Use of Different Agrochemicals and Neem Oil to Control Whitefly (Bemisia tabaci) in Cotton Under Different Field Conditions
Bibliographic record
Abstract
Cotton, the white gold, is an important fiber crop that is grown as a commercial crop all over the world. Among the several factors contributing to the low yield of cotton, biotic constraints appear to be very important that are ravages caused by insect pests assume greater importance. Sucking the cell sap caused by whitefly gives a great reaction, which transfers viral diseases to the cotton after secreting the honeydew. For this, a trial was conducted Ayub Agricultural Research Institute (AARI) and a local farmer’s field during 2023-2024 cotton season to check the efficacy of different insecticides against the critical cotton insect pest, whitefly (Bemisia tabaci). In this study, Dufire 70% WDG, Crunch Super 75% WDG, Ulala 50%, Oshin 20% SG, and neem oil were applied in the cotton field and the data of the whitefly population were recorded the day after application, 72 hrs., and 7 days after application of insecticides. The data was taken accordingly, and it was seen that each insecticide significantly reduced the population to a certain level, but Oshin and Ulala proved to be the best chemicals to decrease the whitefly population in field conditions. The population of whitefly in treated areas of Ayub Agricultural Research Institute was always maximum in control conditions after the pesticides were applied and the least value was observed in neem oil and control-treated plots.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".