Screening of Different Varieties of Okra (Abelmoschus esculentus L.) against Sucking Insect Pests
Bibliographic record
Abstract
The screening of different varieties is one of the key strategies used in Integrated Pest Management to control the insect population and to escape the use of synthetic insecticides. This study was aimed to screen different okra varieties against sucking insect pest complex such as thrip, jassid, aphid, whitefly and mealybug. The experiment was conducted at Agriculture Research Institute Tandojam. The varieties such as Rama Krishna, Silky-460 and Bharat Kawairi were cultivated in RCBD layout and each treatment was replicated five times. The results revealed that the most infested variety was Bharat Kaiwari followed by Silky-460 and Rama Krishna throughout the experimental period. The highest mean population of the sucking insect pests (thrip, jassid, aphid, whitefly and mealybug) was 9.61±0.35, 3.22±0.13, 18.33±0.50, 3.25±0.15 and 3.75±0.19 respectively was observed on Bharat Kaiwari and the lowered on Rama Krishna. However, the attack of aphid was prominent on all okra varieties and overall pest attack was higher in the month of June. Similarly, the co-efficient correlation analysis showed a positive relationship of temperature and humidity (r= 0.012; r = 0.128) with thrip population whereas there was a negative relationship between temperature and humidity with remaining sucking pests. Both jassid and mealybug population indicated a significant difference with temperature. Similarly, relative humidity displayed a significant impact on population of mealybug (r = 0.365) and aphid (r = -0.096). Thus, it could be concluded based on the results that Rama Krishna is the most resistance against sucking insect pests as compared to Silky-460 and Bharat Kaiwairi.
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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".