The Addition of Saflufenacil to Glyphosate plus Dicamba Improves Glyphosate-Resistant Canada Fleabane (Erigeron canadensis L.) Control in Soybean
Bibliographic record
Abstract
Glyphosate + dicamba has provided variable glyphosate-resistant Canada fleabane (GRCF) control in glyphosate/dicamba-resistant (GDR) soybean. Previous research has indicated improved GRCF control when a third herbicide was added to glyphosate + dicamba, though research is limited. The objective of this research was to ascertain if the level and consistency of GRCF control can be improved when adding tiafenacil, metribuzin, bromoxynil, pyraflufen-ethyl/2,4-D, 2,4-D ester, halauxifen-methyl or saflufenacil to glyphosate + dicamba applied preplant (PP) in GDR soybean. Four field trials were conducted in 2020 and 2021 in commercial fields in southwestern Ontario, Canada. Glyphosate + dicamba controlled GRCF 57, 93 and 94% at 2, 4 and 8 WAA, respectively. Adding bromoxynil to glyphosate + dicamba improved GRCF control from 57 to 77% at 2 WAA; adding saflufenacil to glyphosate + dicamba improved GRCF control from 57 to 92, 93 to 99, and 94 to 99% at 2, 4 and 8 WAA, respectively. All three-way tank-mixtures improved the consistency of GRCF control, except for glyphosate + dicamba + 2,4-D ester at 2 WAA, glyphosate + dicamba + 2,4-D ester, tiafenacil or metribuzin at 4 WAA, and glyphosate + dicamba + tiafenacil or bromoxynil at 8 WAA. This study concludes that the level and consistency of GRCF control was improved when saflufenacil was added to a PP application of glyphosate + dicamba 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 | 0.000 |
| Science and technology studies | 0.001 | 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".