Soil organic carbon and black carbon storage and dynamics under different fire regimes in temperate mixed-grass savanna

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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Soil organic carbon storage by shaded and unshaded coffee systems and its implications for climate change mitigation in China

The Journal of Agricultural Science ◽

10.1017/s002185962100006x ◽

2021 ◽

pp. 1-8

Author(s):

Ziwei Xiao ◽

Xuehui Bai ◽

Mingzhu Zhao ◽

Kai Luo ◽

Hua Zhou ◽

...

Keyword(s):

Climate Change ◽

Linear Regression ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Multiple Linear Regression ◽

Carbon Storage ◽

Soil Ph ◽

Soil Bulk Density ◽

Soil N ◽

And Storage

Abstract Shaded coffee systems can mitigate climate change by fixation of atmospheric carbon dioxide (CO2) in soil. Understanding soil organic carbon (SOC) storage and the factors influencing SOC in coffee plantations are necessary for the development of sound land management practices to prevent land degradation and minimize SOC losses. This study was conducted in the main coffee-growing regions of Yunnan; SOC concentrations and storage of shaded and unshaded coffee systems were assessed in the top 40 cm of soil. Relationships between SOC concentration and factors affecting SOC were analysed using multiple linear regression based on the forward and backward stepwise regression method. Factors analysed were soil bulk density (ρb), soil pH, total nitrogen of soil (N), mean annual temperature (MAT), mean annual moisture (MAM), mean annual precipitation (MAP) and elevations (E). Akaike's information criterion (AIC), coefficient of determination (R2), root mean square error (RMSE) and residual sum of squares (RSS) were used to describe the accuracy of multiple linear regression models. Results showed that mean SOC concentration and storage decreased significantly with depth under unshaded coffee systems. Mean SOC concentration and storage were higher in shaded than unshaded coffee systems at 20–40 cm depth. The correlations between SOC concentration and ρb, pH and N were significant. Evidence from the multiple linear regression model showed that soil bulk density (ρb), soil pH, total nitrogen of soil (N) and climatic variables had the greatest impact on soil carbon storage in the coffee system.

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Estimations of soil organic carbon storage in cropland of China based on DNDC model

Geoderma ◽

10.1016/j.geoderma.2005.10.005 ◽

2006 ◽

Vol 134 (1-2) ◽

pp. 200-206 ◽

Cited By ~ 117

Author(s):

Huajun Tang ◽

Jianjun Qiu ◽

Eric Van Ranst ◽

Changsheng Li

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Dndc Model ◽

Soil Organic Carbon Storage ◽

Organic Carbon Storage

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Estimating soil organic carbon storage and distribution in a catchment of Loess Plateau, China

Geoderma ◽

10.1016/j.geoderma.2009.10.011 ◽

2010 ◽

Vol 154 (3-4) ◽

pp. 261-266 ◽

Cited By ~ 69

Author(s):

Fengpeng Han ◽

Wei Hu ◽

Jiyong Zheng ◽

Feng Du ◽

Xingchang Zhang

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Loess Plateau ◽

Soil Organic Carbon Storage ◽

Organic Carbon Storage

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Soil organic carbon storage, distribution, and influencing factors at different depths in the dryland farming regions of Northeast and North China

CATENA ◽

10.1016/j.catena.2021.105934 ◽

2022 ◽

Vol 210 ◽

pp. 105934

Author(s):

Zhiqing Zhuo ◽

Qianqian Chen ◽

Xianglin Zhang ◽

Songchao Chen ◽

Yuxuan Gou ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Influencing Factors ◽

Carbon Storage ◽

North China ◽

Soil Organic Carbon Storage ◽

Dryland Farming ◽

Different Depths ◽

Organic Carbon Storage ◽

Storage Distribution

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Carbon storage along altitudes in agrisilviculture system- Case study of Devprayag Block, Tehri District, Uttarakhand, India

International Journal of Current Research in Biosciences and Plant Biology ◽

10.20546/ijcrbp.2020.709.004 ◽

2020 ◽

Vol 7 (9) ◽

pp. 29-38

Author(s):

K.K. Vikrant ◽

D.S. Chauhan ◽

R.H. Rizvi

Keyword(s):

Climate Change ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Carbon Stock ◽

Total Carbon ◽

Total Biomass ◽

Biomass Carbon ◽

Total Carbon Stock ◽

Crop Biomass

Climate change is one of the impending problems that have affected the productivity of agroecosystems which calls for urgent action. Carbon sequestration through agroforestry along altitude in mountainous regions is one of the options to contribute to global climate change mitigation. Three altitudes viz. lower (286-1200m), middle (1200-2000m), and upper (2000-2800m) have been selected in Tehri district. Ten Quadrates (10m × 10 m) were randomly selected from each altitude in agrisilviculture system. At every sampling point, one composite soil sample was taken at 30 cm soil depth for soil organic carbon analysis. For the purpose of woody biomass, Non destructive method and for crop biomass assessment destructive method was employed. Finally, aboveground biomass (AGB), belowground biomass carbon (BGB), Total tree Biomass (TTB), Crop biomass (CB), Total Biomass (TB), Total biomass carbon (TBC), soil organic carbon (SOC), and total carbon stock (TC) status were estimated and variables were compared using one-way analysis of variance (ANOVA).The result indicated that AGB, BGB, TTB, CB , TB, TBC, SOC, and TC varied significantly (p < 0.05) across the altitudes. Results showed that total carbon stock followed the order upper altitude Ëƒ middle altitudes Ëƒ lower altitude. The upper altitude (2000-2800 m) AGB, BGB,TTB, TBC,SOC, and TC stock was estimated as 2.11 Mg ha-1 , 0.52 Mg ha-1, 2.63 Mg ha-1, 2.633 Mg ha-1, 1.18 Mg ha-1 , 26.53 Mg ha-1, 38.48 Mg ha-1 respectively, and significantly higher than the other altitudes. It was concluded that agrisilviculture system hold a high potential for carbon storage at temperate zones. Quercus lucotrichophora, Grewia oppositifolia and Melia azadirach contributed maximum carbon storage which may greatly contribute to the climate resilient green economy strategy and their conservation should be promoted.

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