Tatarstan Gets a New Soil Map
Before planning the planting season, agronomists routinely study soil maps. They use them to determine, for example, which crops are most likely to thrive and which fertilizers to apply. In the past, professionals had to rely on paper maps that divided soils into clearly defined color-coded zones with fixed boundaries. But in nature, as we know, straight lines are rare, and transitions occur gradually. A digital interactive model of the land is more effective.
Geographers at Moscow’s HSE University, working with scientists from the Institute of Geography of the Russian Academy of Sciences, Lomonosov Moscow State University and Kazan Federal University, have created a digital soil map of arable land in the Republic of Tatarstan, covering about 4.5 million hectares. This is more than a digitization of old data – it is a new approach to agriculture not only in Tatarstan but across Russia.
Agronomy Without Boundaries
On older maps, boundaries between soil types were drawn as straight lines. Each area was assigned to a single class. In practice, however, one soil type gradually and imperceptibly transitions into another, and a single field may show characteristics of several types at once. The new digital map moves beyond this “black-and-white” approach by assessing each area on a probability scale, showing, for example, that a given location may be 70% chernozem and 30% forest soil.
The final Tatarstan map has a spatial resolution of 250 meters and covers four main soil groups – sod-podzolic, gray forest, chernozem and alluvial soils. The researchers combined data from the Soil Map of the RSFSR at a scale of 1:2.5 million with MODIS satellite observations from 2013 to 2025, as well as information on terrain, climate and clay content. Analyzing long-term satellite time series made it possible to identify persistent landscape characteristics associated with vegetation productivity, moisture conditions and the intensity of photosynthesis, and distinguish them from short-term changes caused by weather and agricultural activity.
As a result of the modeling, the share of gray forest soils was revised from 48% to 27%, while the share of sod-podzolic soils rose from 4% to 16% and alluvial soils from 5% to 16%. The prevalence of chernozems changed only slightly, with their share declining from about 43% to 41%.
Soil for Precision Agriculture
Creating a unified digital soil map of Russia is already being addressed jointly by RosAgrokhimsluzhba (Russian Agrochemical Service), Rosselkhozzemmonitoring (Russian Agricultural Land Monitoring Service) and the V. Dokuchaev Soil Science Institute. The project aims to harmonize large-scale soil maps and bring different types of mapping data under a common standard for assessing land resources, modeling natural processes, planning land use and agricultural activities.
Novosibirsk Region already has a digital soil map. Farmers can click on any area of the region to see what type of soil is found there, what its properties are and how much land it covers. Agriculture is one of the key sectors of Tatarstan’s economy. The digital map could become a foundation for precision-farming systems, soil-degradation monitoring, assessments of farmland productivity and management decisions at the municipal level.
A more precise understanding of soil conditions will help farmers use fertilizers more efficiently, select crops based on the characteristics of specific plots, lower production costs and make yields more stable. Digital methods can update data from long-term soil surveys without requiring vast areas to be completely surveyed again.
Lower Costs, Higher Yields
Russia’s total planted area for the 2026 harvest was nearly 77 million hectares, according to Rosstat. By 2030, it could grow by another 5 million hectares. Abandoned farmland will be brought back into production, while soil maps updated to reflect current changes in climate, land-use patterns and soil conditions will be used to restore fertility. HSE University’s digital project could accelerate that process by combining remote sensing of Earth with machine-learning methods.
The technology could also become part of a broader spatial-data ecosystem. Russia is already developing a National Spatial Data System that brings together information on territories, real estate and land resources. Integrating soil models into such systems would make the data useful not only for agriculture but also for regional planning, environmental monitoring and natural-resource management.
Russian agricultural holdings and farms are interested in technologies that reduce fertilizer costs, make land management more efficient and improve yield forecasting. Digital soil maps will likely be combined over time with other data sources, including weather services, satellite monitoring of crops, farm-equipment management systems and government platforms. This could lead to comprehensive digital models of agricultural territories, a particularly important development for Russia given the scale of its farmland and the need to use it more efficiently.
