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A dynamic adoption model with Bayesian learning: an application to U.S. soybean farmers

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  • Xingliang Ma
  • Guanming Shi

Abstract

Adoption of agricultural technology is often sequential, with farmers first adopting a new technology on part of their lands and then adjusting their use of the new technology in later years based on what was learned from the initial partial adoption. Our article explains this experimental behavior by using a dynamic adoption model with Bayesian learning, in which forward-looking farmers take account of future impacts of their learning from both their own and their neighbors’ experiences with the new technology. We apply the analysis to a panel of U.S. soybean farmers surveyed from 2000 to 2004 to examine their adoption of the genetically modified (GM) seed technology. We compare the results of the forward-looking model to that of a myopic model, in which farmers maximize current benefits only. Results suggest that the forward-looking model fits data better than the myopic model does. And potential estimation biases arise when fitting a myopic model to forward-looking decision makers.

Suggested Citation

  • Xingliang Ma & Guanming Shi, 2015. "A dynamic adoption model with Bayesian learning: an application to U.S. soybean farmers," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 25-38, January.
  • Handle: RePEc:bla:agecon:v:46:y:2015:i:1:p:25-38
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    File URL: http://hdl.handle.net/10.1111/agec.12124
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    1. repec:eee:wdevel:v:115:y:2019:i:c:p:94-106 is not listed on IDEAS
    2. Mishra, Khushbu & Abdoul, Sam G. & Miranda, Mario J. & Diiro, Gracious M., 2015. "Gender and Dynamics of Technology Adoption: Evidence from Uganda," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206550, Agricultural and Applied Economics Association.
    3. repec:eee:jfpoli:v:83:y:2019:i:c:p:271-284 is not listed on IDEAS
    4. Tesfay, M., 2018. "Adoption of diversified farm technology in a semi arid of northern Ethiopia: A Panel Data Analysis," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276008, International Association of Agricultural Economists.

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