Novel programming pipeline for herbicide resistance prediction

Abstract Background Weeds evolve resistance to specific herbicide Mode of Actions (MoAs) through repeated herbicide use. Delaying herbicide resistance is a primary area of concern because there are a limited number of herbicide MoAs remaining. Objective The objective of this study was to create a generic model on weed resistance when various management practices are used. Academic herbicide resistant modeling tools have been developed to integrate knowledge about weed biology (species specific), genetics, weed management (herbicide dose response, herbicide rotation) because interactions may not always be intuitive. A variety of other interacting factors such as seed bank density, cropping system, and the initial resistant allele frequency in weed populations contribute to this dilemma. Methods Methods used provide details about a weed resistance model, along with representative results when two experimental herbicides with similar MoA are simulated for potential weed resistance over time to provide the ability to prioritize predevelopment molecules for advancement if resources are limited. Results Management practices provide insight into how these herbicides can be used to extend product performance over its lifetime (~25 years). Tillage is a sensitive management practice that can delay the onset of weed resistance, but modeling suggests an additional deep till, years later, would only re-expose resistant seed and spore population which was buried during the first deep till. Conclusion: Having a modelling tool that can predict possible onset of resistance, especially if different management practices are followed, will put these quantitative tools into the hands of experts where little/no new herbicide MoA exists.