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@ARTICLE{Wallach:889706,
      author       = {Wallach, Daniel and Palosuo, Taru and Thorburn, Peter and
                      Gourdain, Emmanuelle and Asseng, Senthold and Basso, Bruno
                      and Buis, Samuel and Crout, Neil and Dibari, Camilla and
                      Dumont, Benjamin and Ferrise, Roberto and Gaiser, Thomas and
                      Garcia, Cécile and Gayler, Sebastian and Ghahramani, Afshin
                      and Hochman, Zvi and Hoek, Steven and Hoogenboom, Gerrit and
                      Horan, Heidi and Huang, Mingxia and Jabloun, Mohamed and
                      Jing, Qi and Justes, Eric and Kersebaum, Kurt Christian and
                      Klosterhalfen, Anne and Launay, Marie and Luo, Qunying and
                      Maestrini, Bernardo and Mielenz, Henrike and Moriondo, Marco
                      and Nariman Zadeh, Hasti and Olesen, Jørgen Eivind and
                      Poyda, Arne and Priesack, Eckart and Pullens, Johannes
                      Wilhelmus Maria and Qian, Budong and Schütze, Niels and
                      Shelia, Vakhtang and Souissi, Amir and Specka, Xenia and
                      Srivastava, Amit Kumar and Stella, Tommaso and Streck, Thilo
                      and Trombi, Giacomo and Wallor, Evelyn and Wang, Jing and
                      Weber, Tobias K. D. and Weihermüller, Lutz and de Wit,
                      Allard and Wöhling, Thomas and Xiao, Liujun and Zhao,
                      Chuang and Zhu, Yan and Seidel, Sabine J.},
      title        = {{H}ow well do crop modeling groups predict wheat phenology,
                      given calibration data from the target population?},
      journal      = {European journal of agronomy},
      volume       = {124},
      issn         = {1161-0301},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2021-00328},
      pages        = {126195 -},
      year         = {2021},
      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.},
      cin          = {IBG-3},
      ddc          = {640},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000620810400008},
      doi          = {10.1016/j.eja.2020.126195},
      url          = {https://juser.fz-juelich.de/record/889706},
}