Coffee rust forecast systems: development of a warning platform in a Minas Gerais state, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
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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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 |
_version_ |
1815438985900589056 |