Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-23T14:51:32.708Z Has data issue: false hasContentIssue false
  • English
  • Français

Factors affecting farmers' crop diversity decisions: An integrated approach

Published online by Cambridge University Press:  30 October 2009

Laurence B. Cutforth ,
Charles A. Francis ,
Gary D. Lynne ,
David A. Mortensen  and
Kent M. Eskridge
Laurence B. Cutforth*
Affiliation:
Graduate Research Assistant, Institute for Environmental Studies, Land Resources Program, University of Wisconsin-Madison, Madison, WI 53706;
Charles A. Francis
Affiliation:
Professor, Department of Agronomy, Department of Biometry, University of Nebraska-Lincoln, Lincoln, NE 68583.
Gary D. Lynne
Affiliation:
Professor, Department of Agricultural Economics, Department of Biometry, University of Nebraska-Lincoln, Lincoln, NE 68583.
David A. Mortensen
Affiliation:
Professor, Department of Agronomy, Department of Biometry, University of Nebraska-Lincoln, Lincoln, NE 68583.
Kent M. Eskridge
Affiliation:
Professor, Department of Biometry, University of Nebraska-Lincoln, Lincoln, NE 68583.
*
Corresponding author is L.B. Cutforth ([email protected]).
Get access

Abstract

Sustainable cropping systems use crop diversity as a foundation to reduce the environmental impacts of agrichemicals and risks of pest and disease outbreaks. Although greater diversity in the agricultural landscape is an important goal, the decisions of individual farmers determine the diversity of crops used in each farming system. Therefore, it is essential to gain an understanding of important factors in farmers' decisions as part of any proposed solution to low crop diversity. Our objective was to develop and test an integrated socioeconomic model of farmers' decisions on crop rotations as an indicator of overall crop diversity. The model incorporated measures of farmers' attitudes, net household income, control over decisions, social norms, and regional location to address the social, economic, and environmental factors of crop diversity. Mail survey responses from 197 farmers from a western Corn Belt county in 1998 supplied the data for the analysis. Location had the strongest influence on crop diversity in the model, on the basis of a standardized beta statistic of 0.46 in the ordinary least squares regression analysis. Farmers in the region with the highest degree of sloping land had significantly higher crop diversity than farmers in the most productive, relatively flat area. Lower household net income and positive attitudes toward crop rotations were also associated with higher crop diversity. We believe social norms and control would exert more influence in uncertain decisions. The results of this study suggest that, to maintain and possibly expand crop diversity in the future, targeting farmers in sloping landscapes with positive attitudes toward rotations would be the best approach to focus economic incentives for diverse crop rotations and technical support from Extension. In any effort to support crop diversity greater than a typical cornsoybean rotation, it is also critical to preserve integrated forage/crop/livestock systems. Further adaptation and application of our behavioral model in other settings would help to better evaluate, understand, and target the integrated decisions people make relative to crop diversity.

Keywords

agroecological zonesbehavioral modelingcrop rotationsfarmers' attitudesfarmers' decision-makingsocial normssocioeconomic modelsU.S. Corn Belt
Type
Articles
Information
American Journal of Alternative Agriculture , Volume 16 , Issue 4 , December 2001 , pp. 168 - 176
Copyright
Copyright © Cambridge University Press 2001

