CC BY
4DOI: 10.29141/2658-5081-2021-22-3-6
JEL classification: L66, R58
Ilya V. Naumov
Institute of Economics (Ural branch of RAS), Ekaterinburg, Russia
Vladislav M. Sedelnikov Institute of Economics (Ural branch of RAS), Ekaterinburg, Russia
Interregional relationships in the Russian dairy market:
Spatial growth poles
Abstract. Significant differentiation between Russian regions in the production and consumption of milk and dairy products hampers the food security. The paper investigates interregional relationships in the indicated processes. Methodologically, the research relies on the regional and spatial economics, in particular on Hirschman's theory of backward and forward linkages, Perroux's theory of growth poles, Christaller's central place theory, Losch's theory of spatial organisation of the economy, Friedman's core-periphery theory, and some others. Using the spatial autocorrelation analysis by Moran procedure and the research of inter-territorial interactions according to Anselin, the authors determine the main regional centers of milk production and consumption, cluster the regions according to the level of dairy production, and specify clusters of closely interconnected regions of Russia on the basis of the identified interregional relationships. The agro-industrial cluster structures functioning in these territories confirm the tightness of the identified interregional cooperative direct and reverse relationships. The four clusters include the Siberian cluster (Novosibirsk, Omsk, Tyumen oblasts, etc.), the Southern cluster (Krasnodar krai, Rostov and Belgorod oblasts, etc.), the Central cluster (Vladimir and Nizhny Novgorod oblasts, Republic of Mordovia, etc.), and the Ural-Volga cluster (Republic of Tatarstan, Sverdlovsk and Nizhny Novgorod oblasts). The results of the study can be useful for researchers involved in the design of spatial strategies, regional programmes and models of socioeconomic development, as well as for regional and municipal authorities implementing the Strategy for the spatial development of the Russian Federation for the period up to 2025.
Keywords: regional development; spatial development; dairy market; interregional relationships; clusters; autocorrelation.
Acknowledgements: The paper is prepared in accordance with the R&D Plan for the Laboratory for Spatial Territorial Development Modelling of the Institute of Economics (Ural Branch of RAS) for 2021 on the topic "Methodology for modelling the spatial development of macroregions in the context of ensuring their economic security" For citation: Naumov I. V., Sedelnikov V. M. (2021). Interregional relationships in the Russian dairy market: Spatial growth poles. Journal of New Economy, vol. 22, no. 3, pp. 103-124. DOI: 10.29141/2658-5081-2021-22-3-6 Received February 3, 2021.
Introduction
The imposition of a food embargo by the Government of the Russian Federation on certain food categories produced abroad as well as the global coronavirus pandemic have made the issue of ensuring national, economic and food security of Russia especially acute. Mechanisms for solving this problem are specified in the Declaration of the World Summit on Food Security1, the Doctrine of Food Security of the Russian Federation2, the Strategy for the Economic Security of the Russian Federation for the period up to 20303, the Strategy for the Development of the Agro-industrial and Fisheries Complexes of the Russian Federation for the period up to 20 304, the Forecast of the Social and Economic Development of the Russian Federation for the period up to 20365, the state program for the development of agriculture and the regulation of markets for agricultural products, raw materials and supply6 and in other documents designed to reduce interstate and interregional imbalances and spatial differentiation.
First of all, it is necessary to provide the population of Russia with essential goods, which include milk and dairy products. However, the market for these products is significantly fragmented and characterised by a high level of regional differentiation in both production and consumption.
The research looks at the Russian dairy market, and in particular, at the spatial differences in its development between regions. Overcoming regional imbalances in this area is possible due to the intensification of interregional relationships, the formation of intersectoral production strings and an increase in the intensity of interregional trade.
The purpose of the article is to assess the spatial differentiation and the level of interregional relationships in the milk and dairy products market in the process of the production and consumption, which will help to identify key growth poles for ensuring food security and economic development of the Russian economy. To achieve this purpose, the paper's objectives are to:
• review the evolutionary aspects of the formation of the theory of spatial development;
1 Declaration of the World Summit on Food Security. World Summit on Food Security. Rome, November 1618, 2009. https://www.un.org/ru/documents/decl_conv/ declarations/pdf/summit2009_declaration.pdf. (in Russ.)
2 Doctrine of Food Security of the Russian Federation: Decree of the President of the Russian Federation of January 21, 2020 no. 20. https://www.garant.ru/products/ipo/prime/doc/73338425/.(in Russ.)
3 On the Strategy of Economic Security of the Russian Federation for the period up to 2030: Decree of the President of the Russian Federation of May 13, 2017 no. 208. http://www.kremlin.ru/acts/bank/41921. (in Russ.)
4 On the approval of the Strategy for the Development of the Agro-industrial and Fisheries Complexes of the Russian Federation for the period up to 2030: Order of the Government of the Russian Federation of April 12, 2020 no. 993-p. http: // docs.cntd.ru/document/564654448. (in Russ.)
5 Forecast of the social and economic development of the Russian Federation for the period up to 2036. https://economy.gov.ru/material/directions/makroec/prognozy_socialno_ekonomicheskogo_razvitiya/prognoz_ socialno_ekonomicheskogo_razvitiya_rossiyskoy_federacii_na_period_do_2036_goda.html. (in Russ.)
6 On the state program for the development of agriculture and regulation of agricultural products, raw materials and supply for the period of 2013-2020: Resolution of the Government of the Russian Federation of July 14, 2012 no. 717 (as amended on December 31, 2020). http://docs.cntd.ru/document/902361843. (in Russ.)
