Medany 24Mar AM

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Central Laboratory for Agricultural Climate (CLAC) Methodology of Studying the Impact of Climate Change on Crop Productivity By Dr. Mahmoud Medany Dakkar, 24 March 2004

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Integrated Crop Management Information System by using DSSAT program

Who Uses DSSAT Tools? : 

Who Uses DSSAT Tools? Agronomic Researchers and Extension Specialists Policy Makers Farmers and their Advisors Private Sector Educators

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The program presents a table that includes fertilizer N added , N taken up by crop, N leached below 1.8m, and final Nitrate –N in soil (Kg/ha) and grain yield of crop (Kg/ha) for that run

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DSSAT was designed to allow users to : Input, organize and store data on crop, soil and weather “data base”· Retrieve, analyze and display data. Calibrate and evaluate crop growth models. Evaluate different management practices and compare simulation results with their own measured results to give them confidence that models work adequately. DSSAT allow users to simulate option for crop management over a number of years to assess the risks associated with each option. Create different management strategies and the simulated performance indicators that can be analyzed.

Applications of Crop Models: 

Applications of Crop Models Based on understanding of plants, soil, weather and management interactions Predict crop growth, yield, timing (Outputs) Optimize Management using Climate Predictions Diagnose Yield Gaps, Actual vs. Potential Optimize Irrigation Management Greenhouse Climate Control Quantify Pest Damage Effects on Production Precision Farming Climate Change Effects on Crop Production Can be used to perform “what-if” experiments on the computer to optimize management

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Daily Increase in Dry Matter Growth: Photosynthesis and Respiration Daily Growth = CVF * Gross Photosynthesis - Respiration dW/dt = CVF * ((30/44) * A - MC * W) dW/dt = Plant Growth Rate, g m-2 s -1 CVF = Conversion Efficiency, g tissue (g glucose)-1 30/44 = Converts CO2 into Glucose, g glucose (g CO2 )-1 A = Gross Photosynthesis, g [CO2] m-2 s -1 MC = Maintenance Respiration Coefficient, s -1 W = Plant Tissue Mass, g m-2 or Updating Growth Masst+1 = Masst + Growtht - Abortt

Conversion Factor (CVF): 

Conversion Factor (CVF) 1/CVF= fleaf/0.68 + fstem/0.66 + froot/0.68 + fstorage /Co CVF= Conversion factor (g product g-1glucose) f = Fraction of each organ in the increase in total dry matter (f=1) Co = Conversion factor of storage organ (g product g-1glucose) For example, Co is 0.67 for maize, 0.78 for potato, 0.46 for soybean, and 0.40 for peanut.

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Water Management N Application + Organic Crop (Genetic Coefficients ) Development Mass of Crop Kg/ha Duration of Phases Growth Partitioning Leaf Stem Root Fruit Weather CO2 Photosynthesis Respiration Temperature Photoperiod Soil

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File x ExperimentalData File File C Cultivar Code File A Crop Data at Harvest File T Crop Data during season Output Depending on Option Setting and Simulation Application File w Weather Data File S Soil Data Crop Models INPUTS

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Seventy different soil location were chosen and soil properties were determined as follow: - Soil physical conditions of the profile by layer. - Soil chemical conditions of the profile by layer - Sand, Clay& Silt % . - Organic carbon. - Coarse fraction < 2mm,% of whole soil. - pH of soil. - Soil classification. - Soil horizon. - Root abundance information. - Slope %. - Soil color. - Permeability code. - Drainage. - Latitude - Longitude - Soil texture - Number of layer - Bulk density 1/3 bar (g/cm3) - % Total nitrogen - CEC Soil analysis and fertility measurements

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Historical weather data: Thirty-five years of weather data for different experimental locations have already been collected.   The minimum required weather data includes: -Latitude and longitude of the weather station, . -Daily values of incoming solar radiation (MJ/m²-day), -Maximum and minimum air temperature (°C), and -Rainfall (mm).

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COEFF DEFINITIONS VAR# Identification code or number for a specific cultivar VAR-NAME Name of cultivar ECO# Ecotype code or this cultivar, points to the Ecotype in the ECO file (currently not used). P1 Thermal time from seedling emergence to the end of the juvenile phase (expressed in degree days above a base temperature of 8ّ C(during which the plant is not responsive to changes in photoperiod. P2 Extent to which development (expressed as days) is delayed for each hour increase in photoperiod above the longest photoperiod at which development proceeds at a maximum rate (which is considered to be 12.5 hours). P5 Thermal time from silking to physiological maturity (expressed in degree days above a base temperature of 8ّC). G2 Maximum possible number of kernels per plant. G3 Kernel filling rate during the linear grain filling stage and under optimum conditions (mg/day). PHINT Phylochron interval; the interval in thermal time (degree days)between successive leaf tip appearances. @VAR# VRNAME.......... ECO# P1 P2 P5 G2 G3 PHINT EG0011 S.C. 9 IB0001 400.0 0.200 620.0 650.0 11.4 40.00 EG0004 SC 10 IB0001 400.0 0.300 865.0 720.0 11.5 38.90 EG0013 S.C-103 IB0001 295.0 0.520 593.0 695.0 13.4 38.90 EG0007 S.C-122 IB0001 270.0 0.500 580.0 650.0 13.6 38.90 EG0008 S.C-124 IB0001 290.0 0.500 630.0 630.0 14.8 38.90 EG0002 T.W.C.310 IB0001 430.0 0.200 868.0 700.0 10.0 40.00 EG0014 T.W.C.323 IB0001 290.0 0.300 680.0 635.0 12.2 38.90 MAIZE GENOTYPE COEFFICIENTS

