Challenges and innovations for sustainable ruminant production based upon One Health principles
Bibliographic record
Abstract
Abstract Almost 12 % of the human population have insufficient access to food and hence are at risk from nutrient deficiencies and related conditions, such as anaemia and stunting. Ruminant meat and milk are rich in protein and micronutrients, making them a highly nutritious food source for human consumption. Conversely, ruminant production contributes to methane (CH 4 ) emissions, a greenhouse gas (GHG) with a global warming potential (GWP) 27–30 times greater than that of carbon dioxide (CO 2 ). Nonetheless, ruminant production plays a crucial role in the circular bioeconomy in terms of upcycling agricultural products that cannot be consumed by humans, into valuable and nutritional food, whilst delivering important ecosystem services. Taking on board the complexities of ruminant production and the need to improve both human and planetary health, there is increasing emphasis on developing innovative solutions to achieve sustainable ruminant production within the ‘One Health’ framework. Specifically, research and innovation will undoubtedly continue to focus on (1) Genetics and Breeding; (2) Animal nutrition and (3) Animal Health, to achieve food security and human health, whilst limiting environmental impact. Implementation of resultant innovations within the agri-food sector will require several enablers, including large-scale investment, multi-actor partnerships, scaling, regulatory approval and importantly social acceptability. This review outlines the grand challenges of achieving sustainable ruminant production and likely research and innovation landscape over the next 15 years and beyond, specifically outlining the pathways and enablers required to achieve sustainable ruminant production within the One Health framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".