Impact of derived global weather data on simulated crop yields
Identifieur interne : 001440 ( Istex/Corpus ); précédent : 001439; suivant : 001441Impact of derived global weather data on simulated crop yields
Auteurs : Justin Van Wart ; Patricio Grassini ; Kenneth G. CassmanSource :
- Global Change Biology [ 1354-1013 ] ; 2013-12.
Abstract
Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.
Url:
DOI: 10.1111/gcb.12302
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Cassman</name><affiliation><mods:affiliation>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: kcassman1@unl.edu</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Global Change Biology</title><title level="j" type="abbrev">Glob Change Biol</title><idno type="ISSN">1354-1013</idno><idno type="eISSN">1365-2486</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><date type="published" when="2013-12">2013-12</date><biblScope unit="volume">19</biblScope><biblScope unit="issue">12</biblScope><biblScope unit="page" from="3822">3822</biblScope><biblScope unit="page" to="3834">3834</biblScope></imprint><idno type="ISSN">1354-1013</idno></series><idno type="istex">EFD0B6D043D64A8FC33B12FEE8D01837946C2B08</idno><idno type="DOI">10.1111/gcb.12302</idno><idno type="ArticleID">GCB12302</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1354-1013</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Justin van Wart</name><affiliations><json:string>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</json:string></affiliations></json:item><json:item><name>Patricio Grassini</name><affiliations><json:string>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</json:string></affiliations></json:item><json:item><name>Kenneth G. Cassman</name><affiliations><json:string>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</json:string><json:string>E-mail: kcassman1@unl.edu</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>crop model</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>maize</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>rice</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>weather data</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>wheat</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>yield potential</value></json:item></subject><articleId><json:string>GCB12302</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.</abstract><qualityIndicators><score>10</score><pdfVersion>1.7</pdfVersion><pdfPageSize>595.276 x 782.362 pts</pdfPageSize><refBibsNative>true</refBibsNative><abstractCharCount>1903</abstractCharCount><pdfWordCount>8507</pdfWordCount><pdfCharCount>51133</pdfCharCount><pdfPageCount>13</pdfPageCount><abstractWordCount>298</abstractWordCount></qualityIndicators><title>Impact of derived global weather data on simulated crop yields</title><genre><json:string>article</json:string></genre><host><volume>19</volume><publisherId><json:string>GCB</json:string></publisherId><pages><total>13</total><last>3834</last><first>3822</first></pages><issn><json:string>1354-1013</json:string></issn><issue>12</issue><subject><json:item><value>Primary Research Article</value></json:item></subject><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><eissn><json:string>1365-2486</json:string></eissn><title>Global Change Biology</title><doi><json:string>10.1111/(ISSN)1365-2486</json:string></doi></host><categories><wos><json:string>science</json:string><json:string>environmental sciences</json:string><json:string>ecology</json:string><json:string>biodiversity conservation</json:string></wos><scienceMetrix><json:string>natural sciences</json:string><json:string>biology</json:string><json:string>ecology</json:string></scienceMetrix></categories><publicationDate>2013</publicationDate><copyrightDate>2013</copyrightDate><doi><json:string>10.1111/gcb.12302</json:string></doi><id>EFD0B6D043D64A8FC33B12FEE8D01837946C2B08</id><score>0.04101784</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/EFD0B6D043D64A8FC33B12FEE8D01837946C2B08/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/EFD0B6D043D64A8FC33B12FEE8D01837946C2B08/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/EFD0B6D043D64A8FC33B12FEE8D01837946C2B08/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Impact of derived global weather data on simulated crop yields</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Blackwell Publishing Ltd</publisher><availability status="free"><p>Open Access</p></availability><date>2013-07-04</date></publicationStmt><notesStmt><note>Figure S1. Comparison of weather data from control and NOAA‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in USA.Figure S2. Comparison of weather data from control and NCEP global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in USA.Figure S3. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in USA.Figure S4. Comparison of weather data from control and NASA global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in USA.Figure S5. Comparison of weather data from control and NOAA‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in China.Figure S6. Comparison of weather data from control and NCEP global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in China.Figure S7. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in China.Figure S8. Comparison of weather data from control and NASA global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in China.Figure S9. Comparison of weather data from control and NOAA‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in Germany.Figure S10. Comparison of weather data from control and NCEP global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in Germany.Figure S11. