Caracterização epidemiológica da ferrugem do cafeeiro conilon

• Ano de defesa: 2023
• Autor(a) principal: Anjos, Breno Benvindo dos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Doutorado em Agronomia; Centro de Ciências Agrárias e Engenharias; UFES; Programa de Pós-Graduação em Agronomia
• Palavras-chave em Português: Ambiente; Coffea canephora; Hemileia vastatrix; Modelagem; Previsão; subject.br-rjbn; Agronomia
• Link de acesso: http://repositorio.ufes.br/handle/10/16611

Brazil stands out on the world stage as one of the main producers and exporters of coffee. However, the occurrence of diseases, with emphasis on coffee rust, considered the main disease, is one of the main limiting factors in increasing the production and productivity of the crop. Knowledge of the environmental conditions and how they are related to the intensity of the disease allows the planning and evaluation of strategies, helping in decision-making and in the rational elaboration of phytosanitary management for this disease, mitigating the damage and losses caused. Therefore, the objective was to analyze the behavior of rust at different altitudes over time, verifying the variables correlated with the disease intensity and, based on this, to generate a risk prediction model for the conilon coffee leaf rust epidemic. Thus, the thesis was organized into three chapters: 1) Analysis of the temporal progress of conilon coffee leaf rust; 2) Relation of coffee leaf rust intensity in conilon plantations with meteorological variables; 3) Logistic models based on meteorological data to estimate the probability of the occurrence of coffee leaf rust epidemics in conilon trees. For this, four areas of conilon coffee cultivation at different altitudes (<100 m; >100m and <300m; >300m and <500 m; >500m) propagated by seeds were selected. In each area, 80 points were evaluated, from September 2017 to December 2019, at each evaluation the intensity of coffee leaf rust was quantified and meteorological data were obtained from stations installed in each of the evaluated areas. Therefore, it was possible to conclude that the Logistic and Gompertz models were the ones that best fit the conilon coffee leaf rust incidence data, accurately describing the epidemics, with the highest intensity observed >100 m and 500 m. Regarding the meteorological variables, it was found that variables correlated with disease intensity were TMax, TMín, TAvg, TAvgLW(6 pm – 9 am, RH≥90%), TAvgLW(6 pm – 9 am, RH≥80%), TAvgLW(6 pm – 6 am, RH≥90%), TAvgLW(6 pm – 6 am, RH≥80%), NDHT(≥15°C e <26°C). Finally, it was possible to develop a prediction model to estimate the probability of the occurrence of conilon coffee leaf rust using the logistic regression modeling approach.