Statistical analysis of current development of agriculture in Russian regions

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Abstract


Regional social and economic development is characterized by presence of wide imbalances in structure of industry specialization, which are largely due to spatial development of individual territories. As part of a statistical study of agro-industrial complex development in regions of Russia, uneven agricultural development of certain territories, their involvement, as well as degree of participation in single national economic complex of the country are reflected. At the level of statistical significance, two of the region’s most important in terms of accumulated agro-industrial potential are identified - Krasnodar Territory and Rostov Region. Based on the account of a wide range of socio-economic indicators, the subjects of the Russian Federation were ranked by integral indicator of development of agricultural sector and by urbanization level. The regions dominating for certain types of agricultural indicators were identified and their general dynamics over a long period of statistical observations were reflected. In the process of the research, a general conclusion is made step by step about development opportunities of agro-industrial complex in the regions in strategy for sustainable development of rural territories not only at the territorial level, but, more significantly, at the federal and local levels. In order to develop rural territories, among other things, it is necessary to create imperative social conditions that will preserve existing national economic potential and ensure fulfillment of not only production, but also demographic, cultural, historical and laborfunction in the village.


Introduction The agro-industrial complex in modern Russia is the most important socio-economic resource of the country, importance of which is growing in conditions of increasing role of natural factors in the system of sustainable development of civilization. The trajectory of rural development is characterized by extreme unevenness. Even taking into account the achieved growth indicators of agricultural production after 2008-2009 crisis we can state a significant lag in quality and standard of living of rural population compared to urban one. There is a widening gap in innovation [1], investment, informational component of social development [2]. The degradation of cultural and social infrastructure is observed [3-5]. These and other unfavorable factors lead to an unsatisfactory structure of agricultural production funds. The negative impact of migration processes, expressed in the negative migration balance for rural areas, is becoming increasingly apparent [6-8]. There is an irrevocable loss of labor results in developing vast and prosperous in the past territories, and agro-industrial potential that has taken place in the past is being vanished [9-11]. Insufficient development of agricultural production in Russia hinders implementation of one of the most important reforms in post-Soviet Russia - land, which began three decades ago, which provides for replacement of collective farms and state farms as agricultural producers by hundreds of thousands of farmers and peasants owning their own land. Privatization of agricultural land and provision of it to those who want and can cultivate it seemed an obvious necessity [12]. Currently, in relation to agricultural land plots, the tasks still remain paramount: ensuring stability of land legislation and unity of the land policy pursued at the territorial level [13, 14]; developing effective information and analytical tools for accounting and monitoring the state of the land fund in the country in the context of constituent entities of the Russian Federation, digital transformation and digitalization of land market [15]; improving quality and expansion of the services provided by public authorities, ensuring information availability of land market indicators for all interested participants [16]; completion of the process of creating the Unified State Register of Real Estate, a multiple increase in the data contained in it both spatially and temporally, and increase their reliability [17]; realization of the rights of owners of land shares (units), expanding possibilities of their use in civil circulation; improving the mechanism of land acquisition through involvement in land production of agricultural land from agricultural land in case of their non-use, improper use or misuse; creating a unified methodological approach for all subjects of permissible state interference in private property for the speediest resolution of the accumulated contradictions of market institution of land acquisition for state and municipal needs [18]; development of security measures for access to land plots of federal, regional and municipal significance, as well as administrative reduction in number of cases of land plots without bidding [19]; accelerating the development of the institution of lease and redemption of land [20]; legislative