Economic Analysis of Solo Cropping and Mixed Cropping with Maize in Yield of Potato in Rasuwa, Nepal
Bibliographic record
Abstract
Potato, the second most produced crop in Nepal, is critical for rural livelihoods, yet farmers in the Rasuwa district lack an economic comparison between solo potato cropping and mixed potato-maize cropping to optimize their practices.This study's primary objective was to comprehensively assess and compare the yield and profitability of these two systems.Utilizing a structured household survey, data were collected from 90 farmers selected through simple random sampling in the Kalika and Gosaikunda municipalities, with analysis centered on the Benefit-Cost (B/C) ratio.The results conclusively demonstrate that mixed cropping is significantly more profitable, achieving a B/C ratio of 2.77 compared to 1.62 for solo cropping (p-value=0.001).Although mixed cropping had a higher total average cost (NRs/ha 228,557 vs. NRs/ha 193,123 with p value of 0.001), it yielded vastly greater average benefits (NRs/ha 379,915 vs. NRs/ha 100,523 (p-value=0.012)).Crucially, the mixed system's primary benefit was its effectiveness in reducing the risk of crop failure, and regression analysis identified chemical fertilizer and potato tuber costs as key positive determinants of cost.These findings strongly advocate for the adoption of mixed potato-maize cropping as a superior, more economical strategy to enhance both farm productivity and financial stability in the region.
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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 | 0.000 | 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) | 0.001 | 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".