Use of the Environmental Impact Quotient to Estimate Health and Environmental Impacts of Pesticide Usage in Peruvian and Ecuadorian Potato Production
Bibliographic record
Abstract
Currently there is no effective mechanism for measuring the potential benefits of integrated pest and disease interven-tions in terms of reducing pesticide risks in potato production in developing countries. The environmental impact quotient (EIQ), a composite hazard indicator, was applied to data from potato field trials implemented in Ecuador to evaluate the practical boundaries of this metric related to potato production practices in the Andes. The EIQ was also applied to data from two independent farmer surveys, one from Peru and one from Ecuador to compare potato farming practices and the utility of the EIQ when applied to existing survey data. In the Ecuadorian field trials, the EIQ values, i.e., environmental impact (EI) per ha, varied greatly among the different potato systems tested and ranged from 40 for an integrated pest management system (resistant cultivar plus less hazardous pesticides) to 1235 for a high-input conventional system (susceptible cultivar plus frequent use of hazardous pesticides). Thus, this parameter demonstrates substantial variation under different conditions and different crop management approaches. EI per ha values from the two surveys fell within the range found in the field trial, but in the survey values were toward the lower end, ranging from 64 to 213. Methodical and biophysical factors are discussed that may account for the relatively low EI per ha found in the field survey data. Our study demonstrates the utility of the EIQ for assessing health and environmental hazards of potato production in the Andes and potentially other areas in the developing world. Nonetheless, there are limitations to the EIQ as presently used and care is needed in the interpretation of results. We see our work as an initial step in the development of an integrated metric to estimate environmental and human health hazards related to pesticide use in potato production in the diverse conditions of developing countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".