Benchmarking of farms with automated milking systems in Canada and associations with milk production and quality
Bibliographic record
Abstract
The objective of this study was to benchmark the herd-level housing and management strategies of automated milking system (AMS) farms across Canada and assess the associations of these herd-level housing factors and management practices with milk production and quality. Canadian AMS farms (n = 197; Western Canada: n = 50, Ontario: n = 77, Quebec: n = 59, Atlantic Canada: n = 11) were each visited once from April to September 2019, and details were collected related to barn design and herd management practices. Milk-recording data for the 6 mo before farm visits were collected. Farms averaged (± standard deviation) 110 ± 102 lactating cows, 2.4 ± 1.9 AMS units/farm, 47.5 ± 14.9 cows/AMS, 36.7 ± 5.0 kg/d of milk, 4.13 ± 0.34% fat, 3.40 ± 0.16% protein, and a herd-average somatic cell count of 186,400 ± 80,800 cells/mL. Farms mainly used freestall housing systems (92.5%), organic bedding substrates (73.6%), and free flow cow traffic systems (87.8%); farms predominantly milked Holsteins (90.4%). Multivariable regression models were used to associate herd-level housing factors and management practices with milk production and quality. At the herd level, feed push-up frequency (mean = 12.8 ± 8.3 times per day) and feed bunk space (mean = 64 ± 21.5 cm/cow) were positively associated with milk yield. Greater milk yield was associated with herds using inorganic (sand) versus organic bedding, milking Holsteins versus non-Holsteins, and using a form of mechanical ventilation versus natural ventilation alone. Milk fat and milk protein content were only associated with breed. Herds with lower somatic cell counts had more frequent alley cleaning (mean = 12.1 ± 7.5 times per day), wider lying alleys (mean = 304.5 ± 40.0 cm), and sand bedding. The results highlight the importance of using sand bedding, using mechanical ventilation, keeping feed pushed up, ensuring alleys are clean, and ensuring adequate space at the feed bunk for maintaining herd-level productivity and milk quality in farms with AMS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".