Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
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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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) |
instacron_str |
SBMET |
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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1752122087441432576 |