How well do crop models predict phenology, with emphasis on the effect of calibration?

Plant phenology, which describes the timing of plant development, is a major aspect of plant response to environment and for crops, a major determinant of yield. Many studies have focused on comparing model equations for describing how phenology responds to climate but the effect of crop model calibration, also important for determining model performance, has received much less attention. The objectives here were to obtain a rigorous evaluation of prediction capability of wheat phenology models, to analyze the role of calibration and to document the various calibration approaches. The 27 participants in this multi-model study were provided experimental data for calibration and asked to submit predictions for sites and years not represented in those data. Participants were instructed to use and document their “usual” calibration approach. Overall, the models provided quite good predictions of phenology (median of mean absolute error of 6.1 days) and did much better than simply using the average of observed values as predictor. The results suggest that calibration can compensate to some extent for different model formulations, specifically for differences in simulated time to emergence and differences in the choice of input variables. Conversely, different calibration approaches were associated with major differences in prediction error between the same models used by different groups. Given the large diversity of calibration approaches and the importance of calibration, there is a clear need for guidelines and tools to aid with calibration. Arguably the most important and difficult choice for calibration is the choice of parameters to estimate. Several recommendations for calibration practices are proposed. Model applications, including model studies of climate change impact, should focus more on the data used for calibration and on the calibration methods employed.