Abstract
Pre-harvest pest management tools are essential to minimizing crop loss. The development of predictive models using early warning signs of pest abundance to predict imminent crop loss can guide management decisions and enable targeted, well-calibrated intervention. With sufficient data, in-season measures of pest abundance can be an important factor in generating accurate predictions of damage. However, sampling plans for tracking insect phenology and those designed to guide informed pest-management may not be equivalent. Using data from a commercial almond orchard setting, we evaluated five different lure-trap combinations used for monitoring navel orangeworm (Amyelois transitella; a primary pest of almond) under different management conditions. Using these data, we developed a predictive model of almond damage and evaluated the contribution of in-season measures of pest abundance towards accurate predictions of end-of-season damage. Mating disruption in orchard management influenced the efficacy of multiple lure-trap combinations. Despite this effect, there was as strong correlation between measured pest abundance and damage across multiple trap types regardless of management method. While statistically significant, measures of pest abundance did not enhance the accuracy of predictive models and characteristics of almond variety were stronger predictors of damage at harvest. Repeated significant correlations between navel orangeworm abundance and almond damage across multiple trap types justifies continued exploration of predictive modeling as a management tool. Lure-trap combinations exhibiting resistance to external factors are clear candidates for further development. However, generating accurate predictions of damage using these data will likely require calibration or modification of current sampling protocols. © 2024 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Published Version
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