Disentangling environmental effects on plant disease epidemics at the regional scale

Abstract

The environment plays an essential role in driving the occurrence and dynamics of plant disease epidemics. The weather is known to influence pathogen and host biology and should modulate the stages of the disease cycle. On the other hand, climate, the average of weather within long periods of time (i.e. 30 years), should set the predominance of pathogens and host genotypes across regions, and therefore the spatial distribution of plant diseases and their respective intensities. In this thesis, three studies aiming to unravel the effects of environmental factors on plant disease epidemics will be presented. The first study will demonstrate how the climate shapes the spatial distribution of citrus Huanglongbing prevalence in Minas Gerais. In the second study, the time to onset of soybean rust in commercial soybean fields from Southern Brazil was associated with the El Nino Southern Oscillation, a phenomenon that triggers extreme weather events around the globe and also in Brazil; The third study integrates cutting- edge statistical methodology to associate weather time series and soil properties data to white mold prevalence in snap bean fields in New York, United States. The results led to novel insights into pathogen biology and disease risk at the regional and local scales for the three pathosystems under study. Keywords: Epidemiology. Huanglongbing. Soybean rust. White mold. Bayesian. Machine learning.