Agent-based modeling of bioenergy crop adoption and farmer decision-making
We formulate an agent-based simulation model to analyze farmers' decision-making in bioenergy crops adoption and predict related farmers' group behavior. Agents include farmers and biofuel producers, and each is represented with their own decision-making mechanism. We quantitatively model the decisionmaking of farmers in Iowa, USA as an optimization model based on values derived from published literature and social survey data; results were further validated with social survey data. Economic and environmental impacts of growing conventional row crops versus dedicated energy crops are considered and evaluated under a series of operational constraints including neighborhood influence. We apply the model to forecast biomass supply in the next three decades and reveal that Iowa has the potential to produce 2.27 billion gallons (8.59 billion liters) of cellulosic biofuel by 2022, which constitutes 14.2% of RFS2 cellulosic ethanol mandate. We also find improved publicity to be more efficient in promoting biomass production than raising the contract price. This paper provides a new approach to study farmers' behavior, and insights we derive on managerial operations may be of interest to bioenergy investors, producers, and government policy makers.