期刊
PLOS ONE
卷 11, 期 4, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0151782
关键词
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资金
- German Federal Ministry of Food and Agriculture (BMEL) through Federal Office for Agriculture and Food (BLE) [2851ERA01J]
- FACCE MACSUR through Finnish Ministry of Agriculture and Forestry (MMM) [3200009600]
- Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning [220-2007-1218]
- faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences)
- INRA ACCAF metaprogram
- UR MIAT INRA
- Helmholtz project REKLIM - Regional Climate Change
- HGF Alliance Remote Sensing and Earth System Dynamics (EDA)
- German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) [FOR1695]
- German Science Foundation [EW 119/5-1]
- BBSRC [BB/K009265/1, BB/N004922/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/N004922/1, BB/K009265/1] Funding Source: researchfish
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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