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@ARTICLE{Wallach:889347,
      author       = {Wallach, Daniel and Palosuo, Taru and Thorburn, Peter and
                      Hochman, Zvi and Andrianasolo, Fety and Asseng, Senthold and
                      Basso, Bruno and Buis, Samuel and Crout, Neil and Dumont,
                      Benjamin and Ferrise, Roberto and Gaiser, Thomas and Gayler,
                      Sebastian and Hiremath, Santosh and Hoek, Steven and Horan,
                      Heidi and Hoogenboom, Gerrit and Huang, Mingxia and Jabloun,
                      Mohamed and Jansson, Per-Erik and Jing, Qi and Justes, Eric
                      and Kersebaum, Kurt Christian and Launay, Marie and Lewan,
                      Elisabet and Luo, Qunying and Maestrini, Bernardo and
                      Moriondo, Marco and Olesen, Jørgen Eivind and Padovan,
                      Gloria 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 Kumar Srivastava, Amit 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        = {{M}ulti-model evaluation of phenology prediction for wheat
                      in {A}ustralia},
      journal      = {Agricultural and forest meteorology},
      volume       = {298-299},
      issn         = {0168-1923},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-00236},
      pages        = {108289 -},
      year         = {2021},
      abstract     = {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.},
      cin          = {IBG-3},
      ddc          = {550},
      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:000610797100011},
      doi          = {10.1016/j.agrformet.2020.108289},
      url          = {https://juser.fz-juelich.de/record/889347},
}