Wireworm infestations in multiple high value/hectarage Scottish crops are being reported in increasing numbers, however there is little information on the species involved, or their numbers, locations or damage levels in Scotland. This lack of local data on species presence across Scotland makes it difficult to develop control strategies or issue advice for Scottish growers, with a risk of yield loss and/or potentially inappropriate pesticide usage as a result.

The outputs of this project will help to obtain an up-to-date data set on which wireworm species are present in Scotland, along with information on locations and risk factors. This will establish the foundation for on-going species monitoring in future. The project will lead to updated advice and guidelines on integrated pest management, with clear links to monitoring information. Knowledge exchange arising from the project will target Scottish growers and agronomists about the updated risk factors for wireworm, how and when to effectively set up traps themselves and myth bust outdated information. This will give the project findings longevity as growers and agronomists will be guided to the best monitoring methods for them to use themselves, rather than being dependent on centralised monitoring information.


Image Credit: David Cappaert, Bugwood.org (CC) BY-NC 3.0

Scottish seed potatoes are a premium global product, and the industry underpins UK potato production that is worth an estimated £4-5bn across all upstream and downstream sectors. The sustainability of the industry is under threat, however, due to increasing levels of potato virus Y and potato leaf roll virus worldwide.

These viral diseases are transmitted by aphids, and they cause growth disorders, leaf symptoms, and yield losses. The recent increase in the occurrence of these diseases is likely due to multiple factors, such as climate change, the loss of important aphicides, and a general lack of coordinated management strategies, including accurate predictive models to support decision-making. Current methods used to forecast these diseases at a national scale do not offer sufficient resolution to tackle localised virus health issues.

This project will employ state-of-the-art machine learning techniques to develop improved models for forecasting these diseases and contribute to a better understanding of virus epidemiology, which will support management strategies to reduce disease impacts.