The use of broccoli agro-industrial waste in dairy cattle diet for environmental mitigation
Bibliographic record
Abstract
Livestock feed production for the intensive dairy industry has a significant environmental impact. This study evaluated the potential to reduce the environmental impacts of milk production in Guanajuato, Mexico, by incorporating broccoli stems (BS), an abundant agro-industrial waste product with high nutritional value, into dairy cattle feed. The potential reduction of environmental impacts from adding BS to cattle diet formulation was estimated using a life cycle assessment and a linear programming model which considered nutritional requirements as constraints. Two scenarios for milk production were considered: an optimized conventional diet and an optimized diet including BS. The results indicated that incorporating BS in cattle feed could reduce greenhouse gas emissions by 118 g CO 2 eq kg −1 fat-and-protein corrected milk (FPCM and agricultural land occupation by 0.002 m 2 a kg −1 FPCM but increased fossil depletion by 4 g oil eq kg −1 FPCM. BS can replace 11.1% of conventional feeds and maximize the incorporation feeds with low environmental impacts in the diet, such as alfalfa hay and maize silage. A sensitivity analysis of the economic allocation showed that the maximum price of BS to remain environmentally viable was 19.28 USD t −1 on a fresh matter basis.
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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) | 0.001 | 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".