Effect of a Corn Silage-Based Finishing Diet on Growth, Carcass Composition, Meat Quality, Methane Emissions and Carbon Footprint of Crossbred Angus Young Bulls
Bibliographic record
Abstract
Using locally produced forage and agro-industrial by-products can reduce dependence on imported feed and competition for human food sources, while improving meat quality. However, the overall effect of this feeding strategy on global greenhouse gas emissions must be evaluated to provide a comprehensive assessment of sustainability. This study aimed to test whether replacing the conventional concentrate finishing diet with a total mixed ration (TMR) diet based on maize silage and brewer’s spent grains (BSG) would improve meat quality without compromising productive performance, carcass composition, and the carbon footprint (CFp) of finishing beef cattle. Twenty crossbred young bulls were randomly distributed among 4 pens and randomly allocated to 2 treatments: Control—a conventional diet based on commercial concentrate and wheat straw or TMR—a maize silage-based diet with BSG, concentrate, and straw. Dry matter intake and average daily gain were 13% and 15%, respectively, lower in the TMR treatment than in the Control treatment. Daily methane emissions were 59% higher in the TMR treatment. However, life cycle assessment results revealed no differences in the CFp, and the beef from TMR treatment achieved higher meat quality. In conclusion, a maize silage-based diet offers a cost-effective alternative to conventional diets, with a lower environmental impact and improved beef quality.
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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".