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@ARTICLE{Kuhnert:820796,
      author       = {Kuhnert, Matthias and Yeluripati, Jagadeesh and Smith, Pete
                      and Hoffmann, Holger and van Oijen, Marcel and Constantin,
                      Julie and Coucheney, Elsa and Dechow, Rene and Eckersten,
                      Henrik and Gaiser, Thomas and Grosz, Balász and Haas, Edwin
                      and Kersebaum, Kurt-Christian and Kiese, Ralf and Klatt,
                      Steffen and Lewan, Elisabet and Nendel, Claas and Raynal,
                      Helene and Sosa, Carmen and Specka, Xenia and Teixeira,
                      Edmar and Wang, Enli and Weihermüller, Lutz and Zhao, Gang
                      and Zhao, Zhigan and Ogle, Stephen and Ewert, Frank},
      title        = {{I}mpact analysis of climate data aggregation at different
                      spatial scales on simulated net primary productivity for
                      croplands},
      journal      = {European journal of agronomy},
      volume       = {88},
      issn         = {1161-0301},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2016-06063},
      pages        = {41–52},
      year         = {2016},
      abstract     = {For spatial crop and agro-systems modelling, there is often
                      a discrepancy between the scale of measured driving data and
                      the target resolution. Spatial data aggregation is often
                      necessary, which can introduce additional uncertainty into
                      the simulation results. Previous studies have shown that
                      climate data aggregation has little effect on simulation of
                      phenological stages, but effects on net primary production
                      (NPP) might still be expected through changing the length of
                      the growing season and the period of grain filling. This
                      study investigates the impact of spatial climate data
                      aggregation on NPP simulation results, applying eleven
                      different models for the same study region (∼34,000 km2),
                      situated in Western Germany. To isolate effects of climate,
                      soil data and management were assumed to be constant over
                      the entire study area and over the entire study period of 29
                      years. Two crops, winter wheat and silage maize, were tested
                      as monocultures. Compared to the impact of climate data
                      aggregation on yield, the effect on NPP is in a similar
                      range, but is slightly lower, with only small impacts on
                      averages over the entire simulation period and study region.
                      Maximum differences between the five scales in the range of
                      1–100 km grid cells show changes of $0.4–7.8\%$ and
                      $0.0–4.8\%$ for wheat and maize, respectively, whereas the
                      simulated potential NPP averages of the models show a wide
                      range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2
                      d−1 for wheat and maize, respectively). The impact of the
                      spatial aggregation was also tested for shorter time
                      periods, to see if impacts over shorter periods attenuate
                      over longer periods. The results show larger impacts for
                      single years (up to $9.4\%$ for wheat and up to $13.6\%$ for
                      maize). An analysis of extreme weather conditions shows an
                      aggregation effect in vulnerability up to $12.8\%$ and
                      $15.5\%$ between the different resolutions for wheat and
                      maize, respectively. Simulations of NPP averages over larger
                      areas (e.g. regional scale) and longer time periods (several
                      years) are relatively insensitive to climate data
                      aggregation. However, the scale of climate data is more
                      relevant for impacts on annual averages of NPP or if the
                      period is strongly affected or dominated by drought stress.
                      There should be an awareness of the greater uncertainty for
                      the NPP values in these situations if data are not available
                      at high resolution. On the other hand, the results suggest
                      that there is no need to simulate at high resolution for
                      long term regional NPP averages based on the simplified
                      assumptions (soil and management constant in time and space)
                      used in this study.},
      cin          = {IBG-3},
      ddc          = {630},
      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:000405255100005},
      doi          = {10.1016/j.eja.2016.06.005},
      url          = {https://juser.fz-juelich.de/record/820796},
}