Soil degradation in arid ecosystems is a major threat to sustainable development and food security, especially under accelerating climate change. Kazakhstan, where more than 70% of agricultural land suffers from salinisation, erosion, and humus loss, offers a representative case for studying climate-driven degradation. This study quantitatively assessed the influence of air temperature, precipitation, aridity index, and extreme climatic events on soil properties in the arid regions of western Kazakhstan (Atyrau and Mangystau). The analysis integrated long-term meteorological time series (1941–2023) with field and laboratory data (1967–2024) into a harmonised dataset of 1330 records. Principal component analysis (PCA) identified four degradation gradients explaining 73.6% of total variance, while Random Forest and SHAP algorithms quantified variable importance. Mean annual temperature, frequency of arid years, and aridity index were the strongest predictors of humus, salinity, pH, and CO₂ parameters, with climate factors accounting for up to 30% of soil variability. The findings demonstrate that climatic stressors are the main drivers of soil degradation in arid zones, with climate factors explaining up to 30% of the variability in key soil properties (humus, salinity, pH, and CO₂)—a substantial proportion that underscores their dominant role relative to local geochemical and anthropogenic influences. The proposed hybrid PCA—Random Forest/SHAP framework provides a robust tool for analysing climate–soil interactions and supports the design of adaptive land-use strategies to achieve Land Degradation Neutrality (LDN) in Kazakhstan and other arid regions.

The matter of the effectiveness of applying smart technologies in agriculture of the Republic of Kazakhstan will be of interest to the academic community, regardless of territorial affiliation. The relevance of the research is in the need to inform farmers on the efficiency of such technologies and how to use them. Economic valuation, cost-benefit analysis were used to compare traditional and digital agricultural technologies in grain production. Wealthy farmers from developed countries widely use digital technology, reducing operating costs for wage labour and productive resources. In this research, the authors attempted to prove the hypothesis of significant economic and environmental efficiency in the application of precision technologies. The purpose of this paper is to evaluate the results of the application of precision farming tools from an economic standpoint, these results can contribute to the study of the efficiency of digital technologies at the global and local levels. This research displays that with application of modern digital technologies in production, under certain circumstances, a farm can increase wheat yield by at least 25-30%. Gross profit with traditional technologies in agriculture amounted to 31 US dollars per hectare, while application of digital technologies brought 54 US dollars per hectare.

This study presents a spatial assessment of agro-landscape soil contamination by phosphorite plant emissions under semi-arid conditions in the vicinity of Taraz, Kazakhstan. A total of 120 soil samples (0–20 cm) were collected from eight wind-oriented directions (N, S, W, E, NW, NE, SE, SW) at...

Solonetz and Solonetzic rangelands are widespread in arid regions of Central Kazakhstan, where pasture degradation is often difficult to assess because surface vegetation patterns do not always reflect subsurface soil constraints. This study aimed to evaluate degradation mechanisms in Solonetz pasture ecosystems of the Ulytau region by integrating field soil-profile descriptions, laboratory analyses, vegetation observations, forage productivity data and Sentinel-2A-derived MSAVI. Ten monitoring soil profiles were examined for particle-size distribution, soluble salts, ionic composition, exchangeable cations, available N, P and K, vegetation cover and forage yield. USDA textural classification, salt-distribution analysis, Pearson correlation, PCA, RDA and MSAVI-based mapping were used to link soil-profile properties with vegetation and spectral response. The results showed strong profile heterogeneity, with clay enrichment, subsurface salt accumulation, alkalinity and Na- or Mg-related exchange-complex imbalance forming several degradation pathways. Surface horizons were often weakly saline, whereas deeper layers contained stronger chemical and physical limitations. MSAVI values were low across the monitoring sites and reflected vegetation–soil surface conditions rather than salinity or sodicity directly. Combining soil-profile diagnostics with Sentinel-2A MSAVI improved the interpretation of degradation patterns and provides a practical framework for monitoring spatially heterogeneous Solonetz rangelands in arid pasture systems.

Solonetz and Solonetzic rangelands are widespread in arid regions of Central Kazakhstan, where pasture degradation is often difficult to assess because surface vegetation patterns do not always reflect subsurface soil constraints. This study aimed to evaluate degradation patterns in Solonetz pasture ecosystems of the Ulytau region by integrating field soil-profile descriptions, laboratory analyses, vegetation observations, forage productivity data and Sentinel-2A-derived MSAVI. Ten monitoring soil profiles were examined for particle-size distribution, soluble salts, ionic composition, exchangeable cations, available N, P and K, vegetation cover and forage yield. USDA textural classification, salt-distribution analysis, Pearson correlation, PCA, RDA and MSAVI-based mapping were used to link soil-profile properties with vegetation and spectral response. The first two PCA axes explained 65.5% of the total variance, while selected soil profile constrains accounted for 58% of the variation in vegetation cover, forage yield and MSAVI in the RDA analyses. The results showed strong profile heterogeneity, with clay enrichment, subsurface salt accumulation, alkalinity and Na- or Mg-related exchange–complex imbalance associated with several degradation pathways. Surface horizons were often weakly saline, whereas deeper layers contained stronger chemical and physical limitations. MSAVI values were low across the monitoring sites and reflected vegetation–soil surface conditions rather than salinity or sodicity directly. MSAVI ranged from 0.0888 to 0.2148, with a mean value of 0.1248. Combining soil-profile diagnostics with Sentinel-2A MSAVI improved the reliability of interpreting spatial degradation patterns and provides a practical framework for monitoring spatially heterogeneous Solonetz rangelands, supporting sustainable rangeland management under arid conditions.

The dynamics of organic matter, nitrogen status, and biological activity in soils in southern Kazakhstan under various land-use systems were studied. A key feature of the research is the comprehensive comparison of humus status, nitrogen state, and biological activity of virgin and arable dark Kastanozem, Gleyic Calcisol, and Haplic Calcisol, as well as identification of their correlation with signs of functional depletion of organic component. The assessment was conducted using set of agrochemical and biological methods, including determination of humus content, available nitrogen forms, C/N ratio, microbial population, and enzymatic activity. It has been determined that the highest humus content is typical for dark chestnut soils under natural vegetation, while plowing of them is accompanied by decrease in humus content due to increased mineralization processes. Gleyic Calcisol - are characterized by more stable humus state, in some cases with increased organic matter content under arable conditions. Minimum humus values were found in Haplic Calcisol, due to arid conditions and limited supply of organic residues. It is shown that arable soils are characterized by a decreased C/N ratio and increased rates of organic matter transformation. Soil biological activity is linked to mineralization processes, as confirmed by microbial population dynamics and enzymatic activity. Additional assessment using digital tools reveals signs of functional depletion of organic component in agrocenoses. The obtained results indicate the need to consider biological indicators when assessing soil conditions and developing sustainable land management systems in arid climates.