The purpose of this study was the scientific justification of the concept of assessment of the agro-resource potential of agricultural landscapes and the improvement of the methodology for such assessment, on the basis of knowledge integration principles that allow for the combining of various fields of science in order to create an integrated methodological approach to addressing scientific and practical problems of environmental management. Based on the analysis of modern worldviews and natural scientific ideas on the mechanisms of biomass production in natural systems, we propose a methodology for the assessment of the agro-resource potential of agricultural landscapes that is an integral function of four key components (groups of factors)—agroclimatic resources (ACR), soil–land resources (SLR), agrobiological resources (ABR) and water resources (WR)—and that is based on the laws of nature and the principles of agricultural nature management. The proposed algorithm for predicting the natural state of agricultural landscapes based on agroclimatic, agrochemical and agrobiological integrated indexes allowed us to develop a unified integrated approach to the methodology for the assessment of the agro-resource potential of agricultural landscapes that makes it possible to determine the logical sequence of the trend of changes in the natural process, fully characterizing its state in the space–time scale.

To study the impact of soil salinity type and degree in irrigated lands on the process of crop yield formation, multiparametric and single-parameter mathematical models were used. The methodological basis of the study was the materialist theory of scientific knowledge (analysis and synthesis) and the laws of ecology, using graph-analytical methods based on artificial intelligence and the applied software product Microsoft Office. To create the database, an empirical method of generalizing research results was used to study the effect of soil salinity type and degree in irrigated lands on the yield of agricultural crops in various natural and climatic zones of Central Asia for the period from 1932 to 2020. Based on plotting graphs of the dependence of the relative yield of agricultural crops on the dimensionless (relative) value of soil salinity type and degree, based on research data, the following results were obtained: first, differential equations describing the studied process were derived; second, within the framework of a very high determination index confirming a strong correlation between the function arguments and yield, a system of exponential, logarithmic, and polynomial equations was obtained using the applied software product Microsoft Office, which enables the management of agricultural crop yields on saline soils; and third, it creates prerequisites for the design of ecologically sustainable agro-landscapes.

This article presents the results of a study assessing changes in climatic resources in various natural zones of the Zhetysu region, Republic of Kazakhstan, conducted based on long-term climate data for the period 1966 to 2024 (from 12 meteorological stations). The study examines current trends in climatic indicators in spatial and temporal aspects that influence agricultural land use within the region. The first part of this study examines current trends in climate indicators from both spatial and temporal perspectives within the Zhetysu Region of the Republic of Kazakhstan; the second part focuses on studying trends in climate indicators using the non-parametric Mann–Kendall test and the Sen’s slope test, as well as Fisher’s t-test. The authors identified divergent trends in relative air humidity and precipitation and detected a steady trend toward an increase in the average annual air temperature across the region. Based on the analysis of time series of climate-forming and climate–environment-forming indicators, a persistent increasing trend in mean annual air temperature was identified, while relative humidity, precipitation, and evaporation exhibited divergent (both positive and negative) trends across the territory of the region. The developed climate–resource-forming models and a series of estimated applied maps of climate indicators for 1966–1975 and 2016–2024 serve as the scientific basis for climate change forecasting and can be used by administrative bodies to improve agricultural land use strategies in the region.

Trends in average annual river discharge were analyzed to identify regional and local features of the hydrological regime. Trends in average annual river discharge were derived by processing time series of these values in Microsoft Excel, which makes it possible to obtain regression hydrological models in the form of a linear function of time. This made it possible to determine the trend and intensity of change over time, within allowing for the analyze and forecasting of water resources dynamics in the present and future, considering the process of climate change.

To study the spatial and temporal variability of the mean annual river discharge within the Nura River basin, long-term hydrological data from the monitoring stations Bes-Oba, Sheshenkara, Balykty, Akmeshit, and R. Koshkarbayev, as well as from the rivers Sherubainura, Sokyr, Akbastau, Ulken-Kundyzdy, and Kon, were utilized. These stations are relatively evenly distributed across the basin, covering an area of 58,100 km², with observation periods spanning from 1932 to 2021. The analysis of hydrological process dynamics within the Nura River basin watershed reveals that long-term fluctuations in mean annual river discharge comprise both stochastic and deterministic components, the latter represented by a linear trend. Statistically significant estimates of the parameters characterizing the cyclic structure of long-term fluctuations in mean annual river discharge (phase, amplitude, and synchronicity) were obtained. It has been demonstrated that the alternation of high-water and low-water years, associated with the phases of long-term discharge fluctuation development, can be represented as time-dependent functions exhibiting dynamic means with varying intensities. This phenomenon may serve as a scientific basis for river discharge forecasting.