Based on the systematization of relevant problems in the agricultural sector of Kazakhstan and other countries, the purpose of the research is to aid in the development and implementation of a methodology for the econometric analysis of sustainability, the classification of economic growth, and an alternative strategy for gross value added depending on time phases with time lags of 0, 1, and 2 years, and on the gross fixed capital formation in the agricultural sector of Kazakhstan. The research has used a variety of quantitative techniques, including the logistic growth difference equation, applied statistics, econometric models, operations research, nonlinear mathematical programming models, economic modeling simulations, and sustainability analysis. In the work on three criteria: equilibrium, balanced and optimal growth, we have defined the main trends of growth of Gross added value of agriculture, hunting and forestry. The first, depending on the time phases, the second, depending on the Gross fixed capital formation transactions for equilibrium growth, for the growth of an alternative strategy, for the endogenous growth rate and the growth of exogenous flows. And we also received a classification of the trend of Productive, Moderate and Critical growth for the agricultural industry depending on the correlated linkaged industry of the national economy of Kazakhstan. The results of this work can be used in data analytics and artificial intelligence, digital transformation and technology in agriculture, as well as in the areas of sustainability and environmental impact.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

The purpose of the scientific article is to study the problem of increasing the business activity of the rural population in the conditions of the «new» economy of Kazakhstan and to develop proposals for stimulating the economic activity of personal subsidiary farms. The use of classical methods of scientific cognition and literature review allowed us to study the theoretical foundations and reveal the economic essence of the categories of «business activity» regarding its applicability to the economy of the household. Based on the macroeconomic analysis, the main indicators and trends of the development of the economy of Kazakhstan were studied, the share of the agricultural economy in the country’s GDP was determined. It is revealed that the agricultural economy in the overall structure of the national economy can become a priority industry in the long term, which is justified by the effectiveness of the activities of agribusiness entities in conditions of developed competition. It is confirmed that 58% of the rural population are self-employed, and 46% of the population cannot provide for their basic needs, since the only source of income is transfer payments (social benefits) from the state budget. It is determined that the inflation rate has a significant impact on the real income of households. The model of economic stimulation of the business activity of personal subsidiary farms is presented, taking into account their peculiarities of formation and development as a form of agribusiness organization, which clearly affects the agricultural economy of Kazakhstan.

This study introduces a novel approach to predicting the success of small businesses in Kazakhstan, leveraging Graph Neural Networks (GNNs) to analyze a comprehensive set of business parameters. Recognizing the critical role of small businesses in the national economy, this research aims to provide stakeholders with a predictive tool that utilizes advanced machine learning techniques to evaluate business outcomes. By integrating data on revenue, number of employees, market dynamics, and other key operational metrics, the model captures the complex interactions within the business ecosystem. The methodology involves constructing a graph-based representation of the business landscape, where nodes represent individual businesses and edges denote relationships and influences among them. Through this framework, the GNN model learns to identify patterns and predictors of success, offering insights that traditional linear models might overlook. Preliminary results indicate a strong correlation between specific business parameters and their likelihood of success, highlighting the potential of GNNs in strategic decision-making. This paper not only contributes to the academic discourse on predictive analytics in business but also proposes a practical tool for entrepreneurs, investors, and policymakers in Kazakhstan to foster a thriving small business sector. Future work will focus on refining the model, incorporating real-time data, and expanding its applicability to other regions and sectors.