Efficacy of Extraction Methods of Moringa oleifera Leaf Extract for Enhanced Growth and Yield of Wheat
Bibliographic record
Abstract
A field study was conducted to assess effective method to get Moringa leaf extract, through its response on growth and yield traits of wheat. Extracts of whole or chopped and dry or fresh Moringa leaves were used exogenously i.e. pre-sowing seed soaking as well as water diluted solution based foliar spray application at tillering and booting stages. Significantly higher growth response in term of leaf area index, leaf area duration, and crop growth rate was observed when combination of 30 times diluted moringa leaf extract (MLE) was applied at both crop stages. Yield contributing traits of wheat such as fertile tillers, spikelet’s spikelet’s per spike, grains per spike, 1000 grain weight, biological and grain yields were recorded in significantly higher due to 30 times diluted fresh MLE followed by 20 times dilution of Moringa dried leaf powder (DLP). While, control treatment and hydro-priming showed at comparable results in the form of significantly lesser fertile tillers, grains per spike, 1000-grain weight and grain or biological yields. Conclusively, 30 times diluted MLE proved the best among the treatments combinations for improved wheat growth and yield however, the biochemical features responsible for such promotive response are yet to be investigated prior to dissemination of this technology to the farmer field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| 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 teacher head, 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".