) in which the decisions in the first stage are made to meet the uncertain outcomes in the second stage. The use of a two-stage planning model comes natural to agricultural planning given the time elapsed from the time of planting to harvest (10–15 weeks). Most of the time, the long lead time between the time of planting and the time of harvest require that all planting decisions are made before there is a single crop harvested. These physical constraints prevent the tactical plan from being reevaluated, thus reducing the planning decisions to a single stage. At the beginning of the planting season, the farmer should decide on how much of each product will be planted without having certain information of future weather and market conditions.
In accordance with the two-stage approach, the information available to the farmer at the time that tactical decisions are made is divided in two sets. The first stage set incorporates the planting constraints and the costs associated with the planting decisions, such as labor cost and availability. In the second stage the information available is the random distribution of crops’ prices and crops’ yields. Also in the second stage, there are transportation, harvesting and distribution costs. Other relevant features in the second stage include demand requirements that must be met, such as preexisting contracts, market demand, and transportation available during the harvesting period. The solution for the two-stage problem is then dependent on the first stage decisions (planting), the random realizations (Crop yield and prices) and the second stage decisions (harvesting and distribution).
As mentioned before, the benefit of using stochastic programs (SPs) is that, unlike the deterministic solutions, which are based in expectations, the stochastic approach can be used to consider specific scenarios that occur according to the realizations of the different random variables explicitly considered in the model