Two-Pass Weed Management with Preemergence and Postemergence Herbicides in Glyphosate-Resistant Soybean
Bibliographic record
Abstract
There is little information on the efficacy and profitability of two-pass weed control strategies in soybean when a preemergence (PRE) residual herbicide is followed by glyphosate applied late postemergence (LPOST) under Ontario, Canada environmental conditions. Ten field trials were conducted during 2011-2013 in Ontario, Canada to determine the level of weed control, yield and net returns of various preemergence/postemergence programs in glyphosate-resistant soybean. Crop injury was 2% or less with the herbicides evaluated except for chlorimuron + flumioxazin (PRE) and pyroxasulfone + flumioxazin (PRE) which caused 4% and 7% visible injury in soybean, respectively. A single application of glyphosate resulted in variable weed control (73% - 98%) while the sequential application of glyphosate provided excellent weed control (98% - 100%). The control of all weeds 8 WAA after the LPOST glyphosate application was equivalent regardless of the PRE herbicide applied (96% - 100%). Soybean yield was equivalent to the weed free control regardless of the PRE herbicide applied. Soybean yield was lower than the sequential application of glyphosate with chlorimuron or pyroxasulfone/flumioxazin PRE fb glyphosate LPOST. Generally net return with the two-pass programs was equivalent to the sequential application of glyphosate. Net returns were lower than the sequential application of glyphosate with chlorimuron or s-metolachlor + flumetsulam followed by glyphosate LPOST. Based on these results, a sequential application of glyphosate or a two-pass program of a preemergence residual herbicide followed by glyphosate LPOST are the preferred weed management programs in glyphosate-resistant soybean. The two-pass programs have the potential to reduce selection pressure for glyphosate-resistant weeds.
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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 | 0.000 | 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".