At harvest time, rice farmers can sell their paddy at the existing price, or can choose to store their paddy and sell at a future, uncertain price. The decisions are made under conditions of uncertainty. The paddy prices in the future months are the primary source of the uncertainty;The objective of this study was to develop marketing decision models to determine expected prices and expected net returns on farm storage. These models might be used to assist the rice farmers in making better decisions. Two Bayesian marketing decision models were developed: (1) Single-stage model; the decisions are made only at the harvest time. The post-harvest months are the possible courses of action, the ranges of cash prices in the post-harvest months are the states of nature and conditional means of the states of nature are the payoffs which are expressed in the term of paddy prices. The prior probability distributions are subjective. Four price forecast models were developed to generate forecast prices. These forecast prices were used to determine conditional probability distributions; the posterior probability distributions were computed by using Bayes' theorem. The expected prices were determined and the month with the highest expected net returns was selected. (2) Multi-stage model; the decisions are revised every decision period by using current information. The structure of this model is the same as the first except the decision periods are added. There are two possible solutions to this model: continue storing or sell at the decision period;The 1978/79 marketing season was selected to simulate the marketing decision. The Bayesian strategies obtained were compared to the actual net returns. Close resemblance between the expected and actual net returns was found.