A focus group study exploring dairy farmers’ perspectives of cull cow management in Ontario, Canada
Bibliographic record
Abstract
Introduction: Maintaining the welfare of cull dairy cows from the farm to slaughter is an ongoing challenge for the dairy industry. Recent research suggests that some cull dairy cows within the marketing system are in physical states that are below regulatory standards, and further research is required to determine why these unfit cows are found throughout the journey to abattoirs. Since dairy farms are the origin of these cows, decision making by dairy farmers has been identified as key to preventing cull cows that are considered unfit for transport from entering the marketing system. The objectives of this study were to understand dairy farmers' perspectives on their cull dairy cow management practices, recommendations and requirements of regulations, management tools, and welfare issues. Methods: Four focus groups with a total of 21 participants were each conducted virtually, video recorded, and transcribed verbatim, with dairy farmers from Ontario, Canada. A thematic analysis of focus group discussions was conducted utilizing deductive reasoning. Results: There were three themes identified including deciding to cull or not, management of cows being culled, and knowledge and perceptions of cull cow regulations. When making culling decisions, farmers utilize multiple sources of information including personal experiences and values and external referents like veterinarians, family members and other farmers. The welfare of their cows was a high priority but one that was often weighed against the financial outcomes of culling decisions. Finally, most participants considered recent regulatory changes for the management of cows before shipment to be of little importance on their farms. Discussion: In conclusion, the farmers from this study showed the diversity of considerations they make in culling decisions and the large contribution of animal productivity and economic factors. There was a general lack of knowledge of recent regulatory changes for the shipment of cull cows, and there is room for improving the uptake of new recommendations for culling only cows fit for transportation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".