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in Germany.Figure S12. Comparison of weather data from control and NASA global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in Germany.Table S1–S3. Management parameters used in simulation models at four sites in three countries for three crops. Dates of planting, transplanting, and physiological maturity are reported as day of the year (DOY). Maize crop variety expressed in relative maturity days (CRM). Table S4. Elevation within 100 km of simulation sites in China (m). Source: CGIAR‐CSI (2006): NASA Shuttle Radar Topographic Mission available for download at: http://srtm.csi.cgiar.org/ Table S5. Mean error (ME) and root mean square error (RMSE) using different global weather databases compared with local, high‐quality control data during the growing season time period used in simulations of crop yields at each of four sites for rainfed maize in USA, irrigated rice in China, and rainfed wheat in Germany.</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Impact of derived global weather data on simulated crop yields</title><author xml:id="author-1"><persName><forename type="first">Justin</forename><surname>van Wart</surname></persName><affiliation>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</affiliation></author><author xml:id="author-2"><persName><forename type="first">Patricio</forename><surname>Grassini</surname></persName><affiliation>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</affiliation></author><author xml:id="author-3"><persName><forename type="first">Kenneth G.</forename><surname>Cassman</surname></persName><email>kcassman1@unl.edu</email><affiliation>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</affiliation></author></analytic><monogr><title level="j">Global Change Biology</title><title level="j" type="abbrev">Glob Change Biol</title><idno type="pISSN">1354-1013</idno><idno type="eISSN">1365-2486</idno><idno type="DOI">10.1111/(ISSN)1365-2486</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><date type="published" when="2013-12"></date><biblScope unit="volume">19</biblScope><biblScope unit="issue">12</biblScope><biblScope unit="page" from="3822">3822</biblScope><biblScope unit="page" to="3834">3834</biblScope></imprint></monogr><idno type="istex">EFD0B6D043D64A8FC33B12FEE8D01837946C2B08</idno><idno type="DOI">10.1111/gcb.12302</idno><idno type="ArticleID">GCB12302</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2013-07-04</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.</p></abstract><textClass><keywords scheme="keyword"><list><head>keywords</head><item><term>crop model</term></item><item><term>maize</term></item><item><term>rice</term></item><item><term>weather data</term></item><item><term>wheat</term></item><item><term>yield potential</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>article-category</head><item><term>Primary Research Article</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2013-04-23">Received</change><change when="2013-05-24">Registration</change><change when="2013-07-04">Created</change><change when="2013-12">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/EFD0B6D043D64A8FC33B12FEE8D01837946C2B08/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component type="serialArticle" version="2.0" xml:id="gcb12302" xml:lang="en"><header><publicationMeta level="product"><doi origin="wiley" registered="yes">10.1111/(ISSN)1365-2486</doi><issn type="print">1354-1013</issn><issn type="electronic">1365-2486</issn><idGroup><id type="product" value="GCB"></id></idGroup><titleGroup><title sort="GLOBAL CHANGE BIOLOGY" type="main">Global Change Biology</title><title type="short">Glob Change Biol</title></titleGroup></publicationMeta><publicationMeta level="part" position="120"><doi origin="wiley">10.1111/gcb.2013.19.issue-12</doi><copyright ownership="publisher">Copyright © 2013 John Wiley & Sons Ltd</copyright><numberingGroup><numbering type="journalVolume" number="19">19</numbering><numbering type="journalIssue">12</numbering></numberingGroup><coverDate startDate="2013-12">December 2013</coverDate></publicationMeta><publicationMeta level="unit" position="220" status="forIssue" type="article" accessType="open"><doi>10.1111/gcb.12302</doi><idGroup><id type="unit" value="GCB12302"></id></idGroup><countGroup><count number="13" type="pageTotal"></count></countGroup><titleGroup><title type="articleCategory">Primary Research Article</title><title type="tocHeading1">Primary Research Articles</title></titleGroup><copyright ownership="publisher">© 2013 John Wiley & Sons Ltd</copyright><legalStatement type="creativeCommonsBy"><p>This is an open access article under the terms of the <url href="http://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution</url> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</p></legalStatement><eventGroup><event date="2013-04-23" type="manuscriptReceived"></event><event date="2013-05-24" type="manuscriptAccepted"></event><event agent="SPS" date="2013-07-04" type="xmlCreated"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.2.6 mode:FullText" date="2013-12-02"></event><event type="publishedOnlineAccepted" date="2013-06-26"></event><event type="publishedOnlineEarlyUnpaginated" date="2013-09-24"></event><event type="publishedOnlineFinalForm" date="2013-11-08"></event><event type="firstOnline" date="2013-09-24"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.6.4 mode:FullText" date="2015-10-03"></event></eventGroup><numberingGroup><numbering type="pageFirst">3822</numbering><numbering type="pageLast">3834</numbering></numberingGroup><correspondenceTo>Correspondence: Kenneth G. Cassman, tel. +402 474 5554, fax +402 472 7904, e‐mail: <email>kcassman1@unl.edu</email></correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:GCB.GCB12302.pdf"></link></linkGroup></publicationMeta><contentMeta><titleGroup><title type="main">Impact of derived global weather data on simulated crop yields</title><title type="shortAuthors">J. van Wart <i>et al</i>.