Modeled effects of moderate and strong `Los Niños' on crop productivity in North America
Identifieur interne : 000805 ( Istex/Corpus ); précédent : 000804; suivant : 000806Modeled effects of moderate and strong `Los Niños' on crop productivity in North America
Auteurs : R. C Izaurralde ; N. J Rosenberg ; R. A Brown ; D. M Legler ; M. Tiscare O L Pez ; R. SrinivasanSource :
- Agricultural and Forest Meteorology [ 0168-1923 ] ; 1999.
Abstract
The El Niño of 1997–1998 rivaled in strength that of 1982–1983 and called attention worldwide not only for that reason but also because advances in understanding of ENSO phenomena allowed for its early forecast and preparations to deal with its impacts. We used the Erosion Productivity Impact Calculator (EPIC) to evaluate the impacts of El Niño on North American agriculture through an assemblage of 140 representative farms. We distinguish between `regular' (EN) and `strong' (SEN) El Niño defined by the extent to which sea surface temperatures in the central and eastern tropical Pacific ocean deviate from the long term normal. Each condition produced a distinct geographic distribution of temperature and precipitation anomalies with respect to Neutral (N) years (within 0.5°C of the long term normal). Using daily weather records, EPIC accounted for 87% of the total variation in historical yields in a sample of the farms. Yields simulated in EPIC with a stochastic weather generator predicted different geographic distributions of `winner' and `loser' regions for corn and wheat during EN and SEN years. Changes in water stress during EN and SEN with respect to N years was the variable that accounted for a significant proportion in the variation of simulated yield changes. Yields simulated under SEN tended to have larger variability than under EN. Further evaluation of the methodology presented here could arise from the near real-time simulation of El Niño events. Application of this type of methodology at a regional level could take advantage of interannual climatic variability for agricultural production.
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DOI: 10.1016/S0168-1923(98)00128-2
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A Brown</name><affiliation><mods:affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</mods:affiliation></affiliation></author><author><name sortKey="Legler, D M" sort="Legler, D M" uniqKey="Legler D" first="D. M" last="Legler">D. M Legler</name><affiliation><mods:affiliation>Center for Ocean-Atmospheric Prediction Studies (COAPS), The Florida State University, Tallahassee, FL 32306-3041, USA</mods:affiliation></affiliation></author><author><name sortKey="Tiscare O L Pez, M" sort="Tiscare O L Pez, M" uniqKey="Tiscare O L Pez M" first="M" last="Tiscare O L Pez">M. Tiscare O L Pez</name><affiliation><mods:affiliation>Instituto de Investigaciones Forestales y Agropecuarias (INIFAP), Teniente I. Alemán 294, Morelia, Michoacán 58260, Mexico</mods:affiliation></affiliation></author><author><name sortKey="Srinivasan, R" sort="Srinivasan, R" uniqKey="Srinivasan R" first="R" last="Srinivasan">R. 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C Izaurralde</name><affiliation><mods:affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</mods:affiliation></affiliation></author><author><name sortKey="Rosenberg, N J" sort="Rosenberg, N J" uniqKey="Rosenberg N" first="N. J" last="Rosenberg">N. J Rosenberg</name><affiliation><mods:affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</mods:affiliation></affiliation></author><author><name sortKey="Brown, R A" sort="Brown, R A" uniqKey="Brown R" first="R. A" last="Brown">R. A Brown</name><affiliation><mods:affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</mods:affiliation></affiliation></author><author><name sortKey="Legler, D M" sort="Legler, D M" uniqKey="Legler D" first="D. M" last="Legler">D. M Legler</name><affiliation><mods:affiliation>Center for Ocean-Atmospheric Prediction Studies (COAPS), The Florida State University, Tallahassee, FL 32306-3041, USA</mods:affiliation></affiliation></author><author><name sortKey="Tiscare O L Pez, M" sort="Tiscare O L Pez, M" uniqKey="Tiscare O L Pez M" first="M" last="Tiscare O L Pez">M. Tiscare O L Pez</name><affiliation><mods:affiliation>Instituto de Investigaciones Forestales y Agropecuarias (INIFAP), Teniente I. Alemán 294, Morelia, Michoacán 58260, Mexico</mods:affiliation></affiliation></author><author><name sortKey="Srinivasan, R" sort="Srinivasan, R" uniqKey="Srinivasan R" first="R" last="Srinivasan">R. Srinivasan</name><affiliation><mods:affiliation>Blacklands