Adverse weather conditions, once rare anomalies, are now becoming increasingly commonplace, causing heavy losses to crops and livestock. One of the most immediate and far-reaching concerns is the potential impact on agricultural productivity and global food security. Although studies combining crop models and future climate data have been previously carried out, such research work in Central Asia is limited in the international literature. The current research aims to harness the predictive capabilities of the CRAFT (CCAFS Regional Agricultural Forecasting Toolbox) to predict and comprehend the ramifications stemming from three distinct RCPs, 2.6, 4.5, and 8.5, on wheat yield. As a result, the arid steppe zone was found to be the most sensitive to an increase in greenhouse gases in the atmosphere, since the yield difference between RCPs 2.6 and 8.5 accounted for almost 110 kg/ha (16.4%) and for 77.1 kg/ha (10.4%) between RCPs 4.5 and 8.5, followed by the small hilly zone with an average loss of 90.1 and 58.5 kg/ha for RCPs 2.6–8.5 and RCPs 4.5–8.5, respectively. The research findings indicated the loss of more than 10% of wheat in the arid steppe zone, 7.6% in the small hilly zone, 7.5% in the forest steppe zone, and 6% in the colo steppe zone due to climate change if the modeled RCP 8.5 scenario occurs without any technological modernization and genetic modification. The average wheat yield failure in the North Kazakhstan region accounted for 25.2, 59.5, and 84.7 kg/ha for RCPs 2.6–4.5, 4.5–8.5, and 2.6–8.5, respectively, which could lead to food disasters at a regional scale. Overall, the CRAFT using the DSSAT crop modeling system, combined with the climate predictions, showed great potential in assessing climate change effects on wheat yield under different climate scenarios in the North Kazakhstan region. We believe that the results obtained will be helpful during the development and zoning of modified, drought-resistant wheat varieties and the cultivation of new crops in the region.

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.

В условиях полевых научных опытов на территории черноземов обыкновенных. В статье представлены результаты исследования 2024 года по влиянию различных систем обработки почвы и сельскохозяйственных культур на содержание органического углерода, общего азота, а также фракционный состав гумуса в условиях черноземов обыкновенных Костанайской области. Опыт проводился на посевах яровой пшеницы, гороха и на паровом участке при традиционной и нулевой обработке почвы (No-Till), в период вегетации для определения показателей были отобраны почвенные образцы с глубины 0–20 см., с каждого деляночного участка в 3 периода: июнь, август и октябрь. Проведён корреляционный анализ между содержанием органического углерода и общего азота, а также между гуминовыми (Сгк) и фульвокислотными (Сфк) кислотами. Выявлено, что система No-Till в большинстве случаев способствует накоплению органического вещества. На паровом участке установлена высокая положительная корреляция между Cорг. и Nобщ. (r = 0.78), а между Сгк и Сфк — прочная связь (r = 0.978). Полученные результаты позволяют глубже понять механизмы накопления органического вещества и оценить влияние агротехнологий на накопление и потерю углерода.

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.

Spring wheat breeding compares candidate lines across seasons with different growing conditions. We evaluated seasonal transfer of models based on multitemporal UAV observations from a northern Kazakhstan breeding nursery, considering absolute yield, line ranking and yield margins over standards of comparable maturity. Models developed based on 2024 and 2025 data were tested at six retrospectively defined cutoffs in 2026. Pooled development R² was 0.814 for a random forest and 0.779 for a gated recurrent unit (GRU), whereas within-season R² for line means ranged from 0.140 to 0.375. In 2026, line-mean R² was negative at every cutoff, and no positive rank correlation was supported by its bootstrap interval. Referencing candidates to maturity-matched standards removed 67–98% of shared prediction bias but mean zero-margin skill remained negative. For the 20 candidates previously grown in the nursery, historical mean yield gave higher point-estimate rank correlations than either model. Among 16 candidates without historical yields, neither model showed a demonstrated ranking advantage over NDVI from the latest acquisition. Exploratory calibration using harvested yields from 20 lines partly improved prediction and ranking for 16 unseen candidates but did not represent an early-season procedure. In this nursery, reducing shared seasonal prediction bias did not recover reliable estimates of the yield advantages of individual lines.