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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Pest Management Scie...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Pest Management Science
Article . 2025 . Peer-reviewed
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Inferring herbicide non‐target‐site cross‐resistance from dose response and mixture treatments

Authors: Chun Liu; Stephen R. Moss; Anastasia Perraki; Will Plumb; Shiv S. Kaundun;

Inferring herbicide non‐target‐site cross‐resistance from dose response and mixture treatments

Abstract

AbstractBACKGROUNDHerbicide cross‐resistance is of increasing concern because it compromises the effectiveness of both existing and new chemical options. However, a common misconception is that if a weed population shows dose–response shifts to two herbicides, it is cross‐resistant to both. The possibility that individual plants may possess different resistance mechanisms is often overlooked.RESULTSTo better characterise non‐target‐site cross‐resistance, we propose that the accession be treated with mixtures of the two herbicides of interest. A population model could be used to simulate the expected dose responses to the mixtures, assuming different cross‐resistance levels in the population, as well as synergistic or antagonistic effects between the two herbicides. The simulated responses can then be compared with the actual responses, and the cross‐resistance level approximated. We demonstrated this approach from a mathematical standpoint and validated it with experimental data from glasshouse tests on four well‐characterised biotypes of Lolium multiflorum and two commercial herbicides, clodinafop and iodosulfuron. Results also showed that understanding chemical interactions such as synergy and antagonism is crucial to a better estimate of cross‐resistance levels.CONCLUSIONBecause this method utilises standard dose–response tests, it is potentially easier and less time‐consuming than those involving plant cloning or divergent recurrent selection. Quick characterisation of the degree of cross‐resistance provides important insights as to whether chemical rotations or mixtures can still effectively control the weed population displaying resistances to multiple herbicides. This approach could be applied more broadly in cross‐resistance studies with other xenobiotics. © 2025 Society of Chemical Industry.

Keywords

Dose-Response Relationship, Drug, Herbicides, Lolium, Plant Weeds, Herbicide Resistance

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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