Spatial sampling for agricultural data

The importance of sampling spatial units is recently acknowledged in various practical studies. In most cases, spatial units are defined across a geographical domain partitioned into a number of predetermined regularly or irregularly shaped locations. In standard sampling theory, spatial units have been traditionally represented as a mosaic of areas in which individual primary units are essentially viewed as identical members of the same population. The dependence between nearest units is an inherent feature of spatial data that should be exploited at least in the sample design. In this paper, we present a review of the main topic concerning sampling spatial units. In particular, we focus our attention to spatially distributed agricultural data, which are extremely important as a support for both policy makers and market stakeholders. Our narrative aims at raising some research questions that can be explored in the near future.