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27 September 2017 Predicting Nut Damage at Harvest Using Different in-Season Density Estimates of Amyelois Transitella: Analysis of Data from Commercial Almond Production
Jay A. Rosenheim, Bradley S. Higbee, Jonathan D. Ackerman, Matthew H. Meisner
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Abstract

Despite decades of research on management tactics for the navel orangeworm, Amyelois transitella (Walker) (Lepidoptera: Pyralidae), on almonds, we still do not have an established means of using in-season pest-density estimates to predict damage to nuts at harvest. As a result, hull-split pesticide applications, although timed carefully to coincide with navel orangeworm oviposition and with crop vulnerability, are not tied to pest densities—thus falling short of our goals under modern pest management. Here we use an ecoinformatics approach, analyzing a pre-existing data set collected in commercial almond production in California, to ask: 1) are navel orangeworm density estimates obtained using different sampling methods in strong agreement with one another? and 2) can we use either single density estimates or combinations of density estimates to explain variation in nutmeat damage at harvest? We find that correlations between density estimates of navel orangeworm made over a single growing season are often weak, and suggest that density estimates taken closer to the time of harvest (catches of adult females between hull split and harvest; infestation of early-split nuts) may be most useful for predicting damage at harvest. Single-density estimates explained ≤39.1% of variation in harvest damage, whereas a combination of predictors explained 51.5% of the total variance in nutmeat damage at harvest. Our results suggest that density estimates taken just prior to harvest may, with refinement, be usable within a predictive framework to guide late-season control decisions.

© The Author(s) 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Jay A. Rosenheim, Bradley S. Higbee, Jonathan D. Ackerman, and Matthew H. Meisner "Predicting Nut Damage at Harvest Using Different in-Season Density Estimates of Amyelois Transitella: Analysis of Data from Commercial Almond Production," Journal of Economic Entomology 110(6), 2692-2698, (27 September 2017). https://doi.org/10.1093/jee/tox226
Received: 14 February 2017; Accepted: 14 July 2017; Published: 27 September 2017
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KEYWORDS
collinearity
ecoinformatics
navel orangeworm
predicting damage
sampling methods
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