -14.9036
0.8399
-1.0846
-3.7600
LWD
2.2387
2.1979
-0.2259
-0.1003
-0.0196
RLWD
-1.1193
-3.4383
0.2869
0.1184
0.0782
52
Table 2-7. Relative sensitivities of parameters used in the FODIS-TMEB model to the proportion of disease severity (PDS), the area under the disease progress curve (AUDPC), the integrated leaf area index (LAI), biomass at maturity, and yield when no fungicide applications were simulated. Descriptions of variable are given in Table 2-5. Variable
Outputs PDS AUDPC
LAI Biomass
Yield
PST_INIT
0.3800
0.0171
-0.2110
-0.2183
-0.1405
MIN_LP
-0.2609
-0.3035
0.1403
0.1454
0.1012
LP
-1.0157
-1.1586
0.4915
0.5696
0.3921
MIN_FP
0.0005
0.0009
-0.0001
-0.0002
0.0021
FP
0.0278
0.0377
0.0000
-0.0149
0.0000
ONSET_AGE
0.1841
-0.2431
0.0496
-0.0841
-0.0495
RMAX
0.7061
0.8209
-0.3592
-0.3987
-0.2675
FAV_LEVEL INT_SS
-0.0447
-0.0750
0.0288
0.0189
0.0150
-0.3321
-0.3481
0.1353
0.1755
0.1001
KLEX
0.0281
0.0441
-0.0099
-0.0146
-0.0010
TOLCFF
-2.6308
-3.1836
1.3828
1.1425
0.6764
PST_TOLR
0.6191
0.3458
-0.3913
-0.4257
-0.3343
WLIDISDOT
0.0774
0.1824
0.0387
-0.0143
0.0351
TAIRHR
-2.8213
-0.0049
4.3857
-0.1761
-2.2869
LWD
-1.0012
0.0418
-0.8016
-0.5731
-0.2646
RLWD
-1.4994
-2.4552
0.8016
0.5731
0.2646
0.8 PDS
6
-20% 0% +20%
+20%
0.6
-20%
0.4
2
0.0
0 60
80 100 Days after transplanting
30
90
120
Figure 2-23. Sensitivity of LAI to changes in PST_INIT without fungicide applications. -20% 0% +20%
5000
-20%
6000
60 Days after transplanting
Yield (kg/ha)
8000
0
120
6000
-20% 0% +20%
10000
+20%
+20%
4000 2000
4000
-20%
3000
+20%
2000 1000
0
0 0
40
80
120
Days after transplanting
Figure 2-24. Sensitivity of tops weight to changes in PST_INIT without fungicide applications.
0
40
80
120
Days after transplanting
Figure 2-25. Sensitivity of yield to changes in PST_INIT without fungicide applications.
53
12000
-20%
3
1
Figure 2-22. Sensitivity of the proportion of disease severity to changes in PST_INIT without fungicides.
Tops weight (kg/ha)
4
0.2
40
-20% 0% +20%
5 Healthy LAI
1.0
0.8 PDS
6
-33% 0% +33%
-33%
0.6 0.4
-33% 0% +33%
5 Healthy LAI
1.0
+33%
0.2
4 3 2
-33%
1
0.0
0 40
60
80
100
120
0
20
40
Days after transplanting
8000
+33% -33%
6000
-33% 0% +33%
5000 Yield (kg/ha)
10000
100
120
Figure 2-27. Sensitivity of LAI to changes in MIN_LP without fungicide applications. 6000
-33% 0% +33%
80
4000
4000
+33% -33%
3000 2000 1000
2000
0
0 0
20
40
60
80
100
120
Days after transplanting
Figure 2-28. Sensitivity of tops weight to changes in MIN_LP without fungicide applications.
0
20
40
60
80
100
120
Days after transplanting
Figure 2-29. Sensitivity of yield to changes in MIN_LP without fungicide applications.
54
12000
60
Days after transplanting
Figure 2-26. Sensitivity of the proportion of disease severity to changes in MIN_LP without fungicides.
Tops weight (kg/ha)
+33%
1.0
-20% 0% +20%
5 Healthy LAI
0.8 PDS
6
-20% 0% +20%
-20%
0.6 0.4
+20%
0.2
4 3
-20%
2 1
0.0
0 40
60
80
100
120
0
20
40
Days after transplanting
8000
+20% -20%
6000 4000
4000
0
0 40
60
80
100
120
Days after transplanting
Figure 2-32. Sensitivity of tops weight to changes in LP without fungicide applications.
+20% -20%
2000 1000
20
120
3000
2000 0
-20% 0% +20%
5000 Yield (kg/ha)
10000
100
Figure 2-31. Sensitivity of LAI to changes in LP without fungicide applications. 6000
-20% 0% +20%
80
0
20
40
60
80
100
120
Days after transplanting
Figure 2-33. Sensitivity of yield to changes in LP without fungicide applications.
55
12000
60
Days after transplanting
Figure 2-30. Sensitivity of the proportion of disease severity to changes in LP without fungicides.
Tops weight (kg/ha)
+20%
0.8 PDS
6
-50% 0% +50%
-50% 0% +50%
5
-50%
0.6
Healthy LAI
1.0
+50%
0.4 0.2
4 3
+50%
2 1
0.0
-50%
0 40
60
80
100
0
120
20
40
8000
6000
+50%
6000 4000
4000
0
0 100
120
Figure 2-36. Sensitivity of tops weight to changes in MIN_FP without fungicide applications.
-50%
2000 1000
40 60 80 Days after transplanting
+50%
3000
2000 20
-50% 0% +50%
5000
-50%
0
120
Figure 2-35. Sensitivity of LAI to changes in MIN_FP without fungicide applications.
Yield (kg/ha)
Tops weight (kg/ha)
10000
100
0
20
40
60
80
100
120
Days after transplanting
Figure 2-37. Sensitivity of yield to changes in MIN_FP without fungicide applications.
56
Figure 2-34. Sensitivity of the proportion of disease severity to changes in MIN_FP without fungicides. -50% 0% +50%
80
Days after transplanting
Days after transplanting
12000
60
1.0
-20% 0% +20%
5 Healthy LAI
0.8 PDS
6
-20% 0% +20%
+20%
0.6
-20%
0.4 0.2
4 3
+20%
2
-20%
1
0.0
0 40
60
80
100
0
120
40 80 Days after transplanting
Days after transplanting
10000 8000
6000
*Three lines are overlapped.
-20% 0% +20%
6000 4000
4000 3000 2000
2000
1000
0
0 0
20
40
60
80
100
120
Days after transplanting
Figure 2-40. Sensitivity of tops weight to changes in FP without fungicide applications.
*Three lines are overlapped.
-20% 0% +20%
5000 Yield (kg/ha)
Tops weight (kg/ha)
12000
Figure 2-39. Sensitivity of LAI to changes in FP without fungicide applications.
0
20
40
60
80
100
120
Days after transplanting
Figure 2-41. Sensitivity of yield to changes in FP without fungicide applications.
57
Figure 2-38. Sensitivity of the proportion of disease severity to changes in FP without fungicides.
120
0.8 PDS
6
-20% 0% +20%
+20% -20%
0.6
-20% 0% +20%
5
0.4
Healthy LAI
1.0
0.2
4
-20%
3 2 1
0.0
0 40
60
80
100
0
120
20
40
8000
+20% -20%
6000 4000
4000
0
0 40
60
80
100
120
Days after transplanting
Figure 2-44. Sensitivity of tops weight to changes in ONSET_AGE without fungicides.
+20% -20%
2000 1000
20
120
3000
2000 0
-20% 0% +20%
5000 Yield (kg/ha)
10000
100
Figure 2-43. Sensitivity of LAI to changes in ONSET_AGE without fungicide applications. 6000
-20% 0% +20%
80
58
Figure 2-42. Sensitivity of the proportion of disease severity to changes in ONSET_AGE without fungicides. 12000
60
Days after transplanting
Days after transplanting
Tops weight (kg/ha)
+20%
0
20
40
60
80
100
Days after transplanting
Figure 2-45. Sensitivity of yield to changes in ONSET_AGE without fungicide applications.
120
1.0 0.8
-20% 0% +20%
5
+20%
0.6
Healthy LAI
PDS
6
-20% 0% +20%
0.4
-20%
0.2
4
+20%
3 2 1
0.0
0 40
60
80
100
0
120
20
40
8000
-20% +20%
6000 4000
4000
0
0 40
60
80
100
120
Days after transplanting
Figure 2-48. Sensitivity of tops weight to changes in RMAX without fungicides.
-20% +20%
2000 1000
20
120
3000
2000 0
-20% 0% +20%
5000 Yield (kg/ha)
10000
100
Figure 2-47. Sensitivity of LAI to changes in RMAX without fungicide applications. 6000
-20% 0% +20%
80
0
20
40
60
80
100
120
Days after transplanting
Figure 2-49. Sensitivity of yield to changes in RMAX without fungicide applications.
59
Figure 2-46. Sensitivity of the proportion of disease severity to changes in RMAX without fungicides. 12000
60
Days after transplanting
Days after transplanting
Tops weight (kg/ha)
-20%
0.8 PDS
6
-80% 0% +80%
0, -80%
0.6
+80%
0.4
0 80 100 Days after transplanting
0
120
+80% 0, -80%
6000 4000
4000
0
0 40
60
80
100
120
Days after transplanting
Figure 2-52. Sensitivity of tops weight to changes in FAV_LEVEL without fungicide applications.
120
0, -80%
2000 1000
20
100
+80%
3000
2000 0
-80% 0% +80%
5000 Yield (kg/ha)
8000
40 60 80 Days after transplanting
Figure 2-51. Sensitivity of LAI to changes in FAV_LEVEL without fungicide applications. 6000
-80% 0% +80%
10000
20
60
12000
0, -80%
2
0.0 60
+80%
3
1
Figure 2-50. Sensitivity of the proportion of disease severity to changes in FAV_LEVEL without fungicides.
Tops weight (kg/ha)
4
0.2
40
-80% 0% +80%
5 Healthy LAI
1.0
0
20
40
60
80
100
Days after transplanting
Figure 2-53. Sensitivity of yield to changes in FAV_LEVEL without fungicide applications.
120
0.8 PDS
6
-20% 0% +20%
-20%
0.6
+20%
0.4
0
-20%
4000
4000
0 80
120
100
120
Days after transplanting
Figure 2-56. Sensitivity of tops weight to changes in INT_SS without fungicide applications.
+20% -20%
2000
0 60
100
3000
1000 40
80
-20% 0% +20%
5000
2000 20
60
Figure 2-55. Sensitivity of LAI to changes in INT_SS without fungicides.
+20%
6000
0
40
Days after transplanting
Yield (kg/ha)
8000
20
6000
-20% 0% +20%
10000
0
120
0
20
40
60
80
100
120
Days after transplanting
Figure 2-57. Sensitivity of yield to changes in INT_SS without fungicide applications.
61
12000
-20%
2
0.0 60 80 100 Days after transplanting
+20%
3
1
Figure 2-54. Sensitivity of the proportion of disease severity to changes in INT_SS without fungicides.
Tops weight (kg/ha)
4
0.2
40
-20% 0% +20%
5 Healthy LAI
1.0
0.8 PDS
6
-20% 0% +20%
-20%
0.6
+20%
0.4
4 3 2
0.2
1
0.0
0 40
-20% 0% +20%
5 Healthy LAI
1.0
60
80
100
+20% -20% 0
120
20
40
8000
6000
+20% -20%
6000 4000
4000
2000
0
0 40
60
80
100
120
Days after transplanting
Figure 2-60. Sensitivity of tops weight to changes in KLEX without fungicide applications.
-20%
3000
1000 20
+20%
-20% 0% +20%
5000
2000 0
120
Figure 2-59. Sensitivity of LAI to changes in KLEX without fungicide applications.
Yield (kg/ha)
Tops weight (kg/ha)
10000
100
0
20
40
60
80
100
120
Days after transplanting
Figure 2-61. Sensitivity of yield to changes in KLEX without fungicide applications.
62
Figure 2-58. Sensitivity of the proportion of disease severity to changes in KLEX without fungicides. -20% 0% +20%
80
Days after transplanting
Days after transplanting
12000
60
0.8 PDS
6
-20% 0% +20%
-20%
0.6 0.4
+20%
0.2
-20% 0% +20%
5 Healthy LAI
1.0
4 3
-20%
2 1
0.0
0 40
60
80
100
0
120
20
40
8000
+20%
-20%
6000
120
4000 2000
+20%
-20% 0% +20%
5000
Yield (kg/ha)
10000
100
Figure 2-63. Sensitivity of LAI to changes in PST_TOLR without fungicides. 6000
-20% 0% +20%
80
63
Figure 2-62. Sensitivity of the proportion of disease severity to changes in PST_TOLR without fungicides. 12000
60
Days after transplanting
Days after transplanting
Tops weight (kg/ha)
+20%
4000
-20%
3000 2000 1000
0
0 0
20
40
60
80
100
120
Days after transplanting
Figure 2-64. Sensitivity of tops weight to changes in PST_TOLR without fungicides.
0
20
40
60
80
100
Days after transplanting
Figure 2-65. Sensitivity of yield to changes in PST_TOLR without fungicides.
120
0.8 PDS
6
-20% 0% +20%
+20%
0.6
-20%
0.4
-20% 0% +20%
5 Healthy LAI
1.0
0.2
4
+20%
3 2 1
0.0
0 40
60
80
100
0
120
20
40
8000
-20% +20%
6000 4000
3000 2000 1000
0
0 20
40
60
80
100
120
Days after transplanting
Figure 2-68. Sensitivity of tops weight to changes in HALO without fungicide applications.
+20%
4000
2000 0
120
-20%
-20% 0% +20%
5000
Yield (kg/ha)
10000
100
Figure 2-67. Sensitivity of LAI to changes in HALO without fungicide applications. 6000
-20% 0% +20%
80
0
20
40
60
80
100
120
Days after transplanting
Figure 2-69. Sensitivity of yield to changes in HALO without fungicide applications.
64
Figure 2-66. Sensitivity of the proportion of disease severity to changes in HALO without fungicides. 12000
60
Days after transplanting
Days after transplanting
Tops weight (kg/ha)
-20%
0.8 PDS
6
-20% 0% +20%
+20%
0.6
-20%
0.4
-20% 0% +20%
5 Healthy LAI
1.0
0.2
4 3 2
+20%
1
0.0
-20%
0 40
60
80
100
0
120
20
40
8000
6000
-20%
4000
4000
2000 1000
0
0 20
40
60
80
100
120
Days after transplanting
Figure 2-72. Sensitivity of tops weight to changes in WLIDISDOT without fungicide applications.
-20%
3000
2000 0
+20%
-20% 0% +20%
5000
+20%
6000
120
Figure 2-71. Sensitivity of LAI to changes in WLIDISDOT without fungicide applications.
Yield (kg/ha)
Tops weight (kg/ha)
10000
100
0
20
40
60
80
100
Days after transplanting
Figure 2-73. Sensitivity of yield to changes in WLIDISDOT without fungicide applications.
120
65
Figure 2-70. Sensitivity of the proportion of disease severity to changes in WLIDISDOT without fungicides. -20% 0% +20%
80
Days after transplanting
Days after transplanting
12000
60
0.8 PDS
8
-20% 0% +20%
-20% 0% +20%
7
-20% 0%
0.6 0.4
Healthy LAI
1.0
6
+20%
5 4
0%
3 2
0.2
1
+20%
0.0 40
60
80
100
0 0
120
20
40
8000
0% -20% +20%
6000
-20% 0% +20%
6000
4000 2000
Yield (kg/ha)
10000
100
120
Figure 2-75. Sensitivity of LAI to changes in TAIRHR without fungicide applications. 7000
-20% 0% +20%
80
66
Figure 2-74. Sensitivity of the proportion of disease severity to changes in TAIRHR without fungicides. 12000
60
Days after transplanting
Days after transplanting
Tops weight (kg/ha)
+20%
5000
0%
4000 3000
-20%
2000
+20%
1000
0
0 0
20
40
60
80
100
120
Days after transplanting
Figure 2-76. Sensitivity of tops weight to changes in TAIRHR without fungicide applications.
0
20
40
60
80
100
120
Days after transplanting
Figure 2-77. Sensitivity of yield to changes in TAIRHR without fungicide applications.
1.0
0.6
4 3 2
0.2
1
0.0
0 40
60
80 100 Days after transplanting
10000
7000
* +20% and 0% overlapped.
6000
5000
2000
1000
0
0 60
80
120
3000 2000
40
100
4000
4000
20
80
100
120
Days after transplanting
Figure 2-80. Sensitivity of tops weight to changes in LWD without fungicide applications.
* +20% and 0% overlapped.
-20% 0% +20%
6000
8000
0
60
Figure 2-79. Sensitivity of LAI to changes in LWD without fungicide applications.
Yield (kg/ha)
12000
40
0
20
40
60
80
100
120
Days after transplanting
Figure 2-81. Sensitivity of yield to changes in LWD without fungicide applications.
67
-20% 0% +20%
20
Days after transplanting
Figure 2-78. Sensitivity of the proportion of disease severity to changes in LWD without fungicides. 14000
0
120
* +20% and 0% overlapped.
-20% 0% +20%
5
0.4
Tops weight (kg/ha)
6
Healthy LAI
0.8 PDS
* +20% and 0% overlapped.
-20% 0% +20%
1.0
0.6
4 3 2
0.2
1
0.0
0 40
60
80 100 Days after transplanting
10000
7000
* -20% and 0% overlapped.
6000
5000
2000
1000
0
0 60
80
120
3000 2000
40
100
4000
4000
20
80
100
120
Days after transplanting
Figure 2-84. Sensitivity of tops weight to changes in RLWD without fungicides.
* -20% and 0% overlapped.
-20% 0% +20%
6000
8000
0
60
Figure 2-83. Sensitivity of LAI to changes in RLWD without fungicide applications.
Yield (kg/ha)
12000
40
0
20
40
60
80
100
120
Days after transplanting
Figure 2-85. Sensitivity of yield to changes in RLWD without fungicide applications.
68
-20% 0% +20%
20
Days after transplanting
Figure 2-82. Sensitivity of the proportion of disease severity to changes in RLWD without fungicides. 14000
0
120
* -20% and 0% overlapped.
-20% 0% +20%
5
0.4
Tops weight (kg/ha)
6
Healthy LAI
0.8 PDS
* -20% and 0% overlapped.
-20% 0% +20%
69 The relative sensitivities of five outputs were calculated for the parameters used in the graphical sensitivity analyses with two cases: 1) when weekly fungicide applications were simulated (Table 2-6) and 2) when no fungicide applications were simulated (Table 2-7). As in the graphical analyses, hourly temperature had a great impact on all outputs in both cases. Aside from the hourly temperature, parameters related to the calculation of the basic infection rate (Equation 2-3), such as the tolerance coefficient, the fungicide efficiency, and the leaf wetness duration were most important. For outputs of the disease progress (PDS and AUDPC), the tolerance coefficient had the greatest negative impacts, which were -4.4774 to PDS and –5.1285 to AUDPC with fungicides and –2.6308 to PDS and –3.1836 to AUDPC without fungicides. The leaf wetness duration parameter positively impacted both PDS and AUDPC, whereas the required leaf wetness duration parameter negatively impacted both outputs. Given the role of leaf wetness in infection initiation, and given that the conditions for infection were amply met with each rainfall event past day 30 (Figure 2-12), outputs were not particularly sensitive to negative changes in required leaf wetness duration. However, when the standard leaf wetness duration satisfied the required leaf wetness duration at 56 days, a change of +20% in the required leaf wetness duration satisfied the required leaf wetness duration on only 23 days. Since the tolerance coefficient was the most important parameter in the relative sensitivity analyses aside from the hourly temperature, further graphical sensitivity analyses were performed with it to describe the effects of different values of the tolerance coefficient on tomato early blight development and on tomato growth. The following outputs were considered: AUDPC (Figure 2-86), the integrated healthy leaf area index
70 (Figure 2-87), the tops weight (Figure 2-88), and the fruit weight (Figure 2-89). The sensitivity of AUDPC to the tolerance coefficient (Figure 2-86) showed that disease development is a linear function of the tolerance coefficient. The other four outputs increased non-linearly as the tolerance coefficient increased. The base value of the cultivar tolerance coefficient used in the sensitivity analyses was calibrated at 0.78 on a 0 to 1 scale in which the most tolerant cultivar was assumed to have a tolerance coefficient of 1.00 and the most susceptible was assumed to have a value of 0.00. However, especially for the fruit yield, a tolerance coefficient value above 0.80 did not cause large changes although it did decrease AUDPC and increased leaf area index and biomass. In other words, the fruit weight approximately reached their attainable values with a tolerance coefficient 0.80, so any further suppression of tomato early blight development did produce additional yield increases. 25
y = -21.373x + 21.389 2
R = 0.9906
AUDPC
20 15 10 5 0 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Tolerance coefficient Figure 2-86. Simulated sensitivity of AUDPC to changes in the tolerance coefficient to in Ft. Pierce, 1998, under simulated weekly fungicide application.
71
Integrated Healthy LAI
400 350 300 250 200 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Tolerance coefficient Figure 2-87. Simulated sensitivity of IHLAI (Integrated Healthy Leaf Area Index) to changes in the tolerance coefficient to in Ft. Pierce, 1998, under simulated weekly fungicide application.
Tops Weight (kg/ha)
12000
10000
8000
6000 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Tolerance coefficient Figure 2-88. Simulated sensitivity of tops weight to changes in the tolerance coefficient to in Ft. Pierce, 1998. Weekly fungicide application was simulated.
72
Fruit Weight (kg/ha)
6000 5500 5000 4500 4000 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Tolerance coefficient Figure 2-89. Simulated sensitivity of the fruit weight to changes in tolerance coefficient to in Ft. Pierce, 1998. Weekly fungicide application was simulated. Fungicide efficiency was also an important parameter in the relative sensitivity analyses (Table 2-5). The same outputs that were used in graphical sensitivity analyses for the tolerance coefficient were used to illustrate the effects of fungicide efficiency on tomato early blight development and tomato growth. The AUDPC decreased linearly as fungicide efficiency increased (Figure 2-90). Biomass (Figure 2-92) and leaf area index (Figure 2-93) increased linearly as the fungicide efficiency increased. However, the fruit yield (Figure 2-94) increased non-linearly and approximately reached their attainable values at a fungicide efficiency of 0.80. Thus, fungicides with efficiency better than 0.80 would not increase the yield.
73
20
y = -15.249x + 16.396 2
R = 0.9935
AUDPC
15 10 5 0 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Fungicide Efficiency Figure 2-90. Simulated sensitivity of AUDPC to changes in fungicide efficiency in Ft. Pierce, 1998. Weekly fungicide application was simulated.
Integrated Healthy LAI
350
y = 110.06x + 235.22 2
R = 0.9853 300
250
200 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Fungicide Efficiency Figure 2-91. Simulated sensitivity of integrated healthy LAI to changes in fungicide efficiency in Ft. Pierce, 1998. Weekly fungicide application was simulated.
74
Tops Weight (kg/ha)
12000
y = 2722.5x + 9194.4 2
R = 0.9971 11000
10000
9000 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Fungicide Efficiency Figure 2-92. Simulated sensitivity of tops weight to changes in fungicide efficiency in Ft. Pierce, 1998. Weekly fungicide application was simulated.
Fruit Weight (kg/ha)
6000
5500
5000 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Fungicide Efficiency Figure 2-93. Simulated sensitivity of fruit weight to changes in fungicide efficiency in Ft. Pierce, 1998. Weekly fungicide application was simulated.
75 5500-6000 5000-5500 4500-5000 4000-4500 3500-4000 3000-3500
Yield (kg/ha) 6000 5500 5000 4500 4000 3500 3000 1.0
0.8
1.0 0.8 0.6 0.4 0.6
Tolerance Coefficient
0.4
0.2 0.2
0.0 0.0
Fungicide Efficiency
Figure 2-94. Simulated sensitivity of fruit weight (yield) to changes in fungicide efficiency and tolerance coefficient in Ft. Pierce, 1998. Weekly fungicide application was simulated. The sensitivities of fruit yield to fungicide efficiency and the tolerance coefficient were plotted together (Figure 2-94). Note how values of 0.80 for both parameters can closely achieve the maximum attainable yield. Validation with LATESPOT
The FODIS-TMEB model was converted to model peanut late leafspot disease with information available in Bourgeois’ dissertation (1989). Total of eighteen parameters in FODIS-TMEB were converted to corresponding parameters for peanut late leafspot (Table 2-8). Then, PDS (Figure 2-95), LAI (Figure 2-96), and seed weight (Figure 2-97) outputs from the converted model, FODIS-PNLS, were compared with outputs from the LATESPOT model (Bourgeois, 1989) using the same input data set. For each output variable, three sets of outputs, from i) the FODIS-PNLS model, ii) the LATESPOT
76 model, and iii) measured, were statistically compared with Duncan’s multiple range method. With 0.90 as the confidence coefficient, no significant differences were observed. The new model, FODIS-PNLS, predicted disease progress, LAI, and seed weight as accurately as the old model, LATESPOT, did. These results imply that the proposed disease model structure has a potential to be portable across different pathosystems. Same leaf cohort model was used in FODIS-PNLS, but some components were entirely rewritten to implement different conceptual models. For example, defoliated leaf area is a linear function of the proportion of visible lesion in FODIS-TMEB (Equation 27), but it is a non-linear function of the proportion of infection, which includes latent infection, in FODIS-PNLS. Although the conversion process showed potential of the structure of FODIS-TMEB, more efforts to make each component parameterized would be necessarily to be used as a fully parameterized generic model. For disease progress outputs (Figure 2-95), a fluctuation of disease severity was calculated as shown in the observed data. This fluctuation was due to abscission or defoliation of leaves and the consequently decreased visible diseased LAI used in the calculation of disease severity (Equation 2-6). For seed weight outputs (Figure 2-97), both models showed different values near harvest maturity and it was because neither model accounted for mature pod detachment that occurs after seeds mature (Jones, personal communication).
Reference Bourgeois, 1989. P.65 Bourgeois, 1989. P.54
Bourgeois, 1989. P.60 Arbitrarily assumed 77
Table 2-8. Parameters of FODIS-TMEB model converted to simulate the peanut late leafspot pathosystem. Reference FODIS-PNLS Variable FODIS-TMEB* Defoliation f(YVISIBLE) Vloutoglou, 2000. f(YINFECTION) Effect of environment f(temperature) x f(leaf f(temperature, RH≥93% Berger, personal conditions on communication. hours) wetness duration) infection (Not used) Effect of leaf wetness f(rainfall, RH≥93 hours, Rotem, 1994. P.86 plant age) duration on infection Rotem, 1994. P.225 TMin : TOpt : TMax = Effect of temperature TMin : TOpt : TMax = 5 : 27 : 35 (ºC) 16 : 24 : 32 (ºC) on infection Favorable level of 0.20 Arbitrarily assumed. 0.20 environment Fungicide efficiency 0.80 when applied Calibrated with (Not simulated) measured data. Halo factor 2.0 / day Arbitrarily assumed. 2.0 / day f(temperature) Rotem, 1994. P.72 i1 = 2 (days), i50 = 5 Infectious period (days) Initial condition of 0.25% Johnson and Teng, 1990 1% disease severity Introducing inoculum Two inoculum sources Arbitrarily assumed. Three inoculum sources (outside of canopy and (outside of field, within within canopy) with an field, and within infectious area based canopy) with a spore calculation. density based calculation. Interval between 5 days (latent period O’Leary, 1985 9 days (min. latent
Arbitrarily assumed Bourgeois, 1989. P.70 Bourgeois, 1989. P.51 Bourgeois, 1989. P.50
Bourgeois, 1989. P.70
symptom and sporulation kINF
incubation period = 8 – 3 = 5) min. infection period infectious period
Walker, 1952
period -min. incubation period = 19-10 = 9)
Manetsch, 1976
i1 × f (temperature) i 50
Bourgeois, 1989. P.138
kLAT
min. latent period latent period
Manetsch, 1976
Bourgeois, 1989. P.138
Latent period
f(physiological age of cohort) f(KLEX ,diseased area, YNONDISEASED)
Johnson and Teng, 1990
Bourgeois, 1989. P.70 Bourgeois, 1989. P.71
14 days
Rotem, 1994, P.72
3 days
Johnson and Teng, 1990
19 days
Rmax
9.85 / day
Lesion expansion
Variable i1 i50 inc1
0.79
Johnson and Teng, 1990
Bourgeois, 1989. P.71 Bourgeois, 1989. P.70
Calibrated with 0.00 (Not simulated) measured data Calibrated with 0.23177 / day Bourgeois, 1989. P.17 measured data * Details of each function can be found in Materials and Methods section of this chapter.
Description Infectious period to first spores removed from the sporulating lesions. Infectious period to 50% of spores removed from the sporulating lesions. Incubation period to first necrotic lesion.
78
Minimum infectious period Minimum latent period Tolerance coefficient
ρ1 × f (temperature) ρ 50 ρ1 = 19 (days), ρ50 = 25 (days) f(infected area, inc1, inc50, f(temperature), expansion factor, YINFECTION) 2 days
inc50 KINF KLAT KLEX TMax TMin TOpt Rmax
79
YNONDISEASED YINFECTION YVISIBLE ρ1 ρ50
Incubation period to 50% of necrotic lesions. Parameter for distributed delay function in the infectious stage. Parameter for distributed delay function in the latent stage. Lesion expansion rate in TMEB. 0.012 cm2/m2/day (Berger et al., 1997) Maximum temperature for infection in ºC. Minimum temperature for infection in ºC. Optimum temperature for infection in ºC. Maximum basic infection rate. Proportion of non-diseased leaf area in a cohort. Proportion of infection on leaf area in a cohort. Proportion of lesion on leaf area in a cohort. Latent period to first lesions showing sporulation. Latent period to 50% of lesions showing sporulation.
80
1.00
FODIS-PNLS LATESPOT Observations
Disease Severity
0.80 0.60 0.40
LATESPOT FODIS-PNLS
0.20 0.00 0
50
100
150
Days after planting Figure 2-95. Comparisons of observed disease severity data with the corresponding FODIS-PNLS and LATESPOT simulations. The same input data were used for the simulations of both models. FODIS-PNLS LATESPOT Measured
5
2
2
LAI (m /m )
4 3
LATESPOT FODIS-PNLS
2 1 0 0
50
100
150
Days after planting
Figure 2-96. Comparisons of observed LAI with the corresponding FODIS-PNLS and LATESPOT simulations. The same input data were used for the simulations of both models.
81
3500
FODIS-PNLS LATESPOT Measured
Seed Weight (kg/ha)
3000 2500
LATESPOT FODIS-PNLS
2000 1500 1000 500 0 0
50 100 Days after planting
150
Figure 2-97. Comparisons of observed seed weight with the corresponding FODIS-PNLS and LATESPOT simulations. The same input data were used for the simulations of both models. Conclusions
The CROPGRO-Tomato and FODIS-TMEB model was developed and adequately simulated the disease progression when compared with a measured dataset. The disease development of tomato early blight and tomato growth were relatively sensitive to the maximum basic infection rate, the cultivar tolerance coefficient, and the fungicide efficiency. However, these parameters had to be calibrated with a limited number of measured data due to unavailability of published data with naturally occurring and homogenous tomato early blight disease epidemic. Especially for the cultivar tolerance coefficient, validations for different cultivars will be necessary. FODIS-TMEB was linked to the CROPGRO-Tomato model with two coupling points: the diseased leaf area and the defoliated leaf mass, through which tomato growth and yield were affected. A leaf cohort approach was used to calculate a basic infection rate instead of using an entire canopy basis. However, no interrelationship among cohorts’
82 sporulation was implemented. Further studies considering pathogen’s spore dynamics in the tomato canopy may be useful to better understand disease development in leaf cohorts. A model was implemented to simulate fungicide persistence in two canopy layers. Fungicide efficiency was one of the most sensitive parameters for disease development and tomato growth as it directly affects basic infection rate. When weekly fungicide applications were simulated, both canopy layers were well protected by their residues. The parts of the FODIS-TMEB model that described tomato early blight infection were converted to describe peanut late leafspot infection to test its portability for simulating another foliar pathosystem. The resulting FODIS-PNLS peanut late leafspot model and a previous one, LATESPOT (Bourgeois, 1989), were compared. The FODISPNLS predicted disease progress as accurately as LATESPOT. Thus, the structure of FODIS-TMEB can be used to implement other foliar disease pathosystems. However, some epidemiological components may have to be modified for pathosystems. To be used as a generic foliar disease model, further studies to parameterize those components will be necessary.
