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Geoinformation techniques were applied to develop predictive models to study the areas of risk to soybean rust (Phakopsora pachyrhizi Sydow) in soybean (Glycine max L.); coffee leaf rust (Hemileia vastatrix Berk & Br) in coffee; and black Sigatoka (Mycosphaerella fijiensis var. difformis) in banana, considering Brazil's climatic characterization and the distribution of soybean, coffee and banana crops. Temperature and rainfall data were obtained for the period from 1950 to 2000, for which observational data are available, and of simulations for 2020, 2050 and 2080 using the SRES A2 climate change scenarios. Using principal components analysis, a single variable was generated based on 57 variables, in order to determine an index explaining 87%, 88% and 90% of the variability of soybean, coffee and banana crops, respectively, in municipal districts across Brazil. The climatic model was used to generate the zoning of the three plant diseases, using temperature and leaf wetness as input. Areas of favorability for the diseases were plotted against the main coffee, soybean and banana growing areas in Brazil. This methodology enabled the visualization of the changes in areas favorable for epidemics under possible future scenarios of climate change

Ecological zoning of soybean rust, coffee rust and banana black sigatoka based on Brazilian climate changes