Investment

Disease control in agriculture is very often ephemeral with resistance based on genetic approaches often showing little durability. A major source of this instability lies in the fact that host-pathogen systems are biotic interactions in which both components have potential to evolve. In the past the widespread use of major resistance genes has taken precedence over the design of disease control strategies that reduce the evolutionary pressures exerted on pathogens to rapidly evolve infectivity and aggressiveness. This project aims to redress this balance by investigating the application of evolutionary principles to the development of a range of resistance gene deployment strategies and assess their impact on disease incidence and the evolution of novel pathotypes in two distinctly different pathogen types - a necrotroph (representing blackleg of canola) and a biotroph (representing rust of wheat). In its initial phase the project will develop agriculturally realistic simulation models to consider various spatial and temporal deployment strategies, and their effect on (i) short-term epidemic development; and (ii) longer-term evolution of infectivity and aggressiveness. Particular consideration will be given to the ways in which R genes are deployed. Models will incorporate both epidemiological and pathogen evolution to permit assessment of the durability of different strategies. To empirically validate specific predictions, the modelling approach will be complemented with epidemiological and genetic data collected from field trials run under realistic farming conditions.