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Opportunities exist to improve decision support systems through the use of dispersal information gained from epidemiological research. However, dispersal and demographic information is often fragmentary in plant pathology, and this uncertainty creates a risk of inappropriate action whenever such information is used as a basis for decision making. In this article, a scenario-based simulation approach is used to evaluate crop and economic risks and benefits in the use of dispersal information for decision making using the potato late blight pathosystem (Phytophthora infestans-Solanum tuberosum) as a case study. A recently validated spatiotemporal potato late blight model was coupled to submodels for crop growth, tuber dry matter production, and fungicide efficacy. The yield response of a range of management scenarios to a single influx of primary inoculum (the initial spore load) was calculated. Damage curves (relative yield loss versus initial spore load) from a range of combinations of varietal susceptibility and fungicide treatments were used to classify the various management scenarios as either sensitive to initial spore load or tolerant to initial spore load, thus identifying where a high degree of accuracy world be required in dispersal information for appropriate decision making, and where a greater degree of uncertainty could be tolerated. General epidemics, resulting from spatially homogeneous initial spore loads, responded more strongly to the size of the initial spore load than focal epidemics, resulting from an initial spot infection. Susceptible cultivars responded with sizeable yield losses even at low levels of initial spore load, regardless of the fungicide management regime used. These results indicated that, for susceptible cultivars (late cultivars in particular), the degree of accuracy that would be required in dispersal information for appropriate decision making is unlikely to be practically attainable. The results also indicated that, contrary to "folk wisdom," spore loads of a few hundred spores per square meter do not lead to appreciable crop loss in resistant cultivars and are therefore acceptable. We conclude that scope exists for including dispersal information in decision making for potato late blight with resistant potato cultivars but not for susceptible cultivars. The modeling framework used in this study can be extended to investigate the scope for inclusion of dispersal information in decision support for other aerially transmitted pathogens. [ABSTRACT FROM AUTHOR]

A spatiotemporal/integro-difference equation model was developed and utilized to study the progress of epidemics in spatially heterogeneous mixtures of susceptible and resistant host plants. The effects of different scales and patterns of host genotypes on the development of focal and general epidemics were investigated using potato late blight as a case study. Two different radial Laplace kernels and a two-dimensional Gaussian kernel were used for modeling the dispersal of spores. An analytical expression for the apparent infection rate, r, in general epidemics was tested by comparison with dynamic simulations. A genotype connectivity parameter, q, was introduced into the formula for r. This parameter quantifies the probability of pathogen inoculum produced on a certain host genotype unit reaching the same or another unit of the same genotype. The analytical expression for the apparent infection rate provided accurate predictions of realized r in the simulations of general epidemics. The relationship between r and the radial velocity of focus expansion, c, in focal epidemics, was linear in accordance with theory for homogeneous genotype mixtures. The findings suggest that genotype mixtures that are effective in reducing general epidemics of Phytophthora infestans will likewise curtail focal epidemics and vice versa. [ABSTRACT FROM AUTHOR]

A spatiotemporal, integrodifference equation model of the potato late blight pathosystem is described. Formerly, the model was used in a theoretical context to analyze and predict epidemic dynamics in spatially heterogeneous mixtures of host genotypes. The model has now been modified to reflect a research interest in interactions between genotype, environment, landscape, and management. New parameter values describing host-pathogen interactions were determined and new environment-pathogen relationships included. A new analytical equation describing lesion expansion and associated necrosis has also been developed. These changes prompted a need to assess the quality of model predictions. Cultivar-isolate-specific interactions were characterized in the model using three quantitative components of resistance: infection efficiency, lesion growth rate, and sporulation intensity. These were measured on detached potato leaflets in the laboratory. Results of a sensitivity analysis illuminate the effect of different quantitative components of resistance and initial conditions on the shape of disease progress curves. Using the resistance components, the epidemic process of lesion expansion was separated from the epidemic process of lesion propagation providing two reference curves for diagnosing observed epidemics. The spatial component of the model was evaluated graphically in order to determine if realistic rates of focal expansion for potato late blight are produced. In accordance with theory, the radius of a predicted focus increased linearly with time and a constant focal velocity was reached that compared well with published experimental data, Validation data for the temporal model came from 20 late blight epidemics observed in field trials conducted in the Netherlands in 2002 and 2004. The field data and model were compared visually using disease progress curves, and numerically through a comparison of predicted and observed t5 and t50 points (time in days until 5 and 50% disease severity is reached, respectively) and relative areas under the disease progress curve values. Temporal model predictions were in close agreement with observational data and the ability of the model to translate measured resistance components, weather data, and initial conditions into realistic disease progress curves without the need for calibration confirms its utility as a tool in the analysis and diagnosis of epidemics. [ABSTRACT FROM AUTHOR]