Assessment of the farm-economic impact of reducing antimicrobial use in livestock production
Bibliographic record
Abstract
The overall objective of this dissertation was to assess the scope for, and the farm-economic impact of reducing veterinary antimicrobial use (AMU). The dissertations’ underlying assertion is that an assessment of these issues can help in understanding pathways for reducing veterinary AMU. First, a conceptual framework was developed that provides an integrated assessment of measures and strategies that can be applied within the supply chain in order to reduce both (the need for) AMU and the prevalence of (pathogenic) microorganisms, and consequently the risks of human exposure to AMR. The farmer, the farm and the animals are considered as main decision areas in order to reduce AMU successfully. In addition, a theoretical framework was developed for deriving the economic value of AMU and determining the factors that affect the economic value of AMU. Microeconomic theory postulates that the main determinants of the economic value of AMU are the prices of productive inputs, damage abatement inputs and outputs, the production technology, the damage abatement function, the risk attitude of the farmer and the variance of profit. The next step was to assess the relation between technical farm performance and AMU. The results indicate that farms have unique combinations of technical farm performance and AMU, and therefore require farm-specific strategies to reduce AMU successfully. Finally, the impact of farm-specific interventions on farm performance was assessed. The results indicate that successful strategies for reducing AMU need to target combined interventions regarding the farmer, the farm and the animals. Overall, this dissertation underlined that there are possibilities for reducing AMU without necessarily having negative consequences with respect to technical farm performance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".