|Van Den Bosch, Frank|
Submitted to: Elsevier
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/23/2012
Publication Date: 3/30/2012
Citation: Luo, W., Pietravalle, S., Parnell, S., Van Den Bosch, F., Gottwald, T.R., Irey, M.S., Parker, S.R. 2012. An improved regulatory sampling method for mapping and representing plant disease from a limited number of samples. Elsevier. 4:68-77. Interpretive Summary: Plant diseases are inherently difficult to manage. An important issue for management is knowing where the disease is in the field or in an orchard and what the incidence of diseased (the proportion of plants infected) plants is at any point in time. Knowing where and how much disease occurs may lead to quite different management strategies by farmers. Therefore, sampling for disease becomes very critical. You have to be able to sample and accurately estimate how much disease is present, where it is present, and the farmer must be able to find initial infections before they spread. This paper describes a new sampling method that can be used to find very low numbers of infections, and by doing a limited amount of sampling, be able to predict what the disease looks like a cross an entire field or orchard. This methodology will be useful to both scientists to study plant diseases, and to regulatory agencies and farmers to facilitate improved disease management.
Technical Abstract: A key challenge for plant pathologists is to develop efficient methods to describe spatial patterns of disease spread accurately from a limited number of samples. Knowledge of disease spread is essential for informing and justifying plant disease management measures. A mechanistic modelling approach is adopted for disease mapping which is based on disease dispersal gradients and consideration of host pattern. The method is extended to provide measures of uncertainty for the estimates of disease at each host location. In addition, improvements have been made to increase computational efficiency by better initialising the disease status of unsampled hosts and speeding up the optimisation process of the model parameters. These improvements facilitate the practical use of the method by providing information on: (a) mechanisms of pathogen dispersal, (b) distance and pattern of disease spread, and (c) prediction of infection probabilities for unsampled hosts. Two data sets of disease observations, Huanglongbing (HLB) of citrus and strawberry powdery mildew, were used to evaluate the performance of the new method for disease mapping. The result showed that our method gave better estimates of precision for unsampled hosts, compared to both the original method and spatial interpolation. This enables decision makers to understand the spatial aspects of disease processes, and thus formulate regulatory actions accordingly to enhance disease control.