Presentation on theme: "Central Laboratory for Agricultural Climate (CLAC) Methodology of Studying the Impact of Climate Change on Crop Productivity By Dr. Mahmoud Medany Dakkar,"— Presentation transcript:
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
DSSAT CLAC Integrated Crop Management Information System by using DSSAT program
Who Uses DSSAT Tools? `Agronomic Researchers and Extension Specialists `Policy Makers `Farmers and their Advisors `Private Sector `Educators
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
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 `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
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 CO 2 into Glucose, g glucose (g CO 2 ) -1 A = Gross Photosynthesis, g [CO 2 ] m -2 s -1 MC = Maintenance Respiration Coefficient, s -1 W = Plant Tissue Mass, g m -2 or Updating Growth Mass t+1 = Mass t + Growth t - Abort t
Conversion Factor (CVF) 1/CVF= f leaf / f stem / f root / f storage /Co CVF= Conversion factor (g product g -1 glucose) f = Fraction of each organ in the increase in total dry matter ( f=1) C o = Conversion factor of storage organ (g product g -1 glucose) For example, Co is 0.67 for maize, 0.78 for potato, 0.46 for soybean, and 0.40 for peanut.
Water Management N Application + Organic Crop (Genetic Coefficients ) Development Mass of Crop Kg/ha Duration of Phases Growth Partitioning LeafStemRootFruit Weather CO 2 Photosynthesis Respiration Temperature Photoperiod Soil
File x Experimental Data 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
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/cm 3 ) - % Total nitrogen - CEC Soil analysis and fertility measurements
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).
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 8ّC(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 VRNAME ECO# P1 P2 P5 G2 G3 PHINT EG0011 S.C. 9 IB EG0004 SC 10 IB EG0013 S.C-103 IB EG0007 S.C-122 IB EG0008 S.C-124 IB EG0002 T.W.C.310 IB EG0014 T.W.C.323 IB MAIZE GENOTYPE COEFFICIENTS
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 PlantEmerge 1st Flower1st Seed Phys. Maturity Harvest Maturity Vegetative Growth PeriodReproductive 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
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.
Model validation Conduct sensitivity analysis on the crop models to evaluate the modal Experimental dataOther inputs Modification model Parameter test Simulation DSSAT program Compare simulation with measured
Building New Software for Data Entry
*RUN 6 : GIZA 164 MODEL : GECER980 - WHEAT EXPERIMENT : EGDK9101 WH DK&BN TREATMENT 6 : GIZA 164 CROP : WHEAT CULTIVAR : GIZA STARTING DATE : NOV PLANTING DATE : NOV PLANTS/m2 :110.0 ROW SPACING : 20.cm WEATHER : EGNA 1991 SOIL : EGNA 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 = R 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
*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 % 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 End Veg-Beg Ear Growth Begin Ear-End Ear Grwth End Ear Grth-Beg Grn Fi Linear Grain Fill Phase
*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 NOV 0 Sowing NOV 0 Start Sim NOV 1 Germinate NOV 10 Emergence JAN 69 Term Spklt FEB 90 End Veg MAR 103 End Ear Gr MAR 117 Beg Gr Fil APR 157 Maturity APR 157 Harvest
*MAIN GROWTH AND DEVELOPMENT VARIABLE PREDICTED MEASURED FLOWERING DATE (dap) PHYSIOL. MATURITY (dap) GRAIN YIELD (kg/ha;dry) WT. PER GRAIN (g;dry) GRAIN NUMBER (GRAIN/m2) GRAINS/EAR MAXIMUM LAI (m2/m2) BIOMASS (kg/ha) AT ANTHESIS BIOMASS N (kg N/ha) AT ANTHESIS BIOMASS (kg/ha) AT HARVEST MAT STALK (kg/ha) AT HARVEST MAT HARVEST INDEX (kg/kg) FINAL LEAF NUMBER GRAIN N (kg N/ha) BIOMASS N (kg N/ha) STALK N (kg N/ha) SEED N (%)
Comparison of measured and predicted of Wheat grain yield
GIS map showing analysis grain yield simulation of Maize single cross 10 in different location.
THE IMPACT OF CLIMATE CHANGE ON PRODUCTION OF DIFFERERENT CULTIVARS OF MAIZE (Zea mays L.) Minia Governorate, Malawi
Fertilizer levels, additions date and amounts Material code (1) = Ammonium nitrate Method code (2) = Broadcast, incorporate
Variety V1: SC10 (Single cross 10) V2: TW310 (Three way cross 310) Combination between varieties and nitrogen levels
Temperature, precipitation and solar radiation for the current (CO2=300ppm ) and the expected change situation(CO2=600ppm) by the year 2040.
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)