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 paper explains this experimental behavior 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 farmers in our sample are more likely to be forward-looking decision makers. The myopic model underestimates the value of early adoption for forward-looking farmers, and predicts lower adoption rates at the beginning of our study period. We also find evidence that farmers tend to rely more on learning from their own experience than on learning from their neighbors.