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Relative efficiency of methods to estimate cabbage stem flea beetle (Psylliodes chrysocephala) larval infestation in oilseed rape (Brassica napus).

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BACKGROUND The cabbage stem flea beetle (CSFB; Psylliodes chrysocephala) is an important pest of winter oilseed rape (OSR) crops in Europe. Damage is caused by adults feeding on young leaves… Click to show full abstract

BACKGROUND The cabbage stem flea beetle (CSFB; Psylliodes chrysocephala) is an important pest of winter oilseed rape (OSR) crops in Europe. Damage is caused by adults feeding on young leaves and by larvae mining in the leaves and stems. The most common method to estimate the severity of CSFB larval infestation is by plant dissection, which is time consuming and labour intensive. Two alternative methods have been proposed; extraction of larvae from desiccating plants and counting the percentage of leaves with scars left by larvae on plant petioles. These methods are easy to implement and less time consuming than plant dissection but have not been properly validated. RESULTS OSR plants were sampled in five different experiments and the two alternative methods tested; assessment of the total number of scars per plant as a predictor of the number of larvae was also tested. The number of larvae remaining in plants following various periods of desiccation was checked via plant dissection. We found that the desiccation method is efficient, giving reliable results after 7 days (76 % of the total larvae extracted) with good accuracy in estimating the number of larvae per plant (± 0.38 larvae per plant). The total number of scars also gives a reliable estimation of the number of larvae (r = 0.76, accuracy of ± 1.01 larvae per plant). CONCLUSION OSR plant desiccation and the total number of scars per plant are reliable methods to estimate CSFB larval infestation and will facilitate monitoring of this cryptic pest stage. This article is protected by copyright. All rights reserved.

Keywords: per plant; number; larval infestation; larvae; plant

Journal Title: Pest management science
Year Published: 2022

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