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Weed Management Practice Selection Among Midwest U.S. Organic Growers

Published online by Cambridge University Press:  20 January 2017

James J. DeDecker ,
John B. Masiunas ,
Adam S. Davis  and
Courtney G. Flint
James J. DeDecker*
Affiliation:
Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801
John B. Masiunas
Affiliation:
U.S. Department of Agriculture–Agricultural Research Service Global Change and Photosynthesis Research Unit, Urbana, IL
Adam S. Davis
Affiliation:
Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801
Courtney G. Flint
Affiliation:
Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801
*
Corresponding author's E-mail: [email protected]
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Abstract

Organic agricultural systems increase the complexity of weed management, leading organic farmers to cite weeds as one of the greatest barriers to organic production. Integrated Weed Management (IWM) systems have been developed to address the ecological implications of weeds and weed management in cropping systems, but adoption is minimal. Organic agriculture offers a favorable context for application of IWM, as both approaches are motivated by concern for environmental quality and agricultural sustainability. However, adoption of IWM on organic farms is poorly understood due to limited data on weed management practices used, absence of an IWM adoption metric, and insufficient consideration given to the unique farming contexts within which weed management decisions are made. Therefore, this study aimed to (1) characterize organic weed management systems; (2) identify motivations for, and barriers to, selection of weed management practices; and (3) generate guiding principles for effective targeting of weed management outreach. We surveyed Midwestern organic growers to determine how specified psychosocial, demographic, and farm structure factors influence selection of weed management practices. Cluster analysis of the data detected three disparate, yet scaled, approaches to organic weed management. Clusters were distinguished by perspective regarding weeds and the number of weed management practices used. Categorization of individual farms within the identified approaches was influenced by primary farm products as well as farmer education, years farming, and information-seeking behavior. The proposed conceptual model allows weed management educators to target outreach for enhanced compatibility of farming contexts and weed management technologies.