support of providing land for specific agricultural production purposes: poultry, deer, pig, feed, grain, vegetable, potato, wine, industrial gardening and others [21]; accounting, monitoring, protection of valuable and productive agricultural lands [22]; application of advanced scientific developments and technological advances aimed at improving soil fertility [23]. The purpose of the study was to determine the dominant regions by integrated indicator of development of agro-industrial complex based on study of statistical dynamics for certain types of agricultural indicators over a long observation period. Materials and methods A preliminary assessment of the effects of pursuing a policy of sustainable growth at the federal and regional levels was obtained by constructing histograms and diagrams (Figs. 1-6). Tibco's STATISTICA program was widely used as a software tool for processing initial information on the Russian market. For the purposes of the current study, the official version of STATISTICA 13.3 Academic EN was used. The officially published data of the Federal State Statistics Service [24] were the statistical base of the study. To compare regions by a significant range of statistical indicators, taxonometric method can be successfully used. The Russian school of regional studies successfully applied it to solve the most important national economic issues of planning and managing territorial production complexes [13]. A descriptive analysis of statistical characteristics, which can be obtained by constructing various kinds of diagrams and calculating elementary mathematical quantities (for example, average values, deviations from average values), has well-known limitations and is mainly focused on formation of a priori judgments about the phenomenon under study. For the purposes of conducting in-depth socio-economic studies, methods of multivariate statistical comparisons are quite popular both in Russia and abroad [25]. One of the successful examples of the use of multivariate analysis in the agricultural production management system is the use of cluster analysis. These methods are also relevant for conducting interregional comparisons, rating, and also for calculating accumulated potential of individual territorial units using a set of interrelated indicators [26-29]. Modern automation procedures for multidimensional statistical calculations allow compactly presenting large volumes of published agricultural data that characterize the regions of Russia. The theoretical basis of stages of cluster analysis, as well as possibility of using modern software products for statistical data processing, is widely discussed in specialized literature, therefore, the authors publish only the results of the study. Results and discussion During the analyzed period, significant changes occurred in the structure of sown areas of agricultural structures (Fig. 1). In connection with the increase in Far East strategic importance in structure of economic and food security of the Russian Federation in the east of the country, the increase in agricultural land amounted to more than 70%. Moderate growth rates of 10…15% are characteristic for the territories of the Central Black Earth Region, the North Caucasus and the south of Russia. The area of agricultural land used in Central Siberia remained virtually unchanged. The significant retirement of agricultural land in the Northwestis of concern. Such negative changes are associated with low efficiency of the agro-industrial complex under adverse climatic influences. In this regard, it should also be added that the lands of the Northwestern Federal District account for less than 2% of the cultivated area of Russia. The economy of sustainable growth requires intensification of all production factors [30, 31], including to increase crop yields. The latter is impossible without increasing fertilizer application (Fig. 2). As more than 70% of the Russian Federation territory is located in the North [32] and is associated with risk farming, yield growth is possible in two ways: artificial improvement of land quality and development of greenhouse system. At the same time, in the second direction, the Russian school of regional studies offers practical developments that can bring the productivity of the northern territories of Russia to the European level. For example, under the guidance of Chemodin Yu.A. (Candidate of Technical Science, associate professor, Department of Economic Theory and Management, State University of Land Management) a practical model “Year-round, guaranteed, risk-free provision of country's population with agricultural products by combining greenhouse complexes with alternative low-cost sources of energy, heat (cogeneration) and cold (trigeneration)” was developed1. 