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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".