An Empirically-Grounded Comparison of the Johnson System versus the Beta as Crop Yield Distribution Models

Previous research established that the expanded Johnson system can accommodate any theoretically possible mean-variance-skewness-kurtosis combination. Therefore, it has been hypothesized that this system can provide for a reasonably accurate modeling approximation of any probability distribution that might be encountered in practice. In order to test that hypothesis, this manuscript develops a more flexible expanded form of the Beta distribution which, in its original form, has been widely used to model and simulate crop yields for risk analysis. Empirically grounded evaluations suggest that the Johnson system can model a variety of typical yield data-generating processes that are based on the Beta distribution much more precisely than the Beta can model representative crop yield data simulated from the Johnson system. The accuracy with which the Johnson system approximates the Beta supports the previously stated hypothesis.