Milk feeding and calf housing practices on British Columbia dairy farms
Bibliographic record
Abstract
The primary aim of this study was to describe rearing practices of dairy calves on farms in British Columbia (BC), Canada. Measures of calf growth are sometimes used to assess success in calf rearing, so a secondary aim was to describe methods used to assess calf growth on these farms. All 437 dairy farms in the province were invited to participate in a survey distributed from June-December 2023. A total of 63 complete responses were received (representing 14.4% of the farms in BC). Milking herd size averaged (± SD) 167 + 172 cows, and the primary breed was Holstein for 84 % of respondents. Participants reported having an average of 2.8 + 1.5 employees responsible for pre-weaned calf care. Most (63.5%) farms housed calves individually before weaning, but some (25.4%) socially housed calves in groups of two or more and others (11.1%) used a combination of individual and social housing. The mean maximum milk allowance was 9.4 + 2.8 L/d, with 87% of respondents offering >8 L/d. Teat feeding was used on 71.7% of farms, with 13.1% using automated milk feeders. Two participants reported feeding calves via the dam or nurse cows. Weaning age averaged 76 ±16.3 d, with calf age being the primary criterion for weaning. About half (52.4%) of farms reported measuring calf growth, and 31.7% reported to having a target growth rate. Our results suggest that milk feeding practices in BC are changing, such that calves are now often fed higher milk rations via a teat. Individual housing remains common, suggesting further research is needed to understand the barriers to adopting social housing on commercial farms. In addition, our findings suggest room for improvement in monitoring calf growth; improved tracking of calf growth may facilitate evidence-based evaluations of calf rearing and weaning protocols on dairy farms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".