Practices to Reduce Milk Carbon Footprint on Grazing Dairy Farms in Southern Uruguay: Case Studies
Bibliographic record
Abstract
Carbon footprint (CF) is an increasingly important indicator of the impact of a product on climate change. This study followed international guidelines to quantify the CF of milk produced on 24 grazing-based dairy farms in southern Uruguay. Cows grazed all year-round and were supplemented with concentrate feeds. Dairy farms varied in annual milk yield per cow (5672 ± 1245 kg fat and protein corrected milk [FPCM]), milk production per ha (4075 ± 1360 kg FPCM/ha), cow stocking rate (0.71 ± 0.12 cows/ha), feed intake (13.3 ± 2.2 kg dry matter [DM]/cow/day) and percentage of concentrate in the diet (36 ± 12% DM) giving an average CF of 0.99 ± 0.10 kg CO2 (equivalent [eq]/kg FPCM) over all farms. Total milk production per ha, milk yield per cow and dry matter intake explained most of the variation in CF. Strategies that provide the highest milk production per ha using high yielding cows and a high portion of lactating cows in the herd were identified as the best management practices for reducing CF. Low forage intake in Uruguay is often a consequence of low yielding pastures and high stocking rates. Overall, this study concluded that a reduction in CF is not achieved through increased concentrate intake unless forage consumption is also unconstrained. Improved pasture and feeding management can be used to reduce the CF of milk produced in Uruguay.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".