Economic and climatic models for estimating coffee supply
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/S0100-204X2017001200004 http://hdl.handle.net/11449/175679 |
Resumo: | The objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000-2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz. |
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Economic and climatic models for estimating coffee supplyClimateCoffea arabicaEconometricsModellingRural creditThe objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000-2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz.Universidade Estadual Paulista (Unesp) Faculdade de Ciências Agrárias e Veterinárias Departamento de Economia Administração e Educação, Via de Acesso Prof. Paulo Donato Castellane, s/noUnesp Faculdade de Ciências Agrárias e Veterinárias Departamento de Ciências Exatas, Via de Acesso Prof. Paulo Donato Castellane, s/noUniversidade Estadual Paulista (Unesp) Faculdade de Ciências Agrárias e Veterinárias Departamento de Economia Administração e Educação, Via de Acesso Prof. Paulo Donato Castellane, s/noUnesp Faculdade de Ciências Agrárias e Veterinárias Departamento de Ciências Exatas, Via de Acesso Prof. Paulo Donato Castellane, s/noUniversidade Estadual Paulista (Unesp)de Moraes-Oliveira, Adriana Ferreira [UNESP]Aparecido, Lucas Eduardo de Oliveira [UNESP]Figueira, Sérgio Rangel Fernandes [UNESP]2018-12-11T17:17:03Z2018-12-11T17:17:03Z2017-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1158-1166application/pdfhttp://dx.doi.org/10.1590/S0100-204X2017001200004Pesquisa Agropecuaria Brasileira, v. 52, n. 12, p. 1158-1166, 2017.1678-39210100-204Xhttp://hdl.handle.net/11449/17567910.1590/S0100-204X2017001200004S0100-204X20170012011582-s2.0-85038855650S0100-204X2017001201158.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPesquisa Agropecuaria Brasileira0,469info:eu-repo/semantics/openAccess2024-06-06T14:54:18Zoai:repositorio.unesp.br:11449/175679Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:54:05.025649Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Economic and climatic models for estimating coffee supply |
title |
Economic and climatic models for estimating coffee supply |
spellingShingle |
Economic and climatic models for estimating coffee supply de Moraes-Oliveira, Adriana Ferreira [UNESP] Climate Coffea arabica Econometrics Modelling Rural credit |
title_short |
Economic and climatic models for estimating coffee supply |
title_full |
Economic and climatic models for estimating coffee supply |
title_fullStr |
Economic and climatic models for estimating coffee supply |
title_full_unstemmed |
Economic and climatic models for estimating coffee supply |
title_sort |
Economic and climatic models for estimating coffee supply |
author |
de Moraes-Oliveira, Adriana Ferreira [UNESP] |
author_facet |
de Moraes-Oliveira, Adriana Ferreira [UNESP] Aparecido, Lucas Eduardo de Oliveira [UNESP] Figueira, Sérgio Rangel Fernandes [UNESP] |
author_role |
author |
author2 |
Aparecido, Lucas Eduardo de Oliveira [UNESP] Figueira, Sérgio Rangel Fernandes [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
de Moraes-Oliveira, Adriana Ferreira [UNESP] Aparecido, Lucas Eduardo de Oliveira [UNESP] Figueira, Sérgio Rangel Fernandes [UNESP] |
dc.subject.por.fl_str_mv |
Climate Coffea arabica Econometrics Modelling Rural credit |
topic |
Climate Coffea arabica Econometrics Modelling Rural credit |
description |
The objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000-2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12-01 2018-12-11T17:17:03Z 2018-12-11T17:17:03Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/S0100-204X2017001200004 Pesquisa Agropecuaria Brasileira, v. 52, n. 12, p. 1158-1166, 2017. 1678-3921 0100-204X http://hdl.handle.net/11449/175679 10.1590/S0100-204X2017001200004 S0100-204X2017001201158 2-s2.0-85038855650 S0100-204X2017001201158.pdf |
url |
http://dx.doi.org/10.1590/S0100-204X2017001200004 http://hdl.handle.net/11449/175679 |
identifier_str_mv |
Pesquisa Agropecuaria Brasileira, v. 52, n. 12, p. 1158-1166, 2017. 1678-3921 0100-204X 10.1590/S0100-204X2017001200004 S0100-204X2017001201158 2-s2.0-85038855650 S0100-204X2017001201158.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pesquisa Agropecuaria Brasileira 0,469 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1158-1166 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128433570447360 |