Efficacy of Bio-Pesticides for Managegement of Sucking Insect Pests of Cotton, Gossipium hirsutum (L.)
Bibliographic record
Abstract
The studies were conducted consecutively for two years, 2006 and 2007 for management of cotton insect pests through eco-friendly measures. Bio-pesticides Neem seed extract, Neem oil, Asafoetida (Hing) and Tobacco leaf extract were evaluated against sucking complex. The experiment regarding evaluation botanical pesticides showed that among all bio-pesticides, the highest percent reduction of thrip (67.65%) was recorded in Neem seed extract followed by Neem oil (60.00%), Tobacco (63.59%) and Hing (Asafoetida) (52.68%) after 96 h. of application. Overall maximum mean reduction (64.69%) was recorded in Neem seed extract followed by Neem oil (57.74%), Tobacco (52.91%) and Asafoetida (46.52%). The highest reduction of jassid (71.97%) was recorded followed by Neem oil (70.06%), Hing (Asafoetida) (68.15%) and Tobacco (23.56%) after 96 h., of application of pesticides. With regards to reduction percent of whitefly revealed that maximum reduction (60.18%) was recorded in Hing (Asafoetida) followed by Neem oil (59.78%), Neem extract (59.38%) and tobacco (40.61%) after 96 h., of spray application. The botanical pesticides started reducing their toxicity after 96 h. However, the effective reduction of pests was recorded up to one week. Integrated pest management (IPM) model was developed for the control of sucking insect pests of cotton, for benefit of farming community through seminars, trainings and pamphlets. Using the safe botanical pesticides remained effective against sucking pests and is recommended against cotton pests, which showed less effective to natural enemies and environment friendly.
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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".