Annual Ryegrass Control in Corn With Glyphosate plus Residual Herbicides Applied Preplant in the Spring
Bibliographic record
Abstract
Glyphosate needs to be partnered with other herbicides that have residual biological activity on ryegrass (Lolium multiflorum Lam.) to improve the efficacy and consistency of annual ryegrass control in corn. Five field experiments were conducted from 2019 to 2021 near Exeter, Ontario to evaluate various glyphosate tank mixes applied preplant (PP) in the spring in corn for the control of annual ryegrass seeded as a cover crop in the fall of the previous year (2018 to 2020). At 1 week after application (WAA), all glyphosate tank mixes evaluated provided minimal annual ryegrass control (14-28%). At 2 WAA, the addition of dimethenamid-p/saflufenacil to glyphosate improved annual ryegrass control from 55% to 68%; there was no improvement in annual ryegrass control with the other 14 tank mixes evaluated. At 3 WAA, the addition of dimethenamid-p/saflufenacil and mesotrione + rimsulfuron to glyphosate controlled annual ryegrass 91 and 90%, respectively. At 4 WAA, the addition of dimethenamid-p, dimethenamid-p/saflufenacil, mesotrione + rimsulfuron, S-metholachlor, or bicyclopyrone/mesotrione/S-metolachlor to glyphosate improved annual ryegrass control 7, 7, 10, 8, and 6%, respectively. At 6 WAA, the addition of pyroxasulfone, pyroxasulfone + atrazine, dimethenamid-p, dimethenamid-p/saflufenacil, mesotrione + rimsulfuron, S-metholachlor, atrazine/S-metolachlor, or bicyclopyrone/mesotrione/S-metolachlor to glyphosate improved annual ryegrass control 10, 9, 12, 10, 17, 13, 10, and 12%, respectively but the addition of all other herbicides to glyphosate did not improve annual ryegrass control. Density and biomass reductions of annual ryegrass with glyphosate tank mix evaluated generally followed a similar trend as the visible control. Annual ryegrass interference reduced corn yield by up to 83% compared to the non-treated control. The addition of a residual herbicide to glyphosate did not result in an improvement in the seed yield of corn. Among the glyphosate tank mixes evaluated glyphosate + mesotrione + rimsulfuron provided the most consistent control of annual ryegrass in corn.
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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 | 0.000 |
| 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".