Cow-level associations of lameness, behavior, and milk yield of cows milked in automated systems
Bibliographic record
Abstract
This study evaluated differences in behavior and productivity between lame and nonlame cows in herds with automated milking systems (AMS). We monitored 30 cows per herd on 41 farms with AMS in Canada (26 herds in Ontario and 15 herds in Alberta). During a 6-d period, milking data (n = 1,184) and lying behavior data (n = 1,209) were collected from cows on 41 farms. Rumination behavior (n = 569) and activity (n = 615) data were available for cows at 22 farms. Locomotion was scored using a numerical rating system (NRS; 1 = sound; 5 = extremely lame). Cows were defined as clinically lame with NRS ≥ 3 (n = 353, 29%) and nonlame with NRS < 3 (n = 865, 71%). Greater parity, lower body condition, and lower environmental temperature were factors associated with lameness. When accounting for other factors, lame cows produced 1.6 kg/d less milk in 0.3 fewer milkings/d. Lame cows were 2.2 times more likely to be fetched more than 1 time during the 6-d period and spent 38 min/d more time lying down in bouts that were 3.5 min longer in comparison with nonlame cows. As the number of cows per AMS unit increased, the frequency of milkings and refusals per cow per day decreased and cow activity increased. For each 13.3-percentage-point increase in freestall stocking density (cows per stall), daily lying time decreased by 13 min/d and cows were 1.6 times more likely to be fetched more than 1 time during the 6-d period. There was no difference in daily rumination or activity between lame and nonlame cows or in night:day rumination time, but lame cows had greater night:day activity ratios. This study supports the growing knowledge that lameness has negative effects on milk production, voluntary milking behavior, and lying behavior of cows in herds with AMS. These results may help dairy producers gain a better appreciation of the negative effects of even moderate cases of lameness and may help motivate them to improve their lameness monitoring and treatment protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".