CHAPTER 3 MODELING THE IMPACTS OF CLIMATE VARIABILITY ON TOMATO DISEASE MANAGEMENT AND PRODUCTION Introduction
Plant pathologists often describe the development of plant disease as interactions among three components: pathogen, host, and environment. Among these three components, the environment is assumed to be an actual driving force (Hardwick, 1998). Each species of pathogen requires a specific range of temperature and humidity or leaf wetness duration for disease to occur. Within those ranges, environmental factors influence rate responses of activities such as epidemic rates of plant pathogens (Agrios, 1997). With weather-based empirical knowledge, a disease forecasting system (DFS) has been developed to predict plant disease development after the occurrence of weather conditions favorable for disease development. By using a DFS, one may decide whether to apply a chemical treatment now or wait until the best timing of application. Such models can help farmers to reduce unnecessary fungicide applications without an increase in risk of losing the crop. For example, BLITECAST (Krause et al., 1975) forecasts potato late blight, and TOMCAST (Jasinski, 1999) forecasts tomato early blight. DFS assigns a numerical daily disease severity value (DSV) from zero (no disease risk) to four (high disease risk). Each DSV indicates the likelihood of disease development on a given day based on hours of leaf wetness and average temperature
83
84 during the day, it is used to make a recommendation to schedule fungicide application with weather data of past few days. Most DFS use past weather data, which may be adequate to schedule treatment for many diseases (Jasinski, 1999; Krause et al., 1975). However, for some diseases, such as polycyclic diseases that infect a crop very rapidly, there is no guarantee of not losing crops at all by using the previous day’s weather conditions. Often, only a short period of disease-favorable condition turns out to be sufficient time for a disease epidemic to occur in a large field. Thus, if one can predict weather conditions two days (Raposo et al., 1993) or four days (Shtienberg and Elad, 1997) in advance, one might control disease earlier and better with protective fungicides before the disease threatens the crop. As weather forecasts can help farmers make tactical decisions during a crop season on a day-to-day basis, climate prediction may help to make strategic decisions (Kropff et al., 1995). For example, if the probability of excess rainfall is higher than normal in a particular crop growth season, a decision support system considering the climate prediction could issue a warning of higher probability of specific disease occurrence for the specific field conditions. This warning may be used as a basis to improve a disease management strategy prior to the crop season, such as purchasing fungicides prior to the season when prices may be lower, a change in crop variety that is more resistant to the disease, or a change in planting date to avoid the most risky climate events. Among various climatic events, variability in rainfall and temperature, primarily caused by the El Niño-Southern Oscillation (ENSO) phenomenon, may have an important impact on disease epidemics. ENSO has three phases of El Niño, neutral, and La Niña. The El Niño phase normally occurs every two to seven years. ENSO is
85 associated with extreme climatic variability on a worldwide scale. In China, coincidences of severe occurrence of wheat stripe rust disease and El Niño were reported in the years of 1952-1990 (Scherm and Yang, 1998). Outbreaks of late blight were reported in tomato and potato production areas of the U.S. and Canada in 1992-1993, and this period coincided with an exceptionally wet growing-season condition resulting from the strong El Niño that occurred during that time (Weingartner, 1997). However, Coakley (1999) reviewed numerous studies that showed veterinary and medical epidemiology established relationships between ENSO and outbreaks of infectious disease, but there had been a conspicuous lack of published work on plant disease in relation to ENSO. ENSO is regarded as a major cause of climate variability in Florida, especially for the fall and winter seasons (Hansen et al., 1999). Generally, a longer period of humid conditions during El Niño is believed to give a more favorable condition for disease occurrence than the neutral condition in Florida (Hildebrand et al., 1999). Moreover, Florida's warm and humid climate is an ideal environment for disease development in tomatoes. The Homestead area is one of the major fresh tomato production areas in Florida (Florida Tomato Committee, 1999) (Figure 3-1). The climate records of this area show that there was 20-30% more precipitation in El Niño and 20-30% less precipitation in La Niña years during the winter season, which is December through February, compared to normal precipitation (Hansen et al., 1999). This winter season is an important growth period in tomato farming in this area (Florida Tomato Committee, 1999).
86
Figure 3-1. The location of the study area in Florida. Weather data used in this study was observed in the Miami International Airport for 51 years (1948-1999) (Florida Tomato Committee, 1999). Tomato early blight, caused by Alternaria solani Sorauer, is economically one of the most important diseases of tomatoes, not only in Florida but also in the USA (Jones et al., 1993). In the tomato early blight pathosystem, more precipitation during El Niño in winter in the Homestead area may cause a longer duration of leaf wetness required for pathogen spore germination. At the same time, more frequent and abundant rainfall may cause lower efficiency of fungicide application and facilitate its weathering. For these reasons, a greater chance of tomato early blight epidemic was hypothesized in El Niño compared to La Niña winters. The objectives of this study were: 1) to verify if and how ENSO affects tomato early blight epidemic and tomato growth in South Florida and 2) to investigate its possible
87 usage to improve management of tomato early blight in the study area, Homestead, Florida. Materials and Methods Foliar Disease and Tomato Growth Model
In Chapter 2, a foliar disease model for tomato early blight, the FODIS-TMEB model, was developed and linked to the tomato growth model, CROPGRO-Tomato. FODISTMEB simulates early blight epidemic as influenced by weather conditions. Sensitivity analyses showed that outputs of the proportion of disease severity (PDS), yield, leaf area index (LAI), and biomass are relatively sensitive to weather effects that include temperature and leaf wetness duration (Table 2-5; Table 2-6). A fungicide persistence model was implemented and linked to FODIS-TMEB (see Chapter 2). The fungicide persistence model simulated fungicide scheduling and dynamic persistence of residues on each of upper and lower tomato canopy layers. Fungicide persistence was influenced by weather conditions (rainfall amount and temperature). FODIS-TMEB and the fungicide persistence model were linked to the CROPGROTomato model and the linked model simulates disease epidemics in each leaf cohort. The model simulates disease development and defoliation, which affect tomato growth by reducing leaf area and mass. Simulation Study
CROPGRO-Tomato linked with FODIS-TMEB was used to analyze disease progress and tomato growth and yield with daily weather data for 51 years (from 1948 through 1998) observed by NOAA (http://www.ncdc.noaa.gov) at Miami International Airport, Florida. The weather data contained 12 years of El Niño, 12 years of La Niña, and 27 years of neutral ENSO phases.
88 To calibrate the cultivar tolerance coefficient and the fungicide efficiency for simulations, disease severity data measured by McGovern (1993) were used. He transplanted tomatoes in September 1991 in Bradenton, Florida, and measured the disease severity 115 days after the transplanting date. During this time period, his field was naturally infected by early blight and he applied a variety of fungicides to test their efficiencies. The final proportion of disease severity was 0.70 for the control without fungicide applications and 0.25 for the chlorothalonil-applied treatment. Based on these measurements, calibrations of the cultivar coefficient and the fungicide efficiency were performed. First, the tomato crop tolerance coefficient was calibrated so that the simulated final proportion of disease severity was approximately 0.70 at 115 days after transplanting when no fungicides were applied. Secondly, the fungicide efficiency was calibrated so that the simulated proportion of disease severity was approximately 0.25 at 115 days after transplanting when weekly chlorothalonil applications were simulated. Yearly simulation outputs of PDS, yield, biomass at maturity, and the integrated LAI were analyzed to analyze if climate variability affected these output variables. Three types of yield were analyzed in this study. Firstly, a potential yield is the simulated yield that was not limited by water or nutrient factors (Ivens et al., 1992). Secondly, an actual yield is the simulated yield that is reduced by early blight epidemic (Ivens et al., 1992). Finally, a marketable yield is the simulated yield that is adjusted by transportation losses and the fraction of culls (FCT) (Messina et al, 2001). Actual yield was not adjusted by FCT because it may include a yield reduction factor due to the early blight epidemic.
89 Messina et al. (2001) analyzed effects of various tomato transplanting dates on marketable yield and concluded the ENSO forecasts have a potential to benefit tomato growers in Florida by selecting different transplanting dates based on the ENSO phase. Similarly, changing transplanting date in terms of the ENSO phase may have a potential to improve disease management if there is a certain range of transplanting date that causes a significant yield loss in each ENSO phase. To test this hypothesis, the yield loss was calculated when the simulation was run using biweekly transplanting dates from September 10 to January 25, classified by ENSO phase, and statistically analyzed with Duncan’s multiple range method that compares sample mean values. Computing Marketable Fruit Yield
Tomatoes are culled when their quality does not meet USDA standards (USDA-AMS, 2001; Sargent, 1997). The CROPGRO-Tomato model simulates the number of mature fruit and their weights. Some of these fruit may not be marketable due to their small sizes. Small fruit in the model may be due to various factors, such as high temperature that decreases fruit, cloudy conditions that reduces photosynthesis, and other factors that may alter relationship between fruit set, fruit growth and development, and photosynthesis processes. In reality, pests and diseases or nutrient imbalance may also result in fruit being “culled” or not marketed. Messina et al. (2001) estimated the FCT in each ENSO phase as 0.37 in El Niño, 0.30 in neutral, and 0.19 in La Niña years. Variations in FCT were due to fruit size, nonuniform maturity or pest and disease epidemic. Simulation analyses generated: 1) the attainable yield without any disease loss, 2) the actual yield when the early blight epidemic was simulated, and 3) the marketable yield when the attainable yield was
90 adjusted by the FCT. In addition, the fraction of yield loss due to the early blight epidemic was calculated in each ENSO phase. Statistical Analysis
Duncan's multiple range method was used to test the significance of all mean values from each output result in terms of the ENSO phase. For the confidence coefficient, α=0.10 was used. For some outputs, probability of each output exceeding any percentage value was plotted to see probability differences for getting an output higher than certain values in each ENSO phase. Results and Discussion Calibration of Coefficients
For the calibration process, tomato growth in Bradenton, Florida in 1991 was simulated with the tomato early blight epidemic and weekly fungicide applications. First, the tolerance of the cultivar used in this study, Sunny, was estimated to be 0.89. This value resulted in the proportion of disease severity value at 115 days after transplanting to be 0.70 without fungicide applications. Secondly, the fungicide efficiency was estimated to be 0.65, which resulted in the proportion of disease severity value at 115 days after transplanting of 0.25. Figure 3-2 shows simulated value of the disease severity value for no fungicide and fungicide application using tolerance coefficient and fungicide efficiency values of 0.89 and 0.65, respectively. Additionally, Figure 3-3 shows the sensitivity of the proportion of disease severity to small changes in these values.
Proportion of disease severity
91
1.0
w/ fungicide application w/o fungicide application
0.8 0.6
w/o fungicide
0.4 w/ fungicide
0.2 0.0 0
20
40
60
80
100
120
140
160
Days after transplanting
Proportion of disease severity
Figure 3-2. Simulated disease severity values in Bradenton, 1991. Fungicide application was turned on and off. Tolerance coefficient and fungicide efficiency values of 0.89 and 0.65 were used, respectively. The proportion of disease severity at 115 days after transplanting was 0.26 with fungicide application and 0.72 without fungicide application. 1.0
w/ fungicide efficiency 0.52 (-20%)
0.8
w/ fungicide efficiency 0.65
-20%
0.6 0.4 +20%
0.2 0.0 0
20
40
60
80
100
120
140
160
Days after transplanting Figure 3-3. Simulated disease severity values in Bradenton, 1991. Fungicide efficiency was changed ±20% from its original value, 0.65. Weekly fungicide applications were simulated.
Proportion of disease severity
92
1.0
w/ tolerance coefficient 0.80 (-10%) w/ tolerance coefficient 0.89 w/ tolerance coefficient 0.98 (+10%)
0.8
-10%
0.6 0.4 0.2 +10%
0.0 0
20
40
60
80
100
120
140
160
Days after transplanting Figure 3-4. Simulated disease severity values in Bradenton, 1991. Tolerance coefficient was changed ±10% from its original value, 0.89. Weekly fungicide applications were simulated. Duration of Tomato Growth and Early Blight Infection
The tomato plant is assumed resistant to the tomato early blight infection until a certain physiological age is reached. In El Niño years, solar radiation is lower in the studied area than in La Niña years (Hansen et al., 1999; Messina et al, 2001), which results in lower temperature. This solar radiation variability influences temperature variability and that indirectly affects physiological aging of tomato. Therefore, tomatoes reached their harvest maturity significantly earlier in La Niña years than in El Niño years due to warmer temperatures. However, differences in date of early blight infection onset and the duration of early blight infection were not significant. Disease Progress
Tomato early blight epidemic in each year was simulated with weekly fungicide applications using observed weather data in Miami for 51 years (1948-1999). To compare the disease progress in each ENSO phase, the final value of proportion of disease severity
93 value (PDS) and the area under the disease progress curve (AUDPC) were calculated for each year’s disease progress. No significances were found among the final PDS values in different ENSO phases (Figure 3-5). However, 7 out of 12 values in El Niño years were above the averaged neutral years’ value and 10 out of 12 values in La Niña years were below the averaged neutral years’ value.
Proportion of disease severity
1.0 0.8 0.6 0.4 El Niño [a] Neutral Averaged [a] La Niña [a]
0.2 0.0 48
58
68
78
88
98
Year Figure 3-5. The distribution of final PDS (Proportion of Disease Severity). Neutral line represents the averaged value over the simulation period. Duncan’s multiple range method did not show any significant differences between El Niño [a] and La Niña [a] years (α=0.10). Simulation analyses were performed with observed weather data in Miami for 51 years (1948-1998). Weekly fungicide applications were simulated.
94 Probability of exceedance showed higher probabilities of having higher PDS in El Niño years than in La Niña years (Figure 3-6). In La Niña years, there was only a 10% chance of having PDS higher than 0.8, but it was 50% in El Niño year. This implied there was higher probability of having more severe disease epidemics in El Niño years than in La Niña years.
Probability of exceedance
1.0
El Niño Neutral
0.8
La Niña
0.6 0.4 0.2 0.0 0.4
0.6
0.8
1.0
Proportion of disease severity Figure 3-6. Probability of simulated tomato early blight PDS exceeding any value with weekly fungicide applications. Seasonal analyses were performed with observed weather data observed in Miami for 51 years (1948-1998).
95 The AUDPC for El Niño years were significantly higher than for La Niña years (Figure 3-7). In El Niño years, 7 out of 12 AUDPC values were higher than neutral, whereas only 2 of 12 AUDPC of La Niña years were higher than neutral. El Niño [a] Neutral [ab] La Niña [b]
AUDPC
25
15
5 48
58
68
78
88
98
Year
Figure 3-7. The distribution of final AUDPC (Area Under the Disease Progress Curve). Neutral line represents the averaged value over the simulation period. Duncan’s multiple range method showed significant differences between El Niño [a] and La Niña [b] years (α=0.10). Simulation analyses were performed with observed weather data observed in Miami for 51 years (1948-1998). Weekly fungicide applications were simulated. Probability of exceedance showed higher probabilities of having higher AUDPC in El Niño years than in La Niña years (Figure 3-8). In La Niña year, there was no probability of having AUDPC higher than 20.0, but it was 50% in El Niño year. As shown in the results of PDS, higher probabilities of having more severe disease epidemics in El Niño years than in La Niña years were implied.
96
Probability of Exceedance
1.0
El Niño Neutral
0.8
La Niña
0.6 0.4 0.2 0.0 5
10
15
20
25
30
AUDPC
Proportion of disease severity
Figure 3-8. Probability of simulated tomato early blight AUDPC exceeding any value with weekly fungicide applications. Seasonal analyses were performed with observed weather data observed in Miami for 51 years (1948-1998). 1.0
1997 (El Niño) 1996 (Neutral) 1998 (La Niña)
0.8
1997
0.6
1996
0.4
1998
0.2 0.0 0
20
40
60
80
100
120
140
Days after transplanting Figure 3-9. Simulated disease progresses in 3 years: 1996 (neutral), 1997 (El Niño), and 1998 (La Niña). Seasonal analyses of tomato early blight were performed with observed weather data in Miami. Weekly fungicide applications were simulated. Three years, which represent one each of three ENSO phases (1996 as neutral, 1997 as El Niño, and 1998 as La Niña year), were selected as examples to visualize differences of the disease progress in each ENSO phase for examples (Figure 3-9). The disease progress
97 in El Niño year (1997) showed a higher disease epidemic and a longer infection period than in La Niña year (1998) throughout the tomato growth season. Yield
El Niño [b] Neutral [a] La Niña [a]
Marketable yield (ton/ha)
6 5 4 3 2 1 0 48
58
68
78
88
98
Year Figure 3-10. The distribution of marketable yields. Neutral line represents the averaged value over the simulation period. Marketable yield was obtained by multiplying the attainable yield by the fraction of culls (Messina et al., 2001). Duncan’s multiple range method showed significant differences between El Niño [b] and La Niña [a] and neutral [a] years (α=0.10). Seasonal analyses were performed with observed weather data in Miami for 51 years (1948-1998). Weekly fungicide applications were simulated. When tomato early blight epidemic and tomato growth in each year were simulated with weekly fungicide applications and the observed weather data in Miami for 51 years (1948-1999), no statistical differences were found for potential and actual yields. However, marketable yield was significantly lower in El Niño years than in other years (Figure 3-10). In La Niña, 7 of 12 marketable yields were higher than neutral, whereas only 3 of 12 marketable yields of El Niño years were higher than neutral. The lower marketable yields in El Niño years were partially due to fewer fruit set in those years in
98 response to 8% lower solar radiation during the reproductive period (Messina et al., 2001). Three types of yields were compared with average values (Figure 3-11). When the yield ratio between 1) the potential and actual yields and 2) the marketable and actual yields were compared (Figure 3-12), the gap between the potential and actual yields was smaller in El Niño years than in La Niña years. This implies that more culls due to early blight may occur in La Niña years than in El Niño years. However, the gap between marketable and actual yields was bigger in El Niño years than in La Niña years. In El Niño years, other factors than early blight disease, such as lower solar radiation, might have contributed more to the culls. 6
Potential
Actual with early blight
Marketable
Yield (ton/ha)
5 4 3 2 1 0
NS
NS
El Niño
NS
NS
A
Neutral
NS
NS
A
La Niña
Figure 3-11. Comparison of three types of simulated yields from the seasonal analyses. Duncan’s method showed that the marketable yields were significantly lower in El Niño years than in other years (α=0.10). Seasonal analyses were performed with observed weather data in Miami for 51 years (1948-1998). Weekly fungicide applications were simulated.
99
1.4 1.3
Potential/Actual Marketable/Actual
Yield Ratio
1.2 1.1 1.0 0.9 0.8 0.7 0.6 El Niño
Neutral
La Niña
Figure 3-12. Yield ratio of simulated potential and marketable yields and simulated actual yield. Each type of yield was averaged from the seasonal analyses. Seasonal analyses were performed with observed weather data in Miami for 51 years (1948-1998). Weekly fungicide applications were simulated. To quantify how much attainable yield was reduced by the epidemic of early blight in each year, a percentage loss was calculated with the following equation: actual yield × 100 yield loss (%) = 1 − potential yield
(3-1)
Calculated percentage yield loss in La Niña years was significantly higher than in El Niño years (Figure 3-13) although the quantified amount of disease development, AUDPC, was significantly higher in El Niño years (Figures 3-7).
100 El Niño [b] Neutral Averaged [b] La Niña [a]
50
Loss (%)
40 30 20 10 0 48
58
68
78
88
98
Year Figure 3-13. The distribution of yield loss due to the tomato early blight epidemics. Neutral line represents the averaged value over the simulation period. Duncan’s multiple range method showed significant differences between El Niño [b] and La Niña [a] years (α=0.10). Seasonal analyses were performed with observed weather data in Miami for 51 years (1948-1998). For a better understanding of higher yield losses in La Niña years, tomato yield and number of fruit in representative years (1997 as El Niño, 1996 as neutral, and 1998 as La Niña) for each of three ENSO phases were compared (Figure 3-13). Since potential values of tomato yield and fruit number were much lower in the El Niño than in the La Niña year, calculated losses due to early blight disease were higher in La Niña than in El Niño year.
101
4 3 2 1
# of fruits at harvest maturity
Yield (ton/ha)
w/ EB
w/o EB
70
w/o EB
5
w/ EB
60 50 40 30 20 10 0
0 E
N
L
E
N
L
Figure 3-14. Simulated differences of tomato yield and number of fruit at harvest maturity between potential value without tomato early blight epidemic (EB) and actual value with EB in terms of ENSO phases (E: El Niño in 1997, N: neutral in 1996, and L: La Niña in 1998). Comparatively, losses due to EB were more severe in La Niña year than in El Niño year. Weekly fungicide applications were simulated. Differences between potential and actual value of the net fruit growth (Figure 3-15) and the number of fruit (Figure 3-16) in each ENSO phase were plotted over a growth period. Both figures illustrated that the relative losses of both outputs were conceivably higher in La Niña year than in El Niño year. Losses in El Niño year were relatively small compared to La Niña year (Figure 3-17).
Net fruit growth differences (Potential-Actual, g/m2/day)
102
80
El Niño Neutral La Niña
60
Neutral
40
La Niña
20 0
El Niño
-20 0
20
40
60
80
100
120
140
Days after transplanting
Number of fruit differences (Potential-Actual, #/m2/day)
Figure 3-15. Differences of daily net fruit mass growth between values without and with tomato early blight epidemic in terms of ENSO phases (El Niño in 1997, neutral in 1996, and La Niña in 1998). Losses due to the disease epidemic more severe in La Niña year than in El Niño year. 40
El Niño Neutral La Niña
30 20
Neutral
10
La Niña
0
El Niño
-10 0
20
40 60 80 100 Days after transplanting date
120
140
Figure 3-16. Differences of daily total number of fruit between values without and with tomato early blight epidemic in terms of ENSO phases (El Niño in 1997, neutral in 1996, and La Niña in 1998). Losses due to the disease epidemic are more severe in La Niña year than in El Niño year.
103 Leaf Area
To compare LAI simulated in each year, the integration of LAI, LAD, from the transplanting date until harvest maturity was calculated. Waggoner and Berger (1986) used this term to estimate amount of yield loss due to defoliations. They defined crop loss in terms of the LAD of a healthy crop and the HAD, which is the integral of LAI of a healthy leaf portion of the diseased crop, as follows: Loss = (1 −
HAD ) LAD
(3-2)
In this study, the leaf area loss was calculated in percentage as follows: actual LAD × 100 Leaf area loss (%) = 1 − potential LAD
(3-3)
A state variable for the healthy leaf area index of the CROPGRO-Tomato model (XHLAI) was used to calculate LAD so the actual LAD replaced HAD in Equation 3-3. Without tomato early blight losses, averaged potential LAD was higher in neutral year but no significant differences were found. With the disease epidemic, averaged actual LAD was lower in El Niño years than in La Niña years but differences were not
significant. Leaf area loss comparing actual LAD with the potential LAD did not show any significant differences (Figure 3-17).
104 El Niño [a] Neutral [a] La Niña [a]
Leaf area loss (%)
40
30
20
10 48
58
68
78
88
98
Year Figure 3-17. The distribution of leaf area loss due to the tomato early blight epidemics. Neutral line represents the averaged value over the simulation period. Duncan’s multiple range method showed no significant differences (α=0.10). Seasonal analyses were performed with observed weather data in Miami for 51 years (1948-1998). Biomass
To analyze biomass changes, the output variable of tops weight at harvest maturity calculated in the CROPGRO-Tomato model was used. Without tomato early blight losses, averaged potential biomass was higher in La Niña years but no significant differences were found. With the disease epidemic, averaged actual biomass was lower in El Niño years than in La Niña years, but were not significant. However, the loss comparing the actual biomass with the potential biomass (Equation 3-4) was significantly higher in La Niña years than in El Niño years (Figure 3-18), as the yield loss was significantly higher in La Niña years (Figure 3-13). actual biomass × 100 Biomass loss (%) = 1 − potential biomass
(3-4)
105 El Niño [b] Neutral [ab] La Niña [a]
Biomass loss (%)
40
30
20
10 48
58
68
78
88
98
Year
Figure 3-18. The distribution of biomass loss due to the tomato early blight epidemics. Neutral line represents the averaged value over the simulation period. Duncan’s multiple range method showed significant differences among years. As a confidence coefficient, 0.90 (α=0.10) was used. Seasonal analyses were performed with observed weather data in Miami for 51 years (1948-1998). Fungicide Applications
Weekly fungicide applications were simulated starting from 7 days after transplanting until two days before harvest. If it rained on a given scheduled day, a fungicide application was postponed to the next day. On average, the number of application times was significantly more in El Niño years than in La Niña years by 1.5 (Table 3-1). This significance was due to the significantly longer tomato growth period in El Niño years (Table 3-1). When the application times were normalized with the tomato growth period, the significance was lost. A risky day was defined as a day on which the amount of fungicide residue simulated for either of lower or upper canopy layer was lower than a certain threshold level. Simulated number of risky days was slightly higher in El Niño years than in La Niña years with no significant differences. Although more precipitation was recorded at the
106 studied area in El Niño years (Hansen et al., 1999), leaves were well protected with the fungicide’s enhanced stickiness (Patterson and Nokes, 2000).
Table 3-1. Summary of statistical test results analyzed with Duncan’s method for parameters used in Chapter 3. As a confidence coefficient, 0.90 (α=0.10) was used. Means with the same letter from Duncan’s method are not significantly different. ENSO Statistically tested parameter Method El Niño Neutral La Niña Tomato growth period Average 142.33 139.26 134.67 (days) Std. 9.77 11.05 10.60 Duncan a a / / / b b Infection period Average 88.33 85.63 84.50 (days) Std. 7.66 8.28 7.72 Duncan a a a Final PDS Average 0.71 0.66 0.59 Std. 0.22 0.18 0.15 Duncan a a a AUDPC Average 17.00 15.54 13.28 Std. 6.15 5.26 2.89 Duncan a a / / / b b Potential yield Average 3864 4741 4731 (kg/ha) Std. 1546 1478 1565 Duncan a a a Actual yield Average 3281 3919 3723 (kg/ha) Std. 1308 1231 1406 Duncan a a a Marketable yield Average 2434 3319 3832 (kg/ha) Std. 974 1035 1268 Duncan b a a Yield loss due to disease Average 14.52 17.00 21.98 (%) Std. 5.36 5.84 6.99 Duncan b b a Potential LAD Average 100.27 112.71 100.07 Std. 45.62 34.74 48.70 Duncan a a a
107 Actual LAD
Leaf area loss (%) Potential biomass (kg/ha) Actual biomass (kg/ha) Biomass loss (%)
Fungicide application times (times)
Riskydays (days)
Average Std. Duncan Average Std. Duncan Average Std. Duncan Average Std. Duncan Average Std. Duncan / Average Std. Duncan / Average Std. Duncan
77.24 35.35 a 23.18 5.88 a 5489 1956 a 4346 1588 a 20.92 5.34 / b 16.17 1.47 a / 10.08 2.50 a
87.52 27.98 a 22.66 4.88 a 6431 1811 a 5062 1481 a 21.42 4.33 a b 15.59 1.53 a b 11.26 6.15 a
79.07 41.06 a 21.72 4.95 a 6221 2121 a 4748 1773 a 24.16 5.39 a / 14.92 1.56 / b 8.58 2.07 a
Transplanting Date
With various transplanting dates, potential yields (Figure 3-22) and actual yields (Figure 3-23) were simulated. For each transplanting date, yield losses due to tomato early blight (Equation 3-1) relative to potential yields were calculated (Figure 3-24). Based on the mean value comparison with Duncan’s method, significant yield losses due to the early blight epidemic in La Niña years occurred when transplanting was done from 328 through 343 day of each year. That period included most of the typical transplanting date, which is from 310 through 340 day of year, in the study area (Florida Tomato Committee, 1999). In order to reduce possible yield loss due to early blight epidemics, a
108 later transplanting date than usual within the typical transplanting date may be helpful without losing potential yield in both of El Niño and La Niña years.
Potential yield (ton/ha)
5
El Niño Neutral La Niña
4 3 2 1 237
253
268
283
298
313
328
343
359
11
25
Transplanting Date (Day of the year) Figure 3-21. Averaged attainable yield for each ENSO phase with biweekly transplanting date varying in Miami for 51 years (1948-1998). Typical transplanting date is indicated with a line (Florida Tomato Committee, 1999).
Actual Yield (ton/ha)
5
El Niño Neutral La Niña
4 3 2 1 0 237
253
268
283
298
313
328
343
359
11
25
Transplanting Date (Day of the year) Figure 3-22. Averaged actual yield for each ENSO phase with biweekly transplanting date varying in Miami for 51 years (1948-1998). Typical transplanting date is indicated with a line (Florida Tomato Committee, 1999).
109
Yield loss (%)
40
El Niño Neutral La Niña
30 20 10 0 237
253
268
283
298
313
328
343
359
11
25
Transplanting date (day of the year) Figure 3-24. Averaged yield loss for each ENSO phase with biweekly transplanting date varying in Miami for 51 years (1948-1998). Typical transplanting date is indicated with a line (Florida Tomato Committee, 1999). Losses caused by early blight in La Niña years were significantly higher on date 328 (Nov. 24) and 343 (Dec. 9). Conclusion
Seasonal analyses with the CROPGRO-Tomato model linked with the tomato early blight model and the fungicide persistence model were performed for Miami, Florida. A total of 51 years of observed weather data were categorized into three phases of ENSO and used in the simulation analyses to find significant impacts of climate variability on early blight development and tomato production in each phase. Output variables for the simulation analyses included: tomato growth period, the final value of the proportion of disease severity (PDS), the area under the disease progress curve (AUDPC), yield, leaf area index (LAI), and biomass. Duncan's multiple range method was used to test the significance of the results (α=0.10). Tomato growth period was significantly longer in El Niño years than in La Niña years. The longer growth period was consequently related to: 1) significantly 1.5 more
110 fungicide applications in El Niño years as weekly applications were simulated and 2) significantly higher AUDPC in El Niño years since the longer tomato growth period provided the more time for early blight disease to develop. Probability of exceedance graph for PDS showed the probability of occurrence of severe tomato early blight epidemic is higher in El Niño than in other years. Analyses on different types of yield (potential, actual, and marketable yields) implied more factors other than early blight epidemic causes culls in El Niño years than in La Niña years, such as lower solar radiation during tomato productive. Fruit quality was not simulated in this study, but the effects of tomato early blight development on fruit may have also contributed to the culls. The yield and biomass losses were significantly higher in La Niña years, and lower potential yield and biomass in El Niño years contributed comparatively lower losses than in La Niña years. Overall simulated results that suggested higher probability of more severe tomato early blight epidemic in El Niño and higher yield loss in La Niña years. Given the results, the following discussions may benefit tomato farmers in the study area to improve management of tomato early blight. In El Niño years: 1) Farmers should be aware of tomato early blight epidemic. More tolerant tomato varieties should be selected. 2) Farmers should be aware of lower potential of tomato yield. They may be advised to plant more acreage to compensate for this situation. 3) As more fungicide applications are expected, farmers may be advised to acquire extra amounts of fungicide. 4) If market prices permit, later transplanting date within typical range may be helpful to increase potential yield and decrease losses due to early blight. And, in La Niña years: 1) Farmers
111 should be aware of higher potential of yield and higher probability of yield losses due to diseases. 2) Farmers should expect shorter tomato growth season. 3) Later transplanting date may be helpful to reduce probability of losing yield due to early blight disease.
