Assessment of Fertilizer Policy, Farmers’ Perceptions and Implications for Future Agricultural Development in Nepal
Bibliographic record
Abstract
This paper assesses the origins of and changes to fertilizer policy in Nepal over a period of time. It assesses farmers’ awareness of the recent changes to the subsidy policy and examines their perceptions of the extension services. This paper looks at the environmental implications of the concentrated application of chemical fertilizer, particularly as far as food security is concerned. Questionnaire surveys, group discussions, a workshop, soil analyses and archival materials were used to collect data for this study. Changes in fertilizer policy have occurred in four different phases: (i) without subsidy; (ii) with subsidy; (iii) with deregulation of fertilizer trade; and (iv) the current phase of subsidies for fertilizer. However, timely and effective fertilizer distribution by the government has always been a problem. Only few farmers (12 %) know about recent changes in the fertilizer policy; most of them (44 %) were satisfied with the new subsidy scheme. Valid proof of land ownership is a requirement for qualifying for subsidized fertilizer, and this makes it difficult for some small farmers who are tenant. The soil analysis indicated a significant decrease in the soil pH as a result of intensified agriculture. One reason is due to the intensive use of chemical fertilizers and the declining use of farmyard manure. The ineffectiveness of the extension services also influences farmers’ use of fertilizer as they are not aware of which fertilizer and how much to use. The use of fertilizer may increase yields in the short term, but in the longer term, it may worsen the food insecurity in the country.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".