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 & 13 others 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

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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 <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.

Original languageEnglish
Article numbere0151782
Pages (from-to)1-23
Number of pages23
JournalPloS ONE
Volume11
Issue number4
DOIs
Publication statusPublished - 7 Apr 2016

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Climate
Soil
Agglomeration
climate
Soils
Crops
soil
crop models
Germany
ex ante analysis
Silage
Water
corn silage
Triticum
Zea mays
winter wheat
crop yield
water
Sampling
crops

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Hoffmann, H., Zhao, G., Asseng, S., Bindi, M., Biernath, C., Constantin, J., ... Ewert, F. (2016). Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations. PloS ONE, 11(4), 1-23. [e0151782]. https://doi.org/10.1371/journal.pone.0151782

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

In: PloS ONE, Vol. 11, No. 4, e0151782, 07.04.2016, p. 1-23.

Research output: Contribution to journalArticle

Hoffmann, H, Zhao, G, Asseng, S, Bindi, M, Biernath, C, Constantin, J, Coucheney, E, Dechow, R, Doro, L, Eckersten, H, Gaiser, T, Grosz, B, Heinlein, F, Kassie, BT, Kersebaum, K-C, Klein, C, Kuhnert, M, Lewan, E, Moriondo, M, Nendel, C, Priesack, E, Raynal, H, Roggero, PP, Rötter, RP, Siebert, S, Specka, X, Tao, F, Teixeira, E, Trombi, G, Wallach, D, Weihermüller, L, Yeluripati, J & Ewert, F 2016, 'Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations' PloS ONE, vol. 11, no. 4, e0151782, pp. 1-23. https://doi.org/10.1371/journal.pone.0151782
Hoffmann H, Zhao G, Asseng S, Bindi M, Biernath C, Constantin J et al. Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations. PloS ONE. 2016 Apr 7;11(4):1-23. e0151782. https://doi.org/10.1371/journal.pone.0151782
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. / Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations. In: PloS ONE. 2016 ; Vol. 11, No. 4. pp. 1-23.
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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 <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.",
author = "Holger Hoffmann and Gang Zhao and Senthold Asseng and Marco Bindi and Christian Biernath and Julie Constantin and Elsa Coucheney and Rene Dechow and Luca Doro and Henrik Eckersten and Thomas Gaiser and Bal{\'a}zs Grosz and Florian Heinlein and Kassie, {Belay T.} and Kurt-Christian Kersebaum and Christian Klein and Matthias Kuhnert and Elisabet Lewan and Marco Moriondo and Claas Nendel and Eckart Priesack and Helene Raynal and Roggero, {Pier P.} and R{\"o}tter, {Reimund P.} and Stefan Siebert and Xenia Specka and Fulu Tao and Edmar Teixeira and Giacomo Trombi and Daniel Wallach and Lutz Weiherm{\"u}ller and Jagadeesh Yeluripati and Frank Ewert",
note = "This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRA ACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. CB was funded by the Helmholtz project “REKLIM—Regional Climate Change”. CK was funded by the HGF Alliance “Remote Sensing and Earth System Dynamics” (EDA). FH was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the Grant FOR1695. FE and SS acknowledge support by the German Science Foundation (project EW 119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.",
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AU - Hoffmann, Holger

AU - Zhao, Gang

AU - Asseng, Senthold

AU - Bindi, Marco

AU - Biernath, Christian

AU - Constantin, Julie

AU - Coucheney, Elsa

AU - Dechow, Rene

AU - Doro, Luca

AU - Eckersten, Henrik

AU - Gaiser, Thomas

AU - Grosz, Balázs

AU - Heinlein, Florian

AU - Kassie, Belay T.

AU - Kersebaum, Kurt-Christian

AU - Klein, Christian

AU - Kuhnert, Matthias

AU - Lewan, Elisabet

AU - Moriondo, Marco

AU - Nendel, Claas

AU - Priesack, Eckart

AU - Raynal, Helene

AU - Roggero, Pier P.

AU - Rötter, Reimund P.

AU - Siebert, Stefan

AU - Specka, Xenia

AU - Tao, Fulu

AU - Teixeira, Edmar

AU - Trombi, Giacomo

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AU - Yeluripati, Jagadeesh

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N1 - This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRA ACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. CB was funded by the Helmholtz project “REKLIM—Regional Climate Change”. CK was funded by the HGF Alliance “Remote Sensing and Earth System Dynamics” (EDA). FH was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the Grant FOR1695. FE and SS acknowledge support by the German Science Foundation (project EW 119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

PY - 2016/4/7

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N2 - 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.

AB - 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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