Effect of Various Mulching Methods on Growth and Yield Parameters of Potato (<i>Solanum tuberosum</i>) Varieties in Achham, Nepal
Bibliographic record
Abstract
A field experiment was conducted from February to June 2022 at Thulasen of Mangalsen Municipality-Achham, Nepal to determine the suitability of various mulching materials on the growth and yield of different potato varieties.Two major varieties i.e.Khumal Seto and Cardinal grown with four types of mulching material (Black plastic mulch, Silver plastic mulch, Organic mulch, and Control) were set up in a Randomized Complete Block Design (RCBD) with three replications.The vegetative parameters evaluated were germination percentage at 20 and 30 DAP, plant height, aerial stem number, plant spread, and stem girth at 60, 75, and 90 days after planting (DAP).In contrast, the reproductive parameters including tuber number/m 2 , yield (kg/m 2 ) and graded tuber (number/m 2 ) for three grades were taken after potato harvest.Data entry and analysis were done in MS-Excel and R-studio software respectively.Findings revealed that at a 5% level of significance, a significant difference was found in the interaction effect of variety and mulching material respective to germination, height, average yield, and tuber numbers for (>100 gm).Khumal Seto in black plastic mulch was found significantly superior for germination percentage at 30 DAP (93.33%), plant height (40.87 m), and yield parameters like average tuber yield (5.10 kg/m 2 ) and tuber number above 100 gm (12.67).In addition, the Khumal seto variety had greater stem girth (3.03 cm) and tuber yield while the Cardinal variety had a maximum number of stems (11.17).The highest plant spread (48.13 cm) and stem girth (3.10 cm) were observed in black plastic mulch and the lowest was in control in 60, 75, and 90 DAP.Khumal Seto in black plastic mulch was found to be the most effective treatment for increasing the overall production of potatoes.
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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 | 0.000 | 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) | 0.001 | 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".