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@ARTICLE{Maharjan:858069,
      author       = {Maharjan, Ganga Ram and Hoffmann, Holger and Webber, Heidi
                      and Srivastava, Amit Kumar and Weihermüller, Lutz and
                      Villa, Ana and Coucheney, Elsa and Lewan, Elisabet and
                      Trombi, Giacomo and Moriondo, Marco and Bindi, Marco and
                      Grosz, Balazs and Dechow, Rene and Kuhnert, Mathias and
                      Doro, Luca and Kersebaum, Kurt-Christian and Stella, Tommaso
                      and Specka, Xenia and Nendel, Claas and Constantin, Julie
                      and Raynal, Hélène and Ewert, Frank and Gaiser, Thomas},
      title        = {{E}ffects of input data aggregation on simulated crop
                      yields in temperate and {M}editerranean climates},
      journal      = {European journal of agronomy},
      volume       = {103},
      issn         = {1161-0301},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2018-06987},
      pages        = {32 - 46},
      year         = {2019},
      abstract     = {Soil-crop models are used to simulate ecological processes
                      from the field to the regional scale. Main inputs are soil
                      and climate data in order to simulate model response
                      variables such as crop yield. We investigate the effect of
                      changing the resolution of input data on simulated crop
                      yields at a regional scale using up to ten dynamic crop
                      models. For these models we compared the effects of spatial
                      input data aggregation for wheat and maize yield of two
                      regions with contrasting climate conditions (1) Tuscany
                      (Italy, Mediterranean climate) and (2) North Rhine
                      Westphalia (NRW, Germany, temperate climate). Soil and
                      climate data of 1 km resolution were aggregated to
                      resolutions of 10, 25, 50, and 100 km by selecting the
                      dominant soil class (and corresponding soil properties) and
                      by arithmetic averaging, respectively. Differences in yield
                      simulated at coarser resolutions from the yields simulated
                      at 1 km resolution were calculated to quantify the effect
                      of the aggregation of the input data (soil and climate data)
                      on simulation results.The mean yield difference (bias) at
                      the regional level was positive due to the upscaling of
                      productive dominant soil(s) to coarser resolution. In both
                      regions and for both crops, aggregation effects (i.e. errors
                      in simulation of crop yields at coarser spatial resolution)
                      due to the combined aggregation of soil and climate input
                      data increased with decreasing resolution, whereby the
                      aggregation error for Tuscany was larger than for North
                      Rhine Westphalia (NRW). The average absolute percentage
                      yield differences between grid cell yields at the coarsest
                      resolution (100 km) compared to the finest resolution
                      (1 km) were by about $20–30\%$ for Tuscany and less than
                      15 and $20\%$ for NRW for winter wheat and silage maize,
                      respectively.In the Mediterranean area, the prediction
                      errors of the simulated yields could reach up to $60\%$ when
                      looking at individual crop model simulations. Additionally,
                      aggregating soil data caused larger aggregation errors in
                      both regions than aggregating climate data.Those results
                      suggest that a higher spatial resolution of climate and
                      especially of soil input data are necessary in Mediterranean
                      areas than in temperate humid regions of central Europe in
                      order to predict reliable regional yield estimations with
                      crop models. For generalization of these outcomes, further
                      investigations in other sub-humid or semi-arid regions will
                      be necessary.},
      cin          = {IBG-3},
      ddc          = {640},
      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)16},
      UT           = {WOS:000456753000004},
      doi          = {10.1016/j.eja.2018.11.001},
      url          = {https://juser.fz-juelich.de/record/858069},
}