Control Strategies of American Leaf Miner (<i>Tuta absoluta</i>) (Meyrick) Adopted by Farmers in Lalitpur District, Nepal
Bibliographic record
Abstract
This study evaluates the control strategies adopted by vegetable farmers in Lalitpur, Nepal, to combat Tuta absoluta during April-May 2023.Five wards in Godawari municipality were selected due to the significant infestation of Tuta absoluta.A total of 70 households were chosen through random sampling.Data collection methods included pre-tested interviews, Focus Group Discussions, and Key Informant Surveys for primary information, while secondary information was obtained from relevant publications.The primary vegetables grown were tomato, potato, cauliflower, cabbage, and onion.Farmers faced major issues with diseases and pests, particularly blight and Tuta absoluta, which caused yield losses up to 90%, with an average loss of 56%.While 35.7% of farmers used chemical pesticides, 71.4% relied on botanical pesticides and cultural methods.Among control measures, botanical methods were rated most effective with a relative index of 0.933.The study highlights the pressing need for integrated pest management training, as only 25.7% of farmers had received it, and underscores the significance of botanical methods in managing Tuta absoluta effectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".