Is There a Benefit of Adding Atrazine to HPPD-Inhibiting Herbicides for Control of Multiple-Herbicide-Resistant, Including Group 5-Resistant, Waterhemp in Corn?
Bibliographic record
Abstract
The evolution of multiple-herbicide-resistant (MHR) waterhemp (resistant to Groups 2, 5, 9, and 14) in Ontario, Canada is challenging for growers. The complementary activity of the co-application of hydroxyphenylpyruvate dioxygenase (HPPD)-inhibiting herbicides with atrazine has been well documented. The objective of this research was to determine if the addition of atrazine to 4-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibiting herbicides applied postemergence improves their consistency of MHR waterhemp (including Group 5 resistance) in corn. Five field trials were conducted over a two-year period (2018, 2019) in Ontario, Canada. Five HPPD-inhibiting herbicides [isoxaflutole (105 g ha-1), mesotrione (100 g ha-1), topramezone (12.5 g ha-1), tembotrione (90 g ha-1), and tolpyralate (30 g ha-1)] were applied postemergence with and without atrazine to 10-cm-tall waterhemp. Corn injury (≤ 10%) was observed at specific sites where the application of tembotrione, isoxaflutole and isoxaflutole + atrazine resulted in characteristic white bleaching of corn foliage; however, yield was not affected. Averaged across field sites, the addition of atrazine to isoxaflutole, mesotrione, topramezone, or tembotrione improved MHR waterhemp control 15%, 11%, 7%, and 7%, respectively at 4 weeks after application (WAA). Averaged across herbicide treatments and sites, the addition of atrazine reduced the standard error of MHR waterhemp control by 13% to 100%. This study concludes that the co-application of atrazine with HPPD-inhibitors applied postemergence reduced the risk of herbicide failure and resulted in greater and more consistent control of MHR waterhemp.
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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 | 0.001 | 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".