Glyphosate-Resistant Giant Ragweed (<i>Ambrosia trifida</i>) Control in Dicamba-Tolerant Soybean
Bibliographic record
Abstract
Glyphosate-resistant (GR) giant ragweed has been confirmed in Ontario, Canada. Giant ragweed is an extremely competitive weed and lack of control in soybean will lead to significant yield losses. Seed companies have developed new herbicide-resistant (HR) crop cultivars and hybrids that stack multiple HR traits. The objective of this research was to evaluate the efficacy of glyphosate and glyphosate plus dicamba tank mixes for the control of GR giant ragweed under Ontario environmental conditions in dicamba-tolerant (DT) soybean. Three field trials were established over a 2-yr period (2010 and 2011) on farms near Windsor and Belle River, ON. Treatments included glyphosate (900 g ae ha−1), dicamba (300 g ae ha−1), and dicamba (600 g ha−1) applied preplant (PP), POST, or sequentially in various combinations. Glyphosate applied PP, POST, or sequentially provided 22 to 68%, 40 to 47%, and 59 to 95% control of GR giant ragweed and reduced shoot dry weight 26 to 80%, 16 to 50%, and 72 to 98%, respectively. Glyphosate plus dicamba applied PP followed by glyphosate plus dicamba applied POST consistently provided 100% control of GR giant ragweed. DT soybean yield correlated with GR giant ragweed control. This is the first report in Canada of weed control in DT soybean, specifically for the control of GR giant ragweed. Results indicate that the use of dicamba in DT soybean will provide an effective option for the control of GR giant ragweed in Ontario.
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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) | 0.001 | 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".