Control of Multiple-Herbicide-Resistant Green Pigweed (Amaranthus powellii) with Preemergence and Postemergence Herbicides in Ontario Soybean Production
Bibliographic record
Abstract
Green pigweed [Amaranthus powellii S. Wats.] is a competitive, annual, broadleaf weed that can significantly reduce soybean yield due to rapid early growth and biomass production. As a consequence of green pigweed’s high competitiveness with crops, the weed species is generally managed with the use of herbicides; this may, however, lead to the selection of resistance. A green pigweed biotype with resistance to MCPA, mecoprop, dichlorprop-p, aminocyclopyrachlor (synthetic auxins), and imazethapyr (acetolactate synthase-(ALS)-inhibitor) was recently confirmed in Ontario, Canada. Research was conducted to identify alternative effective preemergence (PRE) and postemergence (POST) herbicides for the control of multiple-herbicide-resistant (MHR) green pigweed in Ontario soybean production. Four field trials, two with PRE and two with POST herbicides were conducted near Dresden, Ontario in 2019 and 2020. Visible soybean injury, green pigweed control, density, aboveground biomass, and soybean yield were evaluated following application with 19 PRE herbicide treatments and 12 POST herbicide treatments in separate studies. At 8 wk after application (WAA), pyroxasulfone/flumioxazin applied PRE controlled green pigweed 95% and was the most effective soil-applied herbicide. In the POST study, glyphosate was the most effective herbicide, controlling green pigweed 94% at 8 WAA. Imazethapyr, applied PRE or POST did not control this green pigweed biotype as it is also resistant to ALS-inhibitors. Control with other ALS-inhibiting herbicides as well as with PPO-inhibiting herbicides was variable, and further research is needed to determine the reason for variable control with these herbicides.
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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 | 0.000 | 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 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".