The paper presents an innovative approach to cropping scheme classification based on fad n data with two main goals. First, the identification at the regional level (NUTS 2) of land use patterns common to similar farms defined ‘group cropping scheme’. Second, the farm-level construction of farm cropping schemes, which expand the observed crop mix and identify suitable variation ranges considering the farm production context. The schemes are based on the observed behaviour of homogeneous farms and capture their common structural characteristics regarding land use.
The schemes can be used at the territorial scale to analyse landuse trends and patterns over time. At the farm level, the method is designed to analyse short-term adaptations and is suitable to be used, together with other data, in mathematical programming models to run policy analysis exercises. At this latter scale, crop substitution within a scheme allows the set of eligible crops to be expanded while remaining linked to the observed behaviour on a spatial basis.
The paper applies the methodology to identify and quantify the cropping schemes using FADN data on Italian farms specialising in annual field crops. An algorithm implemented in gams automates the process. Results confirm the validity of the method and open a field of research for future applications.