Spatial distribution of grassland soil organic carbon and potential carbon storage on the Qinghai Plateau

AbstractKey MessageBastin et al. 2019 used flawed assumptions in calculating the carbon storage of restored forests worldwide, resulting in a gross overestimate.ContextBastin et al. 2019 use two flawed assumptions: 1) that the area suitable for restoration does not contain any carbon currently, and 2) that soil organic carbon (SOC) from increased canopy cover will accumulate quickly enough to mitigate anthropogenic carbon emissions.AimsWe re-evaluated the potential carbon storage worldwide using empirical relationships of tree cover and carbon.Methods and ResultsWe use global datasets of tree cover, soil organic carbon, and above ground biomass to estimate the empirical relationships of tree cover and carbon stock storage. A more realistic range of global carbon storage potential is between 71.7 and 75.7 GtC globally, with a large uncertainty associated with SOC. This is less than half of the original 205 GtC estimate.ConclusionThe potential global carbon storage of restored forests is much less than that estimated by Bastin et al. 2019. While we agree on the value of assessing global reforestation potential, we suggest caution in considering it the most effective strategy to mitigate anthropogenic emissions.

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Elevation Distribution of Fine Root Biomass and Soil Organic Carbon Storage of Mature Chinese Fir (Cunninghamia lanceolata) Plantations in East China

Polish Journal of Ecology ◽

10.3161/15052249pje2020.68.1.007 ◽

2020 ◽

Vol 68 (1) ◽

pp. 84

Author(s):

Qin Wang ◽

Zhen Teng ◽

Jingjing Wang ◽

Xinyun Xie ◽

Xiaoniu Xu

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Root Biomass ◽

Fine Root ◽

Fine Root Biomass ◽

Chinese Fir ◽

Cunninghamia Lanceolata ◽

East China ◽

Organic Carbon Storage

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Influence of Slope Gradient and Aspect on Soil Organic Carbon Content in the Region of Niš, Serbia

Sustainability ◽

10.3390/su13158332 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8332

Author(s):

Snežana Jakšić ◽

Jordana Ninkov ◽

Stanko Milić ◽

Jovica Vasin ◽

Milorad Živanov ◽

...

Keyword(s):

Land Use ◽

Spatial Distribution ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Slope Aspect ◽

Available Phosphorus ◽

Organic Carbon Content ◽

Slope Gradient ◽

Phosphorus And Potassium ◽

Uncultivated Land

Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

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Topographic Position, Land Use and Soil Management Effects on Soil Organic Carbon (Vineyard Region of Niš, Serbia)

Agronomy ◽

10.3390/agronomy11071438 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1438

Author(s):

Snežana Jakšić ◽

Jordana Ninkov ◽

Stanko Milić ◽

Jovica Vasin ◽

Milorad Živanov ◽

...

Keyword(s):

Land Use ◽

Surface Layer ◽

Spatial Distribution ◽

Soil Erosion ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Compaction ◽

Soil Management ◽

Forest Land ◽

Topographic Position

Spatial distribution of soil organic carbon (SOC) is the result of a combination of various factors related to both the natural environment and anthropogenic activities. The aim of this study was to examine (i) the state of SOC in topsoil and subsoil of vineyards compared to the nearest forest, (ii) the influence of soil management on SOC, (iii) the variation in SOC content with topographic position, (iv) the intensity of soil erosion in order to estimate the leaching of SOC from upper to lower topographic positions, and (v) the significance of SOC for the reduction of soil’s susceptibility to compaction. The study area was the vineyard region of Niš, which represents a medium-sized vineyard region in Serbia. About 32% of the total land area is affected, to some degree, by soil erosion. However, according to the mean annual soil loss rate, the total area is classified as having tolerable erosion risk. Land use was shown to be an important factor that controls SOC content. The vineyards contained less SOC than forest land. The SOC content was affected by topographic position. The interactive effect of topographic position and land use on SOC was significant. The SOC of forest land was significantly higher at the upper position than at the middle and lower positions. Spatial distribution of organic carbon in vineyards was not influenced by altitude, but occurred as a consequence of different soil management practices. The deep tillage at 60–80 cm, along with application of organic amendments, showed the potential to preserve SOC in the subsoil and prevent carbon loss from the surface layer. Penetrometric resistance values indicated optimum soil compaction in the surface layer of the soil, while low permeability was observed in deeper layers. Increases in SOC content reduce soil compaction and thus the risk of erosion and landslides. Knowledge of soil carbon distribution as a function of topographic position, land use and soil management is important for sustainable production and climate change mitigation.

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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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Predicting spatial distribution of soil organic carbon and total nitrogen in a typical human impacted area

Geocarto International ◽

10.1080/10106049.2021.1886344 ◽

2021 ◽

pp. 1-19

Author(s):

Yingcong Ye ◽

Yefeng Jiang ◽

Lihua Kuang ◽

Yi Han ◽

Zhe Xu ◽

...

Keyword(s):

Spatial Distribution ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Total Nitrogen

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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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