Effects of cow feed efficiency, longevity, heterosis, and lifetime productivity on profitability of heifer selection and cow–calf operations
Bibliographic record
Abstract
This study investigated factors influencing heifer replacement and cow–calf profitability using 361 cows (born 2011–2018) at the Lacombe Research and Development Centre, Alberta, Canada. Profitability was measured by marginal returns (MR) incorporating feed costs, heifer opportunity cost, calf and cull revenues, and a premium for cows retained in the herd. The study evaluated the linear effects of lifetime productivity, feed efficiency (residual feed intake adjusted for off-test backfat thickness; RFIfat), and genomic retained heterozygosity, an indicator of heterosis, on MR, feed costs, total costs, and net revenue (NR). Lifetime productivity, defined by the cumulative weight of calves weaned, was positively associated with MR and NR ( P < 0.01). RFIfat influenced total cost, MR, and NR ( P < 0.05), with MR improved by $168.50 cow−1 year−1 for each unit decrease in RFIfat, although regression and group mean comparisons were not fully consistent. Genomic retained heterozygosity positively impacted MR and NR, with a 10% increase enhanced MR and NR by $21.80 and $20.50 cow−1 year−1, respectively. Cow breed type did not affect longevity, MR, or NR. In conclusion, RFIfat and genomic diversity were important factors to consider in heifer replacement decisions when lifetime DMI was estimated as described in the present study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".