The adoption of social housing for milk-fed calves on dairy farms in British Columbia
Bibliographic record
Abstract
Rearing milk-fed dairy calves in pairs or groups (i.e., social housing) can be beneficial for their welfare, but individual housing remains the norm on many farms in North America. Although some research has investigated farmer perceptions of social housing, little research has investigated farmers' experiences with transitioning from individual to social housing. I aimed to describe dairy farmers' experiences of transitioning from individual to social calf housing using the Innovation-Decision Process, a framework that includes the processes leading up to, implementing, and sustaining (or discontinuing) a practice. I conducted in-person semi-structured interviews with 17 dairy farmers from 15 farms in British Columbia, Canada, that transitioned calf housing during the milk feeding period from an individual to a social setup (n = 12 farms); as well as farms that returned to individual housing after the transition (n = 3 farms). Interviews averaged 86 min in length and were anonymized, transcribed, and thematically coded. The findings indicate that the initial motivation to adopt social housing was influenced in part by participants' social environment, but also by calf-based (e.g., growth) and farm-based (e.g., labor saving) reasons. After implementing social housing, participants varied in the challenges (e.g., competition between calves for feed access) and benefits (e.g., improved calf growth) they experienced. Participants also discussed problem-solving approaches to improve outcomes, such as modifying housing and management practices, sometimes cycling through multiple approaches before settling on one that worked for them. The results of this thesis contribute to our understanding of farmer motivators, needs, concerns, and approaches when transitioning to social housing for calves. More generally, the results provide insights into how changes in farm practices occur, helping to inform the adoption of other practices on 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".