Reproductive management practices on dairy farms: The Canadian National Dairy Study 2015
Bibliographic record
Abstract
The objectives of this cross-sectional study were to characterize reproductive management practices on Canadian dairy farms and describe differences based on regional and demographic factors. A questionnaire was offered to all licensed Canadian dairy producers and included 189 questions regarding producer and farm background information, herd dynamics, biosecurity, disease prevalence, calf health, animal welfare, milking practices, reproduction, and internet use. Twenty-four questions were related to estrus detection, hormonal protocols for reproduction, insemination, and pregnancy diagnosis. A total of 1,373 producers responded to the survey, representing a response rate of 12.5%. Estrus detection practices in lactating cows were associated with herd size, barn type, region, organic production, breeding method, and age of respondent. The most commonly used estrus-detection method in cows was visual (51.0% of farms for first insemination; 45.5% for subsequent inseminations). Estrus detection for nulliparous heifers was associated with herd size, barn type, region, and breeding method, with visual detection also the most common method for heifers (71.3% of farms). Eighty percent of farms used strictly artificial insemination, 2.8% used natural service only, and 16.8% used a combination of artificial insemination and natural service. Breeding method was associated with herd size, barn type, region, and education level of the respondent. Pregnancy diagnosis method was associated with herd size, barn type, region, and organic production. Ultrasound was the most commonly used method of pregnancy diagnosis (used by 52.2% of farms). Sixty-nine percent of farms rechecked cows for pregnancy, and rectal palpation was the most commonly used method (employed by 48.7%). Reproductive management practices vary considerably among Canadian dairy farms and decisions are associated with farm-level factors, including region, herd size, and barn type, as well as producer-level factors, such as age, managerial role, and education level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| 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".