CHAPTER 4 SUMMARY AND CONCLUSIONS This study presented the development of the tomato early blight epidemic model FODIS-TMEB and its application to study climate variability impacts on tomato disease management and production. Based on a systems analysis of the tomato early blight pathosystem, FODIS-TMEB was developed as a module and coupled with the crop growth model, CROPGROTomato. The linked model simulated interrelationships between disease development and its impacts on crop growth and yield. A fungicide persistence model was also developed and linked to simulate fungicide applications and its residue persistence that directly affected the basic infection rate in each cohort. The potential of the FODIS-TMEB model can be used to implement other foliar disease pathosystems. Parts of the FODIS-TMEB model that described tomato early blight infection were converted to describe the peanut late leafspot infection to verify the validity of this disease model structure and to test its portability for simulating another foliar pathosystem. The resulting FODIS-PNLS model predicted disease progresses as accurately as the old model, LATESPOT, did. However, a different approach was adopted to introduce inoculums into the infection process in FODIS from LATESPOT. Details on differences and consequences of two approaches were not analyzed in detail in this study, but may provide an insight to generalization of disease model structure in further studies.
112
113 Seasonal analyses with the CROPGRO-Tomato model linked with the tomato early blight model and the fungicide persistence model were performed for Miami, Florida. A total of 51 years of observed weather data were used in the simulation analyses to find significant impacts of climate variability on early blight development and tomato production in each ENSO phase. Duncan's multiple range method showed significances in: 1) longer tomato growth period in El Niño, 2) higher AUDPC in El Niño, 3) lower marketable yield in El Niño, 4) higher yield loss in La Niña, 5) higher biomass loss in La Niña, and 6) more fungicide applications in El Niño. Given simulated results in this study, possible strategies in terms of an ENSO phase were discussed. In El Niño years: 1) more tolerant tomato varieties should be selected, 2) farmers may be advised to plant more acreage to compensate for lower potential yield, 3) farmers may be advised to acquire extra amounts of fungicide, and 4) later transplanting date within typical range may be helpful to increase potential yield and decrease losses due to early blight. Likewise, in La Niña years: 1) farmers should be aware of higher probability of yield losses, 2) farmers should expect a shorter tomato growth season, and 3) a later transplanting date may reduce the probability of losing yield due to early blight disease. Further studies are needed to analyze the economic values and risks of using ENSO-based climate forecasts.
APPENDIX A TECHNICAL ASPECTS OF INTERCHANGING VALUES OF VARIABLE In the modularized CROPGRO v3.7, there is one main driver program that controls initializations, rate calculations, and integrations in each module as well as communications between all components. This top-down structure for modules requires a cumbersome way to communicate between two individual modules. Let's assume that: 1) there is a main driver, which is connected to sub-modules from SubOneA through SubFourB, 2) SubFourA module has a state variable, SVA (Figure A-1). If the SubFourB
module needs to know the value of SVA calculated in the SubFourA module, all the upper level modules, including the main driver, also need to know the value of SVA to pass it to SubFourB although the other modules does not need to use this variable at all. This problem gets more problematic when the value of a variable must pass through a number of modules because, each of those modules would need to be modified. As the FODIS model was modularized and coupled with the CROPGRO-Tomato model, many variables from other modules, such as PLANT, GROW, IPWTH, etc, were needed. So, a number of modifications were necessary in these modules as well as their upper level modules and the main driver. To avoid these many modifications, a new approach was developed with the third module, named as a messenger module (Figure A2). The messenger module is a small module that can be called from any other module including its ‘mother’ module. In the above example, SubThreeA would be a mother module. With an identifier, the Messenger module distinguishes which module calls it 114
115 whenever other module calls it. When its mother module calls it with the value of SVA, the Messenger module stores its value in a temporary variable. When any other module, SubFourB in this case, calls this Messenger module, it returns the value of SVA, which
was temporarily stored. Using this way, modifications to the CROPGRO source code could be minimized. Main Driver
SubOneA
SVA
SubTwoA SVA SubThreeA
SubOneB
SVA SubFourA
SubTwoB
SVA
SVA SubThreeB
SVA
SVA SubFourB SVA SVA
Figure A-1. An example of top-down module structure. The main driver has two submodules and each of them has chained sub-sub-modules. The value of SVA is calculated in SubFourA module. When SubFourB module needs to know the value of SVA, all other upper modules as well as the main driver need to know that value.
116
Main Driver
SubOneA SubTwoA SubThreeA
SubOneB
SubFourA
SubTwoB SubThreeB
SVA SubFourB Messenger
SVA SVA
Figure A-2. An example of top-down module structure with the messenger module to pass a variable. With the use of messenger module, all other modules do not need to be modified.
APPENDIX B TECHNICAL ASPECTS OF SENDING FEEDBACKS TO THE CROPGRO MODEL As discussed in Chapter 2, two coupling points of CROPGRO were used to send feedback from FODIS. In this appendix, technical aspects of updating these coupling points or variables are described. TDLA
CROPGRO PLANT (1)
PEST
(2)
PEST_TM
(3)
PT_TMEB
(4)
(5)
(7)
PESTCP
(6)
MESSENGER
VEGDM
Figure B-1. Diagram of interacting modules to feed back simulated value of TDLA from FODIS to CROPGRO. Descriptions of each numbered steps are given in text. The TDLA coupling point represents the total diseased leaf area on a given day (cm2[leaf area]/m2[ground area]/day) (Boote et al., 1983; Batchelor et al., 1993). In CROPGRO, TDLA is updated from an input data file in the PESTCP module. In order to 117
118 use this variable as a coupling point, FODIS model calculates the diseased leaf area and updates TDLA prior to the CROPGRO’s updating it in PESTCP module. Figure B-1 illustrates a chronological flow of using this coupling point to send TDLA to CROPGRO: (1) PLANT module calls PEST module if there is pest damage to apply to crop growth, (2) PEST module calls PEST_TM module if simulating tomato, (3) PEST_TM module calls PT_TMEB module if there is tomato early blight damage, (4) PT_TMEB module calculates daily amount of diseased leaf area due to tomato early blight and sends its value to the MESSENGER module, (5) PEST module calls PESTCP module to update coupling points variables, (6) PESTCP module calls MESSENGER module to update TDLA with the diseased leaf area calculated in PT_TMEB module, and (7) PEST module calls VEGDM to calculates the reduction in vegetative variables and the value of TDLA is used to update DISLA, which is then used to reduce healthy leaf area in GROW module. WLIDOT
The WLIDOT is a CROPGRO coupling point representing daily pest or freeze damage to leaf mass (g[leaf mass]/m2[ground area]/day) (Boote et al., 1983; Batchelor et al., 1993). This variable was used to apply defoliated leaf damage, which is not included in TDLA variable. In CROPGRO, WLIDOT is updated using an input data in the VEGDM
module. FODIS model calculates the defoliated leaf mass due to early blight and updates WLIDOT after the CROPGRO’s updating it in VEGDM module.
Figure B-2 illustrates a chronological flow of using this coupling point to send a feedback: (1) PLANT module calls PEST module if there are pest damages to apply to crop growth, (2) PEST module calls PEST_TM module if the crop simulating is tomato, (3) PEST_TM module calls PT_TMEB module if there is a tomato early blight damage,
119 (4) PT_TMEB module calculates daily amount of defoliated leaf mass due to tomato early blight and stores its value to MESSENGER module, (5) PEST module calls PESTCP module to update coupling points variables, (6) PEST module calls VEGDM to calculates the reduction in vegetative variables, and (7) VEGDM module calls MESSENGER to update WLIDOT with the defoliated leaf mass calculated in PT_TMEB module. CROPGRO PLANT (1)
PEST
(2)
PEST_TM
(3)
PT_TMEB
(4)
(5)
(6)
PESTCP
VEGDM
MESSENGER
(7)
Figure B-2. Diagram of interacting modules to feed back simulated value of WLIDOT from FODIS to CROPGRO. Descriptions of each numbered steps are given in text.
APPENDIX C IMPLEMENTING CROP CULTIVAR TOLERANCE The Tolerance Framework
In Plant Pathology, the tolerance is the inherent ability of a plant to endure the effects of a disease without dying or suffering serious injury or crop loss (Agrios, 1997). Not only for a plant disease, a crop cultivar may also have tolerances to many other damaging factors that could result in yield losses, such as drought, flood, nematode, weed, etc. In order to implement the effects of crop tolerance to these damaging factors in CROPGRO, Batchelor (unpublished) proposed a framework of applying the cultivar tolerance. The tolerance framework consists of: (1) the tolerance coefficient input from the *.TOL file, (2) modifications to the experiment input (*.TMX) file, (3) IPTOL module that reads the tolerance coefficient from the TOL file, and (4) modifications on CROPGRO source code that calculates quantified damage. Figure C-1 illustrates the information flow of tolerance coefficient within the framework. Description of each numbered step is given in Table C-1.
120
121
CROPGRO (1)
IPIBS
(2)
*.INP Experiment input file
PLANT
(3)
(4)
PEST
PEST_TM (5)
IPTOL (6)
(7)
*.TOL Cultivar tolerance input file
PESTCP (8)
VEGDM (9)
Figure C-1. Diagram of the tolerance framework: two input files (EXP and TOL) and four new modules linked to the PEST module. Descriptions of each numbered steps are given in text.
Table C-1. Description of steps illustrated in Figure C-1. Step Description (1) IPIBS module reads the CROPGRO experiment input file (*.INP) (2) The input file (*.INP) has a section that indicates i) which type of damage (e.g. name of disease or pest) and ii) when to occur (described in a later section) (3) The main driver, CROPGRO, calls PLANT module (4) PLANT module calls PEST module with the pest initial condition data (5) PEST module calls PEST_TM module if the crop simulating is tomato (6) PEST_TM module calls IPTOL module (7) IPTOL module reads the corresponding tolerance coefficient from the tolerance input file (*.TOL) (8) PESTCP module updates coupling points variables (9) VEGDM module calculates the reduction in vegetative variables
122 Tolerance Coefficient Input File
The tolerance coefficient input file (*.TOL) has tolerance coefficients for each cultivar and each type of damage (Figure C-2). Coefficients have a 0 to 1 scale in which the most tolerant cultivar was assumed to have a tolerance coefficient of 1.00 and the most susceptible was assumed to have a value of 0.00. Up to twenty type of damage can be added in the same format. If the tolerance coefficient for the corresponding type of damage is not found in this file, IPTOL module assumes the cultivar is susceptible for that type of damage. *CROP VARIETY SUSCEPTIBILITY COEFFICIENTS
! COEFF
DEFINITIONS
! ========
===========
! VAR#
Identification code or number for a specific cultivar.
! VAR-NAME
Name of cultivar
! !
Relative resistance to...
!
--------------------------------------
!
0 = No resistance, 1 = Full resistance
!
--------------------------------------
! SCN1
Soybean cyst nematode to photosynthesis (Type I)
! SCN2
Soybean cyst nematode to root water uptake (Type II)
! RIZC
Relative resistance to Rizoctonia
! CHLR
Chlorosis
! BSTR
Brown stem rot
! EMRG
Emergence hardness(cold & wet)
! DRGT
Drought
! WLOG
Excessive water
@
VAR# VAR-NAME....... RIZC SCN1 SCN2 CHLR BSTR EMRG DRGT WLOG IB0001 BRAGG (7)
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
IB0002 COBB (8)
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Figure C-2. An example tolerance coefficient file for soybean, SBGRO980.TOL. Coefficient values are not defined.
123 Experiment Input File
Experimental input information are defined in X-file (i.e. *.TMX for tomato) and then interpreted into INP file (*.INP). In order to implement the tolerance coefficient, one section (A in Figure C-3) and a factor level identifier for this section (B in Figure C-3) were added in the X-file format. The new section, PEST INITIAL CONDITION, defines input to apply damages and tolerances for each treatment level. Description of each column in data (from line three) is given in Table C-2. *EXP.DETA ILS: U FGA780 1SB HA RTZ, S ENSITI VITY
*GENERAL
A
@PEOPLE BOOTE,K. J. JON ES,J.W . HAMM OND,L. C. @ADDRESS UNIVERSI TY OF FLORID A, GAI NESVIL LE, FL , USA
---------------FACTOR LEVELS-------------
@SITE CRESTVIE W, GEO RGIA, FIELD1 0
*TREATMEN TS
CU FL SA IC MP MI MF MR MC MT ME MH SM PT
---- ------ -----F ACTOR LEVELS ------ ------ -
@N R O C TNAME. ...... ...... ...... . CU F L SA I C MP M I MF M R MC M T ME M H SM P T 1 1 0 0 RITCHI E
1
1
0
5
1
0
0
0
0
0
0
0
1
1
2 1 0 0 SAXTON
1
2
0
5
1
0
0
0
0
0
0
0
1
2
1
1
0
5
1
0
0
0
0
0
0
0
1
1
1
2
0
5
1
0
0
0
0
0
0
0
1
2
*CULTIVAR S @C CR ING ENO CN AME 1 SB 990 007 HA RTZ
*FIELDS @L ID_FIE LD WST A....
FLSA
1 GA2300 04 GA2 39801 -99.0 2 GA2300 05 GA2 39801 -99.0
FLOB
FLDT
0 DR000 0 DR000
FLDD
FLDS
FLST SLTX
SLDP ID_SOI L
0
0 00000 LOSA
180 IBSB91 0501
0
0 00000 LOSA
180 IBSB91 0502
@L ...... .....X CRD .. ...... ...YCR D .... .ELEV ...... ...... .AREA .SLEN .FLWR .SLAS 1
0.00 000
0.0000 0
0.00
0.0
0
0.0
0.0
2
0.00 000
0.0000 0
0.00
0.0
0
0.0
0.0
*INITIAL CONDIT IONS @C
PCR ICDAT
5
ICND
ICRN
ICRE
1.00
1.00 -99.0
-99
-99
SH2O
SNH4
SNO3
5
30 0.129
0.0
0.0
5
60 0.124
0.0
0.0
5
90 0.115
0.0
0.0
5
120 0.165
0.0
0.0
@C
BA 98161
ICRT
ICBL
ICWD ICRES ICREN ICREP ICRIP ICRID 0
0.00
0.00
0
0
B
@T DATE
*PLANTING DETAI LS @P PDATE EDATE 1 98161
-99
PPOP
PPOE
PLME
PLDS
PLRS
PLRD
PLDP
PLWT
29.9
29.9
S
R
45
0
4.0
-99
PAGE
PLPH
SPRL
-99 -99.0 -99.0
PENV
0.0
*PEST INI TIAL C ONDITI ON @T DATE
PSTT P VAL..
1 79180 SCN1
25000
1 79180 SCN2
25000
1 79220 RIZC
0.50
2 79180 SCN2
1000
*SIMULATI ON CON TROLS @N GENERA L 1 GE @N OPTION S 1 OP @N METHOD S 1 ME @N MANAGE MENT 1 MA @N OUTPUT S 1 OU
@
NYERS NREPS START SDATE RSEED SNAME. ...... ...... ...... . 1
1
S 98148
N
N
N
N
N
CHEM
TILL
N
N
WTHER INCON LIGHT EVAPO INFIL PHOTO HYDRO M
M
E
R
PSTT PVAL..
1 79180 SCN1
25000
1 79180 SCN2
25000
1 79220 RIZC
0.50
2 79180 SCN2
1000
2150 HARTZ, SWCME THODS
WATER NITRO SYMBI PHOSP POTAS DISES Y
*PEST INITIAL CONDITION
S
L
R
PLANT IRRIG FERTI RESID HARVS R
R
N
N
M
FNAME OVVEW SUMRY FROPT GROUT CAOUT WAOUT NIOUT MIOUT DIOUT N
Y
Y
3
Y
N
Y
N
N
N
LONG CHOUT OPOUT Y
N
N
AUTOMA TIC MA NAGEME NT
@N PLANTI NG 1 PL @N IRRIGA TION 1 IR @N NITROG EN 1 NI @N RESIDU ES 1 RE @N HARVES T 1 HA
PFRST PLAST PH2OL PH2OU PH2OD PSTMX PSTMN 98148 98173
40
100
30
40
5
IMDEP ITHRL ITHRU IROFF IMETH IRAMT IREFF 30
50
100 GS000 IR001
10
0.75
NMDEP NMTHR NAMNT NCODE NAOFF 30
50
25 FE001 GS000
RIPCN RTIME RIDEP 100
1
20
HFRST HLAST HPCNP HPCNR 0 98301
100
0
Figure C-3. An example experiment input data file for soybean with modified sections to implement the tolerance coefficient to CROPGRO.
Table C-2. Description of input data used in PEST INITIAL CONDITION section (B in Figure C-3) to apply damages and tolerances. Header Description T The treatment level number defined for each experiment.
124 DATE PSTT PVAL
The date when a specific damage occurs. The type code of damage occurs on a crop. Must be same as the code appeared on the tolerance coefficient file. The initial value for each type of damage
Modifications of CROPGRO Source Code
Depending on the type of damage to apply, certain state variables as well as coupling point variables can be modified or coupled with the tolerance coefficient. Equation C-1 shows an example code for applying damages to the root water uptake, where RWUDAM1 is the damage rate of root water uptake due to Rhizoctonia disease and RIZC_TOLR is the tolerance coefficient. Source code for IPTOL module is given in
Appendix D. RWUDAM1 = RWUDAM1 X (1-RIZC_TOLR)
(C-1)
Use of tolerance framework in FODIS to implement cultivar resistance
The FODIS model adopted the tolerance framework to apply the crop cultivar resistance for each pathosystem. Figure C-3 shows the tolerance coefficient file and Figure C-4 shows the experiment input file used in this study. Details about tolerance input data in the experiment file are given in Table C-3. Details about implementing the tolerance coefficient on calculating a basic infection rate to apply the cultivar resistance are explained in Chapter 2.
125 *CROP VARIETY SUSCEPTIBILITY COEFFICIENTS
! COEFF
DEFINITIONS
! ========
===========
! VAR#
Identification code or number for a specific cultivar
! VAR-NAME
Name of cultivar
! !
Relative resistance to...
!
--------------------------------------
!
0 = No resistance, 1 = Full resistance
!
--------------------------------------
! TMEB
Tomato early blight
@
VAR# VAR-NAME....... TMEB 990001 Tomato Example
0.78
TM0001 SUNNY S-D.
0.92
Figure C-4. Tomato cultivar tolerance coefficients file used in this study, TMGRO980.TOL. *EXP.DETA ILS: U FFP980 1TM
*GENERAL @PEOPLE
@NOTES
*TREATMEN TS
---- ------ ---FAC TOR LE VELS-- ------ ------ -
@N R O C TNAME. ...... ...... ...... . CU F L SA I C MP M I MF M R MC M T ME M H SM P T 1 1 0 0 SUNNY SUBIRR IGATED
1
1
0
1
1
0
0
0
0
0
0
0
1
1
*CULTIVAR S @C CR ING ENO CN AME 1 TM TM0 001 SU NNY S- D.
*FIELDS @L ID_FIE LD WST A....
FLSA
1 UFBR00 01 UFF P9801
0.0
FLOB
FLDT
0 000
FLDD 0
FLDS
FLST SLTX
0 0000
SA
SLDP ID_SOI L 180 UFBR95 0001
@L ...... .....X CRD .. ...... ...YCR D .... .ELEV ...... ...... .AREA .SLEN .FLWR .SLAS 1
0.00 000
0.0000 0
0.00
0.0
0
0.0
0.0
*INITIAL CONDIT IONS @C
PCR ICDAT
1 @C
ICRT
ICND
ICRN
ICRE
1
0
1.00
1.00 -99.0
SNH4
SNO3
PR 98068 ICBL
SH2O
1
7 0.086
0.2
3.0
1
15 0.086
0.1
0.1
1
22 0.086
0.0
0.1
1
30 0.086
0.0
0.0
1
45 0.086
0.0
0.0
1
90 0.076
0.0
0.0
1
120 0.076
0.0
0.0
1
150 0.130
0.0
0.0
1
180 0.258
0.0
0.0
ICWD ICRES ICREN ICREP ICRIP ICRID 0
0.00
0.00
100
15
*PLANTING DETAI LS @P PDATE EDATE 1 98077
PPOP
PPOE
PLME
PLDS
PLRS
PLRD
PLDP
PLWT
PAGE
PENV
PLPH
SPRL
1.0
1.0
T
R
137
90
1.0
3
28
25.0
1.0
0.0
*PEST INITIAL CONDITION
*PEST INI TIAL C ONDITI ON @T DATE
PSTT P VAL..
A1 98080 TMEB
0.25
@T DATE
*SIMULATI ON CON TROLS @N GENERA L 1 GE @N OPTION S 1 OP @N METHOD S 1 ME @N MANAGE MENT 1 MA @N OUTPUT S 1 OU
@
NYERS NREPS START SDATE RSEED SNAME. ...... ...... ...... . 1
1
S 98077
N
N
N
N
N
Y
CHEM
TILL
N
N
1 98080 TMEB
WTHER INCON LIGHT EVAPO INFIL PHOTO HYDRO M
M
E
R
PSTT PVAL.. INMOD
2150 POTENT IAL YI ELD
WATER NITRO SYMBI PHOSP POTAS DISES
S
L
0.25
A
R
PLANT IRRIG FERTI RESID HARVS R
R
R
N
M
FNAME OVVEW SUMRY FROPT GROUT CAOUT WAOUT NIOUT MIOUT DIOUT N
Y
Y
1
Y
N
Y
Y
N
N
LONG CHOUT OPOUT Y
N
N
AUTOMA TIC MA NAGEME NT
@N PLANTI NG 1 PL @N IRRIGA TION 1 IR @N NITROG EN 1 NI @N RESIDU ES 1 RE @N HARVES T 1 HA
PFRST PLAST PH2OL PH2OU PH2OD PSTMX PSTMN 98 77 98 77
40
100
30
45
40
IMDEP ITHRL ITHRU IROFF IMETH IRAMT IREFF 45
50
100
-99 SI001
10
0.75
NMDEP NMTHR NAMNT NCODE NAOFF 200
50
25 SI001 SI001
RIPCN RTIME RIDEP 100
60
20
HFRST HLAST HPCNP HPCNR 98139 98162
75
0
Figure C-5. An added section in the experiment input file to implement the tolerance coefficient to CROPGRO.
126 Table C-3. Description of input data used in PEST INITIAL CONDITION section (B in Figure C-4) to apply damages and tolerances. Header Description T Treatment level number defined for each experiment. DATE Date when a specific damage occurs. When A option is chosen in INMOD, this date will not be used in simulation. PSTT Type code of damage occurs on a crop. TMEB is used for tomato early blight pathosystem. PVAL Initial value for each type of damage. This value is used to calculate initial amount of infected leaf area in this study in percentage scale. INMOD Switch to choose how the infection process begins. A Infection will be automatically begun when the environmental conditions are favorable enough. M Infection will be begun only after the given date.
APPENDIX D FODIS FILES WRITTEN IN FORTRAN Based on CROPGRO v3.7 (Porter and Jones, 2000), seven files were created and eight files were modified in this study (Table D-1). Those created seven files were included below. Full source code includes modified or inserted portion can be downloaded from the Internet (http://csml.ifas.ufl.edu/jawoo/fodis). Description of variables used in these files is given in Appendix E.
Table D-1. List of files written or modified in this study. File Name
Description
PT_TMEB.FOR
Tomato early blight model
PT_PNLS.FOR
Peanut late leafspot model
FCIDE_CT2L.FOR Fungicide (chlorothalonil) persistence model PEST_TM.FOR
Couples tomato pest models
PEST_PN.FOR
Couples peanut pest models
GetNDLA.FOR
Read NDLA from an input data file
MESSENGER.FOR Pass a value of variable between two different modules
PT_TMEB.FOR C======================================================================= C PT_TMEB: Tomato early blight module C----------------------------------------------------------------------C Called by: Pest_TM C Calls: GetNDLA C C UpdateTDLA_TMEB .. to pass TDLA for PESTCP C UpdateWLIDOT_TMEB .. to pass WLIDOT for VEGDM C GetDAP .. to get DAP from PEST C GetF .. to get F from DEMAND C GetGROW .. to get AREALF,CLW,WLDOTN,SLA,WTLF C from GROW C GetHMET .. to get TAIRHR,RHUMHR from HMET C GetRAIN .. to get RAIN from IPWTH C GetRSD .. to get RSD from FCIDE_CT2L C GetTAVG .. to get TAVG from HMET
127
128 C Outputs: TDLA, WLIDOT .. through the messenger modules C======================================================================= SUBROUTINE PT_TMEB( & PST_STRT, PST_INIT, PST_TOLR, YRDOY, !Input & DYNAMIC) !Control !----------------------------------------------------------------------IMPLICIT NONE !----------------------------------------------------------------------INTEGER RUNINIT, SEASINIT, EMERG, RATE, INTEGR, OUTPUT, FINAL PARAMETER (RUNINIT = 1, SEASINIT = 2, EMERG = 3, RATE = 3, & INTEGR = 4, OUTPUT=5, FINAL = 6) INTEGER DYNAMIC
!
// INPUT REAL AREALF, WLDOTN, F, PST_INIT, SLA, PST_TOLR, WTLF CHARACTER*1 PST_STRT
!
// DATE INTEGER YRDOY, YY, YRSIM
!
// WEATHER DATA REAL RHUMHR(24), TAIRHR(24), TAVG, RAIN INTEGER RAINYHRS, LWD, RLWD
!
// FUNGICIDE REAL RSD(2), RSD1_EX REAL LAISL, AREASL, AREASL_CH REAL FCIDECFF, FC_EFF INTEGER FCIDEDAT, UPPER_CH
!
// MANAGING COHORT INTEGER CH, I, J, H, L, ONSET_AGE, DAP REAL COHORT(200,82) REAL D_VAC(200), D_LAT(200,40), D_INF(200,40), D_POS(200) REAL INI_INF(200), INI_LAT(200) REAL STR_SLA(200), CLW REAL COHORT_PAGE(200), COHORT_TOTAL(200), COHORT_VAC(200) REAL COHORT_LAT(200), COHORT_INF(200), COHORT_POS(200) REAL COHORT_ABSCISED(200), COHORT_DISEASED(200) REAL LOST_AREA, COHORT_TOTAL_AS(200), COHORT_TOTAL_AD(200) LOGICAL EMERGENCE, EMERGENCE_CH(200), DIS_PLANT, ISONSET
!
// DEFOLIATION CONTROL REAL OL_RATE, DL_RATE_CH(200), DL_AREA_CH(200) REAL WLIDISDOT_CH(200), WLIDISDOT_TOTAL(200), WLIDISDOT REAL DEFOL(200), PDS(200), DEF_AREA(200), DEF_MASS(200) REAL DEF_AREA_TOTAL(200), PDS_TOTAL REAL EX_DEF_MASS, EX_DEF_AREA REAL NDLA, NDLA_CH(200)
!
// PROPORTION OF DISEASE SEVERITY (PDS) CALCULATION REAL Y_DEF, Y_VISIBLE, TDISLA_FD REAL Y_DEF_CH(200), Y_VISIBLE_CH(200), TDISLA_CH
!
// AGE CALCULATION REAL DTX, P_AGE INTEGER AGE
!
// LATENT AND INFECTIOUS PERIOD, DISEASE EFFICIENCY INTEGER LS, LE, IS, IE, RH93HRS, HR REAL MIN_LP, MIN_FP, LP, FP, INT_SS REAL INI_DIS, INF_CH, FLCINF_CH(200)
129 REAL K_LAT, K_INF, RMAX, R(200) REAL F_LW, F_TMP, F_ENV, FAV_LEVEL
!
// LESION EXPANSION AND MULTIPLICATION FACTOR FOR THE AREA OF HALO REAL KLEX, AREA_EXP, HALO
!
// TOTAL AREA CALCULATION FOR ALL COHORTS REAL SUM_VAC(200), SUM_LAT(200), SUM_INF(200), SUM_POS(200) REAL SUM_DISEASED(200), SUM_AREA(200), SUM_CH
C*********************************************************************** C*********************************************************************** C Run Initialization - Called once per simulation C*********************************************************************** IF (DYNAMIC.EQ.RUNINIT) THEN ! ! !
// READING AND ASSIGNING INPUT PARAMETERS .. Minimum latent period is set to 3 days. .. Ref: Johnson & Teng, 1990, P.419 (Fig.4) MIN_LP = 3.0
! !
.. Minimum infectious period. .. Ref: Rotem, 1994, P.72 MIN_FP = 14
!
.. Estimated from calibrating with observed data RMAX = 9.85
!
.. Interval between showing symptom and sporulation INT_SS = 5.0
! !
.. Susceptible plant physiological age. .. Younger than this p.age is more resistance. ONSET_AGE = 50.5
! !
.. Physiological age at the transplanting date .. when the plant is assume to be transplanted at 25-day old. P_AGE = 20.38
!
.. Initial ID of cohort. CH = 1
! ! !
.. Defoliation due to disease. .. Integrated to WLIDOT, .. the global coupling point for leaf weight reduction. WLIDISDOT = 0.0
!
.. Disease Severity Value for whole plant. PDS_TOTAL = 0.0
! ! !
.. Diseased leaf area. .. Integrated into DISLA, .. the global coupling point for healthy leaf area reduction. TDISLA_FD = 0.0
! !
.. Favorable level of DE which initiates infection process .. Arbitrary set to 0.50 FAV_LEVEL = 0.50
! ! !
.. Multiplication factor for halo area surrounding lesion. .. Moved from GROW module into each disease specific module. .. Existing value is used. HALO = 2.0
!
.. Yesterday's fungicide residue level for the upper layer. RSD1_EX = 0.0
!
.. Fungicide(Chlorothalonil)'s efficiency (1.0 is the best).
130 ! !
.. Here set to average PDS for years to be approximately 0.25 .. , which is based on McGovern's testing result, 1993, P.200. FC_EFF = 0.78 !0.00 for no fungicide applications !0.78 for Seasonal Analysis
!
.. Lesion expansion rate KLEX = 0.012
! ! !
// MAXIMUM OF .. Limited to .. to make it MIN_LP = MIN( MIN_FP = MIN(
! ! !
// .. .. LS LE IS IE
! ! !
// INITIAL AMOUNT OF DISEASE .. Read from the inp file, PST_INIT is 0.25% on disease onset. .. Ref: Johnson & Teng, 1990. INI_DIS = PST_INIT/100.0
!
// INITIALIZING VARIABLES D_VAC(1:200) = 0.0 D_POS(1:200) = 0.0 DEF_AREA(1:200) = 0.0 DEF_MASS(1:200) = 0.0 DEF_AREA_TOTAL(1:200) = 0.0 DEFOL(1:200) = 0.0 PDS(1:200) = 0.0 WLIDISDOT_CH(1:200) = 0.0 WLIDISDOT_TOTAL(1:200) = 0.0 SUM_VAC(1:200) = 0.0 SUM_LAT(1:200) = 0.0 SUM_INF(1:200) = 0.0 SUM_POS(1:200) = 0.0 SUM_AREA(1:200) = 0.0 SUM_DISEASED(1:200) = 0.0 COHORT_PAGE(1:200) = 0.0 COHORT_TOTAL(1:200) = 0.0 COHORT_VAC(1:200) = 0.0 COHORT_LAT(1:200) = 0.0 COHORT_INF(1:200) = 0.0 COHORT_POS(1:200) = 0.0 COHORT_ABSCISED(1:200) = 0.0 COHORT_TOTAL_AS(1:200) = 0.0 COHORT_TOTAL_AD(1:200) = 0.0 DO 6 I = 1, 200 D_LAT(I,1:40) = 0.0 D_INF(I,1:40) = 0.0 COHORT(I,1:82) = 0.0 CONTINUE DIS_PLANT = .FALSE. EMERGENCE = .FALSE. ISONSET = .FALSE. EMERGENCE_CH(1:200) = .FALSE. CALL UpdateWLIDOT_TMEB(0.0, 0.0, 'TMEB') CALL UpdateTDLA_TMEB(0.0, 0.0, 'TMEB')
6
LATENT AND INFECTIOUS PERIOD be 40 days, which is presumably set more than enough for other diseases as well. MIN_LP + INT_SS, 40.0 ) MIN_FP, 40.0 )
LOCATION SETTING INSIDE OF EACH COHORT'S ARRAY Cohort area is divided into each cell of array to apply distributed delay function. = 2 = LS + MIN_LP - 1 = LE + 1 = IS + MIN_FP - 1
C*********************************************************************** C***********************************************************************
131 C Daily rate calculations C*********************************************************************** ELSEIF (DYNAMIC .EQ. RATE) THEN
!
