Evaluation of the characteristics of dairy farms producing Holstein calves to increase their valuation in the heavy veal sector in Québec
Bibliographic record
Abstract
main reason why dairy producers are limiting their efforts of valorisation, which reverberates on the health and well-being of the calves. Since these two parameters are essential for the optimal expression of their potential in feedlot, the association “Producteurs des bovins du Québec” (PBQ) requested the expertise of the Faculty of Veterinary Medicine (FMV) research team. Objective: The study highlights the most common calves rearing practices implemented in farms aiming the production calves with greater valuation in the Québec heavy veal industry. The objective is to identify and highlight the critical factors and good practices that ensure the production of high-quality calves for the general adoption of better long-term management (on animal health and welfare). Materials and methods: A total of 166 questionnaires were constructed and applied to Québec producers. Each questionary is composed by 56 questions covering selected parameters namely Calf management at birth, Nutrition and Housing management, in accordance with the priority stated by the producers questioned. Other parameters were also evaluated in this review such as producer background, farm general profile, biosecurity practices, calving management and conditions of departure to auction that remain in the Appendix for academic purpose. The results are based on the statistical analysis, under the form of a descriptive analyse realized with the software R studio (Version 2022.07.2+576), of the 56 questions. Results: The most important priorities for the producers were colostrum management (40,6%), housing management (25,1%) and milk nutrition (11,1%). As an example, when questioning the colostrum management more specifically, it turned out that 19,88% of the producers are respecting the literature standards but 5,42% of the producers make distinctions between replacement calves and calves for auction, subjects of our study. The results denote an under-valorisation of calves for auction and the questionnaires show a wish to find solutions to alleviate this lack of consideration. Future work: The continuation of the project will consist of developing a list of approximately 25 farms from the 166 farms requested in the questionnaire, to be contacted and visited for field data collection and will include the verification of the reliability of the results obtained on this report.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".