Cow comfort in tie-stalls: Increased depth of shavings or straw bedding increases lying time
Bibliographic record
Abstract
Over half of US dairy operations use tie-stalls, but these farming systems have received relatively little research attention in terms of stall design and management. The current study tested the effects of the amount of 2 bedding materials, straw and shavings, on dairy cattle lying behavior. The effects of 4 levels of shavings, 3, 9, 15, and 24 kg/stall (experiment 1, n = 12), and high and low levels of straw in 2 separate experiments: 1, 3, 5, and 7 kg/stall (experiment 2, n = 12) and 0.5, 1, 2, and 3 kg/stall (experiment 3, n = 12) were assessed. Treatments were compared using a crossover design with lactating cows housed in tie-stalls fitted with mattresses. Treatments were applied for 1 wk. Total lying time, number of lying bouts, and the length of each lying bout was recorded with data loggers. In experiment 1, cows spent 3 min more lying down for each additional kilogram of shavings (11.0, 11.7, 11.6, and 12.1 +/- 0.24 h/d for 3, 9, 15, and 24 kg/stall shavings, respectively). In experiment 2, cows increased lying time by 12 min for every additional kilogram of straw (11.2, 12.0, 11.8, and 12.4 +/- 0.24 h/d for 1, 3, 5, and 7 kg/stall of straw, respectively). There were no differences in lying behavior among the lower levels of straw tested in experiment 3 (11.7 +/- 0.32 h/d). These results indicated that additional bedding above a scant amount improves cow comfort, as measured by lying time, likely because a well-bedded surface is more compressible.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".