// GET DAP, YRSIM CALL GetDAP(DAP, 'TMEB') CALL GetYRSIM(YRSIM, 'TMEB')
! !
// STORING DAILY VARIABLES Get variables from GROW module CALL GetGROW(AREALF, CLW, WLDOTN, SLA, WTLF, 'TMEB')
!
.. Storing daily SLA. IF (DAP.GE.1) STR_SLA(DAP) = SLA
! !
// GET WEATHER DATA .. Get hourly relative humidity from HMET. CALL GetHMET(TAIRHR, RHUMHR, 'TMEB')
!
.. Get daily average temperature from HMET. CALL GetTAVG(TAVG, 'TMEB')
!
.. Get daily precipitation from IPWTH. CALL GetRAIN(RAIN, 'TMEB')
! !
// PHYSIOLOGICAL AGE CALCULATION .. Physiological age calculation is based on DTX variable CALL GetDTX(DTX, 'TMEB') P_AGE = P_AGE + DTX
! ! !
// REAL AGE CALCULATION .. Real age according to the calendar when the plant is assumed to be .. transplanted at 25-day old. Add DAP to AGE when it's transplanted. AGE = 25 + DAP
! ! !
// WETNESS PERIOD .. Hours having higher than 93% of RH. .. Ref: Gaetan, 1989 RH93HRS = 0 DO 10 HR = 1, 24 IF ( RHUMHR(HR) .GE. 93 ) RH93HRS = RH93HRS + 1 CONTINUE
10 !
.. Estimate rainy hours RAINYHRS = AINT( 6.3397*(1-EXP(-RAIN*0.4402/2.0)) )
!
.. Estimate leaf wetness duration LWD = MIN(24, RH93HRS+RAINYHRS)
! !
// INFECTION EFFICIENCY .. Function of leaf wetness duration RLWD = MAX( 4.0, MIN(24.0, -0.4348*AGE + 30.087) ) IF (LWD.GE.RLWD) THEN F_LW = 1.0 ELSE F_LW = 0.0 END IF
! ! !
.. .. .. IF
Function of temperature for infection efficiency Min 5C, Opt 27C, Max 35C Ref: Rotem's book p.225 (TAVG.LE.27.0) THEN F_TMP = 1 - COS(0.073*(TAVG-5)) ELSE
132 F_TMP = COS(0.196*(TAVG-27)) END IF F_TMP = MAX( 0.0, MIN(1.0, F_TMP) ) ! !
.. Environmental factor for infection .. Only after plant's susceptible age IF (P_AGE.GE.ONSET_AGE) THEN F_ENV = F_LW * F_TMP ELSE F_ENV = 0.0 END IF
! !
// ONSETDAP .. To check when the plant is mature enough to be infected IF (ISONSET.EQ..FALSE. .AND. F_ENV.GE.FAV_LEVEL) THEN CALL GetOnsetDAP_TMEB(DAP, 'TMEB') ISONSET = .TRUE. END IF
! ! !
// .. .. IF
! ! !
INFECTIOUS PERIOD Function of daily average temperature. Ref: Rotem, 1994, P.72. ( TAVG .LT. 10.0 ) THEN .. Parameter for distributed delay function .. in the infectious period. .. Ref: Manetsch, 1976. K_INF = 0.0
ELSE IF ( TAVG .LT. 35.0 ) THEN FP = -1.3 * TAVG + 62.167 K_INF = MIN_FP / FP ELSE K_INF = 0.0 END IF
! ! ! ! !
// GET RSD .. The fungicide residue from Fcide_CT2L module, .. which calculate residual amount of applied Chlorothalonil. .. RSD is an array that has RSD(1) for upper layer .. and RSD(2) for lower one. CALL GetRSD(RSD, 'TMEB')
!
.. To see if the fungicide is applied today. IF ( RSD(1).GT.RSD1_EX ) FCIDEDAT = DAP
! ! !
// GET LAISL .. The sunlit LAI calculated in ETPHOT module. .. It's used to decide which cohorts are in upper layer. CALL GetLAISL(LAISL, 'TMEB')
!
.. Convert LAISL to leaf area (cm2/m2) AREASL = LAISL * 10000
!
.. Initializing a temporary variable AREASL_CH = 0.0
!
.. Loop to find how many cohorts are in sunlit DO 15 CH = 200, 1, -1 AREASL_CH = AREASL_CH + COHORT_TOTAL(CH)
!
.. If found, cohorts older than UPPER_CH are in lower canopy IF (AREASL_CH.GT.AREASL) THEN UPPER_CH = CH EXIT END IF
133 15
CONTINUE
!
// DO LOOP FOR COHORT DO 30 CH = 1, 200
! ! !
// .. .. IF
ASSIGNING COHORT AREA Initial area of a cohort is assigned as a newly grown leaf mass multiplied by F factor. ( CH.EQ.DAP ) THEN
!
.. Get F from DEMAND or DEMAND_2 module. CALL GetF(F, 'TMEB')
!
.. WTLF is used initially only IF (.NOT.EMERGENCE .AND. AREALF.GT.0.0) THEN
!
.. Total area of a cohort COHORT_TOTAL(CH) = WTLF * F
!
.. Vacant area of a cohort COHORT_VAC(CH) = WTLF * F COHORT(CH,1) = WTLF * F
!
.. Logical switch EMERGENCE = .TRUE. EMERGENCE_CH(CH) = .TRUE. ELSE
!
.. Total area of a cohort COHORT_TOTAL(CH) = WLDOTN * F
!
.. Vacant area of a cohort COHORT_VAC(CH) = WLDOTN * F COHORT(CH,1) = WLDOTN * F
!
.. Logical switch for emergence IF (COHORT(CH,1).GT.0.0) EMERGENCE_CH(CH) = .TRUE.
!
.. IF: (.NOT.EMERGENCE .AND. AREALF.GT.0.0) END IF
!
..IF: (CH.EQ.DAP) END IF
! !
// AREA LOSS DUE TO DISEASE .. Only if there is a cohort area IF ( COHORT_TOTAL(CH).NE.0.0 ) THEN
! &
.. Cohort_Total area after defoliation COHORT_TOTAL_AD(CH) = COHORT_TOTAL(CH) - DL_AREA_CH(CH)
&
.. Rate of area loss due to disease is calculated .. to subtract defoliated area from this cohort's area DL_RATE_CH(CH) = COHORT_TOTAL_AD(CH) / COHORT_TOTAL(CH)
! !
ELSE !
.. Rate 1.0 means there is no disease loss DL_RATE_CH(CH) = 1.0 END IF
!
.. Maximum 1.0, Minimum 0.0 DL_RATE_CH(CH) = MAX( 0.0, MIN( 1.0, DL_RATE_CH(CH) ) )
134 ! ! ! ! !
// .. .. .. .. IF
LESION EXPANSION RATE 0.012cm2/day from Johnson and Teng, 1990. Its original value was 0.20 with unclear unit, but converted to this unit area based value by the reference. Ref: Berger et al., 1997. ( COHORT_TOTAL(CH).NE.0.0 ) THEN
! ! ! ! ! !
.. Expanded area in actual is calculated logistically here. .. Expansion rate * diseased area * no diseased area yet .. -------------------.. total area in cohort .. , where disease area shows actual symptom, .. and the other doesn't. AREA_EXP = KLEX * COHORT_DISEASED(CH) * ( COHORT_TOTAL(CH)-COHORT_DISEASED(CH) ) /COHORT_TOTAL(CH)
& & & ELSE
AREA_EXP = 0.0 END IF
! !
// ESTIMATION OF FUNGICIDE EFFECT .. If today is the first emerging day for the cohort IF (CH.EQ.DAP) THEN
!
.. If fungicide is applied today IF (FCIDEDAT.EQ.DAP) THEN
!
.. This cohort area is protected FCIDECFF = FC_EFF ELSE
!
.. If not applied, no effect FCIDECFF = 0.0 END IF
!
.. If this cohort has not been protected yet ELSE IF (FCIDEDAT.LT.CH) THEN FCIDECFF = 0.0
!
.. If this cohort is in between applications ELSE
!
.. Lower canopy (old leaves) IF (CH.LT.UPPER_CH) THEN
! !
.. Threshold level is set to 1.5 microgram/cm2 .. Ref: Elliott and Spurr, 1993. IF (RSD(2).GT.1.5) THEN FCIDECFF = FC_EFF ELSE FCIDECFF = 0.0 END IF
!
.. Upper canopy (young leaves) ELSE IF (RSD(1).GT.1.5) THEN FCIDECFF = FC_EFF ELSE FCIDECFF = 0.0 END IF END IF END IF
! !
// BASIC INFECTION RATE .. Driving force of infection. Here R is intermediate product.
135 !
&
! !
.. Ref: Dr. Berger's class note 'Rmax' IF (COHORT_TOTAL(CH).GT.0.0) THEN R(CH) = RMAX * F_ENV * (1.0-PST_TOLR) * (1.0-FCIDECFF) * (COHORT_VAC(CH)/COHORT_TOTAL(CH)) ELSE R(CH) = 0.0 END IF
// ASSIGNING INITIAL AMOUNT OF INFECTION/DISEASE .. When it's started manually on specified date IF ( PST_STRT.EQ.'M' .OR. PST_STRT.EQ.'m' ) THEN
!
.. Initial area of disease infection IF ( .NOT.DIS_PLANT .AND. PST_INIT.GT.0.0 ) THEN INI_INF(CH) = COHORT_VAC(CH)*INI_DIS*R(CH) INI_LAT(CH) = 0.0 IF (INI_INF(CH).NE.0.0) THEN DIS_PLANT = .TRUE. END IF
!
.. If its disease process has begun ELSE IF ( DIS_PLANT ) THEN
!
.. To initiate further infection IF ( F_ENV.GE.FAV_LEVEL ) THEN INI_INF(CH) = 0.0 INI_LAT(CH) = COHORT_VAC(CH)*INI_DIS*R(CH)
! !
.. There is no favorable condition for .. further infection ELSE INI_INF(CH) = 0.0 INI_LAT(CH) = 0.0 END IF
!
!
.. IF: Initial area of infection END IF .. When it's started automatically ELSE
! ! !
.. .. .. IF
Initial area of latent infection. Starts when the disease efficiency is higher than certain level. ( F_ENV.GE.FAV_LEVEL ) THEN INI_INF(CH) = 0.0 INI_LAT(CH) = COHORT_VAC(CH)*INI_DIS*R(CH)
!
.. No need to initiate infections ELSE INI_INF(CH) = 0.0 INI_LAT(CH) = 0.0 END IF
!
.. IF: (PST_STRT.EQ.'M' .OR. PST_STRT.EQ.'m') END IF
! ! !
// .. .. IF
P.AGE OF LEAF TISSUE To be used in latent period calculation. Accumulated only after the cohort is created. (COHORT_TOTAL(CH).GT.0.0) THEN COHORT_PAGE(CH) = COHORT_PAGE(CH) + DTX END IF
! ! ! !
// .. .. ..
LATENT PERIOD Function of leave's physiological age. Johnson and Teng, 1990. Precisely, this equation is for the incubation period (ingress->symptom), but here used for
136 ! !
.. .. LP LP
the latent period (ingress->producing inoculum) with adding INT_SS that is interval between showing symptom and sporulation. = ( -0.078 * COHORT_PAGE(CH) ) + 8.80 + INT_SS = MAX( LP, MIN_LP )
! !
.. Parameter for distributed delay function in latent period. .. Ref: Manetsch, 1976. K_LAT = MIN_LP / LP
! !
// D_VAC .. Only if vacant area is not zero IF ( COHORT_VAC(CH).NE.0.0 ) THEN
!
.. In general case, D_VAC(CH)
! ! &
=
!
.. Disease came from diseased area .. as R calculated based on the diseased area - R(CH) * COHORT_INF(CH)
&
.. Vacant area availability * ( COHORT_VAC(CH) / COHORT_TOTAL(CH) )
&
.. Subtract area losses due to disease - COHORT(CH,1) * ( 1.00-DL_RATE_CH(CH) )
&
.. Subtract initially infected area - INI_LAT(CH) - INI_INF(CH)
&
.. Subtract expanded lesion area - AREA_EXP
!
!
!
!
.. To avoid minus cohort area D_VAC(CH) = MAX( -COHORT(CH,1), D_VAC(CH) ) ELSE
!
.. If there is no vacant area D_VAC(CH) = 0.0 END IF
! !
// D_LAT .. Only if cohort area is not zero IF ( COHORT_TOTAL(CH).NE.0.0 ) THEN
!
.. When vacant area is zero, latent area can't increase. IF ( COHORT(CH,1).EQ.0.0 ) THEN D_LAT(CH,1) = - K_LAT * COHORT(CH,LS) - COHORT(CH,LS) * ( 1.00-DL_RATE_CH(CH) )
& & !
.. If there are not enough vacant area to be decreased ELSE IF ( COHORT(CH,1)+D_VAC(CH).EQ.0.0 ) THEN
& & &
D_LAT(CH,1) = + D_VAC(CH) - K_LAT * COHORT(CH,LS) - COHORT(CH,LS) * (1.00-DL_RATE_CH(CH))
&
D_LAT(CH,1) = MAX( -COHORT(CH,2), D_LAT(CH,1) )
!
.. In general case, ELSE D_LAT(CH,1) ! & &
=
.. Area increasing from the infectious area + R(CH) * COHORT_INF(CH) * ( COHORT_VAC(CH) / COHORT_TOTAL(CH) )
137
! &
.. Expanded lesion + AREA_EXP
&
.. Latently infected area at initial + INI_LAT(CH)
&
.. Distributed area from (ch,1) to (ch,2) - K_LAT * COHORT(CH,LS)
&
.. Decreasing defoliated area - COHORT(CH,LS) * ( 1.00-DL_RATE_CH(CH) )
!
!
!
END IF D_LAT(CH,1) = MAX( -COHORT(CH,2), D_LAT(CH,1) ) ELSE D_LAT(CH,1) = 0.0 !
.. IF: ( COHORT_TOTAL(CH).NE.0.0 ) END IF
!
.. Making loops for the rest of latent area DO 18 I = 2, LE-1 D_LAT(CH,I) = + ( K_LAT*COHORT(CH,I) ) - ( K_LAT*COHORT(CH,I+1) ) - ( COHORT(CH,I+1)*(1.00-DL_RATE_CH(CH)) ) D_LAT(CH,I) = MAX( -COHORT(CH,I+1), D_LAT(CH,I) ) CONTINUE
& & & 18
! !
// D_INF .. Only if cohort area is not zero IF ( COHORT_TOTAL(CH).NE.0.0 ) THEN
! ! & & & & & !
& & & & 20 !
.. Diseased area loss is subtracted from .. infectious area in each cohort. D_INF(CH,1) = + INI_INF(CH) + ( K_LAT * COHORT(CH,LE) ) - ( K_INF * COHORT(CH,IS) ) - ( COHORT(CH,IS) * ( 1.00-DL_RATE_CH(CH) ) ) D_INF(CH,1) = MAX( -COHORT(CH,IS), D_INF(CH,1) ) .. Making loops for rest of infectious area DO 20 I = IS+1, IE D_INF(CH,I-LE) = + ( K_INF * COHORT(CH,I-1) ) - ( K_INF * COHORT(CH,I) ) - COHORT(CH,I) * ( 1.00-DL_RATE_CH(CH) ) D_INF(CH,I-LE) = MAX( -COHORT(CH,I), D_INF(CH,I-LE) ) CONTINUE .. If cohort area is zero, there is no D_INF ELSE DO 22 I = IS, IE D_INF(CH,I-LE) = 0.0 CONTINUE
22 !
.. IF: ( COHORT_TOTAL(CH).NE.0.0 ) END IF
!
// D_POS D_POS(CH) = + ( K_INF * COHORT(CH,IE) ) - ( COHORT(CH,IE+1) * ( 1.00-DL_RATE_CH(CH) ) )
& &
138 ! 30
.. CLOSING: DO LOOP FOR COHORT CONTINUE
!
// DO LOOP FOR COHORT AREA INTEGRATION DO 50 CH = 1, 200
!
.. INTEGRATION: VAC COHORT(CH,1) = COHORT(CH,1) + D_VAC(CH)
!
.. INTEGRATION: LAT DO 32 I = LS, LE COHORT(CH,I) = COHORT(CH,I) + D_LAT(CH,I-1) CONTINUE
32 !
34
.. INTEGRATION: INF DO 34 I = IS, IE COHORT(CH,I) = COHORT(CH,I) + D_INF(CH,I-LE) CONTINUE
!
.. INTEGRATION: POS COHORT(CH,IE+1) = COHORT(CH,IE+1) + D_POS(CH)
!
.. AREA CAN'T BE ZERO DO 36 I = 1, IE+1 COHORT(CH,I) = MAX( 0.0, COHORT(CH,I) ) CONTINUE
36 !
38
.. Intermediate Cohort_Total COHORT_TOTAL(CH) = 0.0 DO 38 I = 1, IE+1 COHORT_TOTAL(CH) = COHORT_TOTAL(CH) + COHORT(CH,I) CONTINUE
50
CONTINUE
!
// SUMMATION OF COHORT AREAS SUM_CH = 0.0 DO 52 CH = 1, 200 SUM_CH = SUM_CH + COHORT_TOTAL(CH) CONTINUE
52
! ! ! ! !
// SUBTRACT SENESCENCE .. Natural senescence is assumed to be come from .. the oldest cohort. .. Area should be subtracted is a difference between .. total cohort area and total leaf area. LOST_AREA = MAX( 0.0, SUM_CH-AREALF )
!
.. Internal loop to cover all cohort DO 53 CH = 1, 200
!
.. Only if the cohort is not abscised yet IF (COHORT_ABSCISED(CH).EQ.0.0) THEN
! !
.. _AS stands for 'After Senescenced' .. Initially set to be same as _TOTAL COHORT_TOTAL_AS(CH) = COHORT_TOTAL(CH)
! ! !
.. .. .. IF
! !
.. If a cohort area is greater than the lost area, .. cohort area will be reduced by the lost area and
If a cohort area is less than the lost area, that cohort will be abscised and the lost area will be reduced by the disposed area. (COHORT_TOTAL(CH).LE.LOST_AREA) THEN COHORT_TOTAL_AS(CH) = 0.0 COHORT_ABSCISED(CH) = COHORT_TOTAL(CH) LOST_AREA = LOST_AREA - COHORT_TOTAL(CH)
139 ! !
.. lost area becomes zero. COHORT_TOTAL_AS will remember .. the cohort area after senescence occurred. ELSE COHORT_TOTAL_AS(CH) = COHORT_TOTAL(CH) - LOST_AREA LOST_AREA = 0.0 END IF
! ! !
.. .. .. IF
This section adjusts areas in each cohort array. If a cohort area became zero after senescence, all of boxcars in the cohort become zero. (COHORT_TOTAL_AS(CH).EQ.0.0) THEN COHORT(CH,1:IE+1) = 0.0 ELSE
! ! ! !
.. If a cohort area should be reduced after .. senescence, calculate the rate of cohort area, .. which should be used for each array boxcar's .. reduction. OL_RATE = COHORT_TOTAL_AS(CH) / COHORT_TOTAL(CH) DO 55 J = 1, IE+1 COHORT(CH,J) = COHORT(CH,J) * OL_RATE CONTINUE
55 END IF !
.. IF: (COHORT_ABSCISED(CH).EQ.0.0) END IF
53
CONTINUE
! !
// DO LOOP FOR COHORT .. Total area calculation for each stage DO 70 CH = 1, 200
!
.. Cohort_LAT(*): Total latent area in a cohort COHORT_LAT(CH) = 0.0 DO 62 I = LS, LE COHORT_LAT(CH) = COHORT_LAT(CH) + COHORT(CH,I) CONTINUE
62 !
.. Cohort_INF(*): Total infectious area in a cohort COHORT_INF(CH) = 0.0 DO 64 I = IS, IE COHORT_INF(CH) = COHORT_INF(CH) + COHORT(CH,I) CONTINUE
64 !
.. Storing VAC and POS area in a cohort COHORT_VAC(CH) = COHORT(CH,1) COHORT_POS(CH) = COHORT(CH,IE+1) COHORT_DISEASED(CH) = COHORT_INF(CH) + COHORT_POS(CH)
! ! !
.. .. .. DO &
Cohort_Diseased corresponds area shows symptom, which includes (1)infectious, (2)post-infectious, and (3)latent but has symptom 66 I = 0, INT_SS-1 COHORT_DISEASED(CH) = COHORT_DISEASED(CH) + COHORT(CH,LE-I)
66
CONTINUE
! !
.. Summation of V, L, I and P in a Cohort .. COHORT_TOTAL(*): Total area in a cohort COHORT_TOTAL(CH) = COHORT_VAC(CH) + COHORT_LAT(CH) + COHORT_INF(CH) + COHORT_POS(CH)
& 70
CONTINUE
!
// PDS & DEFOLIATION CALCULATION IN COHORT
140 !
.. Get a cohort area when there is no disease IF (DAP.GE.1) CALL GetNDLA('TMEB', YRSIM, DAP, NDLA_CH)
!
.. Loop for PDS & defoliation DO 75 CH = 1, 200
! ! ! ! !
.. .. .. .. .. IF
PDS in cohort Definition of PDS includes defoliation effects. Abscised leaf contributes to the PDS. Ref: Berger's class note 'Calculation..', Berger and Plaut, 1981. (DAP.LE.1) THEN PDS(CH) = 0.0 ELSE
!
.. No PDS when it's not emerged yet IF (.NOT.EMERGENCE_CH(CH) .OR. DAP.LT.CH) THEN Y_VISIBLE_CH(CH) = 0.0 Y_DEF_CH(CH) = 0.0
!
.. Only if it's not abscised or defoliated ELSE IF ( COHORT_TOTAL(CH).NE.0.0 .AND. NDLA_CH(CH).NE.0.0 ) THEN
& !
.. Y_VISIBLE TDISLA_CH = COHORT_DISEASED(CH) IF (COHORT_TOTAL(CH).NE.0.0) THEN Y_VISIBLE_CH(CH) = TDISLA_CH/COHORT_TOTAL(CH) ELSE Y_VISIBLE_CH(CH) = 0.0 END IF
&
!
.. Y_DEF Y_DEF_CH(CH) = MIN(1.0, 1-COHORT_TOTAL(CH)/NDLA_CH(CH))
& ELSE
Y_DEF_CH(CH) = 1.0 END IF ! & &
=
!
.. IF:(DAP.EQ.1) END IF
! ! !
.. Calculating FLCINF .. Fraction of infected area in each cohort is used for .. cohort output. INF_CH = COHORT_LAT(CH)+COHORT_INF(CH)+COHORT_POS(CH) IF (COHORT_TOTAL(CH).NE.0 .AND. COHORT_ABSCISED(CH).EQ.0.0) THEN FLCINF_CH(CH) = INF_CH/COHORT_TOTAL(CH) ELSE FLCINF_CH(CH) = 0.0 END IF
&
! ! ! &
!
.. Calculation of PDS PDS(CH) MAX( 0.0, MIN( (1-Y_DEF_CH(CH))*Y_VISIBLE_CH(CH) + Y_DEF_CH(CH), 1.0 ) ) * 100.00
.. Defoliation rate due to disease calculated from diseased area .. unless the leaf has abscised. .. Ref: Vloutoglou, 2000 (Fig.4, p.343). DEFOL(CH) = ( -1.53 + (0.80*Y_VISIBLE_CH(CH)*100.0 ) ) / 100. DEFOL(CH) = MIN( 1.0, MAX( 0.0, DEFOL(CH) ) ) .. Defoliation
141 IF (DAP.GE.1) THEN !
.. Save to get today's increment EX_DEF_AREA = DEF_AREA(CH)
!
.. Defoliated area in A cohort up to today. DEF_AREA(CH) = MAX( DEF_AREA(CH), DEFOL(CH)*NDLA_CH(CH) )
& ! !
.. If a leaf has abscised, the defoliated area .. is the abscised area. IF (Y_DEF.GT.0.0) THEN DEF_AREA(CH) = MAX( COHORT_ABSCISED(CH), DEF_AREA(CH) ) END IF
&
! &
=
.. Today's increment of defoliated area DL_AREA_CH(CH) DEF_AREA(CH) - EX_DEF_AREA
=
.. Defoliated area in ALL cohorts up to today. DEF_AREA_TOTAL(DAP) DEF_AREA_TOTAL(DAP) + DEF_AREA(CH)
! & !
.. To remember the value of previous day EX_DEF_MASS = DEF_MASS(CH)
!
.. Defoliated mass in a cohort up to today. IF (STR_SLA(DAP).GT.0.0 .AND. COHORT_TOTAL(CH).NE.0.0) THEN DEF_MASS(CH) = MAX( EX_DEF_MASS, DEF_AREA(CH) / STR_SLA(DAP) ) ELSE DEF_MASS(CH) = EX_DEF_MASS END IF
& & &
! ! &
.. Defoliated mass in a cohort on today only. .. (up to today's def.) - (up to yesterday's def.) WLIDISDOT_CH(CH) = MAX( 0.0, DEF_MASS(CH) - EX_DEF_MASS )
&
.. Defoliated mass in ALL cohorts on today only. WLIDISDOT_TOTAL(DAP) = WLIDISDOT_TOTAL(DAP) + WLIDISDOT_CH(CH)
!
!
.. Defoliation: IF (DAP.GE.1) END IF
!
.. Defoliated mass shouldn't be bigger than WTLF. IF (DAP.GE.1) WLIDISDOT_TOTAL(DAP) = MAX( MIN( WLIDISDOT_TOTAL(DAP), WTLF ), 0.0 )
& ! 75
.. Closing: DO LOOP FOR PDS & DEFOLIATION CALCULATION CONTINUE
!
// SUMMATION OF V, L, I & P FOR ALL COHORTS IF (DAP.GE.1) THEN DO 80 CH = 1, 200 SUM_VAC(DAP) = SUM_VAC(DAP) + COHORT_VAC(CH) SUM_LAT(DAP) = SUM_LAT(DAP) + COHORT_LAT(CH) SUM_INF(DAP) = SUM_INF(DAP) + COHORT_INF(CH) SUM_POS(DAP) = SUM_POS(DAP) + COHORT_POS(CH) SUM_DISEASED(DAP) = SUM_DISEASED(DAP) + COHORT_DISEASED(CH) CONTINUE IF (DAP.GE.1) SUM_AREA(DAP) = SUM_VAC(DAP) + SUM_LAT(DAP) + SUM_INF(DAP) + SUM_POS(DAP) END IF
& 80 &
142
! ! ! ! ! ! !
// .. .. .. .. .. .. IF
!
.. Update defoliated mass to WLIDISDOT. CALL UpdateWLIDOT_TMEB(0.0, WLIDISDOT, 'TMEB')
! ! ! !
// .. .. .. IF &
COUPLED WITH WLIDISDOT Defoliated mass in ALL cohorts on yesterday only , which was used in today's DL subtraction. This leaf mass have gone and not considered in today's leaf cohort area calculations. And, it is going to be sent to VEGDM module with this coupling point to increase WLIDOT. (DAP.GT.1) WLIDISDOT = WLIDISDOT_TOTAL(DAP-1)
TDISLA_FD Diseased area due to this foliar disease. This includes halo surrounding diseased area. Halo multiplication is moved to here from GROW module. (DAP.GE.1) TDISLA_FD = MIN( SUM_AREA(DAP), HALO*SUM_DISEASED(DAP) )
! ! !
.. Update diseased area due to this foliar disease. .. TDISLA will be integrated into DISLA, the global coupling point .. to reduce healthy leaf area. CALL UpdateTDLA_TMEB(0.0, TDISLA_FD, 'TMEB')
! !
// PDS_TOTAL .. To be used later in Y_DEF calculation. NDLA = 0.0 DO 82 CH = 1, 200 NDLA = NDLA + NDLA_CH(CH) CONTINUE
82 ! ! &
! !
.. Definition of PDS followed Berger and Plaut's, .. which includes defoliation effect IF (NDLA.NE.0.0 .AND. DAP.GE.1 .AND. SUM_AREA(DAP).NE.0.0) THEN Y_DEF = MAX( 0.0, MIN( 1.0, 1.0 - SUM_AREA(DAP)/NDLA ) ) Y_VISIBLE = SUM_DISEASED(DAP)/SUM_AREA(DAP) ELSE Y_DEF = 0.0 Y_VISIBLE = 0.0 END IF PDS_TOTAL = MIN(1.0, (1-Y_DEF)*Y_VISIBLE + Y_DEF )
// Save RSD(1) .. To be used tomorrow. IF (DAP.GT.2) RSD1_EX = RSD(1)
C*********************************************************************** C*********************************************************************** C END OF DYNAMIC IF CONSTRUCT C*********************************************************************** ENDIF C*********************************************************************** END SUBROUTINE ! End of PT_TMEB
PT_PNLS.FOR C======================================================================= C PT_PNLS: Peanut late leafspot module. C----------------------------------------------------------------------C Called by: Pest_PN C Calls: GetNDLA C
143 C UpdateTDLA_PNLS .. to pass TDLA for PESTCP C UpdateWLIDOT_PNLS .. to pass WLIDOT for VEGDM C GetDAP .. to get DAP from PEST C GetDTX .. to get DTX from PHENOL C GetF .. to get F from DEMAND C GetGROW .. to get AREALF,CLW,WLDOTN,SLA,WTLF C from GROW C GetHMET .. to get TAIRHR,RHUMHR from HMET C GetTAVG .. to get TAVG from HMET C----------------------------------------------------------------------C Outputs: TDLA, WLIDOT .. through the messenger modules C======================================================================= SUBROUTINE PT_PNLS( & PST_STRT, PST_INIT, PST_TOLR, YRDOY, !Input & DYNAMIC) !Control !----------------------------------------------------------------------IMPLICIT NONE !----------------------------------------------------------------------INTEGER RUNINIT, SEASINIT, EMERG, RATE, INTEGR, OUTPUT, FINAL PARAMETER (RUNINIT = 1, SEASINIT = 2, EMERG = 3, RATE = 3, & INTEGR = 4, OUTPUT=5, FINAL = 6) INTEGER DYNAMIC
!
// INPUT & OUTPUT REAL AREALF, WLDOTN, DTX, F, PST_INIT, SLA, PST_TOLR, WTLF CHARACTER*1 PST_STRT
!
// DATE INTEGER YRDOY, YY, YRSIM
!
// WEATHER DATA REAL RHUMHR(24), TAIRHR(24), TAVG
!
// MANAGING COHORT INTEGER CH, I, J, H, L, DAP REAL COHORT(200,82) REAL D_VAC(200), D_LAT(200,40), D_INF(200,40), D_POS(200) REAL INI_INF(200), INI_LAT(200) REAL TOTAL_INF, TOTAL_LAT, STR_SLA(200), CLW REAL COHORT_TOTAL(200), COHORT_VAC(200) REAL COHORT_LAT(200), COHORT_INF(200), COHORT_POS(200) REAL COHORT_ABSCISED(200), COHORT_DISEASED(200) REAL LOST_AREA, COHORT_TOTAL_AS(200), COHORT_TOTAL_AD(200) LOGICAL EMERGENCE, EMERGENCE_CH(200), DIS_PLANT
!
// DEFOLIATION CONTROL REAL OL_RATE, DL_RATE_CH(200), DL_AREA_CH(200), DL_AREA_TOTAL REAL WLIDISDOT_CH(200), WLIDISDOT_TOTAL(200), WLIDISDOT REAL DEFOL(200), PDS(200), DEF_AREA(200), DEF_MASS(200) REAL DEF_AREA_TOTAL(200), PDS_TOTAL REAL EX_DEF_MASS, EX_DEF_AREA REAL NDLA, NDLA_CH(200)
!
// PROPORTION OF DISEASE SEVERITY (PDS) CALCULATION REAL Y_DEF, Y_VISIBLE, TDISLA_FD REAL Y_DEF_CH(200), Y_VISIBLE_CH(200), TDISLA_CH
!
// LATENT AND INFECTIOUS PERIOD, DISEASE EFFICIENCY INTEGER LS, LE, IS, IE, RH93HRS, HR, INT_SS REAL MIN_LP, MIN_FP, LP, FP REAL INI_DIS, INF_AREA, INF_CH(200), FLCINF_CH(200) REAL K_LAT, K_INF, RMAX, R REAL F_LW, F_TMP, F_ENV, FAV_LEVEL
144
!
