The strong dependence on exogenously determined states of nature (weather, diseases, pests, etc.) is a constituent element of most agricultural production processes. While this state contingency creates uncertainties, it likewise offers various possibilities to react to particular states of nature (e.g. through irrigation or pest management). A concept which in principle accounts for these contexts is the so called state contingent approach originally developed by CHAMBERS and QUIGGIN. This approach comprises the state contingent depiction of the production process under uncertainty as basis for a realistic representation of individual decision making as well as the resulting market reactions. The following article deals with the state contingent approach in the context of mathematical programming. It starts with the description of the conceptual foundations of the approach and subsequently focuses on its implementation in the context of mathematical programming under uncertainty. The comparison with conventional mathematical programming approaches using an example documents the conceptual advantage of the state contingent approach, but also clarifies the methodical challenges which result from its complexity.