Land is a limited yet vital resource, which is fundamental for food security and national stability. Ensuring its effective use requires modern tools to support informed decision making. This study evaluates land suitability for irrigation in the Kyzylorda region of Kazakhstan, with the objective of identifying areas appropriate for crop expansion. A multidimensional approach was employed, integrating soil properties, topography, proximity to water sources, and existing land use patterns, with suitability classes defined according to the Food and Agriculture Organization’s framework. The Analytic Hierarchy Process, based on pair-wise comparisons and Saaty’s scale, was used to assign weights to each factor. The findings reveal that 30% of highly suitable land and 80% of suitable land remain uncultivated. Conversely, 10% of the current cropland is located in marginally suitable area—likely a result of historical land use decisions or the limited availability of optimal land. This research responds to the specific challenges of arid climates and water scarcity by providing a decision-support tool that promotes sustainable land use, strengthens food security, and encourages responsible land management.

Scientific labour organization is becoming relevant in the context of the rapidly changing requirements of the modern market, especially in the growing human interaction with robotic systems and artificial intelligence. The purpose of this study is to conduct a bibliometric and content analysis of scientific labour organization in agriculture, with a focus on the integration of remote sensing technologies and precision farming. The methodological basis of the work included bibliometric and content analysis of scientific articles selected from the Web of Science database for the period 1992-2025, using clusterization (CiteSpace 6.3.R1). The results showed a steady increase in publication activity: since 2017, the number of papers has increased to four per year, and the peak of citations occurred in 2022. Cluster analysis revealed two dominant areas: “Industry 4.0” (77 articles, the average publication year is 2016, S = 0.99) and “Precision Agriculture” (34 articles, the average year is 2014, S = 1.0). These clusters have shown that sustainable land use technologies and precision farming innovations are changing the organization of labor and management of agricultural enterprises. The results demonstrate the growing interest in the problems of labor organization in the context of the digitalization of the agricultural sector, the strengthening of interdisciplinary ties and the expansion of the range of applied research. In the future, it is advisable to expand databases for analysis, include more intersectoral research and develop organizational models that take into account the social and ethical aspects of the introduction of new technologies.

Sustainable expansion of irrigated agriculture requires the identification of areas with favorable biophysical conditions while also considering water-resource and infrastructure constraints. This study assessed the biophysical land suitability for potential irrigation development within the Ertis Water Management Basin (WMB), Kazakhstan, using an integrated remote sensing, geographic information system (GIS), and Analytic Hierarchy Process (AHP) framework. Four criteria—slope, normalized difference vegetation index (NDVI), land use/land cover (LULC), and soil texture—were standardized into five suitability classes and integrated using an AHP-based weighted overlay. The derived weights were 0.51 for slope, 0.26 for NDVI, 0.14 for LULC, and 0.09 for soil texture, with a consistency ratio of approximately 1%. The resulting assessment classified approximately 10% of the basin as highly suitable (S1), 43% as suitable (S2), 18% as moderately suitable (S3), 2% as marginally suitable (S4), and 27% as not suitable (NS), indicating that approximately 53% of the basin falls within the two highest suitability classes. Regional analysis showed that substantial shares of suitable land are concentrated in Pavlodar and Abay Regions, while suitable areas were identified across a wide range of distances from the river network. These findings indicate considerable biophysical potential for irrigation development across the Ertis WMB, with suitable areas occurring both near and farther from the river network. The resulting suitability map provides a spatial basis for identifying areas where more detailed assessments of water availability, irrigation infrastructure, and development feasibility can subsequently be undertaken.

Water scarcity during the growing season remains a major constraint for irrigated agriculture in Central Asia, particularly in transboundary river basins where water availability depends on mountain snow accumulation and melt processes. This study aims to assess how seasonal snow dynamics influence water availability in the Kuragaty sub-basin of the Shu river basin, with focus on downstream agricultural areas located in Kazakhstan and Kyrgyz Republic. Snow cover dynamics were derived from Sentinel-2 satellite imagery for elevations above 1000 m over the period from November to May, using a threshold-based snow detection approach. Evapotranspiration over agricultural lands was estimated from Landsat data using an energy balance model, while river discharge data from hydrometric stations were used to characterize the hydrological response. The results show a stable seasonal pattern of snow cover, with maximum extent observed in winter, where mean values exceed 55% and reach 59 ± 11.7% in December. In spring, snow cover rapidly decreases to 27.6 ± 12.7% in April and below 10% in May. The spring snow index (March-April) averages 37 ± 8.2%, reflecting strong interannual variability. Evapotranspiration follows a consistent seasonal cycle, increasing to 2.1 ± 0.6 mm/day in May and reaching peak values of up to 7.4 ± 1.2 mm/day in June. The total evapotranspiration during the growing season, averages 214.4 ± 24.2 mm. River discharge during this period shows moderate variability, with mean values of 4.26 ± 1.34 m3/s and peak flows occurring in early summer. A consistent positive relationship was observed between late-winter to early-spring snow cover and evapotranspiration during May-June, indicating that snowmelt contributes to water supply at the onset of the irrigation period. At the same time, evapotranspiration patterns are also influenced by vegetation development and increasing atmospheric demand, suggesting that snow dynamics act as an initial, but not the only, controlling factor.