Soil bulk density (ρb) is required to estimate, evaluate, and calculate many physical soil properties and processes and is essential to convert data from weight‐based to volume‐ and area‐related data. One of the dominating factors changing ρb is the soil's organic matter (SOM) concentration that alters the soil's compressibility; ρb is an important soil structure attribute. Currently, no parameter for characterizing soil compactness giving directly comparable values for all soils is available. Therefore, our aim was to develop a general approach to calculate the effect of SOM concentration on ρb that would be universally valid for soils different in their genesis, compaction, and type of land use. To describe the effect of SOM on ρb mathematically, we used a nonlinear regression model that was parameterized and validated using published data from experiments where SOM concentration was the main ρb–affecting factor (long‐term fertilization and proctor experiments, wetlands, reclaimed soils, and volcanic soils). To obtain a standardized parameter describing the present compaction status of a site, we introduced the standardized bulk density sρb Mathematically, sρb is the intercept parameter of the used nonlinear regression model, and ranged between 0.7 and 2.1 Mg m−3 and was very simple to estimate. Another distinct advantage of this novel concept is that only one representative pair of ρb and SOM has to be known to calculate sρb as well as the bulk densities corresponding to other SOM concentrations measured on the site. This concept might also be helpful for identifying similar universal approaches to standardize the effect of other ρb affecting parameters (e.g., texture, soil depth, tillage regime), however, reassessed from the SOM effect.

Precision farming overcomes the paradigm of uniform field treatment by site‐specific data acquisition and treatment to cope with within‐field variability. Precision farming heavily relies on spatially dense information about soil and crop status. While it is often difficult and expensive to obtain precise soil information by traditional soil sampling and laboratory analysis some geophysical methods offer means to obtain subsidiary data in an efficient way. In particular, geoelectrical soil mapping has become widely accepted in precision farming. At present it is the most successful geophysical method providing the spatial distribution of relevant agronomic information that enables us to determine management zones for precision farming. Much work has been done to test the applicability of existing geoelectrical methods and to develop measurement systems applicable in the context of precision farming. Therefore, the aim of this paper was to introduce the basic ideas of precision farming, to discuss current precision farming applied geoelectrical methods and instruments and to give an overview about our corresponding activities during recent years. Different experiments were performed both in the laboratory and in the field to estimate first, electrical conductivity affecting factors, second, relationships between direct push and surface measurements, third, the seasonal stability of electrical conductivity patterns and fourth, the relationship between plant yield and electrical conductivity. From the results of these experiments, we concluded that soil texture is a very dominant factor in electrical conductivity mapping. Soil moisture affects both the level and the dynamic range of electrical conductivity readings. Nevertheless, electrical conductivity measurements can be principally performed independent of season. However, electrical conductivity field mapping does not produce reliable maps of spatial particle size distribution of soils, e.g., necessary to generate input parameters for water and nutrient transport models. The missing step to achieve this aim may be to develop multi‐sensor systems that allow adjusting the electrical conductivity measurement from the influence of different soil water contents.

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