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An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes
Zhang, Weixin1; Chen, Yuanqi2,3; Shi, Leilei; Wang, Xiaoli4; Liu, Yongwen5; Mao, Rong6; Rao, Xingquan1; Lin, Yongbiao1; Shao, Yuanhu1; Li, Xiaobo1; Zhao, Cancan7; Liu, Shengjie8; Piao, Shilong5; Zhu, Weixing9; Zou, Xiaoming10; Fu, Shenglei1
2019
Source PublicationECOLOGY AND EVOLUTION
ISSN2045-7758
Volume9Issue:13Pages:7586-7596
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.

KeywordCLIMATE-CHANGE SEQUESTRATION LAND CHRONOSEQUENCE NITROGEN FOREST CULTIVATION MANAGEMENT STORAGE MATTER
Subject AreaEnvironmental Sciences & Ecology ; Evolutionary Biology
DOI10.1002/ece3.5308
Indexed BySCI
Language英语
WOS IDWOS:000476593800015
Citation statistics
Document Type期刊论文
Identifierhttp://ir.xtbg.org.cn/handle/353005/11445
Collection土壤生态组
Affiliation1.Henan Univ, Coll Environm & Planning, Minist Educ, Key Lab Geospatial Technol Middle & Lower Yellow, Kaifeng 475004, Peoples R China
2.Chinese Acad Sci, South China Bot Garden, Key Lab Vegetat Restorat & Management Degraded Ec, Guangzhou, Guangdong, Peoples R China
3.Hunan Univ Sci & Technol, Hunan Prov Key Lab Coal Resources Clean Utilizat, Xiangtan, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Qinghai Univ, Qinghai Acad Anim & Vet Sci, State Key Lab Plateau Ecol & Agr, Xining, Qinghai, Peoples R China
6.Peking Univ, Coll Urban & Environm Sci, Beijing, Peoples R China
7.Jiangxi Agr Univ, Coll Forestry, Jiangxi Prov Key Lab Silviculture, Nanchang, Jiangxi, Peoples R China
8.Henan Univ, Coll Life Sci, Kaifeng, Peoples R China
9.Chinese Acad Sci, Xishuangbanna Trop Bot Garden, Mengla, Peoples R China
10.SUNY Binghamton, Dept Biol Sci, Binghamton, NY USA
11.Univ Puerto Rico, Dept Environm Sci, POB 70377, San Juan, PR 00936 USA
Recommended Citation
GB/T 7714
Zhang, Weixin,Chen, Yuanqi,Shi, Leilei,et al. An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes[J]. ECOLOGY AND EVOLUTION,2019,9(13):7586-7596.
APA Zhang, Weixin.,Chen, Yuanqi.,Shi, Leilei.,Wang, Xiaoli.,Liu, Yongwen.,...&Fu, Shenglei.(2019).An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes.ECOLOGY AND EVOLUTION,9(13),7586-7596.
MLA Zhang, Weixin,et al."An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes".ECOLOGY AND EVOLUTION 9.13(2019):7586-7596.
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