• cluster the regions and identify leading regions in the production and consumption of dairy products;
• identify direct and reverse interregional relationships;
• identify growth poles in the production of dairy products and areas of their influence, analyse existing and potential interregional clusters of the production of dairy products.
The novelty of the research lies in the substantiation of spatial and sectoral differentiation, including the high concentration and localisation of production in some regions and consumption centers in the others, in the identification of interregional relationships within the framework of the creation of potential clusters and functioning of already existing clusters on the dairy market, as well as in the integrated use of tools of Moran's spatial autocorrelation analysis and Anselin's spatial weights matrices.
Interregional relationships, identified on the basis of cluster structures of the production of milk and dairy products, will not only satisfy the domestic needs of the population, but also reduce import dependence on foreign products, thereby solving the problem of ensuring the country's food security by intensifying interregional exchange in the dairy market in Russia.
Theoretical approaches to the study of the features of spatial development
The problem of ensuring food security is examined in the scientific literature from the standpoint of protecting interests at various levels: global, subregional, interstate, state, regional, local and household level. On February 11, 2021, in Rome, members of the Committee on World Food Security (WFS) endorsed the first ever Food Systems and Nutrition Guidelines, designed to support efforts to eradicate all forms of hunger and malnutrition through an integrated approach to food systems1.
Russian policies follow the lead of the international agenda. Thus, the state program for the development of agriculture and regulation of the markets of agricultural products, raw materials and supply was adopted2, which is aimed to ensure food independence of Russia, accelerate import substitution of certain types of products, as well as increase the competitiveness of Russian agricultural products in the domestic and foreign markets.
These tasks are solved according to the parameters specified in the Doctrine of Food Security of the Russian Federation, which defines: "Food security is the state of the economy of the Russian Federation, in which food independence is ensured, including the guarantee for every citizen of the country of the physical and economic accessibility of
1 CFS Members endorsed the new Food Systems and Nutrition Guidelines. February 11, 2021. Rome. Food and Agriculture Organization of the United Nations. http://www.fao.org/news/story/ru/item/1374891/icode/.
2 On the State Program for the Development of Agriculture and Regulation of Agricultural Products, Raw Materials and Supply for 2013-2020: Decree of the Government of the Russian Federation of July 14, 2012 no. 717 (as amended on December 31, 2020). http://docs.cntd.ru/document/902361843. (in Russ.)
food products that meet the requirements ... technical regulations, in an amount not lower than the rational consumption rates required for an active, healthy lifestyle"1.
However, despite the growth in agricultural production, its consumption differs significantly across the territory of Russia. In our opinion, the theoretical aspects of the study of interregional disproportions (interregional differentiation) should be considered in accordance with the theories of spatial development. In the course of the study, the advantages and disadvantages of key theories of the spatial development of regions were analysed in detail [Naumov, Sedelnikov, Averina, 2020, p. 386].
In the process of evolution, these theories went through several stages. In the second half of the 19th century - the beginning of the 20th century, von Thunen, Launhardt, Weber and others studied the location of a single enterprise within the framework of microeconomics. By the mid-1950s, Christaller, Losch [2001] and Isard [1960, p. 830] identified the factors that determine the spatial distribution of enterprises in a certain territory, studied the aspects of their localisation in the region from the perspective of mesoeconomics.
Based on the theory of backward and forward linkages, Hirschman showed that the economic growth of a region tends to be imbalanced [Hirschman, 1958, p. 468]. The availability of the necessary resources can lead to regional development, while the lack of them can act as an incentive to identify its reserves. Perroux pointed out that economic growth cannot be observed at all points in space at the same time, but manifests itself in growth poles, which are based on a basic industry with significant development potential, a set of local industries and spatial agglomeration of production [Perroux, 2007, p. 82].
The 1980s saw the emergence of the theories, which targeted spatial development, including the theories of central places and agglomerations, where spatial effects from the influence of economic factors are assessed; new economic geography; new theory of trade; spatial models of industrial objects development; new theory of growth, which considers human capital, knowledge and competencies of workers as an endogenous resource for development [Izotov, 2013, p. 135; Zamyatina et al., 2020, p. 568]. They significantly modified the research of scientists of the 1950s, complementing and expanding the theories of cumulative growth used for the spatial development of territories. The main disadvantage of the above theories was that they did not allow reducing spatial differentiation and reducing interregional and interstate differences in economic development.
The 1990s were marked by the appearance of the cluster theory aimed at studying the spatial effects of creating of network communities of enterprises and their participation in cooperative relationships, spatial clustering and inter-territorial interactions, the effects of growth poles.
1 Doctrine of Food Security of the Russian Federation: Decree of the President of the Russian Federation of January 21, 2020 no. 20. https://www.garant.ru/products/ipo/prime/doc/73338425/. (in Russ.)
Currently, researchers [Demidova, 2011, p. 125; Firsova, Balash, Nosov, 2012, p. 302; Guriev, Vakulenko, 2015, p. 635; Gluschenko 2018, p. 48; Naumov, 2020, p. 19; Sedelnikov, 2020, p. 216] actively apply methods of spatial autocorrelation and clustering [Moran, 1948, p. 250; Geary, 1954, p. 132; Getis, Ord, 1995, p. 289; Anselin, 2002, p. 253]. These methods allow studying spatial effects influencing socioeconomic processes, and modelling inter-territorial interactions in various directions. Their use makes it possible to enhance the effect of interregional cooperation relationships and reduce the spatial differentiation of the development of milk and dairy products manufacture processes in territorial systems of different levels.
