Application of DNA parentage testing and EnVigour HX™ to evaluate bull prolificacy and heifer performance in beef cattle breeding programs in Western Canada
Bibliographic record
Abstract
Abstract The use of multi-sire breeding pastures is a common practice in Western Canada for beef cattle management. However, the number of progenies sired by each bull may not be known or producers will not be sure if performance traits were passed on. Hybrid vigor is defined as the superiority of crossbred progeny over their parents’ average to increase production efficiency, longevity and reproductive rate of beef cows. A DNA parentage test was performed to identify relative sires to their progeny. Then, 109 bull prolificacy indexes (BPI) were calculated for 46 sires over 6 breeding seasons (some had more than one BPI). They ranged from 0.04 to 3.47, with values larger than one showing high prolific sires. Yearling sires had a significantly lower BPI value than 2-year-old and mature bulls. Regardless of the level of BPI, each sire produced the majority of all his born calves in the first cycle of the calving period (R2 = 0.89). This demonstrated the importance of identifying high BPI sires, as they showed a tendency to have a greater percentage of the first cycle born grand calves from their first cycle born heifers (R2 = 0.86). Also, they produced a greater number of grand calves from their retained daughters with a significant impact on total kg weaned (P < 0.01). The EnVigour HX™ test was applied to estimate the effect of bulls’ and heifers’ vigor scores (scores were based on percentage) on fertility and production traits. The breeder’s interest in the utilization of crossbred heifers and bulls positively affected the longevity of replacement females. There was a decrease in age at first calving relative to the EnVigour HX™ test commercialized suggestions per 10% increase in heifers’ vigor scores. Moreover, larger productivity was observed for average WW per calf and cow life productivity, which could be due to different genomic breed compositions between this study and the test animals. Overall, DNA parentage and EnVigour HX™ tests are essential tools for beef cattle production profitability. However, up to a 75% vigor score cutoff is suggested when selecting crossbred females with EnVigour HX™ to retain production and fertility efficiency in crossing with Beef Booster bulls.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".