Data from: An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes

Quantifying soil organic carbon (SOC) changes is a fundamental issue in
ecology and sustainable agriculture. However, the algorithm-derived biases
in comparing SOC status have not been fully addressed. Although the
methods based on equivalent soil mass (ESM) and mineral-matter mass (EMMM)
reduced biases of the conventional methods based on equivalent soil volume
(ESV), they face challenges in ensuring both data comparability and
accuracy of SOC estimation due to unequal basis for comparison and using
un-conserved reference systems. We introduce the basal mineral-matter
reference systems (soils at time zero with natural porosity but no organic
matter) and develop an approach based on equivalent mineral-matter volume
(EMMV). To show the temporal bias, SOC change rates were re-calculated
with the ESV method and modified methods that referenced to soils at time
t1 (ESM, EMMM, EMMV-t1) or referenced to soils at time zero (EMMV-t0)
using two datasets with contrasting SOC status. To show the spatial bias,
the ESV and EMMV-t0 derived SOC stocks were compared using datasets from
six sites across biomes. We found that, in the relatively C-rich forests,
SOC accumulation rates derived from the modified methods that referenced
to t1 soils and from the EMMV-t0 method were 5.7-13.6% and 20.6% higher
than that calculated by the ESV method, respectively. Nevertheless, in the
C-poor lands, no significant algorithmic biases of SOC estimation were
observed. Finally, both the SOC stock discrepancies (ESV vs EMMV-t0) and
the proportions of this unaccounted SOC were large and site-dependent.
These results suggest that although the modified methods that referenced
to t1 soils could reduce the biases derived from soil volume changes, they
may not properly quantify SOC changes due to using un-conserved reference
systems. The EMMV-t0 method provides an approach to address the two
problems and is potentially useful since it enables SOC comparability and
integrating SOC datasets.