Improving the Level and Consistency of Glyphosate-Resistant Canada Fleabane (Erigeron canadensis L.) Control With Bromoxynil-Based Tankmixes Applied Preplant to Soybean
Bibliographic record
Abstract
Glyphosate-resistant (GR) Canada fleabane (Erigeron canadensis L.) is a problematic weed in soybean. Bromoxynil-based tankmixes provide control of GR Canada fleabane in monocot crops; however, there is limited research on preplant (PP) applications in soybean. The objective of this study was to determine the best third tankmix partner with glyphosate plus bromoxynil applied PP to improve the level and consistency of GR Canada fleabane control in soybean. Four field trials were conducted over a two-year (2020, 2021) period. Glyphosate plus bromoxynil controlled GR Canada fleabane 52, 58, and 69% at 2, 4, and 8 weeks after application (WAA) respectively. The addition of saflufenacil to glyphosate plus bromoxynil improved GR Canada fleabane control 39% at 2 WAA and the addition of dicamba to glyphosate plus bromoxynil improved GR Canada fleabane control 39 and 29% at 4 and 8 WAA, respectively. The addition of bromoxynil to glyphosate plus 2,4-D ester or dicamba improved GR Canada fleabane control by 36 and 35%, respectively, at 2 WAA. The addition of bromoxynil to glyphosate plus tiafenacil or pyraflufen-ethyl/2,4-D improved GR Canada fleabane control by 19% at 8 WAA. The addition of bromoxynil to glyphosate plus metribuzin improved GR Canada fleabane control 31, 32, and 36% at 2, 4, and 8 WAA, respectively. Antagonism was observed between bromoxynil plus glyphosate plus tiafenacil or halauxifen-methyl 8 WAA, while the interaction between bromoxynil plus glyphosate plus pyraflufen-ethyl/2,4-D was considered synergistic. The addition of metribuzin, tiafenacil, pyraflufen-ethyl/2,4-D, 2,4-D ester, halauxifen-methyl, saflufenacil or dicamba to glyphosate plus bromoxynil improved the consistency of GR Canada fleabane at 2, 4, and 8 WAA. This study concludes that the addition of saflufenacil or dicamba to glyphosate plus bromoxynil can improve the level and consistency of GR Canada fleabane control applied PP to 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".