Potato leafhoppers in alfalfa: effects of alfalfa–grass mixtures, cultivar resistance status, and insecticides on forage yields
Bibliographic record
Abstract
Alfalfa ( Medicago sativa L.) is increasingly affected by high potato leafhopper (PLH, Empoasca fabae (Harris)) populations in some regions of Quebec. A study was conducted in three environments to contrast the use of different strategies to mitigate the effects of PLH on alfalfa including the addition of grasses in mixture with alfalfa, the use of a PLH-resistant cultivar, and insecticide applications. Foliar insecticide applications in the seeding year temporarily reduced PLH populations, but only increased alfalfa yield of a PLH-susceptible cultivar in one out of three environments. Insecticides also had an indirect residual effect in the first post-seeding year in low PLH conditions in the same environment increasing alfalfa yield of the PLH-susceptible cultivar at the first two harvests, compared to plots not treated with insecticide. The use of a PLH-resistant cultivar overall provided limited benefits across years and environments, it actually yielded less than a susceptible cultivar in some environments with low PLH populations. The addition of small percentages of grasses to alfalfa overall had minimal effect in high PLH conditions. Of the three strategies investigated, the use of insecticide had the greatest effect on alfalfa response to PLH, it is although important to note that harvesting was also effective in reducing PLH populations. Results need to be further validated across a wider range of environments as PLH populations we observed were low in most post-seeding years.
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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 | 0.001 | 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 | 0.001 | 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".