(1.) Yield potential and tillage depth, two example VRT-applications of PF in Holstein. Computer algorithms of Variable Rate Technology (VRT) demand for sets of management, crop and soil parameters. Often the latter are – due to a heavy-going access and high local variabilities of the soil resource – a minimum factor in PF. By two VRT applications, the drilling rate and tillage depth, the algorithmic demands, data acquisition and integrated evaluation of specific soil parameters will be summarized (fig. application&parameter sets). Related to agronomic rules and standards, the Seeding Rate SR depends on the Target Yields TY of cereal`s field means and the Organic Matter of Ap horizons (SR=f(OMp,TY)). Another algorithm varies the Tiller Depth TD by the Clay content, OMp, and a soil-reliefform related Hydromorphy Factor (TD=f(Clay,Omp,HF)). The latter depends on climate, too. Therefore, a mean temperature of 8 degree C. and annual precipitation of 750mm are boundary conditions for the 1400 ha cashcrop example farm on hilly, young morainic soilscapes in Holstein, North Germany. Mean field yields (Y) of the standard crop rotation winter wheat, barley, oil-seed rape W-B-R are YW=9.2, YB=7.5, YR=4.2 Mg/ha (fig.farmloc.+yield map). (2.) Soil resource access 1: Applying the Extended Causal Chain of Pedology (ECCP). Jenny, and his pre- and post-pedologists, have evolved the ECCP: the soil factors climate, organisms, relief, parent material and humans (cl-o-r-p-t+h) act by processes on morphologic soil attributes which react by pedofunctions on the feedback components of the soil-ecosystem (fig.SES+ECCP). The ECCP is the pedo-logical and paradigmatic basis for soil inventories by experts, but it faces problems when confronted with the specific human-shaped conditions of the study soilscapes. (a) r-factor: the (relictic) hydromorphy is related to landforms, but changed by drainage ameliorations; (b) h-factor: land clearing, water and tillage erosion, and fertilizing have changed the landforms and soils (erosion, colluviation etc.); (c) p-factor: a non-relief-dependant, difficult-to-detect petrovariance modifies texture substrates and fertilities of soils, very locally (figs. multi-profile, catena&aerial images). (3.) Soil resource access 2: Landscape Geo-Information (GIS) and on-the-go soil sensors. Each VRT-application demands to fill-up (measure, predict) a set of data for each pedocell, the smallest steerable soil unit of PF (~10*10m; fig.). Results from variogram studies in Germany (poster Herbst&Lamp) emphasize that soil inventories for PF can not rely on augering only, but must collect and use dense, low-cost landscape and/or on-the-tramline-go soil sensor data, efficiently. For all fields of the farm, GIS map layers of old soil ratings, elevation incl. derived relief parameters, drainage plans and annual yield maps since 1999 (poster Reimer&Lamp) are stored in the Soil Information System (SIS) of the farm. Better than airborne images, a ground-based hyperspectral sensor is able to map the OMp of fields (poster Reimer&Lamp). Texture substrates of profiles can be sensed by bulked soil Electrical Conductivities (EC) of the inductive EM38-sonde. EC values correlate - higher than German soil ratings - with the data of yield maps (poster Herbst&Lamp). As a new, multi-depth sensor of electrical soil conductivities (ECi, figs.), the spike-wheel-equipped “Pluripol” is presented which predicts clay contents in four soil layers as basis for several applications. (4.) A challenging future task: Three-D Rule-based Continuous Soil modelling (TRCS). Beyond special uses in PF, the data stock of the farm-SIS may flow into TRCS (Ameskamp, figs.). Based on fuzzificated landscape GIS data, ECCP rules and “fuzzy profiles”, the system allows soil experts to represent the soil resource space, appropriately in 3D.