Characterization of heifer rearing costs and their association with farm factors of Québec Holstein dairy farms
Bibliographic record
Abstract
Although raising heifers is an important investment for dairy farms, heifer rearing costs and their contributing factors are rarely assessed. The objective of the study was to quantify and characterize the cost of raising replacement Holstein heifers from birth to calving and per growing phases, and to identify the management practices associated with the lowest rearing cost. This analysis was conducted using rearing practices and the related costs from 87 conventional Holstein dairy farms in Québec, Canada. Data were collected directly from the farm and their financial management services. The average total cost was calculated to be $4870 ± 757 CAD heifer−1 with a great variation throughout the growing phases. In particular, the period from weaning to 12 months had the most advantageous cost of gain. A cluster analysis by total rearing cost revealed that farms in the Low rearing cost cluster ($4145 CAD heifer−1; P < 0.001) had better labor organization and optimized feed costs, leading to raising heifers at lower cost without negative impact on growth performances nor maturity at first breeding and maturity at first calving. By identifying key factors that influence rearing cost, the findings can help dairy producers to improve economic sustainability and enhance profitability.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".