Assessment of Phytosanitary Risks in Cocoa Rubber and Rice Production via the Quebec Pesticide Risk Indicator in Central West Ivory Coast
Bibliographic record
Abstract
Agricultural chemicalization poses critical threats to sustainable resource management in tropical regions. In Ivory Coast, a global agricultural leader, intensive production of cocoa, rubber, and rice depends heavily on pesticides, endangering biodiversity, water resources, and human health. This study addresses the crucial gap in standardized risk assessment tools by pioneering the adaptation and application of the Quebec Pesticide Risk Indicator (QPRI) to West African tropical agro-ecosystems. Using a structured questionnaire, systematic farm visits, and data triangulation with 160 farmers (71.9% cocoa, 20.6% rubber, and 7.5% rice) cultivating 931.5 hectares in the Lakota department, we evaluated the health (HRI) and environmental (ERI) risks of 37 pesticides. Our findings demonstrate extreme risk levels from insecticide mixtures, particularly neonicotinoid-pyrethroid combinations (e.g., CABOS PLUS 50 SC, HRI=2109). Triazole fungicides (e.g., cyproconazole, ERI=495) and herbicides containing triclopyr emerged as significant threats to aquatic ecosystems, especially the Gazolilié dam, due to their high persistence and mobility. The assessment also revealed significant acute risks to terrestrial invertebrates and birds from pyrethroid use, with products like bifenthrin showing high ERI values (ERI=361). The research uncovered alarming disparities between regulatory frameworks and field practices, including improper waste disposal and inadequate protective measures. We establish that the adapted QPRI serves as an essential tool for evidence-based policy and sustainable agricultural transition. Our study provides a scientifically-grounded framework for prioritizing pesticide regulation, promoting eco-friendly alternatives such as biopesticides, and implementing targeted awareness campaigns. This work offers a replicable model for achieving sustainable agriculture and effective natural resource management in Ivory Coast and comparable tropical agro-ecosystems, directly contributing to conservation objectives, agricultural sustainability, and the UN Sustainable Development Goals through improved pesticide risk assessment and management strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".