Mathematical models of regional reference standards of intrazonal and azonal soils in the Altai Region

Abstract

The regional soil reference standards were developed in accordance with the natural geomorphological soil zoning of the Altai Region. The azonal soil region (19th Soil District) of meadow and alluvial soils of the floodplains and the pine-forest terraces of the Ob River and large rivers stretches through several steppe and forest-steppe zones and subzones. The most common (regional) soils are meadow, chernozem-meadow, meadow- chernozem, alluvial and alluvial-meadow, and alluvial-marsh soils. All soils are influenced by various extents of hydromorphic conditions which affect their properties. The obtained quantitative models of regional soil reference standards in the studied area showed that the most significant differences between them were due to the processes of biogenic accumulation (accumulation of biogenic elements, phosphorus, potassium, and absorbed bases). Unlike zonal soils, they are characterized by significant reserves of humus and organic matter from 6.0 to 10.0%, slightly acidic or close to neutral pH in the upper humus horizon which is replaced by alkaline reaction at depth. The particle-size composition of the regional soil reference standards of the 19th Soil District is characterized by significant variation, both within a separate taxon and among themselves, which is associated with the specifics of their genesis, namely, the synlithogenic accumulation of soil material on already formed soils. In accordance with the substantial genetic classification soils of Russia, alluvial meadow and alluvial marsh soils belong to the trunk of synlithogenic soils; meadow soils belong to the postlithogenic trunk - the taxonomic group of hydrometamorphic soils, and meadow-chernozem soils belong to the postlithogenic trunk - the taxonomic group of accumulative humus soils (hydro-metamorphosed chernozems). The qualitative and quantitative model for the diagnosis of soils in the 19th Soil District was obtained. Its forecasting ability makes 60% error-free prediction, and with error of 1 rank, the accuracy increases to 100%.