PSVIII-B-4 Differences in Bull Prolificacy and Offspring Body Performance in Western Canadian Multi-Sire Commercial Herds
Bibliographic record
Abstract
Abstract Beef cattle producers employing multi-sire natural mating systems expect superior breeding and production performance from the bulls. The bulls that sire more healthy calves are more economical considering that the annual maintenance cost for each bull could exceed $2000. Industry benchmarks on bull prolificacy and the production performance of their offspring will enable producers to make better selection and culling decisions. This 3-yr study evaluated 2,130 calves from 62 bulls and 1, 869 cows to assess bull prolificacy and calf production performance in multi-sire breeding pastures – VS (VS1, VS2), OS, CF, KF, DIF, and DEF across Alberta Canada. Calves were matched to bulls using DNA paternity analysis. Prolificacy was assessed among bulls engaged in breeding activities year-over-year. Percentage calves assigned to each sire from sire-progeny matches were assessed within sites over consecutive years (VS1: 11.5 - 46.34%, VS2: 4 - 75%, OS: 2 - 17%, CF: 6 - 33%, KF: 5 - 38%, DIF: 3 - 26%, DEF: 16 - 62%). The differences in body and growth performance of calves were analyzed with linear mixed models. The average birth weight of calves varied by sires (p < 0.05) in VS1 (39.92kg – 42.74kg; P < 0.05), OS (34.09kg–39.49kg), CF (35.72-40.27kg) but not in VS2 (36.35 – 38.02kg; P > 0.05). Similarly, the average calf weaning weights among sires did not differ in VS1 (309.44 – 316.77kg; P = 0.33) but were different (P < 0.05) in OS (219.25 - 236.36kg), CF (216 -243 kg) and KF (178 - 232kg). Differences were also observed in calves' average daily body gain among sires in VS1 (0.93 – 1.14 kg d-1; P < 0.0001). Verifying the sires of calves in multi-sire herds will enable beef producers to make effective management and economic decisions. Measuring the production performance of calves from different bulls is important for the beef industry's profitability and sustainability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".