Spatio-temporal analysis of a plant disease in a non-uniform crop: a Monte Carlo approach

Bin Li, R. S. Sanderlin, Rebecca Melanson and Qingzhao Yu

Journal of Applied Statistics, 2011, vol. 38, issue 1, 175-182

Abstract: Identification of the type of disease pattern and spread in a field is critical in epidemiological investigations of plant diseases. For example, an aggregation pattern of infected plants suggests that, at the time of observation, the pathogen is spreading from a proximal source. Conversely, a random pattern suggests a lack of spread from a proximal source. Most of the existing methods of spatial pattern analysis work with only one variety of plant at each location and with uniform genetic disease susceptibility across the field. Pecan orchards, used in this study, and other orchard crops are usually composed of different varieties with different levels of susceptibility to disease. A new measure is suggested to characterize the spatio-temporal transmission patterns of disease; a Monte Carlo test procedure is proposed to test whether the transmission of disease is random or aggregated. In addition, we propose a mixed-transmission model, which allows us to quantify the degree of aggregation effect.

Keywords: hypothesis testing; lattice system; Monte Carlo; spatial; spatio-temporal analysis (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1080/02664760903301150

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