Nurlan Bekmukhamedov

In Kazakhstan, scientific, methodological and practical approaches to the management and monitoring of salt pasture lands based on digital technologies are at the stage of formation and require further development. The authors have developed and used data from ground-based studies of saline lands on pastures to compile an information database and digitalize agricultural lands in Kazakhstan. The current state of saline lands on pastures has been studied through field work along routes covering the territory of 12 administrative regions of the republic. A database of salt pastures in desert, foothill semi-desert (vertical), semi-desert (latitudinal) and dry-steppe zones has been compiled, including the following indicators: soil type and subtype, profile morphology, humus and nutrient content, water-soluble salts, granulometric composition, absorbed bases and cation exchange capacity.

Understanding the interactions among mountain snow resources, river discharge, and irrigated agriculture is essential for sustainable water management in transboundary river basins of Central Asia. This study evaluated the relationships among high-elevation snow conditions, hydroclimatic factors, summer river discharge, and agricultural vegetation dynamics in the transboundary Chu River Basin during 2001–2025 using MODIS snow-cover, snow-persistence, and NDVI products together with meteorological and hydrological observations. The results showed that snow cover was widespread during winter but declined substantially during spring, while the Mann–Kendall analysis indicated that no statistically significant trend predominated across most of the high-elevation snow zone, although significant decreases in snow persistence occurred across 12.83% of the analyzed area. For summer cropland NDVI, 61.15% of the analyzed area showed no significant trend, while significant decreases and increases occurred across 26.24% and 12.62%, respectively. Spring temperature exhibited a significant negative correlation with snow persistence (r = −0.64, p < 0.001), indicating the sensitivity of mountain snow resources to warming conditions. Summer river discharge was strongly correlated with summer precipitation (r = 0.77, p < 0.0001), vegetated cropland area (r = 0.76, p < 0.0001), and mean summer NDVI (r = 0.69, p < 0.001). In addition, snow persistence showed a significant positive relationship with summer river discharge (r = 0.63, p < 0.001), vegetated cropland area (r = 0.52, p = 0.008), and mean summer NDVI (r = 0.42, p = 0.036), suggesting a linkage between high-elevation snow conditions, seasonal water availability, and downstream agricultural vegetation. Canonical correlation analysis further revealed a strong multivariate association between hydroclimatic and hydroagricultural variables (Rc = 0.900, Rc² = 0.810). Overall, the findings indicate significant statistical linkages among climate conditions, snow resources, river discharge, and irrigated agriculture, providing a scientific basis for water-resource management and climate-change adaptation in transboundary mountain-fed basins.

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.

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.