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How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

Daniel Wallach 1 Taru Palosuo 2 Peter Thorburn 3 Emmanuelle Gourdain 4 Senthold Asseng 5 Bruno Basso 6 Samuel Buis 7 Neil Crout 8 Camilla Dibari 9 Benjamin Dumont 10 Roberto Ferrise 9 Thomas Gaiser 11 Cécile Garcia 12 Sebastian Gayler 11 Afshin Ghahramani 13 Zvi Hochman 14 Steven Hoek 12 Gerrit Hoogenboom 15, 16 Heidi Horan 12 Mingxia Huang 17 Mohamed Jabloun 18 Qi Jing 19 Eric Justes 20 Kurt Christian Kersebaum 21 Anne Klosterhalfen 22 Marie Launay 23 Qunying Luo 24 Bernardo Maestrini 25 Henrike Mielenz 26 Marco Moriondo Hasti Nariman Zadeh 27 Jørgen Eivind Olesen 28 Arne Poyda 29 Eckart Priesack 30 Johannes Wilhelmus Maria Pullens 28 Budong Qian 19 Niels Schütze 31 Vakhtang Shelia 16 Amir Souissi 32 Xenia Specka 21 Amit Kumar Srivastava 33 Tommaso Stella 21 Thilo Streck 11 Giacomo Trombi 34 Evelyn Wallor 21 Jing Wang 35 Tobias K.D. Weber 11 Lutz Weihermüller 36 Allard de Wit 25 Thomas Wöhling 31 Liujun Xiao 37 Chuang Zhao 16 Yan Zhu 37 Sabine Seidel 38
Abstract : 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.
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https://hal.inrae.fr/hal-03131656
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Submitted on : Thursday, February 4, 2021 - 3:22:57 PM
Last modification on : Friday, May 21, 2021 - 6:12:02 PM

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Daniel Wallach, Taru Palosuo, Peter Thorburn, Emmanuelle Gourdain, Senthold Asseng, et al.. How well do crop modeling groups predict wheat phenology, given calibration data from the target population?. European Journal of Agronomy, 2021, 124, ⟨10.1016/j.eja.2020.126195⟩. ⟨hal-03131656⟩

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