Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil

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...

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Autores principales:	Edson Ampélio Pozza, Éder Ribeiro dos Santos, Nilva Alice Gaspar, Ximena Maira de Souza Vilela, Marcelo de Carvalho Alves, Mário Roberto Nogueira Colares
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	incidence multiple linear regression models meteorological variables Brazil Agriculture S
Acceso en línea:	https://doaj.org/article/e8a1bd9fa8334c179de4498d0d894e00
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spelling	oai:doaj.org-article:e8a1bd9fa8334c179de4498d0d894e002021-11-25T16:10:18ZCoffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil10.3390/agronomy111122842073-4395https://doaj.org/article/e8a1bd9fa8334c179de4498d0d894e002021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2284https://doaj.org/toc/2073-4395This 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.Edson Ampélio PozzaÉder Ribeiro dos SantosNilva Alice GasparXimena Maira de Souza VilelaMarcelo de Carvalho AlvesMário Roberto Nogueira ColaresMDPI AGarticleincidencemultiple linear regression modelsmeteorological variablesBrazilAgricultureSENAgronomy, Vol 11, Iss 2284, p 2284 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	incidence multiple linear regression models meteorological variables Brazil Agriculture S
spellingShingle	incidence multiple linear regression models meteorological variables Brazil Agriculture S Edson Ampélio Pozza Éder Ribeiro dos Santos Nilva Alice Gaspar Ximena Maira de Souza Vilela Marcelo de Carvalho Alves Mário Roberto Nogueira Colares Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil
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.
format	article
author	Edson Ampélio Pozza Éder Ribeiro dos Santos Nilva Alice Gaspar Ximena Maira de Souza Vilela Marcelo de Carvalho Alves Mário Roberto Nogueira Colares
author_facet	Edson Ampélio Pozza Éder Ribeiro dos Santos Nilva Alice Gaspar Ximena Maira de Souza Vilela Marcelo de Carvalho Alves Mário Roberto Nogueira Colares
author_sort	Edson Ampélio Pozza
title	Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil
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
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e8a1bd9fa8334c179de4498d0d894e00
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Coffee Rust Forecast Systems: Development of a Warning Platform in a Minas Gerais State, Brazil

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