Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-27T10:01:06.542Z Has data issue: false hasContentIssue false
  • English
  • Français

Mental models of organic weed management: Comparison of New England US farmer and expert models

Published online by Cambridge University Press:  27 June 2013

Randa Jabbour ,
Sarah Zwickle ,
Eric R. Gallandt ,
Katherine E. McPhee ,
Robyn S. Wilson  and
Doug Doohan
Randa Jabbour*
Affiliation:
Department of Plant, Soil and Environmental Sciences, University of Maine, Orono, ME 04469, USA.
Sarah Zwickle
Affiliation:
School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA.
Eric R. Gallandt
Affiliation:
Department of Plant, Soil and Environmental Sciences, University of Maine, Orono, ME 04469, USA.
Katherine E. McPhee
Affiliation:
Department of Plant, Soil and Environmental Sciences, University of Maine, Orono, ME 04469, USA.
Robyn S. Wilson
Affiliation:
School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA.
Doug Doohan
Affiliation:
Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH 44691, USA.
*
*Corresponding author: randa.jabbour@maine.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Weeds are a major challenge for organic farmers, yet we know little about the factors influencing organic farmers’ weed management decisions. We hypothesized that farmers and scientist ‘experts’ differ in fundamental areas of knowledge and perceptions regarding weeds and weed management. Moreover, these differences prevent effective communication, outreach programming and research prioritization. An expert mental model, constructed primarily from interviews with research scientists and extension professionals, revealed expert emphasis on knowledge of ecological weed management as crucial for successfully implementing such strategies. We interviewed 23 organic farmers in northern New England, yielding an aggregate farmer mental model to compare with the expert model. Farmers demonstrated knowledge of the major concepts discussed by experts, but differed in emphasis. Farmers placed less emphasis on ecological complexity than experts. One-third of farmers interviewed discussed the potential role of weeds as indicators of soil nutrient status, a concept of which experts were skeptical. Farmer beliefs about the weed seedbank highlighted potential misconceptions regarding seed persistence, with one-fourth of farmers focusing on the concept that seeds can live for an exceptionally long time in the soil, while experts focused on the concept of the seed half-life. Farmers emphasized the role of experience, both their own and that of other farmers, rather than knowledge derived from scientific research. Farmers considered yield and the cost of time and labor as equally at risk because of weeds, whereas experts predominantly discussed yield loss. During discussions of management, both farmers and experts most emphasized risks associated with cultivation and benefits associated with cover cropping. These results have prompted us, first, to develop new educational materials focused on weed seed longevity and management of the weed seedbank, and, second, to conduct regional focus groups with farmers who prioritize fertility management in their efforts to control weeds, especially manipulations of soil calcium and magnesium.

Keywords

mental modelsorganic agriculturefarmer beliefsweed risksseedbank managementcritical weed-free management
Type
Research Papers
Information
Renewable Agriculture and Food Systems , Volume 29 , Issue 4 , December 2014 , pp. 319 - 333
Copyright
Copyright © Cambridge University Press 2013 

