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000820796 1001_ $$0P:(DE-HGF)0$$aKuhnert, Matthias$$b0$$eCorresponding author
000820796 245__ $$aImpact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands
000820796 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2016
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000820796 520__ $$aFor 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.
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000820796 7001_ $$0P:(DE-HGF)0$$aYeluripati, Jagadeesh$$b1
000820796 7001_ $$0P:(DE-HGF)0$$aSmith, Pete$$b2
000820796 7001_ $$0P:(DE-HGF)0$$aHoffmann, Holger$$b3
000820796 7001_ $$0P:(DE-HGF)0$$avan Oijen, Marcel$$b4
000820796 7001_ $$0P:(DE-HGF)0$$aConstantin, Julie$$b5
000820796 7001_ $$0P:(DE-HGF)0$$aCoucheney, Elsa$$b6
000820796 7001_ $$0P:(DE-HGF)0$$aDechow, Rene$$b7
000820796 7001_ $$0P:(DE-HGF)0$$aEckersten, Henrik$$b8
000820796 7001_ $$0P:(DE-HGF)0$$aGaiser, Thomas$$b9
000820796 7001_ $$0P:(DE-HGF)0$$aGrosz, Balász$$b10
000820796 7001_ $$0P:(DE-HGF)0$$aHaas, Edwin$$b11
000820796 7001_ $$0P:(DE-HGF)0$$aKersebaum, Kurt-Christian$$b12
000820796 7001_ $$0P:(DE-HGF)0$$aKiese, Ralf$$b13
000820796 7001_ $$0P:(DE-HGF)0$$aKlatt, Steffen$$b14
000820796 7001_ $$0P:(DE-HGF)0$$aLewan, Elisabet$$b15
000820796 7001_ $$0P:(DE-HGF)0$$aNendel, Claas$$b16
000820796 7001_ $$0P:(DE-HGF)0$$aRaynal, Helene$$b17
000820796 7001_ $$0P:(DE-HGF)0$$aSosa, Carmen$$b18
000820796 7001_ $$0P:(DE-HGF)0$$aSpecka, Xenia$$b19
000820796 7001_ $$0P:(DE-HGF)0$$aTeixeira, Edmar$$b20
000820796 7001_ $$0P:(DE-HGF)0$$aWang, Enli$$b21
000820796 7001_ $$0P:(DE-Juel1)129553$$aWeihermüller, Lutz$$b22
000820796 7001_ $$0P:(DE-HGF)0$$aZhao, Gang$$b23
000820796 7001_ $$0P:(DE-HGF)0$$aZhao, Zhigan$$b24
000820796 7001_ $$0P:(DE-HGF)0$$aOgle, Stephen$$b25
000820796 7001_ $$0P:(DE-HGF)0$$aEwert, Frank$$b26
000820796 773__ $$0PERI:(DE-600)2016158-X$$a10.1016/j.eja.2016.06.005$$gp. S1161030116301186$$p41–52$$tEuropean journal of agronomy$$v88$$x1161-0301$$y2016
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