Facilities and housing, husbandry, and health management practices on Quebec dairy farms: A retrospective descriptive study
Bibliographic record
Abstract
The objective of this retrospective descriptive study was to characterize housing and herd management practices of Quebec dairy farms. Pre-existent survey data (housing, husbandry, and herd health; 36 questions) collected in person by Lactanet technicians (n = 116; March 2020 to February 2021) from 1,965 herds were used. Results were segregated by facility type (freestall [FS] vs. tiestall herds [TS]) and summarized using descriptive statistics. The average herd size was 76 ± 56 cows, with 65 ± 47 milking cows, peaking at 40 ± 5 kg/d, with most herds housed their milking cows in TS facilities (80%). Of the FS herds (20%), 36% transitioned from TS after 2016. Based on the Canadian Code of Practice and peer-reviewed literature, management strengths included frequent bedding in TS, feed reduction before dry-off (>59% FS; >75% TS), routine hoof trimming (≥2×/yr in >80%), and adequate lighting (>85% maintained >200 lx for 14-17 h/d). Areas needed improvement included the adoption of secondary ventilation systems (observed in <45% FS and <20% TS), implementation of targeted dry-off protocols (>80% of herds applied a single dry-off protocol, regardless of milk yield) and greater adoption of teat sealant use (45% reported using intramammary antibiotics without teat sealants). Deep-bedded lying surfaces were uncommon (30% of far-off and lactating groups in FS; <20% of dry and lactating groups in TS). In FS herds, horizontal bars were most frequent in lactating groups (40%). In TS herds, 58% of herds calved cows in tiestalls and >80% lacked pasture or exercise pens access. Regarding hoof health, footbaths and sprays were largely absent in TS herds (90%), whereas FS herds more often used footbaths, especially for lactating cows (70%). These findings establish benchmarks for Quebec dairy herds, highlighting well-adopted practices and identifying opportunities to increase the uptake of specific management practices across the province to further enhance herd health and welfare.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".