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Bitesize Biosecurity: A tool and framework for curating and summarising expert biosecurity advice for farmers using
Alexander F B Carmichael1, Lorna A Pate2, Andrew J Duncan1,3
1Centre for Epidemiology and Planetary Health Scotland's Rural College Inverness UK.
This study developed a framework using large language models (LLMs) to simplify biosecurity guidance for livestock farmers. The tool provides accessible, evidence-based recommendations to improve farm biosecurity practices and animal health.
Area of Science:
- Agricultural Science
- Veterinary Medicine
- Artificial Intelligence
Background:
- Biosecurity practices are crucial for mitigating disease risk in livestock farming.
- Variable adoption and implementation of biosecurity measures by farmers present a significant challenge.
- Effective communication of biosecurity information is key to improving uptake, reducing disease, and enhancing farm productivity and animal welfare.
Purpose of the Study:
- To develop and demonstrate a framework utilizing large language models (LLMs) for transforming complex biosecurity guidance into accessible information for livestock farmers.
- To create a proof-of-concept system targeting specific UK livestock health challenges (sheep scab, liver fluke, Johne's disease) for cattle and sheep.
- To improve the dissemination and practical application of evidence-based biosecurity recommendations.
Main Methods:
- Leveraging large language models (LLMs) to process and condense expert biosecurity advice from UK government and veterinary sources.
- Developing a purpose-built platform to host condensed, accessible biosecurity summaries.
- Focusing initial implementation on three prevalent diseases affecting cattle and sheep in the UK.
Main Results:
- A centralized repository, 'Bitesize Biosecurity,' was established, offering farmers easy access to evidence-based recommendations.
- The system successfully transforms complex scientific literature into practical, actionable guidance.
- Scientific integrity is maintained while ensuring information is presented in an accessible format for farmers.
Conclusions:
- The developed framework addresses critical gaps in agricultural knowledge transfer between research and practice.
- The system's scalable architecture allows for future expansion to more diseases, farm types, and geographical regions.
- This approach has the potential to significantly transform biosecurity information communication across the entire agricultural sector.
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