Sustainable livestock production by utilising forages, supplements, and agricultural by-products: Enhancing productivity, muscle gain, and meat quality – A review
Bibliographic record
Abstract
Global food consumption is rising due to population growth and increased demand for animal protein, necessitating sustainable livestock production systems. This paper examines strategies to address inefficiencies in meat production, including high resource use and environmental impacts, by utilising low-value feedstuffs, agricultural by-products, and innovative supplements. A comprehensive literature review was conducted, synthesising recent research from databases such as Scopus and Web of Science, focusing on forage-based diets, grain supplements, marine-derived additives, agrifood by-products, and micronutrient interventions. Findings reveal that forage-based diets enhance health-enhancing fatty acids in ruminant meat, while marine supplements like Asparagopsis seaweed may reduce methane emissions without compromising meat safety. Agricultural by-products, such as grape pomace and olive cake, improve oxidative stability and fatty acid profiles, aligning with circular economy principles. Mineral and vitamin supplementation, including selenium and vitamin E, boosts antioxidative capacity, extending meat shelf life and retail storage quality. However, outcomes depend on feed type, inclusion levels, and animal species, with antinutritional factors requiring careful management to avoid metabolic disorders. The review concludes that integrating diverse feed resources, such as forages, marine additives, and by-products, can enhance sustainability, reduce environmental footprints, and improve meat quality. Strategic implementation of these practices, tailored to regional feed availability and livestock needs, is critical for balancing economic viability, ecological resilience, and nutritional enhancement in future food systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".