Plant pathogens destroy around one-quarter of food production, significantly threatening our ability of achieving food security for an ever-growing global population. Septoria tritici Blotch (STB) is caused by the fungus Zymoseptoria tritici and is the most damaging disease of wheat in temperature climates, reducing wheat yields by up to 20%. To ensure control strategies are optimally employed, the ability to predict disease risk in time and space is an important aspect of disease risk management. This is especially the case for pathogens such as Z. tritici that show high resistance to multiple fungicides, and are therefore very difficult to control.

Here, we created a new weather-dependent model of STB disease risk. Whilst many alternative approaches can be employed to predict STB disease risk, our new model focusses exclusively on the biology of the pathogen – a largely unexplored area. We drive our model (model A) with experimentally-derived data for temperature- and wetness-dependent germination, growth and death of Zymoseptoria tritici. We also develop a second, comparative model (model B) that utilises an alternative approach to predict temperature-dependent STB risk. Developing both models allowed us to predict regions of the UK with high or low STB disease risk. Neither model was able to predict annual disease risk throughout the UK. However, model A could differentiate between areas of the UK with differing average risk over a 15-year period; model B could not.

The first key limitation of both models is the broad spatial resolution of the climate data, including between-farm differences in climate, as well as microclimate differences that occur within a plant canopy, that we could not account for. The second key limitation of both models is the lack of data concerning the host wheat plants, including cultivar resistance status, growth stage, and planting regime. Model A outperformed model B; likely because model B is additionally limited by its lack of scope for incorporation of pathogen death, leading to a cumulative overestimation of disease over the course of the wheat growing season. To successfully predict disease risk in space and time, and hence inform disease management, model A will likely need to be combined with explicit, experimentally-derived data concerning host wheat plants, as well as the inclusion of additional environmental factors.

Tom Chaloner, SWBio DTP student

Paper: A new mechanistic model of weather-dependent Septoria tritici blotch disease risk by Thomas M. Chaloner , Helen N. Fones , Varun Varma , Daniel P. Bebber and Sarah J. Gurr in Philosophical Transactions of the Royal Society B: Biological Sciences.