1 In October 2017, a useful development was awarded the medal of the Golden Autumn Russian agro-industrial exhibition, and a corresponding application for registration of an invention was submitted to Rospatentin December 2017. In April 2018, the department team was awarded a silver medalin the competition “The Best Innovative Project of ArhimedSalon” by the International Jury at the XXI Moscow International Salon of Inventions and Innovative Technologies “Arhimed 2018”. Changing the area of agricultural crops, % 80 60 40 73,0 20 0 -20 -40 11,0 -23,7 12,8 14,3 1,7 3,2 -1,9 Central Federal district North-West Federal district Southern Federal district North Caucasus Federal district Volga Federal district Ural Federal district Siberian Federal district Far Eastern Federal district Fig. 1. Change of cropacreage in farms of all categories in 2005-2017 Fertilizers application growth rate, % Central Federal district Siberian Federal district Southern Federal district NorthWest Federal district Far Eastern Federal district Ural Federal district North Caucasus Federal district Volga Federal district Fig. 2. Growth rates of mineral fertilizers (in terms of 100% nutrients) in 2005-2017 Fig. 3. Agricultural land dynamics in 2005-2017 (percentage) In the regional context, changes in the area of agricultural land at the level of -0.3% over 12 years throughout the country should be recognized as insignificant. Chelyabinsk and Volgograd regions are characterized by the greatest growth. Increase in construction pace and changing types of permitted land use resulted in significant reduction in agricultural land occurred in the Moscow Region (Fig. 3). Emissions in the form of points on the graphs used indicate extreme values. In fig. 3, the Moscow and Murmansk regions with the maximum retirement of agricultural land in the amount of -6.6 and -5.5%, respectively, were among the extreme values (emissions). The maximum increase in the share of agricultural land in the total land fund of the region occurred in the Yamalo-Nenets Autonomous Okrug in the amount of +11.5%. 6 regions can be distinguished with a negative increase in agricultural land, the Republic of Kalmykia is only one subject of the Russian Federation with a positive increase (1%). Values for the remaining 73 regions are in the range from -0.5 to -2.0% with a median of -0.3%. Obviously, share of agricultural land retirement from circulation over the past tenplus years in the vast majority of Russian regions is very small. Perhaps, an analysis of agricultural land by category or land by regions of the Russian Federation will help to interpret reasons for low profitability of this economical sector. In absolute terms, the largest increase of agricultural land was recorded in the Volgograd region and the Republic of Kalmykia. The Moscow region, Altai region, Kemerovo region and the Khanty-Mansiysk Autonomous Okrug - Yugra are leaders in agricultural land reduction at the regional level (Fig. 4). According to the Ministry of Agriculture of the Russian Federation, threshold values of food safety indicators provided for by the Food Safety Doctrine for grain, vegetable oil, sugar, meat and fish were met in 2018. The threshold indicators for milk and milk products, potatoes were not reached. Fig. 4. Dynamics of agricultural land in 2005-2017 (thousands of hectares) Currently, agricultural development is characterized by steady growth in agricultural exports, with growth faster than most other economical sectors. In 2018, growth in exports of food products and agricultural raw materials compared to the previous year amounted to 19%, reaching $ 25.7 billion. The main growth was due to an increase in wheat exports by 39.5% (by $ 3.0 billion), in which Russia ranks first in the world. The target growth rate for agricultural exports is up to $ 45 billion by 2024. The major work in agricultural production is carried out by residents of the countryside. It is important to ensure a comfortable standard of living in the village that meets modern requirements, attract young specialists to the countryside with possibility of continuous improvement of their skills, create conditions for recreation and leisure (sports, walking with children in landscaped areas and recreation areas, improving living conditions). Equally important is preservation of historical and cultural monuments, restoration of natural landscapes, solution of issues related to civilized collection of household waste and its disposal. The dynamics of sown areas for the analyzed period may be characterized as weak. The Tyumen region, the Ryazan region, the Rostov region, the Yamalo-Nenets Autonomous District, the Republic of Buryatia, the Republic of Bashkortostan, the Kursk Region and the Republic of Sakha-Yakutia demonstrate success in agricultural production (Fig. 5). Fig. 5. Dynamics of cultivated areas in 2005-2017 Fig. 6. Dynamics of crop yield in 2005-2017 The results presented on the graphs very clearly reflect not only dynamics of turnover of agricultural land throughout the country, but also efficient use of available land resources. Some regions have made significant progress in the development of agricultural production for the period from 2005 to 2017. The Volgograd region increased gross harvest of sugar beets by more than 45 times, the Kurgan region and the Omsk regions