Synergistic and Antagonistic Herbicide Interactions for Control of Volunteer Corn in Glyphosate/Glufosinate/2,4-D-Resistant Soybean
Bibliographic record
Abstract
Weed interference from glyphosate/glufosinate-resistant (GGR) volunteer corn can reduce soybean yield and quality. The recent release of glyphosate/glufosinate/2,4-D choline (GG2)-resistant soybean will allow for expanded POST herbicide mixture options for broad-spectrum weed control. Herbicide antagonism between ACCase-inhibiting graminicides and synthetic auxin herbicides has been confirmed for various grass weed species, including volunteer corn. Field experiments (total of 4) were carried out in 2021 and 2022 in southwestern Ontario to assess volunteer corn control with combinations of glufosinate, 2,4-D choline, or dicamba plus clethodim or quizalofop-p-ethyl applied POST to GG2-resistant soybean. Quizalofop-p-ethyl and quizalofop-p-ethyl + glufosinate controlled GGR volunteer corn 95 and 98%, respectively, 6 weeks after application (WAA); adding 2,4-D choline or dicamba to quizalofop-p-ethyl reduced control to ≤ 15%. Clethodim controlled GGR volunteer corn 81%, and the addition of glufosinate increased control to 97%; the co-application of 2,4-D choline or dicamba with clethodim reduced GGR volunteer corn control to 58 and 45%, respectively at 6 WAA. ACCase-inhibiting herbicides co-applied with glufosinate resulted in a synergistic improvement in GGR volunteer corn control while co-applications with synthetic auxin herbicides resulted in an antagonistic decrease in GGR volunteer corn control. Greater antagonism occurred when the synthetic auxin herbicides were co-applied with quizalofop-p-ethyl than clethodim. All mixtures of quizalofop-p-ethyl or clethodim with 2,4-D or dicamba resulted in unacceptable control of GGR volunteer corn.
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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 | 0.001 | 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".