Control of Glyphosate-Resistant Common waterhemp (<i>Amaranthus rudis</i>) in Three New Herbicide-Resistant Soybean Varieties in Ontario
Why this work is in the frame
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Bibliographic record
Abstract
Glyphosate-resistant (GR) common waterhemp (CW) is a localized weed in Ontario and one of the most problematic weeds in the US Corn Belt. First confirmed in Ontario in 2014, GR CW has now been confirmed in forty fields in three counties in Ontario as of 2015. Historically, the primary POST herbicides used for the control of CW in soybean were glyphosate, acifluorfen and fomesafen, but resistance to all three has been confirmed in many US states. Research was conducted in 2015 and 2016 to determine the control of GR CW with some of the new herbicide-resistant soybean technologies including glufosinate (LibertyLink), 2,4-D and glyphosate (Enlist), and isoxaflutole, mesotrione, and glufosinate (HPPD-resistant). Glyphosate-resistant CW was controlled (≥90%) all season with a two-pass weed control system across all herbicide-resistant soybean technologies evaluated. The two-pass weed control system in this research is defined as a PRE herbicide followed by a POST herbicide. At 12 WAA, the two-pass programs in LibertyLink, Enlist, and HPPD-resistant systems controlled GR CW up to 98, 98, and 92%, respectively, and reduced GR CW densities to 0 to 2% of the weedy control at 4 WAA. The two-pass programs provided greater GR CW control than PRE or POST herbicides alone. This study found that the use of two-pass weed control programs in glufosinate-resistant, glyphosate DMA/2,4-D choline-resistant and HPPD-resistant soybean can provide excellent control of GR CW, and can be valuable tools to reduce the selection intensity for herbicide-resistant weeds. Through the rotational use of different technologies, growers may be able to better manage their weed populations in reducing the risk of resistance when compared to the use of one herbicide repeatedly.
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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) | 0.001 | 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 | 0.000 |
| 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 it