Influence of individual animal characteristics on beef cattle performance while grazing northern temperate rangeland
Bibliographic record
Abstract
This study evaluated the effects of age, genomic retained heterozygosity (gRHET), and residual feed intake corrected for back-fat thickness (RFI fat ) on weight gain in heifers, cows, and calves grazing native pasture over three years (2021–2023), during summer and fall in a northern temperate region. Three groups were studied: A) annual cohorts of 1-year-old heifers and 3-year-old Kinsella Composite (KC) crossbred cows (n = 182); B) a multi-aged purebred Angus cow herd (3–11 years; n = 133) monitored for one year; and C) a multi-aged KC crossbred cow herd (3–9 years; n = 132) tracked for one year. Average daily gain (ADG) was generally higher in summer than fall, except in 2023 within the multi-aged cattle when increased fall precipitation influenced ADG in crossbred cows. Pre-weaning calves gained more weight in summer ( P <0.001). Among crossbred cows, age and RFI fat were key predictors of ADG, with older cows (>9 years) consistently gaining less weight (3yr old = 0.49±0.073; 9yr old = 0.22±0.15; P <0.05). RFI fat measured as heifers was negatively associated with their ADG (β = -0.0937; P = 0.02) in multi-aged cattle, particularly during fall, suggesting moderate life-stage consistency. In purebred Angus cows, fall ADG also declined with age, and cow and calf performance were linked: higher cow ADG often correlated with lower calf weaning weights, especially in older cows (>10-yr old), indicating a trade-off in energy allocation. Additionally, higher maternal gRHET in crossbreds was associated with increased calf ADG in fall (β = 0.63; P = 0.022), supporting the value of retained heterozygosity in grazing-based production systems. These results highlight the importance of intrinsic traits, age, genomic diversity, and early-life feed efficiency, in optimizing productivity in pasture-based beef systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".