Creating soil texture maps for precision liming using electrical resistivity and gamma ray mapping

Predicting the Lime Demand of Arable Soils from pH Value, Soil Texture and Soil Organic Matter Content

“…However, Stocker and Walthert [29] reported that solely 68%, 31% and 56% of clay, silt and sand variability, respectively, could be explained by the finger texturing method (predicted by sieving and sedimentation analyses). They took more than 11,000 soil samples from approximately 90 Swiss forest sites from six depth levels down to 1.5 m. This rather imprecise soil texture determination can lead to unrealistic and severe differences in the lime application recommendations of management units [19]. ii.…”

“…To provide reliable soil parameters for lime management at a higher spatial resolution and low costs, on-the-go soil sensor systems, such as the Geophilus [31] and the Veris pH manager [32], are appropriate solutions. Meyer et al [19] used the mentioned sensor systems in combination with targeted soil samples that were taken for sensor calibrations by analyzing the soil pH, SOM and soil texture with standardized laboratory methods. Finally, they produced 2 × 2 m maps of the actual soil acidity (pH), SOM and soil texture (clay, silt, and sand).…”

“…Farmers can use these tables without calculations and without special expert knowledge to assess the lime demand for their fields. Over the last 40 years, the VDLUFA look-up table system has served the agricultural needs of lime fertilization very well but has to be considered very rough, especially in relation to new results of soil texture mapping [17][18][19]. As was shown in these studies, soil data can now be determined very accurately and with a high degree of spatial accuracy using on-the-go soil sensors and digital soil mapping techniques.…”

“…However, when using the conventional VDLUFA look-up table system with these new input data for lime fertilization, much of the input information from the new soil texture and the SOM mapping systems is lost because the classes of this system are rather coarse. Meyer et al [19] reported a data acquisition strategy to generate digital soil texture and SOM maps in a precision farming context, combining spatially high-resolution soil sensor data and point reference samples for soil texture and SOC. Nevertheless, this information needs to be implemented in a finer lime recommendation system with a continuous scale, rather than a coarse class-based system (see Section 2.1).…”

Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data

“…Fig. 8 Geophilus mapped soil texture classes (derived from the German KA5 soil texture classification (Eckelmann et al 2005)) Ss pure sand, St2 slightly loamy sand, St3 medium clayey sand, Sl4 highly sandy loam, Ls4 highly sandy loam, Lts clayey sandy loam (a), the MSP-mapped pH values (b) and SOM content (c) and the lime supply level at 2 × 2 m resolution In other studies, Boenecke et al (2018) and Meyer et al (2019) used data from the Geophilus system to successfully generate predictive soil texture maps of the clay, silt and sand fractions of the topsoil for practical purposes. Meyer et al (2019) achieved the best prediction results by deriving the soil texture of the topsoil using the gamma mapping results and by calculating the dimensionless relationship between the gamma and electrical resistivity mapping results.…”