Estimating organic carbon storage in temperate wetland profiles in Northeast China

Geoderma ◽  
2008 ◽  
Vol 146 (1-2) ◽  
pp. 311-316 ◽  
Author(s):  
Wen-Ju Zhang ◽  
He-Ai Xiao ◽  
Cheng-Li Tong ◽  
Yi-Rong Su ◽  
Wan-sheng Xiang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Storage ◽  
Northeast China ◽  
Temperate Wetland ◽  
Organic Carbon Storage

No-tillage with continuous maize cropping enhances soil aggregation and organic carbon storage in Northeast China

Geoderma ◽  
2018 ◽  
Vol 330 ◽  
pp. 204-211 ◽  
Author(s):  
Yan Zhang ◽  
Xiujun Li ◽  
Edward G. Gregorich ◽  
Neil B. McLaughlin ◽  
Xiaoping Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Storage ◽  
Northeast China ◽  
Soil Aggregation ◽  
No Tillage ◽  
Continuous Maize ◽  
Organic Carbon Storage

scholarly journals A Two-Stage Approach to the Estimation of High-Resolution Soil Organic Carbon Storage with Good Extension Capability

Land ◽  
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.

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

Geoderma ◽  
2006 ◽  
Vol 134 (1-2) ◽  
pp. 200-206 ◽  
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

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

Geoderma ◽  
2010 ◽  
Vol 154 (3-4) ◽  
pp. 261-266 ◽  
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

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

Soil Research ◽  
2006 ◽  
Vol 44 (3) ◽  
pp. 233 ◽  
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.

Shifts in priming partly explain impacts of long-term nitrogen input in different chemical forms on soil organic carbon storage

2018 ◽  
Vol 24 (9) ◽  
pp. 4160-4172 ◽  
Author(s):  
Minghua Song ◽  
Yu Guo ◽  
Feihai Yu ◽  
Xianzhou Zhang ◽  
Guangmin Cao ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Storage ◽  
Chemical Forms ◽  
Soil Organic Carbon Storage ◽  
Nitrogen Input ◽  
Organic Carbon Storage
Sign in / Sign up

Export Citation Format

Share Document