Agricultural planning at the district level requires a transparent comparison of investments, production, financial returns, and resource availability. This study evaluates the relative production and economic performance of 13 districts in the North Kazakhstan Region based on seven groups of indicators: investment activity, gross agricultural output, crop yields, livestock productivity, crop profitability, livestock profitability, and capital intensity. To compare heterogeneous indicators, proportional normalization was used, in which each district’s value is expressed as a share of the region’s total value. The final score is calculated by summing the scores for the seven factors, after which the districts are grouped into five levels of production and economic activity using the equal-interval method. The absence of certain types of livestock products is treated as a zero contribution to the corresponding subindex and is interpreted with regard to the territorial specialization of the districts. A sensitivity analysis using Min-Max normalization demonstrated high stability of the ranking (Spearman’s correlation coefficient ρ = 0.94). The Kyzylzhar District received the highest total score (140.9), while the Ualikhanov District received the lowest (59.0). The results obtained are intended for a comparative spatial–economic assessment of districts and to support regional agricultural planning.

Seasonal snow is a critical water resource in Northern Kazakhstan, where spring snowmelt dominates surface runoff and agricultural water supply. However, traditional rainwater harvesting frameworks often neglect snow water equivalent (SWE), limiting their applicability in cold, snow-dominated steppe environments. This study develops an integrated remote sensing and GIS-based approach to identify potential sites for spring runoff harvesting. Using Sentinel-2 imagery, snow cover fraction (SCF) was derived and combined with a digital elevation model (DEM) to estimate snow depth through a quadratic regression model, validated against in situ data from 23 meteorological stations. Snow water equivalent was then calculated by incorporating field-measured snow density. Alongside SWE, six additional parameters—slope, drainage density, land use/land cover (LULC), and soil texture (sand, silt, clay)—were integrated within an Analytical Hierarchy Process (AHP) multi-criteria decision analysis. Results show that SWE, drainage density, and LULC collectively contributed 75% of the weight in site suitability, underscoring their dominant role in snowmelt-driven hydrology. The final suitability map indicates that 28.03% of the region falls into the “Very High” class, concentrated in central and northwestern districts. Developing just 30% of these potential zones could yield approximately 4.2 × 10⁸ m³ of usable meltwater, supporting up to 105,000 ha under full irrigation or 280,000 ha under deficit irrigation. The study demonstrates that integrating high-resolution SWE with MCDA provides a scalable framework for enhancing agricultural resilience, mitigating floods, and strengthening water security in cold, semi-arid landscapes.

Sustainable development of the agro-industrial complex (AIC) requires an integrated assessment of investment activity, production performance, financial returns, and resource provision at the district level. This study evaluates the production and economic performance of 13 districts of the North Kazakhstan Region using a composite scoring approach based on seven groups of indicators: investment activity, gross agricultural output, crop yields, livestock productivity, crop-production profitability, livestock-production profitability, and capital intensity. The information base includes official district-level statistics, materials of the regional agricultural authorities, data on fixed production assets for 2021–2023, and national land-use reporting. The results reveal substantial territorial differentiation. Kyzylzhar and Taiynsha districts form the high-performance group, Esil District is classified above average, whereas Ualikhanov District occupies the lowest position in the aggregate ranking. The analysis also identifies a scale-efficiency mismatch: large agricultural territories do not necessarily demonstrate high profitability or productivity, while several medium-sized districts achieve stronger returns on land and production resources. The findings provide a comparative basis for territorially differentiated agricultural policy, targeted investment support, technological modernization, and the development of processing infrastructure in the North Kazakhstan Region.