Comparing the Effectiveness of Garlic (Allium sativum L.) and Hot Pepper (Capsicum frutescens L.) in the Management of the Major Pests of Cabbage Brassica oleracea (L.)
Bibliographic record
Abstract
The use of chemical insecticides in crop production has resulted in increased food production in Africa, but their use has resulted in the destruction of beneficial organisms and development of resistance by some insects to the insecticides. The effectiveness of garlic Allium sativum and hot pepper, Capsicum frutescens in controlling the pests of cabbage, Brassica oleracea was evaluated. These botanicals were compared with a standard chemical insecticide Attack® (Emamectin benzoate). The experiment was conducted in a randomized complete block design, with 3 treatments and a control, each of which was replicated 3 times. Plutella xylostella, Brevicoryne brassicae, Hellula undalis and Trichoplusia ni were found on cabbage plants. Significantly fewer of them were found on the treated plants than the control plants. The use of the plant extracts resulted in a reduction in mortality ranging from 10.76% to 55.94%. Fewer natural enemies of B. brassicae were sampled on the insecticide-sprayed plots than the garlic and pepper-sprayed plots. The cost of protecting cabbage plants from insect infestation using Attack was higher than the botanicals. Garlic-treated plots recorded the highest cost: benefit ratio of 1:16 while Attack®-treated plots recorded the least of 1: 9.2. The control effects of the botanicals compared favourably with that of the chemical insecticides. Thus these botanicals can be used as substitutes to chemical insecticides.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".