Chemical technology adoption and risk awareness among apple growers of Kashmir
Bibliographic record
Abstract
The adoption of chemical technologies, such as pesticides and fertilizers, plays a significant role in enhancing apple cultivation productivity in Kashmir, a region known for its high-value apple cultivation. The reliance on fertilizers and pesticides in modern agriculture is widespread, driven by the need to enhance crop quality and meet global food demands. However, it is imperative to weight these technologies against environmental and health implications by thorough analysis towards balancing productivity with sustainability. The present study investigates the risks associated with such technologies, and perceived impacts on the environment as a consequence of the use of chemical technology by the apple farmers. We adopt a mixed-method approach through structured surveys driven by Garret’s ranking technique to prioritize farmers’ concerns regarding various identified parameters. Overall, 300 apple farmers were surveyed from the major apple-growing districts of Kashmir. The results are reported with regard to two dimensions, viz., (i) signifying impacts on health and (ii) signifying impacts on environment. The results reveal pesticide resistance, impact on water quality, and emergence of pests and diseases as the most pressing environmental concerns, with significant implications for ecosystem balance and agricultural productivity. The results highlight headache as the most critical health concern, followed by eye irritation and dizziness which indicate noticeable health effects linked to exposure. Approximately 50 per cent of the farmers reported Cyclone (Chlorpyrifos) as the most harmful chemical. These findings underline paths leading towards the need for alignment of educational interventions, policy support, and the promotion of sustainable agricultural practices to mitigate adverse effects while ensuring economic viability forapple cultivation in Kashmir.
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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".