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Abstract Discrete stochastic sequential programming (DSSP) is a mathematical programming technique that allows for random coefficients in the objective function and constraint set, and a multi‐stage decision process. Despite its intuitive appeal, DSSP is often overlooked in empirical research because 1) the size of the programming matrix becomes large as the number of states of nature and stages increases, and 2) the data requirements associated with these models are formidable. The purpose of this article is to document the construction and use of a DSSP model of a Midwestern corn‐soybean farm. The model is general in the decision variables and the sources of risk it includes. The costs of using DSSP are decreasing, and such models should be considered seriously for a wide variety of empirical problems.
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