Control of multiple-herbicide-resistant waterhemp (<i>Amaranthus tuberculatus</i>) with acetochlor-based herbicide mixtures in corn
Bibliographic record
Abstract
Abstract Waterhemp is a summer annual, broadleaf weed with high fecundity, short seed longevity in the soil, and wide genetic diversity. Populations have evolved resistance to five herbicide modes of action (Groups 2, 5, 9, 14, and 27), which are present across southern Ontario; this has increased the challenge of controlling this competitive weed species in corn, the most important grain crop produced worldwide and the highest-value agronomic crop in Ontario. Acetochlor is a Group 15 soil-applied residual herbicide that has activity on many grass and broadleaf weeds but has yet to be registered in Canada. The objective of this study was to ascertain whether mixtures of acetochlor with flumetsulam, dicamba, atrazine, isoxaflutole/diflufenican, or mesotrione + atrazine applied preemergence would increase the control of multiple-herbicide-resistant (MHR) waterhemp in corn. Five field trials were conducted between 2022 and 2023. No corn injury was observed. Acetochlor applied alone controlled MHR waterhemp 97% 12 wk after application (WAA). All herbicide mixtures controlled MHR waterhemp similarly at ≥98% 12 WAA; there were no differences among herbicide mixtures. Flumetsulam, dicamba, and atrazine provided lower MHR waterhemp control than all other herbicide treatments and did not reduce density or biomass. Acetochlor reduced waterhemp density 98%, while the acetochlor mixtures reduced density similarly at 99% to 100%. This study concludes that the acetochlor mixtures evaluated provide excellent waterhemp control; however, control was not greater than acetochlor alone. Herbicide mixtures should be used as a best management practice to mitigate the evolution of herbicide resistance.
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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".