Seed potatoes are small tubers that are used to “seed” the soil and grow new potatoes. Most potatoes grown in the UK originate from Scottish seed, therefore Scottish seed underpins the wider UK potato industry, which is worth an estimated £5 billion p.a. Our potatoes are under threat, however, from potato cyst nematodes (PCN), which are microscopic, soilborne, worms that feed on potato roots. PCN drastically reduce crop yields and quality and are incredibly difficult to control. The main route by which PCN spreads is through the movement of infested soil, which may be transferred with tubers. Current legislation aimed at preventing PCN spread therefore targets seed production, and all land used for seed must be tested and the soil sample declared free from PCN prior to planting. Crops that are grown for consumption or processing, therefore not for further propagation, are not subject to statutory soil testing. Despite these measures, PCN has spread to infest a large proportion of prime potato production land. It is estimated that PCN could cause the end of the Scottish seed industry and subsequent collapse of the wider UK potato industry by 2050, potentially only 5 rotations away.

In this study we will investigate if state-of-the-art machine learning techniques can be used to develop a model for predicting the presence or absence of different PCN species in any potential crop location. This will enable data-driven, species-specific decision-making in policy and practice, and would be particularly useful for prediction in crop locations that are not subject to statutory testing, as well as providing a tool to predict where new infestations are more likely to occur.

Image of Potato by Jai79 from Pixabay

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.

Potato Cyst Nematode (PCN) is a threat to the Scottish seed potato and bulb industries: SASA estimates that over 13% of the area regularly planted with potatoes in Scotland is now infested with PCN, with an estimated increase in spread of 5% per year. Recently, following a working group on PCN, a report entitled ‘Potato cyst nematode (PCN) and the future of potato production in Scotland’ was submitted to Scottish Government highlighting the PCN problem in detail and outlining practical recommendations for its reduction. However, in order to maximise and target resources for such a reduction, a better understand is needed of i) the main areas of infestation in Scotland and how this is changing over time, ii) what key factors are responsible for that spread (be it geographic, climatic, agronomic etc) and iii) which management options would allow us to make the biggest impact on reducing spread.

Impact: The outcomes of this project to provide guidance to both policy and industry on the most effective ways to reduce spread of PCN.