Management in Soybean and Investigating mechanisms of glyphosate resistance in an Ontario common ragweed (Ambrosia artemisiifolia L.) biotype
Bibliographic record
Abstract
Glyphosate-resistant (GR) common ragweed was first reported near Windsor, Ontario from seed collected in 2011. Common ragweed was previously reported to be resistant to glyphosate in the United States in 2004; however, the mechanism(s) conferring glyphosate resistance in these biotypes has (have) not been identified. Therefore, the purpose of this research was to identify alternative herbicides to control glyphosate-resistant common ragweed in soybean and investigate known mechanisms of resistance to determine the mechanism of glyphosate resistance in the Ontario GR common ragweed biotype. Field experiments established close to Windsor, Ontario concluded that sequential applications of saflufenacil/dimethenamid-P, linuron, or metribuzin preplant (PP) followed by (fb) fomesafen postemergence (POST) provided more consistent control of GR common ragweed than a single application of any of these herbicides in soybean. The “metribuzin biologically effective rate” study concludes that a minimum of 786 g a.i. ha-1 is required for 95% control of GR common ragweed in soybean. This study concludes that GR common ragweed can be controlled with a PP followed by POST herbicide program and that metribuzin has a role in the control of GR common ragweed in soybean. In addition, in dicamba-resistant (DR) soybean, control of GR common ragweed was most consistent with sequential applications of dicamba. At 28 days after the post-emergence application (DAB), dicamba applied PP (fb) POST at 300 fb 300 g a.e. ha-1, 300 fb 600 g a.e. ha-1, or 600 fb 300 g a.e. ha-1 provided up to 98, 99 and 99% GR common ragweed control, respectively. Glyphosate resistance in the GR Ontario common ragweed biotype was not due to mutations at positions 102 and 106 in the EPSPS gene, or due to reduced absorption and translocation of glyphosate compared to the susceptible biotype. Further research is required to determine the mechanism of resistance in this biotype.
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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 | 0.000 | 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 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".