Research methodology for spatial differentiation
To investigate the spatial differentiation of regions in the production of dairy products, identify the spatial localisation of the corresponding industries, conduct spatial clustering of territories by the level of dairy production and search for close interregional cooperative relationships in this area, we suggest using a spatial autocorrelation analysis according to Moran's method [Moran, 1948, p. 248]. This analysis will allow not only establishing the existence of relationships between regional systems by indicators of dairy production, but also determining their direction (direct and reverse relationships), identifying the growth poles in the production of dairy products and their distribution zones. At the same time, using Moran scatter plot, it is possible to determine the features of spatial development and key scenarios for the development of the dairy industry in Russia.
The accuracy of the results of spatial autocorrelation analysis largely depends on the spatial weight matrices used to account for the distances between the objects under study. Currently, in the study of spatial auto-correlation relationships, matrices of adjacent boundaries between territorial systems, matrices of linear distances, distances between objects along highways, railways, aviation, river communications, distances between the centers of territorial systems or to their boundaries are used. In addition, there are also unconventional spatial weight matrices, such as the matrix of the minimum travel time between the main cities of the regions, the matrix of trade flows [Beck, Gleditsch, Beardsley, 2006, p. 38], a matrix reflecting the differentiation in cultural values [di Guardo, Marrocu, Paci, 2016, p. 835], the matrix of the market potential of the regions, the migration matrix [Guriev, Vakulenko, 2015, p. 637] and others.
Spatial autocorrelation is correctly described by only one set of spatial weights W, while using other weights can lead to false results. Therefore, we consider it important to use different matrices of spatial weights and select for further research those that allow calculating statistically significant coefficients. We propose calculating these coefficients according to the traditional method of Moran's spatial autocorrelation:
N
x
SA-M)2
(1)
= TV x-'—L-
zfc*,-#o2
(2)
where IG denotes the global autocorrelation index of the studied regions; ILi is the local autocorrelation index of the studied regions; N is the number of regions; Wij is the element of the matrix of spatial weights for regions i and j; ^ is the average value of the indicator; Xj is an analysed indicator of one region; Xj is an analysed indicator of another region.
The global index is necessary to assess the possibility of clustering the territories by the level of dairy production, whereas local indices are used to search for direct and inverse spatial effects that affect the production of dairy products in the studied regional systems. The use of various matrices of spatial weights in calculating the indicated indices will allow for a more balanced approach to the study of spatial effects, namely, identifying the optimal matrices for spatial clustering of dairy production processes and confirming the results of calculating Moran's indices by various methods of measuring the distances between territorial systems. Spatial clustering of territories is supposed to be implemented using Moran scatter plot.
This tool makes it possible to differentiate territorial systems by categories HL, HH, LH, LL based on the level of dairy production and the characteristics of their spatial distribution. However, unlike traditional approach, we propose in each quadrant of the diagram additionally considering territories that have values above and below the average of the indicator of the level of regional two-way influence (local index of spatial autocorrelation) (Figure 1).
This will make it possible to select territories with the highest level of relationships from the entire set of territorial systems belonging to one category or another (HH, HL, LL, LH) and thereby confirm the results of clustering of territorial systems obtained at the stage of scatter plot formation. This refinement of the traditional methodological approach to the implementation of spatial autocorrelation analysis allows us eliminating the ambiguity of the results obtained. Thus, according to Moran scatter plot, the HL category includes regional systems that have high values for the analysed indicators, which are surrounded by territories with rather low values. This means that regional systems from the HL category, in fact, are growth poles for nearby regional systems.
However, the practice of calculating the local indices of Moran's spatial autocorrelation shows that territories with weak interregional relationships that are not related to growth poles can also be classified in this category. Moreover, such regions may be notable for not the highest values for the studied attribute of clustering.
LH Zone of influence of spatial clusters and growth poles HH Spatial clusters, having high values of the indicator
Spatial two-way influence level Spatial two-way influence level
High Low High Low
are a zone of strong influence of spatial clusters (HH) and growth poles (HL) are experiencing weak influence spatial clusters (HH) and growth poles (HL) • are not growth poles • are influenced by growth poles (HL) • and are located around them • are experiencing weak influence of growth poles (HL) • are the periphery of the cluster
LL Clustering territories with low indicator values HL
Spatial two-way influence level
High Low
• are not related to others territorial systems • are not influenced by spatial clusters (HH) and growth poles (HL) • growth poles (cores of a spatial cluster) • areas with a high concentration of resources • outliers (extremums) • are not growth poles
Fig. 1. Spatial clusterisation of territories using Moran scatter plot
Regional systems located in the HH category are not growth poles, centers of attraction of resources, but have high values of the studied indicator. Identifying among them territorial systems with a high and low level of mutual influence relative to the average value of the local autocorrelation index will make it possible to identify the regions on which the growth poles have a strong or weak influence. The same division of regions by the strength of two-way influence is presented in the LH category. According to Moran scatter plot, this category of territorial systems is the zone of influence of spatial clusters (HH) and growth poles (HL). As a result of this approach to the formation of the scatter plot in relation to the territorial systems of the HH and LH quadrants, which have two-way influence, we propose considering only those regions whose local autocorrelation indices are significantly higher than the average value for all the subjects of the Russian Federation. Territorial systems with a low value of the local autocorrelation index, in our opinion, can receive an impulse for development from growth poles and spatial clusters, but do not have close relationships with them. As in the calculation of the global and local indices of spatial autocorrelation, we propose forming the Moran scatter plot taking into account various matrixes of spatial weights. This is necessary for the reasonable assignment of the studied regional systems to the categories HH, HL, LL, LH, and for
the search of growth poles, spatial clusters and zones of their influence all confirmed by different methods.
