Greenhouse Gas Emissions from Calf- and Yearling-Fed Beef Production Systems, With and Without the Use of Growth Promotants
Bibliographic record
Abstract
A spring calving herd consisting of about 350 beef cows, 14-16 breeding bulls, 60 replacement heifers and 112 steers were used to compare the whole-farm GHG emissions among calf-fed vs. yearling-fed production systems with and without growth implants. Carbon footprint ranged from 11.63 to 13.22 kg CO₂e per kg live weight (19.87-22.52 kg CO₂e per kg carcass weight). Enteric CH₄ was the largest source of GHG emissions (53-54%), followed by manure N₂O (20-22%), cropping N₂O (11%), energy use CO₂ (9-9.5%), and manure CH₄ (4-6%). Beef cow accounted for 77% and 58% of the GHG emissions in the calf-fed and yearling-fed. Feeders accounted for the second highest GHG emissions (15% calf-fed; 35-36% yearling-fed). Implants reduced the carbon footprint by 4.9-5.1% compared with hormone-free. Calf-fed reduced the carbon footprint by 6.3-7.5% compared with yearling-fed. When expressed as kg CO₂e per kg carcass weight per year the carbon footprint of calf-fed production was 73.9-76.1% lower than yearling-fed production, and calf-fed implanted was 85% lower than hormone-free yearling-fed. Reducing GHG emissions from beef production may be accomplished by improving the feed efficiency of the cow herd, decreasing the days on low quality feeds, and reducing the age at harvest of youthful cattle.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".