where μd is SOC stock (Mg OC ha− 1); BDd is soil bulk density (g cm− 3); OCd is the concentration of OC in soil (b 2 mm; mg OC g−1 soil); D is soil depth interval (cm); gr is fractional gravel content, the soil fraction N 2mm. Table A2 shows the cumulative mass coordinate approach
used to calculate SOC stocks across the different land uses. The cumula- tive mass coordinate approach consists of collection and quantification of all the soil mass in a given depth interval. Sampling by mass instead of volume minimizes potential biases derived from varying bulk density caused by land use change or agricultural practices (Gifford and Roderick, 2003; McBratney and Minasny, 2010; Rovira et al., 2015). We conducted sampling at fixed depth intervals in order to compute for soil bulk density and be able to inter-convert between the spatial co- ordinate and the cumulative mass coordinate approach (Saiz and Albrecht, 2015).
The different datasets were tested for normal distribution by the Kolmogorov-Smirnov test, and where necessary data were log- transformed to conduct parametric statistical tests. Within each site and depth, one-way ANOVA was performed to compare soil variables between different sampling locations. Post hoc comparisons using Tukey HSD test were conducted to find out which of the locations dif- fered. The same statistical procedure was used to compare soil proper- ties between different land uses. Analyses of covariance (ANCOVA) were performed to test for significant different differences between re- gressions. SPSS 17.0 (SPSS Inc. Chicago, IL, USA) was used for all statisti- cal analyses.