Efficacy of Botanical Insecticides from <I>Piper</I> Species (Piperaceae) Extracts For Control of European Chafer (Coleoptera: Scarabaeidae)
Bibliographic record
Abstract
Biopesticides, including botanicals, can offer a safe and effective alternative to conventional insecticides for controlling major insect pests within an integrated pest management program. The current study highlights the practical application of a botanical insecticide for controlling a major insect pest of turfgrass: European chafer, Rhizotrogus majalis (Razoumowsky). Greenhouse and field trials were conducted to test the efficacy of a botanical formulation based on black pepper, Piper nigrum L. (Piperaceae), seed extracts to R. majalis larvae. The 7-d P. nigrum extract LC50 for R. majalis third instars was 2.5%. Successful treatment in the field was accomplished with the application of a 2% P. nigrum formulation to turfgrass infested with R. majalis second and third instars, whereas 4% extract was required in a second field trial with older third instars. The 2% pepper extract activity was comparable with the conventional insecticide diazinon in the first field trial. However, the 4% pepper extracts significantly affected the earthworm populations in treated plots compared with diazinon in the second field trail. The analysis of soil residues for piperamides in the P. nigrum extract determined a half-life of 1 - 2.6 d in the first and second field trials, respectively. This confirmed the expectation that under field conditions the residual activity would be less than conventional insecticides, thereby reducing the environmental risk associated with pesticide use. We recommend the pepper formulation for spot treatment applications when population densities reveal an epicenter of infestation rather than broadcasting over large areas, thus helping to minimize cost and negative affects on nontarget invertebrates.
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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".