Evolution of Resistance to Auxinic Herbicides: Historical Perspectives, Mechanisms of Resistance, and Implications for Broadleaf Weed Management in Agronomic Crops
Bibliographic record
Abstract
Auxinic herbicides are widely used for control of broadleaf weeds in cereal crops and turfgrass. These herbicides are structurally similar to the natural plant hormone auxin, and induce several of the same physiological and biochemical responses at low concentrations. After several decades of research to understand the auxin signal transduction pathway, the receptors for auxin binding and resultant biochemical and physiological responses have recently been discovered in plants. However, the precise mode of action for the auxinic herbicides is not completely understood despite their extensive use in agriculture for over six decades. Auxinic herbicide-resistant weed biotypes offer excellent model species for uncovering the mode of action as well as resistance to these compounds. Compared with other herbicide families, the incidence of resistance to auxinic herbicides is relatively low, with only 29 auxinic herbicide-resistant weed species discovered to date. The relatively low incidence of resistance to auxinic herbicides has been attributed to the presence of rare alleles imparting resistance in natural weed populations, the potential for fitness penalties due to mutations conferring resistance in weeds, and the complex mode of action of auxinic herbicides in sensitive dicot plants. This review discusses recent advances in the auxin signal transduction pathway and its relation to auxinic herbicide mode of action. Furthermore, comprehensive information about the genetics and inheritance of auxinic herbicide resistance and case studies examining mechanisms of resistance in auxinic herbicide-resistant broadleaf weed biotypes are provided. Within the context of recent findings pertaining to auxin biology and mechanisms of resistance to auxinic herbicides, agronomic implications of the evolution of resistance to these herbicides are discussed in light of new auxinic herbicide-resistant crops that will be commercialized in the near future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".