Influence of Glufosinate Rate, Ammonium Sulfate, and Weed Height on Annual Broadleaf Weed Control
Bibliographic record
Abstract
Weed control efficacy with contact herbicides can depend on weed height at application. Four field experiments were conducted at sites with multiple weed species at different heights to determine the effect of weed height, glufosinate rate, and the addition of ammonium sulfate (AMS) on annual broadleaf weed control in soybean in southwestern Ontario, Canada, during 2021 and 2022. Glufosinate was applied at 300 or 500 g ai ha-1 without or with 6.50 L ha-1 of AMS to 5, 10, and 15 cm tall common lambsquarters (Chenopodium album L.), common ragweed (Ambrosia artemisiifolia L.), velvetleaf (Abutilon theophrasti Medik.), and redroot pigweed (Amaranthus retroflexus L.). Glufosinate provided excellent common ragweed control (> 98%) at both rates, without and with AMS, and at all three heights 4 weeks application (WAA). In contrast, glufosinate efficacy declined when applied to common lambsquarters, velvetleaf, and redroot pigweed > 5 cm in height. The addition of AMS to glufosinate improved common lambsquarters control. Increasing the rate of glufosinate to 500 g ai ha-1 and the addition of AMS improved control of velvetleaf and redroot pigweed. The results of this study demonstrate that the effect of glufosinate rate, AMS addition, and weed height at application timing is weed species-specific. In general, glufosinate (300 g ai ha-1) controlled common annual broadleaf weeds if weeds were < 5 cm in height; otherwise, glufosinate needed to be applied at 500 g ai ha-1 with AMS for control of annual broadleaf weeds, especially common lambsquarters, velvetleaf and redroot pigweed.
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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".