Category: SLIMERS+

As Research Associate at the UK Agri-Tech Centre and a core member of the SLIMERS project, Dr Kerry McDonald-Howard is combining expertise in sustainable pest control with advanced digital tools to transform how farmers manage slugs.

While studying at Liverpool John Moores University, Kerry’s PhD focused on improving the efficacy of parasitic nematodes, specifically Phasmarhabditis hermaphrodita, as a biological control for slugs.

These nematodes, which are natural predators of slugs, thrive in the same moist, organic-rich soils favoured by slugs. Like slugs they also thrive in soils with higher organic matter and clay content, and at lower temperatures.

Nematodes, often called roundworms, are among the most abundant animals on earth. While most are harmless, a select few, like those in the Phasmarhabditis genus, are specialised slug parasites. These microscopic hunters track slugs by following their slime trails, enter through a small breathing hole, and ultimately kill their host within a few weeks. As they are harmless to other wildlife and the environment, they are considered a sustainable biological alternative to chemical molluscicides.

The key to effective nematode use is precision explains Kerry: “Nematodes can’t travel far in the soil, and applying them across an entire field would be prohibitively expensive. But if we can pinpoint exactly where the slugs are, we can target applications, making biological control both affordable and effective.”

Kerry works from the UK Agri-Tech Centre’s Phenotyping laboratory based at Rothamsted Research where they lead experiments using advanced imaging and AI to distinguish slugs. These technologies are being trained using thousands of images and data points collected by the project’s Slug Sleuth farmers and partners.

“Together with Fotenix we are building AI models that will recognise slugs, monitor populations, and apply nematodes precisely where they’re needed.”

So far, the SLIMERS team has successfully trained AI models to distinguish slugs from soil, plants, and stones and Kerry has more recently been working in Slug Sleuth’s fields, collecting more data, and refining both the technology and the biological control strategies.

This has involved taking the highly specialised Fotenix multi-spectral imaging equipment out into fields to collect more images of slugs
in a variety of locations. As slugs generally reach a period of peak activity on the soil surface starting two to three hours after dark Kerry has been heading out into the farmers’ fields late at night.

“With it being so dry it has been a challenge to find slugs this spring so we have had to wait until late when there is more moisture.”

Kerry is optimistic about the project’s impact. “The agricultural industry is in a constant struggle of needing to produce more with less, especially chemical inputs – not only because of the detrimental impacts to the environment and non-target organisms, but also the rising costs.
“The ability to monitor and control a massive pest of UK agriculture, in a sustainable and cost-effective manner is fundamental therefore, this project delivers economic, environmental and societal benefits.

Researchers at the John Innes Centre have been making progress towards identifying what makes some wheat varieties more ‘slug resistant’ than others.

This work is based on previous research that screened varieties from the centre’s Watkins collection of landrace wheat varieties. This singled out Watkins 788 as a potential front runner for ‘slug resistant’ properties after 78% of slugs demonstrated a dislike to it.

From there, the team at JIC, led by Dr Simon Griffiths, crossed Watkins 788 with commercial variety Paragon, to create 85 Recombinant Inbred Lines (RILs) for further testing.

Quantitative Trait Locus (QTL) analysis is a genetic tool that helps identify specific regions of a plant’s DNA linked to desirable traits, such as slug resistance in wheat. By combining data from field trials with lab-based genetic testing, researchers are getting closer to identifying which part of the genome influences the apparent resistance.

Researchers use DNA markers to scan the genomes of resistant and susceptible RILs. Regions consistently linked to lower slug damage are flagged. Once the QTLs are confirmed, those genetic markers can potentially be used to create new slug resistant varieties.

The centre’s Head of Entomology and Insectory Dr Victor Soria-Carrasco has run trials on these RILS using slugs that have been posted in by ‘Slug Scouts’. These are members of the public (including farmers) who collect and post containers of grey field slugs to the centre.

Dr Simon Griffiths, Group Leader and ‘Delivering Sustainable Wheat’ Programme Lead says: “Early results have highlighted a number of RILs that appear to be slug resistant, as well as those that are susceptible. Using the diverse wheat in our historic Watkins collection in these trials is really exciting, as we’re starting to see it becoming an increasingly useful resource for tackling the challenges that farmers face in the field.”

Two of these RILs as well as Watkins 788 were multiplied up for testing by six Slug Sleuth farmers who took on the additional responsibility within the SLIMERS project. The farmers established blocks of Watkins 788 and the two RILS – one believed to be resistant and the other susceptible to slugs – alongside their farm standard wheat. Over the 2024-5 growing year they have taken measurements and samples to find out if these are indeed spurned by slugs and could offer potential solutions to farmers.

Meanwhile the on-going lab-based feeding trials have identified other even more resistant RILs that are currently being multiplied up at the John Innes Centre field station near Norwich, and will be put into field trials in autumn 2025.

Research led by Professor Keith Walters is transforming understanding and management of slugs.

One of the key insights driving the SLIMERS project is that slugs don’t spread evenly across fields. Instead, they gather in patches hotspots with high slug numbers.

Previous research led by Keith established that these patches are not only common, but also stable throughout the cropping season, making them potential targets for selective control.

But there was still a lot to learn about the specific factors that caused slugs to form patches where they do, and how those locations can be reliably predicted.

Over the past two years, Slug Sleuths have been counting slugs, and recording data across 1ha plots on their farm. Their efforts, combined with detailed soil mapping and testing by project partner Agrivation and subcontractor Hutchinsons, have created comprehensive datasets on slug distribution.

This enabled Keith and his team to dig deeper into the factors influencing slug patch formation.

They found that soil characteristics including pH, organic matter, and the proportions of sand, silt, and clay all play a role in where slugs settle and breed thus forming patches. For example, slugs tend to avoid areas with higher pH or low organic matter, while clay-rich soils, which retain moisture, are particularly attractive to them.

The large datasets collected have allowed quantification of their varying impact on different aspects of patch formation at a level of detail never before achieved.

The first two years of the SLIMERS project coincided with unusually high rainfall, which gave the opportunity to study how slugs respond to waterlogged soils. The researchers discovered that when fields are saturated, slugs abandon their usual patch forming behaviour in favour of survival, dispersing more randomly. As soils dry out, patch formation resumes but often in the sandier, better-drained parts of the field first.

Predictive models for precision control

Armed with this new understanding, Keiths’ team has developed a suite of mathematical and AI-driven models:

•  A biological model that explains how slugs respond to different soil characteristics under both normal and waterlogged conditions.

•  A binary predictive model that identifies areas likely to exceed a threshold slug count (such as the AHDB’s four slugs per trap), helping farmers know where to focus control efforts.

•  An abundance model that aims to predict the level of surface activity of slugs in each part of a field. This is a more complex task, but one that’s showing promise as more data becomes available.

Initial tests of these models have been encouraging with the binary model, for example, already achieving nearly 80% precision when it predicts locations of high-risk patches. As more data is collected in the upcoming autumn trials, accuracy is expected to improve even further.

Saving money and protecting the environment

The implications for farmers are significant. By targeting slug control measures – whether using conventional pellets or biological alternatives like nematodes – only where they’re needed, growers can reduce input use and limit environmental impacts.

This approach not only makes economic sense, but also supports the industry’s drive toward more sustainable farming practices.

The next phase of the SLIMERS project (2025-26) will see these predictive models put to the test in real- world conditions. Slug Sleuths will receive detailed risk maps for their fields and be asked to treat only the predicted slug hotspots.

The results will help fine-tune the models and bring the vision of precision pest management closer to reality.