The U.S. crop insurance program has major policy implications in terms of resource allocations, with government subsidies playing a major role. Efficient implementation of crop revenue insurance contracts requires accurate measures of risk for both crop prices and yields. In addition, rating methods are to consider the natural hedge between prices and yields. Empirical evidence shows that crop prices tend to be positively skewed with fat tails. This paper analysis is two-fold. It first studies crop prices using a Burr distribution, with parameters that capture skewness and kurtosis (fat tails), providing a better fit than current normal or log-normal distributions being used. It then uses a copula method to measure the correlation between crop prices and yields, for the study of crop revenue insurance. Results indicate a smaller probability of payout than present methods being used, having direct implications on the design and rating of crop and revenue insurance contracts.