High nitrogen enrichment increases the allocation of new organic carbon to deep soil layers

The study was to investigate the change patterns of soil organic carbon (SOC), total nitrogen (TN), and soil C/N (C/N) in each soil sublayer along vegetation restoration in subtropical China. We collected soil samples in four typical plant communities along a restoration chronosequence. The soil physicochemical properties, fine root, and litter biomass were measured. Our results showed the proportion of SOC stocks (Cs) and TN stocks (Ns) in 20–30 and 30–40 cm soil layers increased, whereas that in 0–10 and 10–20 cm soil layers decreased. Different but well-constrained C/N was found among four restoration stages in each soil sublayer. The effect of soil factors was greater on the deep soil than the surface soil, while the effect of vegetation factors was just the opposite. Our study indicated that vegetation restoration promoted the uniform distribution of SOC and TN on the soil profile. The C/N was relatively stable along vegetation restoration in each soil layer. The accumulation of SOC and TN in the surface soil layer was controlled more by vegetation factors, while that in the lower layer was controlled by both vegetation factors and soil factors.

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Root penetration in deep soil layers stimulates mineralization of millennia-old organic carbon

Soil Biology and Biochemistry ◽

10.1016/j.soilbio.2018.06.010 ◽

2018 ◽

Vol 124 ◽

pp. 150-160 ◽

Cited By ~ 22

Author(s):

Tanvir Shahzad ◽

Muhammad Imtiaz Rashid ◽

Vincent Maire ◽

Sébastien Barot ◽

Nazia Perveen ◽

...

Keyword(s):

Organic Carbon ◽

Root Penetration ◽

Deep Soil ◽

Soil Layers

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Storage of Soil Organic Carbon and Its Spatial Variability in an Agro-Pastoral Ecotone of Northern China

Sustainability ◽

10.3390/su12062259 ◽

2020 ◽

Vol 12 (6) ◽

pp. 2259

Author(s):

Yanjiang Zhang ◽

Qing Zhen ◽

Pengfei Li ◽

Yongxing Cui ◽

Junwei Xin ◽

...

Keyword(s):

Spatial Distribution ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Spatial Variability ◽

Strong Dependence ◽

Soil Layer ◽

Northern China ◽

Structural Factors ◽

Deep Soil ◽

Soil Layers

Spatial distribution of soil organic carbon (SOC) is important for the development of ecosystem carbon cycle models and assessment of soil quality. In this study, a total of 732 soil samples from 122 soil profiles (0–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm) were collected by a combination of fixed-point sampling and route surveys in an agro-pastoral ecotone of northern China and the spatial variation of the SOC in the samples was analyzed through classical statistical and geostatistical approaches. The results showed that the SOC contents decreased from 4.31 g/kg in the 0–10 cm to 1.57 g/kg in the 80–100 cm soil layer. The spatial heterogeneity of the SOC exhibited moderate and strong dependence for all the soil layers owing to random and structural factors including soil texture, topography, and human activities. The spatial distributions of the SOC increased gradually from northeast to southwest in the 0–40 cm soil layers, but there was no general trend in deep soil layers and different interpolation methods resulted in the inconsistent spatial distribution of SOC. The storage of SOC was expected to be 25 Tg in the 0–100 cm soil depths for the whole area of 7692 km2. The SOC stocks estimated by two interpolation approaches were very close (25.65 vs. 25.86 Tg), but the inverse distance weighting (IDW) interpolation generated a more detailed map of SOC and with higher determination coefficient (R2); therefore, the IDW was recognized as an appropriate method to investigate the spatial variability of SOC in this region.

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Fresh carbon and nitrogen inputs alter organic carbon mineralization and microbial community in forest deep soil layers

Soil Biology and Biochemistry ◽

10.1016/j.soilbio.2014.01.020 ◽

2014 ◽

Vol 72 ◽

pp. 145-151 ◽

Cited By ~ 63

Author(s):

Qingkui Wang ◽

Yanping Wang ◽

Silong Wang ◽

Tongxin He ◽

Li Liu

Keyword(s):

Microbial Community ◽

Organic Carbon ◽

Carbon Mineralization ◽

Deep Soil ◽

Soil Layers ◽

Carbon And Nitrogen ◽

Nitrogen Inputs ◽

Organic Carbon Mineralization

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Stability of organic carbon in deep soil layers controlled by fresh carbon supply

Nature ◽

10.1038/nature06275 ◽

2007 ◽

Vol 450 (7167) ◽

pp. 277-280 ◽

Cited By ~ 1125

Author(s):

Sébastien Fontaine ◽

Sébastien Barot ◽

Pierre Barré ◽

Nadia Bdioui ◽

Bruno Mary ◽

...

Keyword(s):

Organic Carbon ◽

Deep Soil ◽

Soil Layers ◽

Carbon Supply

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A Two-Stage Approach to the Estimation of High-Resolution Soil Organic Carbon Storage with Good Extension Capability

Land ◽

10.3390/land10050517 ◽

2021 ◽

Vol 10 (5) ◽

pp. 517

Author(s):

Sunwei Wei ◽

Zhengyong Zhao ◽

Qi Yang ◽

Xiaogang Ding

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Guangdong Province ◽

Soil Samples ◽

Estimation Accuracy ◽

Ann Model ◽

Soil Layers ◽

Second Stage ◽

Organic Carbon Storage

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m−2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy.

