Hostname: page-component-7c8c6479df-ph5wq Total loading time: 0 Render date: 2024-03-19T10:05:08.027Z Has data issue: false hasContentIssue false

Modeling the Impact of Harvest Weed Seed Control on Herbicide-Resistance Evolution

Published online by Cambridge University Press:  09 May 2018

Gayle J. Somerville*
Affiliation:
Ph.D student, Australian Herbicide Resistance Initiative, School of Agriculture and Environment, University of Western Australia, Western Australia, Australia
Stephen B. Powles
Affiliation:
Professor and Director, Australian Herbicide Resistance Initiative, School of Agriculture and Environment, University of Western Australia, Western Australia, Australia
Michael J. Walsh
Affiliation:
Director, Weed Research, Plant Breeding Institute, Sydney Institute of Agriculture, University of Sydney, New South Wales, Australia
Michael Renton
Affiliation:
Senior Lecturer, School of Biological Sciences, and School of Agriculture and Environment, University of Western Australia, Western Australia, Australia
*
*Author for correspondence: Gayle J. Somerville, Australian Herbicide Resistance Initiative, School of Agriculture and Environment, University of Western Australia, WA 6009, Australia. (Email: gayesomerville@hotmail.com)

Abstract

Harvest weed seed control (HWSC) techniques have been implemented in Australian cropping systems to target and reduce the number of weed seeds entering the seedbank and thereby reduce the number of problematic weeds emerging in subsequent years to infest subsequent crops. However, the influence of HWSC on ameliorating herbicide-resistance (HR) evolution has not been investigated. This research used integrated spatial modeling to examine how the frequency and efficacy of HWSC affected the evolution of resistance to initially effective herbicides. Herbicides were, in all cases, better protected from future resistance evolution when their use was combined with annual HWSC. Outbreaks of multiple HR were very unlikely to occur and were nearly always eliminated by adding annual, efficient HWSC. The efficacy of the HWSC was important, with greater reductions in the number of resistance genes achieved with higher-efficacy HWSC. Annual HWSC was necessary to protect sequences of lower-efficacy herbicides, but HWSC could still protect herbicides if it was used less often than once per year, when the HWSC and the herbicides were highly effective. Our results highlight the potential benefits of combining HWSC with effective herbicides for controlling weed populations and reducing the future evolution of HR.

