Effect of Different Growing Media on Growth and Germination Parameters of Bell Pepper (<i>Capsicum annuum</i>) Seed in Bhojpur, Nepal
Bibliographic record
Abstract
Bell pepper (Capsicum annuum), particularly the California Wonder variety, is a high-value vegetable crop in Nepal, contributing significantly to farmers' livelihoods and food security.However, optimizing nursery practices remains a challenge, as seedling growth and establishment are highly influenced by the choice of growing media.Selecting an appropriate medium can enhance seedling vigor, leading to improved crop productivity and economic returns.Therefore, this study investigated the impact of different growing media on the germination and growth parameters of bell pepper seedlings in Bhojpur, Nepal, during the spring season (May-June 2023).The experiment followed a two-factorial Completely Randomized Design (CRD) with two levels of treatment combinations.Seeds of California Wonder and Sagar varieties were procured from local agro-vets and sown in seed trays using different growth media: Soil, Soil + Cocopeat (1:1), Soil + Farmyard Manure (FYM) (3:1), Soil + Vermicompost (1:1), and Soil + Cocopeat + FYM + Vermicompost (1:1:1:1).The results demonstrated a significant influence of the growing medium on key germination and growth parameters.The highest germination percentage (90.83%)and germination rate index (67.70)were recorded in Soil + Vermicompost (1:1), whereas the Soil + Cocopeat + FYM + Vermicompost (1:1:1:1) medium yielded the highest values for seedling vigor index (950.05),number of leaves (5.13), seedling length (6.91 cm), stem diameter (0.28 cm), fresh weight (1.13 g), dry weight (0.12 g), root length (5.70 cm), and leaf area (9.75 cm²).Among the tested varieties, California Wonder performed best in most growth parameters, except germination percentage and germination rate index.Optimizing its nursery conditions with sustainable growing media can enhance seedling establishment and boost bell pepper production in 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".