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Perceptions and Beliefs about Weed Management: Perspectives of Ohio Grain and Produce Farmers

Published online by Cambridge University Press:  20 January 2017

Robyn S. Wilson ,
Mark A. Tucker ,
Neal H. Hooker ,
Jeff T. Lejeune  and
Doug Doohan
Robyn S. Wilson*
Affiliation:
Department of Horticulture and Crop Science and Department of Agricultural, Environmental and Development Economics, The Ohio State University, Columbus, OH 43210
Mark A. Tucker
Affiliation:
Department of Agricultural Communication Youth Development and Education, Purdue University, West Lafayette, IN 47907
Neal H. Hooker
Affiliation:
Department of Horticulture and Crop Science and Department of Agricultural, Environmental and Development Economics, The Ohio State University, Columbus, OH 43210
Jeff T. Lejeune
Affiliation:
Department of Veterinary Preventive Medicine and Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH 44691
Doug Doohan
Affiliation:
Department of Veterinary Preventive Medicine and Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH 44691
*
Corresponding author's E-mail: [email protected]
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Abstract

Experts have long sought to understand the factors that underlie farmer decision making for weed management. The majority of this interest has been in relation to the weak adoption of integrated management approaches and more recently, herbicide resistance strategies. In order to increase adoption in these contexts there is a need to understand better the underlying drivers for weed management decisions. The objective of the research reported here was to probe farmers' understanding of weed management to establish a baseline understanding of these key drivers. Thirty Ohio farmers participated in an in-depth interview where they were asked to reflect on how weeds are introduced and spread, what risks and benefits weeds pose, and what management strategies farmers are familiar with and which they prefer. Their responses were mapped, coded, and analyzed for dominant beliefs and major decision-making influences. The results indicate that farmers largely attribute the introduction and movement of weeds to factors outside their control (e.g., the environment, plant characteristics). They frequently cite diverse and integrated management, but their focus is on control as opposed to prevention. In general, they tend to receive messages about integrated and preventive approaches, but do not always put them into practice because of underlying beliefs about the inevitability of new weed introductions and spread.

