Growth, Productivity and Profitability of Different Cultivars of Potato (Solanum tuberosum L.) with and without Straw-Mulch in Dadeldhura, Nepal
Bibliographic record
Abstract
Farmers use different potato cultivars and cultivation methods, but there is a lack of information on the most suitable cultivars and techniques for optimal yields under local conditions in far western mid-hill agro-ecology of Nepal. To assess the impact of cultivars and straw mulching on the growth, yield performance and economics of potato, a field experiment was conducted during the spring, 2023 at Dotighatal, Amargadhi Municipality-03, Dadeldhura, Nepal. The experiment was laid out in two factorial Randomized Complete Block Design (RCBD) with 4 replications. Three potato cultivars (Cardinal, Desiree and Bajhang Local) were evaluated under straw-mulch and no-mulch conditions, wherein the data entry and analysis were done in MS-Excel and R-Studio, respectively. Bajhang Local exhibited the highest plant height (cm), average number of leaves and branches hill-1. But, the highest number of stems hill-1 was observed in Cardinal followed by Bajhang Local. The highest tuber yield was obtained in Cardinal (52.1 t/ha) followed by Desiree (48.0 t/ha) due to the highest weight of tubers hill-1 (1.36 kg) and number of large sized tuber (35-55 mm). Similarly, straw-mulch resulted higher yield (50.31 t/ha) than no-mulch (42.4 t/ha). However, interaction of two factor showed non-significant in both growth parameter and overall yield. This study concluded that potato cultivar Cardinal with straw-mulch was most suitable for improving the productivity, profitability and soil health of potato in the far-western mid hill agro-ecology of Nepal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".