Climate impacts of alternative beef production systems depend on the functional unit used: Weight or monetary value
Bibliographic record
Abstract
Beef production has been identified as a significant source of anthropogenic greenhouse gas (GHG) emissions in the agricultural sector. United States and Canada account for about a quarter of the world’s beef supply. To compare the GHG emission contributions of alternative beef production systems, we conducted a meta-analysis of 32 studies that were conducted between 2001 and 2023. Results indicated that GHG emissions from beef production in North America varied almost fourfold from 10.2 to 37.6 with an average of 21.4 kg CO 2 e/kg carcass weight (CW). Studies that considered soil C sequestration (C-seq) reported the highest mitigation potential in GHG emissions (80%), followed by growth enhancement technology (16%), diet modification (6%), and grazing management improvement (7%). Our study highlights the implications of using carbon intensity per economic activity (i.e., GHG emissions per monetary unit), compared to the more common metric of intensity on per weight of product basis (GHG emissions per kg CW) for comparisons across differentiated beef cattle products. While a positive association was found between the proportion of lifespan on grassland and the conventional weight-based indicator, grass-finished beef was found to have lower carbon intensity per economic activity than feedlot-finished beef. Our study emphasizes the need to incorporate land use and management effects and soil C-seq as fundamental aspects of beef GHG emissions and mitigation assessments.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".