increased the gross harvest of flax fiber by more than 20 times, the Chechen Republic increased the gross harvest of sunflower seeds by a record 50 times (Fig. 6). Oryol region increased sunflower growing area for the analyzed period from 1. thousand hectares in 2005 to 74.8 thousand hectares in 2017, therefore, the indicator was excluded for display on the graph. The region showed amazing dynamics in this indicator. Factors that testify to involvement of agricultural land in agro-industrial circulation allow us to speak about positive shifts in the processes of formation of trajectory of sustained sustainable development at the regional level. Modernization of engineering infrastructure is necessary to ensure a modern standard of living and remain important for rural areas: construction and repair of roads, gas pipelines, water pipelines, and telecommunication networks. According to the Federal State Statistics Service for 2018, less than 40% of rural settlements are provided with central water supply, 35% of villages do not have an asphalt road, gasification rates for houses (apartments) with network gas are uneven across the country and averaged 60.3%. In accordance with the Government’s decree, from January 1, 2018, the implementation of the federal target program “Sustainable Development of Rural Areas for 2014-2017 and Until 2020” was terminated ahead of schedule. The program is integrated into the State Agro-Industrial Complex Program as a separate subprogram “Sustainable Development of Rural Areas”. For the implementation of the subprogram in 2018, 17.1 billion rubles were allocated. from the federal budget, 12.5 billion rubles - from regional and local budgets, 5.0 billion rubles - from extra budgetary sources. Implementation of the subprogramme contributed to improving living conditions of citizens in rural areas. Resource support for program activities does not provide pace of development of housing, social and engineering infrastructure of the village, network of roads necessary for implementation of serious qualitative changes in conditions of rural population. The draft state program for integrated development of rural areas provides for a preferential mortgage of up to 3% per annum for purchase or construction of a house, as well as possibility of using a consumer loan for home improvement and for purchase of equipment for energy supply, water supply, sewage, heating. Russian credit organizations will be reimbursed for lost income in the amount of the Central Bank's key rate. As part of direction to promote rural employment, compensation is provided for costs to agricultural producers who sent employees for additional training, support is provided for future specialists with targeted training and students undergoing practical training, as well as soft loans for creation and connection of facilities to engineering and transport infrastructure. The target indicator of rural employment should be 80%, and unemployment should be 5.7%. The project provides for improvement of rural areas - 42 thousand projects (landscaped recreation areas, playgrounds and sports grounds, well-lit streets, sidewalks and bus stops). Development of engineering and transport infrastructure is also one of the priorities of state policy and basis for improving rural life. The program provides for completion of construction and commissioning of gas pipelines, water pipelines, telecommunication networks, road networks (leading to socially significant objects of rural settlements, objects of production and processing of agricultural products), as well as a comprehensive arrangement of sites for compact housing in 2021. To achieve these goals, the government plans to coordinate the state program with national projects and the country's Spatial Development Strategy. According to the results of the study by the Ministry of Agriculture, more than 6 trillion rubles are needed to solve the priority tasks of rural territories: 2.1 trillion rubles - development of engineering infrastructure; 2 trillion rubles - improving the quality of road infrastructure; 900 billion rubles - improvement of housing conditions; 950 billion rubles - development of education, increasing accessibility of cultural facilities, expanding access to sports facilities, developing a healthcare system. A number of targets are planned in the draft State Program, including increasing well-being of rural population in terms of the ratio of the average monthly disposable resources of rural and urban households to 80% (currently 68%); improvement of the housing stock of the rural population in terms of the proportion of residential premises provided with all types of utilities to the level of 50% (currently 32.5%); maintaining the share of the rural population in the total population of Russia (currently about 35%). As a result of intensification of urbanization processes throughout the regions, the area of agricultural land is decreasing. A separate role in this process is played by cities of federal significance: Moscow, St. Petersburg and Sevastopol. For