Probabilistic assessments of climate change impacts on durum wheat in the Mediterranean region

Recently, the availability of multi-model ensemble prediction methods has permitted a shift from a scenario-based approach to a risk-based approach in assessing the effects of climate change. This provides more useful information to decision-makers who need probability estimates to assess the seriousness of the projected impacts. In this study, a probabilistic framework for evaluating the risk of durum wheat yield shortfall over the Mediterranean Basin has been exploited. An artificial neural network, trained to emulate the outputs of a process-based crop growth model, has been adopted to create yield response surfaces which are then overlaid with probabilistic projections of future temperature and precipitation changes in order to estimate probabilistic projections of future yields. The risk is calculated as the relative frequency of projected yields below a selected threshold. In contrast to previous studies, which suggest that the beneficial effects of elevated atmospheric CO(2) concentration over the next few decades would outweigh the detrimental effects of the early stages of climatic warming and drying, the results of this study are of greater concern.