</title></titleGroup><creators><creator affiliationRef="#gcb12302-aff-0001" creatorRole="author" xml:id="gcb12302-cr-0001"><personName><givenNames>Justin</givenNames> <familyNamePrefix>van</familyNamePrefix> <familyName>Wart</familyName></personName></creator><creator affiliationRef="#gcb12302-aff-0001" creatorRole="author" xml:id="gcb12302-cr-0002"><personName><givenNames>Patricio</givenNames> <familyName>Grassini</familyName></personName></creator><creator affiliationRef="#gcb12302-aff-0001" corresponding="yes" creatorRole="author" xml:id="gcb12302-cr-0003"><personName><givenNames>Kenneth G.</givenNames> <familyName>Cassman</familyName></personName></creator></creators><affiliationGroup><affiliation countryCode="US" type="organization" xml:id="gcb12302-aff-0001"><orgDiv>Department of Agronomy and Horticulture</orgDiv> <orgName>University of Nebraska‐Lincoln</orgName> <address><city>Lincoln</city> <countryPart>NE</countryPart> <postCode>68583‐0915</postCode> <country>USA</country></address></affiliation></affiliationGroup><keywordGroup type="author"><keyword xml:id="gcb12302-kwd-0001">crop model</keyword><keyword xml:id="gcb12302-kwd-0002">maize</keyword><keyword xml:id="gcb12302-kwd-0003">rice</keyword><keyword xml:id="gcb12302-kwd-0004">weather data</keyword><keyword xml:id="gcb12302-kwd-0005">wheat</keyword><keyword xml:id="gcb12302-kwd-0006">yield potential</keyword></keywordGroup><supportingInformation><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0001-FigS1"></mediaResource><caption><b>Figure S1</b>. Comparison of weather data from control and <fc>NOAA</fc>‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in <fc>USA</fc>.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0002-FigS2"></mediaResource><caption><b>Figure S2</b>. Comparison of weather data from control and <fc>NCEP</fc> global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in <fc>USA</fc>.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0003-FigS3"></mediaResource><caption><b>Figure S3</b>. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in <fc>USA</fc>.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0004-FigS4"></mediaResource><caption><b>Figure S4</b>. Comparison of weather data from control and <fc>NASA</fc> global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in <fc>USA</fc>.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0005-FigS5"></mediaResource><caption><b>Figure S5</b>. Comparison of weather data from control and <fc>NOAA</fc>‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in <fc>C</fc>hina.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0006-FigS6"></mediaResource><caption><b>Figure S6</b>. Comparison of weather data from control and <fc>NCEP</fc> global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in <fc>C</fc>hina.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0007-FigS7"></mediaResource><caption><b>Figure S7</b>. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in <fc>C</fc>hina.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0008-FigS8"></mediaResource><caption><b>Figure S8</b>. Comparison of weather data from control and <fc>NASA</fc> global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in <fc>C</fc>hina.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0009-FigS9"></mediaResource><caption><b>Figure S9</b>. Comparison of weather data from control and <fc>NOAA</fc>‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in <fc>G</fc>ermany.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0010-FigS10"></mediaResource><caption><b>Figure S10</b>. Comparison of weather data from control and <fc>NCEP</fc> global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in <fc>G</fc>ermany.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0011-FigS11"></mediaResource><caption><b>Figure S11</b>. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in <fc>G</fc>ermany.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="image/tif" rendition="webOriginal" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0012-FigS12"></mediaResource><caption><b>Figure S12</b>. Comparison of weather data from control and <fc>NASA</fc> global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in <fc>G</fc>ermany.</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting" mimeType="application/msword" href="urn-x:wiley:13541013:media:gcb12302:gcb12302-sup-0013-TableS1-S5"></mediaResource><caption><p><b>Table S1–S3</b>. Management parameters used in simulation models at four sites in three countries for three crops. Dates of planting, transplanting, and physiological maturity are reported as day of the year (<fc>DOY</fc>). Maize crop variety expressed in relative maturity days (<fc>CRM</fc>).</p><p><b>Table S4</b>. Elevation within 100 km of simulation sites in <fc>C</fc>hina (m). Source: <fc>CGIAR</fc>‐<fc>CSI</fc> (2006): <fc>NASA</fc> Shuttle Radar Topographic Mission available for download at: <url href="http://srtm.csi.cgiar.org/">http://srtm.csi.cgiar.org/</url></p><p><b>Table S5</b>. Mean error (<fc>ME</fc>) and root mean square error (<fc>RMSE</fc>) using different global weather databases compared with local, high‐quality control data during the growing season time period used in simulations of crop yields at each of four sites for rainfed maize in <fc>USA</fc>, irrigated rice in <fc>C</fc>hina, and rainfed wheat in <fc>G</fc>ermany.