Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions

Detalhes bibliográficos
Autor(a) principal: Lorençone,Pedro Antonio
Data de Publicação: 2022
Outros Autores: Aparecido,Lucas Eduardo de Oliveira, Lorençone,João Antonio, Torsoni,Guilherme Botega, Lima,Rafael Fausto de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Meteorologia (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862022000100121
Resumo: Abstract This study aimed to estimate the minimum and maximum monthly air temperatures in the sugarcane regions of Brazil. A 30-year historical series (1988-2018) of maximum (Tmax) and minimum (Tmin) air temperatures from the NASA/POWER platform was used for 62 locations that produce sugarcane in Brazil. Multiple linear regression was used for data modeling, in which the dependent variables were Tmin and Tmax and the independent variables were latitude, longitude, and altitude. The comparison between estimation models and the real data was performed using the statistical indices MAPE (accuracy) and adjusted coefficient of determination (R2adj) (precision). The lowest MAPE values of the models for estimating the minimum air temperature occurred mainly in the North during February, March, and January. Also, the most accurate models for estimating the maximum air temperature occurred in the Southeast region during January, February, and March. The MAPE and R2adj values showed accuracy and precision in the models for estimating both the maximum and minimum temperatures, indicating that the equations can be used to estimate temperatures in sugarcane areas. The Tmin estimation model for the Southeast region in July shows the best performance, with a MAPE value of 1.28 and an R2adj of 0.94. The Tmax model of the North region for September presents higher precision and accuracy, with values of 1.28 and 0.96, respectively.
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spelling Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regionsmodelling climatemultiple linear regressionsugarcanelatitudelongitudeair temperatureAbstract This study aimed to estimate the minimum and maximum monthly air temperatures in the sugarcane regions of Brazil. A 30-year historical series (1988-2018) of maximum (Tmax) and minimum (Tmin) air temperatures from the NASA/POWER platform was used for 62 locations that produce sugarcane in Brazil. Multiple linear regression was used for data modeling, in which the dependent variables were Tmin and Tmax and the independent variables were latitude, longitude, and altitude. The comparison between estimation models and the real data was performed using the statistical indices MAPE (accuracy) and adjusted coefficient of determination (R2adj) (precision). The lowest MAPE values of the models for estimating the minimum air temperature occurred mainly in the North during February, March, and January. Also, the most accurate models for estimating the maximum air temperature occurred in the Southeast region during January, February, and March. The MAPE and R2adj values showed accuracy and precision in the models for estimating both the maximum and minimum temperatures, indicating that the equations can be used to estimate temperatures in sugarcane areas. The Tmin estimation model for the Southeast region in July shows the best performance, with a MAPE value of 1.28 and an R2adj of 0.94. The Tmax model of the North region for September presents higher precision and accuracy, with values of 1.28 and 0.96, respectively.Sociedade Brasileira de Meteorologia2022-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862022000100121Revista Brasileira de Meteorologia v.37 n.1 2022reponame:Revista Brasileira de Meteorologia (Online)instname:Sociedade Brasileira de Meteorologia (SBMET)instacron:SBMET10.1590/0102-77863710008info:eu-repo/semantics/openAccessLorençone,Pedro AntonioAparecido,Lucas Eduardo de OliveiraLorençone,João AntonioTorsoni,Guilherme BotegaLima,Rafael Fausto deeng2022-06-21T00:00:00Zoai:scielo:S0102-77862022000100121Revistahttp://www.rbmet.org.br/port/index.phpONGhttps://old.scielo.br/oai/scielo-oai.php||rbmet@rbmet.org.br1982-43510102-7786opendoar:2022-06-21T00:00Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)false
dc.title.none.fl_str_mv Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
title Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
spellingShingle Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
Lorençone,Pedro Antonio
modelling climate
multiple linear regression
sugarcane
latitude
longitude
air temperature
title_short Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
title_full Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
title_fullStr Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
title_full_unstemmed Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
title_sort Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
author Lorençone,Pedro Antonio
author_facet Lorençone,Pedro Antonio
Aparecido,Lucas Eduardo de Oliveira
Lorençone,João Antonio
Torsoni,Guilherme Botega
Lima,Rafael Fausto de
author_role author
author2 Aparecido,Lucas Eduardo de Oliveira
Lorençone,João Antonio
Torsoni,Guilherme Botega
Lima,Rafael Fausto de
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Lorençone,Pedro Antonio
Aparecido,Lucas Eduardo de Oliveira
Lorençone,João Antonio
Torsoni,Guilherme Botega
Lima,Rafael Fausto de
dc.subject.por.fl_str_mv modelling climate
multiple linear regression
sugarcane
latitude
longitude
air temperature
topic modelling climate
multiple linear regression
sugarcane
latitude
longitude
air temperature
description Abstract This study aimed to estimate the minimum and maximum monthly air temperatures in the sugarcane regions of Brazil. A 30-year historical series (1988-2018) of maximum (Tmax) and minimum (Tmin) air temperatures from the NASA/POWER platform was used for 62 locations that produce sugarcane in Brazil. Multiple linear regression was used for data modeling, in which the dependent variables were Tmin and Tmax and the independent variables were latitude, longitude, and altitude. The comparison between estimation models and the real data was performed using the statistical indices MAPE (accuracy) and adjusted coefficient of determination (R2adj) (precision). The lowest MAPE values of the models for estimating the minimum air temperature occurred mainly in the North during February, March, and January. Also, the most accurate models for estimating the maximum air temperature occurred in the Southeast region during January, February, and March. The MAPE and R2adj values showed accuracy and precision in the models for estimating both the maximum and minimum temperatures, indicating that the equations can be used to estimate temperatures in sugarcane areas. The Tmin estimation model for the Southeast region in July shows the best performance, with a MAPE value of 1.28 and an R2adj of 0.94. The Tmax model of the North region for September presents higher precision and accuracy, with values of 1.28 and 0.96, respectively.
publishDate 2022
dc.date.none.fl_str_mv 2022-03-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862022000100121
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862022000100121
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0102-77863710008
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Meteorologia
publisher.none.fl_str_mv Sociedade Brasileira de Meteorologia
dc.source.none.fl_str_mv Revista Brasileira de Meteorologia v.37 n.1 2022
reponame:Revista Brasileira de Meteorologia (Online)
instname:Sociedade Brasileira de Meteorologia (SBMET)
instacron:SBMET
instname_str Sociedade Brasileira de Meteorologia (SBMET)
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institution SBMET
reponame_str Revista Brasileira de Meteorologia (Online)
collection Revista Brasileira de Meteorologia (Online)
repository.name.fl_str_mv Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)
repository.mail.fl_str_mv ||rbmet@rbmet.org.br
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