The development of the traditional methodological approach to spatial auto-correlation analysis was carried out in terms of confirming the identified relationships between territorial systems. To search for these relationships in spatial analysis, Anselin's matrix is usually applied [Anselin, 2019, p. 155], which displays direct and reverse two-way influences of territories using local indices of spatial autocorrelation. We propose identifying territories with high autocorrelation indices (above the average) in this matrix in order to establish close inter-territorial relationships in the studied socioeconomic processes, in particular in the field of dairy production. The use of various spatial weights in the formation of these matrices will make it possible to select the most stable ones from the set of identified relationships, verified by different methods of measuring distances.
The established relationships can be checked by pair correlation analysis using time series. Classical correlation analysis allow assessing the tightness of the relationships between regions not only by spatial, but also by temporal statistical data. An important stage of the research is to confirm the relationships in the production of dairy products by analysing the ongoing joint projects in this area. This analysis involves the search for industrial cooperation, integration of enterprises operating in different territorial systems, as well as ongoing integration projects and cluster structures created in this area, supported by state authorities. Its implementation is necessary to confirm the reliability of the results obtained using economic and mathematical tools. A comprehensive approach to the study of spatial autocorrelation will make it possible not only to reveal spatial effects that affect production processes, but also to carry out spatial clustering of territories, in particular, to establish growth poles in the production of dairy products, spatial clusters and zones of their influence, as well as close inter-territorial relationships in the implementation of these processes.
Research results
The first stage of the study involved the identification of key centers for the production and consumption of milk and dairy products in Russia, the grouping of territories by the level of production and consumption, as well as the identification of regional specifics of the spatial distribution of industries. In Figure 2 black color reflects the leading territories that produce dairy products and have an impact on the surrounding regions. These include the Republic of Tatarstan (the share in Russia's total production is 5.9 %), the Republic of Bashkortostan (5.3 %), Krasnodar krai (4.7 %), etc. The ten territories with the highest indicators of milk production account for 36.71 % of the total volume of dairy products produced in Russia.
Spatial analysis indicated that in the overwhelming majority of the Russian regions dairy products are not produced, which increases the differentiation of regions in this area and weakens the food security of a significant part of them. In terms of consumption of milk and dairy products, the leading regions are Moscow (6.2 % of Russia's total consumption), Moscow oblast (5 %), the Republic of Tatarstan (4.5 %), etc. About 34 % of all dairy products are consumed in 8 regions.
Fig. 2. Leading territories in milk production, 2018
A study of the processes of milk production and consumption showed a strong dependence of certain regions on imported dairy products. According to the data in Table 1, the Sverdlovsk oblast, being the leader in the production of dairy products, has a negative balance of production and consumption and is forced, along with Saint Petersburg, Moscow, the Moscow oblast and other subjects of the Russian Federation, to resort to importing milk from abroad.
Table 1. Balance of milk production and consumption in the Russian regions
Region Balance, thousand tonnes
Altai krai 493
Udmurt Republic 391
Republic of Tatarstan 285
Moscow oblast -1051.3
Saint Petersburg -1517
Moscow -2089.5
Thus, the level of dairy production in Russia is insufficient to meet the needs of a significant part of the regions, which creates serious threats to food security. Spatial autocorrelation analysis for various distance matrices showed that some of them give unsatisfactory results (Table 2).
Table 2. Assessment of the statistical significance of Moran's indices for various distance matrices
Index Normalised matrices Backward distance matrices
road distance matrices linear distance matrices matrices on adjacent boundaries matrices on adjacent boundaries road distance matrices linear distance matrices
Global Moran's Index -3.84 -4.02 17.29 0.26 0.02 0.03
Z-score -284 -298 271 315 158 182
P-value 1.0 1.0 0.0 0.0 0.0 0.0
The obtained spatial correlation index and the global Moran's index are statistically insignificant for the normalised matrix of road distances and the normalised matrix of linear distances. This can be seen based on their P-value, or the probability of statistical insignificance of the spatial autocorrelation coefficients, which takes on a value greater than 0.05. The remaining four types of matrices: of adjacent boundaries, of adjacent boundaries normalised, of road and linear distances delivered good results. As a result of a comparative analysis of the results obtained by the matrices of spatial weights, four types of regional systems were identified according to the level of milk and dairy products production and the specifics of their spatial distribution (Figure 3).
So, according to Moran scatter plot, the category HL encompasses territories with high production indicators, surrounded by territories with rather low indicators. Thus, regions from the HL category are growth poles for other territorial systems. However, the practice of calculating the local indices of Moran's spatial autocorrelation shows that territories with weak interregional interaction that are not growth poles can also be included in this category. Moreover, such regions may have not the highest values in terms of the volume of dairy products produced. These include the Leningrad, Irkutsk, Vologda oblasts and others (in Figure 3 they are highlighted in white with black polka dots), which are surrounded by territories with very low values of milk production (in Figure 3 they are indicated in white). Of all the regions included in the HL-cluster, only the Republic of Dagestan is the leader in the production of milk and dairy products.
