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Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China.

Authors :
Wang, Shuai
Zhuang, Qianlai
Wang, Qiubing
Jin, Xinxin
Han, Chunlan
Source :
Geoderma. Nov2017, Vol. 305, p250-263. 14p.
Publication Year :
2017

Abstract

Estimation of carbon and nitrogen stocks is important for quantifying carbon and nitrogen sequestration as well as greenhouse gas emissions and inventorying national carbon and nitrogen balances. For Liaoning province of China, we estimated the vertical distribution of soil organic carbon (SOC), soil total nitrogen (STN), bulk density (BD), and mapped their spatial distribution at five standard soil depth intervals (0–5, 5–15, 15–30, 30–60 and 60–100 cm) using nine environmental variables as predictors including precipitation, temperature, land use, elevation, system for automated geoscientific analyses (SAGA) wetness index, and Normalized Difference Vegetation Index (NDVI). The highest average contents of SOC and STN were 15.2 g kg − 1 and 1.6 g kg − 1 in the 0–5 cm soil layer, and 1.5 g kg − 1 SOC and 0.4 g kg − 1 STN in the 60–100 cm soil layer, respectively. The prediction precision for SOC, STN and BD all decreased with soil depth. Average SOC and STN stocks for 0–30 cm were 3.1 kg m − 2 and 0.5 kg m − 2 , respectively. For the top 1 m, SOC and STN were 4.5 kg m − 2 and 0.9 kg m − 2 , respectively. In total, the soils stored approximately 588 Tg SOC and 128 Tg STN within the top 1 m. The soils under forest had the highest amount of carbon (356 Tg) and nitrogen (58 Tg) followed by agriculture and wetland that contributed 34% and 48% of the total stock, respectively. > 91% of the total SOC and STN stocks were in Argosols and Cambosols. We adopted a digital soil mapping method to map the spatial distribution of SOC and STN stocks and predict their uncertainties. The estimation was validated with a 10-fold cross-validation procedure. The data and high-resolution maps from this study can be used for future soil carbon and nitrogen assessment and inventorying. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00167061
Volume :
305
Database :
Academic Search Index
Journal :
Geoderma
Publication Type :
Academic Journal
Accession number :
124403634
Full Text :
https://doi.org/10.1016/j.geoderma.2017.05.048