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

Authors :
Qianlai Zhuang
Xinxin Jin
Shuai Wang
Qiu-Bing Wang
Chun-Lan Han
Source :
Geoderma. 305:250-263
Publication Year :
2017
Publisher :
Elsevier BV, 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.

Details

ISSN :
00167061
Volume :
305
Database :
OpenAIRE
Journal :
Geoderma
Accession number :
edsair.doi...........2e9f073c4043551e88a890b25460c6a7
Full Text :
https://doi.org/10.1016/j.geoderma.2017.05.048