// LESION EXPANSION AND MULTIPLICATION FACTOR FOR THE AREA OF HALO REAL AREA_EXP, HALO
!
// TOTAL AREA CALCULATION FOR ALL COHORTS REAL SUM_VAC(200), SUM_LAT(200), SUM_INF(200), SUM_POS(200) REAL SUM_DISEASED(200), SUM_AREA(200), SUM_CH
C*********************************************************************** C*********************************************************************** C Run Initialization - Called once per simulation C*********************************************************************** IF (DYNAMIC.EQ.RUNINIT) THEN ! ! !
// READING AND ASSIGNING INPUT PARAMETERS .. Minimum latent period. .. Ref: Bourgeois, P.71 MIN_LP = 19.0
! !
.. Minimum infectious period. .. Ref: Bourgeois, P.70 MIN_FP = 2.0
! !
.. Calculated based on disease measurement .. Bourgeois, P.17 RMAX = 0.23177
! !
.. Interval between showing symptom and sporulation .. Ref: (Min.Lat.-Min.Inc.=19-10=9) INT_SS = 9.0
!
.. Initial ID of cohort. CH = 1
! ! !
.. Defoliation due to disease. .. Integrated to WLIDOT, .. the global coupling point for leaf weight reduction. WLIDISDOT = 0.0
!
.. Disease Severity Value for whole plant. PDS_TOTAL = 0.0
! ! !
.. Diseased leaf area. .. Integrated into DISLA, .. the global coupling point for healthy leaf area reduction. TDISLA_FD = 0.0
! !
.. Favorable level of F_ENV which initiates infection process .. Arbitrary set to 0.20 FAV_LEVEL = 0.20
! ! !
.. Multiplication factor for halo area surrounding lesion. .. Moved from GROW module into each disease specific module. .. Existing value is used. HALO = 2.0
! ! !
// MAXIMUM OF .. Limited to .. to make it MIN_LP = MIN( MIN_FP = MIN(
! ! !
// .. .. LS LE
LATENT AND INFECTIOUS PERIOD be 40 days, which is presumably set more than enough for other diseases as well. MIN_LP, 40.0 ) MIN_FP, 40.0 )
LOCATION SETTING INSIDE OF EACH COHORT'S ARRAY Cohort area is divided into each cell of array to apply distributed delay function. = 2 = LS + MIN_LP - 1 ! 20
145 IS = LE + 1 IE = IS + MIN_FP - 1
! 21 ! 28
! ! !
// INITIAL AMOUNT OF DISEASE .. Read from the INP file, PST_INIT is 1% on disease onset. .. Ref: Bourgeois, P.51 INI_DIS = PST_INIT/100.0
!
// INITIALIZING VARIABLES D_VAC(1:200) = 0.0 D_POS(1:200) = 0.0 DEF_AREA(1:200) = 0.0 DEF_MASS(1:200) = 0.0 DEF_AREA_TOTAL(1:200) = 0.0 DEFOL(1:200) = 0.0 PDS(1:200) = 0.0 WLIDISDOT_CH(1:200) = 0.0 WLIDISDOT_TOTAL(1:200) = 0.0 SUM_VAC(1:200) = 0.0 SUM_LAT(1:200) = 0.0 SUM_INF(1:200) = 0.0 SUM_POS(1:200) = 0.0 SUM_AREA(1:200) = 0.0 COHORT_TOTAL(1:200) = 0.0 COHORT_VAC(1:200) = 0.0 COHORT_LAT(1:200) = 0.0 COHORT_INF(1:200) = 0.0 COHORT_POS(1:200) = 0.0 COHORT_ABSCISED(1:200) = 0.0 COHORT_TOTAL_AS(1:200) = 0.0 COHORT_TOTAL_AD(1:200) = 0.0 DO 6 I = 1, 200 D_LAT(I,1:40) = 0.0 D_INF(I,1:40) = 0.0 COHORT(I,1:82) = 0.0 CONTINUE DIS_PLANT = .FALSE. EMERGENCE = .FALSE. EMERGENCE_CH(1:200) = .FALSE. CALL UpdateWLIDOT_PNLS(0.0, 0.0, 'PNLS') CALL UpdateTDLA_PNLS(0.0, 0.0, 'PNLS')
6
C*********************************************************************** C*********************************************************************** C Daily rate calculations C*********************************************************************** ELSEIF (DYNAMIC .EQ. RATE) THEN
!
// GET DAP CALL GetDAP(DAP, 'PNLS') CALL GetYRSIM(YRSIM, 'PNLS')
! !
// STORING DAILY VARIABLES .. Get variables from GROW module CALL GetGROW(AREALF, CLW, WLDOTN, SLA, WTLF, 'PNLS')
!
.. Storing daily SLA. IF (DAP.GE.1) STR_SLA(DAP) = SLA
! !
// GET WEATHER DATA .. Get hourly relative humidity from HMET. CALL GetHMET(TAIRHR, RHUMHR, 'PNLS')
!
.. Get daily average temperature from HMET. CALL GetTAVG(TAVG, 'PNLS')
146
! !
// WETNESS PERIOD .. Hours having higher than 93% of RH. RH93HRS = 0 DO 10 HR = 1, 24 IF ( RHUMHR(HR) .GE. 93 ) RH93HRS = RH93HRS + 1 CONTINUE
10
! ! &
// INFECTION EFFICIENCY .. Ref: Bourgeois, P.54 F_ENV = 0.2477 + 0.1548*RH93HRS - 0.0134*TAVG - 0.0036*RH93HRS*TAVG - 0.0015*RH93HRS*RH93HRS IF (TAVG.LE.16 .OR. RH93HRS.LE.3) F_ENV = 0.0 F_ENV = MAX( 0.0, MIN(1.0, F_ENV) )
! !
.. Function of temperature for infection efficiency .. Ref: Bourgeois, P.60 F_TMP = MIN( 1.0, MAX(0.0, COS(0.1339*(TAVG-24.))) ) F_TMP = MAX( 0.0, MIN(1.0, F_TMP) )
! ! ! !
// PARAMETERS FOR DISTRIBUTED DELAY .. kLAT for D.Delay in latent stages .. kLAT = (p1/p50)*temp.factor .. Ref: Bourgeois, P.138 K_LAT = (10./13.16) * F_TMP
! ! !
.. kINF .. kINF .. Ref: K_INF =
!
// DO LOOP FOR COHORT DO 30 CH = 1, 200
! ! !
for D.Delay in infectious stages = (i1/i50)*temp.factor Bourgeois, P.138 (2./5.) * F_TMP
// .. .. IF
ASSIGNING COHORT AREA Initial area of a cohort is assigned as a newly grown leaf mass multiplied by F factor. ( CH.EQ.DAP ) THEN
!
.. Get F from DEMAND or DEMAND_2 module. CALL GetF(F, 'PNLS')
!
.. WTLF is used initially only IF (.NOT.EMERGENCE .AND. AREALF.GT.0.0) THEN
!
.. Total area of a cohort COHORT_TOTAL(CH) = WTLF * F
!
.. Vacant area of a cohort COHORT_VAC(CH) = WTLF * F COHORT(CH,1) = WTLF * F
!
.. Logical switch for emergence EMERGENCE = .TRUE. EMERGENCE_CH(CH) = .TRUE. ELSE
!
.. Total area of a cohort COHORT_TOTAL(CH) = WLDOTN * F
!
.. Vacant area of a cohort COHORT_VAC(CH) = WLDOTN * F COHORT(CH,1) = WLDOTN * F
!
.. Logical switch for emergence
147 IF (COHORT(CH,1).GT.0.0) EMERGENCE_CH(CH) = .TRUE. !
.. IF: (.NOT.EMERGENCE .AND. AREALF.GT.0.0) END IF
!
..IF: (CH.EQ.DAP) END IF
! !
// ASSIGNING INITIAL AMOUNT OF INFECTION/DISEASE .. When it's started manually on specified date IF ( PST_STRT.EQ.'M' .OR. PST_STRT.EQ.'m' ) THEN
!
.. Initial area of disease infection IF ( .NOT.DIS_PLANT .AND. PST_INIT.GT.0.0 ) THEN INI_INF(CH) = COHORT_VAC(CH)*INI_DIS INI_LAT(CH) = 0.0 IF (INI_INF(CH).NE.0.0) THEN DIS_PLANT = .TRUE. END IF
!
.. If its disease process has begun ELSE IF ( DIS_PLANT ) THEN
!
.. To initiate further infection IF ( F_ENV.GE.FAV_LEVEL ) THEN INI_INF(CH) = 0.0 INI_LAT(CH) = COHORT_VAC(CH)*INI_DIS*R
! !
.. There is no favorable condition for .. further infection ELSE INI_INF(CH) = 0.0 INI_LAT(CH) = 0.0 END IF
!
!
.. IF: Initial area of infection END IF .. When it's started automatically ELSE
! ! !
.. .. .. IF
Initial area of latent infection. Starts when the disease efficiency is higher than certain level. ( F_ENV.GE.FAV_LEVEL ) THEN INI_INF(CH) = 0.0 INI_LAT(CH) = COHORT_VAC(CH)*INI_DIS*R
!
.. No need to initiate infections ELSE INI_INF(CH) = 0.0 INI_LAT(CH) = 0.0 END IF
!
.. IF: (PST_STRT.EQ.'M' .OR. PST_STRT.EQ.'m') END IF
! ! !
// ADJUST LOST AREA DUE TO DISEASE .. To make sure the lost area won't make whole cohort area .. less than today's leaf area
!
.. Initialization DL_AREA_TOTAL = 0.0
!
.. Calculate the area should be lost today from all cohorts DO 12 I = 1, 200 DL_AREA_TOTAL = DL_AREA_TOTAL + DL_AREA_CH(I) CONTINUE
12
148 ! &
.. Check if the area calculated above makes too much loss IF (DAP.GE.2 .AND. SUM_AREA(DAP-1)-DL_AREA_TOTAL.LT.AREALF) THEN
!
.. To check all cohorts DO 14 I = 1,200 IF (SUM_AREA(DAP-1).NE.0.0) THEN
!
.. Make DL_AREA_CH smaller DL_AREA_CH(I) = DL_AREA_CH(I)*(AREALF - SUM_AREA(DAP-1)) /SUM_AREA(DAP-1)
& & END IF CONTINUE
14 END IF
! !
// AREA LOSS DUE TO DISEASE .. Only if there is a cohort area IF ( COHORT_TOTAL(CH).NE.0.0 ) THEN
! &
.. Cohort_Total area after defoliation COHORT_TOTAL_AD(CH) = COHORT_TOTAL(CH) - DL_AREA_CH(CH)
&
.. Rate of area loss due to disease is calculated .. to subtract defoliated area from this cohort's area DL_RATE_CH(CH) = COHORT_TOTAL_AD(CH) / COHORT_TOTAL(CH)
! !
ELSE !
.. Rate 1.0 means there is no disease loss DL_RATE_CH(CH) = 1.0 END IF
!
.. Maximum 1.0, Minimum 0.0 DL_RATE_CH(CH) = MAX( 0.0, MIN( 1.0, DL_RATE_CH(CH) ) )
! ! ! ! ! ! ! ! ! !
// .. .. ..
& & & & &
LESION EXPANSION RATE XINEXP=TINFLA(LCOH)*ILAT/ZINCUB*CTEMFC*EXPFAC*EXP(-FEX4*FLCINF) TINFLA(LCOH) : Total infected leaf area ILAT : Incubation period 1 (10 by GB) / ZINCUB : Incubation period 50 .. ZINCUB = ILAT / XLP1 * XLP50 = 10 / 19 * 25 = 13.16 by GB .. CTEMFC : Temperature factor .. EXPFAC : Expansion factor (1.1 by GB) .. FEX4 : Constant (5 by GB) AREA_EXP = INF_CH(CH) * 10. / 13.16 * F_TMP * 1.10 * EXP(-5.*FLCINF_CH(CH)) IF (FLCINF_CH(CH) .GE. 1.0) AREA_EXP = 0.0
! ! ! !
// BASIC INFECTION RATE .. Driving force of infection. Here R is intermediate product. .. Actual R will be calculated in below when the vacant area .. availability is applied. R = RMAX * F_ENV * (1.0-PST_TOLR)
! !
// D_VAC .. Only if vacant area is not zero IF ( COHORT_VAC(CH).NE.0.0 ) THEN
!
.. In general case,
149 D_VAC(CH) ! ! &
=
!
.. Disease came from diseased area .. as R calculated based on the diseased area - R * COHORT_INF(CH)
&
.. Vacant area availability * ( COHORT_VAC(CH) / COHORT_TOTAL(CH) )
&
.. Subtract area losses due to disease - COHORT(CH,1) * ( 1.00-DL_RATE_CH(CH) )
&
.. Subtract initially infected area - INI_LAT(CH) - INI_INF(CH)
&
.. Subtract expanded lesion area - AREA_EXP
!
!
!
!
.. To avoid minus cohort area D_VAC(CH) = MAX( -COHORT(CH,1), D_VAC(CH) ) ELSE
!
.. If there is no vacant area D_VAC(CH) = 0.0 END IF
! !
// D_LAT .. Only if cohort area is not zero IF ( COHORT_TOTAL(CH).NE.0.0 ) THEN
!
& & !
.. When vacant area is zero, latent area can't increase. IF ( COHORT(CH,1).EQ.0.0 ) THEN D_LAT(CH,1) = - K_LAT * COHORT(CH,LS) - COHORT(CH,LS) * ( 1.00-DL_RATE_CH(CH) ) .. If there are not enough vacant area to be decreased ELSE IF ( COHORT(CH,1)+D_VAC(CH).EQ.0.0 ) THEN
& & &
D_LAT(CH,1) = + D_VAC(CH) - K_LAT * COHORT(CH,LS) - COHORT(CH,LS) * (1.00-DL_RATE_CH(CH))
&
D_LAT(CH,1) = MAX( -COHORT(CH,2), D_LAT(CH,1) )
!
.. In general case, ELSE D_LAT(CH,1)
! & & !
=
.. Area increasing from the infectious area + R * COHORT_INF(CH) * ( COHORT_VAC(CH) / COHORT_TOTAL(CH) )
&
.. Expanded lesion + AREA_EXP
&
.. Latently infected area at initial + INI_LAT(CH)
&
.. Distributed area from (ch,1) to (ch,2) - K_LAT * COHORT(CH,LS)
&
.. Decreasing defoliated area - COHORT(CH,LS) * ( 1.00-DL_RATE_CH(CH) )
!
!
!
END IF D_LAT(CH,1) = MAX( -COHORT(CH,2), D_LAT(CH,1) )
150
ELSE D_LAT(CH,1) = 0.0 !
.. IF: ( COHORT_TOTAL(CH).NE.0.0 ) END IF
!
.. Making loops for the rest of latent area DO 18 I = 2, LE-1 D_LAT(CH,I) = + ( K_LAT*COHORT(CH,I) ) - ( K_LAT*COHORT(CH,I+1) ) - ( COHORT(CH,I+1)*(1.00-DL_RATE_CH(CH)) ) D_LAT(CH,I) = MAX( -COHORT(CH,I+1), D_LAT(CH,I) ) CONTINUE
& & & 18
! !
// D_INF .. Only if cohort area is not zero IF ( COHORT_TOTAL(CH).NE.0.0 ) THEN
! ! & & & &
.. Diseased area loss is subtracted from .. infectious area in each cohort. D_INF(CH,1) = + INI_INF(CH) + ( K_LAT * COHORT(CH,LE) ) - ( K_INF * COHORT(CH,IS) ) - ( COHORT(CH,IS) * ( 1.00-DL_RATE_CH(CH) ) ) D_INF(CH,1) MAX( -COHORT(CH,IS), D_INF(CH,1) )
&
=
& & &
.. Making loops for rest of infectious area DO 20 I = IS+1, IE D_INF(CH,I-LE) = + ( K_INF * COHORT(CH,I-1) ) - ( K_INF * COHORT(CH,I) ) - COHORT(CH,I) * ( 1.00-DL_RATE_CH(CH) ) D_INF(CH,I-LE) = MAX( -COHORT(CH,I), D_INF(CH,I-LE) ) CONTINUE
!
& 20 !
.. If cohort area is zero, there is no D_INF ELSE DO 22 I = IS, IE D_INF(CH,I-LE) = 0.0 CONTINUE
22 !
.. IF: ( COHORT_TOTAL(CH).NE.0.0 ) END IF
!
// D_POS D_POS(CH) = + ( K_INF * COHORT(CH,IE) ) - ( COHORT(CH,IE+1) * ( 1.00-DL_RATE_CH(CH) ) )
& & ! 30
.. CLOSING: DO LOOP FOR COHORT CONTINUE
!
// DO LOOP FOR COHORT AREA INTEGRATION DO 50 CH = 1, 200
!
.. INTEGRATION: VAC COHORT(CH,1) = COHORT(CH,1) + D_VAC(CH)
!
.. INTEGRATION: LAT DO 32 I = LS, LE COHORT(CH,I) = COHORT(CH,I) + D_LAT(CH,I-1) CONTINUE
32
151 !
34
.. INTEGRATION: INF DO 34 I = IS, IE COHORT(CH,I) = COHORT(CH,I) + D_INF(CH,I-LE) CONTINUE
!
.. INTEGRATION: POS COHORT(CH,IE+1) = COHORT(CH,IE+1) + D_POS(CH)
!
.. AREA CAN'T BE ZERO DO 36 I = 1, IE+1 COHORT(CH,I) = MAX( 0.0, COHORT(CH,I) ) CONTINUE
36 !
38
.. Intermediate Cohort_Total COHORT_TOTAL(CH) = 0.0 DO 38 I = 1, IE+1 COHORT_TOTAL(CH) = COHORT_TOTAL(CH) + COHORT(CH,I) CONTINUE
50
CONTINUE
!
// SUMMATION OF COHORT AREAS SUM_CH = 0.0 DO 52 CH = 1, 200 SUM_CH = SUM_CH + COHORT_TOTAL(CH) CONTINUE
52
! ! ! ! !
// SUBTRACT SENESCENCE .. Natural senescence is assumed to be come from .. the oldest cohort. .. Area should be subtracted is a difference between .. total cohort area and total leaf area. LOST_AREA = MAX( 0.0, SUM_CH-AREALF )
!
.. Internal loop to cover all cohort DO 53 CH = 1, 200
!
.. Only if the cohort is not abscised yet IF (COHORT_ABSCISED(CH).EQ.0.0) THEN
! !
.. _AS stands for 'After Senescenced' .. Initially set to be same as _TOTAL COHORT_TOTAL_AS(CH) = COHORT_TOTAL(CH)
! ! !
.. .. .. IF
! ! ! !
.. If a cohort area is greater than the lost area, .. cohort area will be reduced by the lost area and .. lost area becomes zero. COHORT_TOTAL_AS will remember .. the cohort area after senescence occurred. ELSE COHORT_TOTAL_AS(CH) = COHORT_TOTAL(CH) - LOST_AREA LOST_AREA = 0.0 END IF
! ! !
.. .. .. IF
!
If a cohort area is less than the lost area, that cohort will be abscised and the lost area will be reduced by the disposed area. (COHORT_TOTAL(CH).LE.LOST_AREA) THEN COHORT_TOTAL_AS(CH) = 0.0 COHORT_ABSCISED(CH) = COHORT_TOTAL(CH) LOST_AREA = LOST_AREA - COHORT_TOTAL(CH)
This section adjusts areas in each cohort array. If a cohort area became zero after senescence, all of boxcars in the cohort become zero. (COHORT_TOTAL_AS(CH).EQ.0.0) THEN COHORT(CH,1:IE+1) = 0.0 ELSE .. If a cohort area should be reduced after
152 ! ! !
.. senescence, calculate the rate of cohort area, .. which should be used for each array boxcar's .. reduction. OL_RATE = COHORT_TOTAL_AS(CH) / COHORT_TOTAL(CH) DO 55 J = 1, IE+1 COHORT(CH,J) = COHORT(CH,J) * OL_RATE CONTINUE
55 END IF !
.. IF: (COHORT_ABSCISED(CH).EQ.0.0) END IF
53
CONTINUE
! !
// DO LOOP FOR COHORT .. Total area calculation for each stage DO 70 CH = 1, 200
!
.. Cohort_LAT(*): Total latent area in a cohort COHORT_LAT(CH) = 0.0 DO 62 I = LS, LE COHORT_LAT(CH) = COHORT_LAT(CH) + COHORT(CH,I) CONTINUE
62 !
.. Cohort_INF(*): Total infectious area in a cohort COHORT_INF(CH) = 0.0 DO 64 I = IS, IE COHORT_INF(CH) = COHORT_INF(CH) + COHORT(CH,I) CONTINUE
64 !
.. Storing VAC and POS area in a cohort COHORT_VAC(CH) = COHORT(CH,1) COHORT_POS(CH) = COHORT(CH,IE+1) COHORT_DISEASED(CH) = COHORT_INF(CH) + COHORT_POS(CH)
! ! !
.. .. .. DO
& 66 ! ! &
Cohort_Diseased corresponds area shows symptom, which includes (1)infectious, (2)post-infectious, and (3)latent but has symptom 66 I = 0, INT_SS-1 COHORT_DISEASED(CH) = COHORT_DISEASED(CH) + COHORT(CH,LE-I) CONTINUE .. Summation of V, L, I and P in a Cohort .. COHORT_TOTAL(*): Total area in a cohort COHORT_TOTAL(CH) = COHORT_VAC(CH) + COHORT_LAT(CH) + COHORT_INF(CH) + COHORT_POS(CH)
70
CONTINUE
! !
// PDS & DEFOLIATION CALCULATION IN COHORT .. Get a cohort area when there is no disease IF (DAP.GE.1) CALL GetNDLA('PNLS', YRSIM, DAP, NDLA_CH)
!
.. Loop for PDS & defoliation DO 75 CH = 1, 200
! ! ! ! !
!
.. .. .. .. .. IF
PDS in cohort Definition of PDS includes defoliation effects. Abscised leaf contributes to the PDS. Ref: Berger's class note 'Calculation..', Berger and Plaut, 1981. (DAP.EQ.1) THEN PDS(CH) = 0.0 ELSE .. No PDS when it's not emerged yet
153 IF (.NOT.EMERGENCE_CH(CH) .OR. DAP.LT.CH) THEN Y_VISIBLE_CH(CH) = 0.0 Y_DEF_CH(CH) = 0.0 !
.. Only if it's not abscised or defoliated ELSE IF ( COHORT_TOTAL(CH).NE.0.0 .AND. NDLA_CH(CH).NE.0.0 ) THEN
& !
.. Y_VISIBLE TDISLA_CH = COHORT_DISEASED(CH) IF (COHORT_TOTAL(CH).NE.0.0) THEN Y_VISIBLE_CH(CH) = TDISLA_CH/COHORT_TOTAL(CH) ELSE Y_VISIBLE_CH(CH) = 0.0 END IF
&
!
.. Y_DEF Y_DEF_CH(CH) = MIN(1.0, 1-COHORT_TOTAL(CH)/NDLA_CH(CH))
& ELSE
Y_DEF_CH(CH) = 1.0 END IF ! & &
=
.. Calculation of PDS PDS(CH) MAX( 0.0, MIN( (1-Y_DEF_CH(CH))*Y_VISIBLE_CH(CH) + Y_DEF_CH(CH), 1.0 ) ) * 100.00
!
.. IF:(DAP.EQ.1) END IF
! ! !
.. Calculating FLCINF .. Fraction of infected area in each cohort is used for cohort .. output and calculating defoliation and expanded lesion area. INF_CH(CH) = COHORT_LAT(CH)+COHORT_INF(CH)+COHORT_POS(CH) IF (COHORT_TOTAL(CH).NE.0 .AND. COHORT_ABSCISED(CH).EQ.0.0) THEN FLCINF_CH(CH) = INF_CH(CH)/COHORT_TOTAL(CH) ELSE FLCINF_CH(CH) = 0.0 END IF
&
! ! !
.. .. .. IF
Defoliation rate due to disease calculated from diseased area unless the leaf has abscised. Regressed from Bourgeios' data, P.65 (FLCINF_CH(CH).LE.0.3) THEN DEFOL(CH) = 0.0 ELSE IF (FLCINF_CH(CH).GE.0.75) THEN DEFOL(CH) = 1.0 ELSE
! ! ! ! ! ! ! ! ! !
.. DEFFAC = DEFPRO * EXP(-FEX3*(DFSMAX-FLCINF) /(DFSMAX-DFSMIN)) .. DEFPRO: Maximum disease induced defoliation factor that can occur in one day (1 by GB) .. DEFFAC: Disease induced defoliation factor .. FEX3 : Constant (4. by GB) .. DFSMAX: Infected leaf area proportion at which maximum defoliation occurs (0.75 by GB) .. DFSMIN: Infected leaf area proportion at which minimum defoliation occurs (0.3 by GB) DEFOL(CH) = 1 * EXP(-4.*(0.75-FLCINF_CH(CH))/0.45) DEFOL(CH) = MIN( 1.0, MAX( 0.0, DEFOL(CH) ) ) END IF
154 !
.. Defoliation IF (DAP.GE.1) THEN
!
.. Save to get today's increment EX_DEF_AREA = DEF_AREA(CH)
!
.. Defoliated area in A cohort up to today. DEF_AREA(CH) = MAX( DEF_AREA(CH), DEFOL(CH)*NDLA_CH(CH) )
& ! !
&
!
.. If a leaf has abscised, the defoliated area .. is the abscised area. IF (Y_DEF.GT.0.0) THEN DEF_AREA(CH) = MAX( COHORT_ABSCISED(CH), DEF_AREA(CH) ) END IF
&
.. Today's increment of defoliated area DL_AREA_CH(CH) = DEF_AREA(CH) - EX_DEF_AREA
&
.. Defoliated area in ALL cohorts up to today. DEF_AREA_TOTAL(DAP) = DEF_AREA_TOTAL(DAP) + DEF_AREA(CH)
!
!
.. Defoliated mass in a cohort up to today. EX_DEF_MASS = DEF_MASS(CH)
!
.. Defoliated mass in a cohort up to today. IF (STR_SLA(DAP).GT.0.0 .AND. COHORT_TOTAL(CH).NE.0.0) THEN DEF_MASS(CH) = MAX( EX_DEF_MASS, DEF_AREA(CH) / STR_SLA(DAP) ) ELSE DEF_MASS(CH) = EX_DEF_MASS END IF
& & &
! ! & ! &
.. Defoliated mass in a cohort on today only. .. (def up to today) - (def up to yesterday) WLIDISDOT_CH(CH) = MAX( 0.0, DEF_MASS(CH) - EX_DEF_MASS ) .. Defoliated mass in ALL cohorts on today only. WLIDISDOT_TOTAL(DAP) = WLIDISDOT_TOTAL(DAP) + WLIDISDOT_CH(CH)
!
.. IF: (DAP.GE.1) END IF
!
.. Defoliated mass shouldn't be bigger than WTLF. IF (DAP.GE.1) WLIDISDOT_TOTAL(DAP) = MAX( MIN( WLIDISDOT_TOTAL(DAP), WTLF ), 0.0 )
& ! 75
.. Closing: DO LOOP FOR PDS & DEFOLIATION CALCULATION CONTINUE
!
// SUMMATION OF V, L, I & P FOR ALL COHORTS IF (DAP.GE.1) THEN DO 80 CH = 1, 200 SUM_VAC(DAP) = SUM_VAC(DAP) + COHORT_VAC(CH) SUM_LAT(DAP) = SUM_LAT(DAP) + COHORT_LAT(CH) SUM_INF(DAP) = SUM_INF(DAP) + COHORT_INF(CH) SUM_POS(DAP) = SUM_POS(DAP) + COHORT_POS(CH) SUM_DISEASED(DAP) = SUM_DISEASED(DAP) + COHORT_DISEASED(CH) CONTINUE IF (DAP.GE.1) SUM_AREA(DAP) = SUM_VAC(DAP) + SUM_LAT(DAP) + SUM_INF(DAP) + SUM_POS(DAP) END IF
& 80 &
155
! ! ! ! ! ! !
// .. .. .. .. .. .. IF
!
.. Update defoliated mass to WLIDISDOT. CALL UpdateWLIDOT_PNLS(0.0, WLIDISDOT, 'PNLS')
! ! ! !
// .. .. .. IF &
COUPLED WITH WLIDISDOT Defoliated mass in ALL cohorts on yesterday only , which was used in today's DL subtraction. This leaf mass have gone and not considered in today's leaf cohort area calculations. And, it is going to be sent to VEGDM module with this coupling point to increase WLIDOT. (DAP.GT.1) WLIDISDOT = WLIDISDOT_TOTAL(DAP-1)
TDISLA_FD Diseased area due to this foliar disease. This includes halo surrounding diseased area. Halo multiplication is moved to here from GROW module. (DAP.GE.1) TDISLA_FD = MIN( SUM_AREA(DAP), HALO*SUM_DISEASED(DAP) )
! ! !
.. Update diseased area due to this foliar disease. .. TDISLA will be integrated into DISLA, the global coupling point .. to reduce healthy leaf area. CALL UpdateTDLA_PNLS(0.0, TDISLA_FD, 'PNLS')
! !
// PDS_TOTAL .. To be used later in Y_DEF calculation. NDLA = 0.0 DO 82 CH = 1, 200 NDLA = NDLA + NDLA_CH(CH) CONTINUE
82 ! ! &
.. Definition of PDS followed Berger and Plaut's, .. which includes defoliation effect IF (NDLA.NE.0.0 .AND. DAP.GE.1 .AND. SUM_AREA(DAP).NE.0.0) THEN Y_DEF = MAX( 0.0, MIN( 1.0, 1.0 - SUM_AREA(DAP)/NDLA ) ) Y_VISIBLE = SUM_DISEASED(DAP)/SUM_AREA(DAP) ELSE Y_DEF = 0.0 Y_VISIBLE = 0.0 END IF PDS_TOTAL = MIN(1.0, (1-Y_DEF)*Y_VISIBLE + Y_DEF )
C*********************************************************************** C*********************************************************************** C END OF DYNAMIC IF CONSTRUCT C*********************************************************************** ENDIF C*********************************************************************** END SUBROUTINE ! End of PT_PNLS
FCIDE_CT2L.FOR C======================================================================= C Fcide_CT2L C > Simulation of pesticide application and weathering C > Active ingredient(A.I.): Chlorothalonil C > 2-layered system adopted from Patterson & Nokes, 2000 C----------------------------------------------------------------------C Called by: PEST C Calls:
156 C GetTAVG .. to get TAVG from HMET C GetRAIN .. to get RAIN from HMET C GetRSTAGES .. to get STGDOY(9) and YRNR1 from RSTAGES C GetRSD .. to pass RSD to PT_TMEB C YR_DOY .. to convert YYDDD to YY and DDD C======================================================================= SUBROUTINE FCIDE_CT2L( & YRDOY, ! Input & DYNAMIC) ! Control
!----------------------------------------------------------------------IMPLICIT NONE !----------------------------------------------------------------------INTEGER RUNINIT, SEASINIT, EMERG, RATE, INTEGR, OUTPUT, FINAL PARAMETER (RUNINIT = 1, SEASINIT = 2, EMERG = 3, RATE = 3, & INTEGR = 4, OUTPUT=5, FINAL = 6) INTEGER DYNAMIC
!
// Parameters REAL PM_A, PM_C, PM_S, PM_T, PM_Z
!
// Rainfall REAL GT, RAIN, RAIN_CM
!
// Volatilization REAL DT, TAVG
!
// Schedule INTEGER YRDOY, TAPP, NUM_APP, INTAPP, YRNR1 INTEGER YY, DDD, EF_YD, EF_Y, EF_D, DA_EF INTEGER RISKYDAY, CMLDD LOGICAL FLOWER, HIGHRATE
!
// Residue at each canopy layer REAL RSD(2), RSD_H, RSD_L
!
// Loop controller INTEGER I, J
C*********************************************************************** C*********************************************************************** C Run Initialization - Called once per simulation C*********************************************************************** IF (DYNAMIC.EQ.RUNINIT .OR. DYNAMIC.EQ.SEASINIT) THEN
!
