Optimizing molecular assays for glyphosate and ALS-inhibitor resistance diagnostics in four weed species
Bibliographic record
Abstract
Herbicide-resistant weeds pose a threat to food production in modern agriculture, causing US$32 billion in crop production losses worldwide. In Michigan, highly troublesome and widespread weeds include waterhemp, Palmer amaranth, common ragweed, and horseweed, with accessions that are resistant to glyphosate (Group 9) and ALS-inhibitors (Group 2), major herbicide sites of action utilized in soybean and corn cropping systems. Molecular assays for rapid resistance diagnostics to confirm the in-field status of herbicide resistance can assist with more effective, timely, and proactive management. In this research, we developed and tested PCR-based assays to identify target site resistance mechanisms to both herbicide groups through Sanger sequencing and EPSPS copy number variation. Nine different SNPs were identified in five ALS positions known to confer herbicide resistance among all species surveyed. Pro197Ser was the most frequent in horseweed and common ragweed accessions, whereas Trp574Leu was the predominant mutation in Palmer amaranth and waterhemp. Four horseweed accessions contained the Pro106Ser mutation in the EPSPS gene, which confers resistance to glyphosate. Additionally, waterhemp and Palmer amaranth had 2–7 and 20–160 copies of EPSPS, respectively. The assays were validated by comparing genotyping of several field-collected accessions of unknown resistance status with known resistant and susceptible accessions. The efficacy of genotyping assays was >98% and required only two days, confirming that molecular assays are a robust tool for rapid resistance diagnostics. These assays can help growers evaluate herbicide resistance status in weed populations within the same growing season, allowing them to adopt effective management practices.
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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 | 0.001 | 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".