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.