Multivariate statistical applied to data costs post-harvest coffee

Detalhes bibliográficos
Autor(a) principal: Santos, Rafael Vargas
Data de Publicação: 2017
Outros Autores: Vieira, Henrique Duarte, Borém, Flávio Meira, Prado, Mariele Vilela Bernardes
Tipo de documento: Artigo
Idioma: por
Título da fonte: Coffee Science (Online)
Texto Completo: https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244
Resumo: The choice of method of processing coffee is decisive on the profitability of the coffee activity, and will depend on several factors. Thus, due to the existence of many variables is common producer to question the viability of certain types of processing. Thus, the objective in this research was carried out a study of the major influencing factors on the cost of post-harvest coffee. Forty-six farms in the regions of the Cerrado, Matas de Minas and southern Minas Gerais State answered a questionnaire in order to enable this analysis. The application of multivariate techniques of cluster analysis, factor analysis and principal component analysis, allowed us to conclude that farms with higher costs simulated were those with the highest percentages of wet coffee production.
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spelling Multivariate statistical applied to data costs post-harvest coffeeEstatística multivariada aplicada em dados de custos da fase de pós-colheita do caféClusteringprincipal componentsfactor analysisAgrupamentocomponentes principaisanálise de fatoresThe choice of method of processing coffee is decisive on the profitability of the coffee activity, and will depend on several factors. Thus, due to the existence of many variables is common producer to question the viability of certain types of processing. Thus, the objective in this research was carried out a study of the major influencing factors on the cost of post-harvest coffee. Forty-six farms in the regions of the Cerrado, Matas de Minas and southern Minas Gerais State answered a questionnaire in order to enable this analysis. The application of multivariate techniques of cluster analysis, factor analysis and principal component analysis, allowed us to conclude that farms with higher costs simulated were those with the highest percentages of wet coffee production.A escolha do modo de processamento do café é decisiva na rentabilidade da atividade cafeeira, e dependerá de diversos fatores. Assim, em decorrência da existência de tantas variáveis é comum o produtor questionar a viabilidade de determinados tipos de processamentos. Desse modo, o objetivo deste trabalho foi realizar um estudo dos principais fatores influenciadores no custo da pós-colheita do café. Quarenta e seis fazendas das regiões do Cerrado, Matas de Minas e Sul de Minas Gerais responderam a um questionário elaborado no sentido de possibilitar esta análise. A aplicação das técnicas multivariadas de análise de agrupamento, análise de fatores e análise de componentes principais, possibilitou concluir que as fazendas com maiores custos simulados foram as que possuíam as maiores porcentagens de produção de café via úmida.Editora UFLA2017-06-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/vnd.openxmlformats-officedocument.wordprocessingml.documentCDF V2 Document, corrupt: Can't expand summary_infoapplication/vnd.openxmlformats-officedocument.wordprocessingml.documentapplication/vnd.openxmlformats-officedocument.wordprocessingml.documenthttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244Coffee Science - ISSN 1984-3909; Vol. 12 No. 2 (2017); 223-230Coffee Science; Vol. 12 Núm. 2 (2017); 223-230Coffee Science; v. 12 n. 2 (2017); 223-2301984-3909reponame:Coffee Science (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAporhttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/pdf_1244_2https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/1729https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/1730https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/1731https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/1732Copyright (c) 2017 Coffee Science - ISSN 1984-3909https://creativecommons.org/info:eu-repo/semantics/openAccessSantos, Rafael VargasVieira, Henrique DuarteBorém, Flávio MeiraPrado, Mariele Vilela Bernardes2017-06-06T13:29:05Zoai:coffeescience.ufla.br:article/1244Revistahttps://coffeescience.ufla.br/index.php/CoffeesciencePUBhttps://coffeescience.ufla.br/index.php/Coffeescience/oaicoffeescience@dag.ufla.br||coffeescience@dag.ufla.br|| alvaro-cozadi@hotmail.com1984-39091809-6875opendoar:2024-05-21T19:54:00.543779Coffee Science (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv Multivariate statistical applied to data costs post-harvest coffee
Estatística multivariada aplicada em dados de custos da fase de pós-colheita do café
title Multivariate statistical applied to data costs post-harvest coffee
spellingShingle Multivariate statistical applied to data costs post-harvest coffee
Santos, Rafael Vargas
Clustering
principal components
factor analysis
Agrupamento
componentes principais
análise de fatores
title_short Multivariate statistical applied to data costs post-harvest coffee
title_full Multivariate statistical applied to data costs post-harvest coffee
title_fullStr Multivariate statistical applied to data costs post-harvest coffee
title_full_unstemmed Multivariate statistical applied to data costs post-harvest coffee
title_sort Multivariate statistical applied to data costs post-harvest coffee
author Santos, Rafael Vargas
author_facet Santos, Rafael Vargas
Vieira, Henrique Duarte
Borém, Flávio Meira
Prado, Mariele Vilela Bernardes
author_role author
author2 Vieira, Henrique Duarte
Borém, Flávio Meira
Prado, Mariele Vilela Bernardes
author2_role author
author
author
dc.contributor.author.fl_str_mv Santos, Rafael Vargas
Vieira, Henrique Duarte
Borém, Flávio Meira
Prado, Mariele Vilela Bernardes
dc.subject.por.fl_str_mv Clustering
principal components
factor analysis
Agrupamento
componentes principais
análise de fatores
topic Clustering
principal components
factor analysis
Agrupamento
componentes principais
análise de fatores
description The choice of method of processing coffee is decisive on the profitability of the coffee activity, and will depend on several factors. Thus, due to the existence of many variables is common producer to question the viability of certain types of processing. Thus, the objective in this research was carried out a study of the major influencing factors on the cost of post-harvest coffee. Forty-six farms in the regions of the Cerrado, Matas de Minas and southern Minas Gerais State answered a questionnaire in order to enable this analysis. The application of multivariate techniques of cluster analysis, factor analysis and principal component analysis, allowed us to conclude that farms with higher costs simulated were those with the highest percentages of wet coffee production.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-04
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244
url https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/pdf_1244_2
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/1729
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/1730
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/1731
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1244/1732
dc.rights.driver.fl_str_mv Copyright (c) 2017 Coffee Science - ISSN 1984-3909
https://creativecommons.org/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2017 Coffee Science - ISSN 1984-3909
https://creativecommons.org/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/vnd.openxmlformats-officedocument.wordprocessingml.document
CDF V2 Document, corrupt: Can't expand summary_info
application/vnd.openxmlformats-officedocument.wordprocessingml.document
application/vnd.openxmlformats-officedocument.wordprocessingml.document
dc.publisher.none.fl_str_mv Editora UFLA
publisher.none.fl_str_mv Editora UFLA
dc.source.none.fl_str_mv Coffee Science - ISSN 1984-3909; Vol. 12 No. 2 (2017); 223-230
Coffee Science; Vol. 12 Núm. 2 (2017); 223-230
Coffee Science; v. 12 n. 2 (2017); 223-230
1984-3909
reponame:Coffee Science (Online)
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Coffee Science (Online)
collection Coffee Science (Online)
repository.name.fl_str_mv Coffee Science (Online) - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv coffeescience@dag.ufla.br||coffeescience@dag.ufla.br|| alvaro-cozadi@hotmail.com
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