The article provides a comprehensive analysis of the European model of digital innovation hubs as a key tool of digitalization policy, designed to bridge the technological gap and support small and medium-sized enterprises (SMEs). It examines in detail the evolution of this model: from the conceptual foundations of the Digital Single Market Strategy to its institutionalization as a core element of the "Digital Europe" programme (2021–2027) and the formation of a standardized network of European Digital Innovation Hubs (EDIH). The multi-level architecture of hub interaction is explored, including internal, regional, cross-border, and European levels, which allows for combining the flexibility of local operations with the fulfilment of overarching strategic objectives. Based on a literature review and a series of detailed sectoral case studies (viticulture in Slovenia, the dairy sector in Portugal, a comprehensive agri-tech hub in Andalusia), the practical role of digital hubs in bridging the gap between technology development and implementation is assessed, particularly within the agri-food sector. Special attention is paid to identified systemic imbalances: despite the strategic importance of the agro-industrial complex for the EU, it accounts for only about 1.7% of all hub service beneficiaries. The article systematizes the key barriers limiting the digitalization of agriculture (low digital maturity of SMEs, infrastructure and personnel shortages, seasonality) and describes the adaptive approaches that hubs employ to overcome them. In conclusion, the potential and conditions for adapting this flexible platform-based model to address regional development and technological modernization challenges in other national contexts are substantiated, taking into account the need for deep customization to the specifics of local agricultural systems.
The article examines the impact of agriculture on trends in the socio-economic development of rural areas in Kuzbass. The relevance of the topic is due to the fact that the rural areas of the region are characterized by a low proportion of rural residents amounting to 13.4 percent and a noticeable lag in living standards from cities, while in the Novosibirsk region the rural population is about 20%. The aim of the research is to determine how the state and development of the agricultural sector affect demographic and economic indicators of rural communities. The study employs statistical and comparative analysis of official data, examining agro-industrial complex indicators and the implementation of government support programs for rural development. It was found that in Kemerovo Oblast agriculture accounts for only ~2% of the regional product but plays a strategic role in food security and rural employment. By contrast, in Novosibirsk Oblast the agricultural sector constitutes ~6–8% of the economy, contributing to higher agricultural output and a slower out-migration of the rural population. The practical significance of the results lies in reflecting the effectiveness of support measures (such as the “Comprehensive Development of Rural Areas” program) and informing regional rural policy planning. The theoretical significance is related to enriching the understanding of regional aspects of sustainable rural development under different economic structures.
This article explores an improved methodology for sequentially processing statistical information, allowing for the description of the state of the food system across a number of key parameters, resulting in the calculation of a final integrated index of regional food security. The proposed methodology combines expert and statistical research approaches, improving the accuracy and reliability of the assessment. An analysis of statistical bulletins from the Federal State Statistics Service was conducted for the period from 2015 to 2023, covering the following indicators: production, affordability, food quality, infrastructure and logistics, and sustainability. Positive trends in self-sufficiency rates for basic foodstuffs were identified: 149% for grain, 102% for meat, 138% for fish, and 252% for vegetable oils. A comparative analysis of food security levels in Russia's regions was conducted using the proposed methodology. According to the data obtained during the pilot study, Rostov Oblast, as a producer region, demonstrates outstanding results in terms of gross indicators, but the final ranking was adjusted (downgraded) due to imbalances in other areas of the system. Rostov Oblast is constrained by factors such as a household expenditure share on food exceeding 30% and a persistent shortage of logistics capacity, indicating structural imbalances. The proposed model can be further supplemented with a forecasting module to account for the sustainability of the food system. The results obtained prove that scenario modeling based on the proposed approach makes it possible to identify the "bottlenecks" of the food system at the regional level and allocate resources in a targeted manner, focusing not only on increasing gross output, but also on reducing logistical losses, as well as increasing the real solvency of the population.
