Analytical Framework

To assess the impact of climate change and adaptations on food security in Mali, we integrated a number of modeling frameworks. First, to assess yield impacts, we used the following biophysical models: (1) the erosion-productivity impact calculator (EPIC) crop growth simulator (Williams et al., 1989) for crops; (2) the PHYGROW plant growth model (Rowan, 1995) for forage yields; and (3) the nutritional balance (NUTBAL) model (Stuth et al., 1999) for livestock feed demand and yield. Second, to assess changes in production, prices, and trade, plus adaptation strategies, we used the Mali agriculture sector model (MASM). MASM, developed by Chen et al. (1999), was adapted for use as described in Butt (2002). The model shows economic conditions in various agricultural regions in Mali. It is a price-endogenous mathematical programming model based on the concepts reviewed by McCarl and Spreen (1980) and Norton and Schiefer (1980). MASM incorporates climate variability following Lambert et al. (1995), and crop subsistence behavior as discussed in Calkins (1981).

MASM was augmented by a methodology to compute a risk of hunger measure (ROH) (Food and Agriculture Organization [FAO], 1996) indicating the incidence of malnourish-ment, and hence, food insecurity in a country. The ROH

measure estimates the percentage of the population whose daily caloric intake falls below requirements for a healthy life.

For the climate change analysis, the biophysical models were run under base HadCM and CGCM climate projections for 2030. (Model predictions are available under two scenarios: greenhouse gas integrations [GG], and greenhouse gas plus sulphate aerosol integrations [GS]. In this study, we used GG, as this scenario has captured the observed signal of global mean temperature changes better than the GS scenario for the past 100-year record.) In turn, the biophysical responses were incorporated into MASM, along with changes in world trade conditions derived from the U.S. national assessment (Reilly et al., 2000a, 2000b) to obtain simulated changes in production, exports/imports, and food for consumption. MASM results provided changes in prices, production, consumption, and risk of hunger in Mali in response to the projected climate change.

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