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Generalized Management Strategies to Delay Herbicide Resistance: A Simulation Approach

Published online by Cambridge University Press:  04 April 2018

Argen M. West ,
Anthony L. Altieri  and
Steven A. Cryer
Argen M. West
Affiliation:
PhD candidate, Department of Mathematics, University of Illinois at Urbana–Champaign, Champaign, IL, USA
Anthony L. Altieri
Affiliation:
Independent researcher, Boston, MA, USA
Steven A. Cryer*
Affiliation:
Fellow, Corteva™ Agriscience, Agricultural Division of DowDuPont™, Indianapolis, IN, USA
*
Author for correspondence: Steven Cryer, DowDuPont, 9330 Zionsville Road, Indianapolis, IN 46268. (Email: sacryer@dow.com)
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Abstract

Weed species develop resistance to herbicides through the repeated use of the same herbicide mechanism of action (MOA). Farmers often resort to different MOAs once a weed population has become resistant to the MOA that resulted in a resistant weed population. Delaying herbicide resistance is of great importance to growers due to the limited number of commercially available MOAs. Resistance may occur through monogenic or polygenic traits, and various academic and industrial modeling tools have been developed to help infer cause–effect from multiple interacting factors that may not be intuitive. This work explores various best management practices in delaying weed resistance, and we give details for monogenic and quantitative polygenic resistance models and investigate combinations of management strategies that lead to maximizing the product life span for a herbicide. Management practices under parametric uncertainty are provided to showcase how various practices can be used to extend lifetime product performance before resistance is manifest. Penalty functions associated with choosing a unique management strategy, based upon grower constraints, are the subject of a companion manuscript.

Keywords

William Vencill, University of Georgia2,4-Dbest management practices (BMPs)glyphosatesimulationweed resistance
Type
Weed Management
Information
Weed Science , Volume 66 , Issue 4 , July 2018 , pp. 530 - 539
Copyright
© Weed Science Society of America, 2018 

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