Research Center, Texas A & M University, 808 East Blackland Rd., Temple, TX 76502, USA</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Agricultural and Forest Meteorology</title><title level="j" type="abbrev">AGMET</title><idno type="ISSN">0168-1923</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1999">1999</date><biblScope unit="volume">94</biblScope><biblScope unit="issue">3–4</biblScope><biblScope unit="page" from="259">259</biblScope><biblScope unit="page" to="268">268</biblScope></imprint><idno type="ISSN">0168-1923</idno></series><idno type="istex">C444CB85AB3DBDC06187183804318983E6A00AE8</idno><idno type="DOI">10.1016/S0168-1923(98)00128-2</idno><idno type="PII">S0168-1923(98)00128-2</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0168-1923</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The El Niño of 1997–1998 rivaled in strength that of 1982–1983 and called attention worldwide not only for that reason but also because advances in understanding of ENSO phenomena allowed for its early forecast and preparations to deal with its impacts. We used the Erosion Productivity Impact Calculator (EPIC) to evaluate the impacts of El Niño on North American agriculture through an assemblage of 140 representative farms. We distinguish between `regular' (EN) and `strong' (SEN) El Niño defined by the extent to which sea surface temperatures in the central and eastern tropical Pacific ocean deviate from the long term normal. Each condition produced a distinct geographic distribution of temperature and precipitation anomalies with respect to Neutral (N) years (within 0.5°C of the long term normal). Using daily weather records, EPIC accounted for 87% of the total variation in historical yields in a sample of the farms. Yields simulated in EPIC with a stochastic weather generator predicted different geographic distributions of `winner' and `loser' regions for corn and wheat during EN and SEN years. Changes in water stress during EN and SEN with respect to N years was the variable that accounted for a significant proportion in the variation of simulated yield changes. Yields simulated under SEN tended to have larger variability than under EN. Further evaluation of the methodology presented here could arise from the near real-time simulation of El Niño events. Application of this type of methodology at a regional level could take advantage of interannual climatic variability for agricultural production.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>R.C Izaurralde</name><affiliations><json:string>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</json:string></affiliations></json:item><json:item><name>N.J Rosenberg</name><affiliations><json:string>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</json:string></affiliations></json:item><json:item><name>R.A Brown</name><affiliations><json:string>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</json:string></affiliations></json:item><json:item><name>D.M Legler</name><affiliations><json:string>Center for Ocean-Atmospheric Prediction Studies (COAPS), The Florida State University, Tallahassee, FL 32306-3041, USA</json:string></affiliations></json:item><json:item><name>M Tiscareño López</name><affiliations><json:string>Instituto de Investigaciones Forestales y Agropecuarias (INIFAP), Teniente I. Alemán 294, Morelia, Michoacán 58260, Mexico</json:string></affiliations></json:item><json:item><name>R Srinivasan</name><affiliations><json:string>Blacklands Research Center, Texas A & M University, 808 East Blackland Rd., Temple, TX 76502, USA</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>El Niño</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>El Niño Southern Oscillation (ENSO)</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>EPIC</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Corn</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Wheat</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>Full-length article</json:string></originalGenre><abstract>The El Niño of 1997–1998 rivaled in strength that of 1982–1983 and called attention worldwide not only for that reason but also because advances in understanding of ENSO phenomena allowed for its early forecast and preparations to deal with its impacts. We used the Erosion Productivity Impact Calculator (EPIC) to evaluate the impacts of El Niño on North American agriculture through an assemblage of 140 representative farms. We distinguish between `regular' (EN) and `strong' (SEN) El Niño defined by the extent to which sea