</p></caption></supportingInfoItem></supportingInformation><abstractGroup><abstract type="main" xml:id="gcb12302-abs-0001"><title type="main">Abstract</title><p>Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (<fc>GWD</fc>) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of <fc>GWD</fc>s to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three <fc>GWD</fc>s (<fc>CRU</fc>,<fc> NCEP</fc>/<fc>DOE</fc>, and <fc>NASA POWER</fc> data) were evaluated for their ability to simulate Yp and Yw of rice in China, <fc>USA</fc> maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (<fc>CWD</fc>). Agreement between simulations of Yp or Yw based on <fc>CWD</fc> and those based on <fc>GWD</fc> was poor with the latter having strong bias and large root mean square errors (<fc>RMSE</fc>s) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the <fc>NOAA</fc> database combined with solar radiation from the <fc>NASA</fc>‐<fc>POWER</fc> database were in much better agreement with Yp and Yw simulated with <fc>CWD</fc> (i.e. little bias and an <fc>RMSE</fc> of 12–19% of the absolute mean). We conclude that results from studies that rely on <fc>GWD</fc> to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Impact of derived global weather data on simulated crop yields</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Impact of derived global weather data on simulated crop yields</title></titleInfo><name type="personal"><namePart type="given">Justin</namePart><namePart type="family">van Wart</namePart><affiliation>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Patricio</namePart><namePart type="family">Grassini</namePart><affiliation>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Kenneth G.</namePart><namePart type="family">Cassman</namePart><affiliation>Department of Agronomy and Horticulture, University of Nebraska‐Lincoln, NE, 68583‐0915, Lincoln, USA</affiliation><affiliation>E-mail: kcassman1@unl.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2013-12</dateIssued><dateCreated encoding="w3cdtf">2013-07-04</dateCreated><dateCaptured encoding="w3cdtf">2013-04-23</dateCaptured><dateValid encoding="w3cdtf">2013-05-24</dateValid><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract>Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.</abstract><note type="additional physical form">Figure S1. Comparison of weather data from control and NOAA‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in USA.Figure S2. Comparison of weather data from control and NCEP global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in USA.Figure S3. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in USA.Figure S4. Comparison of weather data from control and NASA global weather database during pre‐ (black triangles) and post‐ (red circles) silking of simulated rainfed maize in USA.Figure S5. Comparison of weather data from control and NOAA‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in China.Figure S6. Comparison of weather data from control and NCEP global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in China.Figure S7. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in China.Figure S8. Comparison of weather data from control and NASA global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated irrigated rice in China.Figure S9. Comparison of weather data from control and NOAA‐ solar radiation during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in Germany.Figure S10. Comparison of weather data from control and NCEP global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in Germany.Figure S11. Comparison of weather data from control and Climate Research Unit global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in Germany.Figure S12. Comparison of weather data from control and NASA global weather database during pre‐ (black triangles) and post‐ (red circles) anthesis of simulated rainfed wheat in Germany.Table S1–S3. Management parameters used in simulation models at four sites in three countries for three crops. Dates of planting, transplanting, and physiological maturity are reported as day of the year (DOY). Maize crop variety expressed in relative maturity days (CRM). Table S4. Elevation within 100 km of simulation sites in China (m). Source: CGIAR‐CSI (2006): NASA Shuttle Radar Topographic Mission available for download at: http://srtm.csi.cgiar.org/ Table S5. Mean error (ME) and root mean square error (RMSE) using different global weather databases compared with local, high‐quality control data during the growing season time period used in simulations of crop yields at each of four sites for rainfed maize in USA, irrigated rice in China, and rainfed wheat in Germany.</note><subject><genre>keywords</genre><topic>crop model</topic><topic>maize</topic><topic>rice</topic><topic>weather data</topic><topic>wheat</topic><topic>yield potential</topic></subject><relatedItem type="host"><titleInfo><title>Global Change Biology</title></titleInfo><titleInfo type="abbreviated"><title>Glob Change Biol</title></titleInfo><genre type="journal">journal</genre><subject><genre>article-category</genre><topic>Primary Research Article</topic></subject><identifier type="ISSN">1354-1013</identifier><identifier type="eISSN">1365-2486</identifier><identifier type="DOI">10.1111/(ISSN)1365-2486</identifier><identifier type="PublisherID">GCB</identifier><part><date>2013</date><detail type="volume"><caption>vol.</caption><number>19</number></detail><detail type="issue"><caption>no.</caption><number>12</number></detail><extent unit="pages"><start>3822</start><end>3834</end><total>13</total></extent></part></relatedItem><identifier type="istex">EFD0B6D043D64A8FC33B12FEE8D01837946C2B08</identifier><identifier type="DOI">10.1111/gcb.12302</identifier><identifier type="ArticleID">GCB12302</identifier><accessCondition type="use and reproduction" contentType="creativeCommonsBy">This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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