example, in connection with administrative transformations in Moscow, the area of agricultural land changed from 2.1 thousand hectares in 2012 to 48.9 thousand hectares in 2017. The main descriptive statistics on the land fund of the regions are given in table 1. Cities of federal significance as Moscow, St. Petersburg and Sevastopol were previously excluded, therefore, number of observations was 82, and they were disclosed in 7 variables. Land distribution of regional economic systems by categories Table 1 Index Legend for a land category I II III IV V VI VII Mean 4 679.7 242.0 211.4 573.5 13 735.6 342.1 1 094.0 Median value 2 347.4 216.6 74.7 80.4 2 014.5 69.0 145.7 Standard deviation 7 219.9 159.3 561.5 1 755.0 34 784.0 979.0 4 094.1 Amount 383 738.3 19 844.6 17 337.1 47 032.8 1 126 320.3 28 054.8 89 708.4 Minimum value 150.9 12.4 6.7 0 0 0 0.3 Maximum value 39 760.5 738.4 4 918.2 12 225.3 252 820.3 7 814.3 30 310.2 Lower quartile 1 450.3 111.4 46.2 11.4 457.6 12.2 30.8 Upper quartile 4 529.2 361.8 196.6 370.6 10 257.8 217.1 601.6 Note. I - agricultural land; II - lands of settlements; III - lands for industry and other purposes; IV - lands of specially protected territories and objects; V - lands of the forest fund; VI - lands of the water fund; VII - land reserve. Most regions have arable land fund formed in the 20th century, designed to ensure food security of the population. The largest share of land left for a while without cultivation in order to restore their fertility is in the Republic of Mari El (17%), Pskov and Bryansk regions (12 and 8%, respectively). The largest land areas occupied by perennial plantings are located in cities of federal significance, as well as in the Murmansk and Moscow regions. The strongest differentiation is observed in relation to fodder land distribution, so in the Nenets Autonomous Okrug their share is 99% of the total agricultural land. In the diagram below, the block boundaries take 25...75% of values for all the regions (Fig. 7). In the structure of agricultural land (Fig. 8), the largest share is occupied by arable land, they make up half of all farmland. The third part of the land is occupied by fodder plantings. The share of lands occupied by perennial plantations and deposits in the structure of agricultural lands is insignificant. Fig. 7. Distribution of agricultural land in four categories Fig. 8. Ratio of arable and forage land as main categories of agricultural land Fig. 9. Graph of average values of land area in a regional context According to the total area of land occupied by arable land and fodder land, among the regions we can distinguish the Transbaikal region, the Republic of Kalmykia, the Orenburg region and the Altai region (Fig. 9). For forming a grouping of regions according to degree of agro-industrial potential, three indicators were taken into account - area of agricultural land, forest fund land and reserve land. The grouping of regions by other variables did not pass the test for statistical significance of considered variables in the cluster analysis. Interpretation of Fig. 9 is given in table 2. The regions included in the first and second clusters have the largest land resources for further intensification of agricultural development. The area of agricultural land can be increased due to a significant share of forest land. Thus, the constituent entities of the Russian Federation are initially specific for available land and property potential. The administrative management of the regional system is based on land management for sustainable socio-economic development. In order to identify uniformity of agro-industrial development, a taxonometric model of agro-industrial potential of the regions was constructed by summarizing 22 statistical indicators (Table 3) published by Rosstat in a regional context. The taxonometric development indicator is so-called synthetic indicator, which equally characterizes variables under consideration for the entire set of territorial units, which allows linear ordering of variables included in the analysis and ranking of objects of observation with arithmetic procedures. Subjects of the Russian Federation with significant land resources for further agricultural development Table 2 Number in decreasing order of place in the overall ranking Regions X976 X980 X982 Regions with significant land resources 1 Krasnoyarsk region 39.8 155.6 30.3 2 Republic of Sakha (Yakutia) 19.4 252.8 21.4 Regions with average Russian indicators of land provision 1 Komi Republic 1.9 36.0 0.6 2 Transbaikal region 8.0 31.9 1.2 3 Kamchatka Krai 0.2 44.2 0.7 4 Magadan region 0.3 44.6 0.3 5 Amurskaya region 3.5 30.6 0.8 6 Khanty-Mansiysk Autonomous Okrug - Ugra 0.6 48.7 2.0 7 Tomsk region 2.0 28.6 0.5 8 Republic of Buryatia 2.8 26.9 0.6 9 Arkhangelsk region 2.3 27.1 3.9 10 Yamal-Nenets Autonomous Okrug 30.5 31.7 5.0 11 Irkutsk region 2.9 69.3 0.5 12 Khabarovsk region 0.4 73.7 1.4 13 Chukotka Autonomous Okrug 39.4 27.6 4.2 Regions not adequately provided with land for agro-industrial development 14-82 All other subjects of the Russian