Adoption Status of Improved Ginger (<i>Zingiber officinale</i>) Production Technology in Syangja, Nepal
Bibliographic record
Abstract
The study was carried out from February to July 2021 to assess the adoption status of improved ginger production technology in Syangja, Nepal.The sample population of 80 ginger growers was selected using a simple random sampling technique.The data obtained was analyzed by using Statistical Package for Social Sciences (SPSS) and Microsoft Excel.The chi-square test and independent t-test were applied to determine the association between dependent and independent variables.The analysis showed that the majority of the respondents were male, middle-aged group, literate, and had medium size family.Mulching (93.7%) was the most adopted practice followed by intercropping (88.7%), weeding (87.5%), and rhizome preservation whereas use of recommended fertilizer (13.8%) was the least adopted practice.Socioeconomic factors like level of education, and extension-related factors like training and contact with extension agents had positive and significant relationships with the adoption of improved ginger production technology.The majority of the respondents (67.5%) had not received training related to ginger cultivation, were not in contact with extension agents, and were low adopters of improved ginger production technology.The average annual income and production from ginger cultivation were found to be statistically higher for high adopters.Lack of irrigation facilities, high cost of inputs, incidence of diseases, lack of training facilities, and postharvest loss were the major constraints faced by the farmers in ginger cultivation.The study noted that ginger is the potential spice crop in the Syangja district of Nepal and its productivity can be increased by addressing various factors affecting its production technology.
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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 | 0.001 | 0.001 |
| 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".