Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-23T10:07:47.105Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected])
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Falconer, DS, Mackay, TFC (1996) Introduction to Quantitative Genetics. 4th ed. Upper Saddle River, NJ: Prentice Hall Google Scholar
Heap, I (2017) The International Survey of Herbicide Weeds. http://www.weedscience.org. Accessed: March 2016Google Scholar
Keeley, PE, Carter, CH, Thullen, RJ (1987) Influence of planting date on growth of Palmer amaranth (Amaranthus palmeri). Weed Sci 35:199–204 Google Scholar
Liu, C, Bridges, ME, Kaundun, SS, Glasgow, L, Owen, MD, Neve, P (2017) A generalised individual‐based algorithm for modelling the evolution of quantitative herbicide resistance in arable weed populations. Pest Manag Sci 73:462–474 Google Scholar
MacRae, AW, Culpepper, AS, Webster, TM, Sosnoskie, LM, Kichler, JM (2008) Glyphosate-resistant Palmer amaranth competition with Roundup Ready cotton. Pages 8–11 in Proceedings of the Beltwide Cotton Conference. Nashville, TN: National Cotton CouncilGoogle Scholar
Massinga, RA, Currie, RS, Horak, MJ, Boyer, J Jr (2001) Interference of Palmer amaranth in corn. Weed Sci 49:202–208 CrossRefGoogle Scholar
Maxwell, BD, Roush, ML, Radosevich, SR (1990) Prediction the evolution and dynamics of herbicide resistance in weed populations. Weed Technol 4:2–13 Google Scholar
Neve, P, Norsworthy, JK, Smith, KL, Zelaya, IA (2011a) Modeling evolution and management of glyphosate resistance in Amaranthus palmeri . Weed Res 51:99–112 Google Scholar
Neve, P, Norsworthy, JK, Smith, KL, Zelaya, IA (2011b) Modeling glyphosate resistance management strategies for Palmer amaranth (Amaranthus palmeri) in cotton. Weed Technol 25:335–343 Google Scholar
Norsworthy, JK, Neve, P, Smith, KL, Foresman, C, Glasgow, L, Zelaya, IA (2008) Use of a model to develop practical solutions for reducing risks of glyphosate-resistant Palmer amaranth in cotton. Fayetteville, AR. Arkansas Agricultural Experiment Station Research Series 573, Pp 97–102 Google Scholar
Powles, SB, Yu, Q (2010) Evolution in action: plants resistant to herbicides. Annu Rev Plant Biol 61:317–347 Google Scholar
Stewart, J (1995) Calculus. Pacific Grove, CA: International Thomson Google Scholar