Weather Sensitive Crop Models: Applicationsjhansen/jones15july-b.pdf · Climate Variability & Food Security IRI Advanced Training Institute July 15, 2002 Weather Sensitive Crop Models:

Climate Variability & Food SecurityIRI Advanced Training InstituteJuly 15, 2002

Weather Sensitive Crop Models:Applications

James W. JonesAgricultural & Biological Engineering

University of Florida


Applications• Diagnose Problems (Yield Gap Analysis)• Precision Agriculture

– Diagnose Factors Causing Yield Variations– Prescribe Spatially Variable Management

• Adaptive Management using Climate Forecasts• Soil Carbon Sequestration• Land Use Change Analysis• Targeting Aid

– Early Warning of Food Shortages– Fertilizer vs. Food Aid Decision

• Climate Change


Example Applications in EUROPEType of Application References

Crop management Hunkár, 1994; Pfeil et al., 1992a, b; Ruiz-Nogueria et al., 2001; Sau et al. 1999; Zalud et al., 2000

Fertilizer management Gabrielle and Kengni, 1996; Gabrielle et al., 1998; Zalud et al., 2001

Irrigation management Ben Nouna et al., 2000; Castrignano et al., 1998; Gerdes et al., 1994

Tillage management Castrignano et al., 1997

Variety evaluation Brisson et al., 1989, Colson et al., 1995

Precision farming Booltink and Verhagen, 1997; Booltink et al., 2001

Environmental Pollution Kovács and Németh, 1995

Climate change Alexandrov and Hoogenboom, 2001; Iglesis et al., 2000; Semenov et al., 1996; Wolf et al., 1996

Yield forecasting Landau et al., 1998; Saarikko, 2000

Sustainability Hoffmann and Ritchie, 1993

Citations of applications of DSSAT cropping system models. Jones et al. 2002.


NORTH AMERICAType of ApplicationType of Application ReferencesReferencesCrop management Egli. and Bruening, 1992; Jame and Cuutforth, 1996; Sexton et al., 1998

Fertilizer management Beckie et al., 1995; Hodges, 1998

Irrigation mgt. Epperson et al., 1993; Hook, 1994; McClendon et al., 1996; Steele et al., 2000; Swaneyet al., 1983

Pest management Barbour et al., 1994; Barbour and Bridges, 1995; Batchelor et al., 1993; Boote et al., 1993; Lacey et al., 1989; Mishoe et al., 1984

Tillage management Andales et al., 2000

Variety evaluation Irmak et al., 1999; Manrique et al., 1990; Mavromatis et al., 2001; Piper et al., 1996, 98

Genomics Boote and Tollenaar, 1994; Boote et al., 2001; Hoogenboom et al., 1997; White andHoogenboom, 1996

Precision agriculture Han et al., 1995; Sadler et al., 2000; Paz et al., 1998, 1999; Irmak et al., 2001; Paz et al., 2001a, 2001b, Seidl et al. 2001

Environment Gerakis and Ritchie, 1998; Pang et al., 1998

Climate change Hatch et al., 1999; Mearns et al., 2001; Rosenzweig and Tubiello, 1995; Southworth et al., 2000; Tubiello et al., 1995, 2001; Boote et al., 1997

Climate variability Hansen and Jones, 2000; Jones et al., 2000; Mearns et al., 1996

Yield forecasting Carbone, 1993; Carbone et al., 1996; Chipansi et al., 1997,1999; Duchon, 1986;Georgiev and Hoogenboom, 1999; Moulin and Beckie, 1993

Sustainability Bowen et al., 1992, Hasegawa et al., 1999, 2000; Quemada and Cabrera, 1995; Wagner-Riddle et al., 1997

Space technology Fleisher et al., 2000

Education Cabrera, 1994; Meisner et al., 1991


Example Applications in AFRICA

Type of ApplicationType of Application ReferencesReferencesCrop management Fechter et al., 1991; Mbabaliye and Wojtkowski, 1994; Vos and

