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Investigating the Human Dimension of Weed Management: New Tools of the Trade

Published online by Cambridge University Press:  20 January 2017

Doug Doohan ,
Robyn Wilson ,
Elizabeth Canales  and
Jason Parker
Doug Doohan*
Affiliation:
Department of Horticulture & Crop Science, The Ohio State University, Wooster, OH 44691
Robyn Wilson
Affiliation:
School of Environment & Natural Resources, The Ohio State University, Columbus, OH 44210
Elizabeth Canales
Affiliation:
Department of Horticulture & Crop Science, The Ohio State University, Wooster, OH 44691
Jason Parker
Affiliation:
Department of Horticulture & Crop Science, The Ohio State University, Wooster, OH 44691
*
Corresponding author's E-mail: [email protected]
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Abstract

The human dimension of weed management is most evident when farmers make decisions contrary to science-based recommendations. Why do farmers resist adopting practices that will delay herbicide resistance, or seem to ignore new weed species or biotypes until it is too late? Weed scientists for the most part have ignored such questions or considered them beyond their domain and expertise, continuing to focus instead on fundamental weed science and technology. Recent pressing concerns about widespread failure of herbicide-based weed management and acceptability of emerging technologies necessitates a closer look at farmer decision making and the role of weed scientists in that process. Here we present a circular risk-analysis framework characterized by regular interaction with and input from farmers to inform both research and on-farm risk-management decisions. The framework utilizes mental models to probe the deeply held beliefs of farmers regarding weeds and weed management. A mental model is a complex, often hidden web of perceptions and attitudes that govern how we understand and respond to the world. One's mental model may limit ability to develop new insights and adopt new ways of management, and is best assessed through structured, open-ended interviews that enable the investigator to exhaust the subjects inherent to a particular risk. Our assessment of farmer mental models demonstrated the fundamental attribution error whereby farmers attributed problems with weed management primarily to factors outside of their control, such as uncontrolled weed growth on neighboring properties and environmental factors. Farmers also identified specific processes that contribute to weed problems that were not identified by experts; specifically, the importance of floods and faulty herbicide applications in the spread of weeds. Conventional farmers expressed an overwhelming preference for controlling weeds with herbicides, a preference that was reinforced by their extreme dislike for weeds. These preferences reflect a typical inverse relationship between perceived risk and benefit, where an activity or entity we perceive as beneficial is by default perceived as low risk. This preference diminishes the ability of farmers to appreciate the risks associated with overreliance on herbicides. Likewise, conventional farmers saw great risk and little benefit in preventive measures for weed control. We expect that thorough two-way communication and a deeper understanding of farmer belief systems will facilitate the development of audience-specific outreach programs with an enhanced probability of affecting better weed management decisions.

