Glyphosate- and atrazine-resistant Palmer amaranth in New York: Confirmation and Management with Alternative Postemergence Herbicides
Bibliographic record
Abstract
Abstract Palmer amaranth is an increasing concern for producers in the northeastern United States. A new Palmer amaranth population (NY_PA) was identified in a soybean field in Ontario County, New York, in 2024. The main objectives of this research were to 1) confirm whether this NY_PA population is resistant to glyphosate and atrazine, and 2) determine the effectiveness of various postemergence herbicides alone or in mixtures to control it. Along with the NY_PA population, two previously known glyphosate-resistant Palmer amaranth populations from Connecticut (CT_PA) and Kansas (KS_PA), and a known glyphosate-susceptible population from Alabama (AL_SUS) were also evaluated. Results from the quantitative polymerase chain reaction assay revealed that the NY_PA population had an average of 180 copies of the 5-enolpyruvylshikimate-3-phosphate synthase ( EPSPS ) gene with a single EPSPS gene copy in the AL_SUS population. A greenhouse dose-response study revealed that the NY_PA and CT_PA populations had 7-fold to 11-fold resistance to atrazine. Nearly all postemergence herbicides tested, including 2,4-D, dicamba, saflufenacil, glufosinate, and lactofen alone or in mixtures with 2,4-D, dicamba, and glufosinate, provided effective control (90% to 100%) of Palmer amaranth weeds collected in Connecticut, Kansas, and New York. All these postemergence herbicides, alone or in mixtures, reduced shoot dry biomass of all three populations by 82% to 97% compared with plants in nontreated control plots. These results confirm the first report of Palmer amaranth populations from New York and Connecticut with resistance to multiple herbicides (glyphosate and atrazine). Effective postemergence herbicides tested in this research can be used to manage these Palmer amaranth populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".