Herd-level risk factors for lameness in freestall farms in the northeastern United States and California
Bibliographic record
Abstract
The objective was to investigate the association between herd-level management and facility design factors and the prevalence of lameness in high-producing dairy cows in freestall herds in the northeastern United States (NE; Vermont, New York, Pennsylvania) and California (CA). Housing and management measures such as pen space, stall design, bedding type, and milking routine were collected for the high-producing pen in 40 farms in NE and 39 farms in CA. All cows in the pen were gait scored using a 1-to-5 scale and classified as clinically lame (score ≥3) or severely lame (score ≥4). Measures associated with the (logit-transformed) proportion of clinically or severely lame cows at the univariable level were submitted to multivariable general linear models. In NE, lameness increased on farms that used sawdust bedding [odds ratio (OR)=1.71; 95% confidence interval (CI)=1.06-2.76] and decreased with herd size (OR=0.94; CI=0.90-0.97, for a 100-cow increase), use of deep bedding (OR=0.48; CI=0.29-0.79), and access to pasture (OR=0.52; CI=0.32-0.85). The multivariable model included herd size, access to pasture, and provision of deep bedding, and explained 50% of the variation in clinical lameness. Severe lameness increased with the percentage of stalls with fecal contamination (OR=1.15; CI=1.06-1.25, for a 10% increase) and with use of sawdust bedding (OR=2.13; CI=1.31-3.47), and decreased with use of deep bedding (OR=0.31; CI=0.19-0.50), sand bedding (OR=0.32; CI=0.19-0.53), herd size (OR=0.93; CI=-0.89-0.97, for a 100-cow increase), and rearing replacement heifers on site (OR=0.57; CI=0.32-0.99). The multivariable model included deep bedding and herd size, and explained 59% of the variation of severe lameness. In CA, clinical lameness increased with the percentage of stalls containing fecal contamination (OR=1.15; CI=1.05-1.26, for a 10% increase), and decreased with herd size (OR=0.96; CI=0.94-0.99, for a 100-cow increase), presence of rubber in the alley to the milking parlor (OR=0.46; CI=0.28-0.76), distance of the neck rail from the rear curb (OR=0.97; CI=0.95-0.99, for a 1-cm increase), water space per cow (OR=0.92; CI=0.85-0.99, for a 1-cm increase), and increased frequency of footbaths per week (OR=0.90; CI=081-0.99, for a 1-unit increase). The multivariable model included herd size, percentage of stalls containing fecal contamination, and presence of rubber in the alley to the milking parlor, and explained 44% of the variation of clinical lameness. Severe lameness increased with the percentage of stalls containing fecal contamination (OR=1.23; CI=1.06-1.42, for a 10% increase) and decreased with frequency of manure removal in the pen per day (OR=0.72; CI=0.53-0.97, for a 1-unit increase). The final model included both variables and explained 28% of the variation in severe lameness. In conclusion, changes in housing and management may help decrease the prevalence of lameness on dairy farms, but key risk factors vary across regions.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".