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Wallach, D.* ; Palosuo, T.* ; Thorburn, P.* ; Gourdain, E.* ; Asseng, S.* ; Basso, B.* ; Buis, S.* ; Crout, N.* ; Dibari, C.* ; Dumont, B.* ; Ferrise, R.* ; Gaiser, T.* ; Garcia, C.* ; Gayler, S.* ; Ghahramani, A.* ; Hochman, Z.* ; Hoek, S.* ; Hoogenboom, G.* ; Horan, H.* ; Huang, M.* ; Jabloun, M.* ; Jing, Q.* ; Justes, É.* ; Kersebaum, K.C.* ; Klosterhalfen, A.* ; Launay, M.* ; Luo, Q.* ; Maestrini, B.* ; Mielenz, H.* ; Moriondo, M.* ; Zadeh, H.N.* ; Olesen, J.E.* ; Poyda, A.* ; Priesack, E. ; Pullens, J.W.M.* ; Qian, B.* ; Schuetze, N.* ; Shelia, V.* ; Souissi, A.* ; Specka, X.* ; Srivastava, A.K.* ; Stella, T.* ; Streck, T.* ; Trombi, G.* ; Wallor, E.* ; Wang, J.* ; Weber, T.K.D.* ; Weihermueller, L.* ; de Wit, A.* ; Woehling, T.* ; Xiao, L.* ; Zhao, C.* ; Zhu, Y.* ; Seidel, S.J.*

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

Eur. J. Agron. 124:126195 (2021)
Postprint DOI
Open Access Green
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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Crop Model ; Phenology Prediction ; Model Evaluation ; Wheat
ISSN (print) / ISBN 1161-0301
e-ISSN 1873-7331
Journal European Journal of Agronomy
Quellenangaben Volume: 124, Issue: , Pages: , Article Number: 126195 Supplement: ,
Publisher Elsevier
Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Research Unit Environmental Simulation (EUS)
Helmholtz AI - FZJ (HAI - FZJ)
Grants INRA ACCAF meta-programme
project BiomassWeb of the GlobeE programme - Federal Ministry of Education and Research (BMBF, Germany)
Deutsche Forschungsgemeinschaft (DFG, German Research foundation)
German Federal Ministry of Education and Research (BMBF)
Natural Resources Institute Finland (Luke)through a strategic project BoostIA
Academy of Finland
National Key Research and Development Program of China
National Science Foundation for Distinguished Young Scholars
JPI FACCE MACSUR2 project - Italian Ministry for Agricultural, Food, and Forestry Policies
Broadacre Agriculture Initiative
USDA/NIFA
U.S. Department of Agriculture National Institute of Food and Agriculture
DFG Research Unit FOR 1695 'Agricultural Landscapes under Global Climate Change -Processes and Feedbacks on a Regional Scale
Agriculture and Agri-Food Canada
China Scholarship Council
111 project
Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
Collaborative Research Center 1253 CAMPOS(Project 7: Stochastic Modelling Framework) - German Research Foundation (DFG)