Antibiotic use in commercial broiler chicken farming and its consequential resistance development in root colonizing bacteria of carrot grown in manure-applied soils in a middle-income country
Bibliographic record
Abstract
Broiler chicken litter (BCL) is a cheap manure for vegetable crops in developing countries. Extensive antibiotic use in poultry production could increase antibiotic resistant bacteria (ARB) in manure and eventually in crop root microbiome. We investigated the prevalence of ARB in BCL from medium- and large-scale farms (n = 33) and in carrot (Daucus carota) grown in BCL-applied soils in Sri Lanka. All the BCL samples contained aerobic bacteria resistant to 10 μg·mL−1 of oxytetracycline or enrofloxacin. The abundance of ARB determined by viable plate-count method ranged from 0.05% to 30.10% of aerobic bacterial population. Soil from two fields applied with BLC for 3 yr (short history, SH) and 10 yr (long history, LH) were treated with BCL (10%, w/w) and oxytetracycline (10 and 100 mg·kg−1) in a pot experiment alongside an unamended control. Adding BCL and oxytetracycline had a significant (P < 0.05) effect on the abundance of oxytetracycline-resistant epiphytic and endophytic bacteria (EEB) in carrot roots at harvest. Both total and oxytetracycline-resistant EEB increased significantly (P < 0.05) with the application of BCL to LH soil but not to SH soil. Carrot sold at retailed markets (n = 30) contained epiphytic bacteria resistant to 1 μg·mL−1 oxytetracycline (4.13 ± 0.207 log10CFU·g−1 dry carrot). High minimum inhibitory concentrations for oxytetracycline (≥128 μg·mL−1) were observed in 83% and 50% of ARB isolates obtained from BCL (n = 18) and carrot (n = 24), respectively. Results confirmed that BCL acts as a carrier of ARB, and continuous application of BCL to soil increased the prevalence of ARB among EEB in carrot.
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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".