The carbon footprint of beef cattle systems in the Southeast United States
Bibliographic record
Abstract
Grasslands in the Southeast United States (SE US) cover 15.8 million ha and most of this area is dedicated to beef production systems. This region holds 6.3 million beef cows and 12.1 million cattle, including calves. Beef cattle systems in the SE US are mostly cow-calf based, and most of the greenhouse gas emission from cattle occurs during this phase (cow-calf) because of their forage-based diet. This review assessed the carbon footprint (C footprint) of beef cattle systems in the SE US and indicates possible ways to reduce it. Major emissions in beef cattle systems come from livestock enteric fermentation and greenhouse gases from excreta. Cow-calf systems in the SE US are typically low input, although they use some industrial fertilizers, machinery, and fossil fuel, which adds to the C footprint of the sector. There are opportunities to reduce the beef C footprint in the SE US by adopting climate-smart practices, including preservation of natural ecosystems that have potentially high carbon sequestration, afforestation, integration of forage legumes (and reduction of nitrogen fertilizer), use of slow-release fertilizers, and dietary interventions. In fact, depending on the level of adoption of some of these practices, it is possible to establish climate-neutral beef at the farm gate in the SE US. Beef is a key food for humans and has large economic effects. Development of climate-smart beef could create opportunities for a niche market that recognizes the environmental footprint of agricultural production and could incentivize producers to pursue those systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".