Impact of Acetochlor Rate and Application Timing on Multiple-Herbicide-Resistant Waterhemp Control in Corn and Soybean
Bibliographic record
Abstract
Documented 6-way (Groups 2, 4, 5, 9, 14, and 27) and 5-way (Groups 2, 5, 9, 14, and 27) multiple-herbicide-resistant (MHR) waterhemp have been confirmed in the US and Canada, respectively causing corn and soybean yield losses > 70%. The objective of this study was to determine the effect of acetochlor application timing and rate on non-emerged MHR waterhemp control in corn and soybean. Acetochlor is not yet registered in Canada, but it could be useful component of an integrated MHR waterhemp control program. Two studies, one in corn and one in soybean, were conducted in southwestern Ontario, Canada from 2020 to 2022. Three rates of acetochlor were applied preplant (PP), preemergence (PRE) and postemergence (POST) to non-emerged waterhemp. In corn, acetochlor [Emulsifiable Concentrate (EC)] applied at 1,225, 2,100 and 2,950 g ai ha-1 controlled MHR waterhemp 81, 85, and 90%, respectively, at 8 weeks after POST application (WAC). Acetochlor EC applied POST or PRE provided better control than when applied PP at 4, 8, and 12 WAC. In soybean, acetochlor [Capsule suspension (CS)] applied at 1,050, 1,375, and 1,700 g ai ha-1 controlled MHR waterhemp 63, 70, and 74%, respectively, at 8 WAC. The timing of acetochlor CS application did not affect MHR waterhemp control. Acetochlor applied at the low, medium, and high rate reduced waterhemp density by 87, 89, and 92% in corn, and by 82, 84, and 87% in soybean, respectively. The high rate of acetochlor provides acceptable control of MHR waterhemp in corn; control in soybean was inadequate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".