Climate Change Data Portal
DOI | 10.1002/ece3.5308 |
An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes | |
Zhang, Weixin1,2; Chen, Yuanqi3,4; Shi, Leilei1; Wang, Xiaoli5; Liu, Yongwen6; Mao, Rong7; Rao, Xingquan2; Lin, Yongbiao2; Shao, Yuanhu1,2; Li, Xiaobo2; Zhao, Cancan8; Liu, Shengjie9; Piao, Shilong6; Zhu, Weixing10; Zou, Xiaoming11; Fu, Shenglei1,2 | |
发表日期 | 2019 |
ISSN | 2045-7758 |
卷号 | 9期号:13页码:7586-7596 |
英文摘要 | 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. |
WOS研究方向 | Environmental Sciences & Ecology ; Evolutionary Biology |
来源期刊 | ECOLOGY AND EVOLUTION |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/99860 |
作者单位 | 1.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 |
推荐引用方式 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],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. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。