ASSESSING THE OPTIMAL LEVEL OF NPK FERTILIZERS FOR ENHANCING PRODUCTION OF SPRING RICE IN KANCHANPUR, NEPAL
Bibliographic record
Abstract
Spring rice plays important role in ensuring food self-sufficiency by complementing the production of main season rice in Nepal. To assess the optimal level of nitrogen, phosphorous and potassium in spring rice, a field experiment was conducted in Kanchanpur district, during Feb- June 2023. The experiment was laid out in randomized complete block design with three replications and seven treatments viz., (T1), 120:40:40 NPK kg/ha, (T2), 180:40:40 NPK kg/ha, (T3), 60:40:40 NPK kg/ha, (T4), 120:60:40 NPK kg/ha, (T5), 120:20:40 NPK kg/ha, (T6), 120:40:60 NPK kg/ha and (T7), 120:40:20 NPK kg/ha. Hardinath-1 variety of rice was transplanted at spacing of 20 cm × 15 cm in all the plots. Data for growth parameters, yield attributes, and yield were collected and analyzed. Analysis of variance for all the parameters was done and treatments were compared at 5% level of significance. The statistical analysis revealed that the treatment T2, 180:40:40 NPK kg/ha was statistically superior in terms of yielding plant height, effective tillers, panicle length, grain yield and straw yield (P< 0.05). An increase of 20.52%, 30.19% and 50.68% was observed in number of effective tillers, grain yield and straw yield respectively in T2 over the recommended dose of government i.e. T1 (120:40:40 NPK kg/ha). Similarly, T2; 180:40:40 NPK kg/ha, where nitrogen level was increased by 50 % on government recommendation, provided highest gross return, net return and the benefit cost ratio of 2.35. Therefore, the fertilizer dose 180:40:40 NPK kg/ha is suggested for better productivity of spring rice in Kanchanpur, Nepal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".