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Climate and crop modeling

Overview of modelling work in ESA

Agricultural Systems

Climate CRAFT, Downscaling

Climate analysisClimate scenariosYield forecasting

CropAPSIMDSSAT

Ex-Ante AnalysisClimate change impacts

Yield gapRisk analysis

LandscapeSWAT

RUSLE2Watershed impactsBiomass estimation

EconomicToA-MD

IMPACT??Tradeoff analysis

SpatialGIS

Remote sensing

Change detectionCrop suitabilityTarget domains

Livestock???

The Team and Skills

• Gummadi Sridhar – DSSAT, EPIC, APSIM, SWAT, CRAFT etc.

• Gizachew Legesse – SWAT, GIS/Remote sensing

• Pauline Chivenge –SWAT, APSIM • Martin Moyo - APSIM• Lieven Claessens – ToA-MD

Data Resources

• Climate data for >200 stations– Ethiopia, Kenya, Uganda, Tanzania, Zimbabwe,

Sudan, Madagascar, Mozambique

• Soil data for more than 100 Profiles• Crop varieties calibrated - ?????• Farmer survey data• Flow data for catchments Ziway lake in

Ethiopia and Gwayi in Zimbabwe• GIS data layers for Ethiopia (mostly from

available global resources)

On going activities

• Assessing climate change impacts – AgMIP• Ex-ante analysis of agricultural systems - DS• Forecast based decision making - CCAFS• Yield forecasting – CCAFS• Assessing Impacts of watershed

management – WLE• Catchment modelling - WLE• National assessment of sorghum production

- DC

DSSAT vs APSIM

• Much of E. Africa impacts are small

• Impacts are more negative– Short duration varieties – High input management– Long rain season

Changes in SAT

Ex-Ante Analysis

Period of simulation 1982-2013Planting between 1 March and 30 AprilHarvested or killed by 1 Jun32 year average grain yield 897 kg/ha32 year average biomass yield 3748 kg/haAverage contribution of nitrogen @2% N in biomass 75 kg N/ha

Forecast based decision making

Potential for intercropping during kiremtseason

Period of simulation 1982-2013Pigeonpea medium durationMize with no fertilizerPrice of maize: 4.3/kgPP grain price: 7.3/kg

Watershed Impacts

Year Mean Soil Loss (kg/ha)

2001 7.22010 7.72015 4.8

Change 2001-2010

2010-2015

2001-2015

Increase 8.9 88 36Decrease 91.1 12 38

Unchanged 6

Calibration:• R2 = 0.71 (p=0.05)• 16% overestimation• Overestimates low flow;

underestimating high flowsValidation:• R2 = 0.64 (p=0.05)• 56% overall overestimation• Model overestimates all rainfall

flows, esp high rainfall flows

Catchment modelling

Remote Sensing

GIS

Framework for National Assessment

Target domains

Agro-Ecologica

l Zones

Crop Coverage

Soil distributi

on

Database

Simulation

Validation

Calibration

Climatological Tools

Crop simulation

ModelsCatchment

models

CharacterizationDownscaled CC scenarios

Hind and forecastsAdvisory services

Climate impactsEx-Ante analysis

Input responseManagement response

HydrologyEcosystem services

Erosion and land degradationBiomass production

Sorghum performance

Crop Suitability

• Identify areas where the crop is well adapted

• Define the limits for high potential areas– Rainfall– Average temperature– Altitude

• Map the high and low potential areas

Yield Gap

Trade off Analysis

Moving forward

• Organize and strengthen database development

• Modelling frameworks (CRAFTS)– Targeting technologies– Ex-ante assessment/Yield gap– Forecasting and early warning

• More applications• More work on mandate crops and

calibration of relevant varieties• Whole system modelling• Strengthen livestock modelling

ICRISAT is a member of the CGIAR Consortium

Thank you!