Abstract

Zebra chip disease (ZC) is a disease of potato which produces striped necrotic patterns that become pronounced when fried, making potato products such as chips and fries unmarketable. The disease is associated with a bacterium Candidatus Liberibacter solanacearum (Lso) and is transmitted by the potato psyllid, Bactericera cockerelli. An important aspect in managing this disease is the modeling and prediction of potato psyllid occurrence. In this study, potato psyllid numbers were monitored regularly across the southern Idaho region. This unique data set encompasses psyllid counts, collected by multiple sticky traps, set up at 98 growing sites over the growing seasons of 2013, 2014, and 2015. The data are modeled using a nonlinear logistic growth function, which is modified to account for negative skewness inherent in the sticky trap data. The estimated models are subsequently used to compare relevant potato psyllid occurrence parameters between major growing regions of southern Idaho. Comparisons of various trap configurations within each region were also carried out among estimated model parameters. This modeling effort could help researchers and growers efficiently monitor the psyllid populations and anticipate the potential for future disease outbreaks.

Keywords

Potato Psyllid, Zebra Chip Disease, Nonlinear Regression

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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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Jan 1st, 12:00 AM

Modeling Potato Psyllid Occurrence Using Sticky Trap Data for the Management of Zebra Chip Disease

Zebra chip disease (ZC) is a disease of potato which produces striped necrotic patterns that become pronounced when fried, making potato products such as chips and fries unmarketable. The disease is associated with a bacterium Candidatus Liberibacter solanacearum (Lso) and is transmitted by the potato psyllid, Bactericera cockerelli. An important aspect in managing this disease is the modeling and prediction of potato psyllid occurrence. In this study, potato psyllid numbers were monitored regularly across the southern Idaho region. This unique data set encompasses psyllid counts, collected by multiple sticky traps, set up at 98 growing sites over the growing seasons of 2013, 2014, and 2015. The data are modeled using a nonlinear logistic growth function, which is modified to account for negative skewness inherent in the sticky trap data. The estimated models are subsequently used to compare relevant potato psyllid occurrence parameters between major growing regions of southern Idaho. Comparisons of various trap configurations within each region were also carried out among estimated model parameters. This modeling effort could help researchers and growers efficiently monitor the psyllid populations and anticipate the potential for future disease outbreaks.