Mechanical Soil Database—Part I: Impact of Bulk Density and Organic Matter on Precompression Stress and Consequences for Saturated Hydraulic Conductivity

Abstract

The mechanical strength of agricultural soils depends on many soil properties and functions. The database, “soil strength and consequences for sustainable land use and soil management SOILMECHDAT-Kiel”, originates from the “Horn Research Group” includes analyses of undisturbed soil samples taken from more than 460 profiles in and is developed in collaboration with BONARES, a funding initiative of the German Federal Ministry for Education and Research that focuses on the sustainable use of soils. For over 40 years, over 42 different authors recorded 59 physical and 29 chemical parameters for complete soil profiles. In order to the aim of the initial analyses of this data is to determine the influence of bulk density (BD) organic matter (OM) and time (year) on precompression stress (Pc) and saturated hydraulic conductivity (ks) as a function of Pc. Three main textural groups sand, loam, and silt for both topsoils and subsoils (SS) were studied. In loamy and silty subsoils BD and OM are not related to Pc (R2 = 0.17 and R2 = 0.25). OM and bulk density are more related to Pc in sandy soils (R2 0.55–0.59). The link between ks and Pc showed that sandy soils have a significantly higher Pc (>150 kPa) and conductivities did not change much. In loamy soils, with a Pc > 90 kPa, 50% of the ks fell below the critical value of 10 cm d−1. For silty soils, at a Pc of 60 kPa, 50% of the data fall below the critical value of ks. These findings suggest that the stability of loamy and silty soils not only depends on OM and BD, but requires further data to explain the variation in the measurements. With respect to ks, the results show that fertile silty soils are more sensitive than formerly defined.

Effective stress and shear strength parameters of unsaturated soils as affected by compaction and subsequent shearing

“…These patterns can be explained by the different internal processes, which are related mainly to the stress type impacts on the pore continuity and dependency on the hydraulic conductivity (Fredlund & Rahardjo, 1993;Huang et al, 2022;Zhai & Horn, 2019a, 2019b. Compared to compaction, shearing had more obvious effects on the changes of pore water pressure, indicating that wheeling induced shear destroyed the soil pore system more intensely, which has been observed by Dörner and Horn (2006), Schroeder et al (2022), andJanssen et al (2006).…”

Machine learning applications in vadose zone hydrology: A review

“…For instance, as seen in Table 3, the worldwide ML PTF applications are evaluated from field sensor data, physical model simulations, and different regional or continental datasets. Generally, various datasets—USDA‐ARS experiment watershed network data (Goodrich et al., 2021), international SM network (Dorigo et al., 2021), mechanical soil database (Schroeder et al., 2022), ROSETTA (Zhang & Schaap, 2017), and UNSODA (Leij, 1996)—have been collected and produced to characterize soil properties but will need to be further processed or revised by researchers for their own ML application configurations. This data‐wrangling preparation often yields case‐specific model performance conclusion without a generalizable statement for comparison purposes to mark its progress comprehensively.…”