Herbicide programs for control of waterhemp (<i>Amaranthus tuberculatus</i>) resistant to three distinct herbicide sites of action in corn
Bibliographic record
Abstract
Abstract Control of waterhemp is becoming more difficult in Ontario because biotypes have evolved resistance to four herbicide sites of action (SOA), including groups 2, 5, 9, and 14. The objective of this study was to compare PRE, POST, and PRE followed by (fb) POST herbicide programs for their effect on control, density, and biomass of multiple-herbicide–resistant (MHR) waterhemp as well as corn injury and grain yield. Two separate field studies, each consisting of five field trials, were conducted over a 2-yr period (2018 and 2019) in fields where corn was grown in Ontario, Canada. The first experiment evaluated MHR waterhemp control with an inhibitor of 4-hydroxyphenyl-pyruvate dioxygenase (HPPD) applied PRE, PRE fb glufosinate applied POST, and glufosinate applied POST. The second experiment evaluated MHR waterhemp control with a non-HPPD inhibitor applied PRE, then PRE fb a POST application of atrazine + mesotrione, and then atrazine + mesotrione applied POST. Atrazine + isoxaflutole caused 3% to 5% corn injury at environment 1 (E1); no corn injury was observed with PRE and POST herbicide programs at environments E2, E3, E4, and E5. In general, atrazine/bicyclopyrone/mesotrione/S-metolachlor and dimethenamid-P/saflufenacil applied PRE controlled MHR waterhemp ≥95% 12 wk after POST application (WAA). A POST application of glufosinate following atrazine + tolpyralate PRE, and a POST application of atrazine + mesotrione following atrazine/dicamba or atrazine/S-metolachlor PRE, improved control at 4, 8, and 12 WAA in most environments. In general, PRE fb POST applications resulted in better control of MHR waterhemp throughout the growing season than single PRE and POST applications (P < 0.05). We conclude that herbicide programs based on multiple effective SOAs may offer effective control of MHR waterhemp where field corn is grown. It is advisable that when choosing an herbicide application program that excellent control of MHR waterhemp should be the goal given its high fecundity and competitive ability.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".