Glyphosate-Resistant Common Ragweed (<i>Ambrosia artemisiifolia</i>) Control with Postemergence Herbicides and Glyphosate Dose Response in Soybean in Ontario
Bibliographic record
Abstract
Field trials were conducted in Ontario in 2013 and 2014 in soybean to determine the efficacy of POST herbicides on common ragweed resistant to group 2 and group 9 herbicides. Glyphosate dose-response experiments were conducted in the field on two resistant common ragweed populations and one susceptible population. None of the POST herbicides evaluated provided 80% control of glyphosate-resistant (GR) common ragweed. The most effective POST herbicide mixture was glyphosate (Monsanto Canada Inc., 67 Scurfield Blvd., Winnipeg, Manitoba, Canada) plus fomesafen(Syngenta Canada Inc., 140 Research Lane, Research Park Guelph, Ontario, Canada), which provided 68 to 98% control of GR common ragweed. Chlorimuron, cloransulam, imazethapyr, and thifensulfuron provided control similar to glyphosate alone. An application of glyphosate/fomesafen reduced biomass by as much as 95%. Glyphosate plus acifluorfen reduced GR common ragweed biomass by as much as 92%. The remaining POST herbicide tank mixes evaluated reduced GR common ragweed biomass by less than 80%. Glyphosate plus bentazon, glyphosate plus chlorimuron, and glyphosate plus thifensulfuron resulted in soybean yields similar to the weedy control, with yield reductions of 70, 62, and 73%, respectively. An application of glyphosate plus fomesafen or glyphosate/fomesafen had the lowest soybean yield reductions of 29 and 34%, respectively. The resistant biotype required a 2- to 28-fold increase in glyphosate dose compared to the susceptible population to achieve 50% control.
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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".