A review: Breeding behavior and management strategies for improving reproductive efficiency in bulls
Bibliographic record
Abstract
This review focuses on bull breeding behaviors and management strategies to improve reproductive efficiency. Breeding soundness evaluations are utilized to classify a bull's physical ability and sperm quality, yet roughly 20 % of bulls fail to meet the minimum criteria. Furthermore, despite achieving the minimum criteria, few bulls in multi-sire breeding groups sire the majority of calves, indicating a need for better understanding of bull behavior that impact siring capacity, and thus, a bull's reproductive efficiency. Several factors influence bull libido such as age, breed, and environmental conditions. Although service capacity tests have been used to measure libido, standardization and repeatability, along with variability in age and breed, can be problematic. Management in collection facilities largely focuses on the pre-stimulation of bulls through behavioral cues for better sperm quality and quantity during collection, thus improving a bull's reproductive efficiency through fewer collections with increased breeding doses harvested. In management of multi-sire breeding groups, understanding social interactions, bull-to-female ratios, synchronization of females, and DNA testing to determine parentage, are techniques that can be utilized to improve reproductive efficiency. New research utilizing remote monitoring technology is being developed to better understand bull behavior without the constraints of direct observation. This technology may be used to predict siring capacity, better manage bulls based on social dynamics, and potentially detect lameness or injury in bulls that may impact siring capacity. A better understanding of developing management strategies of breeding behaviors should be further investigated to improve reproductive success of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".