Keywords

Mental modelsmental models of weed managementinnovation diffusionorganic farmersconventional farmers
Type
Symposium
Information
Weed Science , Volume 58 , Issue 4 , December 2010 , pp. 503 - 510
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Alhakami, A. S. and Slovic, P. 1994. A psychological study of the inverse relationship between perceived risk and perceived benefit. Risk Anal. 14:1085–1096.Google Scholar
Anderson, R. L. 2008. Diversity and no-till: keys for pest management in the US Great Plains. Weed Sci. 56:141–145.Google Scholar
Andrews, P. W. 2001. The psychology of social chess and the evolution of attribution mechanisms: explaining the fundamental attribution error. Evol. Hum. Behav. 22:11–29.CrossRefGoogle ScholarPubMed
Bar-Shira, Z., Just, R., and Zilberman, D. 1997. Estimation of farmers' risk attitude: an econometric approach. Agric. Econ. 17:211–221.CrossRefGoogle Scholar
Beckie, H. J., Leeson, J. Y., Thomas, A. G., Brenzil, C. A., Hall, L. M., Holzgang, G., Lozinski, C., and Shirriff, S. 2008. Weed resistance monitoring in the Canadian prairies. Weed Technol. 22:530–543.CrossRefGoogle Scholar
Blackshaw, R. E., Harker, N. K., O'Donovan, J. T., Beckie, H. J., and Smith, E. J. 2008. Ongoing development of integrated weed management systems on the Canadian prairies. Weed Sci. 56:146–150.CrossRefGoogle Scholar
Bostrom, A., Fischhoff, B., and Morgan, M. G. 1992. Characterizing mental models of hazardous processes: a methodology and an application to radon. J. Soc. Issues. 48:85–100.Google Scholar
Bostrom, A., Morgan, M. G., Fischhoff, B., and Read, D. 1994. What do people know about global climate change? 1. Mental models. Risk Anal. 14:959–970.Google Scholar
Canales, E., Parker, J., Wilson, R., Tucker, M., and Doohan, D. 2008. Knowledge, perception and attitudes of organic farmers about weed management. Proc. North Cent. Weed Sci. Soc. Abstract 138.Google Scholar
Czapar, G. F., Curry, M. P., and Gray, M. E. 1995. Survey of integrated pest management practices in central Illinois. J. Prod. Agric. 8:483–486.CrossRefGoogle Scholar
Czapar, G. F., Curry, M. P., and Wax, L. M. 1997. Grower acceptance of economic thresholds for weed management in Illinois. Weed Technol. 11:828–831.Google Scholar
Davis, G. B. and Olson, M. H. 1985. Management Information Systems: Conceptual Foundations, Structure, and Development, 2nd ed. New York McGraw Hill.Google Scholar
Eckert, E. and Bell, A. 2006. Continuity and change: themes of mental model development among small-scale farmers. J. Ext. [Online] 44.Google Scholar
Finnoff, D., Shogren, J. F., Leung, B., and Lodge, D. 2006. Take a risk: preferring prevention over control of biological invaders. Ecol. Econ. 62:216–222.Google Scholar
Fischhoff, B. and Downs, J. 1997. Accentuate the relevant. Psychol. Sci. 8:154–158.Google Scholar
Fischhoff, B., Slovic, P., Lichtenstein, S., Reid, S., and Coombs, B. 1978. How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sci. 9:127–152.Google Scholar
Gunsolus, J. L. and Buhler, D. D. 1999. A risk management perspective on integrated weed management. Pages 225–238. In Buhler, D. ed. Expanding the Context of Weed Management. New York Haworth Press.Google Scholar
Hammond, C. L., Luschei, E. C., Boerboom, C. M., and Nowak, P. J. 2006. Adoption of integrated pest management tactics by Wisconsin farmers. Weed Technol. 20:756–767.Google Scholar
Johnson, W. G. and Gibson, K. D. 2006. Glyphosate-resistant weeds and resistance management strategies: an Indiana grower perspective. Weed Technol. 20:768–772.Google Scholar
Jones, E. E. and Nisbett, R. E. 1971. The actor and the observer: divergent perceptions of the causes of behavior. Pages 79–94. In Jones, E. E., et al eds. Attribution: Perceiving the Causes of Behavior. Morristown, NJ General Learning Press.Google Scholar
Jones, R. and Monjardino, M. 2006. The economic benefits of adopting integrated weed management. Pages 1–10. In McGillion, T. and Storrie, A. eds. Integrated Weed Management in Australian Cropping Systems—A Training Resource for Farm Advisors. Adelaide, Australia: CRC for Australian Weed Management.Google Scholar
Kelley, H. H. and Michela, J. L. 1980. Attribution theory and research. Annu. Rev. Psychol. 31:457–501.Google Scholar
