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Statistical models can help in decision-making for the control of plant diseases, leading to less use of inputs, greater economy, and less negative environmental impact. Thus, this study aimed to use environmental variables to fit multiple linear regression (MLR) models for estimating the Phoma leaf spot incidence in coffee to develop a warning system. The experiment was conducted over two years (September 2013 to August 2015) with monthly disease assessments in the Coffea arabica L. cultivar “Catucaí amarelo 2SL”. A regular grid of 7.65 ha with 85 points delimited the area, with the points spaced 30 x 30 m. The incidence progress curve was constructed by considering the overall mean of the 85 points in each month. Fifty-two environmental variables were generated using an automatic station installed in the crop, and these variables were used in the development of the MLR models. A total of 126 models were fit, of which four were more successful in estimating disease dynamics over time. Two of these models allowed the acquisition of estimated values for disease incidence two weeks prior to the disease assessments, with high precision and accuracy. Nowadays the disease management has been performed exclusively with the use of fixed spraying schedules of fungicides. The models obtained in our research can contribute to sustainability of coffee production, to avoid unnecessary use of fungicides and become coffee cultivation more profitable.
