Effect of Green Manure, Sesbania bispinosa Wight Amendment on Incidence of Sucking Insect Pests, their Predators and Yield in Organic Cotton
Bibliographic record
Abstract
Cotton holds the key importance in the economy of Pakistan, but its yield is severely affected due to the infestation of many insect pests. Farmers mostly rely on chemicals to control pests but their adverse effects on human health and their interests are also considerable. Therefore, this study was conducted over two years to evaluate the influence of amendment of soil with green manure (GM) Dhancha, Sesbania bispinosaWight on the population of cotton sucking insect pests and their predators. Significant impact of GM was found in lowering the population of sucking pests of cotton i.e., Thrips tabaci(Lind), Bemisia tabaci (Gennadus), Amrasca bigutulla bigutulla (Ishida) and Tetranychus urticae (Koch) during both years. Population of predators i.e., Chrysoperla carnea, Geocoris punctipesand Orius sp. was also higher in dhancha treated plots in comparison to control. Application of neem oil was found effective in lowering the population of sucking insect pests during 2014; whereas, application of C. carnea cards showed significant impact after the mid cotton season during 2015. Overall growth and yield parameters were better in dhancha amended organic cotton treatment in comparison to control.
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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 | 0.001 | 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".