Performance perception of Canadian dairy producers when transitioning to an automatic milking system
Bibliographic record
Abstract
Adoption of automated milking systems (AMS) has increased exponentially around the world in recent years. The objective of this observational study was to evaluate how producer perception of changes in cow-average milk yield and somatic cell count (SCC) compared with the actual changes in their herds after the introduction of AMS in Canadian commercial dairy herds. Data were collected (in 2014 and 2015) through a survey of 97 Canadian dairy herds that shifted to AMS from 2000 to 2014. Producers were asked their perception about milk yield and SCC changes (increase, decrease, or no change) after AMS introduction. Actual herd performance data were obtained from dairy herd improvement organizations. Differences between the 12-mo rolling herd-average milk yield (kg/cow per year) and SCC (cells/mL) at the closest test 2 yr after transitioning to AMS and at the last test before the transition were calculated and compared with the producer perception answers. After AMS adoption, milking herd size, milk yield, SCC, and number of AMS units per herd averaged (± standard deviation) 99.8 ± 54.4 cows, 9,619 ± 1,354 kg/cow per year, 248,825 ± 97,286 cells/mL, and 1.9 ± 1.1 units, respectively. On average, after AMS introduction, herd size, milk yield, and culling rate increased by 11.3 cows, 441 kg/cow per year, and 1.3%, respectively, and calving interval decreased by 7 d. For producers who perceived an increase, actual milk yield and SCC increases averaged (mean ± standard deviation) +534 ± 1,003 kg/cow per year and +56,679 ± 66,662 cells/mL, respectively. Alternatively, for producers who perceived a decrease, actual milk yield and SCC decreases averaged -984 ± 658 kg/cow per year and -26,976 ± 94,099 cells/mL, respectively. An actual milk yield change of +83.1 ± 1,113.3 kg/cow per year and an SCC change of +6,135 ± 72,609 cells/mL were observed in the herds in which the dairy producers perceived no change with the AMS introduction. Hence, dairy producers were, on average, able to discern their actual milk yield and SCC changes after AMS adoption. However, the proportions of dairy producers who accurately perceived their actual milk yield and SCC changes after AMS introduction were 39.4% for milk yield (increase: 36.3%; decrease: 100.0%; and no change: 45.5%) and 46.7% for SCC (increase: 50.0%; decrease: 39.0%; and no change: 54.1%). From these results, we concluded that several dairy producers distorted their actual milk yield and SCC changes or were not fully aware of those changes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".