Benchmarking cow comfort on North American freestall dairies: Lameness, leg injuries, lying time, facility design, and management for high-producing Holstein dairy cows
Bibliographic record
Abstract
In this paper, we describe a novel approach to corporate involvement in on-farm assessment, driven by the desire to provide a service for dairy producers and to create a vehicle for engagement on issues of dairy cow welfare. This program provides producers with feedback on animal-based (including gait score, leg injuries, and lying time) and facility-based (including freestall design, bedding practices, feed bunk design and management, and stocking density) measures that can be used to better address their management goals. The aim of this paper is to describe variation in the prevalence of lameness and leg injuries, lying behavior, facility design, and management practices for high-producing cows on freestall dairy farms in 3 regions of North America: British Columbia (BC; n=42); California (CA; n=39); and the northeastern United States (NE-US; n=40). Prevalence of clinical lameness averaged (mean ± SD) 27.9±14.1% in BC, 30.8±15.5% in CA, and 54.8±16.7% in NE-US; prevalence of severe lameness averaged 7.1±5.4% in BC, 3.6±4.2% in CA, and 8.2±5.6% in NE-US. Overall prevalence of hock injuries was 42.3±26.2% in BC, 56.2±21.6% in CA, and 81.2±22.5% in NE-US; prevalence of severe injuries was 3.7±5.2% in BC, 1.8±3.1% in CA, 5.4±5.9% in NE-US. Prevalence of swollen knees was minimal in CA (0.3±0.6%) but high (23.1±16.3%) in NE-US (not scored in BC). Lying times were similar across regions (11.0±0.7h/d in BC, 10.4±0.8h/d in CA, 10.6±0.9h/d in NE-US), but individual lying times among cows assessed varied (4.2 to 19.5h/d, 3.7 to 17.5h/d, and 2.8 to 20.5h/d in BC, CA, and NE-US, respectively). These results showed considerable variation in lameness and leg injury prevalence as well as facility design and management among freestall farms in North America. Each of the 3 regions had farms with a very low prevalence of lameness and injuries, suggesting great opportunities for improvement on other farms within the region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".