Ex ante assessment of climatic seasonal predictions for small-scale farming in Burkina Faso

Abstract

Agricultural production in sub-Saharan Africa is mostly rainfed and particularly vulnerable to climatic variations. At the scale of the farm, climatic variability has a direct impact on primary production and on the household’s income and food security. In this study, we assessed the economic benefit of providing rainy season rainfall predictions to producers. The study was carried out in the south-west of Burkina Faso, in the area of Dano. It is based on a linear programming model that maximizes income by optimizing the allocation of land, labor and inputs among several crops, with yields varying in relation to the type of soil and crop management sequence, and to the rainy season rainfall pattern. Four scenarios were analyzed, differing according to the type of information available to farmers concerning the oncoming rainy season rainfall: a control scenario with no forecast given, a low-rainfall rainy season forecast, a normal rainy season forecast, and a wet rainy season forecast. The results of the simulations show that the most important prediction is the one warning of a dry rainy season, but this is also the one for which prediction errors entail the most costly consequences. Overall, the income gain associated with predictions is relatively small, but the cost incurred by a prediction error raises the issue of liability and compensation. These results shed a sobering light on the value of seasonal predictions for alleviating the vulnerability of Sahel communities.