Regional systems, located, according to the methodology of Moran's spatial autocorrelation, in the category of HH, are not growth poles, centers of attraction of resources, but have high values for the studied indicator.
Identifying among them territorial systems with a high and low level of two-way influence relative to the average value of the local autocorrelation index will allow establishing the regions on which the growth poles have a strong or weak influence. According to the methodological approach used by the authors, the territories included in the HH-cluster with high values of spatial autocorrelation have close interregional relationships.
— Direct autocorrelation relationship between territorial systems
.....Inverse autocorrelation relationship between territorial systems
Regions included in the quadrants:
:•:•: HL (index of spatial autocorrelation below the average for the Russian Federation)
■ HH (index of spatial autocorrelation above the average level in the Russian Federation)
■ HH (index of spatial autocorrelation below the average level for the Russian Federation) LH (index of spatial autocorrelation above the average level for the Russian Federation) LH (index of spatial autocorrelation below the average level for the Russian Federation) LL (regions not connected with other territories)
Fig. 3. Moran scatter plot with close interregional relationships for the production
of milk and dairy products
This cluster is formed by the republics of Bashkortostan, Udmurtia, Tatarstan, and Perm krai, Orenburg, Sverdlovsk and Kirov oblasts (Ural-Volga cluster) (marked in dark gray in Figure 3).
Spatial autocorrelation analysis made it possible to establish close interregional relationships in the field of dairy production in this cluster: between the Republic of Bashkortostan and Sverdlovsk oblast, the Udmurt Republic, Perm krai, the Republic of Tatarstan and Orenburg oblast, as well as between the Republic of Tatarstan and Rostov, Nizhny Novgorod, Kirov, Ulyanovsk oblasts, Krasnodar krai. They are marked in Figure 3 by lines. The regions indicated in Figure 3 in gray, have a weak level of spatial mutual influence (low values of the local Moran's index). According to Moran's methodology, these regions are part of a spatial cluster for the production of milk and other dairy products. However, taking into account the low values of the Moran's indices, we consider it necessary to refer them to the emerging spatial clusters. We include among them:
1) Siberian cluster, uniting Novosibirsk, Omsk, Tyumen oblasts and the Altai Republic;
2) Southern cluster, including Krasnodar krai, Stavropol krai, Astrakhan, Rostov, Volgograd, Voronezh, Belgorod oblasts;
3) Central cluster, which includes Vladimirskaya, Ivanovskaya, Nizhny Novgorod oblasts, the republics of Mordovia, Mari El and Chuvashia.
The listed clusters are not real because of the currently not formed stable interregional relationships. They are located near the already formed Ural-Volga cluster, and in the field of dairy production, interregional relationships with it are being formed, in particular, between the Republic of Tatarstan and Krasnodar krai, Nizhny Novgorod oblast.
The division of regions according to the strength of two-way influence was also carried out in the LH category of the Moran scatter plot. This category is the zone of influence of spatial clusters (HH) and growth poles (HL) (they are highlighted in light gray in Figure 3). This includes Moscow, Saint Petersburg, Kurgan oblast, etc. Territorial systems with a low value of the local autocorrelation index (quadrant LL), in our opinion, can receive an impulse for development from the growth poles and spatial clusters, however, they do not have close relationships with them. These territories have rather low production rates and are surrounded by the territories of same level (all the remaining regions marked in white in Figure 3).
Anselin's matrix was used [Anselin, 2019, p. 158] to search for stable relationships between territorial systems, which, by analogy with the pairwise correlation matrix, displays direct and reverse two-way influences of territories using local indices of spatial autocorrelation. To search for stable relationships between regions in the production of dairy products, territories with high autocorrelation indices (above average) were identified in Anselin's matrix. The use of various matrixes of spatial weights (of linear distances, of roads, of adjacent borders, etc.) in the formation of these matrices made it possible to select from the set of identified relationships the most stable ones, tested by different measurement methods.
Thus, direct linkages were established between the Republic of Bashkortostan and the Udmurt Republic, Orenburg and Sverdlovsk oblasts, the Republic of Tatarstan; the Republic of Tatarstan and Kirov, Nizhny Novgorod oblasts, Krasnodar krai; Krasnodar krai and Rostov oblast; the Republic of Ingushetia and the Republic of North Ossetia - Alania.
Backward linkages are observed between the Republic of Mari El, Ulyanovsk oblast and the Republic of Tatarstan; the Republic of Adygea, the city of Sevastopol and Krasnodar krai; the Altai Republic and Altai krai; Leningrad oblast and the city of Saint Petersburg; the city of Moscow and Moscow oblast.
To substantiate the reliability of the interregional relationships established with the help of economic and mathematical tools, we analysed the interregional projects implemented jointly in the field of dairy production. This analysis involved the search for industrial cooperation, integration of enterprises operating in different territorial systems, as well as ongoing integration projects and cluster structures created in this area, supported by state authorities.
According to the map of Russian clusters, today the agro-industrial cluster of the Novgorod oblast and the cluster for the production and processing of dairy products "Donskie dairy products" operate in the agri-food sector . Both clusters are going through the initial stage of development and have a low level of maturity. In addition, the most promising are the dairy cluster of the Vologda oblast and the food cluster of the Republic of Tatarstan (Table 3).
