The Selyaninov hydrothermal coefficient (HTC) is widely used to characterize moisture conditions in agrometeorology. In arid regions of Central Asia, sparse observational networks increase reliance on reanalysis datasets; however, their applicability requires regional validation. This study presents the first comparative assessment of ERA5 and ERA5-Land for calculating the HTC in Eastern Kazakhstan over 1960–2024, using data from 28 Kazhydromet stations distributed from lowland steppes (200–660 m) to mountainous terrain (above 1000 m). Both products systematically overestimate HTC, primarily due to precipitation bias. Performance at lowland stations remains acceptable (median KGE ≈ 0.55, MAE ≈ 0.09–0.17), whereas in mountainous areas, KGE becomes negative. Nevertheless, interannual HTC variability is reasonably captured (Spearman’s correlation = 0.50–0.80). ERA5-Land provides only a moderate improvement over ERA5 in lowlands; in mountainous areas, its higher resolution offers no clear advantage, and both datasets require bias correction. Trend analysis reveals no consistent direction in moisture changes from Kazhydromet observations, while both reanalyses indicate aridification, with sign agreement at only 13 out of 28 stations. The results show that using ERA5/ERA5-Land as proxies in climate studies requires accounting for bias propagation, while their application in CMIP6-based regional projections remains a subject for future research.

This study presents a dataset of meteorological and soil-hydrological instrumental observations collected at three agrometeorological stations in the East Kazakhstan Region during the growing seasons of 2022–2025. The dataset includes time series from automatic weather stations: WS “OCES-1” (Solnechnoe village) provides hourly data over four years (2022–2025; 14,614 records; 65 variables), while WS “OCES-2” (Lugovoe village; 203,279 records) and WS “Altyn Kazan” (Sulusary village; 207,115 records) provide minute-resolution data for 2025 (49 variables each). Measured parameters at 200 cm height include air temperature and humidity, atmospheric pressure, precipitation, wind speed and direction; soil measurements down to 100 cm depth include temperature and moisture. Also, field-based express measurements of volumetric soil moisture within a 1 m profile (every 10 cm) were collected during three campaigns (May–August 2025), resulting in a total of 253 measurements. The stations are located across steppe and forest-steppe landscapes of the transboundary Altai–Sayan mountain region on active agricultural lands under diverse soil–climatic conditions. Climate types correspond to Dfb and Dfa per the Köppen–Geiger classification. Soils are classified under WRB as Chernozems and Calcic Chernozems. The dataset is published in CSV format on Zenodo under a CC-BY 4.0 license.

Modern agriculture faces the challenge of increasing production efficiency in the context of limited resources and variable climatic conditions. This article presents an approach to assessing the impact of various factors on agro-industrial indicators using machine learning methods. The primary focus is on the development and application of a hybrid analysis that includes techniques such as gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE). The study was conducted using data from agro-industrial enterprises in the North Kazakhstan region for the period 2020–2022, encompassing production, climatic, and economic indicators. It was found that crop area, average crop weight, and precipitation are the most significant factors, accounting for up to 93% of the correlation with yield increase. The use of the proposed methods made it possible to reduce forecast uncertainty by 28% and increase the accuracy of key indicator predictions by 15–20%. The results of the analysis, visualized as correlation matrices and feature significance maps, confirm the possibility of applying the proposed approach to optimize the management of agro-industrial production. The application of the developed methodology contributes to the development of strategies aimed at the sustainable development of the agro-industrial complex.