Family coffee and good agricultural practices in Bom Sucesso - MG

Detalhes bibliográficos
Autor(a) principal: Peixoto, Jaqueline Nicole Santos
Data de Publicação: 2017
Outros Autores: Nunes, Márcio, Baliza, Danielle Pereira, Pereira, Sérgio Parreiras, Rosa, Beatriz Terezinha
Tipo de documento: Artigo
Idioma: por
Título da fonte: Coffee Science (Online)
Texto Completo: https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298
Resumo: The objective in this study was to analyze the coffee familiar in the city of Bom Sucesso - MG, in relation to the adoption of Good Agricultural Practices, through the Cluster and discriminant analysis. It was held to recognize the current situation of the coffee familiar of the municipality with questionnaires structured type Survey in rural properties. It used the questionnaire to 26 family farmers associated with the Community Machado Association and the Association Our Lady of Badia. After the questionnaires, the results were tabulated and was performed multivariate statistical analysis of Cluster and later discriminant analysis. The Cluster analysis aims to group individuals (cases) with similar characteristics in terms of a set of selected variables that separated in this case the producers into two distinct groups. Already discriminant analysis showed the seven variables that most discriminate against another group, thus presenting the main differences between them. Group 1 producers have greater organizational characteristics than Group 2 for the adoption of Good Agricultural Practices. However, both the farmers Group 1 as Group 2 feature points that can be improved, which justifies the action planning and Technical Assistance and Rural Extension (ATER) for each group. Thus, it is expected that this study will assist in decision-making both by the local government as the family associations and farmers, so that this activity reach greater sustainability in the municipality.
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spelling Family coffee and good agricultural practices in Bom Sucesso - MGCafeicultura familiar e as boas práticas agrícolas em Bom Sucesso – MGSustainabilitytechnical assistancecertificationCluster analysisSustentabilidadeassistência técnicaanálise de ClustercertificaçãoThe objective in this study was to analyze the coffee familiar in the city of Bom Sucesso - MG, in relation to the adoption of Good Agricultural Practices, through the Cluster and discriminant analysis. It was held to recognize the current situation of the coffee familiar of the municipality with questionnaires structured type Survey in rural properties. It used the questionnaire to 26 family farmers associated with the Community Machado Association and the Association Our Lady of Badia. After the questionnaires, the results were tabulated and was performed multivariate statistical analysis of Cluster and later discriminant analysis. The Cluster analysis aims to group individuals (cases) with similar characteristics in terms of a set of selected variables that separated in this case the producers into two distinct groups. Already discriminant analysis showed the seven variables that most discriminate against another group, thus presenting the main differences between them. Group 1 producers have greater organizational characteristics than Group 2 for the adoption of Good Agricultural Practices. However, both the farmers Group 1 as Group 2 feature points that can be improved, which justifies the action planning and Technical Assistance and Rural Extension (ATER) for each group. Thus, it is expected that this study will assist in decision-making both by the local government as the family associations and farmers, so that this activity reach greater sustainability in the municipality.Objetivou-se, neste estudo, analisar a cafeicultura familiar do município de Bom Sucesso - MG, em relação à adoção das Boas Práticas Agrícolas, por meio das análises de Cluster e discriminante. Foi realizado o reconhecimento da situação atual da cafeicultura familiar do município, através da aplicação de questionários estruturados do tipo Survey, nas propriedades rurais. Aplicou-se o questionário para um total de 26 cafeicultores familiares associados à Associação Comunitária do Machado e à Associação Nossa Senhora da Badia. Após a aplicação dos questionários, os resultados foram tabulados e foi realizada a análise estatística multivariada de Cluster e, posteriormente a análise discriminante. A análise de Cluster tem como objetivo agrupar os indivíduos (casos) com características semelhantes em função de um conjunto de variáveis selecionadas que separaram, neste caso, os produtores em dois grupos distintos. Já a análise discriminante apresentou as sete variáveis que mais discriminam um grupo do outro, apresentando assim as principais diferenças entre eles. Os produtores do Grupo 1 apresentam características de maior organização diante do Grupo 2, em relação à adoção das Boas Práticas Agrícolas. No entanto, tanto os cafeicultores/propriedades do Grupo 1 quanto do Grupo 2 apresentam pontos que podem ser melhorados, o que justifica o planejamento de ações e políticas de Assistência Técnica e Extensão Rural (ATER), para cada um dos grupos. Dessa forma, espera-se que este estudo auxilie na tomada de decisão tanto por parte do poder público local quanto dos cafeicultores familiares e associações, a fim de que esta atividade alcance maior sustentabilidade no município.Editora