Sequential Modeling of White Wheat Marketing Strategies

A discrete stochastic progamming model is developed to directly maximize expected utility resulting from alternative white wheat marketing strategies. Time‐series and regression techniques are used to simulate post‐harvest price events. Farm characteristics and risk attitudes of a small group of Pacific Northwest wheat producers define the constraints and the objective functions for the model. The sequential formulation of the model, in which price uncertainty is continuously resolved, results in more flexible sales patterns over the marketing year than a nonsequential formulation of the problem. Alternative utility functional forms have little influence on optimal marketing plans.