Federation The system of used statistical indicators Table 3 № Index Х1 Agricultural production in farms of all categories (in actual prices), mln. roubles Х2 Indices of agricultural production in farms of all categories (in comparable prices; as a percentage of the previous year), % Х3 Indices of crop production in farms of all categories (in comparable prices; as a percentage of the previous year), % Х4 Livestock production indices in farms of all categories (in comparable prices; as a percentage of the previous year), % Х5 Balanced financial result (profit minus loss) of crop production organizations according to financial statements, mln. roubles Х6 Balanced financial result (profit minus loss) of livestock organizations according to financial statements, mln. roubles Х7 Profitability of sold goods (works, services), crop production, % Х8 Profitability of sold goods (works, services), livestock products, % Х9 Yield of grain and leguminous crops in weight after refinement in farms of all categories (centners from one hectare of harvested area), c Х10 Potato yield on farms of all categories; centners from one hectare of cleaned area, c Х11 Vegetable productivity on farms of all categories; centners from one hectare of cleaned area, c Х12 Application of mineral fertilizers per 1 ha of sowing crops in agricultural organizations (in terms of 100% of nutrients), kg Х13 Application of organic fertilizers per 1 ha of sowing crops in agricultural organizations, t Х14 Number of cattle in farms of all categories at the end of the year, thousand heads Х15 Number of pigs in farms of all categories at the end of the year, thousand heads Х16 Number of sheep and goats in farms of all categories at the end of the year, thousand heads Х17 Cattle and poultry production for slaughter (in slaughter weight) in farms of all categories, thousand tons Х18 Milk production in farms of all categories, thousand tons Х19 Egg production on farms of all categories, million units Х20 Wool production in farms of all categories (in physical weight), t Х21 Honey production in farms of all categories, t Х22 Feed consumption per one conditional head of cattle in agricultural organizations (centners of feed units), c The study was carried out in the following stages: 1. Collection of statistical indicators and bringing them to a comparable form. 2. After preliminary preparation of the initial data, a matrix of regional indicators was formed and standardization was carried out. 3. The procedure for standardization of variables is accompanied by an inevitable loss of information, therefore, to increase the influence of some variables and reduce the influence of others, coefficients of hierarchy of variables were introduced. 4. The variables were differentiated into positive and negative to calculate individual deviations from the reference for each parameter being evaluated. A similar separation was based on determining the point P0 with coordinates z01, z02 , ..., z0n (1) for z0s = maxr zrs, if s Ì I, z0s = minr zrs, if s Ë I (s = 1, ..., n), where I is the set of positively influencing and negatively influencing (negative) variables; zrs is the standardized value of the attribute s for unit r. 5. Determination of the distance сi0 as the distance between the individual values of variables and values of P0, which are the reference by the formula 1 é n 2 ù 2 ci0 = êëås=1(zis - z0 s ) úû (i = 1, ..., w). (2) i 6. The distances ci0 thus obtained are the basis for calculating the indicator of the level of development of the region d* , defined as i d* = ci0 , (3) c = c + 2S , c = 1 w c , S = é 1 c0 w (c 1 -c )2 ù 2 . where 0 0 0 0 w åi=1 i0 0 ê w åi=1 i0 0 ú ë û i The final indicator d* serves as an integral characteristic of the regions according to the totality of the considered indicators and is characterized by a value tending to zero. 7. The reverse value is considered more informative, showing the closer the indicator to unity, the higher the level of socio-economic development of the region. c d = - (4) i 1 i0 . c0 Using the integral indicator obtained by the taxonometric method, it is possible to evaluate the achievement by an individual region at a particular point in time of the average value of the indicators in question. For the purposes of reliability of the analysis, cities of federal significance were excluded, the number of regions participating in the analysis was 82 (Table 4). At the last stage, a matrix of standardized variables, adjusted for the coefficient of hierarchy, was calculated. The result of the analysis was the construction of a ranking of regions by the level of development of agricultural production. The results of rating building can be compactly displayed on a histogram by determining the number of intervals (groups) using the Sturges formula (Fig. 10). Fig. 10. Distribution of regions according to the integrated index of agro-industrial development The ranking of regions by relatively stable groups visually shows a significant gap between the Krasnodar Territory and the Rostov Region by the integral