Comparison of Mineral Oil, Insecticidal, and Biopesticide Spraying Regimes for Reducing Spread of Three <i>Potato virus Y</i> Strains in Potato Crops
Bibliographic record
Abstract
In-field management of Potato virus Y (PVY) faces challenges caused by the changing availability and environmental acceptability of chemical agents to control aphid vectors of the virus and by proliferation of PVY strains with different symptoms and rates of spread. From 2018 to 2020, foliar spray treatments were compared in field experiments in New Brunswick, Canada, to measure effectiveness at reducing spread of PVYO, PVYN:O, and PVYNTN strains. Mineral oil, insecticide, combined oil and insecticide spray, and a biopesticide (i.e., LifeGard WG) were compared. Insecticide-only and mineral oil–only treatments were not effective, but several combined oil and insecticide treatments and biopesticide treatments significantly reduced PVY spread. The biopesticide was proportionately more effective with recombinant PVYN:O and PVYNTN strains, possibly by exciting the plant’s hypersensitive resistance response, caused naturally only in cultivar ‘Goldrush’ by PVYO. Pesticide residue analysis showed that mineral oil increased the retention of pyrethroid insecticide in the potato foliage longer than with insecticide applied alone, which may explain the beneficial synergistic effect of combined sprays for reducing PVY spread. Tuber yields were generally unchanged in chemical insecticide treatments but were slightly lower in biopesticide treatment. The cost per PVY treatment was competitive across all effective treatments, including biopesticide; however, there was some revenue loss from lower yield with the biopesticide. This biopesticide is certified organic, however, and thus a small premium on the price for organic production could offset this yield deficit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".