Evaluation of crop-topping strategies to reduce common lambsquarters (<i>Chenopodium album</i>) seed production in potato production systems
Bibliographic record
Abstract
Abstract Potato producers in Canada’s Atlantic provinces of Prince Edward Island (PE) and New Brunswick rely on photosystem II (PSII)-inhibiting herbicides to provide season-long weed control. Despite this fact, a high proportion of common lambsquarters populations in the region have been identified as resistant to this class of herbicides. Crop-topping is a late-season weed management practice that exploits the height differential between weeds and a developing crop canopy. Two field experiments were conducted in Harrington, PE, in 2020 and 2021, one each to evaluate the efficacy of a different crop-topping strategy, above-canopy mowing or wick-applied glyphosate, at two potato phenological stages, on common lambsquarters viable seed production and potato yield and quality. Mowing common lambsquarters postflowering decreased viable seed production (72% to 91%) in 2020 but increased seed production (78% to 278%) in 2021. Mowing had minimal impact on potato marketable yield across cultivars in both years. In contrast, treating common lambsquarters with wick-applied glyphosate had variable impacts on seed output in 2020 but dramatically reduced seed production (up to 95%) in 2021 when treatments were applied preflowering. Glyphosate damage to potato tubers was not influenced by timing and resulted in a 14% to 15% increase in culled tubers due to black spotting and rot. Our results highlight the importance of potato and common lambsquarters phenology when selecting a crop-topping strategy and demonstrate that above-canopy mowing and wick-applied glyphosate can be utilized for seedbank management of herbicide-resistant common lambsquarters in potato production systems.
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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 | 0.001 | 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".