An alternative approach to reduce algorithm‐derived biases in monitoring soil organic carbon changes. Issue 13 (30th May 2019)

• Record Type: Journal Article
• Authors: Zhang, Weixin; Chen, Yuanqi; Shi, Leilei; Wang, Xiaoli; Liu, Yongwen; Mao, Rong; Rao, Xingquan; Lin, Yongbiao; Shao, Yuanhu; Li, Xiaobo; Zhao, Cancan; Liu, Shengjie; Piao, Shilong; Zhu, Weixing; Zou, Xiaoming; Fu, Shenglei
• Is Part Of: Ecology and evolution. Volume 9:Issue 13(2019)
• Page Start: 7586
• Page End: 7596
• Publication Date: 2019-05-30
• Subjects: algorithm‐derived biases -- basal mineral‐matter reference systems -- equivalent mineral‐matter volume -- reference systems -- SOC comparability -- soil organic carbon -- soil volume change; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.5308
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: 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 unconserved 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 recalculated with the ESV method and modified methods that referenced to soils at time t1 (ESM, EMMM, and 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 unconserved 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. Abstract : Quantifying soil organic carbon (SOC) changes is a fundamental issue in ecology; however, the algorithm‐derived biases in comparing SOC status have not been fully addressed. The limitation of all the current methods, that is, methods based equivalent soil volume (ESV), equivalent soil mass (ESM), or mineral‐matter mass (EMMM), was explored. We then 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 basal mineral‐matter volume (EMMV‐t0). Three datasets were used to show how the algorithm‐derived biases were reduced. Our results suggest that the algorithm‐derived biases in quantifying SOC changes can be attributed to not only ignoring soil volume changes but also using unconserved reference soils, and 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.