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Differential effects of wetting and drying on soil CO2 concentration and flux in near-surface vs. deep soil layers

Biogeochemistry ◽

10.1007/s10533-020-00658-7 ◽

2020 ◽

Vol 148 (3) ◽

pp. 255-269 ◽

Cited By ~ 2

Author(s):

Kyungjin Min ◽

Asmeret Asefaw Berhe ◽

Chau Minh Khoi ◽

Hella van Asperen ◽

Jeroen Gillabel ◽

...

Keyword(s):

Co2 Concentration ◽

Soil Co2 ◽

Deep Soil ◽

Wetting And Drying ◽

Near Surface ◽

Soil Layers ◽

Differential Effects ◽

Soil Co2 Concentration

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Variations of deep soil moisture under different vegetation types and influencing factors in a watershed of the Loess Plateau, China

Hydrology and Earth System Sciences ◽

10.5194/hess-20-3309-2016 ◽

2016 ◽

Vol 20 (8) ◽

pp. 3309-3323 ◽

Cited By ~ 35

Author(s):

Xuening Fang ◽

Wenwu Zhao ◽

Lixin Wang ◽

Qiang Feng ◽

Jingyi Ding ◽

...

Keyword(s):

Soil Moisture ◽

Influencing Factors ◽

Loess Plateau ◽

Vegetation Restoration ◽

Native Vegetation ◽

Watershed Scale ◽

Vegetation Types ◽

Deep Soil ◽

Soil Layers ◽

Management Measures

Abstract. Soil moisture in deep soil layers is a relatively stable water resource for vegetation growth in the semi-arid Loess Plateau of China. Characterizing the variations in deep soil moisture and its influencing factors at a moderate watershed scale is important to ensure the sustainability of vegetation restoration efforts. In this study, we focus on analyzing the variations and factors that influence the deep soil moisture (DSM) in 80–500 cm soil layers based on a soil moisture survey of the Ansai watershed in Yan'an in Shanxi Province. Our results can be divided into four main findings. (1) At the watershed scale, higher variations in the DSM occurred at 120–140 and 480–500 cm in the vertical direction. At the comparable depths, the variation in the DSM under native vegetation was much lower than that in human-managed vegetation and introduced vegetation. (2) The DSM in native vegetation and human-managed vegetation was significantly higher than that in introduced vegetation, and different degrees of soil desiccation occurred under all the introduced vegetation types. Caragana korshinskii and black locust caused the most serious desiccation. (3) Taking the DSM conditions of native vegetation as a reference, the DSM in this watershed could be divided into three layers: (i) a rainfall transpiration layer (80–220 cm); (ii) a transition layer (220–400 cm); and (iii) a stable layer (400–500 cm). (4) The factors influencing DSM at the watershed scale varied with vegetation types. The main local controls of the DSM variations were the soil particle composition and mean annual rainfall; human agricultural management measures can alter the soil bulk density, which contributes to higher DSM in farmland and apple orchards. The plant growth conditions, planting density, and litter water holding capacity of introduced vegetation showed significant relationships with the DSM. The results of this study are of practical significance for vegetation restoration strategies, especially for the choice of vegetation types, planting zones, and proper human management measures.

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Spatial Distribution and Reserves Estimation of Soil Organic Carbon Based on GIS in Maiji Area of Tianshui

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.316-317.299 ◽

2013 ◽

Vol 316-317 ◽

pp. 299-306

Author(s):

Ai Hong Gai ◽

Ren Zhi Zhang ◽

Fang Chen ◽

Xiao Long Wang

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Average Density ◽

Soil Survey ◽

Soil Conditions ◽

Carbon Density ◽

Deep Soil ◽

Soil Organic Carbon Density ◽

Secondary Survey ◽

Organic Carbon Storage

The soil organic carbon density and storage of Maiji Area of Tianshui was estmiated, using the data of 6060 soil profile from the second soil survey of China and formulating fertilization for soil conditions in 2008. Integrating the soil map, land use status map and district map of Maiji Area of Tianshui, the index of the characteristic of soil organic distribution in different soil and soil layers were analyzed. Results showed: the soil of Maiji area have low average density, when soil secondary census, depths of 5cm,20cm,1m average density of organic carbon are 0.92kg•m-2,3.31kg•m-2,7.79kg•m-2 respectively, average density of organic carbon at depth of 20cm is 2.43 kg•m-2 in 2008 years, As a standard of Yu Dongsheng’s (2005) estimation of average density of 9.60 kg•m-2 in the depth of 1m all over the China, Maiji area 1m deep soil organic carbon density is lower 1.91kg•m-2 than the average density of whole country; The calculation of the secondary survey, reserves of organic carbon in surface soil (0-5cm) is about 4.83×106t, reserves of organic carbon in fall (0-20cm) is about 12.46×106t, reserves of soil organic carbon in 1m depth is about 45.17×106t, reserves of soil organic carbon in fall (0-20cm) is about 18.55×106t in 2008 years. In a word, the soil organic carbon storage was relatively indigent in Maiji Area of Tianshui.

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