Effect of Different Mulching Materials on Onion (<i>Allium cepa</i>) Production at Lamahi, Dang, Nepal
Bibliographic record
Abstract
Winter onion production in rainfed regions is constrained by the limited availability of soil moisture.Mulching has proven to be a viable tool to conserve soil moisture and enhance yield.A field experiment was conducted to evaluate the efficiency of different mulching methods on the performance of onion (Allium cepa L. var.Nasik Red N 53) concerning its yield and yield attributes during the winter season (Nov-March) at Lamahi-5, Dang.The experiment was laid out following a randomized complete block design (RCBD) with three replications and seven treatments.The treatments were T1: Control, T2: Saw Dust (1 kg), T3: Straw (1 kg), T4: Banana Leaves (1 kg), T5: Neem Leaves (1 kg), T6: White Polythene (30 µ), and T7: Rice Husk (1 kg).The onion variety Nasik Red was transplanted at a spacing of 20 cm by 10 cm.Biometrical parameters like plant height, and number. of leaves, length of leaves, neck thickness, neck length, and yield-attributing characters like shoot weight, bulb weight, bulb length, bulb diameter, root length, root weight and total yield were observed.The collected data were statistically analyzed for the best mulching materials using analysis of variance (ANOVA), and the separation of means for significant effects was by least significant difference (LSD) at the 5% level of probability.Among different mulching materials, white plastic mulch was best in terms of vegetative and phenological observations like plant height (64.8 cm) and number of leaves (11) at 100 DAT, while length of leaves (32.7 cm) was found to be significant at 60 DAT.White plastic mulching at 30µ was best in terms of yield and yield attributing characteristics like bulb diameter (7 mm), bulb weight (117.5 gm), and yield per plot (27 tons/ha).In the upcoming days, it would be a better idea to use this technique to reduce weeds, conserve moisture, and improve soil health, producing more yield.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".