Milk feeding and calf housing practices on British Columbia dairy farms
Bibliographic record
Abstract
Decades of research have helped inform practices on how to care for calves, but little is known about how well these practices are adopted on commercial dairy farms. The primary aim of this study was to describe rearing practices of dairy calves in British Columbia, 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 via email from the British Columbia Ministry of Agriculture and Food from June to December 2023. A total of 63 completed responses were received (i.e., 14.4% of those invited). Milking herd size averaged (± SD) 167 ± 172 cows, and the primary breed was Holstein for 84.1% of respondents. Most (63.5%) farms housed calves individually before weaning; the remainder used either just social housing (groups of 2 or more; 25.4% of farms) or a combination of individual and social housing (11.1% of farms). Maximum milk allowance averaged 9.5 ± 2.7 L/d, with 86.4% of respondents offering >8 L/d. Teat feeding was used on 71.6% of farms, and 13.1% of farms used automated milk feeders. Two participants reported feeding calves via the dam or nurse cows. Weaning age averaged 75.8 ± 16.3 d, with calf age being the primary criterion for weaning. About half (52.4%) of farms reported monitoring calf growth, and 31.7% of farms reported having a target growth rate. Our results suggest that rearing practices are changing, in that calves are now often fed higher milk rations via a teat, and many farms use social housing. However, individual housing remains prevalent, suggesting research is needed to understand the barriers to adopting social housing on farms. Our findings also suggest the opportunity to improve methods for monitoring calf growth; improved measures may facilitate evidence-based evaluations of calf rearing and weaning protocols on 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 | 0.001 | 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".