Ecotype and Foliar Fertilization with Florovit Affect Herbage Yield and Quality of Greenhouse-Grown Basil (Ocimum basilicum L.)
Bibliographic record
Abstract
Basil is usually grown in 0.5-0.7 dm3 pots, at 25 plants per pot. However, potted basil plants wilt and die easily due to high plant density, low substrate volume, insufficient moisture and nutrient depletion. The aim of this study was to determine the herbage yield and nutritional value of greenhouse-grown basil. A two-factorial experiment was performed in a randomized block design with three replications, in 2012-2013. A two-factorial experiment was performed in a randomized block design with three replications, in 2012-2013 Six basil (Ocimum basilicum L.) ecotypes were analyzed: sweet basil, ‘Queen of Siam’ basil, purple basil, cinnamon basil, lemon basil and ‘Minette’ basil. The second experimental factor was foliar fertilization with Florovit at a concentration of 0.5% and 1%. Basil yield was significantly affected by the ecotype and the interaction between the experimental factors. ‘Minette’ basil fertilized with 1% Florovit solution was characterized by the highest fresh herbage yield. A statistical analysis revealed that Florovit had no significant effect on basil yield. The concentrations of dry matter, total sugars, L-ascorbic acid and nitrates(V) in basil herbage varied across ecotypes. Foliar fertilization had a significant effect on the organic acid content of basil leaves. The accumulation of the analyzed components in basil herbage was significantly affected by the interaction between the experimental factors. Basil yield was significantly affected by the ecotype. ‘Minette’ and ‘Siam Queen’ basil was characterized by the highest fresh herbage yield. The fresh herbage of ‘Minette’ basil contained the lowest concentrations of dry matter, total sugars, L-ascorbic acid, organic acids and nitrates(V).
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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 | 0.000 |
| 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".