Effect of Foliar Application of Plant Growth Regulators on Growth, Flowering and Yield of Tomato (Lycopersicon Esculentum l.) Under Protected Condition
Bibliographic record
Abstract
A field experiment was carried out under greenhouse to assess the performance of tomato cv. Srijana as influenced by sole application of GA3 and NAA during the summer season of 2021–2022 at the horticulture farm of the school of agriculture, Tikapur, Kailali, Nepal. The seven different treatments consisted of two plant growth regulators each having three concentrations was used viz., T1 (GA3 @ 25 ppm), T2 (GA3 @ 50 ppm), T3 (GA3 @ 75 ppm), T4 (NAA @ 20 ppm), T5 (NAA @ 40 ppm), T6 (NAA @ 60 ppm) and T7 (Control: water spray). Treatments were replicated thrice in the single factorial randomized complete block design (RCBD). Max/min, temperature/humidity was measured 30 °C/13 °C, 87%/60%. The results revealed that the treatment T1 (GA3 25 ppm) had a significant effect on growth and flowering parameters mainly plant height, leaf length, leaf width, leaf area meter, number of flower clusters per plant, number of clusters per plant, number of flower per cluster, number of fruit per cluster, number of fruit per plant and number of fruit set per plant. Similarly, a significantly higher yield (60.83 ton/ha) of tomato was attained with GA3 @ 25 ppm. It could be suggested that the production of tomatoes could be improved by the sole application of GA3 @ 20 ppm under the controlled condition of Kailali, 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 | 0.000 | 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".