A crop variety trial typically yields a large number of data. A datamodel is needed to describe the structure of these data and to explain the meaning of the data. Without using an explicit datamodel it is impossible to interpret the data correctly. In addition, a datamodel offers the opportunity for discussion about the experimental methods and goals, to be applied in new trials. The problem however with data models is to find the right trade-off between standardization and flexibility. In this paper we describe the construction of new, application specific layers on top of any standard data model language. Such a new layer consists of carefully chosen data model templates. By using data model templates the freedom of the datamodel designer is kept, yet data exchange and interpretation can be further supported. In addition we describe two alternative languages and their mutual relation, SFD and EXPRESS. We compare these languages and show that SFD can indeed be translated to EXPRESS descriptions.