Promoting Flowering and Yield in Indonesian Shallot Varieties Through the Application of Gibberellins
Bibliographic record
Abstract
Shallot cultivation in Indonesia often encounters significant challenges due to the plant's reluctance to flower, leading to farmers' reliance on consumption bulbs as planting material.This practice results in increased farming costs, lowered production, and the risk of pathogen infection, thereby impacting yield adversely.This study aimed to investigate the role of gibberellin on the flowering and yield of diverse shallot varieties in Indonesia.A two-factor split-plot design was implemented within a completely randomized block structure.The first factor, gibberellin, was applied at three levels (without gibberellin, GA3, and GA4) as the main plot.The second factor, variety, was incorporated as a sub-plot with five levels (Bima Brebes, Biru Lancor, Superb Philip, Maja Cipanas, and Batu Ijo).Each treatment was replicated thrice, resulting in 30 experimental units.Analysis of Variance was performed at a 5% level, followed by the Duncan Multiple Range Test at the 5% level when influence was observed.A significant acceleration in shallot flowering by 35.67 days was noted when the Biru Lancor variety was treated with GA3.A correlation was found between flower fresh weight and the number of shallot seeds per inflorescence.Each variety exhibited different flowering capabilities, with the Maja Cipanas variety portraying the highest flowering percentage (13.78%).GA3 was observed to enhance the percentage of flowering plants and, when combined with the Batu Ijo variety, to support shallot bulb yield.The results indicate that GA3 can effectively promote the flowering and yield of shallots in Indonesia.
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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 | 0.000 |
| 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 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".