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

• Published in: PloS one Vol. 11; no. 4; pp. e0151782 - 23
• Main Authors: Hoffmann, Holger, Zhao, Gang, Asseng, Senthold, Bindi, Marco, Biernath, Christian, Constantin, Julie, Coucheney, Elsa, Dechow, Rene, Doro, Luca, Eckersten, Henrik, Gaiser, Thomas, Grosz, Balázs, Heinlein, Florian, Kassie, Belay T, Kersebaum, Kurt-Christian, Klein, Christian, Kuhnert, Matthias, Lewan, Elisabet, Moriondo, Marco, Nendel, Claas, Priesack, Eckart, Raynal, Helene, Roggero, Pier P, Rötter, Reimund P, Siebert, Stefan, Specka, Xenia, Tao, Fulu, Teixeira, Edmar, Trombi, Giacomo, Wallach, Daniel, Weihermüller, Lutz, Yeluripati, Jagadeesh, Ewert, Frank
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.04.2016; Public Library of Science (PLoS)
• Subjects: Agglomeration; Agricultural production; Agricultural Science; Agriculture; Agriculture - methods; Bioengineering; Biology and Life Sciences; Climate Change; Climate models; Climatic data; Computer Science; Computer Simulation; Consumer protection; Corn; Crop science; Crop yield; Crop yields; Crops; Crops, Agricultural - growth & development; Data management; Databases, Factual; Earth Sciences; Ecology and Environmental Sciences; Ecosystems; Environmental aspects; Environmental health; Environmental science; Environmental Sciences; Errors; Humanities and Social Sciences; Influence; Jordbruksvetenskap; Life Sciences; Mathematics; Natural resources; Nitrogen; Oryza - growth & development; People and Places; Plant pathology; Research and Analysis Methods; Simulation; Soil - chemistry; Soils; Spatial discrimination; Studies; Systems analysis; Triticum - growth & development; Triticum aestivum; Water; Wheat; Winter wheat; Zea mays - growth & development; United States; Italy; Germany; nitrogen dynamics; crop models; systems; data resolution; winter-wheat; scale; variability; water; calibration
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0151782

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.