ArticlePest management science2016
The potential of decision support systems to improve risk assessment for pollen beetle management in winter oilseed rape.
Article in Pest management science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Phenological Mapping of Invasive Insects: Decision Support for Surveillance and Management.Insects · 2023Review
- Sampling pollen beetle (Brassicogethes aeneus) pressure in oilseed rape: which method is best?Pest management science · 2021Article
- Advances in automatic identification of flying insects using optical sensors and machine learning.Scientific reports · 2021Article
- The Potential for Decision Support Tools to Improve the Management of Root-Feeding Fly Pests of Vegetables in Western Europe.Insects · 2020Review
- Migratory flight behaviour of the pollen beetle Meligethes aeneus.Pest management science · 2017Article
- Meteorological and landscape influences on pollen beetle immigration into oilseed rape crops.Agriculture, ecosystems & environment · 2017Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe reliance on and extensive use of pyrethroid insecticides have led to pyrethroid resistance in pollen beetle (Meligethes aeneus). Widespread adoption of best practice in pollen beetle management is therefore needed. Decision support systems (DSSs) that identify the risk period(s) for pest migration can help to target monitoring and control efforts, but they must be accurate and labour efficient to gain the support of growers. Weather data and the phenology of pollen beetles in 44 winter oilseed rape crops across England over 4 years were used to compare the performance of two risk management tools: the DSS proPlant expert, which predicts migration risk according to a phenological model and local weather data, and 'rule-based advice', which depends on crop growth stage and a temperature threshold.
resultsBoth risk management tools were effective in prompting monitoring that would detect breaches of various control thresholds. However, the DSS more accurately predicted migration start and advised significantly fewer days of migration risk, consultation days and monitoring than did rule-based advice.
conclusionThe proPlant expert DSS reliably models pollen beetle phenology. Use of such a DSS can focus monitoring effort to when it is most needed, facilitate the practical use of thresholds and help to prevent unnecessary insecticide applications and the development of insecticide resistance.
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Registered trials
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