surface temperatures in the central and eastern tropical Pacific ocean deviate from the long term normal. Each condition produced a distinct geographic distribution of temperature and precipitation anomalies with respect to Neutral (N) years (within 0.5°C of the long term normal). Using daily weather records, EPIC accounted for 87% of the total variation in historical yields in a sample of the farms. Yields simulated in EPIC with a stochastic weather generator predicted different geographic distributions of `winner' and `loser' regions for corn and wheat during EN and SEN years. Changes in water stress during EN and SEN with respect to N years was the variable that accounted for a significant proportion in the variation of simulated yield changes. Yields simulated under SEN tended to have larger variability than under EN. Further evaluation of the methodology presented here could arise from the near real-time simulation of El Niño events. Application of this type of methodology at a regional level could take advantage of interannual climatic variability for agricultural production.</abstract><qualityIndicators><score>6.989</score><pdfVersion>1.2</pdfVersion><pdfPageSize>522 x 745 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>5</keywordCount><abstractCharCount>1626</abstractCharCount><pdfWordCount>3989</pdfWordCount><pdfCharCount>25248</pdfCharCount><pdfPageCount>10</pdfPageCount><abstractWordCount>252</abstractWordCount></qualityIndicators><title>Modeled effects of moderate and strong `Los Niños' on crop productivity in North America</title><pii><json:string>S0168-1923(98)00128-2</json:string></pii><genre><json:string>research-article</json:string></genre><host><volume>94</volume><pii><json:string>S0168-1923(00)X0055-X</json:string></pii><pages><last>268</last><first>259</first></pages><issn><json:string>0168-1923</json:string></issn><issue>3–4</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><title>Agricultural and Forest Meteorology</title><publicationDate>1999</publicationDate></host><categories><wos><json:string>science</json:string><json:string>meteorology & atmospheric sciences</json:string><json:string>forestry</json:string><json:string>agronomy</json:string></wos><scienceMetrix><json:string>natural sciences</json:string><json:string>earth & environmental sciences</json:string><json:string>meteorology & atmospheric sciences</json:string></scienceMetrix></categories><publicationDate>1999</publicationDate><copyrightDate>1999</copyrightDate><doi><json:string>10.1016/S0168-1923(98)00128-2</json:string></doi><id>C444CB85AB3DBDC06187183804318983E6A00AE8</id><score>0.04017444</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/C444CB85AB3DBDC06187183804318983E6A00AE8/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/C444CB85AB3DBDC06187183804318983E6A00AE8/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/C444CB85AB3DBDC06187183804318983E6A00AE8/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Modeled effects of moderate and strong `Los Niños' on crop productivity in North America</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>©1999 Elsevier Science B.V.</p></availability><date>1999</date></publicationStmt><notesStmt><note type="content">Fig. 1: Geographic distribution of temperature and precipitation anomalies under EN and SEN.</note><note type="content">Fig. 2: Description of the spatial continuity of winter temperature and precipitation deviations from N under EN and SEN using geostatistical analysis. The most traditional tool of `semivariogram' or `variogram' (Englund and Sparks, 1991) was used in (c) and (d) to depict how the semivariance (γ[h]) between pairs of points changes with distance between pairs (h). A variogram is calculated as half the squared difference between pair data points γ(h)= 1 2N(h) ∑ (i,j)hij=h(νi−νj2; where νI and νj are data values separated by distance (lag) h and N is the sample size). As the distance between data values increases, the variogram value should increase if the data set has spatial continuity (ideally from γ(h)=0 at h=0 to a stable value called the `sill' (σ2). The distance at which the variogram reaches a plateau is called the `range'. Alternative methods (Englund and Sparks, 1991) were used to obtain stable expressions of spatial continuity in (a) and (b). In (a) we used an inverse covariance: σ2-C(h) (σ2 is the variance and C(h) is the covariance; C(h)= 1 N(h) ∑ (i,j)|hij=hνiνj−m−hm+h; m−h and m+h are means of data values at locations which are either −h or +h away from some other location). In (b) we used a general relative variogram: γGR(h)= (h) m(h)2 (where γ(h) is as defined and m(h) is the mean of all the data used to calculate γ(h)).