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
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
Updating Growth
Masst+1 = Masst + Growtht - Abortt
Daily Increase in Dry Matter Growth:
Photosynthesis and Respiration
Daily Growth = CVF * Gross Photosynthesis - Respiration
or
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
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.
Soil
Water Management
Weather
N Application + Organic
Crop
(Genetic Coefficients )
Duration of
Phases
Development
Photosynthesis
Respiration
CO2
Mass of Crop
Kg/ha
Leaf
Stem
Growth
Partitioning
Root
Fruit
INPUTS
File x
Experimental
Data File
File S
File w
File C
Soil Data
Weather Data
Cultivar Code
Crop
Models
File A
Crop Data
at Harvest
File T
Crop Data
during season
Output Depending on Option Setting and Simulation Application
Soil analysis and fertility measurements
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
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).
MAIZE GENOTYPE COEFFICIENTS
COEFF
VAR#
DEFINITIONS
Identification code or number for a specific cultivar
VAR-NAME
ECO#
Name of cultivar
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
Genetic Coefficients
Genetic Coefficients for each variety affected by:
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
Crop Development
Vegetative Growth Period
Reproductive Growth Period
Harvest
Maturity
Plant
Emerge
1st Flower
1st Seed
Phys. Maturity
Time
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.
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
Experimental data
Other inputs
DSSAT program
Simulation
Compare simulation
with measured
Modification model
Conduct sensitivity analysis on the crop
models to evaluate the modal
Parameter test
Building New Software
for Data Entry
Wheat
*RUN
6
: GIZA 164
MODEL
: GECER980 - WHEAT
EXPERIMENT
: EGDK9101 WH
TREATMENT
6
DK&BN
: GIZA 164
CROP
: WHEAT
CULTIVAR : GIZA 164
STARTING DATE
: NOV 20 1991
PLANTING DATE
: NOV 20 1991
WEATHER
: EGNA
SOIL
: EGNA870001
PLANTS/m2 :110.0
-
ROW SPACING :
20.cm
1991
TEXTURE : CL
SOIL INITIAL C : DEPTH:120cm EXTR. H2O:148.6mm
- SIDS
NO3:
1.6kg/ha
WATER BALANCE
: IRRIGATE ON REPORTED DATE(S)
IRRIGATION
:
NITROGEN BAL.
: SOIL-N & N-UPTAKE SIMULATION; NO N-FIXATION
N-FERTILIZER
:
380 mm IN
1.6kg/ha
5 APPLICATIONS
150 kg/ha IN
RESIDUE/MANURE : INITIAL :
NH4:
2 APPLICATIONS
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
MANAGEMENT OPT : PLANTING:R
NITROGEN:Y
N-FIX:N
PESTS
IRRIG
FERT :R
RESIDUE:R
:R
:N
PHOTO
:C
ET :R
HARVEST:M
WTH:M
*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERS
SOIL LOWER UPPER
SAT
EXTR
INIT
ROOT
BULK
DEPTH LIMIT LIMIT
SW
SW
SW
DIST
DENS
cm
cm3/cm3
cm3/cm3
cm3/cm3
pH
NO3
NH4
ORG
C
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
TION
MAX
days
ّC
TEMP
MIN
ّC
SOLAR PHOTOP PHOTO GROWTH PHOTO GROWTH
RAD
MJ/m2
[day] SYNTH
SYNTH
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
*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGES
RUN NO.
6
GIZA 164
DATE CROP GROWTH
AGE
BIOMASS
STAGE
LAI
kg/ha
LEAF
NUM.
