ECONOMIC AND MATHEMATICAL MODELING AS A TOOL FOR FORECASTING THE IMPLEMENTATION OF DEVELOPMENT STRATEGIES IN THE AGRICULTURAL SECTOR

Abstract

The article provides a comprehensive examination of the role of economic and mathematical modeling as a critical tool for forecasting and evaluating the implementation of development strategies within Ukraine's agricultural sector. It emphasizes the importance of using quantitative approaches to support strategic decision-making processes, particularly in an environment marked by increasing complexity and uncertainty. The research focuses on the practical application of various modeling techniques, including optimization models, scenario-based analysis, and stochastic simulation methods, which are essential for navigating risks arising from war-related disruptions, climate variability, and challenges in supply chain logistics.

In particular, the study uses empirical data from leading Ukrainian agricultural enterprises to analyze the evolving patterns of market capitalization and investment behavior. It highlights how these firms adapt to external shocks and how managerial effectiveness, underpinned by data-driven insights, correlates with increased resilience and strategic agility. The article also underscores the role of predictive analytics in shaping long-term competitiveness by enabling more accurate planning and resource allocation.

The findings clearly demonstrate that the integration of economic and mathematical modeling into strategic management frameworks leads to more informed and adaptive business practices. Moreover, the paper discusses the broader implications of digital transformation in the agroeconomic domain, suggesting how emerging technologies can further enhance modeling capabilities. Finally, it outlines potential avenues for future research, particularly in the context of incorporating real-time data, machine learning algorithms, and digital platforms to improve forecasting accuracy and policy development in the agricultural sector.