Environment and Soil Conditions Influence Pre- and Postemergence Herbicide Efficacy in Soybean
Bibliographic record
Abstract
Deciding on the most efficacious PRE and POST herbicide options and their ideal application timing can be challenging for soybean producers. Climatic events during the 14 d before and after herbicide application can further complicate decisions because of their influence on herbicide effectiveness. Nine field trials were conducted at three locations in southwestern Ontario from 2003 to 2006, to determine the most effective PRE and POST soybean herbicides for control of common lambsquarters, common ragweed, green foxtail, and redroot pigweed. When precipitation was low at least 7 d before and after herbicide application weed control was reduced in treatments that included imazethapyr (PRE or POST) or flumetsulam/S-metolachlor (a premix formulation) (PRE). Cumulative precipitation during the 12 d after PRE application that exceeded the monthly average by at least 60% reduced common lambsquarters control when metribuzin was applied and green foxtail control when imazethapyr was applied. Delaying application of imazethapyr + bentazon to a later soybean growth stage decreased control of common lambsquarters and green foxtail; however, environmental conditions appeared to influence these results. Precipitation on the day of application decreased control of common ragweed and redroot pigweed more with quizalofop-p-ethyl + thifensulfuron-methyl + bentazon compared with imazethapyr + bentazon. Soybean yield varied among POST herbicide treatments because of reduced weed control. This research confirms that environmental conditions pre- and postapplication, as well as application timing, influence herbicide efficacy and should be considered by growers when selecting an herbicide program.
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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) | 0.001 | 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".