This research aims to evaluate the dynamics of soil loss through soil erosion by water in agricultural lands in steppe areas using a modification of the RUSLE2 model from the 1980s to the 2010s. The calculation was performed using a raster model of data that included a model of the slope angle, slope length, soil erodibility, rainfall and snowmelt erosivity factors, types of land use, and cover management factor. All data were taken from open sources. The average soil erosion in the territory studied amounted to 1.48 t ha⁻¹ year⁻¹ in the 1980s and 1.72 t ha⁻¹ year⁻¹ in the 2010s. The discrepancy with other studies was 12% for the level of the 1980s and 2–7% for the level of the 2010s. The main factor leading to an increase in soil loss was soil erodibility due to the loss of soil organic matter, which affected about 52% of the studied lands. The increase in the amount of soil loss occurred against a background of compensating processes: reduction in precipitation and climate change (getting drier), as well as the overgrowth of agricultural lands with natural steppe vegetation. Modified model RUSLE2 has shown good results correlated with other studies for the research area.

Testate amoebae are unicellular eukaryotic organisms covered with an external skeleton called a shell. They are an important component of many terrestrial ecosystems, especially peatlands, where they can be preserved in peat deposits and used as a proxy of surface wetness in paleoecological reconstructions. Here, we represent a database from a vast but poorly studied region of the Western Siberia Lowland containing information on TA occurrences in relation to substrate moisture and WTD. The dataset includes 88 species from 32 genera, with 2181 incidences and 21,562 counted individuals. All samples were collected in oligotrophic peatlands and prepared using the method of wet sieving with a subsequent sedimentation of aqueous suspensions. This database contributes to the understanding of the distribution of testate amoebae and can be further used in large-scale investigations.

This study is aimed at assessing soil and land degradation of agricultural farmlands of Kalachev district, Volgograd region. To identify the level of land degradation, soil properties of samples taken in summer 2019 in that area were compared with average values for the same area obtained by YUZHGIPROZEM in 1982 [1]. The calculations to evaluate land degradation were based on "Methodology for determine the extent of damage of soil and land degradation" [2]. The conducted chemical and physical analysis of sampled soils demonstrated the highest level of land degradation in that area. The soils were proven to have an increase in exchangeable sodium percentage (ESP); 52% of the agricultural lands of Donskoe demonstrated these values. The assessment of the other land degradation parameters such as density, mobile phosphorus, exchange potassium, heavy metals concentration and arsenic content, toxic salts and pH (H₂O) revealed lower level of degradation and insignificant occurrence in the farmland territory.

The article presents the results of testing a comprehensive scheme of ecological and economic assessment of land degradation in various regions of the Euro

The purpose of this study was to compare two methods for assessing damage from the degradation of soils and agricultural lands located in the Kalachevsky district of the Volgograd Region. The amount of damage from soil and land degradation was calculated by the “Methodology for determining the amount of damage from soil and land degradation” (1994) and the cost of restoration (reclamation) of degraded soils was determined using the SmetaWizard estimate program (version 4.1.3). It was established that the most significant contribution to the total amount of damage from soil and land degradation (175 573 000 rubles or 64 955 rubles/ha) was made by the increase in the share of exchangeable sodium in soils (147 424 528 rubles or 54 541 rubles/ha), which is due to the development of the process of alkalinization in the soils of the studied agricultural sector in the region. This degradation process, which is harmful to the agricultural use of soils and lands, was found in areas that comprise 52% of the total farm area. This indicator’s significant damage is also associated with extended periods of restoration (reclamation) work, 4 years. The cost of reclamation work amounted to 11 647 353 rubles or 4309 rub./ha. The maximum contribution (78%) to this was made by damage from soil organic matter losses (the amount of damage was 9 131 525 rubles or 3378 rubles/ha). The significant difference in the total values of the environmental and economic assessment of soil degradation and the land of the Donskoye agricultural enterprise in the Kalachevsky district, calculated in various ways, is associated with the methodological features of this assessment: the assessment of damage from degradation by the “Methodology …” (1994) is a stricter approach than estimating the cost of reclamation (restoration) measures. Thus, soils may be degraded compared with a standard (considered when assessing damage), but this degradation is “insufficient” to justify reclamation.

Spatial information on soil clay content addresses critical needs in precision agriculture and soil health assessment worldwide. This study utilizes a mapping workflow for topsoil clay (0–20 cm) across croplands in southern Russia using Sentinel-2 bare soil mosaics and regional covariates representing soil-forming factors. We generated 26 multi-temporal mosaics via per-pixel and per-date approaches and then tested five scenarios with regional covariates in combination with three variable selection techniques: variance inflation factor (VIF), recursive feature elimination (RFE), and modified greedy feature selection (MGFS). We found that among 26 temporal mosaics, the best single mosaic (scenario 1) explained 25% of the clay variation (RMSE = 10.03%, R² = 0.25, RPD = 1.16). Using only regional covariates selected after VIF and RFE approaches (scenario 2) yielded comparable results (RMSE = 9.77%, R² = 0.27, RPD = 1.18). Combination of the best bare soil mosaic and all regional covariates without the variable selection method (scenario 3) improved the predictions (RMSE = 9.45%, R² = 0.33, RPD = 1.24), and with the VIF/RFE application (scenario 4), the model showed slightly worse accuracy (RMSE = 9.65%, R² = 0.31, RPD = 1.21). MGFS implementation (scenario № 5) boosted the model performance and resulted in the best predictions (RMSE = 8.73%, R² = 0.42, RPD = 1.34). NIR bands from the bare soil mosaic, and terrain attributes with Landsat and MODIS variables from the regional covariate set were the key variables. We demonstrate that combining multi-temporal Sentinel-2 mosaics with regional covariates, when paired with an appropriate selection strategy, yields superior clay predictions.