Keywords

Mental modelherbicide resistancefarmer decision makingrisk communicationagricultural education
Type
Education/Extension
Information
Weed Technology , Volume 22 , Issue 2 , June 2008 , pp. 339 - 350
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Ajzen, I. and Fishbein, M. 1977. Attitude–behavior relations: a theoretical analysis and review of empirical research. Psychol. Bull. 84:888–918.Google Scholar
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.CrossRefGoogle ScholarPubMed
Arvai, J. L. 2003. Using risk communication to disclose the outcome of a participatory decision making process: effects on the perceived acceptability of risk-policy decisions. Risk Anal. 23:281–289.Google Scholar
Bar-Shira, Z., Just, R., and Zilberman, D. 1997. Estimation of farmers' risk attitude: an econometric approach. Agric. Econ. 17:211–221.Google Scholar
Beckie, H. J. 2006. Herbicide-resistant weeds: management tactics and practices. Weed Technol. 20:793–814.CrossRefGoogle Scholar
Beckie, H. J., Chang, F. Y., and Stevenson, F. C. 1999. The effect of labeling herbicides with their site of action: a Canadian perspective. Weed Technol. 13:655–661.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.CrossRefGoogle 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. 6:959–970.Google Scholar
Buhler, D. D. 2002. Challenges and opportunities for integrated weed management. Weed Sci. 50:273–280.CrossRefGoogle Scholar
Corselius, K. L., Simmons, S. R., and Flora, C. B. 2003. Farmer perspectives on cropping systems diversification in northwestern Minnesota. Agric. Hum. Values. 20:371–383.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.Google 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
Eckert, E. and Bell, A. 2005. Invisible force: Farmers' mental models and how they influence learning and actions. J. Ext. 43/3:http://www.joe.org/joe/2005june/a2.shtml. Accessed: February 28, 2008.Google Scholar
Eckert, E. and Bell, A. 2006. Continuity and change: themes of mental model development among small-scale farmers. J. Ext. 44/1:http://www.joe.org/joe/2006february/a2.shtml. Accessed: February 28, 2008.Google Scholar
Engel, P. G. H. 1997. The Social Organization of Innovation: A Focus on Stakeholder Interaction. Amsterdam KIT Press. 239.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. S. 1997. Communicating foodborne disease risk. Emerg. Infect. Dis. 3:489–495.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
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
Hightower, J. 1972. Hard Tomatoes, Hard Times: The Failure of America's Land Grant Complex. Cambridge, MA Schenkman. 224.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.CrossRefGoogle 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., Kanouse, D. E., Kelley, H. H., Nisbett, R. E., Valins, S., and Weiner, B., editors. Attribution: Perceiving the Causes of Behavior. Morristown, NJ General Learning Press.Google Scholar
Kahneman, D., Slovic, P., and Tversky, A., editors. 1982. Judgment under Uncertainty: Heuristics and Biases. Cambridge, UK Cambridge University Press. 555.Google Scholar
Keeney, R., Von Winterfeldt, D., and Eppel, T. 1990. Eliciting public values for complex policy decisions. Manag. Sci. 36:1011–1030.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. Biol. 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 Prot. 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
Lombard, M., Snyder-Duch, J., and Bracken, C. C. 2002. Content analysis in mass communication: assessment and reporting of intercoder reliability. Hum. Commun. Res. 28:587–604.CrossRefGoogle Scholar
Mace, K., Munier-Jolain, N., and Quere, L. 2007. Time scales as a factor in decision-making by French farmers on weed management in annual crops. Agric. Syst. 93:115–142.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. 351.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
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., Buhler, D. D., and Stewart, B. A., editors. Integrated Weed Management. Chelsea, MI Ann Arbor Press.Google Scholar
Powe, B. D. 1996. Cancer fatalism among African-Americans: a review of the literature. Nurs. Outlook. 44:18–21.Google Scholar
Powe, B. D. and Finnie, R. 2003. Cancer fatalism: The state of the science. Cancer Nurs. 26:454–465.Google Scholar
Powles, S. B., Preston, C., Bryan, I. B., and Jutsum, A. R. 1997. Herbicide resistance: impact and management. Pages 57–93. in Sparks, D. L., editor. Advances in Agronomy. Vol. 58. San Diego, CA Academic Press.Google Scholar
Powell, D. and Leiss, W. 1997. Mad Cows and Mother's Milk: The Perils of Poor Risk Communication. Montreal, Canada McGill-Queen's University Press. 452.Google Scholar
Rogers, E. M. 1988. Social Change in Rural Societies: An Introduction to Rural Sociology. Englewood Cliffs, NJ Prentice Hall. 480. 3rd ed. Google Scholar
Rogers, E. M. 2003. Diffusion of Innovations. New York Free Press. 512. 5th ed. Google Scholar
Ryan, B. 1948. A study in technological diffusion. Rural Sociol. 13:273–285.Google Scholar
Slovic, P. 1987. Perception of risk. Science. 236:280–285.Google Scholar
Swanton, C. J. and Murphy, S. D. 1996. Weed science beyond the weeds: the role of integrated weed management (IWM) in agroecosystem health. Weed Sci. 44:437–445.Google Scholar
Tukey, J. W. 1972. Some graphic and semigraphic displays. Pages 293–316. in Bancroft, T. A., editor. Statistical Papers in Honor of George F. Snedecor. Ames, IA The Iowa State University Press.Google Scholar
Vanclay, F. 1992. Barriers to adoption: A general overview of the issues. Rural Sociol. 2:10–12.Google Scholar
Waller, B. H., Hoy, C. W., Henderson, J. L., Stinner, B., and Welty, C. 1998. Matching innovations with potential users, a case study of potato IPM practices. Agric. Ecosyst. Environ. 70:203–215.Google Scholar
Wearing, C. H. 1988. Evaluating the IPM implementation process. Annu. Rev. Entomol. 33:17–38.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