Breeding Bulls in Alberta: a cross-sectional descriptive survey of breeding bull herds and current management strategies
Bibliographic record
Abstract
Currently, there is very little known about bull demographics and how bulls are managed on cow-calf operations in Alberta. Identifying these knowledge gaps will provide valuable insights on producer practices and potentially influence future management protocols that will improve bull management and the productivity of cow-calf operations. The objectives of this study were to benchmark beef bull herd demographics and bull management strategies in Alberta, Canada and to investigate associations between herd demographics and management strategies. Cow-calf producers were surveyed between February and August 2022 by paper or electronic copies of the survey. Fifty-two of 72 respondents were enrolled. Forty-two percent of the respondents were seedstock producers. The median female herd size exposed for breeding was 160 cows (IQR: 57 to 275; range: 14 to 1600) as reported by 43 respondents. The most common breed of bull used was Angus, followed by Hereford. The median number of bulls kept was 9, (IQR: 4 to 15; range: 1 to 71). Roughly 1 in 6 bulls were culled with the majority of bulls being culled or treated for conditions due to musculoskeletal or infectious foot causes. The median bull:cow ratio was 1:21 (IQR: 1:11 to 1:25) for heifers and 1:23 (IQR 1:20 to 1:26) for cows. The top 3 selection considerations for replacement bulls for breeding heifers was bull birthweight, bull EPD for calving ease, and physical appearance. The selection criteria for cows were bull adjusted weaning weight, physical appearance, and foot and leg conformation. This study contributes novel information on the breeding bull demographics and management strategies of cow-calf producers in Alberta. In addition, it identifies musculoskeletal issues as the major causes of treatment, death, and culling in bulls and recommends further investigation into causes and management strategies to mitigate these issues and improve bull health 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".