Weed Control in Winter Wheat (<i>Triticum aestivum</i> L.) with Preplant Applications of Glyphosate plus Mesotrione or Saflufenacil
Bibliographic record
Abstract
There is limited information on weed control with glyphosate plus mesotrione or saflufenacil when applied prior to seeding winter wheat in the autumn. A total of 12 field trials (six for each herbicide tankmix) were conducted over a three-year (2010-2012) at two locations (Ridgetown and Exeter, Ontario) to evaluate the effect of glyphosate plus mesotrione or saflufenacil at various rates for broadleaf weed control and red clover establishment in winter wheat. Glyphosate (900 g ae ha-1) and tankmixes of glyphosate (900 g ae ha-1) plus mesotrione or saflufenacil at 12.5, 25, 50, 75, 100, 150 and 200 g ai ha-1 applied preplant (PP) in the autumn resulted in minimal visible injury (0% - 2%) in the autumn and on May 1 and June 1 of the following spring in winter wheat. The PP application of glyphosate alone or in combination with mesotrione or saflufenacil provided only 1% - 30% control of common ragweed in the following spring. The PP application of glyphosate in combination with mesotrione or saflufenacil provided 74% - 100% control of wild mustard at 2 and 4 weeks after emergence (WAE) in autumn but the control was only 0% - 35% on June 1 of the following spring. Glyphosate plus mesotrione or saflufenacil did not have any effect on seed moisture content and yield of winter wheat compared to glyphosate alone. Clover establishment was reduced with glyphosate plus mesotrione at the two highest rates but was not affected with glyphosate plus saflufenacil compared to glyphosate alone. Based on these results, glyphosate alone and in combination with mesotrione or saflufenacil applied PP in the autumn at rates evaluated did not provide adequate residual control of common ragweed, common lambsquarters and wild mustard in the spring of the following year in winter wheat.
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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 | 0.001 |
| 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".