Influence of application timing and herbicide rate on the efficacy of tolpyralate plus atrazine
Bibliographic record
Abstract
Abstract Effective POST herbicides and herbicide mixtures are key components of integrated weed management in corn; however, herbicides vary in their efficacy based on application timing. Six field experiments were conducted over 2 yr (2017–2018) in southwestern Ontario, Canada, to determine the effects of herbicide application timing and rate on the efficacy of tolpyralate, a new 4-hydroxyphenyl pyruvate dioxygenase inhibitor. Tolpyralate at 15, 30, or 40 g ai ha−1in combination with atrazine at 500 or 1,000 g ai ha−1was applied PRE, early POST, mid-POST, or late POST. Tolpyralate + atrazine at rates ≥30 + 1,000 g ha−1provided equivalent control of common lambsquarters and Powell amaranth applied PRE or POST, whereas no rate applied PRE controlled common ragweed, velvetleaf, barnyardgrass, or green foxtail. Common ragweed, common lambsquarters, velvetleaf, and Powell amaranth were controlled equally regardless of POST timing. In contrast, control of barnyardgrass and green foxtail declined when herbicide application was delayed to the late-POST timing, irrespective of herbicide rate. Similarly, corn grain yield declined within each tolpyralate + atrazine rate when herbicide applications were delayed to late-POST timing. Overall, the results of this study indicate that several monocot and dicot weed species can be controlled with tolpyralate + atrazine with an early to mid-POST herbicide application timing, before weeds reach 30 cm in height, and Powell amaranth and common lambsquarters can also be controlled PRE. Additionally, this study provides further evidence highlighting the importance of effective, early-season weed control 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 | 0.001 |
| 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".