Leung, B., Lodge, D., Finnoff, D., Shogren, J. F., Lewis, M. A., and Lamberti, G. 2002. An ounce of prevention or a pound of cure: bioeconomic risk analysis of invasive species. Proc. R. Soc. Bio. Sci. 269:2407–2413.Google Scholar
Llewellyn, R. S. and Allen, D. M. 2006. Expected mobility of herbicide resistance via weed seeds and pollen in a Western Australian cropping region. Crop Prod. 25:520–526.Google Scholar
Llewellyn, R. S., Lindner, R. K., Pannell, D. J., and Powles, S. B. 2002. Resistance and the herbicide resource: perceptions of Western Australian grain growers. Crop Prot. 21:1067–1075.Google Scholar
Llewellyn, R. S., Pannell, D. J., Lindner, R. K., and Powles, S. B. 2005. Targeting key perceptions when planning and evaluating extension. Aust. J. Exp. Agric. 45:1627–1633.Google Scholar
Luschei, E. C., Hammond, C. M., Boerboom, C. M., and Nowak, P. J. 2009. Convenience sample of on-farm research cooperators representative of Wisconsin farmers. Weed Technol. 23:300–307.Google Scholar
Maharik, M. and Fischhoff, B. 1993. Risk knowledge and risk attitudes regarding nuclear energy sources in space. Risk Anal. 13:345–353.CrossRefGoogle Scholar
Morgan, M. G., Fischhoff, B., Bostrom, A., and Atman, C. J. 2002. Risk Communication: A Mental Models Approach. Cambridge, UK Cambridge University Press.Google Scholar
Morgan, M. G., Fischhoff, B., Bostrom, A., Lave, L., and Atman, C. J. 1992. Communicating risk to the public. Environ. Sci. Technol. 11:2048–2056.Google Scholar
Moss, S. R. 2008. Weed research: is it delivering what it should? Weed Res. 48:389–393.CrossRefGoogle Scholar
[NAAS] National Agricultural Statistics Service 2009. U.S. Crop Acreage Down Slightly in 2009, but Corn and Soybean Acres Up. http://www.nass.usda.gov/Newsroom/2009/06_30_2009.asp. Accessed: December 8, 2009.Google Scholar
Neve, P. and Powles, S. B. 2005. Recurrent selection with reduced herbicide rates results in the rapid evolution of herbicide resistance in Lolium rigidum . Theor. Appl. Genet. 110:1154–1166.CrossRefGoogle ScholarPubMed
Nisbett, R. E. and Borgida, E. 1975. Attribution and the psychology of prediction. J. Pers. Soc. Psychol. 32:932–943.Google Scholar
Nowak, P. J. and Cabot, P. E. 2004. The human dimension of resource management programs. J. Soil Water Conserv. 59:128–135.Google Scholar
Owen, M. D. K. 1998. Producer attitudes and weed management. Pages 43–59. In Hatfield, J. L., et al eds. Integrated Weed Management. Chelsea, MI Ann Arbor.Google Scholar
Padel, S. 2001. Conversion to organic farming: a typical example of the diffusion of an innovation? Sociol. Ruralis. 41 (1):40–61.Google Scholar
Poweles, S. B., Preston, C., Bryan, I. B., and Jutsum, A. R. 1997. Herbicide resistance: impact and management. Pages 57–93. In Sparks, D. L. ed. Advances in Agronomy. Vol. 58. San Diego, CA: Academic.Google Scholar
Rogers, E. M. 2003. Diffusion of Innovations. 5th ed. New York Free Press.Google Scholar
Sanyal, D., Bhowmik, P. C., Anderson, R. L., and Shrestha, A. 2008. Revisiting the perspective and progress of integrated weed management. Weed Sci. 56:161–167.Google Scholar
Simon, H. A. 1990. Invariants in human behavior. Annu. Rev. Psychol. 41:1–19.Google Scholar
Storrie, A. 2006. Case studies. Pages 211–248. In McGillion, T. and Storrie, A. eds. Integrated Weed Management in Australian Cropping Systems—A Training Resource for Farm Advisors. Adelaide, Australia: CRC for Australian Weed Management.Google Scholar
Strotz, R. H. 1956. Myopia and inconsistency in dynamic utility maximization. Rev. Econ. Stud. 23:165–180.Google Scholar
Swanton, C. J. and Weise, S. F. 1991. Integrated weed management: the rationale and approach. Weed Technol. 5:657–663.Google Scholar
Vanclay, F. 1992. Barriers to adoption: a general overview of the issues. Rural Sociol. 2:10–12.Google Scholar
Wilson, R. S., Tucker, M. A., Hooker, N., LeJeune, J., and Doohan, D. 2008. Perceptions and beliefs about weed management: perspectives of Ohio grain and produce farmers. Weed Technol. 22:339–350.Google Scholar
Wszelaki, A. and Doohan, D. 2003. Comparing weed communities in organic and conventional vegetable farms across Ohio. Proceedings of the OFFER Horticulture Workshop. 24th Annual OEFFA conference, March 8–9, 2003, Johnstown, OH.Google Scholar
Zaksek, M. and Arvai, J. L. 2004. Toward improved communication about wildland fire: mental models research to identify information needs for natural resource management. Risk Anal. 24:1503–1514.Google Scholar