Issue № 6, June 2024, article № 11

Simulation modeling of financial and economic activities of agricultural organizations as a decision making support tool

Simulation modeling of financial and economic activities of agricultural organizations allows for analysis and scenario forecasting, evaluating the effectiveness of management strategies and making informed management decisions. This research helps agricultural enterprises optimize financial flows, reduce risks and increase competitiveness. The dynamics of changes in the share of agricultural products in all categories of farms of the studied subjects of the Russian Federation over several years is presented, which allowed us to conclude that the importance of crop production has increased recently. Simulation modeling was carried out for five grain crops, as well as technical and fodder crops, since they account for about 90% of the entire crop subcomplex. The study is based on data on crop yields for more than 30 years; average data on acreage for the previous 5 years were used, which allows us to obtain smoothed values in order to avoid extreme or random indicators over the past year; data on the cost of a ton of a specific crop according to the Federal State Statistics Service. Based on the results of simulation modeling, optimal strategies for growing crops on the territory of various municipalities of the considered subjects of the Russian Federation have been determined. Ten different types of strategies are identified, for which explanations and recommendations are given. The paper also examines the use of agricultural insurance for agricultural organizations as a decision-making tool in terms of the cultivation of certain crops.

Issue № 6, June 2024, article № 12

Comparative analysis of carbon dioxide emissions in the Russian regions

The article presents estimates of the specific emissions of carbon dioxide released by the soil of land resources in Russian regions. The estimates are compiled based on the T&P model of American scientists J. W. Raich and C. S. Potter. When compiling estimates using the model formula, hydrothermal parameters of the regions of the Russian Federation were used: average monthly temperature and average monthly precipitation during the growing season, which begins and ends at an air temperature greater than or equal to 5°C. The tables of estimates include the first and last months of the growing season, as well as the average monthly air temperature and average monthly precipitation during the growing season of the region. Specific emission table is the subsequent input information for the multiple linear regression equation, which, similar to the T&P model, can be used to calculate specific carbon dioxide emissions in the regions of the Russian Federation. The coefficient of determination of 0.88 of the multiple linear regression equation reflects the high dependence of the function: specific carbon dioxide emissions in the region by the studied factors: average monthly air temperature, average monthly precipitation during the growing season and the duration of the growing season. The article provides evidence of a more significant (9.4 times) influence of the average monthly temperature factor on specific emissions compared to the factor of average monthly precipitation. Estimates of specific carbon dioxide emissions in the regions of the Russian Federation can be used in compiling the soil carbon balance in the region to develop directions for the development of agricultural and non-agricultural production with a low carbon footprint and maintaining a stable state of soil fertility.

Issue № 6, June 2024, article № 13

Typology of rural settlements in the kaliningrad region according to the degree of provision with social infrastructure facilities

The article presents the author's typology of rural settlements in the Kaliningrad region according to the degree of provision with social infrastructure facilities. The influence of the agglomeration factor and the factor of provision of social infrastructure facilities on the population dynamics of rural settlements in the region in the period from 2020 to 2023 is analyzed. The dominant role of the agglomeration factor (proximity to the administrative center - the city of Kaliningrad) on the dynamics of the rural population of the region has been revealed. Social infrastructure facilities were identified in 341 settlements (35% of the total number of rural settlements in the region). Only 3 rural settlements have the maximum score for the level of social infrastructure development. In large (with a population of more than 1000 people) rural settlements of the region, the average score for the degree of provision with social infrastructure facilities is 5.6 points (with a maximum of 13 points), in villages with an average population (from 500 to 1000 people) the average value is 5 ,2 points. At the same time, in the area adjacent to the city of Kaliningrad, demographically rapidly growing settlements that are not provided with social infrastructure facilities have been identified. At the same time, in the non-agglomeration zone there are rural settlements with developed social infrastructure, in which the population has a steady downward trend. This requires, in relation to them, the development and implementation of a program to provide these settlements with new functions, to replace the once lost traditional agricultural specialization.

Issue № 6, June 2024, article № 14

Monitoring the effectiveness of regional management of rural development

The article presents the results of monitoring the effectiveness of managing the development of rural territories in 82 constituent entities of the Russian Federation for 2019-2021. To evaluate the effectiveness of regional management, the rating assessment method based on the values of the inclusive development index was used. The results of the study show high differentiation of regions by the level of inclusive development of rural territories. It was established that the leaders and outsiders of ratings over the course of all three years have practically not changed. 7 regions became the leaders of the rating: Magadan, Murmansk, Kursk, Lipetsk, Oryol, Kaliningrad regions and Kamchatka Territory. They can be considered as a sample and adopting the experience of managing the development of rural areas. 12 regions for the analyzed period significantly improved their position in the ranking, and 10 regions, on the contrary, shifted more than 10 positions down. The outsiders of the rating for all three years were 11 constituent entities of the Russian Federation, of which 7 are located in the Siberian and Far Eastern Federal Districts (SFD, FEFD): Altai, Tuva and Khakassia Republics (SFD); Republics of Buryatia and Sakha (Yakutia), Jewish Autonomous Region, Trans-Baikal Territory (FEFD); Perm Territory, Kostroma Region, Novgorod Region, Komi Republic. It is obvious that the authorities of these regions are not able to independently cope with the current socio-economic situation in the village and need the support of the federal authorities to develop and implement a set of anti-crisis measures.

