Managing soil variability is an integral aspect of precision agriculture (PA). Existing soil databases, however, are found to match few of the requirements for PA. The nature of these requirements and their implications for soil information need to be further explored. Ongoing developments towards a decision support system (DSS) for PA in the Netherlands have shed some light on this issue. Two soil related DSS-components are presented: (i) the construction of a soil database at the farm level and (ii) the delineation of soil functional units at the field level. Developed methods were tested in a case study for two arable fields located on Dutch marine clay soils. Basic soil data were collected in a 1:5000 soil survey and supplemented with secondary data derived through pedotransfer functions. Soil characterization focused on functional properties describing soil-specific characteristics in terms of water regimes and nutrient dynamics. Four properties were considered: (i) water stress, (ii) N-stress, (iii) N-leaching and (iv) residual N-content at harvest. These were quantified for individual soil profiles using a mechanistic–deterministic simulation model. Sensitivity to water stress was evaluated for a dry year (1989), other properties were quantified for a wet year (1987). Based on functional similarity, the soil profiles were grouped into functional classes using a fuzzy c-means classifier. Standard interpolation techniques and a boundary detection algorithm subsequently identified soil functional units in each field. Analysis of variance revealed that >65% of the spatial variation could thus be accounted for. This confirmed that (i) the proposed classification procedure was efficient and (ii) soil functional units are suitable entities to be used as management units for PA.