Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil

Detalhes bibliográficos
Autor(a) principal: Pozza, Edson Ampélio
Data de Publicação: 2021
Outros Autores: Santos, Éder Ribeiro dos, Gaspar, Nilva Alice, Vilela, Ximena Maira de Souza, Alves, Marcelo de Carvalho, Colares, Mário Roberto Nogueira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/49907
Resumo: This study aimed to develop a warning system platform for coffee rust incidence fifteen days in advance, as well as validating and regionalizing multiple linear regression models based on meteorological variables. The models developed by Pinto were validated in five counties. Experiments were set up in a randomized block design with five treatments and five replications. The experimental plot had six lines with 20 central plants of useful area. Assessments of coffee rust incidence were carried out fortnightly. The data collected from automatic stations were adjusted in new multiple linear regression models (MLRM) for five counties. Meteorological variables were lagged concerning disease assessment dates. After the adjustments, two models were selected and calculated for five counties, later there was an expansion to include ten more counties and 35 properties to validate these models. The result showed that the adjusted models of 15–30 days before rust incidence for Carmo do Rio Claro and Nova Resende counties were promising. These models were the best at forecasting disease 15 days in advance. With these models and the geoinformation systems, the warning platform and interface will be improved in the coffee grower region of the south and savannas of the Minas Gerais State, Brazil.
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spelling Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, BrazilIncidenceMultiple linear regression modelsMeteorological variablesCafé - Doenças e pragasFerrugem do cafeeiro - IncidênciaModelo de regressão linear múltiplaVariáveis ​​meteorológicasThis study aimed to develop a warning system platform for coffee rust incidence fifteen days in advance, as well as validating and regionalizing multiple linear regression models based on meteorological variables. The models developed by Pinto were validated in five counties. Experiments were set up in a randomized block design with five treatments and five replications. The experimental plot had six lines with 20 central plants of useful area. Assessments of coffee rust incidence were carried out fortnightly. The data collected from automatic stations were adjusted in new multiple linear regression models (MLRM) for five counties. Meteorological variables were lagged concerning disease assessment dates. After the adjustments, two models were selected and calculated for five counties, later there was an expansion to include ten more counties and 35 properties to validate these models. The result showed that the adjusted models of 15–30 days before rust incidence for Carmo do Rio Claro and Nova Resende counties were promising. These models were the best at forecasting disease 15 days in advance. With these models and the geoinformation systems, the warning platform and interface will be improved in the coffee grower region of the south and savannas of the Minas Gerais State, Brazil.Multidisciplinary Digital Publishing Institute (MDPI)2022-05-10T18:54:19Z2022-05-10T18:54:19Z2021-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfPOZZA, E. A. et al. Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil. Agronomy, Basel, v. 11, n. 11, 2021. DOI: https://doi.org/10.3390/agronomy11112284.http://repositorio.ufla.br/jspui/handle/1/49907Agronomyreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessPozza, Edson AmpélioSantos, Éder Ribeiro dosGaspar, Nilva AliceVilela, Ximena Maira de SouzaAlves, Marcelo de CarvalhoColares, Mário Roberto Nogueiraeng2022-05-10T18:57:56Zoai:localhost:1/49907Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2022-05-10T18:57:56Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
title Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
spellingShingle Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
Pozza, Edson Ampélio
Incidence
Multiple linear regression models
Meteorological variables
Café - Doenças e pragas
Ferrugem do cafeeiro - Incidência
Modelo de regressão linear múltipla
Variáveis ​​meteorológicas
title_short Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
title_full Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
title_fullStr Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
title_full_unstemmed Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
title_sort Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
author Pozza, Edson Ampélio
author_facet Pozza, Edson Ampélio
Santos, Éder Ribeiro dos
Gaspar, Nilva Alice
Vilela, Ximena Maira de Souza
Alves, Marcelo de Carvalho
Colares, Mário Roberto Nogueira
author_role author
author2 Santos, Éder Ribeiro dos
Gaspar, Nilva Alice
Vilela, Ximena Maira de Souza
Alves, Marcelo de Carvalho
Colares, Mário Roberto Nogueira
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Pozza, Edson Ampélio
Santos, Éder Ribeiro dos
Gaspar, Nilva Alice
Vilela, Ximena Maira de Souza
Alves, Marcelo de Carvalho
Colares, Mário Roberto Nogueira
dc.subject.por.fl_str_mv Incidence
Multiple linear regression models
Meteorological variables
Café - Doenças e pragas
Ferrugem do cafeeiro - Incidência
Modelo de regressão linear múltipla
Variáveis ​​meteorológicas
topic Incidence
Multiple linear regression models
Meteorological variables
Café - Doenças e pragas
Ferrugem do cafeeiro - Incidência
Modelo de regressão linear múltipla
Variáveis ​​meteorológicas
description This study aimed to develop a warning system platform for coffee rust incidence fifteen days in advance, as well as validating and regionalizing multiple linear regression models based on meteorological variables. The models developed by Pinto were validated in five counties. Experiments were set up in a randomized block design with five treatments and five replications. The experimental plot had six lines with 20 central plants of useful area. Assessments of coffee rust incidence were carried out fortnightly. The data collected from automatic stations were adjusted in new multiple linear regression models (MLRM) for five counties. Meteorological variables were lagged concerning disease assessment dates. After the adjustments, two models were selected and calculated for five counties, later there was an expansion to include ten more counties and 35 properties to validate these models. The result showed that the adjusted models of 15–30 days before rust incidence for Carmo do Rio Claro and Nova Resende counties were promising. These models were the best at forecasting disease 15 days in advance. With these models and the geoinformation systems, the warning platform and interface will be improved in the coffee grower region of the south and savannas of the Minas Gerais State, Brazil.
publishDate 2021
dc.date.none.fl_str_mv 2021-11
2022-05-10T18:54:19Z
2022-05-10T18:54:19Z
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 POZZA, E. A. et al. Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil. Agronomy, Basel, v. 11, n. 11, 2021. DOI: https://doi.org/10.3390/agronomy11112284.
http://repositorio.ufla.br/jspui/handle/1/49907
identifier_str_mv POZZA, E. A. et al. Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil. Agronomy, Basel, v. 11, n. 11, 2021. DOI: https://doi.org/10.3390/agronomy11112284.
url http://repositorio.ufla.br/jspui/handle/1/49907
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv Agronomy
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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