Climate, Water and Agriculture: Impacts and adaptation in Africa

1. Climate, Water and Agriculture: Impacts and adaptation in Africa Core funding from GEF plus complementary funding from others (WBI Finish Trust, NOAA, CEEPA McArthur, WB ARD, IWMI, FAO) 2002 - 2005 WWW.CEEPA.CO.ZA/CLIMATE CHANGE EEE Program Seminar, ICTP June 10, 2003 Rashid Hassan, CEEPA

2. Motivation • Agro-ecosystems in Africa – most vulnerable to climate change (CC) • Climate already hot in most parts of Africa • productivity decline with warming - crop yields • More pressure: • Higher demand for land conversion • More water for irrigation • Increased intensification (pollution, erosion, etc.) • Introduction of GM plants and animals (biodiversity) • High dependence on agriculture – livelihoods • Low ability of African farmers to adapt • Limited access to capital and technological options • Poor public infrastructure (roads, information, research, extension)

3. Objectives • Improve the capacity of research and policy in participating countries and the region to: • Assess the impacts of CC on agro-ecosyst. • Evaluate alternative adaptation options • Generate improved information and knowledge on impacts of CC and possible options for adaptation

4. Scope • Covers 11 countries from north, south, east and west Africa • Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, Zambia and Zimbabwe (work began in 8) • Involves collaboration with a number of institutions on country level and regional analyses • IWMI, FAO, YALE working with multidisciplinary country teams

5. Approach and Methods • Three approaches integrated: • Ricardian approach to economic impact assessment • River basin hydrology models for assessing impacts on runoff and availability of water • Crop-water response simulation models for assessing the biological impacts (crop yields)

6. The Ricardian Approach 1 • Based on the hypothesis that impacts of changes in climate attributes (temperature, rainfall) like other long-term economic phenomena are capitalized in land values • CC affects crop yields and hence farm revenues capitalized in land value changes over time (present value of the stream of future revenues) • Regression of land values on various determinants of net revenue including climate variables VLt = t NRt (1+ )-t NRt = F(RFt, TMt, Zt, Xt, TKt)

7. The Ricardian Approach 2 • Difficult to have long time series on land values or net revenue by region and all regressors • Cross-sectional data reflecting spatial diversity in climate attributes and all other determinants of land value (NR) alternatively used • Partially controls for adaptation (substitution between inputs in production) • Other adaptation missed (crop switching, transitory adjustment costs – capital goods)

8. Water-crop response simulation models 1 • Describe crops’ behavior (physiological and development) as a function of: • Climate (weather) factors • Soil conditions (fertility, water holding capacity, etc.) • Other determinants under farmer control and management (planting dates and input levels) • Simulate yield response to variability in climate • Other response variables such as crop water consumption and irrigation requirements as climate changes (evapotranspiration, soil moisture, water excess-deficit at critical stages of growth, etc.)

9. Water-crop response simulation models 2 • Compliments the cross-sectional analysis – Ricardian (both spatially implemented) • Overestimates impacts – no control for adaptation (recent modifications to allow) • Crop specific and location specific • Experimental – real world replications of conditions (calibrations to actual locations’ data)

10. River basin Hydrology • Models how CC affects agriculture also indirectly through impacts on basin hydrology (runoff): • Rainfall, temp. and evaporation affect seasonal patterns of river flow and hence availability of water for agriculture • Compliments the cross-sectional analysis – Ricardian method • Both spatially-based

11. Data and Plans of analyses 1 • District level data • Farm survey data at district level (same year 2002) aggregated by country and region (river basins) • Crop yields, prices and production costs (NR) • Other agric. response variables (% land under crops) • Climate and soil attributes data by district again aggregated at river basin • Other data at district level (population, proximity to markets, literacy and disease rate, etc.) • Regional data-base to sport the cross-country assessment of economic impacts

12. Data and Plans of analyses 2 • Hydrology models analyses’ generate input variables to the Ricardian regression by district • Change in runoff, soil moisture, etc. • Crop water response models calibrate the biological response of crops to CC – by district • Yield • Water requirements • The Ricardian analyses of the economic impact applied at both country and district levels • First Year: • Surveys designed and data collection instruments developed and tested in the various countries • Training on the three methods and approaches conducted