How Does the Addition of Biostimulants Affect the Growth, Yield, and Quality Parameters of the Snap Bean (Phaseolus vulgaris L.)? How Is This Reflected in Its Nutritional Value?
Bibliographic record
Abstract
Recently, the use of biostimulants as natural and eco-friendly fertilizers has received increasing attention because of their efficiency in terms of improving crops’ qualitative and quantitative parameters, i.e., growth, yield, and chemical composition. We studied the effect of four biostimulants—humic acid (20 g/L), vermicompost tea (15 mL/L), moringa leaf extract (1:30 v/v), and yeast extract (5 g/L), with tap water as a control treatment—on the qualitative and quantitative characteristics of snap beans. The experiment was designed using a complete randomized block with triplicates. The results showed a significant improvement in treated plant performance (growth and yield), chlorophyll, and chemical composition compared to untreated plants. Using moringa leaf extract increased the plant height, number of leaves and branches/plant, and fresh and dry weight. Additionally, the diameter of the treated plant stems and the quality of the crop and pods were also significantly higher than those of plants treated with vermicompost or humic acid extract. It is also noted that the profile of amino acids was improved using all tested biostimulants. This leads to the conclusion that the addition of moringa leaf extract and vermicompost tea not only positively affects the qualitative and quantitative properties of snap bean but is also reflected in its nutritional value as a plant-based food.
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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) | 0.000 | 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".