Potential of Neem (Azadirachta indica) Extract in Managing Fall Armyworm on Maize
Bibliographic record
Abstract
This research assessed the effect of neem leaf extract on the larval population of the fall armyworm and the level of damage, growth, and yield of maize. A Randomized Complete Block Design (RCBD) with four treatments (Control, 50 g per L, 100 g per L and 200 g per L of neem leaf extract) applied within the two seasons of 2022A and 2022B. The findings showed that a higher concentration (200 g per L) of neem leaf extract-treated maize plants registered the least leaf damage (0.15), severity (0.15), and larvae population (0.1). This effect was most attained during the 1st season of maize production and at the earliest vegetative weeks of maize growth (3 WAP to 5 WAP, i.e., weeks after planting). The higher neem leaf extract concentration of 200 g per L produced the most significant (p < 0.001) effect on maize growth traits. A higher plant height (162.51 cm), longer leaf length (112.5 cm), wider leaf width (11.7 cm), and broader stem girth (11.91 cm) were attained under 200 g per L of neem leaf extract treated plots during the 1st season of maize production and 11 WAP. The findings of this showed that a relatively high concentration of neem leaf extract produced a higher cob weight (200 g per L = 180.1 g) followed by 100 g per L (174.1 g), 50 g per L (140.9 g) and control (139 g). The weight was optimised during the 1st season of maize production (163.8 g). The grain yield was higher under 200 g per L (153.7 g) of neem extract. The above findings demonstrated that a high concentration of neem leaf extract reduced fall armyworm infestation and increased maize plant growth and yield. Farmers should, therefore, be encouraged to apply 200 g per L of neem leaf extract in the management of fall armyworms and improve plant growth and yield because it contains antifeedant and insecticidal properties against the fall armyworm.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".