Dynamical-statistical projections of the climate change impact on agricultural production in Benin by means of a cross-validated linear model combined with Bayesian statistics

Abstract

West Africa is highly vulnerable to climate change and a robust quantification of the societal impacts of climate change is essential to guide the necessary adaptation efforts. Here, we project the potential impacts of climate change on nine important crops using climate change information from a gridded observational data set and a high-resolution regional climate model driven with and without land use changes. Probabilistic crop models are developed and forced with climate predictors until 2050. It is found that large-scale climate predictors are sufficiently robust for crop modelling in the absence of reliable local climate information. Pineapple, maize, groundnuts, cassava and cowpeas will face harmful effects with an average yield reduction in the range of 11%–33% by 2050, whereas sorghum, yam, cotton and rice will benefit from climate change with an average yield gain of 10–39%. Temperature increase rather than precipitation change is responsible for the projected yield changes. Our study also shows that land cover degradation in West Africa tends to reduce yield for most crops whilst favouring the production of yam and cotton.