Control of Multiple-Herbicide-Resistant Green Pigweed (Amaranthus powellii) With Preemergence and Postemergence Herbicides in Ontario Corn Production
Bibliographic record
Abstract
Green pigweed [Amaranthus powellii S.Wats.] is a prolific annual dicot weed that is a prominent weed of crop production in northeastern North America. Green pigweed interference has been documented to reduce corn yields up to 54% in the absence of control strategies. In 2021, a green pigweed biotype from a field near Dresden, Ontario, Canada was determined to be resistant to MCPA, mecoprop, dichlorprop-p, aminocyclopyrachlor (synthetic auxins), and imazethapyr (acetolactate synthase-(ALS)-inhibitor), further impacting control of this weed biotype. Two field studies, with herbicides applied preemergence (PRE) or postemergence (POST), were conducted in 2020 and repeated in 2021. The objective of the research was to determine the most effective PRE and POST herbicides for the control of multiple-herbicide-resistant (MHR) green pigweed in Ontario corn production. 18 PRE and 18 POST herbicide treatments were evaluated in separate studies. Visible crop injury, visible green pigweed control at specified timepoints after herbicide application, green pigweed density, green pigweed biomass, and corn yield at harvest maturity were collected. In the PRE study, rimsulfuron + mesotrione was identified as the most efficacious treatment providing 88% control at 8 WAA. In the POST study, atrazine was identified as the most efficacious treatment providing 94% control at 8 WAA. Control with all PRE herbicide treatments was impacted by rainfall following application. MCPA ester applied POST controlled green pigweed 30% at 8 WAA; reduced control is attributed to herbicide resistance in this biotype. When compared to similar studies, control of green pigweed was reduced with some of the POST herbicides tested. While MHR green pigweed represents an additional challenge for growers, there are efficacious herbicide treatments that would allow it to be managed in corn production.
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 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) | 0.000 | 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 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".