Issue № 6, June 2024, article № 16

Artificial intelligence in agriculture: reality and prospects for use

The purpose of the presented study is to pose the most important problems of the use of artificial intelligence in agriculture: regulatory regulation, economic readiness, economic efficiency and safety of use. The subject of research is regulatory, economic issues of using artificial intelligence. In the past few years, the role of artificial intelligence in agriculture has grown significantly. According to expert estimates, the market for artificial intelligence technologies in agriculture will reach $4 billion by 2026. Quantitative metrics confirm that the contribution of artificial intelligence technologies to agriculture is growing every year. Analysts argue that the effectiveness of the introduction of artificial intelligence directly affects labor productivity and the overall profitability of the agricultural sector. This is a key factor for the sustainable development of the industry. Taking into account climate change and increasing demographic pressure, the effective and intelligent use of AI technologies is becoming not just an innovative practice, but also a necessity. It is known that artificial intelligence technology is economically affordable for large economic entities, and their application has a minimal impact on economic efficiency. Therefore, most likely, in the future, the economic efficiency of using artificial intelligence will be reduced to optimizing business processes based on fast processing of data arrays and their objective assessment.

Issue № 6, June 2024, article № 17

Milk supply chains and the impact of digitalization on their sustainability

The introduction of digital technologies in the dairy complex from the farm to the consumer significantly increases the stability of the functioning of the dairy supply chain. The development of digital technologies is most successfully carried out in long supply chains at all stages of product value creation of milk and dairy products. At short and alternative supply chains in which small business entities participate, the development of digital technologies remains low.The study was conducted in order to study the state of digitalization of the dairy complex of the Northwestern Federal District of the Russian Federation, assess the prospects for its sustainable development, identify problems and develop measures to stimulate the development of digital technologies. Digitalization of the supply chain makes it possible to increase customer orientation, to get information to consumers on each stage of the creation of dairy products in real time. Thermal sensors and electronic tags allow you to monitor compliance with quality standards and the promotion of dairy products along the cold chain from the farm to the consumer, fulfill the order on time, and regulatory authorities identify counterfeit dairy products. At this stage, the digital transformation of the short and ultra-short milk supply chain is fragmented and small business forms have significant financial barriers to the implementation of digital solutions. If these conditions are met, economic ties in the supply chain will be stable and of a regular, non-random nature.

Issue № 5, May 2024, article № 2

Evaluation of the machine and tractor fleet in agriculture of the Russian Federation

The growth of agricultural production is largely based on modern agricultural machinery. In the Russian Federation, products are produced by three categories of farms, their share is changing: the shares of agricultural enterprises are increasing (from 54% in 2015 to 60% in 2023) and farms (from 11.5% to 14.9%), the share of households is decreasing (from 34.5% to 25.1%, respectively). Considering the problem of insufficient equipment, they often operate only with data on the fleet in agricultural organizations (agricultural enterprises), which is decreasing (in 2022 – 196.7 thousand tractors, 1% less than in 2021), 52.3 thousand combine harvesters, 10.7 thousand forage harvesters (2% less). The data of the Ministry of Agriculture of Russia (in the Unified Intersectoral Information Statistical System – EMISS) and the State Technical Supervision authorities give large figures, since they probably take into account the equipment in the farm. According to the All-Russian Agricultural Census of 2016 There were 295046 tractors in the agricultural sector (28% of the total number), and 190486 tractors in the agricultural sector. (18%), in households – 555200 (54%), combine harvesters in agricultural enterprises – 75145 units . (55%) in the farm – 62795 units. (45%). The assessment of the machine and tractor fleet in agriculture of the Russian Federation only by the presence of physical units in the agricultural sector does not give a complete picture. It seems that it is necessary to take into account other categories of farms, primarily farms, as well as to analyze its qualitative condition, which can be assessed by knowing the brand composition. It is proposed to include these indicators (the brand composition of the MTP) in the next All-Union Agricultural Census. Obtaining these data will allow a more objective assessment of the adequacy of the MTP for the country's agriculture.