ET
mm
RAIN IRRIG SWATER CROP
mm
mm
mm
N
kg/ha %
STRESS
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
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
2 MAR
103 End Ear Gr
*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
20
-99
2.41
-99
STALK N (kg N/ha)
SEED N (%)
Comparison of measured and predicted of Wheat grain yield
OBSERVED AND SIMULATED WHEAT GRAIN YIELD
9000
8000
7000
6000
5000
4000
3000
2000
1000
R2 = 0.901
0
0
1000
2000
Observed grain yield
3000
4000
5000
6000
7000
8000
Simulated grain yield
9000
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
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
Fertilizer
level
N1
N2
N3
N4
N5
N6
N7
N8
Date
dd/mm/yy
06/05/1993
06/05/1993
30/05/1993
22/06/1993
15/07/1993
30/05/1993
22/06/1993
15/07/1993
06/05/1993
30/05/1993
22/06/1993
15/07/1993
06/05/1993
30/05/1993
22/06/1993
15/07/1993
06/05/1993
30/05/1993
22/06/1993
15/07/1993
06/05/1993
30/05/1993
22/06/1993
15/07/1993
Material
code
1
1
1
Method
code
2
2
2
1
2
1
2
1
2
1
2
1
2
Material code (1) = Ammonium nitrate
Method code (2) = Broadcast, incorporate
Depth
cm
20
20
2
2
2
2
2
2
20
2
2
2
20
2
2
2
20
2
2
2
20
2
2
2
N
Kg/ha
285
71
71
71
71
103
103
103
286
71
71
71
71
71
71
71
285
103
103
103
71
103
103
103
Combination between varieties and nitrogen levels
Treatment
No.
1
2
3
4
5
6
7
8
Variety V1: SC10
V2: TW310
Treatment Treatment Treatment
No.
V1 N1
V1 N2
V1 N3
V1 N4
V1 N5
V1 N6
V1 N7
V1 N8
(Single cross 10)
(Three way cross 310)
9
10
11
12
13
14
15
16
V2 N1
V2 N2
V2 N3
V2 N4
V2 N5
V2 N6
V2 N7
V2 N8
Temperature , precipitation and solar radiation for the current
(CO2=300ppm ) and the expected change situation(CO2=600ppm)
by the year 2040.
Month
Temperature C0
CO2
CO2 Ratio
0.03% 0.06%
January
11.9
14.8 2.85
February
13.1
17.9 4.84
March
17.2
21
3.86
April
21.5
26.9 5.35
May
26.3
32.3 5.97
June
32
35.9 3.98
July
33.8
37.5 3.63
August
33.7
35.8 2.07
September 29.2
33.5 4.31
October
23.2
26.9 3.69
November 16.2
21.3 5.15
December 12.7
16.9 4.21
Precipitation (mm/day )
CO2
CO2
0.03% 0.06%
0.7
0.5
0.5
0.4
0.9
0.7
0.3
0.2
0.2
0.4
0.2
0.5
0.3
1.2
0.3
1.9
0.8
1.2
0.9
1.1
0.5
0.5
0.5
0.9
Ratio
0.66
0.78
0.84
0.55
2.59
3.1
3.8
5
1.56
1.16
0.93
1.83
Solar ( W/M2 )
CO2
CO2 Ratio
0.03% 0.06%
155
159
1.02
198
199
1.01
259
262
1.01
318
315
0.99
341
338
0.99
350
341
0.97
346
327
0.94
317
302
0.95
275
268
0.97
222
222
1
175
174
1
151
146
0.97
Summary of data produced by the program and compared yield for
measured data.
Treatment
Fert. N
Plant N Leached Final N
Grain yield
No.
N
predicted measured
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
285
71
213
309
498
285
594
380
285
71
213
309
498
285
594
380
133
49
122
151
154
135
164
157
126
47
113
131
132
122
141
134
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)
162
54
94
142
328
144
385
197
168
54
96
151
340
150
402
209
5
5
10
26
18
10
45
31
5
5
10
32
22
12
53
39
5519
3942
5525
5460
5506
5493
5505
5471
4232
2782
4265
4215
4223
4221
4223
4218
5495
4630
5509
5407
5421
5468
5426
5462
3970
3001
3956
3973
3998
4020
3987
3951