Farmers’ perception on the performance of different rice varieties in Kapilvastu district, Nepal
Bibliographic record
Abstract
Rice is the major staple food crop in Nepal. To date, several rice varieties have been developed and released in Nepal. However, rice production is far below in comparison with its production potential. A household survey was conducted in Bangaganga municipality of Kapilvastu district in 2018 to assess farmers’ perception on performance of four different rice varieties (Radha-4, Ramdhan, Gorakhnath, and Sawa). The data were collected from a total of 120 rice farmers (randomly selected) using the interview schedule and analyzed using descriptive statistics, Likert scale, and indexing technique. Statistical analysis showed that the Ramdhan variety had the highest yield (4.95 t/ha), whereas Radha-4 had the lowest yield (3.15 t/ha). The most disease and drought-tolerant variety, as perceived by the farmers, was Radha-4. Smut and Khaira were perceived as the primary diseases whereas Brown planthopper and Rice Gundhi bug were the most important insects of all studied rice varieties. The study recommended that the plant breeders should focus on developing site-specific rice varieties to meet the multiple concerns of the farmers, such as higher yield and stress-tolerant. The farmers should be made aware of varietal selection and crop pest management techniques via training programs, which further helps to reduce the yield gap between farmers’ field and research field.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".