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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".