Minimising Insecticide Application in the Control of Insect Pests of Cowpea (Vigna Unguiculata (L) WALP) in Delta State, Nigeria
Bibliographic record
Abstract
Many commercial cowpea farmers control insect pests on cowpea with synthetic chemicals and may sometimes spray their farms during the growing season as many as 8 to 10 times. This leads to health hazards and environmental pollution. The present study was conducted to reduce the number of times, cypermethrin (conventional chemical) is applied before harvest and still, produce the expected cowpea grains. The experiments were carried out in two agro-ecological zones - Asaba and Abraka of Delta State during the late cropping season. The experiments consisted of 4 treatments - cowpea plots sprayed 4 times (at 7 days’ intervals), cowpea plots sprayed 5 times (at 10 days’ intervals), cowpea plots whose insect infestation were monitored before chemical application and control plots (without chemical treatment). Each treatment was replicated 3 times. The experiments were organised into a randomised complete block design (RCBD). The results indicated that cypermethrin controlled the major insect pests of cowpea. Second, grain yield was high at both locations; significant differences did not exist (P>0.05) in insect number and grain yield among the treatments. The study provides the evidence that (i) high cowpea grain yield is obtained at reduced number of chemical application of 4 or 5 times during the growing season (ii) Grain yield was significantly (P<0.05) higher at Abraka with1400.60kg ha-1 than Asaba (714.40kg ha-1) during the late cropping season.
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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) | 0.000 | 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".