Table 3. Active agro-industrial clusters in the regions of the Russian Federation
Cluster Purpose of creation Partners
Innovative territorial cluster "Donskoye dairy products" for the production and processing of dairy products (Rostov oblast, Southern Federal District) Creation, concentration and further development of the scientific and production potential of cluster partners, necessary for the implementation of the policy of import substitution of dairy products supplied from foreign countries, and increasing the economic efficiency of the dairy subcomplex of the Rostov oblast Enterprises: Salskoe Moloko, Tat-sinsky Dairy Plant, Don-Agro (Millerovsky district), Molagro-don (Ust-Donetsk district), Yuzh-noye moloko (Peshchanokopsky district). Scientific institutes: All-Russian Research Institute of the Dairy Industry, Don State Agrarian University, branch of the Moscow State University of Technology and Management named after K. G. Razumovskiy and others. (in total 20 partners)
Dairy Cluster of the Vologda region (Northwestern Federal District) The strategic goal is to support the sustainable development of dairy enterprises in the Vologda oblast. The following activities are being implemented: • livestock complexes and production lines are modernised (construction of the Urusovsky livestock complex); • the system based on precision agriculture is created and implemented; • a scientific and educational center for training personnel for the needs of the cluster is formed; • the brand "Milk from Vologda" is created and promoted; • an innovative electronic information and trading platform is founded; • innovative products that correspond to the strategy of healthy eating are developed and manufactured Enterprises: Severnoye moloko, Pokrovskoe, Soyuzplemzavod, Zarya, Agricultural production cooperative "Anokhinsky", Shek-sninskaya Zarya, Agrokon-Vo-logda, Agrokorm, Agromolservis, Agro-industrial complex "Chu-shevitsy", Babushkinskoye milk, Agricultural production cooperative "Plemzavod Maysky", Breeding enterprise "Cherepovetskoye", Breeding enterprise "Vologod-skoye", Tarnogsky butter plant, etc. Scientific institutes: Vologda State Dairy Academy named after N. V. Vereshchagin, Educational and experimental dairy plant named after N. V. Vereshchagin. (in total 43 partners)
Table 3 (concluded)
Cluster Purpose of creation Partners
Agro-industrial cluster of the Novgorod oblast (Northwestern Federal District) Creation of a mobile structure of a wide network of partners (enterprises in the economy of the Novgorod oblast) in order to increase their competitiveness and economic potential due to effective interaction at all stages of the value chain and streamlining of information processes Enterprises: Agrostandart, Mstin-skoe moloko, SPSSK "Novgorod-sky Agrariy", SPSSK "Novgorod-sky Fermer", Russian Agricultural Bank, SPK "Urozhai", Ecoprom-stroy Company, SPK Collective farm "Rossiya". Scientific and state institutions: Novgorod State University named after Yaroslav the Wise, Center for Supporting the Development of the Agro-Industrial Complex of the Novgorod oblast, Department of Agriculture and Food of the Novgorod oblast, Department of Economic Development and Trade of the Novgorod oblast, Novgorod Small Business Support Fund. (in total 27 partners)
Food Cluster of the Republic of Tatarstan (Volga Federal District) Creation of effective cooperative relationships and a system of interaction between enterprises of the agro-industrial complex, food industry and scientific and educational institutions in order to increase the economic efficiency and competitiveness of these enterprises due to obtaining state support of innovative and socioeconomic development Enterprises: Agrosila Group, Ag-rifirm "Kama", OOO "FinAgro-Trade", OOO "Chelny-Broiler", Tukayevsky breeding plant. Scientific and state institutions: Kama Center for Cluster Development of Small and Medium-Sized Businesses, Kazan State Academy of Veterinary Medicine named after N. E. Bauman, Naberezhnye Chelny Incubator, Regional Center for Engineering Biotechnologies of the Republic of Tatarstan, Sarmanov Agrarian College, Federal Center for Toxicological, Radiation and Biological Safety. (in total 20 partners)
Source: own representation based on the Map of Russia's clusters. http://map.cluster.hse.ru/ (in Russ.)
Some regions of Russia are initiating projects aimed at the creation agro-industrial clusters as both independent units and participants in the value chain (Table 4).
