Proposal and extensive test of a calibration protocol for crop phenology models

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments and climate change impact is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, comprehensive, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. In the first step, almost additive parameters are identified and estimated. This should nearly eliminate bias in the simulations. In the second step, candidate parameters are identified, which are likely to explain the remaining discrepancies between simulated and observed values. Each candidate is tested, and is added to the list of parameters to estimate only if it causes a sufficient reduction in squared error. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. In both cases, evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.