Alternatives to antibiotics for sustainable livestock production in the context of the One Health approach: tackling a common foe
Bibliographic record
Abstract
The discovery of the growth promoting effects of antibiotics in the 1940s contributed to the economic efficacy of the livestock industry. In response to increased animal protein demand from the 1950s, antimicrobial use at sub-therapeutic levels for growth promotion and disease prevention (antimicrobial growth promoter, AGP) improved feed-to-weight ratio, meat quality and overall health of livestock. These benefits encouraged the heavy use of AGPs such that about 70% of global use of antimicrobials was for food animals. Despite the numerous benefits of AGPs, the emergence of antimicrobial resistance (AMR) associated with their use and impact on human and livestock health, establishes AMR as a global health plague, affecting man, animal and the environment. Although many countries have banned the use of AGPs in livestock production, efforts to identify effective alternatives have yielded inconsistent findings and only few effective alternative products are currently available. This highlights the need to intensify efforts toward identifying more effective AGP alternatives. While current strategies focus on evaluating the efficacy of single products/class of products that can enhance productivity and health, future strategies should focus on combining multiple approaches. It is also important to acknowledge that no single alternative can fully replicate the same mechanism of action attributed to antimicrobials. This comprehensive review presents recent research findings on AGP use trend before and after bans in many countries, the benefit/mode of action of reported AGP alternatives, the economic impact of AGP alternatives in the context of the One Health approach, the factors militating the search for effective AGP alternatives, research gaps and future action plans for AGP-free animal farm management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".