Hoffmann, Holger and Zhao, Gang and Asseng, Senthold and Bindi, Marco and Biernath, Christian and Constantin, Julie and Coucheney, Elsa and Dechow, René and Doro, Luca and Eckersten, Henrik and Gaiser, Thomas and Grosz, Balázs and Heinlein, Florian and Kassie, Belay T. and Kersebaum, Kurt Christian and Klein, Christian and Kuhnert, Matthias and Lewan, Elisabet and Moriondo, Marco and Nendel, Claas and Priesack, Eckart and Raynal, Hélène and Roggero, Pier Paolo and Rötter, Reimund Paul and Siebert, Stefan and Specka, Xenia and Tao, Fulu and Teixeira, Edmar and Trombi, Giacomo and Wallach, Daniel and Weihermuller, Lutz and Yeluripati, Jagadeesh and Ewert, Frank (2016) Impact of spatial soil and climate input data aggregation on regional yield simulations. PLoS One, Vol. 11 (4), e0151782. ISSN 1932-6203. Article.
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 <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
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