Type
Weed Management
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

Ashworth, MB, Walsh, MJ, Flower, KC, Vila‐Aiub, MM Powles, SB (2016) Directional selection for flowering time leads to adaptive evolution in Raphanus raphanistrum (Wild radish). Evol Appl 9:619629 CrossRefGoogle ScholarPubMed
Blanco-Moreno, JM, Chamorro, L, Masalles, RM, Recasens, J Sans, FX (2004) Spatial distribution of Lolium rigidum seedlings following seed dispersal by combine harvesters. Weed Res 44:375387 CrossRefGoogle Scholar
Borger, CPD, Hashem, A Powles, SB (2015) Manipulating crop row orientation and crop density to suppress Lolium rigidum . Weed Res 56:2230 CrossRefGoogle Scholar
Broster, J (2016) 2016 Herbicide Resistance Testing Service Report. Wagga Wagga, NSW: Charles Sturt University. https://www.csu.edu.au/__data/assets/pdf_file/0020/2525024/2016-report.pdf. 4 p Google Scholar
Diggle, AJ, Neve, PB Smith, FP (2003) Herbicides used in combination can reduce the probability of herbicide resistance in finite weed populations. Weed Res 43:371382 CrossRefGoogle Scholar
Friesen, LJS Hall, JC (2004) Herbicide Resistance. Pages 221–225 in Inderjit (ed.) Weed Biology and Management. Dordrecht, The Netherlands: SpringerGoogle Scholar
Izquierdo, J, Blanco-Moreno, JM, Chamorro, L, Gonzalez-Andujar, JL Sans, FX (2009) Spatial distribution of weed diversity within a cereal field. Agron Sustain Dev 29:491496 CrossRefGoogle Scholar
Lacoste, M Powles, S (2014) Upgrading the RIM model for improved support of integrated weed management extension efforts in cropping systems. Weed Technol 28:703720 CrossRefGoogle Scholar
Llewellyn, RS, Ronning, D, Ouzman, J, Walker, S, Mayfield, A Clarke, M (2016) Impact of Weeds on Australian Grain Production: The Cost of Weeds to Australian Grain Growers and the Adoption of Weed Management and Tillage Practices. Kingston, ACT, Australia: GRDC, CSIRO Google Scholar
Michael, PJ, Owen, MJ Powles, SB (2010) Herbicide-resistant weed seeds contaminate grain sown in the Western Australian grainbelt. Weed Sci 58:466472 CrossRefGoogle Scholar
Monjardino, M, Pannell, DJ Powles, SB (2003) Multispecies resistance and integrated management: a bioeconomic model for integrated management of rigid ryegrass (Lolium rigidum) and wild radish (Raphanus raphanistrum). Weed Sci 51:798809 CrossRefGoogle Scholar
Morrison, IN, Nawolsky, KM, Entz, MH Smith, AE (1991) Differences among certified wheat seedlots in response to trifluralin. Agron J 83:119123 CrossRefGoogle Scholar
Neve, P, Diggle, AJ, Smith, FP Powles, SB (2003) Simulating evolution of glyphosate resistance in Lolium rigidum I: population biology of a rare resistance trait. Weed Res 43:404417 CrossRefGoogle Scholar
Neve, P, Vila-Aiub, M Roux, F (2009) Evolutionary-thinking in agricultural weed management. New Phytol 184:783793 CrossRefGoogle ScholarPubMed
Norsworthy, JK, Ward, SM, Shaw, DR, Llewellyn, RS, Nichols, RL, Webster, TM, Bradley, KW, Frisvold, G, Powles, S, Burgos, NR, Witt, WW Barrett, M (2012) Reducing the risks of herbicide resistance: best management practices and recommendations. Weed Sci 60:3162 CrossRefGoogle Scholar
Powles, S Yu, Q (2012) Evolution in action: Plants resistant to herbicides. Annu Rev Plant Biol 61:317347 CrossRefGoogle Scholar
R Core Team (2014) R: A language and environment for statistical computing. http:/www.R-project.org/ Google Scholar
Renton, M, Busi, R, Neve, P, Thornby, D Vila‐Aiub, M (2014) Herbicide resistance modelling: past, present and future. Pest Manag Sci 70:13941404 CrossRefGoogle ScholarPubMed
Renton, M, Diggle, A, Manalil, S Powles, S (2011) Does cutting herbicide rates threaten the sustainability of weed management in cropping systems? J Theor Biol 283:1427 CrossRefGoogle ScholarPubMed
Somerville, GJ, Powles, SB, Walsh, MJ Renton, M (2017a) How do spatial heterogeneity and dispersal in weed population models affect predictions of herbicide resistance evolution? Ecol Modell 362:3753 CrossRefGoogle Scholar
Somerville, GJ, Powles, SB, Walsh, MJ Renton, M (2017b) Why was resistance to shorter-acting pre-emergence herbicides slower to evolve? Pest Manag Sci 73:844851 CrossRefGoogle ScholarPubMed
Walsh, M, Newman, P Powles, S (2013) Targeting weed seeds in-crop: a new weed control paradigm for global agriculture. Weed Technol 27:431436 CrossRefGoogle Scholar
Walsh, M, Ouzman, J, Newman, P, Powles, S Llewellyn, R (2017) High levels of adoption indicate that harvest weed seed control is now an established weed control practice in Australian cropping. Weed Technol 31:341347 CrossRefGoogle Scholar
Walsh, MJ, Harrington, RB Powles, S (2012) Harrington Seed Destructor: a new nonchemical weed control tool for global grain crops. Crop Sci 52:13431347 CrossRefGoogle Scholar
Walsh, MJ Powles, SB (2014) High seed retention at maturity of annual weeds infesting crop fields highlights the potential for harvest weed seed control. Weed Technol 28:486493 CrossRefGoogle Scholar
Supplementary material: File

Somerville et al. supplementary material

Figure S1

Download Somerville et al. supplementary material(File)
File 42.4 KB