Stochastic Linear Programming and Feasibility Problems in Farm Growth Analysis

Abstract

If farm growth is defined in terms of asset accumulation, dynamic linear programming techniques can be employed to determine rates of growth implied by nonstochastic models based on assumed farm situations. Frequently, however, because of the environmental conditions in which farms typically operate, a probabilistic characterization of a number of coefficients in such a model is more realistic. With the introduction of these variable coefficients, the programming model used in calculating feasible rates of growth becomes stochastic. There are two alternative methods of solving these stochastic programming models, of which only one, the distribution method, is considered in this article. This method is examined for its usefulness in establishing feasible rates of growth for a particular farm model. An evaluation of the farm growth model and the distribution method of solving the programming problem is then made in relation to a farm representative of an area in the Texas High Plains.

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