Modelagem da severidade de Phakopsora pachrhizi em soja e relações de seus pontos críticos de desenvolvimento com variáveis meteorológicas

• Ano de defesa: 2022
• Autor(a) principal: Escobar, Otávio dos Santos
• Orientador(a): Lúcio, Alessandro Dal'Col
• Banca de defesa: Sanchotene, Danie Martini, Haesbaert, Fernando Machado, Lopes, Sidinei Jose, Dornelles, Sylvio Henrique Bidel
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Centro de Ciências Rurais
• Programa de Pós-Graduação: Programa de Pós-Graduação em Agronomia
• Departamento: Agronomia
• País: Brasil
• Palavras-chave em Português: Modelo logístico; Modelo gompertz; Análise multivariada; Correlação; Análise de componentes principais ferrugem da soja
• Palavras-chave em Inglês: Logistics model; Gompertz model; Multivariate analysis; Correlation; Principal component analysis soybean rust
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/28384

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.