Modelling: the basis for rational disease management

Abstract

Adequate suppression of plant pathogens is achieved by integration of diverse control measures, which may differ in efficacy, duration of effectiveness and cost. Since the prevalence of pathogens and the intensity of the diseases they cause vary from year to year and from location to location, many factors should be taken into account for rational and cost-effective disease management. Several types of model can be used to achieve that goal. Reliable estimation of disease intensity is crucial for implementation of action thresholds, and modelling of the spatial distribution of the disease is needed for developing disease-monitoring procedures. Forecast or warning systems are models that use past and future weather data for predicting the likelihood of disease outbreaks. Yield loss models that relate the expected reduction in yield to each level of disease intensity are useful for cost–benefit calculations. The complexity of the system forces the decision-makers (growers, extension personnel, and farmer consultants) to seek assistance in their decision-making procedure. Decision support systems are computerized models developed for this purpose, and used not only to time the application of control measures, but also in choosing the appropriate measure to be applied.