Plant disease forecasting

– Features:
– Models predict dispersal, optimal strategy by goal, and time to eradication.
– Model quality benefits from technology and statistical technique improvements.

– Examples of disease forecasting systems:
– Systems may use parameters or a combination of factors to determine disease risk.
– EPIdemiology, PREdiction, and PREvention (EPIPRE) system in the Netherlands focused on multiple pathogens.
– USPEST.org graphs risks of plant diseases based on weather forecasts.
– Forecasting models utilize relationships like linear regression or population growth curves.
– Correct choice of model is crucial for a useful disease forecasting system.

– Future developments:
– Computing power increase and more data may enhance disease forecasting systems.
– Importance of forecasting systems may rise with climate change.
– Accurate prediction of disease outbreaks in new areas may become critical.

– References:
– Agrios, George. “Plant Pathology.” Academic Press.
– Campbell, C. L.; Madden, L. V. “Introduction to Plant Disease Epidemiology.”
– Rimbaud, Loup et al. “Sharka Epidemiology and Worldwide Management Strategies.”
– Esker, P. D. et al. “Ecology and Epidemiology in R: Disease Forecasting.”
– APS Education Centre – Stewarts wilt of corn.

– Objectives:
– Disease forecasting predicts occurrence or change in severity of plant diseases.
– Growers use forecasting systems to make economic decisions on disease treatments.
– Systems consider host susceptibility, current weather conditions, and pathogen interactions.
– Environment plays a crucial role in disease development.
– Reliable, simple, cost-effective forecasting systems are essential for irregular diseases.