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000858069 1001_ $$00000-0003-0201-1742$$aMaharjan, Ganga Ram$$b0$$eCorresponding author
000858069 245__ $$aEffects of input data aggregation on simulated crop yields in temperate and Mediterranean climates
000858069 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2019
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000858069 520__ $$aSoil-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.
000858069 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0
000858069 536__ $$0G:(DE-BLE)2812-ERA-158$$aMACSUR - Modelling European Agriculture with Climate Change for Food Security (2812-ERA-158)$$c2812-ERA-158$$fFACCE MACSUR$$x1
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000858069 7001_ $$0P:(DE-HGF)0$$aHoffmann, Holger$$b1
000858069 7001_ $$0P:(DE-HGF)0$$aWebber, Heidi$$b2
000858069 7001_ $$0P:(DE-HGF)0$$aSrivastava, Amit Kumar$$b3
000858069 7001_ $$0P:(DE-Juel1)129553$$aWeihermüller, Lutz$$b4
000858069 7001_ $$0P:(DE-HGF)0$$aVilla, Ana$$b5
000858069 7001_ $$0P:(DE-HGF)0$$aCoucheney, Elsa$$b6
000858069 7001_ $$00000-0001-9662-3410$$aLewan, Elisabet$$b7
000858069 7001_ $$0P:(DE-HGF)0$$aTrombi, Giacomo$$b8
000858069 7001_ $$0P:(DE-HGF)0$$aMoriondo, Marco$$b9
000858069 7001_ $$0P:(DE-HGF)0$$aBindi, Marco$$b10
000858069 7001_ $$0P:(DE-HGF)0$$aGrosz, Balazs$$b11
000858069 7001_ $$0P:(DE-HGF)0$$aDechow, Rene$$b12
000858069 7001_ $$0P:(DE-HGF)0$$aKuhnert, Mathias$$b13
000858069 7001_ $$0P:(DE-HGF)0$$aDoro, Luca$$b14
000858069 7001_ $$00000-0002-3679-8427$$aKersebaum, Kurt-Christian$$b15
000858069 7001_ $$0P:(DE-HGF)0$$aStella, Tommaso$$b16
000858069 7001_ $$00000-0002-1890-0192$$aSpecka, Xenia$$b17
000858069 7001_ $$00000-0001-7608-9097$$aNendel, Claas$$b18
000858069 7001_ $$0P:(DE-HGF)0$$aConstantin, Julie$$b19
000858069 7001_ $$0P:(DE-HGF)0$$aRaynal, Hélène$$b20
000858069 7001_ $$0P:(DE-HGF)0$$aEwert, Frank$$b21
000858069 7001_ $$0P:(DE-HGF)0$$aGaiser, Thomas$$b22
000858069 773__ $$0PERI:(DE-600)2016158-X$$a10.1016/j.eja.2018.11.001$$gVol. 103, p. 32 - 46$$p32 - 46$$tEuropean journal of agronomy$$v103$$x1161-0301$$y2019
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