Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

Holger Hoffmann, Gang Zhao, Senthold Asseng, Marco Bindi, Christian Biernath, Julie Constantin, Elsa Coucheney, Rene Dechow, Luca Doro, Henrik Eckersten, Thomas Gaiser, Balázs Grosz, Florian Heinlein, Belay T Kassie, Kurt-Christian Kersebaum, Christian Klein, Matthias Kuhnert, Elisabet Lewan, Marco Moriondo, Claas Nendel, Eckart Priesack, Helene Raynal, Pier P Roggero, Reimund P Rötter, Stefan Siebert, Xenia Specka, Fulu Tao, Edmar Teixeira, Giacomo Trombi, Daniel Wallach, Lutz Weihermüller, Jagadeesh Yeluripati and Frank Ewert

PLOS ONE, 2016, vol. 11, issue 4, 1-23

Abstract: We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0151782

DOI: 10.1371/journal.pone.0151782

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