The Carbon Storage of Forest Debris among Different Land-Use Types in North Subtropical Areas in China

This study considered organic carbon storage in fine forest debris( extensively managed bamboo > masson pine > natural secondary forest > shrubs > intensively managed bamboo > agricultural cropped land. Organic carbon of fine forest debris was mainly stored in the leaves, followed by the branches, and then the decomposed debris, while the percentage of the amount in the dead herb was less than 10％. In addition, (2)the underground carbon storage in fine debris, of which the amount in different vegetation types was similar, accounted for more than 50％ of the total carbon storage. Meanwhile, as to the coarse debris, underground carbon storage in both bamboo stands was higher (P Chinese fir > masson pine > natural secondary forest> intensively managed bamboo > shrubs > agricultural cropped land.

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Comparison of bulk density methods in determining soil organic carbon storage under different land use types

Journal of Soil Science and Environmental Management ◽

10.5897/jssem2017.0664 ◽

2018 ◽

Vol 9 (1) ◽

pp. 13-20

Author(s):

Brhan Gebre Abrha

Keyword(s):

Land Use ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Bulk Density ◽

Carbon Storage ◽

Soil Organic Carbon Storage ◽

Land Use Types ◽

Organic Carbon Storage ◽

Density Methods

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Patterns of Biomass, Carbon, and Soil Properties in Masson pine (Pinus massoniana Lamb) Plantations with Different Stand Ages and Management Practices

Forests ◽

10.3390/f10080645 ◽

2019 ◽

Vol 10 (8) ◽

pp. 645 ◽

Cited By ~ 3

Author(s):

Ashfaq Ali ◽

Adnan Ahmad ◽

Kashif Akhtar ◽

Mingjun Teng ◽

Weisheng Zeng ◽

...

Keyword(s):

Organic Carbon ◽

Carbon Storage ◽

Pinus Massoniana ◽

Total Carbon ◽

Stand Age ◽

Biomass Carbon ◽

Soil Physicochemical Properties ◽

Tree Biomass ◽

Masson Pine ◽

Pinus Massoniana Lamb

Masson pine (Pinus massoniana Lamb) has been planted extensively in different parts of China for timber production and habitat restoration. The effects of stand age and management of these plantations on biomass, carbon storage, and soil physicochemical properties are poorly understood. In this study, we investigated biomass, carbon storage, and soil physicochemical properties of Masson pine plantations. The plantations were divided into four age groups (9, 18, 28, and 48 years), and into managed (MS) and unmanaged stands (UMS) in Hubei province, Central China. Tree biomass increased with stand age. A growth model indicated that maximum tree growth occurred when the plantations were 17 years old, and the average growth rate occurred when plantations were 23 years old. Tree biomass in managed stands was 9.75% greater than that in unmanaged ones. Total biomass carbon was estimated at 27.4, 86.0, 112.7, and 142.2 Mg ha−1, whereas soil organic carbon was 116.4, 135.0, 147.4, and 138.1 Mg ha−1 in 9-, 18-, 28-, and 48-year-old plantations, respectively. Total carbon content was 122.6 and 106.5 Mg ha−1, whereas soil organic carbon content was 104.9 and 115.4 Mg ha−1 in MS and UMS, respectively. Total carbon storage in the plantations studied averaged 143.7, 220.4, 260.1, and 280.3 Mg ha−1 in 9-,18-, 28-, and 48-year-old stands, and 227.3 and 222.4 Mg ha−1 in MS and UMS, respectively. The results of our study provide a sound basis for estimating ecosystem carbon as it relates to forest management activity and stand age.

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Comparing Organic Carbon Storage of Upper 15-cm Soils between Different Land Use Types in Korean Inland

Korean Journal of Soil Science and Fertilizer ◽

10.7745/kjssf.2011.44.6.1314 ◽

2011 ◽

Vol 44 (6) ◽

pp. 1314-1319

Author(s):

Kyung-Hwa Han ◽

Hee-Rae Cho ◽

Jeong-Tae Lee ◽

Gye-Jun Lee ◽

Suk-Young Hong ◽

...

Keyword(s):

Land Use ◽

Organic Carbon ◽

Carbon Storage ◽

Land Use Types ◽

Organic Carbon Storage

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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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Response characteristics of organic carbon storage in sandy soil to sandstone

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/514/2/022008 ◽

2020 ◽

Vol 514 ◽

pp. 022008

Author(s):

Zhen Guo ◽

Tingting Cao ◽

Chenxi Yang

Keyword(s):

Organic Carbon ◽

Carbon Storage ◽

Sandy Soil ◽

Response Characteristics ◽

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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