Mallett, 1987; Wafula, 1995Fertilizer management Jagtap et al., 1999; Singh et al., 1993; Thornton et al., 1995;

Keating et al., 1991Irrigation management Kamel et al., 1995; MacRobert and Savage, 1998

Precision management Booltink et al., 2001

Climate change Muchena and Iglesias, 1995

Climate variability Phillips et al., 1998

Food security Pisani, 1987; Thornton et al., 1997



Example Applications in LATIN AMERICA

Crop management Savin et al., 1995; Travasso and Magrin, 1998

Irrigation management Heinemann et al., 2000

Precision management Booltink et al., 2001

Variety evaluation Castelan Ortega et al., 2000; Ferreyra et al., 2000; White et al. 1995

Climate change Baethgen, 1997, Conde et al., 1997; Diaz et al.,1997; Magrin et al., 1997; Maytin et al., 1995

Climate variability Messina et al., 1999; Podesta et al., 2002; Ferreyra et al., 2001; Royce et al., 2002

Yield forecasting Meira and Guevara, 1997; Travasso et al., 1996

Sustainability Giraldo et al., 1998

Education Ortiz, 1998

Type of ApplicationType of Application ReferencesReferences



Example Applications in ASIA

Type of ApplicationType of Application ReferencesReferences

Crop management Alagarswamy et al., 2000; Jintrawet, 1995; Singh et al., 1994a, b; Salam et al., 2001

Fertilizer management Godwin et al., 1994

Irrigation management Hundal and Prabhjyot-Kaur, 1997

Pest management Luo et al., 1997; Pinnschmidt et al., 1995

Climate change Jinghua and Erda. 1996; Lal et al., 1998, 1999; Luo et al., 1995; 1998; Singh and Godwin, 1990

Climate variability Alocilja and Ritchie, 1990; Gadgil et al., 1999

Yield forecasting Kaur and Hundal, 1999; Singh et al., 1999

Sustainability Singh et al., 1999a, b



Outline• Example Applications1

– Agronomic Research– Determine Best Management for Site, Situation– Precision Agriculture– Climate Change– Land Use Management– Climate Forecast Use

• DSSAT Overview• DSSAT Demo

•• 11Think about what is needed for each application!Think about what is needed for each application!


Tool: Synthesize and Interpret Research

• Test published relationships between crop growth processes and light, temperature, water, N.

• Hypothesize causes of yield gaps, whether fertility, weather, or pest effects

• Hypothesize Genetic Improvement in Yield


Diagnose Yield Gaps

• In developing countries (and US), production may be less than the climatic potential.

• Use model as a “What if” tool, to postulate causes associated with water, soil fertility, diseases, pests, & poor management.


Peanut Cropping Systems Analysis in Benin and Ghana

• Low peanut yield and inefficient use of natural resources

• Objective is to identify yield gaps a n d i mprove production efficiency

• Fun ded by Peanut-CRSP• Collaborative research and training progra m between the Institute Nationale des Recherches Agrono mic du Benin; the Savan n a h Agricultural Research Institute in Nyankpala, Gha n a; and the University of


Observed and Simulated BiomassYield Gap Analysis

Peanut - Benin

0 10 20 30 40 50 60 70 80 90 1000

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Days after Planting

Biomass (kg/ha)

Planted Density

Actual Density (AD)

AD + Fertility (F)

AD + F + Disease

Observed


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JULIAN DAY

a) Chinese, 1997 season

b) F-Mix, 1997 season

LEA

F W

EIG

HT,

kg/

J. Naab – Ghana,

Two peanut cult.

Simulated with no disease effect

Simulated with input defoliation to match observed.

Crop had no fungicide applied


JULIAN DAY

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J. Naab – Ghana,

Two peanut cult.

Simulated with no disease effect

Simulated with input defoliation and leafspot injury

Crop had no fungicide applied


Yield gap (kg/ha) of peanut due to water deficit

and biotic stress during 1997 in Ghana.

