Recent improvements in the capability of statistical or dynamic models to predict climate fluctuations several months in advance may be an opportunity to improve the management of climatic risk in rain-fed agriculture. The aim of this paper is to evaluate the potential benefits that seasonal climate predictions can bring to farmers in West Africa. The authors have developed an archetypal bioeconomic model of a smallholder farm in Nioro du Rip, a semiarid region of Senegal. The model is used to simulate the decisions of farmers who have access to a priori information on the quality of the next rainy season. First, the potential economic benefits of a perfect rainfall prediction scheme are evaluated, showing how these benefits are affected by forecast accuracy. Then, the potential benefits of several widely used rainfall prediction schemes are evaluated: one group of schemes based on the statistical relationship between rainfall and sea surface temperatures, and one group based on the predictions of coupled ocean–atmosphere models. The results show that forecasting a dryer than average rainy season would be the most useful to Nioro du Rip farmers if they interpret forecasts as deterministic. Indeed, because forecasts are imperfect, predicting a wetter than average rainy season exposes the farmers to a high risk of failure by favoring cash crops such as maize and peanut that are highly vulnerable to drought. On the other hand, the farmers’ response to a forecast of a dryer than average rainy season minimizes the climate risk by favoring robust crops such as millet and sorghum, which will tolerate higher rainfall in case the forecast is wrong. When either statistical or dynamic climate models are used for forecasting under the same lead time and the same 31-yr hindcast period (i.e., 1970–2000), similar skill and economic values at farm level are found. When a dryer than average rainy season is predicted, both methods yield an increase of the farmers’ income—13.8% for the statistical model and 9.6% for the bias-corrected Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel ensemble mean.
RésuméÀ ces fluctuations climatiques récentes s'ajoutent les conséquences attendues du changement climatique. Le quatrième rapport (AR4) du Groupe d'experts intergouvernemental sur l'évolu-tion du climat (GIEC), publié en 2007, a en effet alerté la communauté internationale sur l' augmentation de la température partout dans le monde ainsi que sur la probable augmentation de la fréquence et de l'intensité des aléas météorologiques majeurs comme les sécheresses, en citant l'Afrique comme le continent le plus vulnérable aux changements climatiques. Les impacts de ces changements sur l'agriculture constituent une contrainte supplémen-taire sur un système de production déjà vulnérable à la variabilité climatique actuelle et confronté à une croissance démographique très rapide.Dans ce contexte, il est apparu essentiel que les progrès en termes de compré-hension de la mousson africaine visés par le programme de recherche AMMA (Janicot et al., dans ce numéro, p. 2-8) puissent contribuer à répondre au besoin sociétal de développer des stratégies pour réduire les impacts socioéconomiques de la variabilité et du changement climatiques. Le programme AMMA, à travers la mobilisation multidisciplinaire qu'il suscite (réunissant notamment climatologues,
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