The quantity and stability of soil organic carbon following vegetation degradation in a salt-affected region of Northeastern China

CATENA ◽  
2022 ◽  
Vol 211 ◽  
pp. 105984
Author(s):  
Pujia Yu ◽  
Yixuan Li ◽  
Shiwei Liu ◽  
Zhi Ding ◽  
Aichun Zhang ◽  
...  
Geoderma ◽  
2019 ◽  
Vol 342 ◽  
pp. 55-64 ◽  
Author(s):  
Yan Wang ◽  
Shuai Wang ◽  
Kabindra Adhikari ◽  
Qiubing Wang ◽  
Yueyu Sui ◽  
...  

2019 ◽  
Vol 20 (3) ◽  
pp. 1241-1252
Author(s):  
Shichao Wang ◽  
Yawen Zhao ◽  
Jinzhou Wang ◽  
Jiajia Gao ◽  
Ping Zhu ◽  
...  

2020 ◽  
Vol 12 (7) ◽  
pp. 1115 ◽  
Author(s):  
Shuai Wang ◽  
Qianlai Zhuang ◽  
Xinxin Jin ◽  
Zijiao Yang ◽  
Hongbin Liu

Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0–30 cm) samples (10.32 kg m−2 (±0.53) for SOC, 1.21 kg m−2 (±0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m−2 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m−2 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.


2018 ◽  
Vol 8 (23) ◽  
pp. 11999-12010 ◽  
Author(s):  
Abdul-Rauf Malimanga Alhassan ◽  
Weiwei Ma ◽  
Guang Li ◽  
Zhirong Jiang ◽  
Jiangqi Wu ◽  
...  

Geoderma ◽  
2018 ◽  
Vol 325 ◽  
pp. 102-109 ◽  
Author(s):  
Yuqiang Li ◽  
Xuyang Wang ◽  
Yayi Niu ◽  
Jie Lian ◽  
Yongqing Luo ◽  
...  

Geoderma ◽  
2012 ◽  
Vol 189-190 ◽  
pp. 532-539 ◽  
Author(s):  
Hongmei Zhao ◽  
Daniel Q. Tong ◽  
Qianxin Lin ◽  
Xianguo Lu ◽  
Guoping Wang

2017 ◽  
Vol 27 (4) ◽  
pp. 516-528 ◽  
Author(s):  
Xiaodong Song ◽  
Feng Liu ◽  
Bing Ju ◽  
Junjun Zhi ◽  
Decheng Li ◽  
...  

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 290
Author(s):  
Pujia Yu ◽  
Shiwei Liu ◽  
Zhi Ding ◽  
Aichun Zhang ◽  
Xuguang Tang

The depth distribution of soil organic carbon (SOC) in a soil profile is important to examine the effects of different treatments on SOC sequestration. This study was conducted to determine the effects of different vegetation types on the concentration, storage, and stratification ratio (SR) of SOC in northeastern China. Five vegetation types, Leymus chinensis (LEY), Puccinellia tenuiflora (PUC), Echinochloa phyllopogon (ECH), saline seepweed (SUA), and Chloris virgata Swartz (CHL), were selected as treatments. Soil bulk density and SOC concentration were measured at 0 to 50 cm depth, and SOC storage and four SRs (SR1 [0–10:10–20 cm], SR2 [0–10:20–30 cm], SR3 [0–10:30–40 cm], and SR4 [0–10:40–50 cm]) were calculated under the five vegetation types. Results showed a pronounced reduction in SOC concentration with increasing soil depth. Vegetation types had significant effects on SOC concentration and storage. Under PUC, ECH, SUA, and CHL treatments, SOC concentrations (2.150, 1.068, 4.110, and 2.542 g kg−1, respectively) and storages (15.075, 7.273, 30.024, and 18.078 Mg ha−1, respectively) at 0–50 cm depth were lower than those under the LEY treatment. SR1 values were all < 2, while SR2, SR3, and SR4 values were all > 2 except for SR2 under ECH and SUA treatments. Vegetation types had significant effects on SR3 (p < 0.001) and SR4 (p = 0.040), while no significant differences were found for SR1 and SR2 due to the narrow range, with values of 0.248 and 0.553 for SR1 and SR2, respectively, among the vegetation types. These results indicated that the degraded soils have great potential to sequester organic carbon in northeastern China, and SR3 could be used as an effective index to show the changes in SOC concentration and soil quality in northeastern China.


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