A comparison of two methods for quantifying soil organic carbon of alpine grasslands on the Tibetan Plateau

Abstract

As CO2 concentrations continue to rise and drive global climate change, much effort has been put into estimating soil carbon (C) stocks and dynamics over time. However, the inconsistent methods employed by researchers hamper the comparability of such works, creating a pressing need to standardize the methods for soil organic C (SOC) quantification by the various methods. Here, we collected 712 soil samples from 36 sites of alpine grasslands on the Tibetan Plateau covering different soil depths and vegetation and soil types. We used an elemental analyzer for soil total C (STC) and an inorganic carbon analyzer for soil inorganic C (SIC), and then defined the difference between STC and SIC as SOCCNS. In addition, we employed the modified Walkley-Black (MWB) method, hereafter SOCMWB. Our results showed that there was a strong correlation between SOCCNS and SOCMWB across the data set, given the application of a correction factor of 1.103. Soil depth and soil type significantly influenced on the recovery, defined as the ratio of SOCMWB to SOCCNS, and the recovery was closely associated with soil carbonate content and pH value as well. The differences of recovery between alpine meadow and steppe were largely driven by soil pH. In addition, statistically, a relatively strong correlation between SOCCNS and STC was also found, suggesting that it is feasible to estimate SOCCNS stocks through the STC data across the Tibetan grasslands. Therefore, our results suggest that in order to accurately estimate the absolute SOC stocks and its change in the Tibetan alpine grasslands, adequate correction of the modified WB measurements is essential with correct consideration of the effects of soil types, vegetation, soil pH and soil depth.

Fig 1. Vegetation map of the 36 sampling sites in the alpine grasslands on the Tibetan Plateau, selected from the Vegetation Map of China [30].

Black triangles represent the sampling sites.

Fig 2. Comparisons of SOC_CNS and SOC_MWB (a), SOC_CNS and STC (b).

Solid line is a linear regression passing through the origin. SOC_CNS, the difference between STC and SIC; SOC_MWB, SOC determined by the MWB method.

Fig 3. Relationships between the modified Walkey-Black recovery and soil CaCO₃ content (a) and pH (b) in all 712 soil samples.

Fig 4. Boxplots of CaCO₃ content (a) and pH (b) in 0–5 cm, 5–10 cm, 10–20 cm, 20–30 cm Soils, and pH (c) in soils of alpine meadow and steppe.

Solied lines within the boxes give the median, dotted lines give the mean, boxes the 25th and 75th percentile. The whiskers are the 5th and 95th percentile and the outliers are closed circles.

Fig 5. Comparisons of SOC_CNS and SOC_MWB, SOC_CNS and STC for alpine medow (a, c) and steppe (b, d).

Solid line is a linear regression passing through the origin. SOC_CNS, the difference between STC and SIC; SOC_MWB, SOC determined by the MWB method.

Fig 6. Relationships between CaCO₃ content (±SE, a) and pH (±SE, b) and the modified Walkey-Black average recovery (±SE) in 9 soil types (accroding to the GSCC, Genetic Soil Classification of China) on the Tibetan Plateau.

Equations, linear fits (R²) and significant levels (P) were obtained when ‘frigid frozen soils (FFS)’ was excluded from the analyses.