A comprehensive scientific understanding of the specifics of agricultural production in risky farming zones contributed to the development of a land reclamation model for irrigation. The scientific support stage includes proposals for developing a new concept for land reclamation in agriculture and modernizing the land reclamation system. The turnover stage involves the commissioning of irrigated lands and equipment to maintain optimal field moisture. The production stage ensures increased efficiency in crop production on irrigated lands. The distribution stage improves relationships between stakeholders, ensures a fair distribution of income among production participants, and the exchange stage regulates supply and demand for irrigated land and reduces transaction costs. The consumption stage promotes land acquisition to expand irrigated land areas. Incentives have been found to be an effective means of increasing agricultural producers' interest in land reclamation. State incentives for the modernization of land resource restoration in irrigated agricultural reclamation are aimed at finding the optimal investment solution and a source of investment resources. A set of state incentives is proposed for the restoration and expansion of irrigated land, the acquisition of mineral fertilizers, the introduction of crop rotations, and the modernization of reclamation equipment, all of which facilitate the transition to expanded, intensive land restoration in agriculture.
The article analyzes the economic efficiency of the implementation of a climate project for the production of biochar from organic waste from livestock farms with its further introduction into the soil to reduce greenhouse gas emissions and increase crop yields. At the current low prices of carbon units for voluntary climate projects at the level of 700-1,000 RUR /CU, payback is not achievable, while the climate effect may be significant. At the same time, the project for the production and use of biochar demonstrates high economic efficiency when increasing the prices of carbon units to the level of 3,750 RUR / CU and above, such prices are possible in the context of stricter climate legislation, including at the international level. Also, the sale of biochar instead of applying it to the soil increases the economic feasibility of the project, but negatively affects CO2 sequestration, reducing this effect by 55%. All this makes it necessary to attract state co-financing to achieve carbon neutrality by 2060 according to the Climate Doctrine of the Russian Federation.
The article analyzes the current state and development trends of the Russian agricultural machinery market in the context of global mechanization and digitalization of the agro-industrial sector. Based on official data from Rosstat, the Ministry of Agriculture of the Russian Federation, the Eurasian Economic Commission, and international organizations (WTO, USDA, Eurostat), the study examines structural changes in production and export, the degree of machinery wear and renewal, and the adoption rate of digital and precision farming technologies. The research identifies key factors affecting market growth, including government support measures, technological modernization, import substitution, and digital transformation of agriculture. Forecasts up to 2030 are formulated using current dynamics and official development programs. Digitalization and precision agriculture are becoming the main growth drivers: the share of farms applying digital technologies increased from 18 % in 2021 to 22% in 2024.
The article proposes a formula for calculating the economic efficiency of organic agriculture for a machine system. The formula is based on the concept of a technological paradigm of agriculture, which includes the most common agricultural technologies. The principle applied to assess economic efficiency postulates a direct relationship between the economic efficiency of the technologies under study and the share of the area, where they are used, in total agricultural land: an increase in economic efficiency should lead to an increase in this share, promoting the integration of these technologies into the technological paradigm. Since a machine system requires a generalized analysis of the technical and economic characteristics of agricultural machinery and technologies, the model employs statistical data aggregated at the country level. Given the lack of statistics on many factors of organic production, the formula in question was derived based on the concept of productivity: here, the output is the share of a particular type of agriculture in the total agricultural area, and the input is the area of agricultural land used per monetary unit of retail sales of the corresponding agricultural produce. Intensive agriculture served as the basis for comparison. Application of the formula with statistical data for a group of 26 countries, including Russia, for the period 2014-2023 revealed the low economic efficiency of organic technologies (approximately 4%), as indicated by their small share in total agricultural land. Thus, despite the two-fold monetary advantage of organic agriculture in retail sales per hectare of agricultural land compared to intensive technologies, the share of organic production was approximately 2% of the studied group’s agricultural land area over the specified period. Due to their low efficiency, organic technologies have little potential to ensure food security—the main conclusion for a machine system. Given the importance of preserving the environment and human health, they require significant improvement, including overcoming the problem of scalability.
The relevance of the study is due to the lack of detailed data on the robotization of agriculture in the Russian Federation, its rapid development as a factor in increasing efficiency and a source of big data, as well as the need to identify real territorial disproportions of these processes. The goal is to identify territorial patterns and form a typology of regions of the Russian Federation by the level of robotization (with a focus on milking robots), its relationship with livestock productivity and the potential for generating big data. The research methods include requests to the territorial offices of the Ministry of Agriculture and online screening for the number of robots, the use of cluster analysis (k-means) with preliminary z-standardization of variables and the use of Euclidean distance on a sample of 79 regions. The main results of the study revealed 4 stable clusters (silhouette = 0.69). The first ("Leaders") includes 18 regions with the highest productivity of ~7384 kg / year and an average number of robots of ~12.8 robots / region (Ryazan, Sverdlovsk regions), including regions with unrealized potential (Vladimir, Belgorod regions). The second cluster ("Outsiders") includes 11 regions of the North Caucasus Federal District and the Far Eastern Federal District with low productivity of ~2183 kg / year, almost zero robotization. The third cluster ("Regions with potential") includes 20 regions with high productivity ~5869 kg/year and moderate robotization ~4.3 robots (Tatarstan, Tyumen region), with many regions without robots but high payback potential (Tver, Bryansk region). The fourth cluster ("Largest group with minimal robotization") includes 30 regions with low productivity ~4467 kg/year and weak robotization ~0.6 robots, isolated "islands". Specific recommendations are given on the need to concentrate government support measures and investments on regions with high unrealized potential (clusters 1 and 3), where high productivity ensures a quick payback of milking robots and the formation of big data. In cluster 4, stimulate the introduction of robots in individual highly productive farms as "growth points". For cluster 2, the priority task is to increase the basic productivity of livestock farming. It is necessary to take into account the role of robotics as a key source of big data when forming programs for digitalization of the agro-industrial complex at the regional level.
