Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil

Detalhes bibliográficos
Autor(a) principal: Medauar, Caique Carvalho
Data de Publicação: 2021
Outros Autores: Silva, Samuel De Assis, Galvao, Icaro Monteiro [UNESP], Franco, Lais Barreto, Carvalho, Luis Carlos Cirilo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1080/15538362.2020.1864698
http://hdl.handle.net/11449/209072
Resumo: The aim of the present study was to construct agroclimatic zoning for the conilon coffee crop in the state of Bahia, Brazil, using fuzzy logic. Historical data series on rainfall, mean air temperature, and relative air humidity were used. Analyses were carried out considering the mean values of the accumulated variables for each month in the historical series. The data were subjected to geostatistical analysis to verify and quantify the existence of spatial dependence between the values of the studied variables. Subsequently, maps with representations of the monthly means of the variables were subjected to continual classification using fuzzy mapping to identify suitable areas and areas of climate favorability for the implantation of conilon coffee in the state of Bahia. Bahia presents great spatial variability in regard to suitability for conilon coffee cultivation, with highly favorable areas, but no totally unsuitable region. The south and extreme south of Bahia were the regions with the lowest temporal-spatial variability for climate favorability for the development of conilon coffee trees, these being the most suitable regions for this crop. The zoning through fuzzy logic assisted in decision-making on which regions of the state had the highest suitability for crop implantation.
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spelling Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, BrazilAgricultural managementagroclimatic zoninggeostatisticsthematic mapsThe aim of the present study was to construct agroclimatic zoning for the conilon coffee crop in the state of Bahia, Brazil, using fuzzy logic. Historical data series on rainfall, mean air temperature, and relative air humidity were used. Analyses were carried out considering the mean values of the accumulated variables for each month in the historical series. The data were subjected to geostatistical analysis to verify and quantify the existence of spatial dependence between the values of the studied variables. Subsequently, maps with representations of the monthly means of the variables were subjected to continual classification using fuzzy mapping to identify suitable areas and areas of climate favorability for the implantation of conilon coffee in the state of Bahia. Bahia presents great spatial variability in regard to suitability for conilon coffee cultivation, with highly favorable areas, but no totally unsuitable region. The south and extreme south of Bahia were the regions with the lowest temporal-spatial variability for climate favorability for the development of conilon coffee trees, these being the most suitable regions for this crop. The zoning through fuzzy logic assisted in decision-making on which regions of the state had the highest suitability for crop implantation.Univ Estadual Santa Cruz, Postgrad Plant Prod, Salvador, BrazilUniv Fed Espirito Santo, Dept Rural Engn, Alegre, BrazilState Univ Julio De Mesquita Filho, Postgrad Agron Irrigat & Drainage, Botucatu, SP, BrazilRural Fed Univ Pernambuco, Postgrad Agr Engn, Pernambuco, BrazilState Univ Julio De Mesquita Filho, Postgrad Agron Irrigat & Drainage, Botucatu, SP, BrazilTaylor & Francis IncUniv Estadual Santa CruzUniversidade Federal do Espírito Santo (UFES)Universidade Estadual Paulista (Unesp)Rural Fed Univ PernambucoMedauar, Caique CarvalhoSilva, Samuel De AssisGalvao, Icaro Monteiro [UNESP]Franco, Lais BarretoCarvalho, Luis Carlos Cirilo2021-06-25T11:47:30Z2021-06-25T11:47:30Z2021-01-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article205-217http://dx.doi.org/10.1080/15538362.2020.1864698International Journal Of Fruit Science. Philadelphia: Taylor & Francis Inc, v. 21, n. 1, p. 205-217, 2021.1553-8362http://hdl.handle.net/11449/20907210.1080/15538362.2020.1864698WOS:000604369300001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal Of Fruit Scienceinfo:eu-repo/semantics/openAccess2021-10-23T19:23:32Zoai:repositorio.unesp.br:11449/209072Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T19:23:32Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil
title Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil
spellingShingle Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil
Medauar, Caique Carvalho
Agricultural management
agroclimatic zoning
geostatistics
thematic maps
title_short Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil
title_full Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil
title_fullStr Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil
title_full_unstemmed Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil
title_sort Fuzzy Mapping of Climate Favorability for the Cultivation of Conilon Coffee in the State of Bahia, Brazil
author Medauar, Caique Carvalho
author_facet Medauar, Caique Carvalho
Silva, Samuel De Assis
Galvao, Icaro Monteiro [UNESP]
Franco, Lais Barreto
Carvalho, Luis Carlos Cirilo
author_role author
author2 Silva, Samuel De Assis
Galvao, Icaro Monteiro [UNESP]
Franco, Lais Barreto
Carvalho, Luis Carlos Cirilo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Univ Estadual Santa Cruz
Universidade Federal do Espírito Santo (UFES)
Universidade Estadual Paulista (Unesp)
Rural Fed Univ Pernambuco
dc.contributor.author.fl_str_mv Medauar, Caique Carvalho
Silva, Samuel De Assis
Galvao, Icaro Monteiro [UNESP]
Franco, Lais Barreto
Carvalho, Luis Carlos Cirilo
dc.subject.por.fl_str_mv Agricultural management
agroclimatic zoning
geostatistics
thematic maps
topic Agricultural management
agroclimatic zoning
geostatistics
thematic maps
description The aim of the present study was to construct agroclimatic zoning for the conilon coffee crop in the state of Bahia, Brazil, using fuzzy logic. Historical data series on rainfall, mean air temperature, and relative air humidity were used. Analyses were carried out considering the mean values of the accumulated variables for each month in the historical series. The data were subjected to geostatistical analysis to verify and quantify the existence of spatial dependence between the values of the studied variables. Subsequently, maps with representations of the monthly means of the variables were subjected to continual classification using fuzzy mapping to identify suitable areas and areas of climate favorability for the implantation of conilon coffee in the state of Bahia. Bahia presents great spatial variability in regard to suitability for conilon coffee cultivation, with highly favorable areas, but no totally unsuitable region. The south and extreme south of Bahia were the regions with the lowest temporal-spatial variability for climate favorability for the development of conilon coffee trees, these being the most suitable regions for this crop. The zoning through fuzzy logic assisted in decision-making on which regions of the state had the highest suitability for crop implantation.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-25T11:47:30Z
2021-06-25T11:47:30Z
2021-01-03
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/10.1080/15538362.2020.1864698
International Journal Of Fruit Science. Philadelphia: Taylor & Francis Inc, v. 21, n. 1, p. 205-217, 2021.
1553-8362
http://hdl.handle.net/11449/209072
10.1080/15538362.2020.1864698
WOS:000604369300001
url http://dx.doi.org/10.1080/15538362.2020.1864698
http://hdl.handle.net/11449/209072
identifier_str_mv International Journal Of Fruit Science. Philadelphia: Taylor & Francis Inc, v. 21, n. 1, p. 205-217, 2021.
1553-8362
10.1080/15538362.2020.1864698
WOS:000604369300001
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Journal Of Fruit Science
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 205-217
dc.publisher.none.fl_str_mv Taylor & Francis Inc
publisher.none.fl_str_mv Taylor & Francis Inc
dc.source.none.fl_str_mv Web of Science
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
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