Control of multiple-herbicide-resistant waterhemp with acetochlor-based herbicide mixtures in soybean
Bibliographic record
Abstract
Abstract Waterhemp has evolved resistance to Group 2, 5, 9, 14, and 27 herbicides in Ontario, Canada, making control of this challenging weed even more difficult. Acetochlor is a Group 15, chloroacetanilide herbicide that has activity on many small-seeded annual grasses and some small-seeded annual broadleaf weeds, including waterhemp. The objective of this study was to ascertain if acetochlor mixtures with broadleaf herbicides (dicamba, metribuzin, diflufenican, sulfentrazone, or flumioxazin), applied preemergence (PRE), increase multiple-herbicide-resistant (MHR) waterhemp control in soybean. Five trials were conducted over 2 yr (2021 and 2022). The acetochlor mixtures caused ≤7% soybean injury, except acetochlor + flumioxazin, which caused 19% soybean injury. Acetochlor applied PRE controlled MHR waterhemp 89% at 4 wk after application (WAA). Dicamba, metribuzin, diflufenican, sulfentrazone, or flumioxazin controlled MHR waterhemp 59%, 67%, 58%, 64%, and 86%, respectively, at 4 WAA. Acetochlor applied in a mixture with dicamba, metribuzin, diflufenican, sulfentrazone, or flumioxazin provided good to excellent control of MHR waterhemp; control ranged from 91% to 98% but was similar to acetochlor applied alone. Acetochlor alone reduced MHR waterhemp density and biomass 98% and 93%; acetochlor + flumioxazin reduced waterhemp density and biomass by an additional 2% and 7%, respectively. This research concludes that acetochlor applied in a mixture with flumioxazin was the most efficacious mixture evaluated for MHR waterhemp control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".