Effect of Different Mulching Materials on Growth and Yield Attributing Characters of Summer Squash in Kanchanpur District
Bibliographic record
Abstract
Mulching in the vegetable crop helps to conserve moisture, regulate temperature, avoid surface compaction, reduce runoff and erosion, improve soil structure, and manage weeds. To study the effect of different mulching materials on growth and yield attributing characters of summer squash, a field experiment was conducted at Kanchanpur district on summer squash during the summer season of 2023. The experiment was laid out in single factor Randomized Complete Block Design (RCBD) design with 4 replications and 5 treatments namely T1: control, T2: Plastic Mulching (silver on black plastic, black on top, silver on bottom), T3: rice straw, T4: sawdust and T5: mustard hulls in an area of 300m2. “F1 Dollar plus” variety was used for the research. Growth and yield characteristics of summer squash were seen better with mulching and provided better results. The effect of different mulching materials on growth and yield attributing characters were found statistically significant except non-significant in a number of leaves at 15DAS. Plant height, number of leaves, and plant spreading were seen highest in treatment T2 and lowest in treatment T1 at 15DAS, 30DAS, 45DAS, and 60DAS. The moisture percentage is retained highest in treatment T3 and the lowest was recorded in treatment T1. The number of fruits per plant, fruit length, yield per plant, and yield were recorded as highest in T2 and lowest in T1. Yield was more than 3 times in treatment T2 and more than double in treatment T3 than in treatment T1. The findings of this research suggest plastic mulching especially silver on black plastic is a better tool for the production of summer squash in the research area compared to non-mulch conditions.
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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 | 0.000 |
| 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) | 0.000 | 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; a candidate call from one teacher head, 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".