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1 15 ABSTRACT 16 Plant phenology, which describes the timing of plant development, is a major aspect of 17 plant response to environment and for crops, a major determinant of yield. Many studies have 18 focused on comparing model equations for describing how phenology responds to climate but 19 the effect of crop model calibration, also important for determining model performance, has 20 received much less attention. The objectives here were to obtain a rigorous evaluation of 21 prediction capability of wheat phenology models, to analyze the role of calibration and to 22 document the various calibration approaches. The 27 participants in this multi-model study 23were provided experimental data for calibration and asked to submit predictions for sites and 24 years not represented in those data. Participants were instructed to use and document their 25 "usual" calibration approach. Overall, the models provided quite good predictions of 26 phenology (median of mean absolute error of 6.1 days) and did much better than simply using 27 the average of observed values as predictor. The results suggest that calibration can 28 compensate to some extent for different model formulations, specifically for differences in 29 simulated time to emergence and differences in the choice of input variables. Conversely, 30 different calibration approaches were associated with major differences in prediction error 31 between the same models used by different groups. Given the large diversity of calibration 32 approaches and the importance of calibration, there is a clear need for guidelines and tools to 33 aid with calibration. Arguably the most important and difficult choice for calibration is the 34 choice of parameters to estimate. Several recommendations for calibration practices are 35 proposed. Model applications, including model studies of climate change impact, should 36 focus more on the data used for calibration and on the calibration methods employed. 37

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multimodeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

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