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

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)

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

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

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)

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 VL t =  t NR t (1+  ) -t NR t = F(RF t , TM t , Z t , X t , TK t )

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)

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.)

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)

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

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

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