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ARS Home » Southeast Area » Fort Pierce, Florida » U.S. Horticultural Research Laboratory » Subtropical Plant Pathology Research » Research » Publications at this Location » Publication #299862
Title: Predicting the establishment and spread of plant disease from regulatory sampling

Author
item LUO, WEIQI - North Carolina State University
item Gottwald, Timothy
item PIETRAVALLE, STEPHANE - The Food And Environment Research Agency
item IREY, MIKE - Us Sugar Corporation

Submitted to: International Research Conference on Huanglongbing
Publication Type: Abstract Only
Publication Acceptance Date: 1/7/2013
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Invasive plant diseases can have devastating consequences on the local plant populations, in both agricultural and natural landscapes. Knowledge of the spatial patterns of pathogen spread can be used to guide more time- and cost-effective disease management strategies. Based on disease dispersal principles and consideration of host pattern, an improved plant disease epidemiological model was developed and tested for plant disease mapping. The model is able to characterize the disease dispersal gradient and predict infection risk, with indication of uncertainty, through heterogeneous environments without reference to the source of infection. As a result, sampling methods can be informed by the predicted prevalence map of the disease. In order to better describe the shapes of the dispersal gradients, three different dispersal functions (Exponential, Modified power law, and Cauchy distribution) were considered in the model. Two data sets of disease observations of Huanglongbing (HLB) of citrus in different landscapes (Southern Garden and Devils Garden plantation) in Florida were used to evaluate the performance of the improved method for disease mapping. The results showed that the improved model provided estimates of greater precision for unsampled hosts. With all different dispersal models compared, the exponential dispersal gradient gave the most satisfactory performance. All the determined information can help decision makers understand the spatial aspects of disease processes, and formulate decisions about disease control accordingly.