Recent advances in on-the-go soil sensing, terrain modelling and yield mapping have made available large quantities of information about the within-field variability of soil and crop properties. But the selection of the key variables for an identification of management zones, required for precision agriculture, is not straightforward. To investigate a procedure for this selection, an 8 ha agricultural field in the Loess belt of Belgium was considered for this study. The available information consisted of. (i) top- and subsoil samples taken at I 10 locations, on which soil properties: textural fractions, organic carbon (OC), CaCO3 and pH were analysed, (ii) soil apparent electrical conductivity (ECa) obtained through an electromagnetic induction based sensor, and (iii) wetness index, stream power index and steepest slope angle derived from a detailed digital elevation model (DEM). A principal component analysis, involving 12 soil and topographic properties and two ECa variables, identified three components explaining 67.4% of the total variability. These three components were best represented by pH, ECa that strongly associated with texture and OC. However, OC was closely related to some more readily obtainable topographic properties, and therefore elevation was preferred. A fuzzy k-means classification of these three variables produced four potential management classes. Three-year average standardized yield maps of grain and straw showed productivity differences across these classes, but mainly linked to their landscape position. In the loess area with complex soil-landscape interactions pH, ECa and elevation can be considered as key properties to delineate potential management classes. (C) 2007 Elsevier B.V. All rights reserved.