DEVELOPMENT OF AN AGROMETEOROLOGICAL MODEL FOR ESTIMATING SUSCEPTIBILITY TO COFFEE LEAF RUST

Abstract

Coffee leaf rust (Hemileia vastatrix) is one of the main diseases that affect coffee crops and can severely compromise production. Its occurrence is associated with both inadequate management practices and specific climatic conditions. Among the environmental factors, wind plays a crucial role, as it aids the dispersion of fungal spores and favors their germination on coffee leaves. The disease also depends on favorable conditions of temperature, humidity, and leaf wetness duration for its establishment. In this context, the objective of this study was to develop a model to evaluate the meteorological conditions that favor the occurrence of coffee leaf rust. To achieve this, forecasts from the Global Forecast System (GFS) numerical model were used. Based on this modeling, indices were generated to identify agrometeorological conditions favorable to rust across the entire state, using meteorological parameters. The results indicate that the index can serve as an effective tool for agricultural monitoring, supporting farmers, institutions, and technical agencies, such as Incaper, in the monitoring, control and prevention of the disease. The use of this index contributes to the development of strategies to mitigate the impacts of coffee rust in the short, medium, and long term. For future perspectives, it is important to integrate these products into Research and Development (R&D) projects, expanding their application to the monitoring of other diseases and crops, as well as to the livestock production, thus strengthening the agroclimatic early warning system in Espírito Santo.