Climate effects on agriculture are of increasing concern in both the scientific and policy communities because of the growing population and the greater uncertainty in the weather during growing seasons. Changes in production are directly linked to variations in temperature and precipitation during the growing season and often to the offseason changes in weather because of soil water storage to replenish the soil profile. This is not an isolated problem but one of worldwide interest because each country has concerns about their food security.

1. Picture here
Climate and crop
modeling

2. Overview of modelling work in ESA
Agricultural
Systems
Climate
CRAFT,
Downscaling
Climate analysis
Climate scenarios
Yield forecasting
Crop
APSIM
DSSAT
Ex-Ante Analysis
Climate change impacts
Yield gap
Risk analysis
Landscape
SWAT
RUSLE2
Watershed impacts
Biomass estimation
Economic
ToA-MD
IMPACT??
Tradeoff analysis
Spatial
GIS
Remote
sensing
Change detection
Crop suitability
Target domains
Livestock???

3. 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

4. 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)

5. 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

6. DSSAT vs APSIM
• Much of E. Africa impacts are
small
• Impacts are more negative
– Short duration varieties
– High input management
– Long rain season

7. Changes in SAT

8. Ex-Ante Analysis
Period of simulation 1982-2013
Planting between 1 March and 30 April
Harvested or killed by 1 Jun
32 year average grain yield 897 kg/ha
32 year average biomass yield 3748 kg/ha
Average contribution of nitrogen @2% N in biomass 75 kg N/ha

9. Forecast based decision making

10. Potential for intercropping during kiremt
season
Period of simulation 1982-2013
Pigeonpea medium duration
Mize with no fertilizer
Price of maize: 4.3/kg
PP grain price: 7.3/kg

11. Watershed Impacts
Year
Mean Soil Loss
(kg/ha)
2001 7.2
2010 7.7
2015 4.8
Change
2001-
2010
2010-
2015
2001-
2015
Increase 8.9 88 36
Decrease 91.1 12 38
Unchanged 6

12. Calibration:
• R2 = 0.71 (p=0.05)
• 16% overestimation
• Overestimates low flow;
underestimating high flows
Validation:
• R2 = 0.64 (p=0.05)
• 56% overall overestimation
• Model overestimates all rainfall
flows, esp high rainfall flows
Catchment modelling

13. 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
Models
Catchment
models
Characterization
Downscaled CC scenarios
Hind and forecasts
Advisory services
Climate impacts
Ex-Ante analysis
Input response
Management response
Hydrology
Ecosystem services
Erosion and land degradation
Biomass production

14. Sorghum performance

15. 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

16. Yield Gap
Trade off Analysis

17. 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

18. ICRISAT is a member of the CGIAR Consortium
Thank you!