Insights into the impact of manure on the environmental antibiotic residues and resistance pool
Bibliographic record
Abstract
The intensive use of antibiotics in the veterinary sector, linked to the application of manure-derived amendments in agriculture, translates into increased environmental levels of chemical residues, AR bacteria (ARB) and antibiotic resistance genes (ARG). The aim of this review was to evaluate the current evidence regarding the impact of animal farming and manure application on the antibiotic resistance pool in the environment. Several studies reported correlations between the prevalence of clinically relevant ARB and the amount and classes of antibiotics used in animal farming (high resistance rates being reported for medically important antibiotics such as penicillins, tetracyclines, sulfonamides and fluoroquinolones). However, the results are difficult to compare, due to the diversity of the used antimicrobials quantification techniques and to the different amounts and types of antibiotics, exhibiting various degradation times, given in animal feed in different countries. The soils fertilized with manure-derived products harbor a higher and chronic abundance of ARB, multiple ARG and an enriched associated mobilome, which is also sometimes seen in the crops grown on the amended soils. Different manure processing techniques have various efficiencies in the removal of antibiotic residues, ARB and ARGs, but there is only a small amount of data from commercial farms. The efficiency of sludge anaerobic digestion appears to be dependent on the microbial communities composition, the ARB/ARG and operating temperature (mesophilic vs. thermophilic conditions). Composting seems to reduce or eliminate most of antibiotics residues, enteric bacteria, ARB and different representative ARG in manure more rapidly and effectively than lagoon storage. Our review highlights that despite the body of research accumulated in the last years, there are still important knowledge gaps regarding the contribution of manure to the AMR emergence, accumulation, spread and risk of human exposure in countries with high clinical resistance rates. Land microbiome before and after manure application, efficiency of different manure treatment techniques in decreasing the AMR levels in the natural environments and along the food chain must be investigated in depth, covering different geographical regions and countries and using harmonized methodologies. The support of stakeholders is required for the development of specific best practices for prudent - cautious use of antibiotics on farm animals. The use of human reserve antibiotics in veterinary medicine and of unprescribed animal antimicrobials should be stopped and the use of antibiotics on farms must be limited. This integrated approach is needed to determine the optimal conditions for the removal of antibiotic residues, ARB and ARG, to formulate specific recommendations for livestock manure treatment, storage and handling procedures and to translate them into practical on-farm management decisions, to ultimately prevent exposure of human population.
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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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".