Keywords

Cluster analysisdecision-makingintegrated weed managementlogistic regression analysis
Type
Weed Management
Information
Weed Science , Volume 62 , Issue 3 , September 2014 , pp. 520 - 531
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bastiaans, L, Paolini, R, Baumann, D (2008) Focus on ecological weed management: What is hindering adoption? Weed Res 48:481–491 CrossRefGoogle Scholar
Buhler, D, Liebman, M, Obrycki, J (2000) Theoretical and practical challenges to an IPM approach to weed management. Weed Sci 48:274–280 Google Scholar
Bürger, J, de Mol, F, Gerowitt, B (2012) Influence of cropping system factors on pesticide use intensity – a multivariate analysis of on-farm data in north East Germany. Eur J Agron 40:54–63 Google Scholar
Burton, RJF, Wilson, G (2006) Injecting social psychology theory into conceptualisations of agricultural agency: towards a post-productivist farmer self-identity? J Rural Stud 22:95–115 CrossRefGoogle Scholar
Cardina, J, Webster, T, Herms, C, Regnier, E (1999) Development of weed IPM: levels of integration for weed management. J Crop Prod 2:239–267 Google Scholar
Ceylan, IC, Koksal, O, Kutlar, I (2010) Determination of effective factors on adoption of integrated pest management practices. J Environ Prot Ecol 11:709–717 Google Scholar
Czapar, G, Curry, M, Gray, M (1995) Survey of integrated pest management practices in central Illinois. J Prod Agr 8:34–46 Google Scholar
Davis, A, Renner, K, Gross, K (2005) Weed seedbank and community shifts in a long-term cropping systems experiment. Weed Sci 53:296–306 Google Scholar
Deytieux, VT, Nemecek, R, Knuchel, F, Gaillard, G, Munier-Jolain, N (2012) Is integrated weed management efficient for reducing environmental impacts of cropping systems? A case study based on life cycle assessment. Eur J Agron 36:55–65 Google Scholar
Dice, LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302 CrossRefGoogle Scholar
Dillman, DA, Smyth, JD, Christian, L (2008) Internet, Mail and Mixed-Mode Surveys. 3rd edn. New York Wiley & Sons. 500 pGoogle Scholar
Doohan, D, Wilson, R, Canales, E, Parker, J (2010) Investigating the human dimension of weed management: new tools of the trade. Weed Sci 58:510 Google Scholar
Edwards-Jones, G (2006) Modelling farmer decision-making: concepts, progress and challenges. Animal Sci 82:783–790 CrossRefGoogle Scholar
Electronic Code of Federal Regulations. 2012. Section 7.B.1.M.205. National organic progrAm. http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&sid=3f34f4c22f9aa8e6d9864cc2683cea02&tpl=/ecfrbrowse/Title07/7cfr205_main_02.tpl. Accessed September 23, 2012.Google Scholar
Fairweather, JR, Campbell, H (1996) The Decision Making of Organic and Conventional Agricultural Producers (No. 233). Canterbury, New Zealand Agribusiness and Economics Research Unit. 30 pGoogle Scholar
Feder, G, Umali, D (1993) The adoption of agricultural innovations: a review. Technol Forecast Soc Change 43:215–239 CrossRefGoogle Scholar
Finch, H (2005) Comparison of distance measures in cluster analysis with dichotomous data. J Data Sci 3:85–100 CrossRefGoogle Scholar
Gintis, H (2009) Decision theory and human behavior. Pages 1–29 in The Bounds of Reason: Game Theory and the Unification of the Behavioral Sciences. Princeton, NJ Princeton University Press Google Scholar
Hair, J, Anderson, R, Tatham, R, Black, W (1992) Multivariate Data Analysis. 2nd edn. New York Macmillan Publishing Company. 816 pGoogle Scholar
Haydu, J (2011) Cultural modeling in two eras of US food protest: Grahamites (1830s) and organic advocates (1960s–70s). Soc Probl 58:461–487 Google Scholar
IBM Corp. (2011) IBM statistics for Windows. Version 20.0. Armonk, NY Google Scholar
Jabbour, R, Zwickle, S, Gallandt, ER, McPhee, KE, Wilson, RS (2013) Mental models of organic weed management: comparison of New England US farmer and expert models. Renew Agr Food Syst FirstView Article 1–15 Google Scholar
Jasinski, J, Eisley, B, Gastier, T, Kovach, J (2001) Scoring IPM adoption in Ohio: It really adds up. http://www.joe.org/joe/2001october/iw1.php. Accessed September 23, 2012.Google Scholar
Kelling, KA, Schulte, E, Peters, J (1996) One hundred years of Ca:Mg ratio research. New Horiz in Soil, No. 8. Madison, WI University of Wisconsin. 6 pGoogle Scholar
Kopittke, PM, Menzies, N (2007) A review of the use of the basic cation saturation ratio and the “ideal” soil. Soil Sci Soc Am J 71:259–265 Google Scholar
Lamine, C (2011) Transition pathways towards a robust ecologization of agriculture and the need for system redesign: cases from organic farming and IPM. J Rural Stud 27:209–219 Google Scholar
Liebman, M, Mohler, C, Staver, C (2001) Ecological management of agricultural weeds. Cambridge, UK Cambridge University Press. 532 pGoogle Scholar
McCann, E, Sullivan, S, Erickson, D, DeYoung, R (1997) Environmental awareness, economic orientation, and farming practices: a comparison of organic and conventional farmers. Environ Manage 21:747–758 Google Scholar
Padgham, J (2011) Book review: weeds and why they grow by Jay McCaman. Organic Broadcaster. September–October 2011.Google Scholar
Park, TA, Lohr, L (2005) Organic pest management decisions: a systems approach to technology adoption. Agric Econ 33:467–478 CrossRefGoogle Scholar
Pennings, JME, Irwin, S, Good, D (2002) Surveying farmers: a case study. Rev Agr Econ 24:266–277 CrossRefGoogle Scholar
Riemens, MM, Groeneveld, R, Kropff, M, Lotz, L, Renes, R, Sukkel, W, van der Weide, R (2010) Linking farmer weed management behavior with weed pressure: more than just technology. Weed Sci 58:496 Google Scholar
Rydberg, N, Milberg, P (2000) A survey of weeds in organic farming in Sweden. Biol Agric Hortic 18:175–185 Google Scholar
Samiee, A, Rezvanfar, A, Faham, E (2009) Factors influencing the adoption of integrated pest management (IPM) by wheat growers in Varamin county, Iran. Afr J Agr Res 4:491–497 Google Scholar
Sattler, C, Nagel, U (2010) Factors affecting farmers' acceptance of conservation measures: a case study from north-eastern Germany. Land Use Pol 27:70–77 CrossRefGoogle Scholar
Schonbeck, M (2000) Balancing soil nutrients in organic vegetable production systems: testing Albrecht's base saturation theory in southeastern soils. Inf Bull 10:17 Google Scholar
Simon, H (1990) Bounded rationality. Pages 15–18 in Eatwell, J, Milgate, M, Newman, P, eds. Utility and Probability. New York W. W. Norton Google Scholar
Smith, DH, Collins, M (2003) Forbs. Pages 215–236 in Barnes, RF, Nelson, CJ, Collins, M, Moore, KJ, eds. Forages: An Introduction to Grassland Agriculture. Ames, IA Iowa State Press Google Scholar
Spall, J (2005) Monte Carlo computation of the Fisher information matrix in nonstandard settings. J Comput Graph Stat 14:889–909 Google Scholar
Straub, ET (2009) Understanding technology adoption: theory and future directions for informal learning. Rev Educ Res 79:625–649 CrossRefGoogle Scholar
[USDA] U.S. Department of Agriculture. (2009) 2007 Census of agriculture. Washington DC National Agricultural Statistics Service. 739 pGoogle Scholar
USDA. (2010) List of certified operations. http://apps.ams.usda.gov/nop/. Accessed September 23, 2012.Google Scholar
Villamil, MB, Alexander, MM, Silvis, A, Gray, M (2012) Producer perceptions and information needs regarding their adoption of bioenergy crops. Renewable Sustainable Energy Rev 16:3604–3612 CrossRefGoogle Scholar
Walz, E (1999) Third Biennial National Organic Farmer's Survey. Santa Cruz, CA Organic Farming Research Foundation. 126 pGoogle Scholar
Walz, E (2004) Fourth National Organic Farmer's Survey. Santa Cruz, CA Organic Farming Research Foundation. 106 pGoogle Scholar
Ward, J (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236 CrossRefGoogle Scholar
Wilson, RS, Hooker, N, Tucker, M, LeJeune, J, Doohan, D (2009) Targeting the farmer decision making process: a pathway to increased adoption of integrated weed management. Crop Prot 28:756–764 Google Scholar