Table 4. Potential clusters in the agro-industrial complex
Subject of the Russian Federation Potential cluster members Reference
Penza oblast (Volga Federal District) The project coordinator: Agro-Industrial Complex Project Management Center; Dairy production: Agricultural holding "Bi-Molo"; Construction of dairy farms: Molochnaya Ferma; Partners: "Bio-Ton" and "Russian Dairy Company" Vinnichek, Stolyarova, Stolyarova [2018, p. 356]; Yakovenko, Ivanenko [2018, p. 99]
Saratov oblast (Volga Federal District) Key sector: dairy factories "Engelskiy", "Saratovskiy", "Maslodel", OOO "Petrovskiy dairy plant" (23 enterprises in total). Sector of agricultural enterprises (producing raw materials): Breeding plant "Trudovoy", "Kolosok", Agricultural company "Volga", APCs "Shturm", "Roscha", and "Novaya Zhizn", Farm "Klimashin", agricultural enterprise "Mikhailovskoye", Breeding enterprise "Meliorator" Sector of trade and promotion of dairy products: Povolzhs-kiy torgovy dom, Milaininvestgroup, Management Company "Selkhozrynok". Feed manufacturers: Saratov feed mill, Company "Standard" Bekkalieva [2016, p. 86]; Likhovtsova et al. [2018, p. 89]
Tambov oblast (Central Federal District) AO AK "Tambovskiy"", Mega-farm "Sheremetyevo", Collective farm & breeding plant named after V. I. Lenin of the Tambov oblast, Educational farm & breeding plant "Komsomolets" of Michurinsky district, FGUPPZ "Prigorodny", Suvorovo, Dairy farm "Zhupikov" Sytova, Minakov, Azzheurova [2016, p. 96]
Perm krai (Volga Federal District) Dairy plants: "Kungursky" (Kungur), "Permsky" (Perm), "Unimilk" (Ural Division), Processing plant "Verkhnemullinsky". Companies: Assistent, Vemol (Vereshchagino), Uralagro, Rus, Khokhlovka. Network organisations: Dobrynya, Family, Vivat, Gastronom
Kostroma oblast (Central Federal District) Cheese production: Manturovsky syrodel (Manturovsky district), Voskresensky syrodel (Buysky district), Bogovar (Ok-tyabrsky district), Cheese factory "Vokhomsky" (Vokhomsky district). Dairy production: Galichmolprod (Galich), Kosmol (Kostroma), Dairy plant "Ostrovsky" (Ostrovsky district). Trading houses: Molprom, Kostromaprom (Kostroma), Zarnit-sa, Voskresenye Agro, Vektor Khomutova, Khomutov, Morozov [2018, p. 1165]
Table 4 (continued)
Subject of the Russian Federation Potential cluster members Reference
Altai krai (Siberian Federal District) Dairy plants and factories and creameries: Barnaulsky, Altai Burenka, Slavgorodsky, Rubtsovsky, Altai Molochnik (Biysk district), Tyumentsevsky MSZ, Pospelikhinsky, Kiprinsky MZ, Kuyagansky MSZ, Barnaul breeding enterprise. Trading houses: Stolitsa moloka - Barnaul, Kholod (Zarinsk), Kiprino, Altai Agroprodukt, Ledyanoy Larets, Siberian Pod-vorye, TriF, Pervomayskoye Milk, MoloPak, Rikon, PepsiCo, Serac group of companies. Scientific institutes: Siberian Research Institute of Cheese Making (SibNIIS), Federal Altai Scientific Center of Agrobiotech-nology, Altai State Agrarian University, Altai State Technical University named after I. I. Polzunov, Altai State University, Pavlovsk Agricultural College Pospelova [2016, p. 186]
Republic of Buryatia (Siberian Federal District) Key sector: Buryatmyasprom, Moloko Buryatii. Raw materials production sector (agricultural enterprises): Agro-V, East-Siberian Pig Breeding Complex, APC "Kolkhoz Iskra", Garantia, Agropodvodstroy, APC "Kolesovsky", APC "Tvorogovsky", APC "Kabansky", Breeding plant "Borgoisky", Breeding plant "Nikolaevsky", Experimental production company "Baikalskoe", Creamery "Bichurskiy", Sokol, Talan-2, APC "Fedotov", APC "Gazar", Farm "Kopytov", APC "Bichura-Agro", APC named after Kalinin, APC "Elansky", APC "Pokrovsky", Buiskaya Niva, Zagustay, APC "Berill", Triumph. Sector of product promotion (markets): Central market, Agricultural market "Stimul", Shopping center "Tula", depots "U istoka", "Fortuna" Tushkayeva, Naydanova [2015, p. 101]
Rostov oblast (Southern Federal District) Agroindustrial centers: Millerovsky ("Russian Pork. Millerovo", branch 'Aston", Millerovoselmash, Amilko), "Morozovsky" (branch "Aston", Morozovskselmash) and "Salsky" (Salsky plant of forging and pressing equipment, Salskselmash, Sal-skoe Moloko) Abdullaev, Mishchenko [2017, p. 806]
Voronezh oblast (Central Federal District) The largest representatives of the cluster are: EkoNivaAgro, Groups of companies "Molvest" and "Prodimex", Management Company "Don Agro", AgroTechGarant Kotarev, Kotareva, Lesnikov [2018, p. 428]
Leningrad oblast (Northwestern Federal District) Lallemant, Galaktika, Canning plant named after Kirov, De-Laval, GEO, Canning plant "Luzhskiy", Petmol, Biotrof Surovtsev [2008, p. 200]
Table 4 (concluded)
Subject of the Russian Federation Potential cluster members Reference
Omsk oblast (Sibirean Federal District) Luzinsky moloko (Omsk, Tavrichesky district), Stud farm "Omsk" (Maryanovsky district), Dairy plant "Luzinsky" (Omsk district), Lyubinsky dairy and canning plant (Lyubinsky district), Inmarko, VNIMI-Siberia, Manros-M (Omsk), Kalachin-sky dairy plant (Kalachinsky district), Butter and cheese plant "Tyukalinsky" (Tyukalinsky district) Epanchintsev [2011, p. 50]
Republic of Adygea (North Caucasian Federal District) Dairy factories and plants: Giaginsky, Krasnogvardeisky, Tam-bovsky, Shovgenovsky, Adygeysky, Novyy Khatukai, Kumpilova, Babalyan [2019, p. 82]
Note: APC stands for Agricultural production cooperative.
