A. A. Dubovitskii, E. A. Klimentova, E. S. Babkina, V. A. Shatskii. Building the Potential for Sustainable Development of the Agricultural: Analysis of Reproduction Processes and Assessment of Regional Prospects Using Neural Network Models

https://doi.org/10.15507/2413-1407.26343.546-571
EDN: https://elibrary.ru/bjzjvhISSN 2413-1407 
УДК / UDC 004.032.26:631 ISSN 2587-8549

Abstract

Introduction. Studying the reproductive processes and spatial characteristics of the formation of sustainable agricultural development potential is a pressing issue given the persistent imbalance between economic, social, and environmental priorities. The purpose of this article is to assess the potential for sustainable development (PSD) of regional agricultural production based on an analysis of the reproduction of artificial, human, and natural capital using neural network tools.

Materials and Methods. The study is based on the authors' concept of sustainable development, which is based on a reproductive approach. For spatial analysis, neural network modeling based on a multilayer perceptron (MLP) was used, using data from 45 regions of the Russian Federation for 2019–2023. A model with two hidden layers (20 and 15 neurons) was constructed, allowing the relative importance of factors influencing PSD to be determined. The information base consisted of open data from Rosstat, Rosreestr, and the Russian Ministry of Agriculture.

Results. The MLP model demonstrated high predictive accuracy: the average relative error was 5.1 %, and the sum of squared errors was 4.6 %. Significant factors in PSD formation were identified: production scale, volume of budget and credit investments, rural unemployment level, and application of organic fertilizers. Based on a comparison of actual and predicted values, three groups of regions were identified: those with negative, average, and positive assessments of PSD formation potential. Leading regions were identified (Moscow, Kirov, and Kaluga Oblasts, the Republic of Dagestan, and others), where actual potential exceeded the predicted level.

Conclusion. The decline in the aggregate PSD from –151.8 to –223.5 billion rubles, despite increased production and investment, indicates a persistent imbalance in production processes – insufficient capital renewal and mounting environmental damage. The proposed approach to studying the spatial characteristics of PSD formation can form the basis for developing territorially differentiated strategies and programs for the socioeconomic development of agriculture at the federal and regional levels.

Keywords: agri-food system, regional differentiation, agriculture, economic growth, sustainable development, forecasting, neural networks

Conflict of interest. The authors declare no conflict of interest.

Funding. The study was supported by grant No. 25-28-01313 from the Russian Science Foundation “Development of a methodology for forecasting agri-food economic systems based on machine learning using artificial neural networks” (https://rscf.ru/project/25-28-01313/).

For citation: Dubovitskii A.A., Klimentova E.A., Babkina E.S., Shatskii V.A. Building the Potential for Sustainable Development of the Agricultural: Analysis of Reproduction Processes and Assessment of Regional Prospects Using Neural Network Models. Russian Journal of Regional Studies. 2026;34(3):546–571. https://doi.org/10.15507/2413-1407.26343.546-571

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About the authors:

Aleksandr A. Dubovitskii, Dr.Sci. (Econ.), Associate Professor, Professor of the Chair of Economics and Commerce, Head of the Research Laboratory of Economic Forecasting at Michurinsk State Agrarian University (101 Internatsionalnaya St., Michurinsk 393760, Russian Federation), ORCID: https://orcid.org/0000-0003-4542-1119, Researcher ID: AAX-8480-2020, Scopus ID: 57211466422, SPIN-code: 1683-4156, daa1-408@yandex.ru

Elvira A. Klimentova, Cand.Sci. (Econ.), Associate Professor, Associate Professor of the Chair of Economics and Commerce, Senior Researcher at the Research Laboratory of Economic Forecas­ting of Michurinsk State Agrarian University (101 Internatsionalnaya St., Michurinsk 393760, Russian Federation), ORCID: https://orcid.org/0000-0001-7628-7181, Researcher ID: ABI-5384-2020,
Scopus ID: 57211466144, SPIN-code: 3256-3838, klim1-408@yandex.ru

Ekaterina S. Babkina, Cand.Sci. (Econ.), Associate Professor of the Chair of Management and Business Administration, Junior Researcher at the Research Laboratory of Economic Forecasting of Michurinsk State Agrarian University (101 Internatsionalnaya St., Michurinsk 393760, Russian Federation),
ORCID: https://orcid.org/0009-0000-3917-5412, Researcher ID: PII-7360-2026, SPIN-code: 3479-5232, babkina_ek.s@mail.ru

Vladislav A. Shatskii, post-graduate student, Junior Researcher at the Research Laboratory of Economic Forecasting of Michurinsk State Agrarian University (101 Internatsionalnaya St., Mi­churinsk 393760, Russian Federation), ORCID: https://orcid.org/0009-0006-0354-8022, Researcher ID: PNH-9947-2026, SPIN-code: 5763-5927, site.mgau@yandex.ru

Contribution of the authors:

A. A. Dubovitskii – conceptualization; supervision; methodology; specifically critical review, commentary or revision.

E. A. Klimentova – investigation; validation; specifically writing the initial draft.

E. S. Babkina – data/evidence collection; formal analysis; specifically writing the initial draft.

V. A. Shatskii – investigation; data curation; visualization.

Availability of data and materials. The datasets used and/or analyzed during the current study are available from the authors on reasonable request.

The authors have read and approved the final manuscript.

Submitted 27.04.2026; revised 18.05.2026; accepted 28.05.2026.