Multi-model evaluation of phenology prediction for wheat in Australia
2021
Wallach, Daniel | Palosuo, Taru | Thorburn, Peter J. | Hochman, Zvi | Andrianasolo, Fety | Asseng, Senthold | Basso, Bruno | Buis, Samuel | Crout, Neil | Dumont, Benjamin | Ferrise, Roberto | Gaiser, Thomas | Gayler, Sebastian | Hiremath, Santosh | Hoek, Steven | Horan, Heidi | Hoogenboom, Gerrit | Huang, Mingxia | Jabloun, Mohamed | Jansson, Per-Erik | Jing, Qi | Justes, Eric | Kersebaum, Kurt Christian | Launay, Marie | Lewan, Elisabet | Luo, Qunying | Maestrini, Bernardo | Moriondo, Marco | et al.,
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 multi-modeling 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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