Bayesian approaching for Asian Suprema soybean rust incidence study in different conditions of temperatures and leaf wetness

Abstract

The Asian soybean rust (Phakopsora pachyrhizi H. Sydow & P. Sydowj, which has been reported in areas of tropical and subtropical climates around the world, causes significant soybean (Glycine max L. Merr.) yield reduction. The disease progress is influenced by biotic factors such as interaction pathogen/host and abiotic factors of the environment. This work presents three models using bayesian approach to study Asian Suprema soybean rust incidence in different temperature and leaf wetness conditions. The models present estimates equivalents to non-linear regression model of Reis et al, fuzzy model of Alves et al and neuro-fuzzy model of Silva et al, when compared on the results from experimental design realized by Alves et al.