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September 10, 2025Open Access

The economic efficiency of the introduction of resource-saving technologies in crop production in Kazakhstan

MBM. BayandinInnovative University of Eurasia ABA. BekmurzayevaInnovative University of Eurasia ZYZeinegul YessymkhanovaAstana Medical University

Key Points

Implementing resource-saving technologies led to a 60% reduction in fuel costs and a significant increase in wheat yield, demonstrating their economic efficiency.
The regression model evidenced high explanatory power with R2=0.948, highlighting the critical role of targeted subsidies for new technologies in driving economic benefits.
The introduction of drip and sprinkler irrigation on 84,000 ha reduced water consumption by 20-40%, showcasing sustainability in drought conditions.
State co-financing of up to 80% contributed to a typical payback period of 3-5 years, underscoring the importance of strategic investment in agricultural technologies.

Abstract

The purpose of this study was to determine whether the implementation of resource-saving technologies in crop production in Kazakhstan during 2021-2024 led to statistically significant improvements in economic efficiency at the level of farming enterprises. The main focus was on the practical results of the application of innovative technologies, such as precision agriculture, Global Positioning System-monitoring, digital crop management, drip irrigation, remote sensing and the use of drones. The study found that the implementation of these technologies allowed to reduce fuel costs by up to 60%, fertilisers by 18-22%, and labour costs by 15-20%. At the same time, wheat yield increased by 50% compared to 2023 and amounted to 1.38 t/ha. The introduction of drip and sprinkler irrigation on an area of about 84 thousand ha allowed to reduce water consumption by 20-40% and increase crop stability in drought conditions. Economic efficiency was confirmed by a high level of payback: typical terms of return on investment in these technologies were 3-5 years with state co-financing of up to 80%. The regression model showed high explanatory power (R2=0.948), with the most significant influencing factors being subsidies for drones (+106.51) and digital platforms (+7.29), while direct funding per 1 ha was not statistically significant (coefficient -0.0004; p=0.685). The results confirmed the economic feasibility of innovative approaches to agriculture in Kazakhstan and proved that the effectiveness of state support depends on its targeting and integration into the institutional environment. The results of this study can be practically applied by government agencies to optimise subsidy policies, by agricultural enterprises to justify investments in digital technologies, and by international donors to support scalable, cost-effective solutions for sustainable agriculture

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