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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.

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  • 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. Seungki Lee & GianCarlo Moschini, 2020. "Estimating the Value of Innovation and Extension Information: SCNResistant Soybean Varieties," Center for Agricultural and Rural Development (CARD) Publications 20-wp603, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    2. Ahmad, Amal, 2022. "Imperfect information and learning: Evidence from cotton cultivation in Pakistan," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 176-204.
    3. Wang, Huaiyu & Bin, Bing & Pede, Valerien O., 2023. "Adoption of ratoon rice and its impact on technical efficiency of rice farming in China," 2023 Annual Meeting, July 23-25, Washington D.C. 335541, Agricultural and Applied Economics Association.
    4. Do‐il Yoo & Jean‐Paul Chavas, 2023. "Dynamic modeling of biotechnology adoption with individual versus social learning: An application to US corn farmers," Agribusiness, John Wiley & Sons, Ltd., vol. 39(1), pages 148-166, January.
    5. Murphy, David M. A., 2017. "Underground Knowledge: Soil Testing, Farmer Learning, and Input Demand in Kenya," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258372, Agricultural and Applied Economics Association.
    6. 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.
    7. Shikuku, Kelvin Mashisia, 2019. "Information exchange links, knowledge exposure, and adoption of agricultural technologies in northern Uganda," World Development, Elsevier, vol. 115(C), pages 94-106.
    8. Khushbu Mishra & Abdoul G. Sam & Gracious M. Diiro & Mario J. Miranda, 2020. "Gender and the dynamics of technology adoption: Empirical evidence from a household‐level panel data," Agricultural Economics, International Association of Agricultural Economists, vol. 51(6), pages 857-870, November.
    9. Menasbo Gebru & Stein T. Holden & Frode Alfnes, 2021. "Adoption analysis of agricultural technologies in the semiarid northern Ethiopia: a panel data analysis," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 9(1), pages 1-16, December.
    10. Vaiknoras, Kate & Larochelle, Catherine & Birol, Ekin & Asare-Marfo, Dorene & Herrington, Caitlin, 2019. "Promoting rapid and sustained adoption of biofortified crops: What we learned from iron-biofortified bean delivery approaches in Rwanda," Food Policy, Elsevier, vol. 83(C), pages 271-284.
    11. Gebru, Menasbo & Holden , Stein T. & Alfnes, Frode, 2020. "Adoption of agricultural technologies in the semi-arid northern Ethiopia: A Panel Data Analysis," CLTS Working Papers 3/20, Norwegian University of Life Sciences, Centre for Land Tenure Studies.
    12. Do-il Yoo & Jean-Paul Chavas, 2021. "An analysis of risk aversion in biotechnology adoption: the case of US genetically modified corn," Empirical Economics, Springer, vol. 60(5), pages 2613-2635, May.
    13. Yongfeng Tan & Apurbo Sarkar & Airin Rahman & Lu Qian & Waqar Hussain Memon & Zharkyn Magzhan, 2021. "Does External Shock Influence Farmer’s Adoption of Modern Irrigation Technology?—A Case of Gansu Province, China," Land, MDPI, vol. 10(8), pages 1-16, August.
    14. Jutao Zeng & Jie Lyu, 2023. "Simultaneous Decisions to Undertake Off-Farm Work and Straw Return: The Role of Cognitive Ability," Land, MDPI, vol. 12(8), pages 1-21, August.
    15. Seungki Lee & GianCarlo Moschini, 2022. "On the value of innovation and extension information: SCN‐resistant soybean varieties," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(4), pages 1177-1202, August.
    16. 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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