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

1 OFRF. 1998. Third Biennial National Organic Farmer's Survey. Organic Farming Research Foundation, Santa Cruz, CA.Google Scholar
2 Riemens, M.M., Groeneveld, R.M.W., Kropff, M.J.J., Lotz, L.P., Renes, R.J., Sukkel, W., and van der Weide, R.Y. 2010. Linking farmer weed management behavior with weed pressure: More than just technology. Weed Science 58:490–496.CrossRefGoogle Scholar
3 Dedecker, J. 2012. Weed management practice selection among Midwest U.S. organic growers. MS thesis, University of Illinois at Urbana-Champaign, Urbana, IL, p. 146.Google Scholar
4 Riemens, M.M., Groeneveld, R.M.W., Lotz, L.P., and Kropff, M.J. 2007. Effects of three management strategies on the seedbank, emergence and the need for hand weeding in an organic arable cropping system. Weed Research 47:442–451.Google Scholar
5 Hawes, C., Squire, G.R., Hallett, P.D., Watson, C.A., and Young, M. 2010. Arable plant communities as indicators of farming practice. Agriculture, Ecosystems and Environment 138:17–26.Google Scholar
6 Nowak, P.J. and Cabot, P.E. 2004. Human dimension of resource management programs. Journal of Soil and Water Conservation 59:128–135.Google Scholar
7 Doohan, D., Wilson, R., Canales, E., and Parker, J. 2010. Investigating the human dimension of weed management: New tools of the trade. Weed Science 58:503–510.Google Scholar
8 Turner, R.J., Davies, G., Moore, H., Grundy, A.C., and Mead, A. 2007. Organic weed management: A review of the current UK farmer perspective. Crop Protection 26:377–382.Google Scholar
9 Zwickle, S. 2011. Weeds and organic weed management: Investigating farmer decisions with a mental models approach. MS thesis, Ohio State University, Columbus, OH, p. 171.Google Scholar
10 Eckert, E. and Bell, A. 2005. Invisible force: Farmers’ mental models and how they influence learning and actions. Journal of Extension [Online] 43. Available at Web site http://www.joe.org/joe/2005june/a2.php (accessed June 12, 2013).Google Scholar
11 Eckert, E. and Bell, A. 2006. Continuity and change: Themes of mental model development among small-scale farmers. Journal of Extension [Online] 44. Available at Web site http://www.joe.org/joe/2006february/a2p.shtml (accessed June 12, 2013).Google Scholar
12 Macé, K., Morlon, P., Munier-Jolain, N., and Quéré, L. 2007. Time scales as a factor in decision-making by French farmers on weed management in annual crops. Agricultural Systems 93:115–142.CrossRefGoogle Scholar
13 Morgan, M.G., Fischhoff, B., Bostrom, A., and Atman, C.J. 2002. Risk Communication: A Mental Models Approach. Cambridge University Press, Cambridge, UK.Google Scholar
14 Wilson, R.S., Tucker, M.A., Hooker, N.H., LeJeune, J.T., and Doohan, D. 2008. Perceptions and beliefs about weed management: Perspectives of Ohio grain and produce farmers. Weed Technology 22:339–350.Google Scholar
15 Wilson, R.S., Hooker, N., Tucker, M., LeJeune, J., and Doohan, D. 2009. Targeting the farmer decision making process: A pathway to increased adoption of integrated weed management. Crop Protection 28:756–764.Google Scholar
16 Parker, J.S., Wilson, R.S., LeJeune, J.T., and Doohan, D. 2012. Including growers in the ‘food safety’ conversation: Enhancing the design and implementation of food safety programming based on farm and marketing needs of fresh fruit and vegetable producers. Agriculture and Human Values 29:303–319.Google Scholar
17 Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50:179–211.Google Scholar
18 Plous, S. 1993. The Psychology of Judgment and Decision Making. McGraw Hill, Columbus, OH.Google Scholar
19 Damasio, A. 1994. Descartes’ Error: Emotion, Reason, and the Human Brain. Cambridge University Press, New York, NY.Google Scholar
20 Corselius, K.L., Simmons, S.R., and Flora, C.B. 2003. Farmer perspectives on cropping systems diversification in northwestern Minnesota. Agriculture and Human Values 20:371–383.Google Scholar
21 Sjöberg, L. 2000. Factors in risk perception. Risk Analysis 20:1–11.Google Scholar
22 Siegrist, M. 1999. A causal model explaining the perception and acceptance of gene technology. Journal of Applied Social Psychology 29:2093–2106.Google Scholar
23 Siegrist, M., Cvetkovich, G., and Roth, C. 2000. Salient value similarity, social trust, and risk/benefit perception. Risk Analysis 20:353–362.Google Scholar
24 MAXQDA, Software for Qualitative Data Analysis. 1989–2013. VERBI Software—Consult—Sozialforschung GmbH, Berlin, DE.Google Scholar
25 Corbin, J. and Strauss, A. 2008. Basics of Qualitative Research. Sage Publications, Los Angeles, CA.Google Scholar
26 Glesne, C. and Peshkin, A. 1992. Becoming Qualitative Researchers: An Introduction. Longman, White Plains, NY.Google Scholar
