Fertile soils are one of the most important resources on earth. Sustainable agriculture should use this resource in such a way that the present and future human needs for food or other agricultural goods are warranted whereas the quality of the environment and the natural resources remain preserved. Plant production needs resources of different origins and most of them are maintained according to certain soil parameters. But as soils are neither static, nor homogenous in space and time, the standard approach of uniform application rates always results in a side by side of over and under supply. Precision Agriculture aims at addressing this spatial variability but for this task physico‐chemical images [Digital Agro Resource Maps (DARM)] of the land are required. Efforts in acquiring these maps are actually one of the major bottlenecks for the exploitation of Precision Agriculture at the farm level. Most recent developments in strategies for reducing sampling efforts without loosing information are in “self surveying”;. This is a method by which basic spatial information of soil texture, organic matter content and geomorphology for instance are gathered just by human skills and “directed”;, or, “smart”; sampling which uses already existing spatial information of a site to guide sampling to representative plots. Optical sensors for major important soil information like soil texture and plant available nutrients are far away from being realized. Already existing approaches for on‐the‐go measurements of soil features are still soil invasive and therefore slow and ineffective as far as human labor is concerned. However, these methods might benefit a lot from developments in robot techniques. Remote sensing is a promising method for gathering spatial information of soils, but will always need a certain amount of work on the ground to allow interpretation and calibration of images. In so far it fits well with the strategy of “directed”; sampling. Temporal availability and visibility are major problems for remote sensing which might be solved by new technological approaches like unmanned air vehicles (UAV) or stationary “surf‐eyes”;. New approaches are also required for the interpretation of spatially distributed soil and plant analysis data and how fertilizer recommendations are derived to be from this information. It is obvious from the first experiences with Precision Agriculture under practical farming conditions that the standard approach of fertilizer recommendation does not provide the best basis in the sense of sustainable farming for a successful implementation of Precision Agriculture.