Control of glyphosate-resistant horseweed with Group 4 herbicides in soybean
Bibliographic record
Abstract
Abstract Little information is available on the relative efficacy of Group 4 herbicides for glyphosate-resistant (GR) horseweed management in soybean. Five field research experiments were conducted in growers’ fields from 2020 to 2021 to determine GR horseweed control with Group 4 herbicides applied preplant (PP) alone and in a mixture. There was minimal soybean injury (≤4%) with herbicides evaluated. Dicamba, 2,4-D, or halauxifen-methyl applied PP controlled GR horseweed 92% to 96%, 73% to 76%, and 85% to 89%, respectively. The mixtures of dicamba + 2,4-D, dicamba + halauxifen-methyl and dicamba + 2,4-D + halauxifen-methyl provided 97% to 99% control of GR horseweed, similar to dicamba applied alone. The mixture of 2,4-D + halauxifen-methyl provided 93% to 94% control of GR horseweed. Dicamba + saflufenacil controlled GR horseweed at 98%. Dicamba alone, dicamba + 2,4-D ester, dicamba + halauxifen-methyl, and dicamba + 2,4-D ester + halauxifen-methyl decreased GR horseweed density 97%, 99%, 99%, and 98%, respectively, similar to a 98% density reduction with dicamba + saflufenacil. Other herbicide treatments had no effect on GR horseweed density. Dicamba, 2,4-D, and halauxifen-methyl applied PP decreased GR horseweed dry biomass by 99%, 76%, and 72%, respectively. The mixtures of dicamba + 2,4-D, dicamba + halauxifen-methyl, and dicamba + 2,4-D + halauxifen-methyl decreased GR horseweed dry biomass by 99% to 100%, similar to a 99% dry biomass reduction with dicamba + saflufenacil. The mixture of 2,4-D + halauxifen-methyl decreased GR horseweed dry biomass by 94%. Soybean yield was decreased by 61% when GR horseweed was left uncontrolled. Results show that Group 4 herbicides that include dicamba applied PP can be very effective in managing GR horseweed in soybean.
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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".