Abstract
Many recent studies with crop models as well as with system models in other fields use multimodel ensembles (MMEs). A major objective is to study the uncertainty due to model structure, which can be estimated based on the variability between models. One can also enlarge MME studies to include variability in model parameters and in approximations to the explanatory variables. MME studies are also of importance for improving predictions. Empirically it is often observed that the mean and median of simulated values are quite good predictors and can be better than even the best individual model. Theoretically it can be shown that the mean is always better than the average model, in expectation over the target population of environments. The important advantages of working with MMEs suggest that this approach may become even more widespread in the future.
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