Invited review: Prevalence, risk factors, treatment, and barriers to best practice adoption for lameness and injuries in dairy cattle—A narrative review
Bibliographic record
Abstract
Lameness and leg injuries are both painful and prevalent across the dairy industry, and are a major welfare concern. There has been a considerable amount of research focused on investigating the risk factors associated with lameness and injuries and how they might be prevented and treated. The objectives of this narrative review were to summarize herd-level prevalence estimates, risk factors, strategies for prevention, control, and treatment of these conditions, and the barriers to best practice adoption for lameness and injuries on dairy farms. There is a relatively high within-herd prevalence of lameness on dairy farms globally, with a recent systematic review estimating the mean prevalence at 22.8%. Similarly, there is a relatively high prevalence of hock injuries, with within-herd estimates ranging from 12% to 81% of cows affected. Knee and neck injuries have been reported to be less common, with 6% to 43% and 1% to 33%, respectively. Numerous risk factors have been associated with the incidence of lameness, notably housing (e.g., access to pasture, bedding depth, bedding type, flooring type, stall design), management (e.g., stall cleanliness, frequency of trimming, holding times, stocking density), and cow-level (e.g., body condition, parity, injured hocks) factors. Risk factors associated with hock injuries can be similarly classified into housing (e.g., bedding type and depth, outdoor access, parlor type, stall design), management (e.g., bedding depth, cleanliness), and cow (e.g., parity, days in milk, lameness) factors. Key preventative approaches for lameness include routine preventative and corrective hoof trimming, improving hoof cushioning and traction through access to pasture or adding rubber flooring, deep-bedded stalls, sand bedding, ensuring appropriate stocking densities, reduced holding times, and the frequent use of routine footbaths. Very little research has been conducted on hock, knee, and neck injury prevention and recovery. Numerous researchers have concluded that both extrinsic (e.g., time, money, space) and intrinsic (e.g., farmer attitude, perception, priorities, and mindset) barriers exist to addressing lameness and injuries on dairy farms. There are many diverse stakeholders in lameness and injury management including the farmer, farm staff, veterinarian, hoof trimmer, nutritionist, and other advisors. Addressing dairy cattle lameness and injuries must, therefore, consider the people involved, as it is these people who are influencing and implementing on-farm decisions related to lameness prevention, treatment, and control.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".