Abstract
There is a lot of previous research on profit maximization in agriculture. In a previous research crop rotation problem and cultivation management problem are solved as one integrated management problem in order to maximize the profit in agriculture. In the previous research the profit is maximized under the condition that probabilities are known. Sufficient historical data is required to estimate probabilities. The previous method cannot be applied to areas where there is no historical data. In this research, a new integrated management method for areas where there is no historical data is proposed. Integrated management is modeled by Markov decision processes with unknown probabilities. Historical data of neighboring areas is used. The proposed method maximizes profit with reference to a Bayes criterion by dynamic programming. In the numerical calculation results, adaptive crop selection and cultivation action selection according to posterior probabilities for neighboring areas are confirmed. This research is a basic research, and future extended research is required.