The objective of this project is to design a decision support system for soybean rust management using gaming software that incorporates farmer's decision making in the face of risks from soybean rust. Learning from past actions and neighbor's actions are also incorporated. Farmers observe rust outbreak in the current and past periods and decide over how much of land to allocate between soybean, corn and other crops. This decision is influenced by maximization of expected profits criterion which entails crop rotation choices that are based upon perceived risks, yield drags and input costs from altering optimum rotation patterns. Adoption of new technology in terms of selecting better rust management practices is also analyzed in an adaptive management framework. The software meets the need of guiding policy formulation besides training stakeholders in making economically sound choices in the absence of empirical data over pest infestation.