</note><note type="content">Fig. 3: Comparison between EPIC-simulated yields and historical-district yields of beans (Mexico), corn (Mexico and US) and wheat (Canada and US) on six representative farms from 1968 to 1994. EPIC yields are derived from model simulation using daily historic weather and plotted against historical regional yields. For Canada, EPIC spring wheat yields were compared with published Statistics Canada regional mean yields for the years 1968–1994. For USA, EPIC yields were compared against yield/harvested acre for a specific county from the NASS–USDA crops county database for the years 1972–1989. For Mexico, historic yields were derived from: C. V. Palacios. `Capacidad en el desarrollo de programas de mejoramiento continuo de la productividad del maiz y diseño de empresas agropecuarias con capacidad para obtener una rentabilidad sostenible.' Proyecto INIFAP–MASECA Región Centro de Jalisco (1995); Secretaria de Agricultura y Recursos Hidraulicos. Mejores opciones de produccion en Temporal: 10 de Septiembre de 1987 Sandovales, Ags. INIFAP-Campo Agric. Exp. Pabellon (1987).</note><note type="content">Fig. 4: Geographic distribution of corn and wheat yield deviations under EN and SEN.</note><note type="content">Table 1: Seasonal temperature and precipitation during N years and deviations from N under EN and SEN for six large regions in North America</note><note type="content">Table 2: Statistics of historical and simulated yields of beans, wheat and corn at 11 sites in Canada, USA and Mexico</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Modeled effects of moderate and strong `Los Niños' on crop productivity in North America</title><author xml:id="author-1"><persName><forename type="first">R.C</forename><surname>Izaurralde</surname></persName><note type="correspondence"><p>Corresponding author: Fax:+1-202-646-7845; e-mail: cesar.izaurralde@pnl.gov.</p></note><affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</affiliation></author><author xml:id="author-2"><persName><forename type="first">N.J</forename><surname>Rosenberg</surname></persName><affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</affiliation></author><author xml:id="author-3"><persName><forename type="first">R.A</forename><surname>Brown</surname></persName><affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</affiliation></author><author xml:id="author-4"><persName><forename type="first">D.M</forename><surname>Legler</surname></persName><affiliation>Center for Ocean-Atmospheric Prediction Studies (COAPS), The Florida State University, Tallahassee, FL 32306-3041, USA</affiliation></author><author xml:id="author-5"><persName><forename type="first">M</forename><surname>Tiscareño López</surname></persName><affiliation>Instituto de Investigaciones Forestales y Agropecuarias (INIFAP), Teniente I. Alemán 294, Morelia, Michoacán 58260, Mexico</affiliation></author><author xml:id="author-6"><persName><forename type="first">R</forename><surname>Srinivasan</surname></persName><affiliation>Blacklands Research Center, Texas A & M University, 808 East Blackland Rd., Temple, TX 76502, USA</affiliation></author></analytic><monogr><title level="j">Agricultural and Forest Meteorology</title><title level="j" type="abbrev">AGMET</title><idno type="pISSN">0168-1923</idno><idno type="PII">S0168-1923(00)X0055-X</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1999"></date><biblScope unit="volume">94</biblScope><biblScope unit="issue">3–4</biblScope><biblScope unit="page" from="259">259</biblScope><biblScope unit="page" to="268">268</biblScope></imprint></monogr><idno type="istex">C444CB85AB3DBDC06187183804318983E6A00AE8</idno><idno type="DOI">10.1016/S0168-1923(98)00128-2</idno><idno type="PII">S0168-1923(98)00128-2</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1999</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>The El Niño of 1997–1998 rivaled in strength that of 1982–1983 and called attention worldwide not only for that reason but also because advances in understanding of ENSO phenomena allowed for its early forecast and preparations to deal with its impacts. We used the Erosion Productivity Impact Calculator (EPIC) to evaluate the impacts of El Niño on North American agriculture through an assemblage of 140 representative farms. We distinguish between `regular' (EN) and `strong' (SEN) El Niño defined by the extent to which sea surface temperatures in the central and eastern tropical