Farmer advisory systems and pesticide use in legume-based systems in West Africa
Bibliographic record
Abstract
Despite the adverse effects of pesticides on the environment and human health, they are a key ingredient in boosting agricultural productivity as a way of meeting global food demand. While global levels of pesticides are towering in high-income countries, pesticide use in many parts of Africa remains low, with significant impacts on agricultural productivity and food production. We use a rich longitudinal dataset to examine the relationship between farmer advisory systems and pesticide use in legume-based production systems in Ghana, Mali, and Nigeria. We find that farmers who are advised by private extension systems are approximately 8 % more likely to use pesticides at an extensive level. They also use pesticides more intensively (41 %). On the other hand, farmers advised by public extension systems are about 5 % more likely to extensively use pesticides. These farmers are observed to reduce the intensive use of pesticides by about 14 %. Furthermore, we also show that farmers advised by joint private-public extension systems are about 4 % more likely to use pesticides as well as reduce their intensity of use by approximately 11 %. At the various country levels, there exists significant heterogeneity in the relationship between advisory systems and pesticide use, suggesting that context matters. Of course, the pesticide regulatory systems and the institutional environments in these countries vary greatly. Given these findings, our study offers key entry and leveraging points for increasing pesticide use at levels that limit their environmental and human effects but may ascertain increased agricultural productivity and food production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".