Benefit of tank-mixing dicamba with glyphosate applied post-emergence for weed control in dicamba plus glyphosate resistant soybean
Bibliographic record
Abstract
Soybean resistant to both glyphosate and dicamba (Roundup Ready 2 Xtend™) has been developed by Monsanto Inc. and was commercially available for the first time in Canada in 2017. Six field trials were conducted over a 2-yr period (2014–2015) at three locations in southwestern Ontario to determine whether there is a benefit of including dicamba in a postemergence application of glyphosate at two application timings for the control of non-glyphosate-resistant weeds in Roundup Ready 2 Xtend™ soybean. Adding dicamba to glyphosate did not increase control of grass weed species. The tank mix of glyphosate and dicamba increased the control of redroot pigweed, common ragweed, common lambsquarters, and lady’s thumb by as much as 14%, 3%, 7%, and 5%, respectively, at 8 weeks after the late-postemergence application. In general, broadleaf weed density and biomass collected 6 weeks after the late-postemergence application was reduced more with dicamba applied alone or together with glyphosate than when glyphosate was applied alone early postemergence. Due to the absence of a grass herbicide, weed interference with dicamba applied alone resulted in a yield loss of 30%–33% while treatments containing glyphosate resulted in a yield loss of only 3%–7%. The tank mix of glyphosate and dicamba improved broadleaf weed control, but it should not be applied alone due to poor control of grass weeds.
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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 | 0.001 | 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".