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Modeling the Herbicide-Resistance Evolution in Lolium rigidum (Gaud.) Populations at the Landscape Scale
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The repeated application of herbicides has led to the development of herbicide resistance. Models are useful for identifying key processes and understanding the evolution of resistance. This study developed a spatially explicit model at a landscape scale to examine the dynamics of Lolium rigidum populations in dryland cereal crops and the evolution of herbicide resistance under various management strategies. Resistance evolved rapidly under repeated herbicide use, driven by weed fecundity and herbicide efficacy. Although fitness costs associated with resistant plants reduced the resistance evolution, they did not affect the speed of its spread. The most effective strategies for slow resistance involved diversifying cropping sequences and herbicide applications. Pollen flow was the main dispersal vector, with seed dispersal also making a significant contribution. Strategies limiting seed dispersal effectively decreased resistance spread. However, the use of a seed-catching device at harvest could unintentionally enrich resistance in the area. It would be beneficial to optimize the movement of harvesters between fields. The model presented here is a useful tool that could assist in the exploration of novel management strategies within the context of site-specific weed management at landscape scale as well as in the advancement of our understanding of resistance dynamics.
Title: Modeling the Herbicide-Resistance Evolution in Lolium rigidum (Gaud.) Populations at the Landscape Scale
Description:
The repeated application of herbicides has led to the development of herbicide resistance.
Models are useful for identifying key processes and understanding the evolution of resistance.
This study developed a spatially explicit model at a landscape scale to examine the dynamics of Lolium rigidum populations in dryland cereal crops and the evolution of herbicide resistance under various management strategies.
Resistance evolved rapidly under repeated herbicide use, driven by weed fecundity and herbicide efficacy.
Although fitness costs associated with resistant plants reduced the resistance evolution, they did not affect the speed of its spread.
The most effective strategies for slow resistance involved diversifying cropping sequences and herbicide applications.
Pollen flow was the main dispersal vector, with seed dispersal also making a significant contribution.
Strategies limiting seed dispersal effectively decreased resistance spread.
However, the use of a seed-catching device at harvest could unintentionally enrich resistance in the area.
It would be beneficial to optimize the movement of harvesters between fields.
The model presented here is a useful tool that could assist in the exploration of novel management strategies within the context of site-specific weed management at landscape scale as well as in the advancement of our understanding of resistance dynamics.
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