-2978631632866295229 May

1473169314212894458724 July189639120293925431626 June76544131243889433029 May

F-MixCultivar22011077402941304824 July148810414842972307626 June

ChineseCultivar

Yield Gap (biotic stress)(2)-(3)

Yield Gap (water deficit)(1)-(2)

Observed Pod Yield

kg/ha(3)

Water limiting (Sim)

(2)

Water non-limiting (Sim)

(1)

SowingDate


Yield and risk of freeze damage for two soybean MG vs. sowing date in Iowa.

Tool: For Optimizing Crop Management for Production vs. Weather Risk.



Optimize Management

• Variety• Planting Date• Sowing Density• Nitrogen Fertilization• Irrigation• Residue Management• Pest Management


Tool: To Optimize and Analyze N Management

• Optimum N rate, optimum N timing,• Yield response to N in specific year or over long-term,• Impact of cover crops, fallow periods, • N uptake versus contribution from N-fixation by

legumes• NO3 leaching in specific years or over long-term.


Model Testing: Comparison to Observed Data and Model Improvement

• Conduct time-series comparison of processes: Dry matter, N accumulation, NO3 leaching, tissue N conc. If dynamics are not right, look at processes and mechanisms.

• Compare final harvest values (yield, dry matter, N uptake, etc.) versus N applied.

• Conclude whether model accurately predicts N balance processes and response to N fert.

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p or

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in M

ass,

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Crop-Low NCrop-High NGrain-Low NGrain-High NCrop-Obs-LCrop-Obs-HGrain-Obs-LGrain-Obs-H

Simulated Crop and Grain Growth of Maize at low or high N (116 vs. 401 kg/ha) on sandy soil in Florida in 1982

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Simulated and Observed Crop N Uptake by Maize Grown at Low and High N (116 and 401 kg/ha) on Sandy Soil in Florida in 1982

CERES-Maize simulation for Maize Experiment, D. Beg, 1989



Optimizing Planting Date and Nitrogen Fertilizer Maize Grown in Camilla, GA

45 Years of Weather (1951-95)

From F. S. Royce


Precision AgricultureThe Problem:• Yield varies considerably within fields• Spatially varying inputs and management may

increase profits and reduce environmental risksHowever:• Understanding what caused yield variability in a

specific field • Determining how to vary management across a field

to optimize profit and meet other goals


• Yield• Soil type• Images• Pests• Elevation• Drainage• Fertility

Genetics Weather

• Causes of Yield Variability• Develop Prescriptions• Risk Assessment• Economics

Crop Models & Precision FarmingCrop Models & Precision Farming


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Simulated versus observed maize grain yield, two years, using field-measuredspatially varying soil parameters in Michigan. R. Braga (2000).

With accurate inputs, crop models can accurately predict yield


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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28Position

Yiel

d (k

g/ha

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MeasuredPredicted

Baker Farm (1994) Transect 1

Soybean

W. D. Batchelor et al., 1999Iowa State University


Soybean Crop Yield Variability,Baker Farm (1994)

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Measured Yield Predicted YieldW. D. Batchelor et al., 1999Iowa State University


Measured vs Predicted Soybean Yield for 1996 & 1998Optimization + revised initial conditions

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Measure d (kg/ha)

Pred

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(kg/h

a)

1996 1998

Y = 0.969x + 113.79r2 = 0.9312

RMSE = 74.35 kg/ha

A. Irmak et al., 2000University of Florida

1996 Season Rain = 418 mm

1998 Season Rain = 700 mm

Simulated Yields After Estimating Soil Properties


598500 .00 598550 .00 598600 .00 598650 .00 598700 .00 598750 .00 598800 .00

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Measured 96 Yield (kg/ha)

465145 0.00

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465165 0.00

5 98500.00 5 98550.00 5 98600.00 5 98650.00 5 98700.00 5 98750.00 5 98800.00

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Observed 98 Yield (kg/ha)

4651450.00

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Climate Change

• Increases in Atmospheric CO2 Levels, ~ 1% per year (Currently about 360 vpm)

• Affects energy balance of the Earth’s surface, hydrologic cycle and global circulation patterns

• Likely increase temperature, change rainfall, melt glacial ice and raise sea levels

• How would such changes affect agriculture?


