Effect of Moringa olifera Leaf Extract on Growth and Productivity of Three Cereal Forages
Bibliographic record
Abstract
Moringa olifera leaf extract at different concentrations were used to evaluate their effects on three cereal forages (Sorghum bicolor L. Moench, Penisetum typhoideum Rich and Sorghum Sudanese) grown under stress environment of soil and water salinity in an arid environment. Three independent experiments carried out at King Abdulaziz University Experimental Station, Makkah province during 2015 and 2016. Treatments consisted of four concentrations of Moringa olifera leaf extract: (C1 = 1 ml of juice + 10 ml of distilled water ,C2 = 1 ml of juice + 20 ml of distilled water, C3 = 1 ml of juice + 30 ml of distilled water and C4 = 1 ml of juice + 40 ml of distilled water in addition to distilled water alone as a control). Results showed that the higher concentration C1 contained the highest amount of inorganic elements and growth hormones compared to other concentrations. This in turn reflected in significant higher growth and forage yields of the three forage crops tested. Significant increases in growth and forage yields in both seasons reported for the higher concentration C1 compared to others. Fresh and dry forage yields of Millet, during 2016, increased by the higher concentration over the control treatment by 17.67 and 4.87%, respectively. Results indicated the effectiveness of Moringa leaf extract in improving growth and increasing productivity of cereal forages under harsh environment of salinity and aridity.
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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) | 0.001 | 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".