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A Test Of Bayesian Learning From Farmer Trials Of New Wheat Varieties

Author

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  • Lindner, Robert K.
  • Gibbs, Melissa

Abstract

In this study, elicited estimates of farmers' subjective beliefs about the mean and variance of wheat variety yields were used to test propositions about Bayesian learning developed in the recent literature on innovation adoption. A series of empirical tests of the Bayesian adoption model were conducted using beliefs elicited from farm surveys conducted in 1982, 1983 and 1984. The results of the analysis neither confirm nor reject the Bayesian approach as a model of how farmers revise subjective beliefs, but do raise serious doubts about its realism, and suggest some issues requiring further investigation. Shortcomings in the elicitation techniques are discussed and the assumptions of the Bayesian model are reviewed.

Suggested Citation

  • Lindner, Robert K. & Gibbs, Melissa, 1990. "A Test Of Bayesian Learning From Farmer Trials Of New Wheat Varieties," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 34(1), pages 1-18, April.
    • Handle: RePEc:ags:ajaeau:22497
    DOI: 10.22004/ag.econ.22497
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    Cited by:

    1. Hardaker, J. Brian & Lien, Gudbrand, 2010. "Probabilities for decision analysis in agriculture and rural resource economics: The need for a paradigm change," Agricultural Systems, Elsevier, vol. 103(6), pages 345-350, July.
    2. Torshizi, Mohammad & Gray, Richard, 2022. "Adaptability and variety adoption: Implications for plant breeding policy in a changing climate," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(04), January.
    3. Kalogeras, Nikos & Pennings, Joost M.E. & Garcia, Philip, 2006. "What Drives Strategic Behavior? A Framework to Explain and Predict SMEs' Transition to Sustainable Production Systems," 2006 Annual meeting, July 23-26, Long Beach, CA 21354, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Llewellyn, Rick S. & Lindner, Robert K. & Pannell, David J. & Powles, Stephen B., 2003. "Effective information and the influence of an extension event on perceptions and adoption," 2003 Conference (47th), February 12-14, 2003, Fremantle, Australia 57911, Australian Agricultural and Resource Economics Society.
    5. Jean‐Paul Chavas & Céline Nauges, 2020. "Uncertainty, Learning, and Technology Adoption in Agriculture," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 42-53, March.
    6. Nelson, R. A. & Holzworth, D. P. & Hammer, G. L. & Hayman, P. T., 2002. "Infusing the use of seasonal climate forecasting into crop management practice in North East Australia using discussion support software," Agricultural Systems, Elsevier, vol. 74(3), pages 393-414, December.
    7. Marra, Michele & Pannell, David J. & Abadi Ghadim, Amir, 2003. "The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve?," Agricultural Systems, Elsevier, vol. 75(2-3), pages 215-234.
    8. Amir K. Abadi Ghadim & David J. Pannell, 1999. "A conceptual framework of adoption of an agricultural innovation," Agricultural Economics, International Association of Agricultural Economists, vol. 21(2), pages 145-154, October.
    9. Galioto, Francesco & Chatzinikolaou, Parthena & Raggi, Meri & Viaggi, Davide, 2020. "The value of information for the management of water resources in agriculture: Assessing the economic viability of new methods to schedule irrigation," Agricultural Water Management, Elsevier, vol. 227(C).
    10. Nadia A. Streletskaya & Samuel D. Bell & Maik Kecinski & Tongzhe Li & Simanti Banerjee & Leah H. Palm‐Forster & David Pannell, 2020. "Agricultural Adoption and Behavioral Economics: Bridging the Gap," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 54-66, March.

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