ISSN 0137-0944
eISSN 2949-6144
En Ru
ISSN 0137-0944
eISSN 2949-6144
Prediction of the bulk density distribution along the soil profile using the data on the resistance to penetration for the retisols of the Educational and Experimental Soil and Ecological Center of Lomonosov Moscow State University “Chashnikovo”

Prediction of the bulk density distribution along the soil profile using the data on the resistance to penetration for the retisols of the Educational and Experimental Soil and Ecological Center of Lomonosov Moscow State University “Chashnikovo”

Abstract

The dependence of soil bulk density on penetration resistance, moisture content, soil organic carbon content and particle size distribution was investigated for Retisols of the Educational and Experimental Soil and Ecological Center of Lomonosov Moscow State University “Chashnikovo”. Penetration resistance was measured with a penetrologger at six key sites with different land-use types, in areas in front of three soil profiles. Soil samples for bulk density were taken from the same profiles using 100 cm³ cutting rings. The samples were subsequently analyzed for moisture content, organic carbon (by the Tyurin method), and particle-size distribution using a laser diffraction analyzer. Within the study, 24 regression models for predicting soil bulk density were developed and analyzed. The quality of these models varied; the coefficients of determination (R²) ranged from 0.23 to 0.89 and the root mean square error (RMSE) from 0.07 to 0.18 g×cm–3. Based on the modelling results, the greatest contribution to predicting bulk density was made by organic carbon content, depth and penetration resistance, in descending order. Adding specific particle-size fractions to the model is more informative than using the principal components of particle-size fractions as predictors. The use of specific particle-size fractions is more informative than using principal components as predictors. In many models, field soil moisture proved to be an insignificant predictor for bulk density. Given the labour-intensive nature of determining a full set of predictors, models with a reduced set of predictors were proposed: 1) depth and penetration resistance; 2) organic carbon content and penetration resistance; and 3) organic carbon content alone. The data from the first of these models are automatically collected during the use of the penetrometer, making it convenient for monitoring surveys. The method has important limitations relating to the particle size distribution of soils and how this is determined. For example, it is impossible to take a top-down measurement of penetration resistance with a penetrologger in the presence of large boulders in the soil, as the measuring tool hits them and cannot be pushed into the underlying horizons. The use of regression equations in which particle-size distribution is determined by a method other than laser diffraction is incorrect. The choice of which particle-size fraction to use as a predictor should be based on a correlation analysis for each territory in question.


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Received: 04/04/2025

Accepted: 12/01/2025

Accepted date: 02/13/2026

Keywords: monitoring; pedotransfer functions; organic carbon stocks

DOI: 10.55959/M SU0137-0944-17-2026-81-1-118-128

Available in the on-line version with: 12.02.2026

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