A Machine Learning approach for solving land allocation problems

Abstract

This paper describes a numerically efficient Machine Learning approach for solving the farm manager’s land allocation problem in a manner that requires neither a distributional assumption on crop returns nor an expected utility specification. The method is based on the idea that a farm manager seeks the allocation of crops that minimizes the chance of realizing a harvest return below some pre-determined target rate of return, a notion supported by results from financial portfolio theory, behavioral economics, and historical land allocation research. The method also includes a way to connect a farm manager’s choice of target rate to their risk attitudes. Corn-soybean return data from Illinois is used to demonstrate the approach for an array of shortfall values; the optimally fitted model matches well to observed planting behavior. The proposed approach is not inherently limited to land allocation decisions; future researchers may discover that it is useful in other types of shortfall and non-shortfall decision processes.