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Economic Analysis: Theory and Practice
 

Cluster approach to the grain industry development

Vol. 23, Iss. 7, JULY 2024

Received: 28 March 2024

Received in revised form: 26 April 2024

Accepted: 13 May 2024

Available online: 30 July 2024

Subject Heading: ECONOMIC ADVANCEMENT

JEL Classification: Q01

Pages: 1272–1287

https://doi.org/10.24891/ea.23.7.1272

Dmitrii V. PROSKURA Nizhny Novgorod State University of Engineering and Economics (NGIEU), Knyaginino, Nizhny Novgorod Oblast, Russian Federation
nvpro@mail.ru

https://orcid.org/0000-0002-3470-1214

Marina L. NECHAEVA Nizhny Novgorod State University of Engineering and Economics (NGIEU), Knyaginino, Nizhny Novgorod Oblast, Russian Federation
khalyavina.mar@yandex.ru

https://orcid.org/0000-0002-5422-3563

Tat'yana N. KUTAEVA Nizhny Novgorod State University of Engineering and Economics (NGIEU), Knyaginino, Nizhny Novgorod Oblast, Russian Federation
kutaeva1959@mail.ru

https://orcid.org/0000-0002-4051-1011

Andrei A. KUTAEV Nizhny Novgorod State University of Engineering and Economics (NGIEU), Knyaginino, Nizhny Novgorod Oblast, Russian Federation
kutaevandrej@yandex.ru

ORCID id: not available

Subject. The article investigates the grain industry development through the cluster analysis, using the case of municipalities of the Nizhny Novgorod Oblast.
Objectives. The purpose is to assess the resource potential of the grain industry development based on the factorial and correlation analysis of the main components affecting the agricultural production of cereals; to study the interaction of agricultural organizations by applying the cluster approach to placement and specialization of agricultural production.
Methods. The study draws on general scientific (analysis and synthesis, formalization, analogy and comparison) and special (statistical, correlation analysis, cluster analysis) methods.
Results. We analyzed the statistical data on grain crops production of municipalities of the Nizhny Novgorod Oblast, performed a cluster analysis. Its procedure included the formation of a database for clustering sample, selection of a measure of proximity (metric) between the sample members, and calculation of its values, the process of cluster formation, listing of results based on the formed clusters, and evaluation of resulting indicators of grain production.
Conclusions. The study shows the effectiveness of multidimensional quantitative analysis. The findings helped identify certain patterns of the grain industry development, which have been verified in dynamics over the past five years.

Keywords: grain industry, cluster analysis, spatial development, cluster

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