Weed Control, Environmental Impact and Profitability of Two-Pass Weed Management Strategies in Glyphosate-Resistant Corn
Bibliographic record
Abstract
Twelve field trials were conducted over a three-year period (2010, 2011, 2012) at different locations in southwestern Ontario, Canada to compare various two-pass weed management strategies in glyphosate-resistant corn for crop injury, weed control, environmental impact, corn yield and profit margin. No visible injury resulted from the herbicide treatments evaluated. One early postemergence (EPOST) application of glyphosate provided good full season control of pigweed species and lady's thumb and fair control of velvetleaf, common ragweed, lamb's-quarters, barnyard grass and green foxtail. One late postemergence (LPOST) application of glyphosate provided excellent control of all weed species evaluated but corn yield was reduced due to early weed interference. The sequential application of glyphosate (EPOST fb LPOST) provided excellent control of all weed species evaluated with no adverse effect on corn yield. The sequential application of a preemergence residual herbicide followed by an application of glyphosate LPOST provided excellent full season control of all weed species evaluated and corn yield was equal to the weed free control. Among the sequential herbicide programs the lowest environmental impact was glyphosate EPOST fb LPOST and saflufenacil/dimethenamid-p, isoxaflutole + atrazine or rimsulfuron + s-metolachlor + dicamba applied PRE fb glyphosate LPOST. Based on this study, the most efficacious and profitable weed management programs in glyphosate-resistant corn are a sequential application of glyphosate or a two-pass program of a preemergence residual herbicide followed by glyphosate LPOST. The two-pass programs have glyphosate stewardship benefits.
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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 | 0.001 | 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".