The Utility of Less-Common Statistical Methods for Analyzing Agricultural Systems: Focus on Kernel Density Estimation, Copula Modeling and Extreme Value Theory

A variety of statistical methods have been developed for multivariate analysis of agricultural systems. Some statistical methods are rarely used to study these systems, although they can contribute to issues such as identifying atypical farms, modeling relations among variables and describing farms with common characteristics. To address these issues, we reviewed studies that applied kernel density estimation (KDE), copula modeling and extreme value theory (EVT) to French dairy farm data. KDE helped identify joint value ranges of forage production and milk production or greenhouse gas emissions that most farms in specific French region were likely to have. Copula modeling formalized the shapes of relations among farm characteristics, while EVT distinguished production strategies and management practices of farms that produced extreme amounts of forage. The present study reviews studies that applied these three methods, recommends when to use the latter and discusses their contribution to improving the understanding of dairy farms.