Sensitivity of crop model predictions to meteorological and soil data

Crop growth models are increasingly used as part of research into areas such as climate change and bioenergy, so it is particularly important to understand the effects of environmental inputs on model results. Rather than investigating the effects of separate input parameters, we assess results obtained from a crop growth model using a selection of entire meteorological and soil input datasets, since these define modelled conditions. Yields are found to vary significantly only where the combination of inputs makes the crop vulnerable to drought, rather than being especially sensitive to any single input. Results highlight the significance of soil water parameters, which are likely to become increasingly critical in areas affected by climate change. Differences between datasets demonstrate the need to consider the dataset-dependence of parameterised model terms, both for model validation and predictions based on alternative datasets.