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@ARTICLE{Hoffmann:820926,
      author       = {Hoffmann, Holger and Zhao, Gang and Asseng, Senthold and
                      Bindi, Marco and Biernath, Christian and Constantin, Julie
                      and Coucheney, Elsa and Dechow, Rene and Doro, Luca and
                      Eckersten, Henrik and Gaiser, Thomas and Grosz, Balázs and
                      Heinlein, Florian and Kassie, Belay T. and Kersebaum,
                      Kurt-Christian and Klein, Christian and Kuhnert, Matthias
                      and Lewan, Elisabet and Moriondo, Marco and Nendel, Claas
                      and Priesack, Eckart and Raynal, Helene and Roggero, Pier P.
                      and Rötter, Reimund P. and Siebert, Stefan and Specka,
                      Xenia and Tao, Fulu and Teixeira, Edmar and Trombi, Giacomo
                      and Wallach, Daniel and Weihermüller, Lutz and Yeluripati,
                      Jagadeesh and Ewert, Frank},
      title        = {{I}mpact of {S}patial {S}oil and {C}limate {I}nput {D}ata
                      {A}ggregation on {R}egional {Y}ield {S}imulations},
      journal      = {PLoS one},
      volume       = {11},
      number       = {4},
      issn         = {1932-6203},
      address      = {Lawrence, Kan.},
      publisher    = {PLoS},
      reportid     = {FZJ-2016-06190},
      pages        = {e0151782 -},
      year         = {2016},
      abstract     = {We show the error in water-limited yields simulated by crop
                      models which is associated with spatially aggregated soil
                      and climate input data. Crop simulations at large scales
                      (regional, national, continental) frequently use input data
                      of low resolution. Therefore, climate and soil data are
                      often generated via averaging and sampling by area majority.
                      This may bias simulated yields at large scales, varying
                      largely across models. Thus, we evaluated the error
                      associated with spatially aggregated soil and climate data
                      for 14 crop models. Yields of winter wheat and silage maize
                      were simulated under water-limited production conditions. We
                      calculated this error from crop yields simulated at spatial
                      resolutions from 1 to 100 km for the state of North
                      Rhine-Westphalia, Germany. Most models showed yields biased
                      by $<15\%$ when aggregating only soil data. The relative
                      mean absolute error (rMAE) of most models using aggregated
                      soil data was in the range or larger than the inter-annual
                      or inter-model variability in yields. This error increased
                      further when both climate and soil data were aggregated.
                      Distinct error patterns indicate that the rMAE may be
                      estimated from few soil variables. Illustrating the range of
                      these aggregation effects across models, this study is a
                      first step towards an ex-ante assessment of aggregation
                      errors in large-scale simulations.},
      cin          = {IBG-3},
      ddc          = {500},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000373608000007},
      pubmed       = {pmid:27055028},
      doi          = {10.1371/journal.pone.0151782},
      url          = {https://juser.fz-juelich.de/record/820926},
}