A Multi-disciplinary Approach Towards Improving Surplus Calf Care on Dairy Farms
Bibliographic record
Abstract
This thesis was conducted to determine how neonatal calf care practices are employed on dairy farms and explore strategies that could be used to motivate improvements. Additionally, we aimed to understand in which circumstances benchmarking could motivate dairy producers to adopt better care practices for surplus and replacement calves.First, a qualitative focus group study explored producer perspectives on their surplus and replacement calf care. Producers experienced barriers to calf care including a lack of clarity on best management practices and prioritization of resources to the lactating herd. Motivating factors included feeling morally obligated to their calves, along with societal and industry expectations. Producers who kept their surplus calves longer felt the cost of neonatal care was worthwhile whereas those selling calves at a young age felt frustrated by a lack of compensation for providing good care.Quantitative evidence collected from a survey of dairy producers showed room for increased uptake of best management practices for colostrum and milk feeding for all calves, and that a minority of farms provided discrepant care to surplus calves. Survey respondents suggested surplus calf care improvements would follow financial incentives and feedback from their calf buyer. Additionally, the Code of Practice for the Care and Handling of Dairy Cattle and the guidance of their herd veterinarian would impact adoption of new practices. A cross sectional study evaluating surplus calves at an assembly facility found that 24% of calves had poor serum total protein concentrations (an indicator of immunity), with better results in crossbred beef calves compared to dairy bred calves.Qualitative analysis was used to evaluate how benchmarking can be used to motivate improved calf care. Most farms were anticipating or considering making a change to their calf management following receiving benchmark data on their calves from their herd veterinarian. This was influenced by farm contexts including (1) farm resources (2) calf productivity (3) management strategies and (4) the producer’s personal values. Depending on these contexts, benchmarking sparked change through providing resources (illustrative data and veterinary advice) and influencing producer decision making. This work provides guidance for motivating improved calf care on dairy farms.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".