Efficacy of Neem Tree (Azadirachta indica A. Jusieu) Extract on White Cabbage Aphid (Brevicoryne brassicae L. (Hem: Aphididae) Control in the Field
Bibliographic record
Abstract
Neem tree extracts from root, bark and leaves have generally been used in the control of pests. The extracts’ efficacy in the control of aphids in cabbages under smallholder farming systems formed the subject of the study. The dose of the admixture of the extract that effectively controlled aphids was determined through experimental methods that involved harvesting and crashing 0.1 × 10-4 of Neem tree parts. The extract was fermented for 18-48 hours.Treatment tests solutions varied were 20 mL, 30 mL and 40 mL of the extract respectively in 10litres of water and applied to cabbage in the field at intervals of once a week, twice a week and once in 2 weeks in a triplicated completely Randomized Block Design. The treated plants were observed for average number of live aphids, number of dead aphids, degree of leaf perforation, leaf curls, stunting, and finally the yield of the cabbage visa vis treatments. Aphid counts at the concentration of 30mL in 10L of water were significantly lower for live aphids and higher for dead aphids (P < 0.05), Although all treatments revealed significant differences from the control in terms of Aphid mortality, overall mean aphid counts showed a reduction in number (implying high mortality) with an increase in the concentration of Neem extract applied and cabbage vigour increased with increase in the frequency of admixture application, with a bi-weekly application producing significant results (P < 0.05). High concentration (40 mL) more effectively controlled aphids but resulted in mild scorched leaves. Smallholder farmers should take advantage of the naturally available Neem tree concoctions of 30 mLs in 10litres of water twice a week to keep aphid’s population below the economic injury level.
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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".