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@ARTICLE{Wallach:1010148,
      author       = {Wallach, Daniel and Palosuo, Taru and Thorburn, Peter and
                      Mielenz, Henrike and Buis, Samuel and Hochman, Zvi and
                      Gourdain, Emmanuelle and Andrianasolo, Fety and Dumont,
                      Benjamin and Ferrise, Roberto and Gaiser, Thomas and Garcia,
                      Cecile and Gayler, Sebastian and Harrison, Matthew and
                      Hiremath, Santosh and Horan, Heidi and Hoogenboom, Gerrit
                      and Jansson, Per-Erik and Jing, Qi and Justes, Eric and
                      Kersebaum, Kurt-Christian and Launay, Marie and Lewan,
                      Elisabet and Liu, Ke and Mequanint, Fasil and Moriondo,
                      Marco and Nendel, Claas and Padovan, Gloria and Qian, Budong
                      and Schütze, Niels and Seserman, Diana-Maria and Shelia,
                      Vakhtang and Souissi, Amir and Specka, Xenia and Srivastava,
                      Amit Kumar and Trombi, Giacomo and Weber, Tobias K. D. and
                      Weihermüller, Lutz and Wöhling, Thomas and Seidel, Sabine
                      J.},
      title        = {{P}roposal and extensive test of a calibration protocol for
                      crop phenology models},
      journal      = {Agronomy for sustainable development},
      volume       = {43},
      number       = {4},
      issn         = {0249-5627},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2023-02976},
      pages        = {46},
      year         = {2023},
      abstract     = {A major effect of environment on crops is through crop
                      phenology, and therefore, the capacity to predict phenology
                      for new environments is important. Mechanistic crop models
                      are a major tool for such predictions, but calibration of
                      crop phenology models is difficult and there is no consensus
                      on the best approach. We propose an original, detailed
                      approach for calibration of such models, which we refer to
                      as a calibration protocol. The protocol covers all the steps
                      in the calibration workflow, namely choice of default
                      parameter values, choice of objective function, choice of
                      parameters to estimate from the data, calculation of optimal
                      parameter values, and diagnostics. The major innovation is
                      in the choice of which parameters to estimate from the data,
                      which combines expert knowledge and data-based model
                      selection. First, almost additive parameters are identified
                      and estimated. This should make bias (average difference
                      between observed and simulated values) nearly zero. These
                      are “obligatory” parameters, that will definitely be
                      estimated. Then candidate parameters are identified, which
                      are parameters likely to explain the remaining discrepancies
                      between simulated and observed values. A candidate is only
                      added to the list of parameters to estimate if it leads to a
                      reduction in BIC (Bayesian Information Criterion), which is
                      a model selection criterion. A second original aspect of the
                      protocol is the specification of documentation for each
                      stage of the protocol. The protocol was applied by 19
                      modeling teams to three data sets for wheat phenology. All
                      teams first calibrated their model using their “usual”
                      calibration approach, so it was possible to compare usual
                      and protocol calibration. Evaluation of prediction error was
                      based on data from sites and years not represented in the
                      training data. Compared to usual calibration, calibration
                      following the new protocol reduced the variability between
                      modeling teams by $22\%$ and reduced prediction error by
                      $11\%.$},
      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:001028161100003},
      doi          = {10.1007/s13593-023-00900-0},
      url          = {https://juser.fz-juelich.de/record/1010148},
}