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Genetic Coefficients Life cycle Photosynthesis Sensitivity to day light(photoperiod) Leaf area Partitioning Re-mobilization Seed growth Seed composition Seed fill duration Vernalization Growing degree days accumulation Genetic Coefficients for each variety affected by:

Crop Development: 

Crop Development Plant Emerge 1st Flower 1st Seed Phys. Maturity Harvest Maturity Vegetative Growth Period Reproductive Growth Period Vegetative Development is mainly affected by Temperature such as appearance of leaves on main stem) Reproductive Development is affected by temperature and daylength (such as duration of seed growth phase) Sensitivity to stresses varies considerably with stage of growth Crop growth in simulation modeling usually refers to the accumulation of biomass with time and its partitioning different organs. Time

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Adapting the DSSAT to our conditions we use the following procedures Conduct field experiments to collect minimum data set required to running and evaluating crop model under Egypt condition. Enter other input soil data for the region and historical weather data for sites in the region(not start calibration of crop parameters before checking the quality of weather data). Run the model to evaluate the ability of model to predict Modify model to evaluation shows that it does not reach the level of precision required. Conduct sensitivity analysis on the crop models to evaluate the modal responses to alternative practices using variances, water use, season length, nitrogen uptake, net profit and other responses. Provide results and recommendations for decision-making . Output can be printed or graphically displayed for conducting sensitivity analysis.

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Model validation Conduct sensitivity analysis on the crop models to evaluate the modal Experimental data Other inputs Modification model Parameter test Simulation DSSAT program Compare simulation with measured

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Building New Software for Data Entry

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Wheat

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*RUN 6 : GIZA 164 MODEL : GECER980 - WHEAT EXPERIMENT : EGDK9101 WH DK&BN TREATMENT 6 : GIZA 164 CROP : WHEAT CULTIVAR : GIZA 164 - STARTING DATE : NOV 20 1991 PLANTING DATE : NOV 20 1991 PLANTS/m2 :110.0 ROW SPACING : 20.cm WEATHER : EGNA 1991 SOIL : EGNA870001 TEXTURE : CL - SIDS SOIL INITIAL C : DEPTH:120cm EXTR. H2O:148.6mm NO3: 1.6kg/ha NH4: 1.6kg/ha WATER BALANCE : IRRIGATE ON REPORTED DATE(S) IRRIGATION : 380 mm IN 5 APPLICATIONS NITROGEN BAL. : SOIL-N & N-UPTAKE SIMULATION; NO N-FIXATION N-FERTILIZER : 150 kg/ha IN 2 APPLICATIONS RESIDUE/MANURE : INITIAL : 0 kg/ha ; 0 kg/ha IN 0 APPLICATIONS ENVIRONM. OPT. : DAYL= .00 SRAD= .00 TMAX= .00 TMIN= .00 RAIN= .00 CO2 = R330.00 DEW = .00 WIND= .00 SIMULATION OPT : WATER :Y NITROGEN:Y N-FIX:N PESTS :N PHOTO :C ET :R MANAGEMENT OPT : PLANTING:R IRRIG :R FERT :R RESIDUE:R HARVEST:M WTH:M

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*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERS SOIL LOWER UPPER SAT EXTR INIT ROOT BULK pH NO3 NH4 ORG DEPTH LIMIT LIMIT SW SW SW DIST DENS C cm cm3/cm3 cm3/cm3 cm3/cm3 g/cm3 ugN/g ugN/g % -------------------------------------------------------------------------------- 0- 5 .170 .299 .388 .129 .299 .35 1.40 7.80 .10 .10 1.20 5- 15 .170 .299 .388 .129 .299 .35 1.40 7.80 .10 .10 1.20 15- 30 .170 .299 .388 .129 .299 .35 1.40 7.80 .10 .10 1.20 30- 45 .243 .367 .382 .124 .367 .20 1.30 7.80 .10 .10 .50 45- 60 .238 .360 .375 .122 .360 .17 1.30 7.87 .10 .10 .30 60- 90 .241 .362 .377 .121 .362 .13 1.30 7.90 .10 .10 .17 90-120 .250 .372 .387 .122 .372 .10 1.30 7.90 .10 .10 .10 ENVIRONMENTAL AND STRESS FACTORS ------------------------------------ENVIRONMENT-----------------STRESS---------- |--DEVELOPMENT PHASE--|-TIME-|-------WEATHER--------| |---WATER--| |-NITROGEN-| DURA TEMP TEMP SOLAR PHOTOP PHOTO GROWTH PHOTO GROWTH TION MAX MIN RAD [day] SYNTH SYNTH days ّC ّC MJ/m2 hr -------------------------------------------------------------------------------- Emergence - Term Spiklt 59 23.31 10.02 15.47 10.24 .000 .006 .271 .473 End Veg-Beg Ear Growth 21 23.58 6.84 15.41 10.78 .000 .000 .000 .302 Begin Ear-End Ear Grwth 13 25.05 8.08 16.41 11.23 .000 .037 .000 .217 End Ear Grth-Beg Grn Fi 14 28.36 13.23 17.40 11.62 .010 .074 .000 .000 Linear Grain Fill Phase 39 32.11 14.68 17.04 12.41 .093 .141 .000 .015

