Analysis of factors and indicators affecting demand for agricultural machinery

Abstract

The study aims to develop a unified analytical model for assessing demand for agricultural machinery, which combines internal market indicators (sales volumes, mechanization coefficient, energy equipment level) with an in‐depth analysis of external factors using the PESTEL methodology. This approach is intended to generate well‐grounded recommendations for manufacturers, distributors, and government bodies regarding the strategic development of the agrarian sector. The paper also assesses the key drivers and risks that shape market conditions. A quantitative analysis of statistical data on machinery sales volumes and energy equipment indicators was applied to achieve this aim. A PESTEL analysis of political, economic, social, technological, environmental, and legal factors was conducted, along with a consolidation of internal strengths and weaknesses, considering regional case studies and expert interviews with representatives of industry organizations. Combining these methods made it possible to obtain a complete picture of both internal and external determinants of demand. The study identifies the key demand drivers as government subsidies, preferential leasing, growth of agricultural GDP, and improved credit access. Technological dynamics manifest in the digitalization of agro‐services and GPS monitoring, while social initiatives include programs for women and young farmers combined with “green” standards. At the same time, internal constraints of small farms and lack of after‐sales support, together with climatic extremes and unstable trade regimes, create significant challenges for further market growth. The results substantiate the need to deploy public-private partnerships to establish mobile service hubs, develop machinery adapted to local conditions with modular digital add‐ons, and implement flexible leasing products that include after‐sales service packages. Targeted training demonstrating the economic impact of mechanization for young farmers and women entrepreneurs is also crucial. For further research, it is recommended to deepen the quantitative analysis using time‐series and spatial econometrics, adapt the model to other regional markets, and assess the influence of AI services on demand formation.