Prediction of plant diseases through modelling and monitoring airborne pathogen dispersal.

Abstract

Abstract Many plant diseases that spread by airborne inocula have had major economic and social impacts worldwide. Plant diseases account for 16% of the yield losses in eight of the most important food and cash crops. Numerical models and monitoring networks have been developed to forecast the spread of these diseases both locally and over long distances. The epidemics of these airborne diseases depend on production of infectious propagules, their aerial transport, specific infectiousness and finally their reproduction. This article first reviews current understanding of these processes with an emphasis on their treatments in disease forecast models as well as the uncertainties the treatments introduce. Then we discuss the concepts and frameworks of forecast models that are broadly classified into epidemic models and aerobiological models and, finally, we present an example of the application of these modelling approaches to soybean rust forecasting.