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Proven Science versus Farmer Perception

Author

Listed:
  • Kelly, Edel
  • Heanue, Kevin
  • Buckley, Cathal
  • O'Gorman, Colm

Abstract

Resource use efficiency is at the core of sustainable farming practices for the future of agriculture. Given the abolition of quotas in the EU and the increasing demands for food globally food producers are faced with a challenge to increase production in an environmentally sustainable manner. This paper examines the adoption of a suite of grassland management practices by Irish dairy farmers which are proven to improved grass utilisation. The Technology Acceptance Model is applied to a nationally representative sample of specialist Irish dairy farmers to investigate the use of belief based variables and traditional socio-economic and demographic variables in predicting intention to use six grassland management practices.

Suggested Citation

  • Kelly, Edel & Heanue, Kevin & Buckley, Cathal & O'Gorman, Colm, 2015. "Proven Science versus Farmer Perception," 2015 Conference, August 9-14, 2015, Milan, Italy 229067, International Association of Agricultural Economists.
  • Handle: RePEc:ags:iaae15:229067
    DOI: 10.22004/ag.econ.229067
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    File URL: http://ageconsearch.umn.edu/record/229067/files/KellyEtAl.pdf
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    References listed on IDEAS

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    1. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    2. Flett, Ross & Alpass, Fiona & Humphries, Steve & Massey, Claire & Morriss, Stuart & Long, Nigel, 2004. "The technology acceptance model and use of technology in New Zealand dairy farming," Agricultural Systems, Elsevier, vol. 80(2), pages 199-211, May.
    3. Lynne, Gary D., 1995. "Modifying the Neo-Classical Approach to Technology Adoption With Behavioral Science Models," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 27(1), pages 67-80, July.
    4. Rehman, T. & McKemey, K. & Yates, C.M. & Cooke, R.J. & Garforth, C.J. & Tranter, R.B. & Park, J.R. & Dorward, P.T., 2007. "Identifying and understanding factors influencing the uptake of new technologies on dairy farms in SW England using the theory of reasoned action," Agricultural Systems, Elsevier, vol. 94(2), pages 281-293, May.
    5. Greenfield, Geoffrey & Rohde, Fiona, 2009. "Technology acceptance: Not all organisations or workers may be the same," International Journal of Accounting Information Systems, Elsevier, vol. 10(4), pages 263-272.
    6. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    7. Neal H. Hooker & Christopher J. Shanahan & Valerie Rake & Eboni Francis & Charles Popovich & Joanne Dehoney, 2009. "A Technology-Enhanced Teaching Tool: Tracking Student Adoption and Performance," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 31(4), pages 963-983, December.
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    10. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, April.
    11. Kellie J. Archer & Stanley Lemeshow, 2006. "Goodness-of-fit test for a logistic regression model fitted using survey sample data," Stata Journal, StataCorp LP, vol. 6(1), pages 97-105, March.
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    Cited by:

    1. Schaak, Henning & Musshoff, Oliver, 2017. "Behavioral Drivers for Grazing Practices in Dairy Farming," 2017 Conference (61st), February 7-10, 2017, Brisbane, Australia 258680, Australian Agricultural and Resource Economics Society.
    2. O’Shea, Robert & O’Donoghue, Cathal & Ryan, Mary & Breen, James, 2018. "Understanding farmers: From adoption to attitudes," 166th Seminar, August 30-31, 2018, Galway, West of Ireland 276203, European Association of Agricultural Economists.

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    Keywords

    Agricultural and Food Policy; Farm Management;

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