Organic Carbon Storage and Spatial Distribution of Forest Soil in Jiangxi

Organic carbon of forest ecosystems is an essential part of the global organic carbon, which plays a dominate role in forest soil organic carbon research. It was estimated that the forest soil organic carbon density, carbon storage and its abundance index of 11 cities in Jiangxi province according to soil survey data for the second time and forest resources survey data in Jiangxi province during 11th five-year plan. Results showed that organic carbon storage of forest soil in 0-20cm and 0-100cm in Jiangxi province was 401.04×106t and 1025.73×106t respectively, in which yellow soil organic carbon density was higher than that of the red soil. Soil organic carbon storage in forest ecosystems was consistent in 0-20cm and 0-100cm of forest soil in eleven cities of Jiangxi province, among them Ganzhou was the highest in Ra20 while Yichun was the lowest one. In addition, it was inconsistent in the abundance index trend of forest soil organic carbon storages, Shangrao was the highest in Ra20, Yichun was the highest in Ra100, while Nanchang was the lowest one both in Ra20 and Ra100. It was the inverse relationship between total GDP and forest soil organic carbon storage..

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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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Number of soil profiles needed to give a reliable overall estimate of soil organic carbon storage using profile carbon density data

Soil Science & Plant Nutrition ◽

10.1111/j.1747-0765.2008.00305.x ◽

2008 ◽

Vol 54 (5) ◽

pp. 819-825 ◽

Cited By ~ 11

Author(s):

Xiaoyuan Yan ◽

Zucong Cai

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Soil Profiles ◽

Carbon Density ◽

Density Data ◽

Soil Organic Carbon Storage ◽

Organic Carbon Storage

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Assessment of Retention Potential and Soil Organic Carbon Density of Agriculturally used Chernozems, Cambisols and Fluvisols

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun201967051131 ◽

2019 ◽

Vol 67 (5) ◽

pp. 1131-1137

Author(s):

Martin Brtnický ◽

Václav Pecina ◽

Tereza Dokulilová ◽

Jan Vopravil ◽

Tomáš Khel ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Storage ◽

Water Retention ◽

Agricultural Land ◽

Agricultural Management ◽

Organic Fertilizers ◽

Soil Types ◽

Carbon Density ◽

Soil Organic Carbon Density

Climate change and the increasing frequency of climatic extremes have led to growing concerns over the sustainability of agriculture during recent years. In this context, soil retention and carbon storage are becoming widely discussed. The aim of this study was to evaluate the retention potential (RP) and soil organic carbon density (SOCD) of Chernozem, Cambisol and Fluvisol topsoil under agricultural management. Despite the different natural assumptions of these soil types, no significant statistical difference was found there. Mean RP values of the soil types varied from 39 to 40 mm and mean SOCD values from 23 to 28 t/ha. This finding may suggest that long-term agricultural management can suppress the naturally diverse potential for water retention and carbon storage of the individual soil types. Comparison of SOCD of the studied soils with agricultural soils in similar studies showed that most of the observed values can be considered as average. Despite this fact, a very strong local degradation has been revealed indicating poor agricultural management. Especially in such cases, there is an urgent need to adjust the management of the agricultural land fund (e.g. increased application of organic fertilizers, change in crop rotation) in order to increase carbon stocks and to improve the water retention capacity of soils.

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Tests of soil organic carbon density modeled by InTEC in China's forest ecosystems

Journal of Environmental Management ◽

10.1016/j.jenvman.2006.09.006 ◽

2007 ◽

Vol 85 (3) ◽

pp. 696-701 ◽

Cited By ~ 11

Author(s):

Y. Shao ◽

J. Pan ◽

L. Yang ◽

J.M. Chen ◽

W.M. Ju ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Forest Ecosystems ◽

Carbon Density ◽

Soil Organic Carbon Density

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Soil organic carbon density and its driving factors in forest ecosystems across a northwestern province in China

Geoderma ◽

10.1016/j.geoderma.2019.05.035 ◽

2019 ◽

Vol 352 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Jin-Hong Guan ◽

Lei Deng ◽

Jian-Guo Zhang ◽

Qiu-Yue He ◽

Wei-Yu Shi ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Forest Ecosystems ◽

Driving Factors ◽

Carbon Density ◽

Soil Organic Carbon Density

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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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