index, determining their dominant position in the agricultural specialization of the regions (Table 4). Due to the fact that the groups were selected at regular intervals, the second group was not present in the final rating, and none of the regions considered fell into the range of values. Distribution of Russian regions by index of agro7industrial development Table 4 Group place Cross-cutting number in the overall ranking Name of the subject of the Russian Federation Rating Index 1 group 1 1 Krasnodar region 0.9833 3 group 1 2 Rostov region 0.9161 4 group 1 3 Republic of Tatarstan 0.8849 2 4 Belgorod region 0.8831 3 5 Stavropol region 0.8691 4 6 Voronezh region 0.8666 5 group 1 7 Republic of Bashkortostan 0.8428 2 8 Altai region 0.8378 3 9 Saratov region 0.8345 4 10 Volgograd region 0.8277 6 group 1 11 Kursk region 0.8193 2 12 Chelyabinsk region 0.8131 3 13 Tambov region 0.8099 Continue of Table 4 Group place Cross-cutting number in the overall ranking Name of the subject of the Russian Federation Rating Index 4 14 Orenburg region 0.8086 5 15 The Republic of Dagestan 0.8064 6 16 Lipetsk region 0.8040 7 17 Moscow region 0.8007 8 18 Samara region 0.7972 9 19 Krasnoyarsk region 0.7964 10 20 Leningrad region 0.7958 7 group 1 21 Omsk region 0.7936 2 22 Novosibirsk region 0.7901 3 23 Penza region 0.7879 4 24 Bryansk region 0.7858 5 25 Nizhny Novgorod region 0.7807 6 26 Sverdlovsk region 0.7805 7 27 Oryol region 0.7785 8 28 Tyumen region 0.7783 9 29 Udmurtian Republic 0.7775 10 30 Republic of Crimea 0.7758 11 31 Irkutsk region 0.7746 12 32 Tula region 0.7719 13 33 Republic of Mordovia 0.7703 14 34 Kemerovo region 0.7693 15 35 Amursk region 0.7664 16 36 Ryazan region 0.7662 8 group 1 37 Perm region 0.7619 2 38 Kabardino-Balkarian Republic 0.7603 3 39 Kurgan region 0.7602 4 40 Chuvash Republic 0.7601 5 41 Mari El Republic 0.7600 6 42 Primorsky Krai 0.7594 7 43 Ulyanovsk region 0.7583 8 44 Astrakhan region 0.7577 9 45 Kirov region 0.7573 10 46 Kaluga region 0.7566 11 47 Vladimir region 0.7554 12 48 Tver region 0.7544 13 49 Yaroslavl region 0.7541 14 50 Karachay-Cherkess Republic 0.7527 15 51 Kaliningrad region 0.7520 16 52 Vologodsk region 0.7518 17 53 Novgorod region 0.7515 18 54 Pskov region 0.7513 19 55 Tomsk region 0.7510 20 56 Republic of Kalmykia 0.7479 21 57 Republic of Ossetia - Alania 0.7476 22 58 Smolensk region 0.7475 23 59 Republic of Sakha (Yakutia) 0.7468 24 60 Khabarovsk region 0.7462 25 61 Kostroma region 0.7457 26 62 Transbaikal region 0.7456 27 63 Republic of Adygea 0.7456 28 64 Chechen Republic 0.7455 29 65 Ivanovo region 0.7431 30 66 Republic of Buryatia 0.7429 End of Table 4 Group place Cross-cutting number in the overall ranking Name of the subject of the Russian Federation Rating Index 31 67 Republic of Khakassia 0.7421 32 68 Arkhangelsk region 0.7399 33 69 Altai Republic 0.7398 34 70 Sakhalin region 0.7395 35 71 Komi Republic 0.7388 36 72 Kamchatka Krai 0.7374 37 73 Republic of Ingushetia 0.7371 38 74 Khanty-Mansiysk Autonomous Okrug - Ugra 0.7371 39 75 Jewish Autonomous region 0.7359 40 76 Tyva Republic 0.7356 41 77 Republic of Karelia 0.7350 42 78 Magadan Region 0.7332 43 79 Murmansk region 0.7326 44 80 Yamal-Nenets Autonomous Okrug 0.7326 45 81 Nenets Autonomous Okrug 0.7321 46 82 Chukotka Autonomous Okrug 0.7317 Conclusions The research confirmed uneven agro-industrial potential of the regions in the Russian Federation. The most developed in this regard can be recognized as the southern regions, the least developed are the northern ones, which is quite logical and logical, given that more than 70% of the country's territory is located in the North. Achieving the goals set for agricultural sector will contribute to a serious improvement in quality of countryside life. As level of agricultural development in most of Russia is low, the following measures are required: comprehensive diversification of regional economic systems, support and development of farm types of production, traditional types of crafts, elimination of administrative restrictions on implementation of products produced in rural areas, creation of affordable conditions for financial equalization transformations, information support of labor activity, infrastructural support of agribusiness processes, increasing interest and economic literacy of the population.

Nikolai I Ivanov

State University of Land Use Planning

Author for correspondence.
Email: nickibut@yandex.ru
Moscow, Russian Federation

Doctor of Economic Sciences, Professor, Vice-rector for social Affairs and educational work, head of the Department of Economic Theory and Management

Tatiana V Shevchenko

State University of Land Use Planning

Email: tatyanavidn@mail.ru
Moscow, Russian Federation

Candidate of Economic Sciences, associate Professor, Department of Economic Theory and Management

Vladimir S Gorbunov

State University of Land Use Planning

Email: vsgorbunov@yahoo.com
Moscow, Russian Federation

Candidate of Geographical Sciences, associate Professor, Department of Economic Theory and Management

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