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

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.