The relevance of this study stems from the need to ensure food security and sustainable development of the agro-industrial complex in the Arctic zone of the Russian Federation in the context of digital transformation and high volatility caused by extreme natural and climatic factors and limited transport and logistics accessibility. Traditional marketing forecasting methods, successfully applied in the southern and central regions, demonstrate limited effectiveness in Arctic conditions due to ignoring specific determinants: the state of ice routes, navigation periods along the Northern Sea Route, permafrost dynamics, and the consumer behavior characteristics of isolated Arctic communities. It has been established for the first time that 40 percent of marketing environment volatility in the Arctic zone is determined by transport and climatic factors. A hybrid forecasting model has been developed that integrates SARIMA with neural network analysis of satellite data, improving the accuracy of medium-term sales forecasts by 25-30 percent. The transformation of the pricing model into an Arctic model of "logistics availability - inventory - price" is mathematically proven, and a risk-based pricing methodology with "Arctic premium" coefficients (1.44-1.69) is synthesized, reducing losses from forced sales by 18 percent. A conceptual architecture for a digital platform with a "double loop" principle is proposed, using ethnographic and shift data as proxy variables. This increases the accuracy of short-term demand forecasts from 72 to 89 percent and ensures a 32 percent reduction in losses from overstocking while reducing shortages by 28 percent. The combination of the proposed methods and models forms a methodological basis for the transition of marketing systems in the Arctic agro-industrial complex from reactive supply management to predictive product supply formation, which is critical for ensuring food security and the sustainable socioeconomic development of Arctic territories.
The main reason for the low competitiveness of a significant part of agricultural producers in the Siberian Federal District is high logistics costs, reaching 40-60% in the structure of the cost of sales operations. It is impossible to solve the problem of their reduction by the efforts of agricultural producers themselves, especially medium, small and micro-businesses, due to their limited financial capabilities and scale of production. The purpose of the study is to develop a comprehensive organizational and economic model for the development of the transport and logistics infrastructure for servicing the agro-industrial complex as a factor in reducing the costs of commodity movement. The research methodology is based on the use of systematic, comprehensive and program-targeted approaches to substantiating the prospects and models of infrastructure development. The main problems include the fragmentation and underdevelopment of the warehouse, the high degree of wear of specialized vehicles, the poor quality of the road network, and the lack of coordination between the participants in the logistics chain. Based on the identified problems, a three-level model of the agrological system was developed (federal hubs, regional ORCs, municipal agricultural hubs), scenario conditions for its development and target indicators were justified. A mechanism for coordinating infrastructure development through the creation of a Coordination Council under the Office of the Plenipotentiary Representative of the President in the Siberian Federal District has been proposed and practical recommendations have been developed to improve state regulation.
This article examines the role of investment incentives as a key driver of development in the agro-industrial complex (AIC) amidst current economic challenges. The relevance of this work stems from the need to enhance the competitiveness of the domestic AIC, ensure food security, and ensure sustainable growth in rural areas. The article analyzes the key barriers hindering investment in the sector, including high capital intensity of projects, long payback periods, climate risks, and insufficient infrastructure. Systemic measures are proposed to stimulate investment by improving government support mechanisms (subsidies, preferential loans, tax incentives), including funding volumes under the federal project to stimulate investment in the AIC, the development of public-private project financing instruments, the creation of AIC clusters, and private investment. An economic and statistical analysis demonstrates a direct correlation between fixed capital investment and indicators of GRP, tax revenues, the export potential of the AIC, and grain yields.
The article presents a comprehensive assessment of the property potential of various forms of agro-industrial complex (AIC) management in the Irkutsk region based on financial statements data for 2021–2023. The study uses comparative, structural, horizontal and vertical balance sheet analysis, as well as financial stability ratios. Total assets of agricultural organizations grew by 24.1% to RUB 56.6 billion, industrial enterprises by 13.8% to RUB 25.2 billion, consumer cooperatives by 27.6% to RUB 2.4 billion. Individual entrepreneurs' revenues in 2023 amounted to RUB 214.3 million with a 43.1% reduction in subsidies. Significant differences in asset structure were identified: industrial enterprises are dominated by current assets (70.5%), while agricultural organizations show a more balanced ratio (57.0/43.0%). The autonomy ratio is highest for industrial enterprises (0.86). The results substantiate a differentiated approach to state support for various forms of agricultural management.