Climate Change• Recent General Circulation Model results

suggest: – Temperature increases across the USA

• from 0.5 to 1.5 C by the year 2035• from 2.0 to 4.0 C by the end of the 21st Century

– Precipitation increases across the USA• -4 to +12 cm/year by 2035• +8 to +30 cm/year by the end of the 21st Century

• Uncertainty exists in magnitude, but evidence is accumulating that changes are indeed occurring


Soybean Production under Climate Change

Purposes

• Understand how soybean yields might change under different climate change situations

• Investigate how changes in management might improve yields under climate change situations


Soybean Study

• Selected 15 locations across the USA soybean growing region

• 20 years of historical weather data (to define “current” weather conditions, including year to year variability

• Systematically changed temperature (-1 C to +5 C) and rainfall (-40% to +40%) by equal increments to produce a range of possible responses


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Area (ha) : 1995Not Estimated<40004000-10,00010,000 to 20,00020,000 to 40,00040,000 to 60,00060,000 +

Soybean Production


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A a (h ) : 995Not Est m t

Location of Study Sites


Table 1. Summary information for the locations used in the study.

Weather StationLocation

Latitude Variety,MG

Planting Date(DOY)

Soil WaterHoldingCapacity,

mm

GrowingSeasonAverage

Temperature,oC

GrowingSeasonRainfall,

mm

Northern StatesFergus Falls, MN 46.28 0 May 17 (137) 267 19.37 296Madison, WI 43.08 1 May 16 (136) 240 19.70 385E Lansing, MI 42.71 2 May 18 (138) 195 19.21 319Ames, IA 42.02 2 May 15 (135) 272 21.02 449Lincoln, NB 40.82 3 May 19 (139) 264 22.77 386Lafayette, IN 40.41 3 May 16 (136) 267 21.20 447Urbana, IL 40.11 3 May 16 (136) 281 21.64 440Manhattan, KS 39.19 4 May 25 (145) 202 23.07 433Average 249 21.00 394

Southern StatesLouisville, KY 38.22 4 Jun 5 (156) 184.2 22.76 410.7Richmond, VA 37.53 4 May 20 (140) 184.2 23.04 446.2Raleigh, NC 35.82 6 Jun 1 (152) 189.0 23.63 445.8Jackson, TN 35.63 5 May 31 (151) 244.3 24.08 437.6Columbia, SC 34.04 7 May 29 (149) 188.0 23.4 513.8Starkville, MS 32.32 5 May 16 (136) 266.6 25.92 431.6Tifton, GA 31.47 7 Jun 9 (160) 184.2 25.28 459.7Average 205.8 24.02 449.3


CROPGRO-Soybean Model• Based on understanding of weather, plant, soil,

management interactionso Vegetative and reproductive developmento photosynthesis, respiration, growtho Root water uptake, stress effects on growth processes

• Predict growth, yield, timing (Outputs)• Require information (Inputs)

o Field, Soil characteristicso Weather (daily)o Cultivar characteristicso Management

• Can be used to perform “what-if” experiments


Simulated Results

• Current Management Practices• All combinations of climate, with and

without considering increases in CO2 levels• Example results for

– Ames, Iowa– Tifton, Georgia


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Temperature Change, oC

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Temperature Change, oC

403020100-10-20-30-40

Ames, Iowa

Current CO2 Levels“Double” CO2 Levels

% Change in Rainfall

Kg/ha


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Tifton, GeorgiaCurrent CO2 Levels “Double” CO2 Levels

% Change in Rainfall

Kg/ha


Generally• Yield in southern states were reduced under

any increases in temperature at current CO2levels and rainfall; yields in more northern states were affected very little

