Evaluating the Impact of Different Growing Media on Germination Parameters and Seedling Growth of Tomato (<i>Solanum lycopersicum</i> L.) in Bhojpur, Nepal
Bibliographic record
Abstract
Tomato (Solanum lycopersicum L.) is one of the most widely cultivated and consumed vegetables globally, valued for its high nutritional content, market demand, and processing potential.However, low-quality seedlings due to improper nursery media selection often led to poor field establishment and reduced yields.To address this challenge, a study was conducted from January to March 2025, to evaluate the effects of different growing media on tomato seedling performance.The experiment involved nine treatments: T1 (Vermicompost), T2 (Cocopeat), T3 (Soil), T4 (FYM + Soil), T5 (Soil + Cocopeat), T6 (Vermicompost + Soil), T7 (Vermicompost + Cocopeat), T8 (Cocopeat + Soil + Vermicompost), and T9 (Vermicompost + FYM + Soil + Cocopeat).The findings revealed that T9 significantly enhanced all measured seedling growth parameters, including root length, shoot length, fresh weight, and dry weight, suggesting a superior growing environment due to balanced nutrient supply, aeration, and water retention.T8 and T6 also showed favorable early stem and leaf development, while T1 consistently underperformed due to poor structural and aeration properties.These results demonstrate the critical role of media composition in promoting early plant vigor and highlight the potential of integrated substrates in nursery management.The study holds substantial practical value for sustainable tomato cultivation, especially in resource-limited settings.By utilizing locally available components like vermicompost, FYM, cocopeat, and soil in strategic combinations, farmers and nursery operators can produce healthier seedlings with better post-transplant growth potential.Future prospects include field-scale validation, economic analysis, and exploring similar media optimization for other high-value vegetable crops.
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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 | 0.001 | 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".