// Parameter initialization PM_A = -0.46 PM_C = -0.01 PM_T = 16.5 PM_S = 0.15 PM_Z = 0.05 TAPP = 1 INTAPP = 7 DA_EF = 0 RSD(1) = 0.0 !Higher canopy RSD(2) = 0.0 !Lower canopy FLOWER = .FALSE. HIGHRATE = .TRUE.
C*********************************************************************** C***********************************************************************
157 C Daily rate calculations C*********************************************************************** ELSEIF (DYNAMIC .EQ. RATE) THEN
!
// Get average temperature from HMET module CALL GetTAVG(TAVG,'FCDE')
!
// Get today's rainfall amount from IPWTH module CALL GetRAIN(RAIN,'FCDE')
! !
// YY, DDD .. Today CALL YR_DOY(YRDOY,YY,DDD)
!
.. Get STGDOY(9) and YRNR1 CALL GetRSTAGES(EF_YD,YRNR1,'FCDE')
!
.. Fruiting day CALL YR_DOY(EF_YD,EF_Y,EF_D)
! !
// Scheduling controller .. Cumulative days after this module has been started. CMLDD = CMLDD + 1
! ! ! !
.. .. .. .. IF
!
.. Fifty percent of plants have at least one flower when YRNR1 is set. IF (FLOWER.EQ..FALSE. .AND. YRNR1.NE.-99) FLOWER = .TRUE.
! !
// Decision to apply .. Days after last application is greater than the application interval. IF (TAPP.GT.INTAPP .AND.
! ! & !
EF_Y is YY of STGDOY(9) To decide how many days left till harvest. Today is STGDOY(9) when EF_Y equals YY. Otherwise, EF_Y = 99. (EF_Y.EQ.YY) DA_EF = DA_EF + 1
.. Not to apply from at least 2 days before harvest. .. Approximately, harvest is two days after STGDOY(9). DA_EF.LE.12) THEN .. It should be a dry day. IF ( RAIN_CM.EQ.0.0 ) THEN
! ! !
.. .. .. IF
Apply higher rate when it's flowering for the first time. Higher rate, 3pt/acre, which is a maximum rate. (FLOWER .AND. HIGHRATE) THEN RSD(1) = RSD(1) + 11.4 RSD(2) = RSD(2) + 4.5 FLOWER = .FALSE. HIGHRATE = .FALSE.
!
.. Otherwise, 2pt/acre, the normal rate. ELSE RSD(1) = RSD(1) + 7.6 RSD(2) = RSD(2) + 2.9 END IF
!
.. Reset 'days after application'. TAPP = 1
!
.. Update number of application. NUM_APP = NUM_APP + 1
158 ELSE !
.. Update days after application without application. TAPP = TAPP + 1 END IF ELSE
!
.. Update days after application without application. TAPP = TAPP + 1 END IF
! !
// Rainfall amount conversion .. Input data is in mm, and used in cm. RAIN_CM = RAIN * 0.1
! !
// gt .. Calculate rainfall effects. IF (RAIN_CM.GE.0.1) THEN GT = EXP( PM_A*(RAIN_CM**(1.0/3.0)) ) ELSE GT = 1.0 END IF
! !
// dt .. Calculate temperature effects. IF (TAVG.GT.PM_T) THEN DT = EXP( PM_C*(TAVG-PM_T) ) ELSE DT = 1.0 END IF
! !
// Residue .. Amount of residue in higher canopy. RSD_H = RSD(1) * GT * DT
!
.. Amount of residue in lower canopy. RSD_L = ((GT*DT)**PM_S) * RSD(2) + (1-(GT*DT))*PM_Z*RSD(1)
!
// Updating RSD(1) = RSD_H RSD(2) = RSD_L
!
// Saving CALL GetRSD(RSD, 'FCDE')
! ! !
// .. .. IF
Risky days Threshold level is 1.5 microgram/cm2. Ref: Elliott and Spurr, 1993. (RSD(1).LT.1.5 .OR. RSD(2).LT.1.5) RISKYDAY = RISKYDAY + 1
C*********************************************************************** C*********************************************************************** C Final C*********************************************************************** ELSEIF (DYNAMIC .EQ. FINAL) THEN
C*********************************************************************** C*********************************************************************** C END OF DYNAMIC IF CONSTRUCT C*********************************************************************** ENDIF
159
C*********************************************************************** END ! End of FCIDE_CT2L
PEST_TM.FOR C======================================================================= C Pest_TM, Subroutine C C Pest damage module for TOMATO C----------------------------------------------------------------------C Revision history C 03/24/01 JK Written C----------------------------------------------------------------------C Called : PEST C Calls : IPTOL C PT_TMEB C YR_DOY C======================================================================= SUBROUTINE PEST_TM( & FILEIO, YRDOY, YRPLT, & DYNAMIC)
!Input !Control
!----------------------------------------------------------------------IMPLICIT NONE !----------------------------------------------------------------------! Variable declaration !----------------------------------------------------------------------INTEGER, PARAMETER :: LUNIO=21, RUNINIT=1, SEASINIT=2, RATE=3 INTEGER DYNAMIC, ERRNUM, LNUM, FOUND INTEGER N_TM_PEST CHARACTER*6 ERRKEY, SECTION, VARNO PARAMETER (ERRKEY = 'IPPLNT') !
// Input data CHARACTER*12 FILEIO INTEGER YRDOY, YRPLT
!
// To save required tolerance CHARACTER*1 PST_STRT(20) ! INTEGER PST_DATE(20) ! CHARACTER*4 PST_TYPE(20) ! REAL PST_INIT(20) ! REAL PST_TOLR(20) !
!
// Adjustment of PST_DATE INTEGER YY_PLT, DD_PLT, YY_PST, DD_PST
!
// Controller INTEGER I LOGICAL SEASONAL_INIT
and pest information from fileio from fileio from fileio from fileio from file-TOL
!*********************************************************************** !*********************************************************************** ! INITIALIZATION !*********************************************************************** IF (DYNAMIC .EQ. RUNINIT) THEN !*********************************************************************** ! // Opening and reading format for fileio OPEN (LUNIO, FILE = FILEIO, STATUS = 'OLD', IOSTAT=ERRNUM) IF (ERRNUM .NE. 0) CALL ERROR(ERRKEY,ERRNUM,FILEIO,0) C----------------------------------------------------------------------C Read Cultivar Section C-----------------------------------------------------------------------
160 SECTION = '*CULTI' CALL FIND(LUNIO, SECTION, LNUM, FOUND) IF (FOUND .EQ. 0) THEN CALL ERROR(ERRKEY, 1, FILEIO, LNUM) ELSE READ (LUNIO,'(6X,A6)') VARNO ENDIF C----------------------------------------------------------------------C Read Pest Initial Condition Section C----------------------------------------------------------------------REWIND (LUNIO) SECTION = '*PEST ' CALL FIND(LUNIO, SECTION, LNUM, FOUND) IF (FOUND .EQ. 0) THEN CALL ERROR(ERRKEY, 1, FILEIO, LNUM) ELSE N_TM_PEST = 0 DO I = 1, 20 READ (LUNIO, 100, IOSTAT=ERRNUM, ERR=200) & PST_STRT(I), PST_DATE(I), PST_TYPE(I), PST_INIT(I) 100 FORMAT(1X, A1, 1X, I5, 1X, A4, 1X, F6.0) !
// Returns tolerance coefficient CALL IPTOL('TMGRO980.TOL', VARNO, PST_TYPE(I), PST_TOLR(I)) N_TM_PEST = N_TM_PEST + 1 ENDDO 200 CONTINUE ENDIF CLOSE (LUNIO)
!*********************************************************************** !*********************************************************************** ! SEASONAL INITIALIZATION !*********************************************************************** ELSEIF (DYNAMIC .EQ. SEASINIT) THEN !*********************************************************************** DO 350 I = 1, N_TM_PEST !
// To adjust YY in PST_DATE CALL YR_DOY(YRPLT,YY_PLT,DD_PLT) CALL YR_DOY(PST_DATE(I),YY_PST,DD_PST) IF (YY_PLT .NE. YY_PST) PST_DATE(I) = (YY_PLT*1000) + DD_PST
!
// To identify type of disease IF (PST_TYPE(I) .EQ. 'TMEB') THEN
!
// Call TMEB to make damages due to early blight CALL PT_TMEB( PST_STRT(I), PST_INIT(I), PST_TOLR(I), YRDOY, RUNINIT)
& & END IF 350
END DO
!*********************************************************************** !*********************************************************************** ! RATE CALCULATIONS !*********************************************************************** ELSEIF (DYNAMIC .EQ. RATE) THEN !*********************************************************************** DO 370 I = 1, N_TM_PEST !
!
// Call each disease module IF (PST_TYPE(I) .EQ. 'TMEB') THEN // Pass the pest initializing value
161 !
.. when today is later than pest initializing date IF ( (PST_STRT(I).EQ.'M' .OR. PST_STRT(I).EQ.'m') .AND. YRDOY.LT.PST_DATE(I) ) THEN CALL PT_TMEB( PST_STRT(I), 0.0, PST_TOLR(I), YRDOY, RATE) ELSE CALL PT_TMEB( PST_STRT(I), PST_INIT(I), PST_TOLR(I), YRDOY, RATE) END IF
& & &
& &
END IF 370
ENDDO
!*********************************************************************** !*********************************************************************** ! END OF DYNAMIC IF CONSTRUCT !*********************************************************************** ENDIF !*********************************************************************** RETURN END SUBROUTINE PEST_TM
PEST_PN.FOR C======================================================================= C Pest_PN, Subroutine C Pest damage module for PEANUT C----------------------------------------------------------------------C Revision history C 03/24/01 JK Written C----------------------------------------------------------------------C Called : PEST C Calls : IPTOL C PT_PNLS C YR_DOY C======================================================================= SUBROUTINE PEST_PN( & FILEIO, YRDOY, YRPLT, & DYNAMIC) !----------------------------------------------------------------------IMPLICIT NONE !----------------------------------------------------------------------! Variable declaration !----------------------------------------------------------------------INTEGER, PARAMETER :: LUNIO = 21, RUNINIT = 1, SEASINIT = 2, RATE = 3 INTEGER DYNAMIC, ERRNUM, LNUM, FOUND INTEGER N_PN_PEST CHARACTER*6 ERRKEY, SECTION, VARNO PARAMETER (ERRKEY = 'IPPLNT') !
// Input data CHARACTER*12 FILEIO INTEGER YRDOY, YRPLT REAL WTLF, AREALF, SLA, CLW
!
// Date INTEGER YY_PLT, DD_PLT, YY_PST, DD_PST
!
// To save required tolerance and pest information CHARACTER*1 PST_STRT(20) ! from fileio INTEGER PST_DATE(20) ! from fileio CHARACTER*4 PST_TYPE(20) ! from fileio
162 REAL REAL !
PST_INIT(20) PST_TOLR(20)
! from fileio ! from file-TOL
// Controller INTEGER I LOGICAL FIRSTDAY
!*********************************************************************** !*********************************************************************** ! INITIALIZATION !*********************************************************************** IF (DYNAMIC .EQ. RUNINIT) THEN !*********************************************************************** ! // Opening and reading format for fileio OPEN (LUNIO, FILE = FILEIO, STATUS = 'OLD', IOSTAT=ERRNUM) IF (ERRNUM .NE. 0) CALL ERROR(ERRKEY,ERRNUM,FILEIO,0) C----------------------------------------------------------------------C Read Cultivar Section C----------------------------------------------------------------------SECTION = '*CULTI' CALL FIND(LUNIO, SECTION, LNUM, FOUND) IF (FOUND .EQ. 0) THEN CALL ERROR(ERRKEY, 1, FILEIO, LNUM) ELSE READ (LUNIO,'(6X,A6)') VARNO ENDIF C----------------------------------------------------------------------C Read Pest Initial Condition Section C----------------------------------------------------------------------REWIND (LUNIO) SECTION = '*PEST ' CALL FIND(LUNIO, SECTION, LNUM, FOUND) IF (FOUND .EQ. 0) THEN CALL ERROR(ERRKEY, 1, FILEIO, LNUM) ELSE N_PN_PEST = 0 DO I = 1, 20 READ (LUNIO, 100, IOSTAT=ERRNUM, ERR=200) & PST_STRT(I), PST_DATE(I), PST_TYPE(I), PST_INIT(I) 100 FORMAT(1X, A1, 1X, I5, 1X, A4, 1X, F6.0) ! // Returns tolerance coefficient CALL IPTOL('PNGRO980.TOL', VARNO, PST_TYPE(I), PST_TOLR(I)) N_PN_PEST = N_PN_PEST + 1 ENDDO 200 CONTINUE ENDIF !
// To control PT modules FIRSTDAY = .TRUE. CLOSE (LUNIO)
!*********************************************************************** !*********************************************************************** ! SEASONAL INITIALIZATION !*********************************************************************** ELSEIF (DYNAMIC .EQ. SEASINIT) THEN !*********************************************************************** DO 350 I = 1, N_PN_PEST !
// To adjust YY in PST_DATE CALL YR_DOY(YRPLT,YY_PLT,DD_PLT) CALL YR_DOY(PST_DATE(I),YY_PST,DD_PST) IF (YY_PLT .NE. YY_PST) PST_DATE(I) = (YY_PLT*1000) + DD_PST
!
// To identify type of disease IF (PST_TYPE(I) .EQ. 'PNLS') THEN
163
!
// Call PNLS to make damages due to leaf spot CALL PT_PNLS( PST_STRT(I), PST_INIT(I), PST_TOLR(I), YRDOY, RUNINIT)
& & END IF 350
END DO
!*********************************************************************** !*********************************************************************** ! RATE CALCULATIONS !*********************************************************************** ELSEIF (DYNAMIC .EQ. RATE) THEN DO 370 I = 1, N_PN_PEST !
// Call each disease module IF (PST_TYPE(I) .EQ. 'PNLS') THEN
! !
// Pass the pest initializing value .. when today is later than pest initializing date IF ( (PST_STRT(I).EQ.'M' .OR. PST_STRT(I).EQ.'m') .AND. YRDOY.LT.PST_DATE(I) ) THEN CALL PT_PNLS( PST_STRT(I), 0.0, PST_TOLR(I), YRDOY, RATE) ELSE CALL PT_PNLS( PST_STRT(I), PST_INIT(I), PST_TOLR(I), YRDOY, RATE) END IF
& & &
& &
END IF 370
ENDDO
!*********************************************************************** !*********************************************************************** ! END OF DYNAMIC IF CONSTRUCT !*********************************************************************** ENDIF !*********************************************************************** RETURN END SUBROUTINE PEST_PN
MESSENGER.FOR C======================================================================= C GetGROW, J. Koo, 08/07/01 C Returns AREALF, CLW, SLA calculated in GROW module C----------------------------------------------------------------------C Input : MDL_NAME, AREALF, WLDOTN, SLA, WTLF from [GROW] C Output: AREALF, WLDOTN, SLA, WTLF to [PT_xxxx] C======================================================================= SUBROUTINE GetGROW(AREALF, CLW, WLDOTN, SLA, WTLF, MDL) IMPLICIT NONE REAL AREALF, TEMP_AREALF REAL CLW, TEMP_CLW REAL WLDOTN, TEMP_WLDOTN REAL SLA, TEMP_SLA REAL WTLF, TEMP_WTLF CHARACTER*4 MDL
164 IF (MDL.EQ.'GROW') THEN TEMP_AREALF = AREALF TEMP_CLW = CLW TEMP_WLDOTN = WLDOTN TEMP_SLA = SLA TEMP_WTLF = WTLF ELSE AREALF = TEMP_AREALF CLW = TEMP_CLW WLDOTN = TEMP_WLDOTN SLA = TEMP_SLA WTLF = TEMP_WTLF END IF END SUBROUTINE
C======================================================================= C GetHMET, J. Koo, 07/26/01 C Returns hourly TEMP and RH C----------------------------------------------------------------------C Input : MDL_NAME, TAIRHR, RHUMHR from [HMET] C Output: TAIRHR, RHUMHR to [PT_xxxx] C======================================================================= SUBROUTINE GetHMET(TAIRHR, RHUMHR, MDL) IMPLICIT NONE CHARACTER*4 MDL REAL TAIRHR(24), TEMP_TAIRHR(24) REAL RHUMHR(24), TEMP_RHUMHR(24) IF (MDL.EQ.'HMET') THEN TEMP_TAIRHR = TAIRHR TEMP_RHUMHR = RHUMHR ELSE TAIRHR = TEMP_TAIRHR RHUMHR = TEMP_RHUMHR END IF END SUBROUTINE
C======================================================================= C GetTAVG, J. Koo, 07/25/01 C Returns today's average temperature C----------------------------------------------------------------------C Input : MDL, TAVG from [HMET] C Output: TAVG to [PT_xxxx] C======================================================================= SUBROUTINE GetTAVG(TAVG, MDL) IMPLICIT NONE CHARACTER*4 MDL REAL TAVG, TEMP_TAVG IF (MDL.EQ.'HMET') THEN TEMP_TAVG = TAVG ELSE TAVG = TEMP_TAVG END IF END SUBROUTINE
C======================================================================= C GetRAIN, J. Koo, 07/30/01 C Returns today's rainfall C----------------------------------------------------------------------C Input : MDL_NAME, RAIN from [IPWTH] C Output: RAIN to [PT_XXXX] C=======================================================================
165 SUBROUTINE GetRAIN(RAIN, MDL) IMPLICIT NONE CHARACTER*4 MDL REAL RAIN, TEMP_RAIN IF (MDL.EQ.'IPWT') THEN TEMP_RAIN = RAIN ELSE RAIN = TEMP_RAIN END IF END SUBROUTINE
C======================================================================= C GetDAP, J. Koo, 08/15/01 C Returns DAP C----------------------------------------------------------------------C Input : MDL_NAME, DAP from [PEST] C Output: DAP to [PT_XXXX] C======================================================================= SUBROUTINE GetDAP(DAP, MDL) IMPLICIT NONE CHARACTER*4 MDL INTEGER DAP, TEMP_DAP IF (MDL.EQ.'PEST') THEN TEMP_DAP = DAP ELSE DAP = TEMP_DAP END IF END SUBROUTINE
C======================================================================= C GetF, J. Koo, 08/07/01 C Returns F calculated in DEMAND module C----------------------------------------------------------------------C Input : MDL_NAME, F from [DEMAND] C Output: F to [PT_xxxx] C======================================================================= SUBROUTINE GetF(F, MDL) IMPLICIT NONE REAL F, TEMP_F CHARACTER*4 MDL IF (MDL.EQ.'DMND') THEN TEMP_F = F ELSE F = TEMP_F END IF END SUBROUTINE
C======================================================================= C GetRSD, J. Koo, 07/25/01 C Returns RSD calculated in the Fcide_CT2L module C----------------------------------------------------------------------C Input : MDL_NAME, RSD from [FCIDE_CT2L] C Output: RSD to [PT_TMEB] C======================================================================= SUBROUTINE GetRSD(RSD, MDL) IMPLICIT NONE CHARACTER*4 MDL REAL RSD(2), TEMP_RSD(2)
166
IF (MDL.EQ.'FCDE') THEN TEMP_RSD(1) = RSD(1) TEMP_RSD(2) = RSD(2) ELSE RSD(1) = TEMP_RSD(1) RSD(2) = TEMP_RSD(2) END IF END SUBROUTINE
C======================================================================= C GetLAISL, J. Koo, 4/1/01 C Returns LAISL calculated in ETPHOT module C----------------------------------------------------------------------C Input : MDL_NAME, LAISL from [ETPHOT] C Output: LAISL to [PT_TMEB] C======================================================================= SUBROUTINE GetLAISL(LAISL, MDL) IMPLICIT NONE REAL LAISL, TEMP_LAISL CHARACTER*4 MDL IF (MDL.EQ.'ETPH') THEN TEMP_LAISL = LAISL ELSE LAISL = TEMP_LAISL END IF END SUBROUTINE C======================================================================= C Variable definitions for GetLAISL C----------------------------------------------------------------------C MDL Module identifier C LAISL LAI for sunlit canopy C TEMP_LAISL Temporary storage for LAISL C=======================================================================
C======================================================================= C UpdateTDLA_PNLS, J. Koo, 07/26/01 C Returns TDLA calculated in the PT_PNLS module C----------------------------------------------------------------------C Input : MDL_NAME, TDISLA_FD from [PNLS] C Output: TDLA to [PESTCP] C======================================================================= SUBROUTINE UpdateTDLA_PNLS(TDLA, TDISLA_FD, MDL) IMPLICIT NONE REAL TDLA, TDISLA_FD, DAM CHARACTER*4 MDL IF (MDL.EQ.'PNLS') THEN DAM = TDISLA_FD ELSE TDLA = TDLA + DAM END IF END SUBROUTINE
C======================================================================= C UpdateWLIDOT_PNLS, J. Koo, 07/26/01 C Returns WLIDOT calculated in the PT_PNLS module C----------------------------------------------------------------------C Input : MDL_NAME, WLIDISDOT from [PNLS] C Output: WLIDOT to [VEGDM] C======================================================================= SUBROUTINE UpdateWLIDOT_PNLS(WLIDOT, WLIDISDOT, MDL)
167
IMPLICIT NONE REAL WLIDOT, WLIDISDOT, DAM CHARACTER*4 MDL IF (MDL.EQ.'PNLS') THEN DAM = WLIDISDOT ELSE WLIDOT = WLIDOT + DAM END IF END SUBROUTINE
C======================================================================= C UpdateTDLA_TMEB, J. Koo, 07/24/01 C Returns TDLA calculated in the PT_TMEB module C----------------------------------------------------------------------C Input : MDL_NAME, TDISLA_FD from [PT_TMEB] C Output: TDLA to [PESTCP] C======================================================================= SUBROUTINE UpdateTDLA_TMEB(TDLA, TDISLA_FD, MDL) IMPLICIT NONE REAL TDLA, TDISLA_FD, DAM CHARACTER*4 MDL IF (MDL.EQ.'TMEB') THEN DAM = TDISLA_FD ELSE TDLA = TDLA + DAM END IF END SUBROUTINE
C======================================================================= C UpdateWLIDOT_TMEB, J. Koo, 07/24/01 C Returns WLIDOT calculated in the PT_TMEB module C----------------------------------------------------------------------C Input : MDL_NAME, WLIDISDOT from [PT_TMEB] C Output: WLIDOT to [VEGDM] C======================================================================= SUBROUTINE UpdateWLIDOT_TMEB(WLIDOT, WLIDISDOT, MDL) IMPLICIT NONE REAL WLIDOT, WLIDISDOT, DAM CHARACTER*4 MDL IF (MDL.EQ.'TMEB') THEN DAM = WLIDISDOT ELSE WLIDOT = WLIDOT + DAM END IF END SUBROUTINE
C======================================================================= C GetRSTAGES, J. Koo, 07/24/01 C Returns STGDOY(9), YRNR1 calculated in RSTAGES module C----------------------------------------------------------------------C Input : MDL_NAME, STGDOY(9), YRNR1 from [RSTAGES] C Output: STGDOY(9), YRNR1 to [FCIDE_2TCL] C======================================================================= SUBROUTINE GetRSTAGES(STGDOY_9, YRNR1, MDL) IMPLICIT NONE INTEGER STGDOY_9, TEMP_STGDOY_9 INTEGER YRNR1, TEMP_YRNR1 CHARACTER*4 MDL
168
IF (MDL.EQ.'RSTG') THEN TEMP_STGDOY_9 = STGDOY_9 TEMP_YRNR1 = YRNR1 ELSE STGDOY_9 = TEMP_STGDOY_9 YRNR1 = TEMP_YRNR1 END IF END SUBROUTINE
GetNDLA.FOR C======================================================================= C GetNDLA, J. Koo, 03/18/02 C Returns NDLA C----------------------------------------------------------------------C Input : YRSIM, DAP C Output: NDLA C======================================================================= SUBROUTINE GetNDLA(PTYPE, YRSIM, DAP, NDLA_CH) IMPLICIT NONE INTEGER YRSIM, DAP INTEGER YRSIM_IN, DAP_IN CHARACTER*4 PTYPE REAL NDLA_CH(200)
!
// Open the raw data file IF (PTYPE.EQ.'TMEB') THEN OPEN (8200, FILE='NDLACH_TMEB.TXT') ELSE IF (PTYPE.EQ.'PNLS') THEN OPEN (8200, FILE='NDLACH_PNLS.TXT') END IF
! 8210
// Format for reading data row FORMAT (I, I, 200(F12.5))
!
// Look up the right line DO 8250 WHILE (YRSIM.NE.YRSIM_IN .OR. DAP.NE.DAP_IN)
! &
// Read line-by-line until header found READ (8200, 8210, END=8230) YRSIM_IN, DAP_IN, NDLA_CH(1:200)
! 8230
// Cares end-of-line error IF (EOF(8200)) THEN PRINT *, "ERR: Corresponding NDLA data not found", DAP GOTO 8252 END IF
! 8250
// Ending DO ENDDO
! 8252
// Closing file CLOSE(8200) END SUBROUTINE
APPENDIX E DESCRIPTION OF VARIABLES USED IN SOURCE FILES For coupling FODIS model with the CROPGRO model, seven source code files were created (Table C-1). Description of variables used in those files is given in this appendix. PT_TMEB and PT_PNLS
Name
DESCRIPTION
UNIT
AGE AREA_EXP
Age of plant daily counted from the planting date Lesion expanded
cm2[leaf]/m2[ground]
AREALF
Area of leaves
cm2[leaf]/m2[ground]
CH
Identifier of a cohort
CLW
Cumulative leaf growth
COHORT
Array to store cohort area
cm2[leaf]/m2[ground]
COHORT_ABSCISED
Abscised area in a cohort
cm2[leaf]/m2[ground]
COHORT_DISEASED
Diseased area in a cohort
cm2[leaf]/m2[ground]
COHORT_INF
Infectious area in a cohort
cm2[leaf]/m2[ground]
COHORT_LAT
Latently infected area in a cohort
cm2[leaf]/m2[ground]
COHORT_PAGE
Physiological age of a cohort
cm2[leaf]/m2[ground]
COHORT_POS
Post-infectious area in a cohort
cm2[leaf]/m2[ground]
COHORT_TOTAL
Total area of a cohort
cm2[leaf]/m2[ground]
COHORT_TOTAL_AD Total area of a cohort after defoliation COHORT_TOTAL_AS Total area of a cohort after senescenced COHORT_VAC Vacant (healthy) area in a cohort
day
g[leaf]/m2[ground]
cm2[leaf]/m2[ground] cm2[leaf]/m2[ground] cm2[leaf]/m2[ground]
D_INF
Rate of change in infectious area
day-1
D_LAT
day-1
D_POS
Rate of change in latently infected area Rate of change in post-infectious area
D_VAC
Rate of change in vacant area
day-1
DAP
Days after transplanting
169
day-1 day
170 DEF_AREA DEF_AREA_TOTAL DEF_MASS DEFOL DIS_PLANT DL_AREA_CH DL_AREA_TOTAL DL_RATE_CH EMERGENCE
Defoliated area in a cohort up to today Defoliated area in all cohorts up to today Defoliated mass in a cohort up to today Defoliation rate in a cohort due to disease Logical variable to indicate the disease or infection process started Defoliated area in a cohort for a day
cm2[leaf]/m2[ground]
Defoliated area in all cohort for a day (cm2/m2) Rate of area defoliation in a cohort
cm2[leaf]/m2[ground]
cm2[leaf]/m2[ground] g[leaf]/m2[ground] day-1
cm2[leaf]/m2[ground]
day-1
EX_DEF_AREA
Logical variable to indicate the day of emergence Previous value of DEF_AREA
EX_DEF_MASS
Previous value of DEF_MASS
g[leaf]/m2[ground]
F
Specific leaf area of new leaf tissue growth (cm2/g) Environmental factor to be used to get the basic infection rate Leaf wetness factor to be used to get the basic infection rate Temperature factor to be used to get the basic infection rate Favorable level of disease efficiency to initiate infection processes Fungicide efficiency coefficient
m2[ground]/g[leaf]
F_ENV F_LW F_TMP FAV_LEVEL FC_EFF FCIDECFF
FP
Effectiveness of fungicide for calculation Date of the most recent fungicide application Fraction of infected area in each cohort Infectious period
H
Loop controller
HALO HR
Multiplication factor to include haloed area surrounding diseased area Loop controller for counting hours
I
Loop controller
FCIDEDAT FLCINF_CH
cm2[leaf]/m2[ground]
day-1
day
day
171 IE INF_CH
Column of the cohort array where the infectious stage ends Fraction of infected area in a cohort
INI_CH_AREA
Initial cohort area
INI_DIS
Initial proportion of disease
INI_INF
Initial area of infectious infection
cm2[leaf]/m2[ground]
INI_LAT
Initial area of latently infection
cm2[leaf]/m2[ground]
INT_SS
J
Interval between showing symptom and sporulation Column of the cohort array where the infectious stage starts Loop controller
K
Loop controller
K_INF
KLEX
Parameter for distributed delay in infectious stage Parameter for distributed delay in latent stage Lesion expansion rate
L
Loop controller
LAISL
LAI for sunlit canopy
LE
Column of the cohort array where the latent stage ends Naturally senescenced area in all cohort Latent period
IS
K_LAT
LOST_AREA LP LS LWD LWD_REQ
Column of the cohort array where the latent stage start Leaf wetness duration
MIN_FP
LWD required to initiate infection process Minimum infectious period
MIN_LP
Minimum latent period
OL_RATE
P_AGE
Area reduction rate to apply losses due to other factors than disease Susceptible tomato physiological age to early blight Physiological age of the plant
P_TIME
Physiological time elapsed today
PDS
Disease severity value in a cohort
ONSET_AGE
cm2[leaf]/m2[ground]
day
day-1 day-1 day-1 m2[leaf]/m2[ground]
cm2[leaf]/m2[ground] day
hour hour day day day-1 physiological age
day-1
172 PDS_TOTAL
day-1
R
Disease severity value for whole canopy Input parameter of initial amount of disease Input switch to decide way to initiate infection process (A/M) Input parameter of tolerance coefficient Basic infection rate
RAIN
Amount of rainfall on today
mm
RAINYHRS
Duration of rainfall
hour
RH93HRS
Hours having RH higher than 93%
hour
RHUMHR
Hourly RH
RMAX
Maximum of the basic infection rate
RSD
SLA
Concentration of fungicide residue in each canopy layer Concentration of fungicide residue in layer 1 on yesterday Specific leaf area
g[leaf]/m2[ground]
STR_SLA
Stored SLA
g[leaf]/m2[ground]
SUM_AREA
Summation of all cohorts' area
cm2[leaf]/m2[ground]
SUM_CH
cm2[leaf]/m2[ground]
TAIRHR
Summation of all cohorts' area to apply natural senescence Summation of diseased area in all cohorts Summation of infectious area in all cohorts Summation of latent area in all cohorts Summation of post-infectious area in all cohorts Summation of vacant area in all cohorts Hourly temperature
TAVG
Daily average temperature
PST_INIT PST_STRT PST_TOLR
RSD1_EX
SUM_DISEASED SUM_INF SUM_LAT SUM_POS SUM_VAC
day-1
% day-1 micro g/cm2[leaf] micro g/cm2[leaf]
cm2[leaf]/m2[ground] cm2[leaf]/m2[ground] cm2[leaf]/m2[ground] cm2[leaf]/m2[ground] cm2[leaf]/m2[ground] ºC ºC 2
2
TDISLA_CH
Total diseased leaf area in a cohort
cm [leaf]/m [ground]
TDISLA_FD
Total diseased leaf area in all cohorts
cm2[leaf]/m2[ground]
WHOLE_AREA
Whole plant leaves area before cm2[leaf]/m2[ground] defoliation Dry weight growth rate of new leaf g[leaf]/m2[ground] day-1 tissue including N but not C reserves
WLDOTN
173
WLIDISDOT_TOTAL
Disease-induced defoliated mass in g[leaf]/m2[ground] day-1 all cohorts on today to be passed to the coupling point Disease-induced defoliated mass in a g[leaf]/m2[ground] day-1 cohort Daily stored WLIDISDOT g[leaf]/m2[ground] day-1
WTLF
Leaf weight
Y_DEF
Proportion of defoliation in all cohorts Proportion of defoliation in a cohort
day-1
day-1
YRDOY
Proportion of visible symptom in all cohorts Proportion of visible symptom in a cohort Today's date (YYDDD)
YY
Year (YY)
WLIDISDOT WLIDISDOT_CH
Y_DEF_CH Y_VISIBLE Y_VISIBLE_CH
g[leaf]/m2[ground]
day-1
day-1
FCIDE_CT2L
Name
Description
CMLDD DA_EF
Cumulative days since this module has been started Number of days after STGDOY(9)
DDD
DDD component of YRDOY
DT
Effect of temperature
EF_D
DDD component of EF_YD
EF_Y
YY component of EF_YD
EF_YD
Fruiting day (YYDDD)
FLOWER
Logical variable to determine if it's flowering season Effect of rainfall
GT HIGHRATE I INTAPP J NUM_APP PM_A
Unit day day day-1
day-1
Logical variable to apply higher rate once in a season Loop controller Interval between fungicide applications. Set to 7 days. Loop controller Number of application for the current season Parameter A
day
174 PM_C
Parameter C
PM_S
Parameter S
PM_T
Parameter T
PM_Z
Parameter Z
RAIN
Amount of rainfall in mm
mm
RAIN_CM
Amount of rainfall in cm
cm
RISKYDAY
day
TAPP
Number of risky day when amount of residue is lower than threshold level Residue concentration in each layer: RSD(1)=RSD_H, RSD(2)=RSD_L Residue in higher canopy after weathering Residue in lower canopy before weathering Number of days since last application
TAVG
Today's average temperature
YRDOY
Today's YYDDD
YRNR1
Day when 50% of plants have at least one flower (YYDDD) YY component of YRDOY
RSD RSD_H RSD_L
YY
µg/cm2[leaf] µg/cm2[leaf] µg/cm2[leaf] day ºC
PEST_TM and PEST_PN
Name
Description
FILEX
Experiment file, e.g., UFGA7801.SBX Loop controller
I MARKER PDATE PNO PST_DATE PST_INIT PST_TOLR
First two character in each line of Xfile Value in DATE field appeared in Pest Initial Condition Treatment level number for Pest Initial Condition section Array of value in DATE field (X-file, PIC section) Array of value in PVAL field (X-file, PIC section) Array of tolerance coefficient corresponding to PST_TYPE
Unit
175 PST_TYPE
VARNO
Array of value in PSTT field (X-file, PIC section) Value of tolerance coefficient in TOL file for a given cultivar Value in PSTT field appeared in Pest Initial Condition Value in PVAL field appeared in Pest Initial Condition Photo-thermal time that occurs in a Photo-thermal days/day real day based on early reproductive development temperature function Treatment number being simulated (from FILEX) Treatment number for PT (Pest Initial Condition) Treatment number in "Factor Level" of TRT_NO Variety number
YRDOY
Current day of simulation (YYDDD)
PTOLCFF PTYPE PVAL TDUMX TRTNO TRT_NO TRT_PT
GetNDLA
Name
Description
DAP
Days after planting date
DAP_IN
DAP read from an input file
NDLA_CH
Unit day day 2
2
cm [leaf]/m [ground]
PTYPE
Leaf area in a cohort of infection-free plant under same environments Type identifier of pest damage
YRSIM
Start of simulation date
YYDDD
YRSIM_IN
YRSIM read from an input file
YYDDD
MESSENGERS
Name
Description
AREALF
Area of leaves
CLW
Cumulative leaf growth
DAM
Daily absolute damage
DAP
Days after Planting
F
Specific leaf area of new leaf tissue growth
Unit cm2[leaf]/m2[ground] g[leaf]/m2[ground] day 2
cm [leaf]/g[leaf]
176 LAISL
LAI for sunlit canopy
MDL
Module identifier
RAIN
Precipitation depth for current day
RHUMHR(24)
Hourly relative humidity
RSD SLA
Concentration of fungicide residue in each canopy layer Specific leaf area
STGDOY_9
Day when stage 9 occurred
TAIRHR(24)
Hourly temperature
TAVG
Daily average temperature
m2[leaf]/m2[ground] mm % µg/cm2 cm2[leaf]/m2[ground] YYDDD C C 2
2
TDISLA_FD
Total diseased leaf area in all cohorts
cm [leaf]/m [ground]
TDLA
Total diseased leaf area
cm2[leaf]/m2[ground]
TEMP_AREALF
Temporary storage for AREALF
cm2[leaf]/m2[ground]
TEMP_CLW
Temporary storage for CLW
g[leaf]/m2[ground]
TEMP_DAP
Temporary storage for DAP
day
TEMP_F
Temporary storage for F
TEMP_LAISL
Temporary storage for LAISL
TEMP_RAIN
Temporary storage for RAIN
TEMP_RHUMHR(24)
Temporary storage for RHUMHR
TEMP_RSD
Temporary storage for RSD
cm2[leaf]/g[leaf]
TEMP_SLA
Temporary storage for SLA
cm2[leaf]/m2[ground]
TEMP_STGDOY_9
Temporary storage for STGDOY_9
TEMP_TAIRHR(24)
Temporary storage for TAIRHR
C
TEMP_TAVG
Temporary storage for TAVG
C
TEMP_WLDOTN
Temporary storage for WLTODN
TEMP_WTLF
Temporary storage for WTLF
TEMP_YRNR1
Temporary storage for YRNR1
WLDOTN
Dry weight growth rate of new leaf tissue including N but not C Disease-induced defoliated mass in g[leaf]/m2[ground] all cohorts on today to be passed to the coupling point Daily pest or freeze damage to leaf g[leaf]/m2[ground]/day mass Dry mass of leaf tissue including C g[leaf]/m2[ground] and N Day when 50% of plants have at least YYDDD one flower
WLIDISDOT WLIDOT WTLF YRNR1
cm2[leaf]/g[leaf] m2[leaf]/m2[ground] mm
YYDDD
YYDDD
APPENDIX F INPUT DATA FILES Experimental Input Data File FODIS-TMEB Bradenton, 1991 *EXP.DETAILS: UFBR9101.TMX *TREATMENTS ---------------FACTOR LEVELS------------@N R O C TNAME.................... CU FL SA IC MP MI MF MR MC MT ME MH SM PT 1 1 0 0 SUNNY SUBIRRIGATED 1 1 0 1 1 1 0 0 0 0 0 1 1 0 *CULTIVARS @C CR INGENO CNAME 1 TM TM0001 SUNNY S-D. *FIELDS @L ID_FIELD WSTA.... FLSA FLOB FLDT FLDD FLDS FLST SLTX SLDP ID_SOIL 1 UFBR0001 UFFP9801 0.0 0. 000 0. 100. 0000 SA 180. UFBR950001 @L ...........XCRD ...........YCRD .....ELEV .............AREA .SLEN .FLWR .SLAS 1 0.00000 0.00000 0.00 1.0 100. 1.0 0.0 *INITIAL @C PCR 1 @C ICBL 5. 15. 30. 45. 60. 90. 120. 150. 180. 203.
