Advancing Soil Texture Estimation Across Agricultural Soil Types in Austria Using Portable Gamma-Ray Spectrometry

Soil texture plays a fundamental role in influencing water retention, nutrient dynamics, erosion susceptibility, and carbon sequestration, making it essential for sustainable agricultural practices. Accurate monitoring and mapping of soil texture components, such as clay, silt, and sand, are crucial for effective soil and water management. This study explores the potential of combining radionuclide monitoring data and Gamma-Ray Spectrometry (GRS) with quantitative modelling techniques for soil texture estimation, focusing on transferring a predictive model developed in one location to another.

The research builds on work conducted in 2023 at the Hydrological Open-Air Laboratory (HOAL) in Petzenkirchen, Lower Austria, to assess the transferability of a predictive model for soil texture to the experimental farm of the University of Natural Resources and Life Sciences (BOKU) in Raasdorf, near Vienna, Austria. Soil sampling campaigns at Petzenkirchen (2023) and Raasdorf (2024) provided input data for the model. Soil texture was analyzed using the PARIO system, which applies the Integral Suspension Pressure (ISP) method, based on Stokes' law, to determine particle size distributions. Portable gamma-ray spectrometry (GRS) was used to measure activity concentrations of radionuclides (⁴⁰K, ²³²Th, and ²³⁸U) at multiple locations in each field, serving as predictors for soil texture components through a Python-based statistical model initially developed in Petzenkirchen.

The integration of GRS data with quantitative modelling revealed critical relationships between radionuclide concentrations and soil texture components. Moderate positive correlations of ²³²Th (0.59) and ²³⁸U (0.72) with silt, and moderate negative correlations with clay (-0.62 and -0.74), indicate that radionuclides preferentially associate with silt-sized particles due to their larger surface area and mineralogical properties. Additionally, a strong inverse relationship between clay and silt (-0.92) reflects their complementary distribution within the soil matrix. Strong correlations were observed between ²³⁸U and both silt (R² = 0.8, p = 4.1 × 10⁻⁵) and clay (R² = 0.78, p = 5.6 × 10⁻⁵) demonstrating its predictive potential. These strong associations formed the basis for selecting ²³⁸U as a key predictor for soil texture estimation in Raasdorf.

The predictive models from Petzenkirchen were applied to estimate silt and clay content in Raasdorf using ²³⁸U as a predictor. The model performed well for silt, achieving a mean error of 10% and an RMSE of 0.07 g, indicating strong agreement between observed and predicted values. However, predictions for clay exhibited greater variability, with a mean error of 25% and an RMSE of 0.28 g. This discrepancy highlights the need for localized calibration to address site-specific differences in soil mineralogy and radionuclide binding affinities between the two fields.

This research demonstrates how integrating radionuclide monitoring with quantitative soil texture modeling provides a scalable and cost-effective approach for digital soil mapping in agricultural landscapes. Future work will refine the model by leveraging advanced GRS data analysis, such as radionuclide ratios (e.g., ²³⁸U/²³²Th) and spatial variability, to improve clay predictions and assess uncertainties in soil property estimations. These efforts aim to enhance the applicability of digital soil mapping for precision agriculture and sustainable land management.