Contribution of agrometeorology to the simulation of crop production and its applications
Identifieur interne : 000F75 ( Istex/Corpus ); précédent : 000F74; suivant : 000F76Contribution of agrometeorology to the simulation of crop production and its applications
Auteurs : Gerrit HoogenboomSource :
- Agricultural and Forest Meteorology [ 0168-1923 ] ; 2000.
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- KwdEn :
Abstract
Weather has a significant impact on crop growth and development. This paper presents an overview of crop modeling and applications of crop models, and the significance of weather related to these applications. To account for the impact of weather and climate variability on crop production, agrometeorological variables are one of the key inputs required for the operation of crop simulation models. These include maximum and minimum air temperature, total solar radiation, and total rainfall. Most models use daily data as input, because variables at a smaller time scale are usually unavailable for most locations. It is important to define standard file formats for weather and other input data; this will expand the applicability of weather data by different models. Issues related to missing variables and data, as well as locations for which no data are available, need to be addressed for model applications, as it can affect the accuracy of the simulations. Weather generators can be used to stochastically generate daily data when data are missing or long-term historical data are unavailable. However, the use of observed weather data for model input will provide more precise crop yield simulations, especially for tropical regions. Many of the crop models have been applied towards strategic and tactical management decision making as well as yield forecasting. The predicted variability of crop yield and related variables as well as natural resource use is mainly due to the short- and long-term variation of weather and climate conditions. The results produced by the models can be used to make appropriate management decisions and to provide farmers and others with alternative options for their farming system. The crop models have been used extensively to study the impact of climate change on agricultural production and food security. Recently, they have also been applied towards the impact of climate variability and the effect of El Niño/Southern Oscillation (ENSO) on agricultural production and food security. It is expected that, with the increased availability of computers, the use of crop models by farmers and consultants as well as policy and decision makers will increase. Weather data in the form of historical data or observations made during the current growing season, and short-, medium-, and long-term weather forecasts will play a critical role in these applications.
Url:
DOI: 10.1016/S0168-1923(00)00108-8
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This paper presents an overview of crop modeling and applications of crop models, and the significance of weather related to these applications. To account for the impact of weather and climate variability on crop production, agrometeorological variables are one of the key inputs required for the operation of crop simulation models. These include maximum and minimum air temperature, total solar radiation, and total rainfall. Most models use daily data as input, because variables at a smaller time scale are usually unavailable for most locations. It is important to define standard file formats for weather and other input data; this will expand the applicability of weather data by different models. Issues related to missing variables and data, as well as locations for which no data are available, need to be addressed for model applications, as it can affect the accuracy of the simulations. Weather generators can be used to stochastically generate daily data when data are missing or long-term historical data are unavailable. However, the use of observed weather data for model input will provide more precise crop yield simulations, especially for tropical regions. Many of the crop models have been applied towards strategic and tactical management decision making as well as yield forecasting. The predicted variability of crop yield and related variables as well as natural resource use is mainly due to the short- and long-term variation of weather and climate conditions. The results produced by the models can be used to make appropriate management decisions and to provide farmers and others with alternative options for their farming system. The crop models have been used extensively to study the impact of climate change on agricultural production and food security. Recently, they have also been applied towards the impact of climate variability and the effect of El Niño/Southern Oscillation (ENSO) on agricultural production and food security. It is expected that, with the increased availability of computers, the use of crop models by farmers and consultants as well as policy and decision makers will increase. Weather data in the form of historical data or observations made during the current growing season, and short-, medium-, and long-term weather forecasts will play a critical role in these applications.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Gerrit Hoogenboom</name><affiliations><json:string>Department of Biological and Agricultural Engineering, the University of Georgia, Griffin, GA, USA</json:string><json:string>E-mail: gerrit@griffin.peachnet.