Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional Manancial UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/28384 |
Resumo: | Asian soybean rust is a disease with a high impact on soybean yield levels, especially in Latin America. As it is a fungal disease, climatic conditions are directly linked to its level of progress and degree of severity in soybean plants. This fungal disease is responsible for the early defoliation of plants, thus affecting the formation and development of grains, causing significant productivity losses. The objective of this work was to model the growth curve of this disease over five seasons, determining critical growth points of the disease and, through multivariate analyses, verify the interaction between these critical points and the climatic variables. The database came from an experimental station in the municipality of Santa Maria, Rio Grande do Sul, in a randomized block design with four replications in five seasons. Nonlinear regression models were fitted for the progress of disease severity growth in the crop cycle. The logistic model is the most suitable, as it represents in a real way the estimates of the parameters and the critical points of the model, being an important way to evaluate this growth rate. To identify the linear relationships between the variables, Pearson's correlation and principal component analysis (PCA) were performed. There are linear relationships between climatic conditions and the emergence of critical points in the progress of disease severity. Where, water regimes and temperature levels prior to hotspots, are important parameters to describe and explain the emergence of hotspots in the progress of disease severity and serve as indices to predict disease behavior. |
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2023-03-27T11:21:32Z2023-03-27T11:21:32Z2022-09-23http://repositorio.ufsm.br/handle/1/28384Asian soybean rust is a disease with a high impact on soybean yield levels, especially in Latin America. As it is a fungal disease, climatic conditions are directly linked to its level of progress and degree of severity in soybean plants. This fungal disease is responsible for the early defoliation of plants, thus affecting the formation and development of grains, causing significant productivity losses. The objective of this work was to model the growth curve of this disease over five seasons, determining critical growth points of the disease and, through multivariate analyses, verify the interaction between these critical points and the climatic variables. The database came from an experimental station in the municipality of Santa Maria, Rio Grande do Sul, in a randomized block design with four replications in five seasons. Nonlinear regression models were fitted for the progress of disease severity growth in the crop cycle. The logistic model is the most suitable, as it represents in a real way the estimates of the parameters and the critical points of the model, being an important way to evaluate this growth rate. To identify the linear relationships between the variables, Pearson's correlation and principal component analysis (PCA) were performed. There are linear relationships between climatic conditions and the emergence of critical points in the progress of disease severity. Where, water regimes and temperature levels prior to hotspots, are important parameters to describe and explain the emergence of hotspots in the progress of disease severity and serve as indices to predict disease behavior.A ferrugem asiática da soja é uma doença com alto impacto nos níveis de rendimento de soja, especialmente na Amárica Latina. Por ser uma doença fúngica, as condições climáticas estão diretamente ligadas ao seu nível de progresso e grau de severidade em plantas de soja. Esta doença fúngica é responsável pela desfolha precoce das plantas, afetando assim a formação e desenvolvimento dos grãos, causando significativas perdas de produtividade. O objetivo deste trabalho foi modelar a curva de crescimento desta doença ao longo de cinco safras, determinação de pontos críticos de crescimento da doença e através de análises multivariadas, verificar a interação entre estes pontos críticos e as variáveis climáticas. O banco de dados foi proveniente a uma estação experimental no município de Santa Maria, Rio Grande do Sul, o delineamento blocos casualizados com quatro repetições em cinco safras. Modelos de regressão não lineares foram ajustados para o progresso de crescimento da severidade da doença no ciclo da cultura. O modelo logístico é o mais indicado, pois representa de maneira real as estimativas dos parâmetros e dos pontos críticos do modelo, sendo uma importante forma para avaliar esta taxa de crescimento. Para a identificação das relações lineares entre as variáveis foram realizadas a correlação de Pearson e análises de componentes principais (PCA). Existem relações lineares entre condições climáticas e o surgimento dos pontos críticos do progresso da severidade da doença. Onde, os regimes hídricos e níveis de temperatura anteriores aos pontos críticos, são importantes parâmetros para descrever e explicar o surgimento de pontos críticos no progresso de severidade da doença e servir como índices para prever o comportamento da doença.porUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em AgronomiaUFSMBrasilAgronomiaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessModelo logísticoModelo gompertzAnálise multivariadaCorrelaçãoAnálise de componentes principais ferrugem da sojaLogistics modelGompertz modelMultivariate analysisCorrelationPrincipal component analysis soybean rustCNPQ::CIENCIAS AGRARIAS::AGRONOMIAModelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicasModeling the severity of Phakopsora pachrhizi in soybean and relationships of its critical development points with weather variablesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisLúcio, Alessandro Dal'Colhttp://lattes.cnpq.br/0972869223145503Sanchotene, Danie MartiniHaesbaert, Fernando MachadoLopes, Sidinei JoseDornelles, Sylvio Henrique Bidelhttp://lattes.cnpq.br/3845262425046995Escobar, Otávio dos Santos500100000009600600600600600600600d51ee404-e7f0-41c1-bf30-c5fa02c4e50966c9809e-fe9c-4213-a23d-6f21923c656393e91f84-5775-42a0-aad9-c68afb73dcd144a5ae44-aad4-4bab-9e57-33c798bfc8cd99941320-bfc9-4f87-830e-85811f64ce3b15921478-ccfb-4be3-80d0-5c764b73fe65reponame:Repositório Institucional Manancial UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGAGRONOMIA_2022_ESCOBAR_OTAVIO.pdfTES_PPGAGRONOMIA_2022_ESCOBAR_OTAVIO.pdfTese de Doutoradoapplication/pdf2265758http://repositorio.ufsm.br/bitstream/1/28384/1/TES_PPGAGRONOMIA_2022_ESCOBAR_OTAVIO.pdfbd7a50180db21b113c01270d600410d7MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas |
