The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils’ properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R²) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R² of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.

Restoring forests in areas where they once stood is an important step towards increasing carbon sequestration. However, reforestation requires an increase in current levels of seedling production in the tree nurseries. The purpose of this work was to study the effectiveness of preparations based on bacteria and humic substances (HSs) to stimulate the growth of tree seedlings in a nursery. Two selected strains of Pseudomonas and humic substances were used to treat pine and poplar plants. The treatment of seedlings was carried out during their transplantation and after it, and the effects of treatment on shoot elongation, shoot and root mass were evaluated. Treatments with both bacterial strains enhanced the growth of poplar and pine shoots and roots, which was explained by their ability to synthesize auxins. P. protegens DA1.2 proved to be more effective than P. sp. 4CH. The treatment of plants with humic substances increased the nitrogen balance index and the content of chlorophyll in the leaves of poplar seedlings, which can elevate carbon storage due to the higher rate of photosynthesis. In addition, the combination of humic substances with P. protegens DA1.2 increased shoot biomass accumulation in newly transplanted pine plants, which indicates the possibility of using this combination in plant transplantation. The increase in length and weight of shoots and roots serves as an indicator of the improvement in the quality of planting material, which is necessary for successful reforestation to increase capture of carbon dioxide.

This paper presents the evaluation and digital mapping of forest growing stock volume (GSV) and carbon stocks of the natural park Kandry-Kul (Republic of Bashkortostan, Russia). The field observations were conducted in the process of forest surveys in 1994 and 2018. According to these materials, we identified the predominant tree species in each studied plot. Then, we produced the digital maps of GSV and carbon stocks for each study year and calculated the annual increment. The results showed that birch (Betula pendula) and pine (Pinus sylvestris) were the dominant species in the studied park. The comparison of the two maps showed that the maximum annual GSV between 1994 and 2018 occurred in areas with a fairly small GSV in 1994. These areas were characterized by young trees of different species and pines of different ages, which had not yet reached the age of maturity, when the growth of trees is significantly reduced. We found that young pine crops contributed the greatest potential of carbon sequestration, with an annual GSV of 4.2 m³/ha per year. The birch trees made a minor contribution to the sequestration potential, characterized by relatively average annual growth (1.2 m³/ha per year). The change in carbon stocks for pine stands was on average 2 t/ha per year. For birch stands, the change in carbon stocks was approximately 0.5 t/ha per year, which is 30% of the average values for the forest-steppe region. Our results provide quantitative information on the carbon accumulation rate in secondary forests under conditions of intensive recreational load.

The ecotoxicological condition of soils around mining areas is most often unsatisfactory, which affects entire ecosystems and human health. This research sought to analyze the morphological, agrochemical properties, and content of heavy metals (Cd, Cu, Zn) and metalloids (As) of soils located in a floodplain. The study was conducted within the city of Sibay (Republic of Bashkortostan, Russia). The soil samples were collected from the floodplains of the rivers Karagayly and Khudolaz. According to morphological studies, the soil cover was represented by the Lithic Leptosols, Stagnic Phaeozems, and Fluvisols. The results showed that the soils were characterized by high values of organic matter, potassium, and low levels of phosphorus. Soils that were located away from the city in the Karagayly River were not contaminated. However, the floodplain areas pertaining to the urban district and located near the quarries were characterized by severe anthropogenic soil pollution, disrupted integrity of the soil cover, decreased vegetation, and accumulating labile forms of heavy metals and metalloids. The highest degree of pollution was observed in the floodplain soil of the river Khudolaz where all elements exceeded the maximum permissible concentration (MPC) level. Soils in the floodplain of the Karagayly river were marked by an increased degree of contamination of Zn: exceeding MPC by 1.6 times. With the trend toward an arid climate, the ecotoxicological condition of floodplain soils is an important challenge.

The process of climate warming significantly affects agroclimatic resources and agricultural production. We study the agroclimatic resources and their variability on the territory of the Republic of Bashkortostan (Russia). The Bashkortostan has a high agricultural potential and holds a leading position in the country in the production of grain crops, potatoes, milk, and honey. Currently, no detailed studies have been conducted for this area to assess the effects of global climate change on agro-climatic resources. World experience shows such research becomes strategically important for regions with powerful agricultural production. We used the sums of average daily air temperatures above 0 and 10 °C, the G.T. Selyaninov hydrothermal coefficient, and the Ped aridity (humidification) index as agroclimatic indicators. We used data of long-term meteorological observations of 30 meteorological stations for the period of 1961–2020. We revealed the long-term dynamics of the agroclimatic indicators and the spatial and temporal regularities in their distribution on the territory of Bashkortostan. There is a steady increase in the sums of average daily air temperatures above 0 and 10 °C. Against this background, aridity increases, which is especially manifested in the southern parts of the Republic of Bashkortostan. We assessed the impact of agroclimatic indicators on the main types of agricultural crops in the republic. We revealed that the greatest positive impact on the yield of oilseeds, cereals, and industrial crops is made by precipitation at the beginning (r = 0.50, r = 0.44, and r = 0.52, respectively) and in the middle of the growing season (r = 0.55, r = 0.76, and r = 0.51, respectively). Temperature and precipitation during the growing season have a complex effect on cereals. This is proven by correlations with HCS and the Ped index (r = 0.45 and r = −0.56, respectively). Aridity at the beginning of the growing season affects the yield of oilseeds and potatoes. This is confirmed by correlations with the Ped index (r = −0.49 and r = −0.52, respectively). In general, the aridity of the growing season has a significant impact on the yield of cereals (r = −0.57). Negative relationships have been found between the air temperature growing season and the yield of potatoes (r = −0.50) and cereals (r = −0.53). The results of the study were compared with data from the Copernicus Climate Change Service database. We identified climate trends under RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios. These scenarios should be taken into account when developing plans for the adaptation of agriculture in the Republic of Bashkortostan to changes in the regional climate. Maximum decrease in precipitation is established for the RCP 6.0 scenario. This can have an extremely negative impact on crop yields. This problem is especially relevant for the southern part of the Republic of Bashkortostan. The information presented in the study will allow for a more effective adaptation of the agricultural sector to current and future climate changes.