Control of glyphosate-resistant horseweed and giant ragweed in soybean with halauxifen-methyl applied preplant
Bibliographic record
Abstract
Abstract Horseweed and giant ragweed are competitive, annual weeds that can negatively impact crop yield. Biotypes of glyphosate-resistant (GR) giant ragweed and horseweed were first reported in 2008 and 2010 in Ontario, respectively. GR horseweed has spread throughout the southern portion of the province. The presence of GR biotypes poses new challenges for soybean producers in Canada and the United States. Halauxifen-methyl is a recently registered selective herbicide against broadleaf weeds for preplant use in corn and soybean. There is limited literature on the efficacy of halauxifen-methyl on GR horseweed and giant ragweed when combined with currently registered products in Canada. The purpose of this study was to determine the effectiveness of halauxifen-methyl applied alone and tank-mixed to control GR giant ragweed and GR horseweed in glyphosate and dicamba-resistant (GDR) soybean in southwestern Ontario. Six field experiments were conducted separately for each weed species over 2018 and 2019. Halauxifen-methyl applied alone offered 72% control of GR horseweed at 8 wk after application (WAA). Control was improved to >91% when halauxifen-methyl applied in combination with metribuzin, saflufenacil, chlorimuron-ethyl + metribuzin, and saflufenacil + metribuzin. At 8 WAA, halauxifen-methyl provided 11% control of GR giant ragweed, and 76% to 88% control when glyphosate/2,4-D choline, glyphosate/dicamba, glyphosate/2,4-D choline + halauxifen-methyl, and glyphosate/dicamba + halauxifen-methyl were used. We conclude that halauxifen-methyl applied preplant in a tank-mixture can provide effective control of GR giant ragweed and horseweed in GDR 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 | 0.000 | 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".