Making tiestalls more comfortable: II. Increasing chain length to improve the ease of movement of dairy cows
Bibliographic record
Abstract
Although most farms in Canada still use tiestall housing for dairy cows, little information is available pertaining to cow comfort and behavior in such systems. Tiestalls are often criticized as they offer a reduced dynamic space to cows, thereby restricting their ability to move. The object of this study was to see if increasing the length of the tie chain provides cows with improved movement opportunities and to measure its effect on cows' rising and lying movements and behaviors. Two treatments were tested: the current recommendation of 1.00 m (recommended) and a longer chain of 1.40 m (long). Twenty-four cows (12/treatment) were blocked by parity number and lactation stage, then randomly allocated to a treatment and a stall within one of 2 rows in the research barn for 10 wk. Leg-mounted accelerometers were used to record lying behaviors and moments of transition between lying and standing positions for all cows. Cows were video-recorded for 24 h/wk using cameras positioned above the stall. The videos were used to evaluate the cows' rising and lying-down movements on wk 1, 2, 3, 6, 8, and 10. Six rising and 6 lying-down motions per cow per week were assessed by a trained observer to detect the presence of abnormal behaviors. Differences between and within treatments over time were analyzed in SAS (SAS Institute Inc., Cary, NC) using a mixed model with treatment, week, and block as fixed effects and with row and cow as random effects. Data from wk 1-3 were grouped together as the short-term effects, and those from wk 8-10 as the long-term effects. Week 6 was used as the mid-term assessment for analysis. Multiple comparisons between terms were accounted for using a Scheffé adjustment. Results indicate that duration of intention movements (exploratory head movements made by cows before lying down) is shorter in cows with longer chains (13.6 ± 1.03 s vs. 16.8 ± 1.01 s). It was also significantly shorter in the long term compared with the short term for both treatments (13.3 ± 0.92 s vs. 16.9 ± 0.81 s). These results suggest that increasing the chain length improves the cows' ease of movement and transitions, although all cows became more at ease in their surroundings with time. It may provide evidence of a potential way to improve the dynamic space provided to cows in tiestall systems, using a simple, affordable modification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".