Central Laboratory for Agricultural Climate (CLAC) Methodology of Studying the Impact of Climate
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Transcript of Central Laboratory for Agricultural Climate (CLAC) Methodology of Studying the Impact of Climate
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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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DSSAT
CLAC
Integrated Crop Management Information System by using DSSAT
program
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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.
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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
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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
TemperaturePhotoperiod
Soil
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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
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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 8 ّ C(during which the plant is not responsive to changes in photoperiod.
P2 Extent to which development (expressed as days) is delayed for each hour increase in photoperiod above the longest photoperiod at which development 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 8 ّC).
G2 Maximum possible number of kernels per plant.
G3 Kernel filling rate during the linear grain filling stage and under optimum conditions (mg/day).
PHINT Phylochron interval; the interval in thermal time (degree days)between successive leaf tip appearances.
@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:
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Crop Development
Plant Emerge 1st Flower 1st SeedPhys. 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
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R2 =0.901
010002000300040005000600070008000
9000
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Observed grain yield Simulated grain yield
OBSERVED AND SIMULATED WHEAT GRAIN YIELD
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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
Fertilizer Date Depth Nlevel dd/mm/yy cm Kg/haN 1 06/05/1993 1 2 20 285N 2 06/05/1993 1 2 20 71
30/05/1993 2 7122/06/1993 2 7115/07/1993 2 7130/05/1993 2 10322/06/1993 2 10315/07/1993 2 10306/05/1993 20 28630/05/1993 2 7122/06/1993 2 7115/07/1993 2 7106/05/1993 20 7130/05/1993 2 7122/06/1993 2 7115/07/1993 2 7106/05/1993 20 28530/05/1993 2 10322/06/1993 2 10315/07/1993 2 10306/05/1993 20 7130/05/1993 2 10322/06/1993 2 10315/07/1993 2 103
Material code
Method code
N 3 1 2
N 4 1 2
N 5 1 2
N 6 1 2
N 7 1 2
N 8 1 2
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Treatment
No.
Treatment Treatment No.
Treatment
1 V1 N1 9 V2 N12 V1 N2 10 V2 N23 V1 N3 11 V2 N34 V1 N4 12 V2 N45 V1 N5 13 V2 N56 V1 N6 14 V2 N67 V1 N7 15 V2 N78 V1 N8 16 V2 N8
Variety V1: SC10 (Single cross 10)
V2: TW310 (Three way cross 310)
Combination between varieties and nitrogen levels
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CO2 CO2 CO2 CO2 CO2 CO20.03% 0.06% 0.03% 0.06% 0.03% 0.06%
January 11.9 14.8 2.85 0.7 0.5 0.66 155 159 1.02February 13.1 17.9 4.84 0.5 0.4 0.78 198 199 1.01March 17.2 21 3.86 0.9 0.7 0.84 259 262 1.01April 21.5 26.9 5.35 0.3 0.2 0.55 318 315 0.99May 26.3 32.3 5.97 0.2 0.4 2.59 341 338 0.99June 32 35.9 3.98 0.2 0.5 3.1 350 341 0.97July 33.8 37.5 3.63 0.3 1.2 3.8 346 327 0.94August 33.7 35.8 2.07 0.3 1.9 5 317 302 0.95September 29.2 33.5 4.31 0.8 1.2 1.56 275 268 0.97October 23.2 26.9 3.69 0.9 1.1 1.16 222 222 1November 16.2 21.3 5.15 0.5 0.5 0.93 175 174 1December 12.7 16.9 4.21 0.5 0.9 1.83 151 146 0.97
Month Temperature C0 Precipitation (mm/day ) Solar ( W/M2 )
Ratio Ratio Ratio
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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predicted measured
1 285 133 162 5 5519 54952 71 49 54 5 3942 46303 213 122 94 10 5525 55094 309 151 142 26 5460 54075 498 154 328 18 5506 54216 285 135 144 10 5493 54687 594 164 385 45 5505 54268 380 157 197 31 5471 54629 285 126 168 5 4232 397010 71 47 54 5 2782 300111 213 113 96 10 4265 395612 309 131 151 32 4215 397313 498 132 340 22 4223 399814 285 122 150 12 4221 402015 594 141 402 53 4223 398716 380 134 209 39 4218 3951
Final N Grain yieldTreatment No.
Fert. N Plant N Leached N
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)
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