Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management.

People living in arid and semi-arid areas with highly variable rainfall often face droughts and floods that affect water availability. Surface runoff harvesting is a historical water delivery system utilized in times of water scarcity to fulfill the ever-increasing demand for water, address climate change, and prevent desertification. However, the study of snowmelt and flood water harvesting in steppe areas with cold and semi-arid climates are understudied in the international literature. This paper, combining remote sensing (RS) and a geographic information systems (GIS)-based analytical hierarchy process (AHP), provides a cost-efficient and reliable tool for assessing potential farm pond sites in the steppe region of Northern Kazakhstan. The research is based on six weighted thematic layers: hydrogeology (5%), slope (10%), drainage density (25.5%), land use/land cover (25.5%), soil (5%), and snow water equivalent (29%), which mainly influence the availability, runoff, infiltration, and accumulation of snowmelt and flood water, in order to identify potential farm pond sites in the Akkayin district (North Kazakhstan). As a result, 3.3% of the study area had a very high potential, 35.5% high, 56.5% medium, 4.6% low, and only 0.1% was recognized as the least preferred. The most suitable sites had medium drainage density, low slope, high snow water equivalent, and were located on flooded vegetation. The accuracy of our model was assessed using the existing farm pond sites from the Soviet era, which showed 82% coincidence. Furthermore, by collecting meltwater from 30% of the study area (135,000 ha), one-fourth of the cultivated land in the Akkayin district (i.e., 54,000 ha) could be transferred to deficit irrigation. This would reduce floods, stabilize farmers’ income in dry years, and open up the possibility of cultivating other highly profitable crops. Overall, the study provides evidence of the great potential of the Akkayin district in snow meltwater harvesting in farm ponds as a response to agricultural drought and spring floods.

This study compares the performances of four existing snow indices (Normalized-Difference Snow Index, Normalized-Difference Snow and Ice Index, Difference Snow Index, and Ratio Snow Index) in estimating snow cover depth at three agricultural enterprises in different soil zones, namely, the “North Kazakhstan Agricultural Experimental Station”, A.I. Barayev “Research and Production Center for Grain Farming”, and “Naidorovskoe”. From 30 January to 9 February 2022, the snow cover thickness and density were measured at 410 and 285 points, respectively, throughout the agricultural enterprise territories. It was found that: (1) snow-covered territories were effectively classified using all spectral indices except both combinations of RSI; (2) the snow cover fraction maps generated from DSI most accurately classified the non-snow areas as forest plantations, settlements, and strongly blown uplands; (3) the maps generated from DSI-2 presented a clear pattern of objects in all three study areas; (4) the liquid water in snowpacks is available in excess for possible reservation and rational use in agriculture during the dry season. At the “North Kazakhstan AES”, A.I. Barayev “Research and Production Center for Grain Farming”, and “Naidorovskoe”, the RMSE varied from 5.62 (DSI-2) to 6.85 (NDSII-2), from 3.46 (DSI-2) to 4.86 (RSI-1), and from 2.86 (DSI-2) to 3.53 (NDSII-1), respectively. The DSI-2-based snow depths best matched the ground truth, with correlations of 0.78, 0.69, and 0.80, respectively.

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.

Cropping systems and agronomic practices play a critical role in regulating soil organic matter dynamics and carbon dioxide (CO₂) emissions, which are key components of the global carbon cycle and climate change mitigation. However, the combined effects of tillage practices and seasonal climatic variability on CO₂ fluxes in chernozem soils (chernozems, WRB classification; highly fertile, humus-rich soils typical of steppe regions) of Northern Kazakhstan remain insufficiently understood. The aim of this study was to quantify soil CO₂ emissions under conventional tillage, no-till, and bare fallow systems during spring wheat cultivation on ordinary chernozems. Field experiments were conducted between 2023 and 2025 in the Kostanay Region (Kazakhstan). Soil CO₂ fluxes were measured using a chamber-based method, while soil temperature, moisture, and microbial community structure were monitored simultaneously. The results revealed pronounced seasonal and interannual variability in CO₂ emissions, ranging from 2 to 27 g CO₂·m⁻²·day⁻¹. Conventional tillage resulted in higher peak emissions due to increased soil aeration and accelerated organic matter mineralization, whereas no-till systems exhibited a more stable seasonal pattern and lower temperature sensitivity of soil respiration (Q₁₀ = 2.40 for no-till and 3.25 for conventional tillage). The application of machine learning techniques (Random Forest) significantly improved the prediction accuracy of CO₂ fluxes (R² = 0.67; RMSE = 3.37 g CO₂·m⁻²·day⁻¹) compared to linear models. These findings provide a scientific basis for the development of climate-smart agricultural practices aimed at improving carbon management in semi-arid steppe agroecosystems.