IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v362y2017icp37-53.html
   My bibliography  Save this article

How do spatial heterogeneity and dispersal in weed population models affect predictions of herbicide resistance evolution?

Author

Listed:
  • Somerville, Gayle J.
  • Powles, Stephen B.
  • Walsh, Michael J.
  • Renton, Michael

Abstract

Weed population simulations can be useful to predict the effects of alternative management practices on herbicide resistance (HR) evolution. Almost all previous simulations have ignored the possibility of within-field spatial structure in a weed population, instead making the implicit assumption of perfect dispersal and spatial homogeneity in population density and genetics. The effects of this simplifying assumption have not been examined, despite the fact that dispersal limitations and spatial structure within the population are likely to occur and to affect the evolution of resistance. Therefore, we developed a new spatially-explicit model called SOMER, and examined how changing the following factors affected the predicted evolution of resistance: the degree of spatial resolution used in the model; whether resistance was semi-dominant or fully-dominant; distances of pollen and natural seed dispersal; and inadvertent collection and grain harvester weed seed dispersal (GHWSD). Simulations showed that spatial resolution is important when modelling HR evolution, with the size of sub-population divisions, the pollen dispersal parameter, the level of dominance, and GHWSD all being important factors in predicting the rate and type of HR evolution. Our results show that accounting for spatial structure and dispersal does affect predictions of HR evolution, with the non-spatial model generally predicting faster resistance evolution compared to the more realistic equivalent spatial model. Most importantly, GHWSD increased the speed of HR evolution. Our spatial model also allowed us to investigate the dynamics of density and genetic structure within patches of herbicide resistant weeds, and we found that resistance genes were spread several times wider than the visible patch, and that homozygous mutations were commonly found in more centrally located weeds. We conclude that an ‘integrated spatial modelling’ approach that accounts for spatial structure should be considered when modelling HR evolution, and the evolution of resistance in general.

