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Large AnimalCase series / Retrospective2 min read · distilled by Vetree AI

On-farm management of vaginal prolapse in ewes: A cross-sectional survey of UK sheep farmers.

Stavisky J, Rigby G, Lockwood S · The Veterinary record · 20 July 2026

Clinical bottom line

Veterinarians must improve farmer education on analgesia, antimicrobial stewardship, and safe retention methods for ovine vaginal prolapse.

Summary

This cross-sectional survey investigated on-farm management practices for vaginal prolapse in ewes among UK sheep farmers during the 2024–2025 lambing season. A total of 127 farmers responded, representing flocks with a median size of 200 ewes. The median prevalence of vaginal prolapse was 1.7%, though it reached up to 20.1% in some flocks. Notably, only 10.2% of respondents sought veterinary intervention for affected animals, confirming that prolapse management is predominantly farmer-led. Encouragingly, the majority of farmers reported washing the prolapse before replacement (87.1%), administering NSAIDs for analgesia (69.9%), using retention harnesses (79.6%), and giving antimicrobials (62.4%). However, significant welfare and stewardship concerns were identified: 6.5% of respondents admitted to suturing the vulva without local anaesthetic, representing a clear welfare deficit. Antimicrobial use without veterinary oversight raises antimicrobial stewardship concerns, particularly given the lack of professional guidance in most cases. The study acknowledges potential social desirability bias, which may mean that problematic practices are underreported. Compared to welfare concerns raised approximately 20 years ago, some improvements in pain management are evident, but gaps remain. The authors conclude that veterinarians have an important educational role in guiding farmers on appropriate analgesia, responsible antimicrobial use, and safe retention methods to improve both animal welfare outcomes and antimicrobial stewardship in sheep production.

Large AnimalReproductionPharmacology

This summary was distilled by AI and may occasionally misinterpret data. Confirm critical details with the primary literature before clinical application.