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000889706 1001_ $$0P:(DE-HGF)0$$aWallach, Daniel$$b0$$eCorresponding author
000889706 245__ $$aHow well do crop modeling groups predict wheat phenology, given calibration data from the target population?
000889706 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2021
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000889706 520__ $$aPredicting 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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000889706 7001_ $$0P:(DE-HGF)0$$aThorburn, Peter$$b2
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000889706 7001_ $$0P:(DE-HGF)0$$aAsseng, Senthold$$b4
000889706 7001_ $$0P:(DE-HGF)0$$aBasso, Bruno$$b5
000889706 7001_ $$0P:(DE-HGF)0$$aBuis, Samuel$$b6
000889706 7001_ $$0P:(DE-HGF)0$$aCrout, Neil$$b7
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000889706 7001_ $$0P:(DE-HGF)0$$aDumont, Benjamin$$b9
000889706 7001_ $$0P:(DE-HGF)0$$aFerrise, Roberto$$b10
000889706 7001_ $$0P:(DE-HGF)0$$aGaiser, Thomas$$b11
000889706 7001_ $$0P:(DE-HGF)0$$aGarcia, Cécile$$b12
000889706 7001_ $$0P:(DE-HGF)0$$aGayler, Sebastian$$b13
000889706 7001_ $$0P:(DE-HGF)0$$aGhahramani, Afshin$$b14
000889706 7001_ $$0P:(DE-HGF)0$$aHochman, Zvi$$b15
000889706 7001_ $$0P:(DE-HGF)0$$aHoek, Steven$$b16
000889706 7001_ $$0P:(DE-HGF)0$$aHoogenboom, Gerrit$$b17
000889706 7001_ $$0P:(DE-HGF)0$$aHoran, Heidi$$b18
000889706 7001_ $$0P:(DE-HGF)0$$aHuang, Mingxia$$b19
000889706 7001_ $$0P:(DE-HGF)0$$aJabloun, Mohamed$$b20
000889706 7001_ $$0P:(DE-HGF)0$$aJing, Qi$$b21
000889706 7001_ $$0P:(DE-HGF)0$$aJustes, Eric$$b22
000889706 7001_ $$0P:(DE-HGF)0$$aKersebaum, Kurt Christian$$b23
000889706 7001_ $$0P:(DE-Juel1)159313$$aKlosterhalfen, Anne$$b24
000889706 7001_ $$0P:(DE-HGF)0$$aLaunay, Marie$$b25
000889706 7001_ $$0P:(DE-HGF)0$$aLuo, Qunying$$b26
000889706 7001_ $$0P:(DE-HGF)0$$aMaestrini, Bernardo$$b27
000889706 7001_ $$0P:(DE-HGF)0$$aMielenz, Henrike$$b28
000889706 7001_ $$0P:(DE-HGF)0$$aMoriondo, Marco$$b29
000889706 7001_ $$0P:(DE-HGF)0$$aNariman Zadeh, Hasti$$b30
000889706 7001_ $$0P:(DE-HGF)0$$aOlesen, Jørgen Eivind$$b31
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000889706 7001_ $$0P:(DE-HGF)0$$aPriesack, Eckart$$b33
000889706 7001_ $$0P:(DE-HGF)0$$aPullens, Johannes Wilhelmus Maria$$b34
000889706 7001_ $$0P:(DE-HGF)0$$aQian, Budong$$b35
000889706 7001_ $$0P:(DE-HGF)0$$aSchütze, Niels$$b36
000889706 7001_ $$0P:(DE-HGF)0$$aShelia, Vakhtang$$b37
000889706 7001_ $$0P:(DE-HGF)0$$aSouissi, Amir$$b38
000889706 7001_ $$0P:(DE-HGF)0$$aSpecka, Xenia$$b39
000889706 7001_ $$0P:(DE-HGF)0$$aSrivastava, Amit Kumar$$b40
000889706 7001_ $$0P:(DE-HGF)0$$aStella, Tommaso$$b41
000889706 7001_ $$0P:(DE-HGF)0$$aStreck, Thilo$$b42
000889706 7001_ $$0P:(DE-HGF)0$$aTrombi, Giacomo$$b43
000889706 7001_ $$0P:(DE-HGF)0$$aWallor, Evelyn$$b44
000889706 7001_ $$0P:(DE-HGF)0$$aWang, Jing$$b45
000889706 7001_ $$0P:(DE-HGF)0$$aWeber, Tobias K. D.$$b46
000889706 7001_ $$0P:(DE-Juel1)129553$$aWeihermüller, Lutz$$b47
000889706 7001_ $$0P:(DE-HGF)0$$ade Wit, Allard$$b48
000889706 7001_ $$0P:(DE-HGF)0$$aWöhling, Thomas$$b49
000889706 7001_ $$0P:(DE-HGF)0$$aXiao, Liujun$$b50
000889706 7001_ $$0P:(DE-HGF)0$$aZhao, Chuang$$b51
000889706 7001_ $$0P:(DE-Juel1)156394$$aZhu, Yan$$b52
000889706 7001_ $$0P:(DE-HGF)0$$aSeidel, Sabine J.$$b53$$eCorresponding author
000889706 773__ $$0PERI:(DE-600)2016158-X$$a10.1016/j.eja.2020.126195$$gVol. 124, p. 126195 -$$p126195 -$$tEuropean journal of agronomy$$v124$$x1161-0301$$y2021
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