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.Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50:179–211.CrossRefGoogle Scholar
2.Anderson, R.L., Aiken, R.M., Sinclair, H.R., Waltman, S.W., and Waltman, W.J.. 1998. Identifying agroecozones in the central Great Plains. In Robert, P.C., Rust, R.H., and Larson, W.E. (eds.). Proc. Fourth Int. Conf. Precision Agric, Minneapolis, MN, July. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison, WI. p. 1347–1353.Google Scholar
3.Anosike, N., and Coughenor, C.M.. 1990. The socioeconomic basis of farm enterprise diversification decisions. Rural Sociol. 55:1–24.CrossRefGoogle Scholar
4.Bagozzi, R.P., and Yi, Y.. 1989. The degree of intention formation as a moderator of the attitude-behavior relationship. Social Psych. Quart. 52:266–279.CrossRefGoogle Scholar
5.Bezdicek, D.F., and Granatstein, D.. 1989. Crop rotation efficiencies and biological diversity in farming systems. Amer. J. Alternative Agric. 4:111–118.CrossRefGoogle Scholar
6.Brown, L.A., Malecki, E.J., and Spector, A.N.. 1976. Adopter categories in a spatial context: Alternative explanations for an empirical regularity. Rural Sociol. 41:99–118.Google Scholar
7.Bultena, G.L., and Hoiberg, E.O.. 1983. Factors affecting farmers' adoption of conservation tillage. J. Soil Water Conserv. 3:281–284.Google Scholar
8.Casey, F., and Lynne, G.D.. 1997. Factors affecting investment in drip irrigation technology: A multiple utility approach. Econ. Rep. 97–1. University of Florida, Gainesville.Google Scholar
9.Cutforth, L.B. 1999. Social, Economic, and Environmental Factors Affecting Crop Diversity. M.S. thesis. University of Nebraska-Lincoln.Google Scholar
10.Dillman, D. 1978. Mail and Telephone Surveys: The Total Design Method. Wiley, New York.Google Scholar
11.Elder, J.A., Beesley, T.E., and McKinzie, W.E.. 1965. Soil Survey of Saunders County, Nebraska. U.S. Dept. of Agriculture, Soil Conservation Service; University of Nebraska-Lincoln, Conservation and Survey Division, Lincoln.Google Scholar
12.ESRI. 1997. Arc View Version 3.0. Environmental Systems Research Institute, Redlands, CA.Google Scholar
13.Fishbein, M., and Ajzen, I.. 1975. Belief, Intention and Behavior: An Introduction to Theory and Research Reading. Addison-Wesley, Reading, MA.Google Scholar
14.Frerichs, S. 1990. Rotations: Influence and constraint from commodity programs. Amer. J. Alternative Agric. 5:134–138.CrossRefGoogle Scholar
15.Helmers, G.A., Langemeier, M.R., and Atwood, J.. 1986. An economic analysis of alternative cropping systems for east-central Nebraska. Amer. J. Alternative Agric. 1:153–158.CrossRefGoogle Scholar
16.Lighthall, D.R. 1996. Sustainable agriculture in the Corn Belt: Production-side progress and demand-side constraints. Amer. J. Alternative Agric. 11:168–174.CrossRefGoogle Scholar
17.Liska, A.E. 1984. A critical examination of the causal structure of the Fishbein/ Ajzen attitude-behavior model. Social Psych. Quart. 47:61–74.CrossRefGoogle Scholar
18.Loomis, R.S., and Connor, D.J.. 1992. Crop Ecology: Productivity and Management in Agricultural Systems. Cambridge University Press, New York.CrossRefGoogle Scholar
19.Lowrance, R., Hendrix, P.F., and Odum, E.P.. 1986. A hierarchical approach to sustainable agriculture. Amer. J. Alternative Agric. 1:169–173.CrossRefGoogle Scholar
20.Lynne, G.D., Casey, C.F., Hodges, A., and Rahmani, M.. 1995. Conservation technology adoption decisions and the theory of planned behavior. Econ. Psych. 16:581–598.CrossRefGoogle Scholar
21.Marshall, I.B., and Schut, P.H.. 1999. A national ecological framework for Canada. Environment Canada, Ecosystems Science Directorate; Agriculture and Agri-Food Canada, Research Branch, Ottawa. Web site http://res.agr.ca/CANSIS/ NSDB/ECOSTRAT/_overview.html (verified January 2001).Google Scholar
22.NASS. 1998. Nebraska Agricultural Statistics 1997–1998. Nebraska Agricultural Statistics Service, Lincoln.Google Scholar
23.Nelson, F.J., and Schertz, L.P. (eds.). 1996. Provisions of the Federal Agriculture Improvement and Reform Act of 1996. Agric. Info. Bull. 729. U.S. Dept. of Agriculture, Economic Research Service, Commercial Agriculture Division, Washington, DC. Web site http://www.ers.usda.gov/publications/aib729/ (viewed January 2001).Google Scholar
24.Olson, R.K. 1998. Procedures for evaluating alternative farming systems: A case study for eastern Nebraska. Vol. 8. North Central Region Sustainable Agriculture Research and Education Program, Lincoln, NE.Google Scholar
25.Pimentel, D., McLaughlin, L., Zepp, A., Lakitan, B., Kraus, T., Kleinman, P., Vancini, F., Roach, W.J., Keeton, W.S., and Selig, G.. 1991. Environmental and economic effects of reducing pesticide use. BioScience 41:402–409.CrossRefGoogle Scholar
26.Salamon, S., Farnsworth, R.L., Bullock, D.G., and Yusuf, R.. 1997. Family factors affecting adoption of sustainable farming systems. J. Soil Water Conserv. 52:265–271.Google Scholar
27.SAS Institute. 1996. SAS Version 6.12. Cary, NC.Google Scholar
28.Shannon, C.E., and Weaver, W.. 1949. The mathematical theory of communication. University of Illinois Press, Urbana.Google Scholar
29.SPSS. 1997. SPSS for Windows 8.0. SPSS Inc., Chicago, IL.Google Scholar
30.Tilley, D.S. 1989. Evaluating alternative agricultural enterprises: The critical role of marketing. Curr. Farm Econ. 62(1):3–13.Google Scholar
31.Tilman, D. 1998. The greening of the green revolution. Nature 396:211–212.CrossRefGoogle Scholar
32.USDA. 1981. Land resource regions and major land resource areas of the U.S. Agric. Handbook No. 296. U.S. Dept. of Agriculture, Soil Conservation Service, Washington, DC.Google Scholar
33.USDA. 1995. Soil Survey Geographic (SSURGO) Database. Misc. Pub. 1527. U.S. Dept. of Agriculture, Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE.Google Scholar
34.Vandermeer, J., van Noordwijk, M., Anderson, J., Ong, C., and Perfecto, I.. 1998. Global change and multi-species agroecosystems: Concepts and issues. Agric. Ecosys. Environ. 67:1–22.CrossRefGoogle Scholar
35.Vogelsberg, J. 1987. Rotations: The key to farm success. Amer. J. Alternative Agric. 2:16.CrossRefGoogle Scholar
36.Welsh, R. 1997. Vertical coordination, producer response, and the locus of control over agricultural production decisions. Rural Sociol. 62:491–507.CrossRefGoogle Scholar
37.Wolf, S.A., and Nowak, P.. 1999. Institutional failure in agro-environmental management. Soc. Prob. Pub. Pol. 7:293–310.Google Scholar