Modelling the factors affecting the spatiotemporal distribution of cabbage stem flea beetle (Psylliodes chrysocephala) larvae in winter oilseed rape (Brassica napus) in the UKOrtega-Ramos, P. A., Mauchline, A. L. ORCID: https://orcid.org/0000-0003-1168-8552, Metcalfe, H., Cook, S. M., Girling, R. D. ORCID: https://orcid.org/0000-0001-8816-8075 and Collins, L. (2023) Modelling the factors affecting the spatiotemporal distribution of cabbage stem flea beetle (Psylliodes chrysocephala) larvae in winter oilseed rape (Brassica napus) in the UK. Pest Management Science. ISSN 1526-4998
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1002/ps.7427 Abstract/SummaryBACKGROUND Cabbage stem flea beetle (CSFB; Psylliodes chrysocephala L.) management in oilseed rape (Brassica napus L.) has become an urgent issue in the absence of permitted and effective insecticides. Understanding the meteorological and management factors affecting their population dynamics has become critical to the development of pest management strategies. RESULTS The spatio-temporal changes in CSFB larval populations were assessed both in autumn and spring, in the UK from 2003 to 2017 (a period encompassing pre-and post-neonicotinoid insecticide restriction). After the neonicotinoid ban in 2013, the number of larvae both in autumn and spring increased 10-fold in the UK. When neonicotinoids were available, later sown crops contained fewer larvae than early sown crops, and bigger fields had fewer larvae than smaller fields, whereas after the ban, bigger fields tended to have more larvae than smaller fields. Wet and mild/hot Septembers were related with higher numbers of larvae when neonicotinoids were available and with lower larval numbers after the neonicotinoid ban. Low temperatures in December and January combined with high rainfall were related with high numbers of larvae in spring both before and after the neonicotinoid ban. CONCLUSION This study will help to produce decision support systems that allow future predictions of regional CSFB population changes and will help growers and consultants to adjust their management methods to reduce the risk of high infestations.
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