Using an agent-based model, we simulate the climate-induced agricultural labor migration for alternative future climate scenarios. For each agent, the probability of migration is calculated as a function of a set of relevant factors using a logistic regression model. Historical U.S. agricultural employment data was used to calibrate the model. The simulation result showed that larger crop yield reduction induced by climate change tends to generate larger migration flows. Furthermore, we observed that the network effects tend to forecast a larger migration difference between alternative climate scenarios.