CONDITIONS ICDAT ICRT 0 0. SH2O SNH4 0.000 0.0 0.000 0.0 0.000 0.0 0.000 0.0 0.000 0.0 0.000 0.0 0.000 0.0 0.000 0.0 0.000 0.0 0.000 0.0
ICND 0. SNO3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
ICRN 0.00
ICRE 0.00
ICWD ICRES ICREN ICREP ICRIP ICRID 0.0 0 0.00 0.00 0. 0.
*PLANTING DETAILS @P PDATE EDATE PPOP 1 91263 0 1.0
PPOE 1.0
PLME T
PLDS R
PLRS 137.
*IRRIGATION AND WATER MANAGEMENT @I EFIR IDEP ITHR IEPT IOFF IAME 1 1.000 0. 0. 0. GS000 IR001
IAMT 0.0
PLRD 90.
PLDP 1.0
PLWT 3.
PAGE 28.
PENV 25.0
PLPH 1.0
SPRL 0.0
*PEST INITIAL CONDITION @T DATE PSTT PVAL.. A 91263 TMEB 0.25 *SIMULATION CONTROLS @N GENERAL NYERS 1 GE 1 @N OPTIONS WATER 1 OP N @N METHODS WTHER 1 ME M @N MANAGEMENT PLANT 1 MA R @N OUTPUTS FNAME 1 OU N
NREPS 1 NITRO N INCON M IRRIG R OVVEW N
START S SYMBI N LIGHT E FERTI R SUMRY N
SDATE 91263 PHOSP N EVAPO R RESID N FROPT 1
RSEED 2150 POTAS N INFIL S HARVS M GROUT Y
SNAME.................... POTENTIAL YIELD DISES CHEM TILL Y N N PHOTO HYDRO L R
CAOUT WAOUT NIOUT MIOUT DIOUT LONG CHOUT OPOUT N N N N N N N N
177
178 @ @N 1 @N 1 @N 1 @N 1 @N 1
AUTOMATIC MANAGEMENT PLANTING PFRST PLAST PL 9177 9177 IRRIGATION IMDEP ITHRL IR 45. 50. NITROGEN NMDEP NMTHR NI 200. 50. RESIDUES RIPCN RTIME RE 100 60 HARVEST HFRST HLAST HA 98182 98162
PH2OL 40. ITHRU 100. NAMNT 25. RIDEP 20 HPCNP 75
PH2OU 100. IROFF -99 NCODE SI001
PH2OD PSTMX PSTMN 30. 45. 40. IMETH IRAMT IREFF SI001 10.0 0.750 NAOFF SI001
HPCNR 0
Ft. Pierce, 1998 *EXP.DETAILS: UFFP9801.TMX *TREATMENTS -------------FACTOR LEVELS--------------@N R O C TNAME.................... CU FL SA IC MP MI MF MR MC MT ME MH SM PT 1 1 0 0 SUNNY SUBIRRIGATED 1 1 0 1 1 0 0 0 0 0 0 0 1 1 *CULTIVARS @C CR INGENO CNAME 1 TM TM0001 SUNNY S-D. *FIELDS @L ID_FIELD WSTA.... FLSA FLOB FLDT FLDD FLDS FLST SLTX SLDP ID_SOIL 1 UFFP0001 UFFP9801 0.0 0 000 0 0 0000 SA 180 UFBR950001 @L ...........XCRD ...........YCRD .....ELEV .............AREA .SLEN .FLWR .SLAS 1 0.00000 0.00000 0.00 0.0 0 0.0 0.0 *INITIAL @C PCR 1 PR @C ICBL 1 7 1 15 1 22 1 30 1 45 1 90 1 120 1 150 1 180
CONDITIONS ICDAT ICRT 98068 1 SH2O SNH4 0.086 0.2 0.086 0.1 0.086 0.0 0.086 0.0 0.086 0.0 0.076 0.0 0.076 0.0 0.130 0.0 0.258 0.0
ICND 0 SNO3 3.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0
ICRN 1.00
ICRE ICWD ICRES ICREN ICREP ICRIP ICRID 1.00 -99.0 0 0.00 0.00 100 15
*PLANTING DETAILS @P PDATE EDATE PPOP 1 98077 1.0
PPOE 1.0
PLME T
PLDS R
PLRS 137
NREPS 1 NITRO N INCON M IRRIG R OVVEW Y
START S SYMBI N LIGHT E FERTI R SUMRY Y
SDATE 98077 PHOSP N EVAPO R RESID N FROPT 1
RSEED 2150 POTAS N INFIL S HARVS M GROUT Y
AUTOMATIC MANAGEMENT PLANTING PFRST PLAST PL 98 77 98 77 IRRIGATION IMDEP ITHRL IR 45 50 NITROGEN NMDEP NMTHR NI 200 50 RESIDUES RIPCN RTIME RE 100 60 HARVEST HFRST HLAST HA 98139 98162
PH2OL 40 ITHRU 100 NAMNT 25 RIDEP 20 HPCNP 75
PH2OU 100 IROFF -99 NCODE SI001
PH2OD PSTMX PSTMN 30 45 40 IMETH IRAMT IREFF SI001 10 0.75 NAOFF SI001
PLRD 90
PLDP 1.0
PLWT 3
PAGE 28
PENV 25.0
PLPH 1.0
SPRL 0.0
*PEST INITIAL CONDITION @T DATE PSTT PVAL.. A1 98080 TMEB 0.25 *SIMULATION CONTROLS @N GENERAL NYERS 1 GE 1 @N OPTIONS WATER 1 OP N @N METHODS WTHER 1 ME M @N MANAGEMENT PLANT 1 MA R @N OUTPUTS FNAME 1 OU N @ @N 1 @N 1 @N 1 @N 1 @N 1
HPCNR 0
SNAME.................... POTENTIAL YIELD DISES CHEM TILL Y N N PHOTO HYDRO L R
CAOUT WAOUT NIOUT MIOUT DIOUT LONG CHOUT OPOUT N Y Y N N Y N N
179
Miami, 1948-1999 *EXP.DETAILS: UFMI4898.SNX *TREATMENTS -------------- FACTOR LEVELS -----------@N R O C TNAME.................... CU FL SA IC MP MI MF MR MC MT ME MH SM PT 1 1 0 0 180 N AUTO IRR. N-B "ON" 1 1 0 1 1 0 1 0 0 0 0 0 1 1 *CULTIVARS @C CR INGENO CNAME 1 TM TM0001 SUNNY S-D. GENERIC *FIELDS @L ID_FIELD WSTA.... FLSA FLOB FLDT FLDD FLDS FLST SLTX SLDP ID_SOIL 1 UFD10001 CBMI 0.0 0 DR000 0 0 0000 SA 180 UF000000D1 @L ...........XCRD ...........YCRD .....ELEV .............AREA .SLEN .FLWR .SLAS 1 0.00000 0.00000 0.00 0.0 0 0.0 0.0 *INITIAL @C PCR 1 PR @C ICBL 1 18 1 36 1 50 1 74 1 81 1 119 1 173 1 190 1 203
CONDITIONS ICDAT ICRT 48328 1 SH2O SNH4 0.066 0.5 0.067 0.5 0.065 0.5 0.076 0.0 0.077 0.0 0.097 0.0 0.080 0.0 0.076 0.0 0.400 0.0
ICND 0 SNO3 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0
ICRN 1.00
ICRE ICWD ICRES ICREN ICREP ICRIP ICRID 1.00 -99.0 0 0.00 0.00 100 15
*PLANTING DETAILS @P PDATE EDATE PPOP 1 48328 -99 1.1
PPOE 1.1
PLME T
PLDS R
PLRS 150
PLRD 90
PLDP 1.0
PLWT 3
FAMN 108 11 11 11
FAMP 0 0 0 0
FAMK 0 0 0 0
FAMC 0 0 0 0
FAMO 0 0 0 0
FOCD
*HARVEST DETAILS @H HDATE HSTG HCOM HSIZE HPC 1 160 -99 -99 -99 -99.0
HBPC 0.0
PAGE 28
PENV 25.0
PLPH 1.0
SPRL 0.0
*PEST INITIAL CONDITION @T DATE PSTT PVAL.. A1 48328 TMEB 0.25 *FERTILIZERS (INORGANIC) @F FDATE FMCD FACD FDEP 1 001 FE001 AP001 10 1 007 FE001 AP001 10 1 015 FE001 AP001 10 1 021 FE001 AP001 10
*SIMULATION CONTROLS @N GENERAL NYERS 1 GE 1 @N OPTIONS WATER 1 OP Y @N METHODS WTHER 1 ME M @N MANAGEMENT PLANT 1 MA R @N OUTPUTS FNAME 1 OU Y @ @N 1 @N 1 @N 1
NREPS 1 NITRO Y INCON M IRRIG A OVVEW Y
START S SYMBI N LIGHT E FERTI D SUMRY Y
SDATE 48328 PHOSP N EVAPO R RESID N FROPT 1
RSEED 2150 POTAS N INFIL S HARVS M GROUT N
SNAME.................... FERT TRIAL 51 YEAR DISES CHEM TILL Y N N PHOTO HYDRO L R
CAOUT WAOUT NIOUT MIOUT DIOUT LONG CHOUT OPOUT N N N N N N N N
AUTOMATIC MANAGEMENT PLANTING PFRST PLAST PH2OL PH2OU PH2OD PSTMX PSTMN PL 94 64 94 64 40 100 30 45 40 IRRIGATION IMDEP ITHRL ITHRU IROFF IMETH IRAMT IREFF IR 40 75 100 -99 SI001 10 1.00 NITROGEN NMDEP NMTHR NAMNT NCODE NAOFF NI 200 50 25 SI001 SI001
180 @N 1 @N 1
RESIDUES RE HARVEST HA
RIPCN RTIME RIDEP 100 60 20 HFRST HLAST HPCNP HPCNR 160 160 75 0
FODIS-PNLS Gainesville, 1987 *EXP.DETAILS: UFGA8701.PNX *TREATMENTS ---------------FACTOR LEVELS------------@N R O C TNAME.................... CU FL SA IC MP MI MF MR MC MT ME MH SM PT 1 1 1 0 NO LEAFSPOT CONTROL, IR 1 1 0 1 1 1 0 0 0 0 0 0 1 1 *CULTIVARS @C CR INGENO CNAME 1 PN IB0002 FLORUNNER *FIELDS @L ID_FIELD WSTA.... 1 UFGA0001 UFGA8701
FLSA 0.0
FLOB FLDT 0 DR000
*INITIAL @C PCR 1 PN @C ICBL 1 5 1 15 1 30 1 45 1 60 1 90 1 120 1 150 1 180
CONDITIONS ICDAT ICRT 87152 1 SH2O SNH4 0.086 0.0 0.086 0.0 0.086 0.0 0.086 0.0 0.086 0.0 0.076 0.0 0.076 0.0 0.130 0.0 0.258 0.0
ICND -99 SNO3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
ICRN 1.00
ICRE ICWD ICRES ICREN ICREP ICRIP ICRID 1.00 -99.0 0 0.00 0.00 0 0
*PLANTING DETAILS @P PDATE EDATE PPOP 1 87153 -99 14.7
PPOE 14.7
PLME S
PLDS R
PLRS 91
IAME -99
IAMT 10
SDATE 87152 PHOSP N EVAPO R RESID R FROPT 3
RSEED 2150 POTAS N INFIL S HARVS M GROUT Y
FLDD 0
FLDS FLST SLTX 0 00000 -99
PLRD 0
PLDP 4.0
SLDP ID_SOIL 180 IBPN910015
PLWT PAGE PENV PLPH -99 -99.0 -99.0 -99.0
SPRL 0.0
*PEST INITIAL CONDITION @T DATE PSTT PVAL.. A1 87153 PNLS 1.00 *IRRIGATION AND WATER MANAGEMENT @I IEFF IDEP ITHR IEPT IOFF 1 0.75 -99 -99 -99 -99 @I IDATE IROP IRVAL 1 87152 IR001 12 1 87155 IR001 11 1 87160 IR001 12 1 87167 IR001 12 1 87240 IR001 17 1 87252 IR001 31 1 87273 IR001 10 1 87288 IR001 10 *SIMULATION CONTROLS @N GENERAL NYERS 1 GE 1 @N OPTIONS WATER 1 OP Y @N METHODS WTHER 1 ME M @N MANAGEMENT PLANT 1 MA R @N OUTPUTS FNAME 1 OU N
NREPS 1 NITRO N INCON M IRRIG R OVVEW Y
START S SYMBI Y LIGHT E FERTI R SUMRY Y
SNAME.................... IRRIGATED, FLORUNNER, 2 D DISES CHEM TILL N N N PHOTO HYDRO C R
CAOUT WAOUT NIOUT MIOUT DIOUT LONG CHOUT OPOUT N Y Y N N N N N
@ AUTOMATIC MANAGEMENT @N PLANTING PFRST PLAST PH2OL PH2OU PH2OD PSTMX PSTMN 1 PL 155 200 40 100 30 40 10
181 @N 1 @N 1 @N 1 @N 1
IRRIGATION IR NITROGEN NI RESIDUES RE HARVEST HA
IMDEP 30 NMDEP 30 RIPCN 100 HFRST 0
ITHRL 50 NMTHR 50 RTIME 1 HLAST 365
ITHRU 100 NAMNT 25 RIDEP 20 HPCNP 100
IROFF GS000 NCODE FE001
IMETH IRAMT IREFF IR001 10 0.75 NAOFF GS000
HPCNR 0
Tolerance Coefficient Input Data File Tomato *CROP VARIETY SUSCEPTIBILITY COEFFICIENTS ! ! ! ! ! ! ! ! ! ! @
COEFF DEFINITIONS ======== =========== VAR# Identification code or number for a specific cultivar VAR-NAME Name of cultivar
TMEB
Relative resistance to... -------------------------------------0 = No resistance, 1 = Full resistance -------------------------------------Tomato early blight
VAR# VAR-NAME....... TMEB 990001 Tomato Example 0.79 TM0001 SUNNY S-D. 0.89
Peanut *CROP VARIETY SUSCEPTIBILITY COEFFICIENTS ! ! ! ! ! ! ! ! ! ! @
COEFF DEFINITIONS ======== =========== VAR# Identification code or number for a specific cultivar. VAR-NAME Name of cultivar
PNLS VAR# 990001 990002 IB0001 IB0002 IB0003 IB0004 IB0005 IB0006 IB0007 IB0008 IB0009 IB0011 IB0013 IB0015 IB0016 IB0017 IB0018 IB0019 IB0020 IB0021 IB0022 IB0023
Relative resistance to... -------------------------------------0 = No resistance, 1 = Full resistance -------------------------------------Peanut leaf spot disease VAR-NAME....... Spanish type Runner Type STARR,v tamnut FLORUNNER FLORIGIANT VALENCIA,v tamn TAMNUT PRONTO,v tamnu MARC I CHICO AGRITEC-127,vMa EARLY RUNNER SUNRUNNER,v flo SOUTHERN RUNNER EARLY BUNCH NC7, VIRGINIA GK3, VIRGINIA SHULAMIT, VA BU TIFTON-8,FLORIG Q18801,earlybun VIRG BUN, MODIF MCCUBBIN, m TAM
PNLS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
182 IB0024 IB0025 IB0026 IB0027 IB0031 IB0032 IB0033 IB0034 IB0035 IB0036 IB0037 IB0038 IB0040
TMV2, mod tamnu TAPIR, mod tamn ROBUT-33,uf v35 TMV-2,uf v 24 F81206,LS-RES MA72*94-12,LS-R 861 VIRGINIA BU 897 VIRGINIA BU ROBUT 33-1 v 5 CIANJUR #5 TAM RANGKASBITUNG 5 PIDIE #5 TAMN FLORIGRAZE PERE
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Weather Input Data File Gainesville, 1987 *WEATHER : UFGA @ INSI LAT UFGA 29.63 @DATE SRAD TMAX 87001 5.1 20.0 87002 10.8 13.3 87003 12.1 14.4 87004 3.6 18.3 87005 12.8 17.2 87006 12.4 21.1 87007 11.1 21.7 87008 12.0 21.7 87009 6.1 20.0 87010 3.5 23.3 87011 13.5 19.4 87012 14.1 15.0 87013 13.3 20.6 87014 12.1 23.3 87015 8.1 26.1 87016 5.3 24.4 87017 4.6 21.1 87018 9.7 27.8 87019 3.7 26.1 87020 8.0 17.8 87021 1.8 17.8 87022 5.5 16.1 87023 15.4 10.0 87024 13.6 16.1 87025 3.2 17.8 87026 5.5 17.2 87027 15.9 8.9 87028 7.6 17.8 87029 14.4 21.7 87030 10.9 22.8 87031 15.8 22.2 87032 15.8 23.3 87033 7.1 23.3 87034 12.3 21.7 87035 10.0 22.2 87036 1.2 16.1 87037 3.8 20.0 87038 2.9 17.2 87039 16.9 21.7 87040 17.8 16.1 87041 17.5 17.8 87042 15.0 20.0 87043 16.7 22.2 87044 17.3 23.9 87045 11.9 23.3 87046 13.9 25.6 87047 13.7 24.4 87048 8.8 17.8 87049 9.3 17.2 87050 17.2 20.0 87051 5.8 19.4 87052 2.1 16.7
LONG 82.37 TMIN 4.4 1.1 1.1 6.1 5.6 5.0 7.2 8.3 8.3 11.1 2.8 -2.2 3.9 2.8 11.1 14.4 14.4 11.1 9.4 10.0 8.9 8.3 1.1 -2.8 6.1 5.0 -1.1 -1.1 2.8 10.0 8.9 0.0 7.2 6.7 10.0 12.2 14.4 12.8 5.6 3.9 -2.2 -0.6 1.7 7.8 3.9 8.3 17.2 9.4 10.6 6.1 11.1 7.2
ELEV RAIN 23.9 0.0 0.0 14.7 0.8 0.0 0.0 0.0 0.0 3.8 0.0 0.0 0.0 0.0 0.0 6.3 2.8 0.0 5.1 0.0 15.5 33.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.6 0.0 0.0 43.7 1.8 1.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 30.2 0.0 0.0 0.0 0.0 32.5
TAV DEWP -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0
AMP REFHT WNDHT WIND -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0 -99.0
PAR 10.70 22.70 25.50 7.60 26.90 26.10 23.40 25.20 12.80 7.40 28.40 29.70 28.00 25.50 17.00 11.20 9.70 20.40 7.80 16.80 3.80 11.60 32.40 28.60 6.70 11.60 33.50 16.00 30.30 22.90 33.20 32.90 14.80 25.60 20.80 2.50 7.90 6.00 35.20 37.00 36.40 31.20 34.70 36.00 24.80 28.90 28.50 18.30 19.30 35.80 12.10 4.40
183 87053 87054 87055 87056 87057 87058 87059 87060 87061 87062 87063 87064 87065 87066 87067 87068 87069 87070 87071 87072 87073 87074 87075 87076 87077 87078 87079 87080 87081 87082 87083 87084 87085 87086 87087 87088 87089 87090 87091 87092 87093 87094 87095 87096 87097 87098 87099 87100 87101 87102 87103 87104 87105 87106 87107 87108 87109 87110 87111 87112 87113 87114 87115 87116 87117 87118 87119 87120 87121 87122 87123 87124 87125 87126 87127 87128 87129 87130 87131 87132 87133
4.6 15.2 5.5 12.8 8.0 15.1 13.1 16.5 9.2 16.4 19.6 18.7 8.1 1.9 16.3 9.3 19.2 11.6 10.9 21.5 21.8 18.9 21.0 19.5 7.1 19.2 16.3 22.3 17.1 17.4 11.9 10.4 3.3 2.7 7.9 7.0 2.8 17.3 25.5 24.5 13.0 26.5 26.1 24.8 22.2 21.5 20.0 21.4 24.9 25.5 24.3 12.4 20.0 21.9 15.8 20.0 25.7 22.9 22.8 23.8 17.7 26.0 26.0 24.8 25.6 25.8 27.1 25.7 25.9 26.8 23.9 22.4 15.9 17.4 22.1 16.0 13.7 15.7 15.7 19.1 18.5
22.8 20.6 20.6 23.3 21.7 27.8 32.8 27.2 20.6 22.2 23.9 21.1 20.0 19.4 23.9 20.0 24.4 22.8 15.6 15.0 22.2 24.4 25.6 25.0 23.9 26.7 22.2 26.1 27.8 26.1 25.6 28.3 28.3 22.8 27.2 26.7 20.6 17.8 16.1 20.6 22.8 21.7 17.8 21.7 23.3 23.3 25.0 26.7 26.7 27.8 30.0 30.0 27.2 25.6 23.3 25.0 27.2 29.4 31.7 33.9 33.9 30.6 30.0 25.0 27.8 30.0 30.0 32.2 32.2 31.7 32.8 32.2 32.2 27.2 30.0 31.1 27.8 29.4 28.3 30.0 29.4
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12.6 21.3 20.5 26.2 25.0 14.8 27.3 14.0 20.7 21.5 24.5 24.4 25.3 19.2 20.9 24.2 24.9 21.3 27.0 24.9 25.4 12.3 16.5 22.5 26.7 25.7 28.3 25.4 22.8 20.7 17.8 13.7 26.2 25.7 22.4 17.8 23.3 19.3 16.1 16.5 16.5 23.9 20.4 17.0 15.6 17.7 13.3 21.4 23.1 19.3 21.0 22.2 17.2 26.4 24.6 21.6 19.4 19.2 22.0 21.7 25.1 9.2 8.4 24.3 25.7 18.6 20.2 19.0 19.0 26.2 24.4 23.8 17.0 22.0 20.3 24.6 22.8 18.7 11.6 21.2 17.3
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27.70 46.80 45.00 57.60 54.90 32.50 60.00 30.80 45.50 47.20 53.80 53.60 55.60 42.20 45.90 53.20 54.70 46.80 59.60 55.00 56.10 27.20 36.40 49.70 59.00 56.70 62.50 56.10 50.30 45.70 39.30 30.20 57.80 56.70 49.50 39.30 51.40 42.60 35.50 36.40 36.40 52.80 45.00 37.50 34.40 39.10 29.40 47.20 51.00 42.60 46.40 49.00 38.00 58.30 54.30 47.70 42.80 42.40 48.60 47.90 55.40 20.30 18.50 53.70 56.70 41.10 44.60 42.00 42.00 57.80 53.90 52.50 37.50 48.60 44.80 54.30 50.30 41.30 25.60 45.70 37.30
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14.5 22.3 16.3 23.5 24.4 23.7 22.8 18.8 15.6 15.0 12.9 7.6 7.3 16.8 22.0 21.4 19.3 23.5 21.0 22.5 22.5 22.4 21.4 22.5 22.1 22.1 21.7 15.3 21.4 14.4 7.6 7.7 12.5 14.3 11.2 21.6 18.2 21.4 18.8 11.5 7.8 17.6 20.0 20.3 20.7 20.7 16.5 14.1 13.2 20.3 10.4 7.6 20.8 19.3 15.8 15.8 14.5 15.3 6.5 21.9 22.5 22.3 22.3 21.8 20.7 19.1 19.9 17.7 11.3 4.1 6.9 19.0 20.1 13.4 20.0 17.7 19.9 16.7 16.6 17.3 16.6
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31.20 48.00 35.10 50.60 52.60 51.10 49.10 40.50 33.60 32.30 27.80 16.40 15.70 36.20 47.40 46.10 41.60 50.60 45.20 48.50 48.50 48.30 46.10 48.50 47.60 47.60 46.80 33.00 46.10 31.00 16.40 16.60 26.90 30.80 24.10 46.50 39.20 46.10 40.50 24.80 16.80 37.90 43.10 43.70 44.60 44.60 35.60 30.40 28.40 43.70 22.40 16.40 44.80 41.60 34.00 34.00 31.20 33.00 14.00 34.20 34.90 34.60 35.20 34.60 33.00 40.80 42.50 28.70 18.20 9.80 13.40 30.40 31.90 22.20 32.10 29.40 31.90 27.20 27.30 27.70 26.30
186 87296 87297 87298 87299 87300 87301 87302 87303 87304 87305 87306 87307 87308 87309 87310 87311 87312 87313 87314 87315 87316 87317 87318 87319 87320 87321 87322 87323 87324 87325 87326 87327 87328 87329 87330 87331 87332 87333 87334 87335 87336 87337 87338 87339 87340 87341 87342 87343 87344 87345 87346 87347 87348 87349 87350 87351 87352 87353 87354 87355 87356 87357 87358 87359 87360 87361 87362 87363 87364 87365
13.9 13.7 7.6 10.7 9.6 18.0 16.9 17.4 7.9 10.5 10.0 2.6 6.8 9.0 14.2 14.8 13.2 12.2 12.4 15.8 11.0 16.9 14.3 15.5 12.6 3.5 8.0 2.2 14.2 16.1 15.2 12.5 11.4 9.6 10.1 10.0 9.0 11.3 10.8 9.3 11.9 12.1 7.8 13.1 8.9 8.2 8.9 11.4 5.4 12.3 10.1 5.3 10.0 1.9 12.2 13.0 12.1 9.9 8.1 9.0 2.6 12.0 8.7 7.7 10.6 7.7 9.4 13.1 12.8 8.3
24.4 27.8 22.2 26.1 31.7 21.1 22.2 25.6 23.9 28.3 27.2 22.2 25.6 26.7 26.1 25.6 26.7 29.4 27.8 18.9 16.7 22.2 26.1 26.7 28.9 26.7 25.0 21.1 17.8 17.8 21.7 24.4 25.6 26.1 26.7 26.7 22.8 25.6 17.8 18.3 17.8 17.2 20.6 15.0 15.6 22.8 26.1 28.3 24.4 22.2 22.2 20.6 26.7 25.0 17.8 12.8 18.9 23.3 25.6 26.7 26.1 21.7 27.2 27.8 28.9 27.2 26.7 20.0 16.1 22.2
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22.40 22.70 13.40 18.10 16.20 28.00 36.10 28.40 13.80 18.50 17.70 7.20 13.20 15.40 20.60 24.30 21.10 19.70 21.20 24.70 23.50 25.70 23.00 25.00 21.90 7.90 14.40 6.90 30.30 24.20 23.60 20.90 24.30 15.40 17.40 17.20 15.60 18.80 17.30 19.50 25.00 25.40 16.40 27.60 18.80 17.20 18.70 24.00 11.30 26.00 21.30 11.20 21.10 4.10 25.70 27.30 25.50 20.80 17.00 19.00 5.40 25.40 18.30 16.10 22.30 16.20 19.80 27.70 26.90 17.50
187 Bradenton, 1991-1992 *WEATHER : BRADENTON, FLORIDA, U.S.A, PARTIAL, WINTER SEASON @ INSI UFBR @DATE 91244 91245 91246 91247 91248 91249 91250 91251 91252 91253 91254 91255 91256 91257 91258 91259 91260 91261 91262 91263 91264 91265 91266 91267 91268 91269 91270 91271 91272 91273 91274 91275 91276 91277 91278 91279 91280 91281 91282 91283 91284 91285 91286 91287 91288 91289 91290 91291 91292 91293 91294 91295 91296 91297 91298 91299 91300 91301 91302 91303 91304 91305 91306 91307 91308 91309 91310 91311 91312 91313 91314
LAT 27.600 SRAD 19.7 13.9 20.7 16.8 20.9 20.8 21.3 11.4 12.4 15.2 16.9 16.8 19.0 18.9 18.9 16.6 11.1 13.8 16.3 15.5 17.0 14.4 12.9 13.1 16.1 15.9 17.1 19.7 16.5 15.7 10.7 4.9 15.2 15.5 13.0 13.7 10.6 15.0 15.4 9.8 14.4 15.1 14.1 16.2 15.4 6.4 17.3 17.3 17.7 3.9 15.2 7.2 13.6 13.6 12.7 8.0 14.3 12.1 13.3 10.6 13.9 15.1 12.4 7.4 9.2 7.0 6.5 12.7 10.1 13.5 12.4
TMAX 34.2 33.6 33.6 33.6 34.7 34.7 35.8 35.8 33.0 33.0 33.6 34.2 34.2 35.3 35.3 35.3 34.2 34.7 33.0 35.3 33.6 33.6 33.6 33.6 34.2 33.0 29.7 30.2 32.5 33.0 31.9 28.6 31.9 33.6 33.6 33.6 32.5 26.9 29.7 29.1 31.4 31.4 29.7 31.4 31.4 28.6 25.2 26.9 29.1 30.2 30.8 29.7 31.9 32.5 31.9 30.8 31.9 31.4 31.4 29.7 29.7 29.1 29.7 28.0 28.0 19.0 18.5 25.8 26.9 23.5 18.5
LONG 82.600 TMIN 23.0 22.4 23.5 23.5 23.5 23.5 23.5 24.6 21.3 23.0 23.0 23.0 22.4 23.5 23.0 23.0 22.4 23.0 21.3 23.0 23.5 22.4 23.5 22.4 23.5 23.0 15.1 16.8 21.3 22.4 22.4 22.4 23.0 22.4 23.0 22.4 17.4 17.4 19.6 20.7 19.6 16.2 15.7 17.9 17.9 17.9 10.1 11.8 15.7 17.9 19.6 20.7 19.0 19.6 21.8 21.8 20.7 18.5 18.5 17.9 12.9 14.0 18.5 19.6 13.4 10.1 11.2 15.7 12.9 11.2 07.3
