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

  • Richard Tiffin
  • Kelvin Balcombe

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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File URL: http://hdl.handle.net/10.1111/j.1467-8489.2011.00549.x
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Article provided by Australian Agricultural and Resource Economics Society in its journal Australian Journal of Agricultural and Resource Economics.

Volume (Year): 55 (2011)
Issue (Month): 4 (October)
Pages: 579-598

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Handle: RePEc:bla:ajarec:v:55:y:2011:i:4:p:579-598
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  1. Michael Burton & Dan Rigby & Trevor Young, 1999. "Analysis of the Determinants of Adoption of Organic Horticultural Techniques in the UK," Journal of Agricultural Economics, Wiley Blackwell, vol. 50(1), pages 47-63.
  2. Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (Bace) Approach," OECD Economics Department Working Papers 266, OECD Publishing.
  3. Kelvin Balcombe & George Rapsomanikis, 2010. "An Analysis of the Impact of Research and Development on Productivity Using Bayesian Model Averaging with a Reversible Jump Algorithm," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(4), pages 985-998.
  4. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
  5. Hoag, Dana L. & Ascough, James C. & Frasier, W. Marshall, 1999. "Farm Computer Adoption In The Great Plains," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 31(01), April.
  6. Leon-Gonzalez, Roberto & Scarpa, Riccardo, 2008. "Improving multi-site benefit functions via Bayesian model averaging: A new approach to benefit transfer," Journal of Environmental Economics and Management, Elsevier, vol. 56(1), pages 50-68, July.
  7. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
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