Pacific ocean deviate from the long term normal. Each condition produced a distinct geographic distribution of temperature and precipitation anomalies with respect to Neutral (N) years (within 0.5°C of the long term normal). Using daily weather records, EPIC accounted for 87% of the total variation in historical yields in a sample of the farms. Yields simulated in EPIC with a stochastic weather generator predicted different geographic distributions of `winner' and `loser' regions for corn and wheat during EN and SEN years. Changes in water stress during EN and SEN with respect to N years was the variable that accounted for a significant proportion in the variation of simulated yield changes. Yields simulated under SEN tended to have larger variability than under EN. Further evaluation of the methodology presented here could arise from the near real-time simulation of El Niño events. Application of this type of methodology at a regional level could take advantage of interannual climatic variability for agricultural production.</p></abstract><textClass><keywords scheme="keyword"><list><head>Keywords</head><item><term>El Niño</term></item><item><term>El Niño Southern Oscillation (ENSO)</term></item><item><term>EPIC</term></item><item><term>Corn</term></item><item><term>Wheat</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1998-10-02">Modified</change><change when="1999">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/C444CB85AB3DBDC06187183804318983E6A00AE8/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity><istex:entity SYSTEM="gr3" NDATA="IMAGE" name="gr3"></istex:entity><istex:entity SYSTEM="gr4" NDATA="IMAGE" name="gr4"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>AGMET</jid><aid>2601</aid><ce:pii>S0168-1923(98)00128-2</ce:pii><ce:doi>10.1016/S0168-1923(98)00128-2</ce:doi><ce:copyright year="1999" type="full-transfer">Elsevier Science B.V.</ce:copyright></item-info><head><ce:title>Modeled effects of moderate and strong `Los Niños' on crop productivity in North America</ce:title><ce:author-group><ce:author><ce:given-name>R.C</ce:given-name><ce:surname>Izaurralde</ce:surname><ce:cross-ref refid="AFF1">a</ce:cross-ref><ce:cross-ref refid="CORR1">*</ce:cross-ref></ce:author><ce:author><ce:given-name>N.J</ce:given-name><ce:surname>Rosenberg</ce:surname><ce:cross-ref refid="AFF1">a</ce:cross-ref></ce:author><ce:author><ce:given-name>R.A</ce:given-name><ce:surname>Brown</ce:surname><ce:cross-ref refid="AFF1">a</ce:cross-ref></ce:author><ce:author><ce:given-name>D.M</ce:given-name><ce:surname>Legler</ce:surname><ce:cross-ref refid="AFF2">b</ce:cross-ref></ce:author><ce:author><ce:given-name>M</ce:given-name><ce:surname>Tiscareño López</ce:surname><ce:cross-ref refid="AFF3">c</ce:cross-ref></ce:author><ce:author><ce:given-name>R</ce:given-name><ce:surname>Srinivasan</ce:surname><ce:cross-ref refid="AFF4">d</ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>a</ce:label><ce:textfn>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>b</ce:label><ce:textfn>Center for Ocean-Atmospheric Prediction Studies (COAPS), The Florida State University, Tallahassee, FL 32306-3041, USA</ce:textfn></ce:affiliation><ce:affiliation id="AFF3"><ce:label>c</ce:label><ce:textfn>Instituto de Investigaciones Forestales y Agropecuarias (INIFAP), Teniente I. Alemán 294, Morelia, Michoacán 58260, Mexico</ce:textfn></ce:affiliation><ce:affiliation id="AFF4"><ce:label>d</ce:label><ce:textfn>Blacklands Research Center, Texas A & M University, 808 East Blackland Rd., Temple, TX 76502, USA</ce:textfn></ce:affiliation><ce:correspondence id="CORR1"><ce:label>*</ce:label><ce:text>Corresponding author: Fax:+1-202-646-7845; e-mail: cesar.izaurralde@pnl.gov.</ce:text></ce:correspondence></ce:author-group><ce:date-received day="20" month="4" year="1998"></ce:date-received><ce:date-revised day="2" month="10" year="1998"></ce:date-revised><ce:date-accepted day="13" month="11" year="1998"></ce:date-accepted><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>The El Niño of 1997–1998 rivaled in strength that of 1982–1983 and called attention worldwide not only for that reason but also because advances in understanding of ENSO phenomena allowed for its early forecast and preparations to deal with its impacts. We used the Erosion Productivity Impact Calculator (EPIC) to evaluate the impacts of El Niño on North American agriculture through an assemblage of 140 representative farms. We distinguish between `regular' (EN) and `strong' (SEN) El Niño defined by the extent to which sea surface temperatures in the central and