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@INPROCEEDINGS{Hoffmann:809227,
      author       = {Hoffmann, H. and Zhao, G. and Asseng, S. and Bindi, M. and
                      Cammarano, D. and Constantin, J. and Coucheney, E. and
                      Dechow, R. and Doro, L. and Eckersten, H. and Gaiser, T. and
                      Kiese, R. and Klatt, S. and Kuhnert, M. and Lewan, E. and
                      Moriondo, M. and Nendel, C. and Raynal, H. and Roggero, P.
                      P. and Rötter, R. and Siebert, S. and Sosa, C. and Specka,
                      X. and Tao, F. and Teixeira, E. and trombi, G. and
                      Yeluripati, J. and Vanuytrecht, E. and Wallach, D. and Wang,
                      E. and Weihermüller, Lutz and Zhao, Z.},
      title        = {{SOIL} {DATA} {AGGREGATION} {EFFECTS} {IN} {REGIONAL}
                      {YIELD} {SIMULATIONS}},
      reportid     = {FZJ-2016-02517},
      year         = {2016},
      abstract     = {Large-scale yield simulations often use data of coarse
                      spatial resolution as input for process-based models.
                      However, using aggregated data as input for process-based
                      models entails the risks of introducing errors due to
                      aggregation (AE). Such AE depend on the aggregation method,
                      on the type of aggregated data as well as on its spatial
                      heterogeneity. However, previous studies indicated that AE
                      in Central Europe might be largely driven by aggregating
                      soil data. AE in yield could therefore be assessed prior to
                      simulation for those regions with a distinct relationship
                      between spatial yield variability and soil heterogeneity.
                      The present study investigates the AE for soil data and its
                      contribution to the total AE for soil and climate data for a
                      range of different crop models. Soil data is aggregated by
                      area majority in order to maintain physical consistency
                      among soil variables. AE are assessed for climate and soil
                      data in North Rhine-Westphalia, German, upscaling from 1 to
                      100 km resolution. We present a model comparison on AE for a
                      range of environmental conditions differing in climate and
                      soil for two crops grown under water-limited conditions.
                      Winter wheat and silage maize yields of 1982-2011 were
                      simulated with crop models after calibration to average
                      regional sowing date, harvest date and crop yield. Results
                      point to the importance of estimating AE for soil data. Ways
                      to generalize from these results to other regions are
                      discussed.},
      month         = {Jun},
      date          = {2016-06-28},
      organization  = {Seeking Sustainable agricultural
                       Solutions AgMip6 Global workshop,
                       Montpellier (France), 28 Jun 2016 - 30
                       Jun 2016},
      subtyp        = {After Call},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / MACSUR - Modelling European Agriculture with
                      Climate Change for Food Security (2812-ERA-158)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-BLE)2812-ERA-158},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/809227},
}