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*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGES RUN NO. 6 GIZA 164 DATE CROP GROWTH BIOMASS LAI LEAF ET RAIN IRRIG SWATER CROP N STRESS AGE STAGE kg/ha NUM. mm mm mm mm kg/ha % H2O N -------------------------------------------------------------------------------- 20 NOV 0 Sowing 0 .00 .0 4 0 70 177 0 .0 .00 .00 20 NOV 0 Start Sim 0 .00 .0 4 0 70 177 0 .0 .00 .00 21 NOV 1 Germinate 0 .00 .0 8 0 70 167 0 .0 .00 .00 30 NOV 10 Emergence 14 .00 2.0 19 0 70 140 0 2.2 .00 .00 28 JAN 69 Term Spklt 2148 2.24 12.0 117 6 225 155 82 3.8 .01 .27 18 FEB 90 End Veg 5488 3.24 15.0 182 11 225 94 132 2.4 .00 .00 2 MAR 103 End Ear Gr 7701 3.06 15.0 226 11 225 50 134 1.7 .06 .00 16 MAR 117 Beg Gr Fil 10037 2.71 15.0 279 13 305 78 133 1.3 .05 .00 25 APR 157 Maturity 12189 .00 15.0 391 17 380 46 142 1.2 .14 .00 25 APR 157 Harvest 12189 .00 15.0 391 17 380 46 142 1.2 .14 .00

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*MAIN GROWTH AND DEVELOPMENT VARIABLES @ VARIABLE PREDICTED MEASURED -------- --------- -------- FLOWERING DATE (dap) 108 106 PHYSIOL. MATURITY (dap) 157 158 GRAIN YIELD (kg/ha;dry) 5064 5063 WT. PER GRAIN (g;dry) .0364 0.038 GRAIN NUMBER (GRAIN/m2) 13917 -99 GRAINS/EAR 29.8 -99 MAXIMUM LAI (m2/m2) 3.25 -99 BIOMASS (kg/ha) AT ANTHESIS 7701 -99 BIOMASS N (kg N/ha) AT ANTHESIS 134 -99 BIOMASS (kg/ha) AT HARVEST MAT. 12189 12302 STALK (kg/ha) AT HARVEST MAT. 7125 -99 HARVEST INDEX (kg/kg) .415 -99 FINAL LEAF NUMBER 15.00 -99 GRAIN N (kg N/ha) 122 -99 BIOMASS N (kg N/ha) 142 -99 STALK N (kg N/ha) 20 -99 SEED N (%) 2.41 -99

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Comparison of measured and predicted of Wheat grain yield

DSSAT v3.5- Models of 16 Crops -: 

DSSAT v3.5 - Models of 16 Crops - Cereals Corn, Wheat, Rice, Barley, Sorghum, Millet Grain Legumes Soybean, Peanut, Dry Bean, Chickpea Root Crops Potato, Cassava Other Crops Tomato, Sunflower, Sugar Cane, Pasture

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GIS map showing analysis grain yield simulation of Maize single cross 10 in different location.

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THE IMPACT OF CLIMATE CHANGE ON PRODUCTION OF DIFFERERENT CULTIVARS OF MAIZE (Zea mays L.) Minia Governorate, Malawi

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Fertilizer levels, additions date and amounts Material code (1) = Ammonium nitrate Method code (2) = Broadcast, incorporate

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Variety V1: SC10 (Single cross 10) V2: TW310 (Three way cross 310) Combination between varieties and nitrogen levels

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Temperature , precipitation and solar radiation for the current (CO2=300ppm ) and the expected change situation(CO2=600ppm) by the year 2040.

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Summary of data produced by the program and compared yield for measured data. Fert. N = Fertilizer N added (Kg/ha) Plant N = N taken up by croup (Kg/ha) Leached N = N leached below 1.8m(Kg/ha) Final N = Final Nitrate –N in soil (Kg/ha) Yield = Grain yield of crop (Kg/ha)