With the aim of contributing to the knowledge of soil organic carbon stocks in dry areas, this work is based on a quantification of SOC stocks in gypsum-bearing soils whose vertical and spatial heterogeneity greatly limits inferring the total SOC stocks solely from soil surface information. Public databases of soil profiles were key to this quantification, through which it was estimated which amounts of organic carbon can potentially be excluded from calculations associated with soil C cycle models in the absence of information regarding deep soil horizons. These databases include two key factors in the quantification of SOC stocks, which are often excluded: the volume of coarse fragments and the thickness of all sampled soil horizons where SOC concentration was determined. The observed average value of SOC stocks in the studied subsurface horizons reaches 73% of the whole soil. Climate, relief, and land use influence the quantity and heterogeneity of SOC stocks in these soils. Information based on the mere surface of the soil is not relevant to quantify the total SOC; however, the calculation of stocks through soil pits of medium depth (30 cm) has proven to be potentially useful as a complementary approach to these stocks.

This research analyzes the relationships between “soil” and “organisms” within the framework of the Jenny equation, a fundamental expression in soil science that is the theoretical basis for modeling the complex occurrence of soils on landscapes. This analysis is based on the interpretation of the indeterminate function “f” of the equation as “statistical dependence between categorical variables”. The categories of the “soil” component of the equation have been defined as “diagnostic horizons”, and those of the “organisms” factor as synthetic types of “land cover”. After applying these criteria to 424 soil profiles studied in a region with an oceanic climate in northern Spain, a multiple correspondence analysis showed pedologically consistent groupings between diagnostic horizons and categories of climate, land cover, relief, and parent material factors. Subsequently, a bivariate analysis detailed pedologically consistent relationships between diagnostic horizons and land cover categories. In the context the scarcity of quantitative information on soil and forming factor relationships, this work provides criteria to statistically assess the role of land cover in such relationships. This soil forming factor is the one whose spatial representation is more generalized and detailed, hence its interest in the development of soil mapping models.