Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations
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 gen...
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| Veröffentlicht in: | PloS one 2016-04, Vol.11 (4), p.e0151782-23 |
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| creator | 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 |
| description | 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 |
| doi_str_mv | 10.1371/journal.pone.0151782 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0151782</identifier><identifier>PMID: 27055028</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2016-04, Vol.11 (4), p.e0151782-23</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Hoffmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Attribution</rights><rights>2016 Hoffmann et al 2016 Hoffmann et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c764t-f5f2bed7b4ae9846303ade979f80f6db75225c1298646953d61a476bb6891a33</citedby><cites>FETCH-LOGICAL-c764t-f5f2bed7b4ae9846303ade979f80f6db75225c1298646953d61a476bb6891a33</cites><orcidid>0000-0002-3492-0564 ; 0000-0001-7594-7280 ; 0000-0001-7608-9097 ; 0000-0001-9647-5374 ; 0000-0003-1404-2255</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824533/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824533/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,554,729,782,786,866,887,2106,2932,23875,27933,27934,53800,53802</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27055028$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.inrae.fr/hal-02636765$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttps://res.slu.se/id/publ/78399$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><contributor>Lightfoot, David A</contributor><creatorcontrib>Hoffmann, Holger</creatorcontrib><creatorcontrib>Zhao, Gang</creatorcontrib><creatorcontrib>Asseng, Senthold</creatorcontrib><creatorcontrib>Bindi, Marco</creatorcontrib><creatorcontrib>Biernath, Christian</creatorcontrib><creatorcontrib>Constantin, Julie</creatorcontrib><creatorcontrib>Coucheney, Elsa</creatorcontrib><creatorcontrib>Dechow, Rene</creatorcontrib><creatorcontrib>Doro, Luca</creatorcontrib><creatorcontrib>Eckersten, Henrik</creatorcontrib><creatorcontrib>Gaiser, Thomas</creatorcontrib><creatorcontrib>Grosz, Balázs</creatorcontrib><creatorcontrib>Heinlein, Florian</creatorcontrib><creatorcontrib>Kassie, Belay T</creatorcontrib><creatorcontrib>Kersebaum, Kurt-Christian</creatorcontrib><creatorcontrib>Klein, Christian</creatorcontrib><creatorcontrib>Kuhnert, Matthias</creatorcontrib><creatorcontrib>Lewan, Elisabet</creatorcontrib><creatorcontrib>Moriondo, Marco</creatorcontrib><creatorcontrib>Nendel, Claas</creatorcontrib><creatorcontrib>Priesack, Eckart</creatorcontrib><creatorcontrib>Raynal, Helene</creatorcontrib><creatorcontrib>Roggero, Pier P</creatorcontrib><creatorcontrib>Rötter, Reimund P</creatorcontrib><creatorcontrib>Siebert, Stefan</creatorcontrib><creatorcontrib>Specka, Xenia</creatorcontrib><creatorcontrib>Tao, Fulu</creatorcontrib><creatorcontrib>Teixeira, Edmar</creatorcontrib><creatorcontrib>Trombi, Giacomo</creatorcontrib><creatorcontrib>Wallach, Daniel</creatorcontrib><creatorcontrib>Weihermüller, Lutz</creatorcontrib><creatorcontrib>Yeluripati, Jagadeesh</creatorcontrib><creatorcontrib>Ewert, Frank</creatorcontrib><creatorcontrib>Sveriges lantbruksuniversitet</creatorcontrib><title>Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.</description><subject>Agglomeration</subject><subject>Agricultural production</subject><subject>Agricultural Science</subject><subject>Agriculture</subject><subject>Agriculture - methods</subject><subject>Bioengineering</subject><subject>Biology and Life Sciences</subject><subject>Climate Change</subject><subject>Climate models</subject><subject>Climatic data</subject><subject>Computer Science</subject><subject>Computer Simulation</subject><subject>Consumer protection</subject><subject>Corn</subject><subject>Crop science</subject><subject>Crop yield</subject><subject>Crop yields</subject><subject>Crops</subject><subject>Crops, Agricultural - growth & development</subject><subject>Data management</subject><subject>Databases, Factual</subject><subject>Earth Sciences</subject><subject>Ecology and Environmental 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of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations</title><author>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, 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USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SwePub Articles full text</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hoffmann, Holger</au><au>Zhao, Gang</au><au>Asseng, Senthold</au><au>Bindi, Marco</au><au>Biernath, Christian</au><au>Constantin, Julie</au><au>Coucheney, Elsa</au><au>Dechow, Rene</au><au>Doro, Luca</au><au>Eckersten, Henrik</au><au>Gaiser, Thomas</au><au>Grosz, Balázs</au><au>Heinlein, Florian</au><au>Kassie, Belay T</au><au>Kersebaum, Kurt-Christian</au><au>Klein, Christian</au><au>Kuhnert, Matthias</au><au>Lewan, Elisabet</au><au>Moriondo, Marco</au><au>Nendel, Claas</au><au>Priesack, Eckart</au><au>Raynal, Helene</au><au>Roggero, Pier P</au><au>Rötter, Reimund P</au><au>Siebert, Stefan</au><au>Specka, Xenia</au><au>Tao, Fulu</au><au>Teixeira, Edmar</au><au>Trombi, Giacomo</au><au>Wallach, Daniel</au><au>Weihermüller, Lutz</au><au>Yeluripati, Jagadeesh</au><au>Ewert, Frank</au><au>Lightfoot, David A</au><aucorp>Sveriges lantbruksuniversitet</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-04-07</date><risdate>2016</risdate><volume>11</volume><issue>4</issue><spage>e0151782</spage><epage>23</epage><pages>e0151782-23</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27055028</pmid><doi>10.1371/journal.pone.0151782</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-3492-0564</orcidid><orcidid>https://orcid.org/0000-0001-7594-7280</orcidid><orcidid>https://orcid.org/0000-0001-7608-9097</orcidid><orcidid>https://orcid.org/0000-0001-9647-5374</orcidid><orcidid>https://orcid.org/0000-0003-1404-2255</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2016-04, Vol.11 (4), p.e0151782-23 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1779457321 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; SWEPUB Freely available online; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| 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 |
| title | Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations |
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