Productivity Determinants and Production Constraints of Apple (<I>Malus</i> Spp) in Jumla District of Nepal
Bibliographic record
Abstract
A study was conducted to determine the factors affecting apple productivity and production constraints in the Jumla district of Nepal from February to June 2023.A total of 70 respondents were selected by using a simple random sampling technique.Semi-structured questionnaires and focus group discussions were used to collect the primary data.Secondary data were collected from ADO reports, Apple super zone and, CBS.SPSS was used to analyze the collected data and descriptive statistics were used to describe socio-demographic characteristics using frequency and percentage.A multiple regression model was used to determine factors affecting Apple's productivity.It showed that 61.7% of the productivity was explained by independent variables used in the model.The result of the regression model showed that years of farming experience and tree density were found positively significant at 10% and 1% level of significance.Intercropping was found negatively significant at 10% level of significance.The relative importance index method was used for ranking the production constraints of Apple.Insect disease damage was found to be the most important constraint to apple production with a high index value (0.911) followed by problem of irrigation (0.703), ineffective extension services (0.497), unavailability of inputs (0.451) and labor costs (0.437).Therefore the study suggests increased tree density, years of farming experience, and the introduction of effective bio pesticides are needed for improving the apple sector in the study area.For the better production and productivity of apple, further study on effective biopesticides, promising resistant varieties and sustainable orchard management practices are needed.
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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 | 0.001 | 0.000 |
| 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".