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The determinants of technology adoption by UK farmers using Bayesian model averaging: the cases of organic production and computer usage

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  • Richard Tiffin
  • Kelvin Balcombe

Abstract

We review and implement a reversible jump approach to Bayesian model averaging for the Probit model with uncertain regressors. Two applications are investigated. The first is the adoption of organic systems in UK farming, and the second is the influence of farm and farmer characteristics on the use of a computer on the farm. While there is a correspondence between the conclusions we would obtain with and without model averaging results, we find important differences, particularly in smaller samples. Concerning the adoption of an organic system, we find that attitudes to the sustainability of the current system along with the ability of organic farms alone to satisfy society’s needs for food are influential. Additionally, the source of management information used by the farmer has a significant impact. Regarding the adoption of computers, we confirm the findings of previous work that the level of education affects uptake and that age is a factor determining adoption. We also find that dairy and organic farms are more likely to use a computer. The physical size of the farm is positively associated with the probability of computer use while net farm income has a limited impact.
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  • Richard Tiffin & Kelvin Balcombe, 2011. "The determinants of technology adoption by UK farmers using Bayesian model averaging: the cases of organic production and computer usage," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(4), pages 579-598, October.
    • Handle: RePEc:bla:ajarec:v:55:y:2011:i:4:p:579-598
    DOI: j.1467-8489.2011.00549.x
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    2. Sirkka Schukat & Heinke Heise, 2021. "Towards an Understanding of the Behavioral Intentions and Actual Use of Smart Products among German Farmers," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    3. Zdenka MALÁ & Michal MALÝ, 2013. "The determinants of adopting organic farming practices: a case study in the Czech Republic," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 59(1), pages 19-28.
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    5. Barnes, A.P. & Soto, I. & Eory, V. & Beck, B. & Balafoutis, A. & Sánchez, B. & Vangeyte, J. & Fountas, S. & van der Wal, T. & Gómez-Barbero, M., 2019. "Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers," Land Use Policy, Elsevier, vol. 80(C), pages 163-174.
    6. Giua, Carlo & Materia, Valentina Cristiana & Camanzi, Luca, 2022. "Smart farming technologies adoption: Which factors play a role in the digital transition?," Technology in Society, Elsevier, vol. 68(C).
    7. Dmytro Serebrennikov & Fiona Thorne & Zein Kallas & Sinéad N. McCarthy, 2020. "Factors Influencing Adoption of Sustainable Farming Practices in Europe: A Systemic Review of Empirical Literature," Sustainability, MDPI, vol. 12(22), pages 1-23, November.
    8. Emileva, Begaiym & Kuhn, Lena & Bobojonov, Ihtiyor & Glauben, Thomas, 2023. "The role of smartphone-based weather information acquisition on climate change perception accuracy: Cross-country evidence from Kyrgyzstan, Mongolia and Uzbekistan," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 41, pages 1-1.
    9. Daniele Mozzato & Paola Gatto & Edi Defrancesco & Lucia Bortolini & Francesco Pirotti & Elena Pisani & Luigi Sartori, 2018. "The Role of Factors Affecting the Adoption of Environmentally Friendly Farming Practices: Can Geographical Context and Time Explain the Differences Emerging from Literature?," Sustainability, MDPI, vol. 10(9), pages 1-23, August.
    10. Payne-Gifford, Sophie & Johnson, Kate & Mauchline, Alice & Gadanakis, Yiorgos & Girling, Laura & Mortimer, Simon, 2020. "Exploring attitudes to technology adoption for cross compliance in Greek and Lithuanian farmers," Land, Farm & Agribusiness Management Department 308133, Harper Adams University, Land, Farm & Agribusiness Management Department.
    11. Wun-Ji JIANG & Yir-Hueih LUH, 2018. "Does higher food safety assurance bring higher returns? Evidence from Taiwan," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(11), pages 477-488.
    12. Payne-Gifford, Sophie & Johnson, Kate & Mauchline, Alice & Gadanakis, Yiorgos & Girling, Laura & Mortimer, Simon, 2020. "Exploring attitudes to technology adoption for cross compliance in Greek and Lithuanian farmers," Agri-Tech Economics Papers 308133, Harper Adams University, Land, Farm & Agribusiness Management Department.
    13. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).
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    More about this item

    JEL classification:

    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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