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Research article - Peer-reviewed, 2019

Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Maharjan, Ganga Ram; Hoffmann, Holger; Webber, Heidi; Srivastava, Amit Kumar; Weihermueller, Lutz; Villa, Ana; Coucheney, Elsa; Lewan, Elisabet; Trombi, Giacomo; Moriondo, Marco; Bindi, Marco; Grosz, Balazs; Dechow, Rene; Kuhnert, Mathias; Doro, Luca; Kersebaum, Kurt-Christian; Stella, Tommaso; Specka, Xenia; Nendel, Claas; Constantin, Julie;
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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.


Data resolution; Scale; Modelling; Regional yield; Climate; Soil

Published in

European Journal of Agronomy
2019, volume: 103, pages: 32-46

Authors' information

Maharjan, Ganga Ram
University of Bonn
Hoffmann, Holger
University of Bonn
Webber, Heidi
Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF)
Srivastava, Amit Kumar
University of Bonn
Weihermueller, Lutz
Helmholtz Association
Villa Solis, Ana
Swedish University of Agricultural Sciences, Department of Soil and Environment
Swedish University of Agricultural Sciences, Department of Soil and Environment
Swedish University of Agricultural Sciences, Department of Soil and Environment
Trombi, Giacomo
University of Florence
Moriondo, Marco
Consiglio Nazionale delle Ricerche (CNR)
Bindi, Marco
University of Florence
Grosz, Balazs
Thünen Institute of Climate-Smart Agriculture
Dechow, Rene
Thünen Institute of Climate-Smart Agriculture
Kuhnert, Mathias
University of Aberdeen
Doro, Luca
Texas AandM AgriLife Res Blackland Res and Extens Ctr
Kersebaum, Kurt-Christian
Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF)
Stella, Tommaso
Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF)
Specka, Xenia
Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF)
Nendel, Claas
Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF)
Constantin, Julie
Institut National de la Recherche Agronomique (INRA)
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UKÄ Subject classification

Agricultural Science
Soil Science

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