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>Decision support system</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Climate variability</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Climate change</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Strategic application</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Tactical application</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Yield forecast</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>Full-length article</json:string></originalGenre><abstract>Weather has a significant impact on crop growth and development. This paper presents an overview of crop modeling and applications of crop models, and the significance of weather related to these applications. To account for the impact of weather and climate variability on crop production, agrometeorological variables are one of the key inputs required for the operation of crop simulation models. These include maximum and minimum air temperature, total solar radiation, and total rainfall. Most models use daily data as input, because variables at a smaller time scale are usually unavailable for most locations. It is important to define standard file formats for weather and other input data; this will expand the applicability of weather data by different models. Issues related to missing variables and data, as well as locations for which no data are available, need to be addressed for model applications, as it can affect the accuracy of the simulations. Weather generators can be used to stochastically generate daily data when data are missing or long-term historical data are unavailable. However, the use of observed weather data for model input will provide more precise crop yield simulations, especially for tropical regions. Many of the crop models have been applied towards strategic and tactical management decision making as well as yield forecasting. The predicted variability of crop yield and related variables as well as natural resource use is mainly due to the short- and long-term variation of weather and climate conditions. The results produced by the models can be used to make appropriate management decisions and to provide farmers and others with alternative options for their farming system. The crop models have been used extensively to study the impact of climate change on agricultural production and food security. Recently, they have also been applied towards the impact of climate variability and the effect of El Niño/Southern Oscillation (ENSO) on agricultural production and food security. It is expected that, with the increased availability of computers, the use of crop models by farmers and consultants as well as policy and decision makers will increase. 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This paper presents an overview of crop modeling and applications of crop models, and the significance of weather related to these applications. To account for the impact of weather and climate variability on crop production, agrometeorological variables are one of the key inputs required for the operation of crop simulation models. These include maximum and minimum air temperature, total solar radiation, and total rainfall. Most models use daily data as input, because variables at a smaller time scale are usually unavailable for most locations. It is important to define standard file formats for weather and other input data; this will expand the applicability of weather data by different models. Issues related to missing variables and data, as well as locations for which no data are available, need to be addressed for model applications, as it can affect the accuracy of the simulations. Weather generators can be used to stochastically generate daily data when data are missing or long-term historical data are unavailable. However, the use of observed weather data for model input will provide more precise crop yield simulations, especially for tropical regions. Many of the crop models have been applied towards strategic and tactical management decision making as well as yield forecasting. The predicted variability of crop yield and related variables as well as natural resource use is mainly due to the short- and long-term variation of weather and climate conditions. The results produced by the models can be used to make appropriate management decisions and to provide farmers and others with alternative options for their farming system. The crop models have been used extensively to study the impact of climate change on agricultural production and food security. Recently, they have also been applied towards the impact of climate variability and the effect of El Niño/Southern Oscillation (ENSO) on agricultural production and food security. It is expected that, with the increased availability of computers, the use of crop models by farmers and consultants as well as policy and decision makers will increase. 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This paper presents an overview of crop modeling and applications of crop models, and the significance of weather related to these applications. To account for the impact of weather and climate variability on crop production, agrometeorological variables are one of the key inputs required for the operation of crop simulation models. These include maximum and minimum air temperature, total solar radiation, and total rainfall. Most models use daily data as input, because variables at a smaller time scale are usually unavailable for most locations. It is important to define standard file formats for weather and other input data; this will expand the applicability of weather data by different models. Issues related to missing variables and data, as well as locations for which no data are available, need to be addressed for model applications, as it can affect the accuracy of the simulations. Weather generators can be used to stochastically generate daily data when data are missing or long-term historical data are unavailable. However, the use of observed weather data for model input will provide more precise crop yield simulations, especially for tropical regions. Many of the crop models have been applied towards strategic and tactical management decision making as well as yield forecasting. The predicted variability of crop yield and related variables as well as natural resource