Pain in Dairy Cattle: A Narrative Review of the Need for Pain Control, Industry Practices and Stakeholder Expectations, and Opportunities
Bibliographic record
Abstract
Pain is an adverse experience causing distress and decreased production in dairy cattle. Pain, and its associated distress, is also undesirable from an animal welfare standpoint. Consumers consider animal welfare an important issue; therefore, ensuring proper pain management, and by extension good welfare, is important to maintain the social acceptability of dairy production. However, while some painful procedures and diseases can be avoided, some are inevitable. When this is the case, it is important to provide pain mitigation where possible. Various dairy quality assurance programs provide guidance on pain management practices for producers; however, guidelines differ across countries and jurisdictions. This narrative review covers common painful conditions and procedures, including disbudding and dehorning, castration, calving and dystocia, surgeries, disease conditions, and lameness. Further, this paper reviews evidence of the necessity and efficacy of pain management in these cases, current uptake of pain management, and quality assurance program standards for addressing pain in dairy cattle. Overall, there are clear advantages to providing pain mitigation for some conditions and procedures. For others, gaps still exist in understanding the best methods for pain control. Further attention should be paid to understanding and reducing the barriers to adoption of pain management strategies, as it is crucial to minimizing pain in animals and ensuring productive and sustainable dairy production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".