Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

预测物候对使品种适应不同的环境条件和作物管理至关重要。因此，重要的是评估不同的作物模拟群体对物候的预测能力。先前已经发表了多个评估研究，但是要归纳此类研究的结果仍然很困难，因为它们经常测试外推到新条件的某些特定方面，或者不测试真正独立于校准数据的数据。在这项研究中，我们分析了在观测的天气和当前管理下法国北部小麦物候的预测，这对小麦管理具有实际重要性。评估了27个建模组的结果，其中建模组包含模型结构，即模型方程式，校准方法以及不受校准影响的那些参数的值。用于校准和评估的数据是从同一目标人群中采样的，因此外推受到限制。校准和评估数据既没有年份也没有地点，以确保对新天气和地点的天气预报进行严格的评估。最好的建模组以及模拟的平均值和中位数，其平均绝对误差（MAE）约为3天，与测量误差相当。几乎所有模型都比使用平均天数或平均天数总和来预测物候更好。另一方面，由于模型结构差异以及使用相同模型结构的组之间的差异，建模组之间存在重要差异，强调仅模型结构并不能完全确定预测准确性。除了提供有关我们特定环境和品种的信息外，这些结果还有助于为建模群体在提供目标人群的校准数据后如何更好地预测物候方面的知识。