Evaluation of the Interaction between Glyphosate and Glufosinate
Bibliographic record
Abstract
Crops transformed to provide resistance to herbicides with two different mechanisms of action provide new opportunities for control of herbicide-resistant weeds. However, unexpected interactions may develop, especially for herbicides not generally used in tank-mixtures. The objectives of this study were to evaluate weed control and determine herbicide interactions and fluorescence responses with combinations of glyphosate and glufosinate on selected weeds prevalent in Michigan cropping systems. Field studies to determine herbicide interactions resulted in synergism only at 0.84 kg ae ha−1of glyphosate and 0.47 kg ai ha−1glufosinate in 2008. Early synergism (7 d after treatment [DAT]) was observed in the field at several combined rates for common lambsquarters and velvetleaf in 2009, and in the greenhouse for giant foxtail. Differences between years were perhaps due to the effect of environmental conditions on herbicide absorption and translocation. Antagonism was observed in the field in 2009 for velvetleaf, common lambsquarters, and giant foxtail especially at 840 g ae ha−1glyphosate and 118 g ai ha−1glufosinate, 28 DAT. Antagonism was also observed in the greenhouse for giant foxtail and Canada thistle, 28 DAT. Fluorescence measurements on Canada thistle in the greenhouse showed that glufosinate and glufosinate plus glyphosate acted rapidly to quench electron transport of photosystem II (PS II) system of photosynthesis, and the fluorescence characteristics of the glyphosate and glufosinate combinations were indistinguishable from glufosinate alone.
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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) | 0.001 | 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".