The estimation model of crop yields is a prerequisite for deriving actuarially sound insurance premiums. A major challenge in estimating crop yield models arises from the non-stationarity of the data generating process due to technological change and climate change. In this paper, we introduce an adaptive local parametric approach to deal with the non-stationarity of crop yields. An empirical application to major crops in the USA indicates that yield risks for corn and cotton are decreasing, but are increasing for winter wheat. A rating analysis suggests that the proposed model shows potential to obtain more accurate rates than commonly used methodology.