VDMBC_vertical_distribution_soil_microbial_biomass_carbon

Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset (VDMBC) is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data were collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data collectd include climate, ecosystem types, and edaphic factors. We develop this dataset by searching the the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data included in this dataset meet two creteria: 1) there were at least three mineral soil layers along a soil profile, and 2) SMBC was measured using the fumigation extraction method. The data were obtained from tables and texts from literature directly, and the data in figures were extracted using GetData Graph digitizer software version 2.25. When climate and soil properties were not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org).

The units of all the variables were converted to the standard international units or commonly used ones and the values were converted correspondingly. For example, the value of soil organic matter (SOM) was converted to SOC using the equation (SOC = SOM × 0.58). Soil depth was calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer.

This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil C models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.