UFLA2017-09-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/vnd.openxmlformats-officedocument.wordprocessingml.documentapplication/vnd.openxmlformats-officedocument.wordprocessingml.documentapplication/vnd.openxmlformats-officedocument.wordprocessingml.documentimage/jpegimage/jpeghttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298Coffee Science - ISSN 1984-3909; Vol. 12 No. 3 (2017); 365 - 373Coffee Science; Vol. 12 Núm. 3 (2017); 365 - 373Coffee Science; v. 12 n. 3 (2017); 365 - 3731984-3909reponame:Coffee Science (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAporhttps://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/pdf_1298https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1761https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1762https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1763https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1764https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1765Copyright (c) 2017 Coffee Scienceinfo:eu-repo/semantics/openAccessPeixoto, Jaqueline Nicole SantosNunes, MárcioBaliza, Danielle PereiraPereira, Sérgio ParreirasRosa, Beatriz Terezinha2017-09-06T14:09:44Zoai:coffeescience.ufla.br:article/1298Revistahttps://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:2024-05-21T19:54:02.068835Coffee Science (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv Family coffee and good agricultural practices in Bom Sucesso - MG
Cafeicultura familiar e as boas práticas agrícolas em Bom Sucesso – MG
title Family coffee and good agricultural practices in Bom Sucesso - MG
spellingShingle Family coffee and good agricultural practices in Bom Sucesso - MG
Peixoto, Jaqueline Nicole Santos
Sustainability
technical assistance
certification
Cluster analysis
Sustentabilidade
assistência técnica
análise de Cluster
certificação
title_short Family coffee and good agricultural practices in Bom Sucesso - MG
title_full Family coffee and good agricultural practices in Bom Sucesso - MG
title_fullStr Family coffee and good agricultural practices in Bom Sucesso - MG
title_full_unstemmed Family coffee and good agricultural practices in Bom Sucesso - MG
title_sort Family coffee and good agricultural practices in Bom Sucesso - MG
author Peixoto, Jaqueline Nicole Santos
author_facet Peixoto, Jaqueline Nicole Santos
Nunes, Márcio
Baliza, Danielle Pereira
Pereira, Sérgio Parreiras
Rosa, Beatriz Terezinha
author_role author
author2 Nunes, Márcio
Baliza, Danielle Pereira
Pereira, Sérgio Parreiras
Rosa, Beatriz Terezinha
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Peixoto, Jaqueline Nicole Santos
Nunes, Márcio
Baliza, Danielle Pereira
Pereira, Sérgio Parreiras
Rosa, Beatriz Terezinha
dc.subject.por.fl_str_mv Sustainability
technical assistance
certification
Cluster analysis
Sustentabilidade
assistência técnica
análise de Cluster
certificação
topic Sustainability
technical assistance
certification
Cluster analysis
Sustentabilidade
assistência técnica
análise de Cluster
certificação
description The objective in this study was to analyze the coffee familiar in the city of Bom Sucesso - MG, in relation to the adoption of Good Agricultural Practices, through the Cluster and discriminant analysis. It was held to recognize the current situation of the coffee familiar of the municipality with questionnaires structured type Survey in rural properties. It used the questionnaire to 26 family farmers associated with the Community Machado Association and the Association Our Lady of Badia. After the questionnaires, the results were tabulated and was performed multivariate statistical analysis of Cluster and later discriminant analysis. The Cluster analysis aims to group individuals (cases) with similar characteristics in terms of a set of selected variables that separated in this case the producers into two distinct groups. Already discriminant analysis showed the seven variables that most discriminate against another group, thus presenting the main differences between them. Group 1 producers have greater organizational characteristics than Group 2 for the adoption of Good Agricultural Practices. However, both the farmers Group 1 as Group 2 feature points that can be improved, which justifies the action planning and Technical Assistance and Rural Extension (ATER) for each group. Thus, it is expected that this study will assist in decision-making both by the local government as the family associations and farmers, so that this activity reach greater sustainability in the municipality.
publishDate 2017
dc.date.none.fl_str_mv 2017-09-06
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298
url https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/pdf_1298
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1761
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1762
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1763
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1764
https://coffeescience.ufla.br/index.php/Coffeescience/article/view/1298/1765
dc.rights.driver.fl_str_mv Copyright (c) 2017 Coffee Science
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2017 Coffee Science
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/vnd.openxmlformats-officedocument.wordprocessingml.document
application/vnd.openxmlformats-officedocument.wordprocessingml.document
application/vnd.openxmlformats-officedocument.wordprocessingml.document
image/jpeg
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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. 3 (2017); 365 - 373
Coffee Science; Vol. 12 Núm. 3 (2017); 365 - 373
Coffee Science; v. 12 n. 3 (2017); 365 - 373
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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