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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

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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 . DSSAT CLAC. Integrated Crop Management Information System by using DSSAT program . Who Uses DSSAT Tools?.

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slide1

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

slide2

DSSAT

CLAC

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
slide4

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

slide5

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
slide7

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

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.

slide9

Soil

Water Management

N Application + Organic

Crop

(Genetic Coefficients )

Weather

Temperature

Photoperiod

Duration of

Phases

Development

Photosynthesis

Respiration

Mass of Crop

Kg/ha

Growth

Partitioning

CO2

Leaf

Stem

Root

Fruit

slide10

INPUTS

File x ExperimentalData File

File S

Soil Data

File w

Weather Data

File C

Cultivar Code

Crop

Models

File T

Crop Data during season

File A Crop Data

at Harvest

Output Depending on Option Setting and Simulation Application

slide11

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

slide12

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).

slide13

MAIZE GENOTYPE COEFFICIENTS

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

slide14

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
Crop Development

Vegetative Growth Period

Reproductive Growth Period

Harvest

Maturity

Phys. Maturity

Plant Emerge

1st Flower

1st Seed

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.
slide16

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.
slide17

Modelvalidation

Experimental data

Other inputs

Parameter test

DSSAT program

Simulation

Compare simulation

with measured

Modification model

Conduct sensitivity analysis on the crop models to evaluate the modal

slide25

*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

slide26

*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

slide27

*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

slide28

*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

slide30

OBSERVED AND SIMULATED WHEAT GRAIN YIELD

9000

2

8000

R

=

0

.

901

7000

6000

5000

4000

3000

2000

1000

0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Simulated grain yield

Observed 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
slide32

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

slide33

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

Minia Governorate, Malawi

slide34

Fertilizer levels, additions date and amounts

Material code (1) = Ammonium nitrate

Method code (2) = Broadcast, incorporate

slide35

Combination between varieties and nitrogen levels

Variety V1: SC10 (Single cross 10)

V2: TW310 (Three way cross 310)

slide36

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

slide37

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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