ELEV TAV AMP REFHT WNDHT 10 -99.0 -99.0 -99.0 -99.0 RAIN 06.3 25.0 00.0 00.0 00.8 00.0 00.0 00.0 23.5 00.0 02.5 02.5 00.0 00.0 00.0 00.0 00.0 00.0 02.0 00.0 00.0 02.5 00.0 00.0 00.0 03.5 00.0 00.0 00.0 00.0 03.5 06.5 00.0 00.0 00.0 03.5 00.8 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 12.5 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 02.8 00.8 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 01.0 00.0
DEWP
WIND
PAR
CO2
188 91315 91316 91317 91318 91319 91320 91321 91322 91323 91324 91325 91326 91327 91328 91329 91330 91331 91332 91333 91334 91335 91336 91337 91338 91339 91340 91341 91342 91343 91344 91345 91346 91347 91348 91349 91350 91351 91352 91353 91354 91355 91356 91357 91358 91359 91360 91361 91362 91363 91364 91365 92001 92002 92003 92004 92005 92006 92007 92008 92009 92010 92011 92012 92013 92014 92015 92016 92017 92018 92019 92020 92021 92022 92023 92024 92025 92026 92027 92028 92029 92030
11.2 13.8 14.3 13.8 13.3 9.3 9.3 11.8 12.1 10.4 8.6 12.2 7.4 12.6 6.9 11.9 9.0 8.8 11.4 10.5 8.8 9.1 8.8 7.7 5.5 12.2 9.0 10.8 11.2 10.7 11.2 10.7 9.3 11.4 9.9 9.6 11.9 11.3 11.5 11.1 8.5 9.8 11.1 11.0 6.2 8.9 7.3 8.5 5.6 9.9 11.0 7.4 5.3 8.1 9.4 8.9 9.0 7.7 12.5 12.3 11.7 6.3 11.7 9.6 10.8 9.1 7.6 12.4 12.7 8.8 2.5 11.0 12.5 10.6 5.7 7.8 13.5 10.9 9.1 3.2 9.4
19.0 22.4 24.1 23.5 28.0 28.0 28.0 30.2 29.7 29.7 30.2 30.2 29.1 26.9 22.4 16.8 17.9 25.8 29.1 29.7 31.4 31.4 30.8 28.6 14.0 22.4 25.2 28.0 29.1 29.1 29.1 28.0 28.0 28.6 29.7 23.5 19.0 23.5 24.6 21.8 24.1 26.3 26.3 25.8 26.9 24.1 26.9 29.1 26.3 24.1 21.3 20.7 24.1 24.6 21.3 18.5 20.7 23.0 24.1 25.8 26.9 25.2 18.5 26.3 26.9 23.0 14.6 15.1 18.5 23.0 21.8 16.2 22.4 27.4 26.3 16.8 21.3 21.8 26.3 23.5 28.0
07.3 05.0 05.0 09.5 13.4 14.0 16.2 16.8 17.9 19.6 17.4 19.0 20.2 11.2 05.0 03.9 06.2 12.3 15.7 15.7 21.8 18.5 21.3 10.1 03.9 06.2 13.4 15.1 14.0 14.0 14.0 14.0 14.0 17.9 14.6 09.0 03.9 07.8 07.8 08.4 10.6 09.0 08.4 12.3 10.6 12.9 15.7 17.4 14.6 10.1 09.0 09.5 16.8 15.1 11.2 06.7 11.2 05.0 06.2 10.1 12.3 04.5 06.2 12.9 19.0 07.3 01.7 -01. 02.8 09.5 07.8 01.7 03.9 12.3 13.4 01.7 04.5 07.8 13.4 17.4 18.5
00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.5 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 10.0 00.5 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.5 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 01.3 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 01.0 00.0 00.0 00.0 00.0 00.0 08.8 00.5 00.0 00.0 00.0 04.8 00.0 00.0 00.0 07.5 00.3
189 92031 92032 92033 92034 92035 92036 92037 92038 92039 92040 92041 92042 92043 92044 92045 92046 92047 92048 92049 92050 92051 92052 92053 92054 92055 92056 92057 92058 92059 92060 92061 92062 92063 92064 92065 92066 92067 92068 92069 92070 92071 92072 92073 92074 92075 92076 92077 92078 92079 92080 92081 92082 92083 92084 92085 92086 92087 92088 92089 92090 92091 92092 92093 92094 92095 92096 92097 92098 92099 92100 92101 92102 92103 92104 92105 92106 92107 92108 92109 92110 92111
9.4 13.34 18.71 12.03 16.85 15.94 14.96 8.40 16.16 17.80 11.39 15.33 15.26 18.15 16.37 17.38 17.36 12.00 17.39 16.32 11.08 14.05 9.18 10.63 15.04 7.92 4.26 11.84 16.48 21.87 22.27 22.44 22.30 20.02 15.89 9.49 11.02 17.55 22.98 22.52 15.17 15.45 3.74 19.91 16.26 24.03 23.32 24.65 19.59 18.45 24.74 23.80 5.96 17.95 23.86 14.84 25.42 25.37 22.25 14.53 21.15 25.01 22.79 22.16 11.07 23.77 25.82 18.22 4.16 19.24 20.38 22.63 19.26 15.98 24.96 22.61 23.12 23.08 24.10 24.10 4.26
28.0 21.1 21.1 21.7 25.0 26.1 25.0 21.1 20.0 20.0 18.3 25.0 24.4 23.9 26.1 27.2 27.2 28.3 28.9 29.4 28.9 25.6 25.6 27.8 28.3 28.3 23.9 24.4 21.1 22.2 23.3 27.2 28.3 27.8 25.0 29.4 29.4 27.8 28.8 29.0 27.2 18.0 13.7 20.2 19.5 22.5 24.3 25.4 26.6 26.8 21.3 22.2 20.5 25.5 25.7 26.2 23.0 24.9 26.6 26.1 27.3 22.1 21.8 24.2 19.7 20.9 26.9 25.0 22.9 28.9 28.1 30.2 29.4 27.7 27.8 26.9 27.5 27.5 27.9 30.0 24.7
18.5 10.0 6.1 9.4 11.1 11.7 15.0 10.6 7.8 4.4 7.8 9.4 8.3 8.3 10.0 13.3 14.4 17.8 16.7 18.9 16.7 13.3 15.6 17.2 18.9 18.3 19.4 15.6 11.7 9.4 10.0 11.1 11.7 13.9 16.1 17.8 18.9 19.3 16.4 14.4 15.6 8.3 7.3 10.2 8.7 8.5 11.1 8.9 13.4 18.2 14.7 8.9 13.0 14.5 13.3 17.0 12.8 10.7 12.4 13.7 16.1 13.9 12.0 14.5 10.8 9.7 14.6 14.8 16.5 18.9 18.1 19.1 19.1 18.5 17.6 17.4 15.5 16.2 16.6 17.1 18.2
00.3 0.3 0.0 0.0 0.0 31.8 6.9 1.3 4.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 55.6 81.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.8 0.0 0.0 0.0 0.0 0.0 0.0 2.0 38.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.1 0.0 0.0 0.3 17.8 12.4 0.0 0.0 0.0 0.0 0.0 0.0 17.5
190 92112 92113 92114 92115 92116 92117 92118 92119 92120 92121 92122 92123 92124 92125 92126 92127 92128 92129 92130 92131 92132 92133 92134 92135 92136 92137 92138 92139 92140 92141 92142 92143 92144 92145 92146 92147 92148 92149 92150 92151 92152 92153 92154 92155 92156 92157 92158 92159 92160 92161 92162 92163 92164 92165 92166 92167 92168 92169 92170 92171 92172 92173 92174 92175 92176 92177 92178 92179 92180 92181 92182 92183 92184 92185 92186 92187 92188 92189 92190 92191 92192
25.16 7.34 21.50 15.85 23.73 21.59 28.10 28.63 27.29 28.60 23.00 27.39 27.64 28.00 19.31 22.80 27.82 17.08 22.85 28.38 27.24 26.47 26.70 26.30 24.76 23.93 24.15 16.82 26.16 27.52 26.13 21.56 25.64 24.79 29.72 28.21 27.67 25.34 25.19 28.32 27.15 28.27 24.24 22.95 8.86 19.32 20.59 22.10 26.68 20.58 21.68 26.18 27.86 22.88 23.19 24.45 26.14 22.28 24.51 24.91 28.94 26.05 26.79 26.55 22.65 8.94 2.48 12.35 19.03 12.51 21.14 23.30 23.57 24.14 23.15 24.27 23.33 22.73 23.82 23.78 18.13
31.6 27.2 29.8 31.5 30.4 27.7 23.7 19.7 19.4 23.2 25.3 25.9 29.1 27.9 26.2 27.4 28.4 21.1 22.2 25.5 30.1 29.9 30.1 28.3 28.6 29.0 30.6 29.2 30.8 30.5 30.0 30.3 30.0 30.9 29.7 30.0 29.0 30.7 30.1 31.7 32.1 33.3 33.6 34.2 31.4 30.5 30.8 32.1 33.8 33.4 32.1 32.4 32.8 32.6 32.8 32.2 32.8 32.8 33.3 33.3 32.8 33.3 33.3 33.3 32.8 27.2 27.8 31.1 31.1 29.4 32.2 32.8 33.3 34.4 33.3 33.9 35.6 34.4 33.9 35.0 33.6
17.8 0.0 20.0 18.3 20.7 1.0 20.8 0.0 21.0 0.0 19.5 0.0 16.8 0.0 14.0 0.0 10.7 0.0 8.3 0.0 12.9 0.0 11.2 0.0 15.5 0.0 15.2 0.0 16.1 0.0 17.8 0.0 17.5 0.0 15.3 0.0 11.1 0.0 11.5 0.0 13.8 0.0 15.1 0.0 15.4 0.0 17.0 0.0 19.1 0.0 16.7 0.0 21.1 0.0 20.8 0.5 20.1 0.0 19.1 0.0 19.2 0.0 19.7 3.3 19.9 0.0 19.1 0.0 17.0 0.0 17.7 0.0 18.4 0.0 21.3 0.0 20.8 0.0 19.7 0.0 19.7 0.0 19.5 0.0 20.9 0.0 22.4 0.8 21.9 2.8 24.0 0.5 25.9 1.3 22.8 3.3 22.1 1.0 22.7 43.9 22.3 25.4 23.1 0.0 23.6 0.0 23.4 0.0 21.1 34.0 22.2 0.0 21.1 7.4 21.7 4.1 22.2 0.0 21.7 12.4 23.3 0.0 23.9 0.0 23.3 0.0 22.8 0.0 21.7 153.7 21.7 13.5 20.6 194.8 23.9 35.1 22.8 22.4 23.3 2.8 23.9 8.4 25.6 0.0 24.4 0.0 23.3 0.0 23.9 0.0 25.0 0.0 23.9 0.0 23.9 0.0 23.9 0.0 22.8 0.0 22.8 0.0
191 92193 92194 92195 92196 92197
21.40 21.80 14.84 22.77 23.31
34.4 35.0 33.9 32.2 34.4
21.7 23.8 21.1 21.7 22.2
0.0 0.0 13.2 0.0 3.8
Ft. Pierce, 1998 *WEATHER : Ft. Pierce, FLORIDA, U.S.A, NCDC(Tmax,Tmin,&Rain)+CDMI(Srad) @ INSI NCDC @DATE 98001 98002 98003 98004 98005 98006 98007 98008 98009 98010 98011 98012 98013 98014 98015 98016 98017 98018 98019 98020 98021 98022 98023 98024 98025 98026 98027 98028 98029 98030 98031 98032 98033 98034 98035 98036 98037 98038 98039 98040 98041 98042 98043 98044 98045 98046 98047 98048 98049 98050 98051 98052 98053 98054 98055 98056 98057 98058 98059 98060 98061
LAT 27.467 SRAD 7.8 4.7 4.2 7.2 7.2 4.2 6.6 12.4 10.4 9.8 13.3 13.7 11.1 8.8 13.1 15.5 11.3 12.9 11.9 8.3 6.4 2.8 11.6 16.3 4.6 8.6 16.4 15.2 16.2 16.3 14.7 13.4 7.5 6.9 11.2 17.1 14.3 12.1 15.0 14.6 15.7 12.4 18.3 18.0 13.3 5.2 19.7 14.5 14.9 14.4 14.5 10.1 15.3 19.8 18.7 19.4 12.8 13.7 17.0 20.3 19.4
TMAX 20.2 23.0 25.8 27.4 27.4 27.4 27.4 30.2 26.9 23.5 24.1 24.6 25.2 25.8 25.8 25.2 21.3 21.3 24.1 24.1 24.1 23.5 25.2 26.9 17.9 25.8 24.6 24.6 23.5 23.5 22.4 24.1 23.5 24.1 21.8 21.8 18.5 16.2 18.5 19.0 23.5 25.8 25.8 25.2 19.6 21.8 25.8 25.8 29.1 25.8 25.8 24.1 28.0 26.9 24.1 24.1 25.2 30.2 31.4 28.0 21.8
LONG ELEV -80.35 7.600 TMIN 03.9 16.2 17.4 18.5 19.6 18.5 21.3 18.5 15.7 10.1 09.0 09.5 11.8 12.9 15.7 10.1 07.8 07.8 06.7 07.8 14.6 17.9 19.0 11.2 07.3 12.3 11.8 05.6 03.9 05.6 06.2 05.0 15.1 16.2 11.2 05.6 03.4 05.6 02.8 02.8 05.6 06.2 05.6 09.0 10.1 08.4 16.2 16.2 12.3 12.3 11.8 09.0 16.2 09.5 07.3 05.6 05.6 12.9 18.5 17.4 07.8
RAIN 00.0 00.3 00.0 09.0 03.3 00.0 00.0 16.8 00.0 00.0 00.0 00.0 00.0 00.0 17.0 04.5 00.0 00.0 00.0 00.0 00.0 00.5 03.8 02.8 00.0 00.5 01.8 00.0 00.0 00.0 00.0 00.0 45.0 03.0 00.0 00.0 17.5 00.8 00.0 00.0 00.0 00.0 00.3 01.5 00.5 22.5 17.0 20.5 00.5 00.0 01.3 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.5 01.5 00.0
TAV 2
AMP REFHT WNDHT 24.5 4.3 -99.0 -99.0
192 98062 98063 98064 98065 98066 98067 98068 98069 98070 98071 98072 98073 98074 98075 98076 98077 98078 98079 98080 98081 98082 98083 98084 98085 98086 98087 98088 98089 98090 98091 98092 98093 98094 98095 98096 98097 98098 98099 98100 98101 98102 98103 98104 98105 98106 98107 98108 98109 98110 98111 98112 98113 98114 98115 98116 98117 98118 98119 98120 98121 98122 98123 98124 98125 98126 98127 98128 98129 98130 98131 98132 98133 98134 98135 98136 98137 98138 98139 98140 98141 98142
18.3 16.2 17.5 11.8 9.2 18.4 21.6 19.1 21.0 20.5 22.1 19.9 15.0 11.5 7.9 12.6 16.1 22.7 20.2 10.9 20.3 22.1 18.7 9.2 8.3 12.2 12.2 12.3 8.4 11.4 17.9 18.0 23.1 18.9 16.5 12.7 11.7 24.6 23.9 15.8 23.0 10.9 16.0 16.9 12.0 11.0 14.1 18.0 20.5 24.0 25.1 24.1 23.3 18.3 14.4 13.4 17.5 3.7 21.7 26.4 25.9 26.5 26.7 26.8 23.8 20.5 24.4 24.4 26.5 26.7 24.0 25.0 23.5 20.7 23.0 19.0 20.8 24.8 17.1 20.0 25.3
22.4 20.2 24.1 27.4 26.9 29.7 26.9 19.0 17.9 15.7 21.3 20.7 24.1 25.8 26.3 27.4 25.2 29.1 21.8 20.7 22.4 25.2 27.4 26.9 26.9 26.9 28.6 27.4 28.6 29.1 30.8 32.5 32.5 27.4 27.4 28.6 30.8 28.0 29.1 22.4 26.3 25.8 28.6 27.4 29.7 29.7 29.7 30.2 32.5 27.4 29.1 25.8 25.2 26.9 27.4 29.1 29.7 28.0 32.5 30.2 31.9 32.5 28.6 30.2 31.9 33.0 34.2 33.6 34.2 34.2 30.8 31.9 30.2 30.2 30.2 31.9 32.5 32.5 31.9 33.0 32.5
04.5 05.0 04.5 12.3 13.4 19.6 12.3 06.7 04.5 03.9 03.9 02.2 08.4 10.1 17.9 19.6 15.7 16.2 09.5 08.4 03.4 07.3 07.3 14.6 17.4 19.6 18.5 19.6 21.3 21.3 19.6 18.5 18.5 11.8 11.2 19.0 19.6 15.7 07.3 13.4 10.6 16.8 14.0 12.9 14.6 14.6 20.2 16.2 11.8 11.2 13.4 10.1 09.0 10.1 14.6 16.8 20.2 19.0 17.9 17.9 16.8 12.9 16.2 15.1 16.2 18.5 18.5 14.0 19.6 19.0 17.9 17.4 16.8 14.6 14.6 16.8 15.7 18.5 15.7 19.0 21.3
00.0 00.0 00.0 00.0 00.0 00.0 21.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.5 50.0 24.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 01.8 00.8 00.0 00.5 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 32.5 22.5 00.0 00.0 00.0 00.0 00.0 00.0 00.0 04.5 18.8 08.3 00.0 00.0 07.3 29.8 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0
193 98143 98144 98145 98146 98147 98148 98149 98150 98151 98152 98153 98154 98155 98156 98157 98158 98159 98160 98161 98162 98163 98164 98165 98166 98167 98168 98169 98170 98171 98172 98173 98174 98175 98176 98177 98178 98179 98180 98181 98182 98183 98184 98185 98186 98187 98188 98189 98190 98191 98192 98193 98194 98195 98196 98197 98198 98199 98200 98201 98202 98203 98204 98205 98206 98207 98208 98209 98210 98211 98212 98213 98214 98215 98216 98217 98218 98219 98220 98221 98222 98223
23.8 17.2 10.6 7.4 12.9 10.6 14.0 17.2 17.3 23.9 25.0 25.5 21.1 19.3 18.3 25.1 24.6 17.3 15.2 15.2 17.3 24.0 26.3 23.4 15.2 17.3 14.1 17.3 19.3 22.7 24.0 17.3 19.3 23.4 24.0 19.3 25.9 25.1 22.6 25.0 23.3 27.6 26.6 19.2 20.2 25.4 21.0 21.0 22.5 22.5 17.2 23.2 15.0 18.2 19.1 18.1 21.6 21.6 23.0 25.2 21.6 12.7 11.6 17.0 11.6 8.3 14.8 14.8 20.5 23.3 18.7 18.7 22.6 21.9 23.7 13.5 17.6 12.4 14.5 15.5 16.5
34.2 32.5 32.5 33.0 31.9 30.2 31.4 31.4 31.9 34.7 36.4 37.0 35.3 38.1 38.1 35.3 31.4 31.9 33.6 34.2 35.8 36.4 37.5 38.1 35.3 35.3 36.4 34.7 36.4 34.7 34.2 34.7 34.2 34.7 35.8 32.5 34.2 33.0 35.8 38.1 35.3 36.4 35.3 34.7 37.5 34.2 35.8 36.4 35.3 33.6 35.8 33.6 33.6 30.2 33.6 35.3 34.7 33.0 33.6 33.6 32.5 33.6 34.2 34.2 34.2 35.3 37.5 37.0 36.4 35.3 34.2 34.2 34.2 33.6 33.0 30.2 31.4 33.6 34.2 33.6 35.8
20.2 19.6 18.5 23.0 24.1 20.7 19.6 21.3 20.2 20.7 21.3 21.8 21.3 20.7 20.7 20.7 18.5 23.5 22.4 21.3 23.5 22.4 23.0 24.1 21.3 21.3 23.5 22.4 18.5 20.7 20.2 23.0 23.5 21.8 20.2 19.6 20.7 21.3 21.8 21.3 22.4 20.7 22.4 22.4 22.4 21.8 21.3 21.3 22.4 23.5 21.3 21.3 21.8 22.4 20.2 21.3 22.4 22.4 21.3 23.0 20.2 22.4 23.0 23.0 23.5 22.4 23.5 23.5 22.4 20.2 21.3 21.8 21.3 20.7 19.6 20.7 21.3 23.0 24.1 23.0 22.4
00.0 00.0 00.0 00.0 00.0 04.5 00.0 08.0 11.0 00.0 00.0 00.0 00.0 00.0 00.0 00.5 58.5 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 11.3 00.0 00.0 57.5 00.0 00.0 00.0 00.0 00.0 22.3 00.0 00.0 00.0 00.0 00.0 06.3 00.0 00.0 00.0 00.0 03.3 12.5 00.0 06.3 00.0 07.5 00.0 05.3 00.0 05.8 00.0 00.0 01.3 03.0 00.5 03.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 28.3 00.0 00.0 26.3 10.3 30.0 10.3 07.3 00.0 00.5 00.0 00.0
194 98224 98225 98226 98227 98228 98229 98230 98231 98232 98233 98234 98235 98236 98237 98238 98239 98240 98241 98242 98243 98244 98245 98246 98247 98248 98249 98250 98251 98252 98253 98254 98255 98256 98257 98258 98259 98260 98261 98262 98263 98264 98265 98266 98267 98268 98269 98270 98271 98272 98273 98274 98275 98276 98277 98278 98279 98280 98281 98282 98283 98284 98285 98286 98287 98288 98289 98290 98291 98292 98293 98294 98295 98296 98297 98298 98299 98300 98301 98302 98303 98304
16.5 23.4 26.1 20.7 17.3 20.7 23.6 19.0 16.2 16.2 16.1 17.0 17.9 20.2 16.0 12.9 11.9 14.9 16.7 21.2 20.5 12.7 14.6 18.9 9.6 13.5 10.5 15.3 15.3 16.9 14.2 12.3 14.1 15.0 12.1 4.7 16.5 12.9 12.8 15.5 16.2 17.6 17.6 16.8 4.6 8.8 9.6 8.7 7.8 13.1 17.5 19.1 16.7 10.2 12.8 12.7 10.0 12.5 14.1 8.2 15.4 17.1 10.6 7.2 7.9 8.7 8.7 9.4 6.2 9.3 13.8 13.7 1.7 5.3 6.0 4.5 8.9 11.2 14.4 15.4 14.8
34.7 35.3 35.3 34.2 33.6 34.2 34.2 33.6 32.5 32.5 33.0 34.2 33.0 34.7 37.0 35.3 34.7 33.6 34.2 35.8 37.5 33.6 35.3 35.3 33.0 33.0 33.6 34.7 33.6 30.2 31.4 30.8 31.4 31.9 31.9 28.6 31.4 33.0 34.2 31.9 34.7 34.7 32.5 33.0 31.9 32.5 33.0 31.9 31.9 34.7 34.2 34.7 35.3 34.2 33.0 32.5 33.0 32.5 35.8 33.0 33.6 32.5 31.9 30.8 31.9 32.5 28.6 30.8 31.4 31.4 31.4 29.7 28.0 29.1 30.2 29.1 29.7 29.7 29.7 30.2 30.8
23.0 22.4 21.8 21.3 23.5 25.8 23.5 24.1 22.4 20.7 23.0 23.0 21.8 23.5 21.8 23.5 24.1 23.5 21.3 21.3 21.3 21.3 21.8 23.0 21.8 23.0 23.5 21.3 21.3 23.0 23.5 20.2 22.4 21.3 22.4 21.3 21.8 21.8 21.8 21.8 21.8 21.3 21.8 20.7 23.5 24.1 24.1 21.3 20.7 21.3 22.4 22.4 22.4 20.7 21.8 22.4 23.0 21.8 21.8 21.3 20.7 20.2 20.7 19.6 19.0 23.5 23.0 20.7 22.4 21.3 21.8 20.7 20.2 20.7 20.7 19.6 21.3 17.4 14.6 12.9 14.0
09.0 01.8 13.0 00.0 00.0 00.0 00.0 00.0 27.0 64.5 02.8 26.3 00.3 00.0 00.0 00.0 00.0 00.0 00.0 00.3 08.8 09.3 00.0 07.5 11.5 00.0 07.0 07.0 03.3 00.0 00.0 00.0 00.0 11.0 05.0 44.3 10.5 25.8 17.0 48.5 00.0 65.8 00.0 00.5 05.8 02.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.3 00.0 01.0 01.3 00.0 17.0 00.0 00.0 00.0 00.0 01.0 08.8 10.3 25.0 03.3 00.0 00.0 00.0 01.8 03.8 00.0 00.0 00.0 00.0 00.0
195 98305 98306 98307 98308 98309 98310 98311 98312 98313 98314 98315 98316 98317 98318 98319 98320 98321 98322 98323 98324 98325 98326 98327 98328 98329 98330 98331 98332 98333 98334 98335 98336 98337 98338 98339 98340 98341 98342 98343 98344 98345 98346 98347 98348 98349 98350 98351 98352 98353 98354 98355 98356 98357 98358 98359 98360 98361 98362 98363 98364 98365
14.1 11.5 10.8 9.3 16.7 11.9 14.7 10.4 6.1 8.2 12.2 12.1 9.5 8.7 11.9 4.0 8.6 8.5 9.8 11.6 9.7 7.7 7.0 7.0 10.1 11.3 11.8 12.2 13.1 11.6 11.6 11.0 10.9 11.4 8.5 6.6 7.9 12.7 10.8 5.3 5.9 4.1 12.9 14.3 11.6 8.9 14.4 10.6 6.5 8.3 10.0 10.6 7.1 8.9 11.1 12.8 7.1 10.6 13.8 12.1 13.2
30.8 29.7 30.2 24.6 25.2 24.6 27.4 27.4 26.9 28.6 29.7 29.1 29.1 28.0 31.9 29.7 33.0 30.2 29.7 30.8 31.4 29.1 28.6 28.0 28.0 29.1 28.0 27.4 28.0 28.6 29.1 29.1 28.0 28.0 28.6 27.4 28.6 28.6 29.1 29.1 27.4 28.6 30.2 24.1 23.0 19.0 23.5 24.1 26.9 28.0 28.6 29.1 27.4 29.1 30.2 28.0 26.9 26.9 28.6 20.7 24.6
14.6 17.9 17.9 17.4 12.9 09.5 11.2 13.4 15.7 21.3 18.5 15.7 15.1 14.6 16.2 15.1 17.9 17.4 21.3 17.4 17.4 19.0 19.0 16.8 15.7 16.8 12.9 12.3 12.3 14.0 14.0 17.4 15.7 16.8 15.7 16.8 14.0 15.1 14.6 16.8 19.6 21.3 20.2 10.6 08.4 05.0 05.0 05.6 18.5 18.5 16.2 15.1 14.0 14.6 17.9 15.1 12.3 17.9 15.7 09.0 07.3
00.0 00.0 00.0 47.8 107. 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 02.0 19.3 25.8 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.0 00.8 02.5 00.0 00.0 00.0 00.0 00.3 02.5 00.0 00.0 00.0 00.0 00.0 00.0 00.0 01.0 00.0 00.0 00.0
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BIOGRAPHICAL SKETCH Jawoo Koo was born on February 17, 1974, in Seoul, Korea. He grew up in an urban area. In spring 1992, he began an undergraduate program in the Agricultural Biology Department of Korea University. After two years of studying in the program, he enrolled in the Seoul Metropolitan Police Department from 1994 until 1996 to fulfill his mandatory service as a Korean citizen. After discharge, he returned to Korea University and graduated in spring 1998. After graduation, he worked for one and half years as a research associate at the Forest Pathology Laboratory of the National Forestry Research Institute, Seoul, Korea. He began his master’s degree study in fall 1999 with Dr. James W. Jones and joined the Crop Systems Modeling Laboratory in the Agricultural and Biological Engineering Department of the University of Florida, Gainesville, Florida. On August 15, 2000, Jawoo married Soonho Kim, who was his classmate in his undergraduate years. His advisor, Dr. Jones, performed their wedding ceremony as the most respected person performs a wedding ceremony in Korean culture. Soonho also began her doctoral studies with Dr. Howard D. Beck in the same department in fall 2000.
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