Recently, the availability of multi-model ensemble prediction methods has permitted the assignment of likelihoods to future climate projections. This allowed moving from the scenario-based approach to the risk-based approach in assessing the effects of climate change, thus providing more useful information for decision-makers that, as reported by Schneider (2001), need probability estimates to assess the seriousness of the projected impacts. The probabilistic approach to evaluate crop response to climate change mainly consists in applying an impact model (such as crop growth model) to a very large number of climate projections so to provide a probabilistic distribution of the variable selected to evaluate the impact. By comparing the outputs of the multi-simulation with a critical threshold (such as minimum yield below which it is not admissible to fall), it is possible to evaluate the risk related to future climate conditions. Unfortunately, such an approach is a time-consuming process due to the large number of model runs needed for such a procedure. An alternative method relies on the set up of impact response surfaces (RS) with respect to key climatic variables on which a probabilistic representation of projected changes in the same climatic variables may be overlaid (Fronzek et al. 2008). This approach was exploited within the ENSEMBLES EU Project aiming at assessing climate change impact on typical Mediterranean crops. This work presents the results of the project with a particular concerning about th
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