Awareness and Adoption Status of Good Agricultural Practices in Mandarin (<i>Citrus reticulata</i> Blanco) among Farmers of Syangja, Nepal
Bibliographic record
Abstract
Mandarin, as one of the important sub-tropical fruits under citrus species, is a high potential bearing crop in different mid-hill regions of Nepal, mostly Syangja due to climatic and topographical suitability.In addition, the use of scientific cultivation practices determines the production output.The research was designed from February to June 2022, to assess the awareness and adoption status of good agriculture practices among mandarin growers in Syangja, Nepal.The command areas of the Mandarin superzone under PMAMP were purposively selected for this study.The sampling was done by stratified proportionate random method to represent farmers of the Superzone area.Primary data was collected by face-to-face interviews, FGDs, and KII using a pre-tested semi-structured questionnaire from 98 respondents.IBM SPSS Statistics 26 software was used to analyze the data and Descriptive statistics, index values were used to interpret the findings.Findings revealed, that most of the respondents (56.1%)only partially knew about GAP and adoption was also at an early level in many farmers (39.8%).Awareness was found significantly higher (p=0.034)among males than females.Respondents from Putalibazar municipality were likely to be more aware (52) and adopting GAP (48) in Mandarin orchards than the rest of the area.The GAP adoption related to standards of planting material fertilizers, and soil additives was found high among (60.7%) and (51.4%) of the respondents respectively while it was low for the irrigation standards i.e. 12.7% of the total.Citrus fruit fly with an index of (0.94) was the severe pest and the powdery mildew with an index value of (0.91) was the major disease reported in the study area.Also, disease and pest problems were more pronounced as the high-ranked production constraints of Mandarin.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".