• Decreases in rainfall affected yields more in southern states

• Increases in CO2 mostly compensated for up to 2 C increase in temperature in southern states and resulted in increases in yield in northern states


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Yiel

d C

hang

e, %

+2C, Normal Precip, +CO2+2C, -20% Precip, +CO2+2C, -30% Precip, +CO2

Simulated Yield Change vs. Latitude of Sites


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Simulated Yield Change vs. Current Growing Season Temperatures


Would Changes in Management be Warranted under Climate

Change?• Simulated combinations of

– Planting dates– Varieties

• For several southern and northern locations• Determined management practice that

maximized yield


Iowa, Non-Irrigated Current Cliamte

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Iowa, Non-Irrigated+2C, -30% Precip,+ CO2

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Georgia, Non-Irrigated Current Climate

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Implications• Northern vs. Southern latitude responses• Most detrimental effects:higher temperature

and lower rainfall• Compensating effects of CO2 for some

temperature increases and rainfall decreases• Potential benefits of switching cultivars and

planting dates under climate change• Need for heat tolerant cultivars, especially

for lower latitudes


International Climate Change Impact Assessment

• Global war ming arising fro m increasing at mospheric carbon dioxide and other trace gases

• I mpacts on global agriculture m ay be substantial -- temperatures, rainfall regi mes

• International study using the DSSAT crop m o dels linked to global climate models, providing yield estimates under different clim ate change scenarios

• Predicted yields fed to a world food trade m o del to investigate econo mic consequences, shifts in trade and food security resulting fro m scenarios

• A co m m o n methodology used by scientists in over 20 ountries study funded by


Model Sites for the International Climate Change


T+2 T+4

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-4

-8

Yield Change, %

Wheat Rice Soybean Maize

Aggregated DSSAT Crop Model Yield Changes for +2 oCand +4 oC Temperature Increase


International Climate Change Study -- Results Summary

• Crop yields in mid- and high-latitude regions are less adversely affected than yields in low-latitude regions

• Si mple far m-level adaptations in the te mperate regions can generally offset the detrimental effects of climate change

• Appropriate adaptations for tropical regions need to be developed and tested further, with particular e mphasis on genetic resour es and infor m ation


Economic and Environmental Impacts of Land Use Changes, South Coast of Puerto Rico

• Sugarcane no longer econo mically viable

• Intensive develop ment of alternative crops could provide important econo mic benefits

• Potential nitrate, pesticide pollution of aquifers and coastal areas

• Applied AEGIS/WIN in DSSAT to evaluate impacts of alternative agricultural practices


Land typebeachsalinewetlandwaterdevelopedagricultural

1 0 1 2 3 4 5 K ilo m e t e rs

Soil seriesSan Anton†

Cortada †

Constancia†

Fraternidad‡

Paso Seco‡

Fe‡

Cintrona †

Jacaguas†

Yauco†

non-agricultural† M olli sols‡ Verti sols

JACAGUASSTUDY AREA

Caribbean Sea

San Juan

Mayagüez

Ponce

N

PUERTO RICO

Isabela

Land and soil types in the RioJacaguas floodplain, Puerto Rico


1 0 1 2 3 4 5 K ilo m e t e r s

Nitrate leached(kg N ha- 1)

54 - 6767 - 8080 - 9393 - 106106 - 119non-agricultural

Irrigation (mm) 96 - 127127 - 156156 - 185185 - 214214 - 243non-agricultural

Fruit yield(Mg ha-1 d.w.b.)

2.8 - 3.73.7 - 4.64.6 - 5.45.4 - 6.36.3 - 7.2non-agricultural

Simulated tomato yields, irrigation requirements and nitrate leached


Summary Results for the Puerto Rico Study

• To m ato-Sorghu m double-crop was m ost profitable

• Sugarcane required most irrigation but resulted in lower erosion and leaching

• Trade-offs occur between econo mic a n d environ mental objectives

• With nitrate and che mical leaching constraints, sugarcane, m aize-soybean, to m ato, to m ato-sorghu m were most profitable

• Results sensitive to soils


Crop Models Used in Climate Forecast Applications for:

• Field scale optimal management• Farm scale, crop mix optimization, considering

risk attitude• Estimate value of climate forecast• Accuracy of climate forecast analysis • Link with climate models at regional scale


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eld

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) MaizeSoybean

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Wheat

Crop Models are Essential Tools

Evaluating models for use in Argentina.On farm tests in Pampas Region. Magrin et al.