eastern tropical Pacific ocean deviate from the long term normal. Each condition produced a distinct geographic distribution of temperature and precipitation anomalies with respect to Neutral (N) years (within 0.5°C of the long term normal). Using daily weather records, EPIC accounted for 87% of the total variation in historical yields in a sample of the farms. Yields simulated in EPIC with a stochastic weather generator predicted different geographic distributions of `winner' and `loser' regions for corn and wheat during EN and SEN years. Changes in water stress during EN and SEN with respect to N years was the variable that accounted for a significant proportion in the variation of simulated yield changes. Yields simulated under SEN tended to have larger variability than under EN. Further evaluation of the methodology presented here could arise from the near real-time simulation of El Niño events. Application of this type of methodology at a regional level could take advantage of interannual climatic variability for agricultural production.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>El Niño</ce:text></ce:keyword><ce:keyword><ce:text>El Niño Southern Oscillation (ENSO)</ce:text></ce:keyword><ce:keyword><ce:text>EPIC</ce:text></ce:keyword><ce:keyword><ce:text>Corn</ce:text></ce:keyword><ce:keyword><ce:text>Wheat</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Modeled effects of moderate and strong `Los Niños' on crop productivity in North America</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Modeled effects of moderate and strong `Los Niños' on crop productivity in North America</title></titleInfo><name type="personal"><namePart type="given">R.C</namePart><namePart type="family">Izaurralde</namePart><affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</affiliation><description>Corresponding author: Fax:+1-202-646-7845; e-mail: cesar.izaurralde@pnl.gov.</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">N.J</namePart><namePart type="family">Rosenberg</namePart><affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">R.A</namePart><namePart type="family">Brown</namePart><affiliation>Battelle Pacific Northwest National Laboratory, 901 D Street, S.W., Suite 900, Washington, DC 20024-2115, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">D.M</namePart><namePart type="family">Legler</namePart><affiliation>Center for Ocean-Atmospheric Prediction Studies (COAPS), The Florida State University, Tallahassee, FL 32306-3041, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">M</namePart><namePart type="family">Tiscareño López</namePart><affiliation>Instituto de Investigaciones Forestales y Agropecuarias (INIFAP), Teniente I. Alemán 294, Morelia, Michoacán 58260, Mexico</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">R</namePart><namePart type="family">Srinivasan</namePart><affiliation>Blacklands Research Center, Texas A & M University, 808 East Blackland Rd., Temple, TX 76502, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">1999</dateIssued><dateModified encoding="w3cdtf">1998-10-02</dateModified><copyrightDate encoding="w3cdtf">1999</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">The El Niño of 1997–1998 rivaled in strength that of 1982–1983 and called attention worldwide not only for that reason but also because advances in understanding of ENSO phenomena allowed for its early forecast and preparations to deal with its impacts. We used the Erosion Productivity Impact Calculator (EPIC) to evaluate the impacts of El Niño on North American agriculture through an assemblage of 140 representative farms. We distinguish between `regular' (EN) and `strong' (SEN) El Niño defined by the extent to which sea surface temperatures in the central and eastern tropical Pacific ocean deviate from the long term normal. Each condition produced a distinct geographic distribution of temperature and precipitation anomalies with respect to Neutral (N) years (within 0.5°C of the long term normal). Using daily weather records, EPIC accounted for 87% of the total variation in historical yields in a sample of the farms. Yields simulated in EPIC with a stochastic weather generator predicted different geographic distributions of `winner' and `loser' regions for corn and wheat during EN and SEN years. Changes in water stress during EN and SEN with respect to N years was the variable that accounted for a significant proportion in the variation of simulated yield changes. Yields simulated under SEN tended to have larger variability than under EN. Further evaluation of the methodology presented here could arise from the near real-time simulation of El Niño events. Application of this type of methodology at a regional level could take advantage of interannual climatic variability for agricultural production.