Rock weathering controls the potential for soil carbon storage at a continental scale

AbstractAs rock-derived primary minerals weather to form soil, they create reactive, poorly crystalline minerals that bind and store organic carbon. By implication, the abundance of primary minerals in soil might influence the abundance of poorly crystalline minerals, and hence soil organic carbon storage. However, the link between primary mineral weathering, poorly crystalline minerals, and soil carbon has not been fully tested, particularly at large spatial scales. To close this knowledge gap, we designed a model that links primary mineral weathering rates to the geographic distribution of poorly crystalline minerals across the USA, and then used this model to evaluate the effect of rock weathering on soil organic carbon. We found that poorly crystalline minerals are most abundant and most strongly correlated with organic carbon in geographically limited zones that sustain enhanced weathering rates, where humid climate and abundant primary minerals co-occur. This finding confirms that rock weathering alters soil mineralogy to enhance soil organic carbon storage at continental scales, but also indicates that the influence of active weathering on soil carbon storage is limited by low weathering rates across vast areas.

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Research Advance in Soil Organic Carbon Change

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.5112 ◽

2012 ◽

Vol 518-523 ◽

pp. 5112-5115

Author(s):

Zhen Hong Xie ◽

Bo Fu ◽

Xiang Liu

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Cycle ◽

Soil Carbon ◽

Carbon Storage ◽

Soil Carbon Storage ◽

Effect Factors ◽

Soil Organic Carbon Storage ◽

The Future ◽

Organic Carbon Storage

Changes of soil organic carbon storage play an important role in carbon balance in the world. Firstly, analyzed the effect factors of the soil organic carbon changes that are limited to qualitative research instead of quantitative studies, and the main effect factors are climate and soil properties , but so far it is still unclear how the temperature changes affect soil organic carbon dynamical changes; then, summarized estimation methods of soil organic carbon storage, and the soil type method is more commonly used estimation method of soil carbon storage in china and abroad for simple method and the data easily accessible, and the study on soil organic carbon storage is static based on a point in time and is lack in dynamics analysis, therefore it is to be solved how to improve the estimation accuracy of soil carbon storage in the future; finally,summarized soil carbon cycle model at home and abroad, and it is a key point that the soil carbon cycle models combined with GIS and RS simulate large-scale soil carbon cycle in the future.

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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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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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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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Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley

Soil Research ◽

10.1071/sr05136 ◽

2006 ◽

Vol 44 (3) ◽

pp. 233 ◽

Cited By ~ 125

Author(s):

Budiman Minasny ◽

Alex. B. McBratney ◽

M. L. Mendonça-Santos ◽

I. O. A. Odeh ◽

Brice Guyon

Keyword(s):

Land Use ◽

Organic Carbon ◽

Soil Carbon ◽

Carbon Storage ◽

Environmental Data ◽

Pedotransfer Functions ◽

Soil Carbon Storage ◽

Depth Function ◽

Terrain Attributes ◽

Organic Carbon Storage

Estimation and mapping carbon storage in the soil is currently an important topic; thus, the knowledge of the distribution of carbon content with depth is essential. This paper examines the use of a negative exponential profile depth function to describe the soil carbon data at different depths, and its integral to represent the carbon storage. A novel method is then proposed for mapping the soil carbon storage in the Lower Namoi Valley, NSW. This involves deriving pedotransfer functions to predict soil organic carbon and bulk density, fitting the exponential depth function to the carbon profile data, deriving a neural network model to predict parameters of the exponential function from environmental data, and mapping the organic carbon storage. The exponential depth function is shown to fit the soil carbon data adequately, and the parameters also reflect the influence of soil order. The parameters of the exponential depth function were predicted from land use, radiometric K, and terrain attributes. Using the estimated parameters we map the carbon storage of the area from surface to a depth of 1 m. The organic carbon storage map shows the high influence of land use on the predicted storage. Values of 15–22 kg/m2 were predicted for the forested area and 2–6 kg/m2 in the cultivated area in the plains.

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