Farm Scale: Determining Optimal Mix of Crops in Argentina, Moderate Risk Aversion

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Neutral El Niño AllLa NiñaNeutral El Niño AllLa Niña

Santa Rosa Pergamino

FromMessina et al.


Credibility of Applications

• One must evaluate crop model results relative to real data – don’t trust results otherwise!

• Some adaptation may be required for a particular region (i.e., soil, management, genetic inputs, or even new model components for some purposes)

• First, convince yourself that results are credible, then be able to provide evidence in discussions, advice, publications, etc.

What is DSSAT?

• Research Tool for Crop Production Analyses• Incorporates

– Crop-Soil-Weather Models– Analysis Tools (Uncertainty, Economics)– Support Software (Graphics, Data Management)– GIS Linkages, Spatial Variability Analysis

• Developed by International Network of Researchers (IBSNAT + ICASA)

What is DSSAT?• Designed to:

– Allow Users to Adapt and Evaluate Models for Their Own Conditions

– Incorporate Their Own Data in Standard Formats. Exchangeability important!!

– Provide Insight into “What-if” Questions About Production, Profitability, and Stability


What are DSSAT Functions?

• Input, organize, store data on crop, soil, weather, and experiments.

• Conduct computer “what if?” experiments• Evaluate crop/soil/weather models• Estimate parameters for models (calibrate)• Conduct economic, risk analyses• Link to spatial data bases, GIS

DSSAT v3.5- Models of 16 Crops -

• Grain Legumes (CROPGRO)– Soybean, Peanut, Dry Bean, Chickpea

• Cereals (CERES)– Corn, Wheat, Rice, Barley, Sorghum, Millet

• Root Crops– Potato, Cassava

• Other Crops– Tomato, Sunflower, Sugar Cane, Pasture


DSSAT v3.5Minimum Data Set

• Level 1 - Operate crop simulation models• Level 2 - Evaluation of model performance• Level 3 - Calibration, parameter estimation• Level 4 - Model development (Maximum)

➜Standard files, formats designed, documented, and implemented in DSSAT and its crop models

DSSAT v3.5- Data Management Tools -

• XCREATE - Input crop management information in standard format.

• WeatherMan - Assist users in cleaning, formating, converting weather data.

• Soil SDB3 - Create soil files for particular fields.

• Genotype - Estimate characteristics for new varieties.

DSSAT v3.5- Analysis Tools -

• Sensitivity Analysis - Vary soil, weather, management, or variety characteristics for insight in model behavior and crop physiology.

• Seasonal Analysis - Multiple year simulations to evaluate uncertainty in biophysical and economic responses.

• Spatial Analysis - Define spatially variable soil, weather, management characteristics across a field or region for analysis.

DSSAT v3.5What is it used for?

• Research– Crop Management Options– Land Use Planning – Global Climate Change– Sustainability

• Teaching– Training Programs– Classroom

• Technology Transfer


Organization of experimentaldata in DSSAT

DSSAT

Crop 1

Crop 2

Crop 3

Crop 16

Treatment 1

Treatment 2

Treatment 3

Treatment x

Experiment 1

Experiment 2

Experiment 3

Experiment m

Subdirectories Sections of a FILEX.

Experimentfiles (FILEX)

:::: ::


DSSAT demonstration


Thank You

Weather Sensitive Crop Models: Applicationsjhansen/jones15july-b.pdf · Climate Variability & Food Security IRI Advanced Training Institute July 15, 2002 Weather Sensitive Crop Models:

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