EJSSEurasian Journal of Soil ScienceEurasian J Soil Sci2147 - 4249Federation of Eurasian Soil Science Societieshttp://ejss.fesss.org/10.18393/ejss.184101810.18393/ejss.1841018Theoretical foundations for developing a digital soil twin for Southern RussiaYurii Litvinov yualitvinov@sfedu.ruAnton Mezhenkov Tanwar Sudip Svetlana Sushkova Alexey Samoylov Eduard Melnik Alexander Kozlovskiy Ekaterina Kuchmenko Tatiana Minkina Evgenyi Shuvaev Anastasia Nemtseva Rahila İslamzade 151141-1482025-05-032025-12-082025-12-122026-01-02 Copyright © 2016 The authors and Federation of Eurasian Soil Science Societies The sustainable management of soil resources in Southern Russia has become increasingly critical due to intensifying agricultural pressures, accelerating climate variability, and cumulative anthropogenic disturbances. Rapid advances in digital technologies now enable the integration of heterogeneous soil datasets into dynamic computational environments capable of representing physical soil systems with unprecedented precision. Within this context, the digital twin (DT) paradigm—originating from engineering sciences and now rapidly expanding into agricultural and environmental domains—offers a transformational framework for real-time soil monitoring, process simulation, scenario forecasting, and risk assessment. This study establishes the theoretical and methodological foundations necessary for developing a comprehensive soil digital twin for the Rostov region by synthesizing more than eighty years of archival soil–geographical surveys, long-term agrochemical monitoring data, multi-scale cartographic sources, remote sensing products, IoT-based soil measurements, climate records, machine-learning algorithms, geostatistical models, semantic graph structures, distributed computing frameworks, federated learning, and blockchain-enabled data governance. Particular emphasis is placed on harmonizing heterogeneous soil legends, vectorizing analog soil maps, constructing unified soil ontologies, and designing a multi-layered DT architecture grounded in contemporary digital twin theory, including mirrored physical–virtual spaces, multidimensional modeling, and state-fusion mechanisms. Machine-learning experiments demonstrate high predictive accuracy for numerous soil attributes, while geostatistical modeling enhances spatial continuity and uncertainty quantification. The integrated framework presented here provides a robust foundation for constructing an operational soil digital twin capable of supporting precision agriculture, environmental monitoring, insurance modeling, and strategic land-use planning. By enabling continuous data ingestion, multi-stakeholder interaction, and dynamic model refinement, the developed digital twin concept has significant potential to strengthen climate resilience, optimize agronomic interventions, and promote sustainable agricultural development across Southern Russia. Digital twin, soil, Rostov region, modeling, GIS, IoT, big data, artificial intelligence.