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Updated: Apr 4, 2026

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
Published on: November 1, 2017
A Predictive Degree Day Model for the Development of Bactericera cockerelli (Hemiptera: Triozidae) Infesting Solanum
O M Lewis1, G J Michels2, E A Pierson3
1Department of Entomology, Texas A&M University, College Station, TX 77843-2475. milolewis3@gmail.com.
Abstract:
Bactericera cockerelli (Sulc) (Hemiptera: Triozidae) is a pest of potato (Solanum tuberosum L.) that vectors the bacterium that putatively causes zebra chip disease in potatoes, 'Candidatus Liberibacter solanacearum.' Zebra chip disease is managed by controlling populations of B. cockerelli in commercial potato fields. Lacking an integrated pest management strategy, growers have resorted to an intensive chemical control program that may be leading to insecticide-resistant B. cockerelli populations in south Texas and Mexico. To initiate the development of an integrated approach of controlling B. cockerelli, we used constant temperature studies, nonlinear and linear modeling, and field sampling data to determine and validate the degree day parameters for development of B. cockerelli infesting potato. Degree day model predictions for three different B. cockerelli life stages were tested against data collected from pesticide-free plots. The model was most accurate at predicting egg-to-egg and nymph-to-nymph peaks, with less accuracy in predicting adult-to-adult peaks. It is impractical to predict first occurrence of B. cockerelli in potato plantings as adults are present as soon cotyledons break through the soil. Therefore, we suggest integrating the degree day model into current B. cockerelli management practices using a two-phase method. Phase 1 occurs from potato planting through to the first peak in a B. cockerelli field population, which is managed using current practices. Phase 2 begins with the first B. cockerelli population peak and the degree day model is initiated to predict the subsequent population peaks, thus providing growers a tool to proactively manage this pest.
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