Control of Glyphosate-Resistant Waterhemp with Two-Pass Weed Control Strategies in Glyphosate/Dicamba-Resistant Soybean
Bibliographic record
Abstract
Waterhemp is a small-seeded, dioecious, broadleaf weed that emerges throughout the growing season. If left uncontrolled, waterhemp interference can reduce soybean yield up to 73%. Glyphosate-resistant (GR) waterhemp was first discovered in one county in Ontario in 2014; as of 2017, it has been found in two other counties. Glyphosate/dicamba-resistant soybean can be sprayed with glyphosate and/or dicamba preplant (PP), preemergence (PRE) and/or postemergence (POST). The objective of this study was to determine the control of GR waterhemp in glyphosate/dicamba-resistant soybean with PRE residual herbicides, glyphosate/dicamba applied POST or a two-pass program of a PRE residual herbicide followed by glyphosate/dicamba applied POST. At 8 weeks after application (WAA), pyroxasulfone (150 g ai ha-1), S-metolachlor/metribuzin (1943 g ai ha-1), pyroxasulfone/sulfentrazone (300 g ai ha-1) and flumioxazin/pyroxasulfone (240 g ai ha-1), applied PRE, resulted in 71%, 85%, 82% and 90% GR waterhemp control, respectively. The same PRE herbicides, followed by glyphosate/dicamba (1800 g ae ha-1) POST, improved control to greater than 96%. This study concludes that a two-pass program of an effective soil applied residual herbicide followed by glyphosate/dicamba POST controlled GR waterhemp in glyphosate/dicamba-resistant soybean.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 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".