Alberta beef industry stakeholders' perspectives towards adoption of preconditioning practices: A thematic analysis
Bibliographic record
Abstract
Preconditioning offers significant benefits for calf health, performance, and a smoother transition to the feedlot environment, while also helping to reduce antimicrobial use in feedlots. Despite these benefits, the adoption of preconditioning by cow-calf producers, as well as the buying and selling of preconditioned calves by feedlot operators and auctioneers, remains relatively low. However, some producers continue to precondition their calves regardless of lack of industry interest. To increase the implementation of preconditioning and improve calves' welfare within the industry, it is crucial to understand the motivators and barriers influencing its adoption among cow-calf operators, feedlot operators, and auctioneers, as well as the associated market dynamics around preconditioned calves. Therefore, semi-structured interviews were conducted with twelve cow-calf operators, three feedlot operators, and five auctioneers in Alberta, and the data were analyzed using inductive thematic analysis. Motivations and barriers were classified as either generic or specific to participant groups. Five key themes were identified regarding stakeholders' perceptions of preconditioning practices: 1) Ranchers' satisfaction and accountability: the heartbeat of preconditioning, 2) Thriving calves: preconditioning for healthier calves, smooth transition, 3) No gain, no preconditioning: financial incentives drive adoption, 4) The quest for preconditioning proof: buyers struggle without verification, and 5) Supply shortfall: shortage of preconditioned calves during periods of high market demands. Producers who preconditioned their calves were primarily motivated by a sense of personal satisfaction, driven by improved welfare of their calves. In contrast, those who did not practice preconditioning emphasized the lack of a price premium as a significant barrier. While all stakeholders recognized the health benefits and quicker acclimatization of preconditioned calves to the feedlot, auctioneers and feedlot operators reported a lack of reliable verification and the low supply of preconditioned calves as major challenges. We concluded that by implementing third-party verification of preconditioning practices, expanding online auctions tailored to preconditioned calves, and establishing trust and financial incentives can encourage greater adoption of preconditioning practices within the beef industry, ultimately leading to improved animal health, productivity, and welfare.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".