use is mainly due to the short- and long-term variation of weather and climate conditions. The results produced by the models can be used to make appropriate management decisions and to provide farmers and others with alternative options for their farming system. The crop models have been used extensively to study the impact of climate change on agricultural production and food security. Recently, they have also been applied towards the impact of climate variability and the effect of El Niño/Southern Oscillation (ENSO) on agricultural production and food security. It is expected that, with the increased availability of computers, the use of crop models by farmers and consultants as well as policy and decision makers will increase. Weather data in the form of historical data or observations made during the current growing season, and short-, medium-, and long-term weather forecasts will play a critical role in these applications.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword" xml:lang="en"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Crop model</ce:text></ce:keyword><ce:keyword><ce:text>Decision support system</ce:text></ce:keyword><ce:keyword><ce:text>Climate variability</ce:text></ce:keyword><ce:keyword><ce:text>Climate change</ce:text></ce:keyword><ce:keyword><ce:text>Strategic application</ce:text></ce:keyword><ce:keyword><ce:text>Tactical application</ce:text></ce:keyword><ce:keyword><ce:text>Yield forecast</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Contribution of agrometeorology to the simulation of crop production and its applications</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>Contribution of agrometeorology to the simulation of crop production and its applications</title></titleInfo><name type="personal"><namePart type="given">Gerrit</namePart><namePart type="family">Hoogenboom</namePart><affiliation>Department of Biological and Agricultural Engineering, the University of Georgia, Griffin, GA, USA</affiliation><affiliation>E-mail: gerrit@griffin.peachnet.edu</affiliation><description>Tel.: +1-770-229-3438; fax: +1-770-228-7218</description><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">2000</dateIssued><copyrightDate encoding="w3cdtf">2000</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">Weather has a significant impact on crop growth and development. This paper presents an overview of crop modeling and applications of crop models, and the significance of weather related to these applications. To account for the impact of weather and climate variability on crop production, agrometeorological variables are one of the key inputs required for the operation of crop simulation models. These include maximum and minimum air temperature, total solar radiation, and total rainfall. Most models use daily data as input, because variables at a smaller time scale are usually unavailable for most locations. It is important to define standard file formats for weather and other input data; this will expand the applicability of weather data by different models. Issues related to missing variables and data, as well as locations for which no data are available, need to be addressed for model applications, as it can affect the accuracy of the simulations. Weather generators can be used to stochastically generate daily data when data are missing or long-term historical data are unavailable. However, the use of observed weather data for model input will provide more precise crop yield simulations, especially for tropical regions. Many of the crop models have been applied towards strategic and tactical management decision making as well as yield forecasting. The predicted variability of crop yield and related variables as well as natural resource use is mainly due to the short- and long-term variation of weather and climate conditions. The results produced by the models can be used to make appropriate management decisions and to provide farmers and others with alternative options for their farming system. The crop models have been used extensively to study the impact of climate change on agricultural production and food security. Recently, they have also been applied towards the impact of climate variability and the effect of El Niño/Southern Oscillation (ENSO) on agricultural production and food security. It is expected that, with the increased availability of computers, the use of crop models by farmers and consultants as well as policy and decision makers will increase. Weather data in the form of historical data or observations made during the current growing season, and short-, medium-, and long-term weather forecasts will play a critical role in these applications.</abstract><subject lang="en"><genre>Keywords</genre><topic>Crop model</topic><topic>Decision support system</topic><topic>Climate variability</topic><topic>Climate change</topic><topic>Strategic application</topic><topic>Tactical application</topic><topic>Yield forecast</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">20000601</dateIssued></originInfo><identifier type="ISSN">0168-1923</identifier><identifier type="PII">S0168-1923(00)X0074-3</identifier><part><date>20000601</date><detail type="volume"><number>103</number><caption>vol.</caption></detail><detail type="issue"><number>1–2</number><caption>no.</caption></detail><extent unit="issue pages"><start>1</start><end>228</end></extent><extent unit="pages"><start>137</start><end>157</end></extent></part></relatedItem><identifier type="istex">7A827BB0C8B06F1D74FE1DFFD81775ACF14D9E0D</identifier><identifier type="DOI">10.1016/S0168-1923(00)00108-8</identifier><identifier type="PII">S0168-1923(00)00108-8</identifier><accessCondition type="use and reproduction" contentType="copyright">©2000 Elsevier Science B.V.</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science B.V., ©2000</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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