The paper substantiates the relevance of studying servitization in the agro-industrial complex (AIC) as a response to structural and institutional challenges that limit the explanatory and managerial power of a purely product-centric value creation logic. Using a conceptual reconstruction approach and qualitative analysis of academic texts, the study systematizes competing interpretations of servitization / servicizing / servicification and proposes a coherent macro–meso–micro analytical framework that reduces terminological ambiguity by explicitly linking definitions to the scale of the phenomenon. The results show that at the macro level servitization appears as a structural shift in value added distribution and an institutional “embeddedness” of services within material production; at the meso level it manifests as a reconfiguration of agri-food value chains and the emergence of service ecosystems supporting coordination, logistics, supply, digital governance, and quality assurance; at the micro level it unfolds as a transformation of firms’ strategies and business models, moving from product sales to delivering functionality, integrated solutions, or outcome-based results. The paper identifies key AIC-specific features of servitization articulated as a consistent “condition–mechanism–effect” linkage: biological production constraints, seasonality and narrow technological windows, spatial dispersion and infrastructural gaps, high uncertainty and risk exposure, and the systemic nature of effects that emerge only when macro, meso, and micro levels are aligned. The scientific novelty lies in developing a multi-level theoretical concept of AIC servitization, specifying its sectoral contours, and proposing an authorial definition of AIC servitization as a multi-level transformational process that increases resilience, risk manageability, and value-in-use in agri-food production.
This article presents a methodology for quantitatively assessing the impact of the technical readiness coefficient of grain harvesting equipment on regional grain self-sufficiency, a key indicator of the production component of food security. Using the Ryazan Region as a case study, a comprehensive analysis of combine fleet dynamics and total grain harvest for 2024-2025 was conducted. An empirical model was developed to predict operational technical readiness based on equipment service life, enabling the conversion of physical wear metrics into direct economic losses. The study shows that a decline in the readiness coefficient from 0.95 to 0.6 increases grain losses by 22 percentage points, resulting in annual economic damage exceeding 10.4 billion RUB at November 2025 baseline prices (11,950 RUB/t). Raising technical readiness to the planned level of 0.8 reduces harvesting costs by 33%, improves cost efficiency by 1.7 times, and increases regional self-sufficiency from 213% to 248%. Practical recommendations for differentiated machine and tractor fleet (MTF) renewal and targeted leasing subsidies confirm the economic feasibility of accelerated MTF modernization to strengthen agricultural production potential.
In the context of the digital transformation of the agricultural sector of the economy, the introduction of digital twin technology for dairy cattle is becoming one of the key factors in the development of animal husbandry. The result of the study was an assessment of the introduction of digital twin technology, which makes it possible to form a production plan, a budget for income and expenses, and a cash flow budget for any agricultural enterprise. The technology of digital twins based on the normative reference information of the physiology of dairy cattle becomes, the rations of dairy cattle becomes, the standards and algorithms embedded in the technology of digital twins can automatically form an optimal and efficient production program. Its result will be a rationally formed movement of dairy cattle, taking into account the effective use of slaughterhouses on farms, as well as the calculation of the optimal amount of necessary feed with high energy value and other commodity values. Based on the production plan and algorithms used in digital twin technology, there will be a need for material and labor resources in kind, based on sales prices, tariffs, and quotations, the system will generate a budget of income and expenses and a cash flow budget. The authors analyzed the strengths and weaknesses, opportunities and risks of the current digital twin technology for the production and financial planning of dairy cattle. The article presents the main directions for improving the budgeting system for dairy cattle based on digital twin technology.