</abstract><note type="content">Fig. 1: Geographic distribution of temperature and precipitation anomalies under EN and SEN.</note><note type="content">Fig. 2: Description of the spatial continuity of winter temperature and precipitation deviations from N under EN and SEN using geostatistical analysis. The most traditional tool of `semivariogram' or `variogram' (Englund and Sparks, 1991) was used in (c) and (d) to depict how the semivariance (γ[h]) between pairs of points changes with distance between pairs (h). A variogram is calculated as half the squared difference between pair data points γ(h)= 1 2N(h) ∑ (i,j)hij=h(νi−νj2; where νI and νj are data values separated by distance (lag) h and N is the sample size). As the distance between data values increases, the variogram value should increase if the data set has spatial continuity (ideally from γ(h)=0 at h=0 to a stable value called the `sill' (σ2). The distance at which the variogram reaches a plateau is called the `range'. Alternative methods (Englund and Sparks, 1991) were used to obtain stable expressions of spatial continuity in (a) and (b). In (a) we used an inverse covariance: σ2-C(h) (σ2 is the variance and C(h) is the covariance; C(h)= 1 N(h) ∑ (i,j)|hij=hνiνj−m−hm+h; m−h and m+h are means of data values at locations which are either −h or +h away from some other location). In (b) we used a general relative variogram: γGR(h)= (h) m(h)2 (where γ(h) is as defined and m(h) is the mean of all the data used to calculate γ(h)).</note><note type="content">Fig. 3: Comparison between EPIC-simulated yields and historical-district yields of beans (Mexico), corn (Mexico and US) and wheat (Canada and US) on six representative farms from 1968 to 1994. EPIC yields are derived from model simulation using daily historic weather and plotted against historical regional yields. For Canada, EPIC spring wheat yields were compared with published Statistics Canada regional mean yields for the years 1968–1994. For USA, EPIC yields were compared against yield/harvested acre for a specific county from the NASS–USDA crops county database for the years 1972–1989. For Mexico, historic yields were derived from: C. V. Palacios. `Capacidad en el desarrollo de programas de mejoramiento continuo de la productividad del maiz y diseño de empresas agropecuarias con capacidad para obtener una rentabilidad sostenible.' Proyecto INIFAP–MASECA Región Centro de Jalisco (1995); Secretaria de Agricultura y Recursos Hidraulicos. Mejores opciones de produccion en Temporal: 10 de Septiembre de 1987 Sandovales, Ags. INIFAP-Campo Agric. Exp. Pabellon (1987).</note><note type="content">Fig. 4: Geographic distribution of corn and wheat yield deviations under EN and SEN.</note><note type="content">Table 1: Seasonal temperature and precipitation during N years and deviations from N under EN and SEN for six large regions in North America</note><note type="content">Table 2: Statistics of historical and simulated yields of beans, wheat and corn at 11 sites in Canada, USA and Mexico</note><subject><genre>Keywords</genre><topic>El Niño</topic><topic>El Niño Southern Oscillation (ENSO)</topic><topic>EPIC</topic><topic>Corn</topic><topic>Wheat</topic></subject><relatedItem type="host"><titleInfo><title>Agricultural and Forest Meteorology</title></titleInfo><titleInfo type="abbreviated"><title>AGMET</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">19990503</dateIssued></originInfo><identifier type="ISSN">0168-1923</identifier><identifier type="PII">S0168-1923(00)X0055-X</identifier><part><date>19990503</date><detail type="volume"><number>94</number><caption>vol.</caption></detail><detail type="issue"><number>3–4</number><caption>no.</caption></detail><extent unit="issue pages"><start>159</start><end>280</end></extent><extent unit="pages"><start>259</start><end>268</end></extent></part></relatedItem><identifier type="istex">C444CB85AB3DBDC06187183804318983E6A00AE8</identifier><identifier type="DOI">10.1016/S0168-1923(98)00128-2</identifier><identifier type="PII">S0168-1923(98)00128-2</identifier><accessCondition type="use and reproduction" contentType="copyright">©1999 Elsevier Science B.V.</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science B.V., ©1999</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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