Greenhouse Gas Emissions of Beef Cow-Calf Grazing Systems in Uruguay
Bibliographic record
Abstract
Evaluating greenhouse gas (GHG) emissions at farm level is an important tool to mitigate climate change. Livestock account for 80% of the total GHG emissions in Uruguay, and beef cow-calf systems are possibly the largest contributors. In cow-calf grazing systems, optimizing forage allowance and grazing intensity may increase pasture productivity, reproductive performance, beef productivity, and possibly reduce GHG emissions. This study estimated GHG emissions per kg of live weight gain (LWG) and per hectare from 20 cow-calf systems in Uruguay, with different management practices. The GHG emissions were on average 20.8 kg CO2-e.kg LWG-1, ranging from 11.4 to 32.2. Beef productivity and reproductive efficiency were the main determinants of GHG emissions. Five farm clusters were identified with different productive and environmental efficiency by numerical classification of relevant variables. Improving grazing efficiency by optimizing the stocking rate and forage production can increase beef productivity by 22% and reduce GHG emissions per kg LWG by 28% compared to “low performance” management. Further improvements in reproductive efficiency can increase productivity by 41% and reduce GHG emissions per kg LWG by 23%, resulting in a “carbon smart” strategy. However, the most intensified farms with highest stocking rate and beef productivity, did not reduce GHG emissions per kg LWG, while increased GHG emissions per ha compared to the carbon smart. This analysis showed that it is possible to simultaneously reduce carbon footprint per kg and per ha, by optimizing grazing management. This study demonstrated that there is high potential to reduce cow-calf GHG emissions through improved grazing management.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".