Biologically effective dose of bromoxynil applied alone and mixed with metribuzin for the control of glyphosate-resistant horseweed in soybean
Bibliographic record
Abstract
Abstract Glyphosate-resistant (GR) horseweed was first confirmed in Ontario in 2010. GR horseweed interference can reduce soybean yield by up to 97%. Bromoxynil is a photosystem II–inhibiting herbicide that is primarily used for annual broadleaf weed control in monocot crops. The objective of this study was to determine the biologically effective dose (BED) of bromoxynil applied alone and when mixed with metribuzin applied preplant for control of GR horseweed in soybean in Ontario. Five field experiments were conducted over a 2-yr period (2019–2020) to determine the predicted dose of bromoxynil with or without metribuzin that would control GR horseweed 50%, 80%, and 95%. No soybean injury was observed. The predicted doses of bromoxynil to achieve 50% and 80% control of GR horseweed were 98 and 277 g ai ha −1 , respectively, at 8 wk after application (WAA). When mixed with metribuzin (400 g ai ha −1 ), the predicted doses of bromoxynil for 50%, 80%, and 95% control of GR horseweed were 10, 25, and 54 g ai ha −1 , respectively. Bromoxynil (280 g ai ha −1 ) plus metribuzin (400 g ai ha −1 ) controlled GR horseweed 97%, a finding that was similar to the industry standards of saflufenacil + metribuzin (99% control) and glyphosate/dicamba + saflufenacil (100% control) at 8 WAA. This study concludes that bromoxynil + metribuzin applied before planting provides excellent control of GR horseweed in 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".