An ecohealth assessment of poultry production clusters (PPCs) for the livelihood and biosecurity improvement of small poultry producers in Asia
Bibliographic record
Abstract
BACKGROUND: Poultry production cluster (PPC) programs are key strategies in many Asian countries to engage small commercial poultry producers in high-value production chains and to control infectious poultry diseases. This study assessed the multiple impacts of PPCs through a transdisciplinary ecohealth approach in four Asian countries, and drew the implications for small producers to improve their livelihoods and reduce the risk of spreading infectious diseases in the poultry sector. METHODS: The data collection combined both quantitative and qualitative methods. It comprised: formal structured household survey questionnaires, measuring the biosecurity level of poultry farms with a biosecurity score card; and key informant interviews. Descriptive statistics were used to process the quantitative data and a content analysis was used to process the qualitative data. RESULTS: This research found that poultry farms in clusters do not necessarily have better economic performance than those outside PPCs. Many farmers in PPCs only consider them to be an advantage for expanding the scale of their poultry operations and improving household incomes, and they are less concerned about-and have limited capacities to-enhancing biosecurity and environmental management. We measured the biosecurity level of farms in PPCs through a 14-item checklist and found that biosecurity is generally very low across all sample sites. The increased flies, mosquitoes, rats, and smells in and around PPCs not only pollute the environment, but also cause social conflicts with the surrounding communities. CONCLUSION: This research concluded that a poultry cluster, mainly driven by economic objectives, is not necessarily a superior model for the control of infectious diseases. The level of biosecurity in PPCs was found to be low. Given the intensity of poultry operations in PPCs (farms are densely packed into clusters), and the close proximity to residential areas of some PPCs, the risk of spreading infectious diseases, in fact, increases. Good management and collective action for implementing biosecurity measures are key for small producers in PPCs to address common challenges and pursue health-based animal production practices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".