Effect of transitioning to automatic milking systems on producers' perceptions of farm management and cow health in the Canadian dairy industry
Bibliographic record
Abstract
Automatic milking systems (AMS), or milking robots, are becoming increasingly common, but there is little documentation of how AMS have affected farms as a whole and what challenges and benefits producers are experiencing during their transition to AMS. The objective of this national survey was to document the effect of transitioning to AMS on producer perceptions of change in housing, farm management, and cow health. In total, 217 AMS producers were surveyed from 8 Canadian provinces. Median time since transition for respondents was 30 mo. The mean number of lactating cows per robot was 51 cows, with a median of 2 AMS units per farm. Fifty-five percent of producers built a new barn to accommodate the AMS. Changing housing systems was necessary for 47% of producers, not necessary for 50%, and not applicable to 3% (as the AMS farm was their first farm). Cleaning and feeding practices remained the same. Overall, farms increased herd size from a median of 77 to 85 lactating cows with the transition to AMS. After the transition to AMS, 66% of producers changed their health-management practices. Producers reported either a decrease or no change in rate of clinical mastitis. Reports on change in rate of lameness and total bacterial count varied. Conception rate was reported to have increased for 63% of producers. Culling rate was perceived to have stayed the same for 59% of producers. Overall, producers perceived their transitions to AMS as successful. Findings from this project provide a benchmark of the effects of AMS on important aspects of Canadian dairy farming, as well as provide producers, AMS manufacturers, veterinarians, and dairy advisors with more detailed knowledge on what to expect when transitioning to 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".