Table 4 shows potential clusters for the production and processing of milk and dairy products, however, there are already cases of interregional relationships within the federal districts [Zaltsman, 2011]. Consider the data illustrating two-way influence in the case of the Siberian Federal District. Thus, Kemerovo, Omsk, Novosibirsk oblasts, as well as the republics of Altai and Khakassia are considered as sales markets for milk and dairy products produced in the Altai krai. The advantageous regional location contributes to this by significantly reducing the transportation costs of dairy enterprises. The enterprises, which are potential consumers of milk from the Altai region, include such as Inmarko and Lyubinsky dairy and canning plant (Omsk oblast), Kuzbaskonservmoloko and Yurginsky Gormolzavod (Kemerovo oblast), Maslosyrodel (Novosibirsk oblast), Slavgorodsky dairy plant and Blagoveshchensk dairy plant (the Altai Republic), Sayan-moloko (the Republic of Khakassia). As we can see, a substantial part of consumers of dairy products manufactured in the Altai krai is located outside of its borders.
Thus, taking into account the existing and potential milk and dairy productions, we can identify the following five clusters of the most interacting regions:
1) Central (Tambov, Kostroma, Voronezh oblasts);
2) Northwestern (Vologda, Leningrad, Novgorod oblasts);
3) Southern (Rostov oblast);
4) Volga (the Republic of Tatarstan, Perm krai, Saratov, Penza oblasts);
5) Siberian (Omsk oblast, the Republic of Buryatia, Altai krai).
These spatial clusters are the key growth poles and the major areas for ensuring food security in the field of dairy production. The development of interregional relationships with these clusters is likely to reduce the severity of spatial imbalances and solve the problem of providing the neighboring regions with high-quality dairy products.
In one of the studies, two groups of regions were identified:
1) cluster 1 (Moscow oblast, Krasnodar krai, the Republic of Tatarstan, Altai krai, Voronezh oblast, the Republic of Bashkortostan), which demonstrates successful organisation of dairy production in terms of import substitution, since the production not only covers the needs of the population in these regions, but also provides exportflows;
2) cluster 2 (Chelyabinsk, Bryansk, Omsk oblasts, the Udmurt Republic, the Republic of Dagestan, etc.), which is also characterised by high production of milk and dairy products, but its subjects are forced to import dairy products from both neighboring regions and foreign countries [Konkina, Martynushkin, 2020].
This once again confirms the necessity to create joint milk clusters, which will allow:
• benefiting from network relationships in the value chain (production, distribution, exchange, consumption of products): using relations with suppliers and credit institutions to organise an effective high-tech chain and reduce considerable costs, inputs and risks, which is impossible in the context of individual agricultural organisations;
• forming zones of specialisation within the federal districts;
• entering new sales markets (researching new market niches), ensuring the promotion of dairy products, as well as qualified service;
• getting access to marketing, innovation, technological, pricing and other types of information;
• applying innovative technologies and modern equipment to form a livestock base and increase the indicators of dairy production;
• supporting employment and living standards of the rural population;
• fully satisfying the needs of the inhabitants of Russia in high-quality and affordable dairy products, thereby solving the problem of food security of the country and dependence on food imports (import substitution) by intensifying interregional exchange and increasing the competitiveness of domestic dairy products when they are exported [Lik-hovtsova, etc., 2018, p. 90].
Conclusion
The study has demonstrated that in order to ensure a high level of food security in the production of milk and dairy products, it is necessary to develop interregional interactions within the framework of the creation of cluster formations, which in the future may become regional growth poles. The presented methodological approach to the research of interregional relationships based on the application of the tools of spatial autocorrelation analysis made it possible to determine the spatial development priorities in this sphere, establish clusters of closely interconnected regional systems in the Russian Federation and their zones of influence, as well as identify backward and forward linkages between regions.
Furthermore, the research identified the functioning spatial cluster in the field of dairy production, more specifically, the Ural-Volga cluster that unites republics of
Bashkortostan, Tatarstan, and Udmurtia, Perm krai, Orenburg, Sverdlovsk, and Kirov oblasts. These regions, according to Moran's methodology, form an HH cluster with high values of spatial autocorrelation and have close interregional relationships. By means of spatial autocorrelation analysis, close interregional relationships in the field of dairy production in this cluster were found between the Republic of Bashkortostan and the Sverdlovsk oblast, the Perm krai, the Republic of Udmurtia, the Republic of Tatarstan and the Orenburg oblast, as well as between the Republic of Tatarstan and Rostov, Nizhny Novgorod, Kirov, Ulyanovsk oblasts and Krasnodar krai.
The paper also distinguished emerging potential spatial clusters, namely Siberian (unites Novosibirsk, Omsk, Tyumen oblasts and the Republic of Altai), Southern (includes Stavropol and Krasnodar krais, Rostov, Astrakhan, Volgograd, Belgorod, Voronezh oblasts) and Central (Vladimir, Nizhny Novgorod , Ivanovo oblasts, the republics of Mari El, Mordovia and Chuvashia). These clusters are territorially bordering on the already formed Ural-Volga spatial cluster and have close relationships with its regions. The development of inter-territorial cooperation in the area under consideration will provide the regions with high-quality dairy products and increase their food security.
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Information about the authors
Ilya V. Naumov, Cand. Sc. (Econ.), Head of the Laboratory for Spatial Territorial Development Modelling, Institute of Economics (Ural branch of RAS), 29 Moskovskaya St., Ekaterinburg, 620014, Russia
Phone: +7 (343) 371-29-65, e-mail: ilia_naumov@list.ru
Vladislav M. Sedelnikov, Jr. Researcher of the Laboratory for Spatial Territorial Development Modelling, Institute of Economics (Ural branch of RAS), 29 Moskovskaya St., Ekaterinburg, 620014, Russia
Phone: +7 (343) 371-29-65, e-mail: vms-1990@mail.ru
© Naumov I. V., Sedelnikov V. M., 2021