The purpose of this article is to systematize methodological approaches to economic forecasting using artificial neural networks. The empirical basis of the research was made up of the works of domestic and foreign researchers in the field of forecasting the economy of agriculture and agriculture, as well as the personal experience of the authors. Based on the generalization of interdisciplinary literature, the article provides a comparative analysis of methodological approaches to building economic forecasts using artificial neural networks, their advantages and disadvantages are considered. The target orientation, advantages, discussion aspects and the relationship between individual approaches were identified, which allowed for their systematization. In relation to economic entities, the approaches were classified as methods of spatial and dynamic forecasting. A conceptual view on the formation of an information base is substantiated, taking into account the specifics of individual approaches. The obtained research results contribute to the understanding of economic work in agribusiness entities in terms of forecasting activities, contribute to building an optimal structure for obtaining reliable information and timely detection of market anomalies.
The article contains a comprehensive analysis of the staffing of the agro-industrial complex of the Republic of Sakha (Yakutia) in the Far North. Based on statistical data for 2017-2024. key problems were identified: low level of self-sufficiency in food (27.8% for meat, 36.4% for vegetables), a shortage of qualified personnel (only 25% of workers with higher education) and a decrease in the profile employment of graduates of agricultural universities (from 42% to 23%). The SWOT analysis method determined the strategic directions for the development of the agricultural education system, including the creation of agricultural clusters, the introduction of distance learning and the digitalization of educational processes. Measures have been proposed to increase the prestige of agricultural professions in the Arctic region.
This article is devoted to a comprehensive study of the dynamics of export activities in the agro-industrial complex of the Republic of Bashkortostan in recent years. The study uses statistical methods of data analysis, comparative analysis, and graphical methods of visualizing trends. It examines the volume and structure of agricultural exports, the geography of export deliveries, and identifies the main factors affecting the dynamics of export activity in the region. The article presents key indicators of growth and development of the export potential of the agro-industrial complex of the Republic of Bashkortostan, identifies the leading industries and types of products with the highest export demand. The authors have analyzed the factors that contribute to and hinder export growth, such as changes in the external environment, government support, the development of logistics infrastructure, and the competitiveness of products. Based on their research, the authors have formulated conclusions about the current state and prospects of export activities in the region's agricultural sector.
Despite the diversity and versatility of concepts and scientific approaches to defining the essence and factors (criteria) of competitiveness, there is currently no universally accepted or unified theory describing the phenomenon of competitiveness at the national or industry levels. The term "competitiveness" as an economic category has not yet been fully defined in the scientific literature. In today's economic environment, competitiveness management issues are particularly important for enterprises in the agro-industrial complex (AIC). This is due to significant changes in the external environment: the rapid development of information technology, the unique conditions for entering international markets, and the strengthening of integration processes in the agricultural sector. Furthermore, the pressing challenges of transitioning to a digital economy in the regions, the introduction of strict environmental standards for food production, the need to ensure food security for the country, and improve the quality of life for the population require the development of new approaches to assessing and managing the competitive potential of agricultural enterprises. This study developed and tested a methodical approach to determining the production competitiveness of various types of agricultural products. The proposed approach utilizes a comprehensive assessment that takes into account the combined significance of parameters (factors) that determine industry-specific production characteristics and directly impact its efficiency. The developed approach was tested using vegetable-growing enterprises, which allowed us to determine the market positioning of specialized economic entities and identify opportunities to enhance their competitive potential. This approach also confirmed its practical significance and the validity of the results. The study's findings can be used by research organizations, regional and municipal agricultural authorities, and agricultural producers.
The article discusses the features of the creation and functioning of dairy farms in Russia. It provides an assessment of methods to stimulate the growth of profits from the sale of milk, which is the raw material produced in dairy farms. The article also identifies the main elements of dairy farms as an infrastructure facility for the development of the dairy industry. The analysis of statistics on milk production and sales reveals trends in the growth of economic potential for expanding markets. The creation of dairy farms as part of the infrastructure development projects for dairy cattle breeding will solve the problem of improving the technological cycle of milk production along the entire value chain. The author presents a comparative assessment of state support for milk production in a commercial dairy farm, depending on the amount of funds provided from the federal or regional budget. Among the factors that actively influence the effectiveness of state support for commercial dairy farms, we can highlight the following: the motivational mechanism of stimulating subsidies for increasing milk production volumes; subsidies for maintaining breeding stock in the context of milk production in Russia; and the diversification of logistics in expanding the production potential of commercial dairy farms in the Russian Federation. The study resulted in conclusions about the need to improve the breeding base for Russian milk production.