What Are the Most Efficacious Herbicides Applied Postemergence for Control of Multiple-Herbicide-Resistant Canada Fleabane [Conyza canadensis (L.) Cronq.] in Corn?
Bibliographic record
Abstract
Multiple-herbicide-resistant (MHR) Canada fleabane [Conyza canadensis (L.) Cronq.] control has become a major concern for corn producers in Ontario. Postemergence (POST) herbicides are critical for the control of emerged MHR Canada fleabane in corn. A study that consisted of five field experiments was conducted in southwestern Ontario in fields with confirmed MHR Canada fleabane to evaluate various herbicide mixtures applied POST for the control of MHR Canada fleabane in corn. Glyphosate + 2,4-D amine, glyphosate/2,4-D choline, glyphosate + clopyralid, glyphosate + S-metolachlor/mesotrione/bicyclopyrone, glyphosate + tolpyralate + atrazine, glyphosate + dicamba, glyphosate + dicamba/atrazine, glyphosate + S-metolachlor/mesotrione/ atrazine, glyphosate + mesotrione + atrazine, glyphosate + bromoxynil + atrazine, glyphosate + S-metolachlor/ mesotrione/bicyclopyrone/atrazine, glyphosate/S-metolachlor/mesotrione + atrazine, glyphosate/dicamba + tembotrione, glyphosate + tembotrione + bromoxynil, glyphosate/dicamba + tembotrione + atrazine, and glyphosate + tembotrione + atrazine applied POST provided 63-99% control, 77-100% density reduction, and 88-100% shoot biomass reduction of MHR Canada fleabane in corn. MHR Canada fleabane interference reduced corn yield up to 58%; reduced MHR Canada fleabane interference with all herbicide treatments resulted in corn yield similar to the weed-free control. Results of this study indicate that among the herbicide mixtures evaluated glyphosate + mesotrione + atrazine, glyphosate + bromoxynil + atrazine, glyphosate + S-metolachlor/ mesotrione/bicyclopyrone/atrazine, glyphosate/S-metolachlor/mesotrione + atrazine, glyphosate/dicamba + tembotrione, glyphosate + tembotrione + bromoxynil, glyphosate/dicamba + tembotrione + atrazine, and glyphosate + tembotrione + atrazine applied POST provided the most consistent control of MHR Canada fleabane in corn.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".