IDEAS home Printed from https://ideas.repec.org/p/ags/aaea11/104577.html
   My bibliography  Save this paper

A Dynamic Adoption Model with Bayesian Learning: Application to the U.S. Soybean Market

Author

Listed:
  • Ma, Xingliang
  • Shi, Guanming

Abstract

Agricultural technology adoption is often a sequential process. Farmers may adopt a new technology in part of their land first and then adjust in later years based on what they learn from the earlier partial adoption. This paper presents a dynamic adoption model with Bayesian learning, in which forward-looking farmers learn from their own experience and from their neighbors about the new technology. The model is compared to that of a myopic model, in which farmers only maximize their current benefits. We apply the analysis to a sample of U.S. soybean farmers from year 2000 to 2004 to examine their adoption pattern of a newly developed genetically modified (GM) seed technology. We show that the myopic model predicts lower adoption rates in early years than the dynamic model does, implying that myopic farmers underestimate the value of early adoption. My results suggest that farmers in my sample are more likely to be forward-looking decision makers and they tend to rely more on learning from their own experience than learning from their neighbors.

Suggested Citation

  • Ma, Xingliang & Shi, Guanming, 2011. "A Dynamic Adoption Model with Bayesian Learning: Application to the U.S. Soybean Market," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 104577, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea11:104577
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/104577
    Download Restriction: no

    References listed on IDEAS

    as
    1. Terrance M. Hurley & Paul D. Mitchell & Marlin E. Rice, 2004. "Risk and the Value of Bt Corn," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 345-358.
    2. Pilar Useche & Bradford L. Barham & Jeremy D. Foltz, 2006. "Integrating Technology Traits and Producer Heterogeneity: A Mixed-Multinomial Model of Genetically Modified Corn Adoption," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(2), pages 444-461.
    3. Lisa A. Cameron, 1999. "The Importance of Learning in the Adoption of High-Yielding Variety Seeds," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 83-94.
    4. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    5. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
    6. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
    7. Feder, Gershon & Just, Richard E & Zilberman, David, 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey," Economic Development and Cultural Change, University of Chicago Press, vol. 33(2), pages 255-298, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Haoyang & Ross, Brent R., 2014. "Farmers’ Switchgrass Adoption Decision Under A Single-Procurer Market: An Agent Based Simulation Approach," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170502, Agricultural and Applied Economics Association.
    2. Liam Graham, 2011. "Individual rationality, model-consistent expectations and learning," CDMA Working Paper Series 201112, Centre for Dynamic Macroeconomic Analysis.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea11:104577. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/aaeaaea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.