An agriculture and health inter-sectorial research process to reduce hazardous pesticide health impacts among smallholder farmers in the Andes
Bibliographic record
Abstract
The use of highly hazardous pesticides by smallholder farmers constitutes a classic trans-sectoral 'wicked problem'. We share our program of research in potato and vegetable farming communities in the Andean highlands, working with partners from multiple sectors to confront this problem over several projects.We engaged in iterative cycles of mixed methods research around particular questions, actions relevant to stakeholders, new proposal formulation and implementation followed by evaluation of impacts. Capacity building occurred among farmers, technical personnel, and students from multiple disciplines. Involvement of research users occurred throughout: women and men farmers, non-governmental development organizations, Ministries of Health and Agriculture, and, in Ecuador, the National Council on Social Participation.Pesticide poisonings were more widespread than existing passive surveillance systems would suggest. More diversified, moderately developed agricultural systems had lower pesticide use and better child nutrition. Greater understanding among women of crop management options and more equal household gender relations were associated with reduced farm pesticide use and household pesticide exposure. Involvement in more organic agriculture was associated with greater household food security and food sovereignty. Markets for safer produce supported efforts by smallholder farmers to reduce hazardous pesticide use.Participatory interventions included: promoting greater access to alternative methods and inputs in a store co-sponsored by the municipality; producing less harmful inputs such as compost by women farmers; strengthening farmer organizations around healthier and more sustainable agriculture; marketing safer produce among social sectors; empowering farmers to act as social monitors; and using social monitoring results to inform decision makers. Uptake by policy makers has included: the Ecuadorian Ministry of Health rolling out pesticide poisoning surveillance modeled on our system; the Ecuadorian Association of Municipalities holding a national virtual forum on healthier agriculture; and the Ecuadorian Ministry of Agriculture promulgating restrictions on highly hazardous pesticides in June 2010.Work with multiple actors is needed to shift agriculture towards greater sustainability and human health, particularly for vulnerable smallholders.
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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 | 0.082 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".