dc.title.alternative.eng.fl_str_mv |
Modeling the severity of Phakopsora pachrhizi in soybean and relationships of its critical development points with weather variables |
title |
Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas |
spellingShingle |
Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas Escobar, Otávio dos Santos Modelo logístico Modelo gompertz Análise multivariada Correlação Análise de componentes principais ferrugem da soja Logistics model Gompertz model Multivariate analysis Correlation Principal component analysis soybean rust CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
title_short |
Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas |
title_full |
Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas |
title_fullStr |
Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas |
title_full_unstemmed |
Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas |
title_sort |
Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas |
author |
Escobar, Otávio dos Santos |
author_facet |
Escobar, Otávio dos Santos |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Lúcio, Alessandro Dal'Col |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/0972869223145503 |
dc.contributor.referee1.fl_str_mv |
Sanchotene, Danie Martini |
dc.contributor.referee2.fl_str_mv |
Haesbaert, Fernando Machado |
dc.contributor.referee3.fl_str_mv |
Lopes, Sidinei Jose |
dc.contributor.referee4.fl_str_mv |
Dornelles, Sylvio Henrique Bidel |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3845262425046995 |
dc.contributor.author.fl_str_mv |
Escobar, Otávio dos Santos |
contributor_str_mv |
Lúcio, Alessandro Dal'Col Sanchotene, Danie Martini Haesbaert, Fernando Machado Lopes, Sidinei Jose Dornelles, Sylvio Henrique Bidel |
dc.subject.por.fl_str_mv |
Modelo logístico Modelo gompertz Análise multivariada Correlação Análise de componentes principais ferrugem da soja |
topic |
Modelo logístico Modelo gompertz Análise multivariada Correlação Análise de componentes principais ferrugem da soja Logistics model Gompertz model Multivariate analysis Correlation Principal component analysis soybean rust CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
dc.subject.eng.fl_str_mv |
Logistics model Gompertz model Multivariate analysis Correlation Principal component analysis soybean rust |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA |
description |
Asian soybean rust is a disease with a high impact on soybean yield levels, especially in Latin America. As it is a fungal disease, climatic conditions are directly linked to its level of progress and degree of severity in soybean plants. This fungal disease is responsible for the early defoliation of plants, thus affecting the formation and development of grains, causing significant productivity losses. The objective of this work was to model the growth curve of this disease over five seasons, determining critical growth points of the disease and, through multivariate analyses, verify the interaction between these critical points and the climatic variables. The database came from an experimental station in the municipality of Santa Maria, Rio Grande do Sul, in a randomized block design with four replications in five seasons. Nonlinear regression models were fitted for the progress of disease severity growth in the crop cycle. The logistic model is the most suitable, as it represents in a real way the estimates of the parameters and the critical points of the model, being an important way to evaluate this growth rate. To identify the linear relationships between the variables, Pearson's correlation and principal component analysis (PCA) were performed. There are linear relationships between climatic conditions and the emergence of critical points in the progress of disease severity. Where, water regimes and temperature levels prior to hotspots, are important parameters to describe and explain the emergence of hotspots in the progress of disease severity and serve as indices to predict disease behavior. |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022-09-23 |
dc.date.accessioned.fl_str_mv |
2023-03-27T11:21:32Z |
dc.date.available.fl_str_mv |
2023-03-27T11:21:32Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/doctoralThesis |
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doctoralThesis |
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publishedVersion |
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http://repositorio.ufsm.br/handle/1/28384 |
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http://repositorio.ufsm.br/handle/1/28384 |
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por |
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por |
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500100000009 |
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600 600 600 600 600 600 600 |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Agronomia |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Agronomia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
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