27 Charmaz, K. 2009. Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage Publications, Los Angeles, CA.Google Scholar
28 Gallandt, E.R. 2006. How can we target the weed seedbank? Weed Science 54:588–596.CrossRefGoogle Scholar
29 Liebman, M. and Gallandt, E.R. 1997. Many little hammers: Ecological management of crop-weed interactions. In Jackson, L.E. (ed.). Ecology in Agriculture. Academic Press, San Diego, CA. p. 291–343.Google Scholar
30 Tilman, E.A., Tilman, D., Crawley, M.J., and Johnston, A.E. 1999. Biological weed control via nutrient competition: Potassium limitation of dandelions. Ecological Applications 9:103–111.CrossRefGoogle Scholar
31 Frederick, S. and Loewenstein, G. 2002. Time discounting and time preference: A critical review. Journal of Economic Literature 40:351–401.Google Scholar
32 Roschewitz, I., Gabriel, D., Tscharntke, T., and Thies, C. 2005. The effects of landscape complexity on arable weed species diversity in organic and conventional farming. Journal of Applied Ecology 42:873–882.Google Scholar
33 José-María, L. and Sans, F.X. 2011. Weed seedbanks in arable fields: Effects of management practices and surrounding landscape. Weed Research 51:631–640.Google Scholar
34 Bàrberi, P., Burgio, G., Dinelli, G., Moonen, A.C., Otto, S., Vazzana, C., and Zamin, G. 2010. Functional biodiversity in the agricultural landscape: Relationships between weeds and arthropod fauna. Weed Research 50:388–401.Google Scholar
35 Evans, D.M., Pocock, M.J.O., Brooks, J., and Memmott, J. 2011. Seeds in farmland food-webs: Resource importance, distribution and the impacts of farm management. Biological Conservation 144:2941–2950.Google Scholar
36 Mt. Pleasant, J. and Schlather, K.J. 1994. Incidence of weed seed in cow (Bos sp.) manure and its importance as a weed source for cropland. Weed Science 8:304–310.Google Scholar
37 Davis, A., Renner, K., Sprague, C., Dyer, L., and Mutch, D. 2005. Integrated Weed Management: ‘One Year's Seeding…’. Michigan State University Extension, East Lansing, MI.Google Scholar
38 Mohler, C.L. and Johnson, S.E. 2009. Crop Rotation on Organic Farms: A Planning Manual. Natural Resource, Agriculture, and Engineering Service, Ithaca, NY.Google Scholar
39 Pfeiffer, E.E. 2008. Weeds and What They Tell. Biodynamic Farming & Gardening Association, Oaks, PA.Google Scholar
40 Walters, C. 1999. Weeds: Control Without Poisons. Acres U.S.A., Austin, TX.Google Scholar
41 Di Tomaso, J.M. 1995. Approaches for improving crop competitiveness through the manipulation of fertilization strategies. Weed Science 43:491–497.Google Scholar
42 Kopittke, P.M. and Menzies, N.W. 2007. A review of the use of the basic cation saturation ratio and the ‘ideal’ soil. Soil Science Society of America Journal 71:259–265.Google Scholar
43 Schonbeck, M. 2000. Balancing soil nutrients in organic vegetable production systems: Testing Albrecht's base saturation theory in southeastern soils. Organic Farming Research Foundation Information Bulletin 10:17.Google Scholar
44 Kelling, K.A., Schulte, E.E., and Peters, J.B. 1996. One hundred years of Ca: Mg ratio research. New Horizons in Soil Science Series 8. Department of Soil Science, University of Wisconsin-Madison, Madison, WI.Google Scholar
45 Warwick, S.L. and Sweet, R.D. 1983. The biology of Canadian weeds. 58. Galinsoga parviflora and G. quadriradiata (=G. ciliata). Canadian Journal of Plant Science 63:695–709.CrossRefGoogle Scholar
46 Ullrich, S.D., Buyer, J.S., Cavigelli, M.A., Seidel, R., and Teasdale, J.R. 2011. Weed seed persistence and microbial abundance in long-term organic and conventional cropping systems. Weed Science 59:202–209.Google Scholar
47 Smith, R.G., Gareau, T.P., Mortensen, D.A., Curran, W.S., and Barbercheck, M.E. 2011. Assessing and visualizing agricultural management practices: A multivariable hands-on approach for education and extension. Weed Technology 25:680–687.Google Scholar
48 Gallagher, R.S., Luschei, E.C., Gallandt, E.R., and DiTommaso, A. 2007. Experiential learning activities in the weed science classroom. Weed Technology 25:680–687.Google Scholar
49 Gareau, T.P., Smith, R.G., Barbercheck, M.E., and Mortensen, D.A. 2010. Spider plots: A tool for participatory extension learning. Journal of Extension [Online] 48. Available at Web site http://www.joe.org/joe/2010october/tt8.php (accessed June 12, 2013).Google Scholar
50 Mirsky, S.B., Gallandt, E.R., Mortensen, D.A., Curran, W.S., and Shumway, D.L. 2010. Reducing the germinable weed seedbank with soil disturbance and cover crops. Weed Research 50:341–352.CrossRefGoogle Scholar
Supplementary material: File

Jabbour Supplementary Materials

Appendix

Download Jabbour Supplementary Materials(File)
File 36.4 KB