Biopesticide for Pest Control and Drought Stress in Potato Crop
Bibliographic record
Abstract
The excessive use of synthetic insecticides and the environmental stress in the crops led us to determine pest control by a biopesticide that induces the concentration of nordiguaracetic acid (NDGA), kaempferol and quercetin as defence activators in the potato crop. A biopesticide was applied to four potato genotypes: Fiana, Silvestre, Norteña and 09-37, under rainfed conditions, the repellent biopesticide was applied to the 16 DAE and the biofungicide to the 27 DAE, the concentration of kaempferol, quercetin and NDGA before applying the biopesticides was made at 14 DAE and the second sample was 37 DAE after applying the doses of the product, it was analyzed by completely random design with 4 treatments and 4 repetitions. The repellency of the potato flea beetle (Epitrix cucumeris), the concentration of ingredients in leaves and the absorption of the biopesticide were determined. The repellency at 27 DAE was 93.5% and at 37 DAE was 97.5%, there was no presence of diseases, at 14 DEA, no incidence of the ingredients was found in the crop, at 37 DAE, the 09-35 genotype had the highest concentration of NDAG with 558.75 mg/kg with 54.09% absorption and kaempferol with 135.92 mg/kg with absorption of 98%, the Silvestre variety with 9404.3 mg/kg with absorption of 64.37%, for the rest of the genotypes are significantly higher. The effect on repellency could be because the insect was not adapted to these ingredients and the absence of diseases to the defence activators and their absorption in the plant, so the plants did not show drought stress.
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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".