Suggested Citation

  • Somerville, Gayle J. & Powles, Stephen B. & Walsh, Michael J. & Renton, Michael, 2017. "How do spatial heterogeneity and dispersal in weed population models affect predictions of herbicide resistance evolution?," Ecological Modelling, Elsevier, vol. 362(C), pages 37-53.
    • Handle: RePEc:eee:ecomod:v:362:y:2017:i:c:p:37-53
    DOI: 10.1016/j.ecolmodel.2017.08.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380017303551
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2017.08.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pannell, David J. & Stewart, Vanessa & Bennett, Anne & Monjardino, Marta & Schmidt, Carmel & Powles, Stephen B., 2004. "RIM: a bioeconomic model for integrated weed management of Lolium rigidum in Western Australia," Agricultural Systems, Elsevier, vol. 79(3), pages 305-325, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Somerville, Gayle. J. & Melander, Bo & Kudsk, Per & Mathiassen, Solvejg K, 2019. "Modelling annual grass weed seed dispersal in winter wheat, when influenced by hedges and directional wind," Ecological Modelling, Elsevier, vol. 410(C), pages 1-1.
    2. Wang, Wenhui & Xu, Feng & Huang, Yunxin & Feng, Hongqiang & Wan, Peng, 2022. "Modeling the evolution of resistance in cotton bollworm to concurrently planted Bt cotton and Bt maize in China," Ecological Modelling, Elsevier, vol. 467(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mitchell, Paul D., 2011. "Economic Assessment of the Benefits of Chloro-s-triazine Herbicides to U.S. Corn, Sorghum, and Sugarcane Producers," Staff Paper Series 564, University of Wisconsin, Agricultural and Applied Economics.
    2. Bohan, David & Schmucki, Reto & Abay, Abrha & Termansen, Mette & Bane, Miranda & Charalabiis, Alice & Cong, Rong-Gang & Derocles, Stephane & Dorner, Zita & Forster, Matthieu & Gibert, Caroline & Harro, 2020. "Designing farmer-acceptable rotations that assure ecosystem service provision inthe face of climate change," MPRA Paper 112313, University Library of Munich, Germany.
    3. Beltran, Jesusa C. & Pannell, David J. & Doole, Graeme J. & White, Benedict, 2012. "Economic analysis of integrated weed management strategies for annual barnyardgrass (Echinochloa crus-galli complex) in Philippine rice farming systems," 2012 Conference (56th), February 7-10, 2012, Fremantle, Australia 124236, Australian Agricultural and Resource Economics Society.
    4. Doole, Graeme J., 2007. "A primer on implementing compressed simulated annealing for the optimisation of a constrained simulation model in Microsoft Excel," Working Papers 7420, University of Western Australia, School of Agricultural and Resource Economics.
    5. Doole, Graeme J., 2008. "Optimal management of annual ryegrass ( Lolium rigidum Gaud.) in phase rotations in the Western Australian Wheatbelt," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 1-24.
    6. Patrick Smith, F. & Holzworth, Dean P. & Robertson, Michael J., 2005. "Linking icon-based models to code-based models: a case study with the agricultural production systems simulator," Agricultural Systems, Elsevier, vol. 83(2), pages 135-151, February.
    7. Morteza Chalak & David J. Pannell, 2015. "Optimal Integrated Strategies to Control an Invasive Weed," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 63(3), pages 381-407, September.
    8. Graeme J. Doole & David J. Pannell, 2008. "Optimisation of a Large, Constrained Simulation Model using Compressed Annealing," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(1), pages 188-206, February.
    9. Walsh, Alison & Kingwell, Ross, 2021. "The Future of Glyphosate in Australian Agriculture," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 29(4), November.
    10. Beltran, Jesusa C. & Pannell, David J. & Doole, Graeme J., 2011. "Economic impacts of high labour cost and herbicide resistance for the management of annual barnyardgrass (Echinochloa crus-galli) in rice production in the Philippines," Working Papers 108770, University of Western Australia, School of Agricultural and Resource Economics.
    11. Graeme J. Doole & David J. Pannell & Clinton K. Revell, 2009. "Economic contribution of French serradella (Ornithopus sativus Brot.) pasture to integrated weed management in Western Australian mixed-farming systems: an application of compressed annealing ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 53(2), pages 193-212, April.
    12. Mace, Karen & Morlon, Pierre & Munier-Jolain, Nicolas & Quere, Lionel, 2007. "Time scales as a factor in decision-making by French farmers on weed management in annual crops," Agricultural Systems, Elsevier, vol. 93(1-3), pages 115-142, March.
    13. Jacobs, A. & Kingwell, R., 2016. "The Harrington Seed Destructor: Its role and value in farming systems facing the challenge of herbicide-resistant weeds," Agricultural Systems, Elsevier, vol. 142(C), pages 33-40.
    14. Graeme J. Doole, 2008. "Optimal management of annual ryegrass (Lolium rigidum Gaud.) in phase rotations in the Western Australian Wheatbelt ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 339-362, September.
    15. Lodovichi, Mariela V. & Blanco, Aníbal M. & Chantre, Guillermo R. & Bandoni, J. Alberto & Sabbatini, Mario R. & Vigna, Mario & López, Ricardo & Gigón, Ramón, 2013. "Operational planning of herbicide-based weed management," Agricultural Systems, Elsevier, vol. 121(C), pages 117-129.
    16. Greijdanus, Auke & Kragt, Marit Ellen, 2014. "A summary of four Australian bio-economic models formixed grain farming systems," Working Papers 170195, University of Western Australia, School of Agricultural and Resource Economics.
    17. Doole, Graeme J. & Pannell, David J., 2009. "Evaluating combined land conservation benefits from perennial pasture: lucerne ( Medicago sativa L.) for management of dryland salinity and herbicide resistance in Western Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 53(2), pages 1-19.
    18. Chalak-Haghighi, Morteza & Pannell, David J., 2010. "Economics of controlling a spreading environmental weed," 2010 Conference (54th), February 10-12, 2010, Adelaide, Australia 58886, Australian Agricultural and Resource Economics Society.
    19. Zull, Andrew F. & Cacho, Oscar J. & Lawes, Roger A., 2009. "Optimising woody-weed control," 2009 Conference (53rd), February 11-13, 2009, Cairns, Australia 47620, Australian Agricultural and Resource Economics Society.
    20. Kragt, Marit Ellen & Llewellyn, Rick S., 2013. "Using choice experiments to improve the design of weed decision support tools," Working Papers 147031, University of Western Australia, School of Agricultural and Resource Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:362:y:2017:i:c:p:37-53. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.