Effect of Native Soap on Insect Pests and Grain Yield of Cowpea (Vigna unguiculata (L) Walp) in Asaba and Abraka during the Late Cropping Season in Delta State, Nigeria
Bibliographic record
Abstract
Studies were conducted to test the effectiveness of native soap against cowpea insect pests during the late cowpea cropping season in two agro-ecological zones-Asaba and Abraka, Delta State. Four major insect pests, namely the cowpea aphid, Aphis craccivora Koch, the legume flower bud thrips, Megalurothrips sjostedti Tryb, the legume pod borer, Maruca vitrata Fab and pod sucking bugs were studied. The experiment was made up of five treatments-1, 2 and 3 percent concentrations of native soap, cypermethrin (as conventional chemical and check) and a control. Each treatment was replicated three times. The experiment was arranged into a randomised complete block design (RCBD). The results showed that all the major insect pests occurred in the study areas but were more at Asaba compared to Abraka. Native soap was effective against A. craccivora and flower bud thrips population at Asaba. Maruca vitrata was not affected by soap application. Grain yield was high at Abraka and significantly (P<0.05) higher than Asaba. The use of native soap as non-conventional insecticide in cowpea insect pests management appears promising, more so as it is not expensive and safe to handle. Farmers may prefer it to synthetic chemical pesticides with their associated dangers.
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 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) | 0.000 | 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 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".