globalchange  > 过去全球变化的重建
DOI: 10.1371/journal.pone.0151782
论文题名:
Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations
作者: 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; Frank Ewert
刊名: PLOS ONE
ISSN: 1932-6203
出版年: 2016
发表日期: 2016-4-7
卷: 11, 期:4
语种: 英语
英文关键词: Agricultural soil science ; Wheat ; Maize ; Winter ; Cereal crops ; Simulation and modeling ; Seasons ; Germany
英文摘要: 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.
URL: http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0151782&type=printable
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/25386
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

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作者单位: Crop Science Group, INRES, University of Bonn, Bonn, Germany;Crop Science Group, INRES, University of Bonn, Bonn, Germany;Agricultural & Biological Engineering Department, University of Florida, Gainesville, Florida, United States of America;Department of Agri-food Production and Environmental Sciences, University of Florence, Florence, Italy;Institute of Biochemical Plant Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany;INRA, Auzeville, France;Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden;Thünen-Institute of Climate-Smart-Agriculture, Braunschweig, Germany;Desertification Research Group, Universitá degli Studi di Sassari, Sassari, Italy;Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden;Crop Science Group, INRES, University of Bonn, Bonn, Germany;Thünen-Institute of Climate-Smart-Agriculture, Braunschweig, Germany;Institute of Biochemical Plant Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany;Agricultural & Biological Engineering Department, University of Florida, Gainesville, Florida, United States of America;Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany;Institute of Biochemical Plant Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany;Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen, Scotland, United Kingdom;Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden;CNR-Ibimet, Florence, Italy;Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany;Institute of Biochemical Plant Pathology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany;INRA, Auzeville, France;Desertification Research Group, Universitá degli Studi di Sassari, Sassari, Italy;Environmental Impacts Group, Natural Resources Institute Finland (Luke), Vantaa, Finland;Crop Science Group, INRES, University of Bonn, Bonn, Germany;Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany;Environmental Impacts Group, Natural Resources Institute Finland (Luke), Vantaa, Finland;Systems Modelling Team (Sustainable Production Group), The New Zealand Institute for Plant and Food Research Limited, Canterbury Agriculture & Science Centre, Lincoln, New Zealand;Department of Agri-food Production and Environmental Sciences, University of Florence, Florence, Italy;INRA, Auzeville, France;Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany;The James Hutton Institute, Craigiebuckler, Aberdeen, Scotland, United Kingdom;Crop Science Group, INRES, University of Bonn, Bonn, Germany

Recommended Citation:
Holger Hoffmann,Gang Zhao,Senthold Asseng,et al. Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations[J]. PLOS ONE,2016-01-01,11(4)
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