Associations of herd-level housing, management, and lameness prevalence with productivity and cow behavior in herds with automated milking systems
Bibliographic record
Abstract
Lameness is problematic for herds with automated milking systems (AMS) due to negative effects on milking frequency and productivity. The objective of this study was to evaluate how management, barn design, and the prevalence of lameness relate to productivity and behavior at the herd level in AMS. Details about barn design, stocking density, and management were collected from 41 AMS farms in Canada (Ontario: n=26; Alberta: n=15). We collected milking data for all cows on each farm, plus lying behavior data for 30 cows/farm during a 6-d period. Farms averaged 105±56 lactating cows and 2.2±1.3 AMS units. Forty cows/farm were gait scored (or 30% of cows for herds with >130 cows) using a numerical rating system (NRS; 1=sound to 5=extremely lame). Cows were defined as clinically lame with NRS ≥3 (mean=26.2±13.0%/herd) and severely lame with NRS ≥4 (mean=2.2±3.1%/herd). The prevalence of both clinical and severe lameness were negatively associated with environmental temperature. Clinical lameness tended to be less prevalent with more frequent scraping of manure alleys. The prevalence of severe lameness was positively associated with stocking density and curb height of the lying stalls. Milking frequency/cow per day was negatively related to the ratio of cows to AMS units. Doubling the prevalence of severe lameness (i.e., from 2.5 to 5%) was associated with reductions in milk production of 0.7kg/cow per day and 39kg/AMS per day. Milk/AMS was positively associated with more cows/AMS (+32kg/cow). Fewer cows were fetched to the AMS with more frequent alley scraping. Lying behavior was associated with the frequency of feed push-ups, stall base, and environmental temperature. These results highlight the need for AMS producers to identify and reduce clinical lameness because 26% of cows/herd were clinically lame. Further, the results indicate that more frequently scraped alleys and optimal stocking densities are associated with improved cow mobility, productivity, and voluntary milking behavior.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".