4.7 Article

Spatial Patterns and Spread of Potato Zebra Chip Disease in the Texas Panhandle

Journal

PLANT DISEASE
Volume 96, Issue 7, Pages 948-956

Publisher

AMER PHYTOPATHOLOGICAL SOC
DOI: 10.1094/PDIS-09-11-0805-RE

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Funding

  1. Texas Department of Agriculture Zebra Chip Initiative
  2. ZC SCRI [2009-51181-20178]
  3. CSS Farms

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Zebra chip (ZC) is a disease that is affecting potato production in the southwestern United States and in other countries, and which has been linked to potato psyllids (Bactericera cockerelli) that harbor the bacterial plant pathogen 'Candidatus Liberibacter solanacearum'. Until recently, the epidemiology of ZC was unknown, motivating research to elucidate the spatial and temporal patterns of ZC infections in potato fields. Studies were performed in multiple commercial potato fields located in the Texas Panhandle, wherein locations of ZC-affected potato plants were georeferenced or counted within large plots and along belt transects consisting of contiguous 10-by-10-m quadrats. By employing distance- and area-based spatial statistical methods, it was determined that locations of ZC infections in potato fields departed from a completely spatially random pattern, instead appearing as clusters comprising infected plants situated in close proximity to one another, with clusters interspersed with numerous solitary infections. Disease progress curves of ZC clusters were generally well described by exponential growth and quadratic polynomial models. Numbers of ZC infections within disease clusters gradually increased over multiple weeks, with foliar disease symptoms first appearing during the tuber bulking stage. ZC infections were not found to be continuously present across fields, because many quadrats along belt transects contained zero or only a few infections while others had numerous infections. Consequently, the frequency of ZC infections within belt transect quadrats was well described by negative binomial and zero-inflated negative binomial distributions, in agreement with observed clustering of infections and distance-based spatial statistical results.

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