The overall objective of this dissertation is to evaluate and model the underlying risk factors that farmers face and their implications for crop insurance programs. Because these risk factors are not isolated from each other, the focus of this research is on investigating the dependence structure and the interactions among the agricultural production risks. Using both estimation and simulation methods, we analyze the interaction of risk in the presence of time-varying dimension, portfolio dimension and spatial correlation dimension. This study evaluates the risk that arises from changes in prices, yields shortfalls, or both. Several models are used for price and yield risk factors for corn and soybeans. For instance, yield risks can be modeled by a family of Beta distributions, whereas price shocks can be modeled by log-normal distributions. In order to develop a multivariate model that preserves a given set of marginals, a copula approach can be used to characterize the joint yield and price risk of corn and soybeans, which are usually highly correlated. The copula approach has been spurred by the recent developments in whole farm insurance, resulting in an increasing need for the modeling of multivariate risk factors and their interaction. As a part of the dissertation, various copula models are investigated for their suitability in modeling yield and price risks. A time-varying model is proposed in order to account for the yield distribution changes under technology progress. Finally, various dynamic spatial autoregressive models are explored to account for county-level dependence of crop yields.
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