scholarly journals Rethinking global carbon storage potential of trees. A comment on Bastin et al. 2019

2019 ◽  
Author(s):  
Shawn D. Taylor ◽  
Sergio Marconi
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Storage ◽  
Canopy Cover ◽  
Tree Cover ◽  
Empirical Relationships ◽  
Storage Potential ◽  
Global Carbon ◽  
Global Datasets ◽  
Potential Carbon

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.

Impact of Farm Management Practices and Agricultural Land Use on Soil Organic Carbon Storage Potential in the Savannah Ecological Zone of Northern Ghana

2014 ◽  
Vol 9 (4) ◽  
pp. 484-500 ◽  
Author(s):  
John Boakye-Danquah ◽  
◽  
Effah Kwabena Antwi ◽  
Osamu Saito ◽  
Mark Kofi Abekoe ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Storage ◽  
Management Practices ◽  
Agricultural Land ◽  
Farm Management ◽  
Northern Ghana ◽  
Storage Potential ◽  
Landscape Level

In recent times, there has been increasing interest in the importance of agricultural soils as global carbon sinks, and the opportunity of enhancing the resilience of degraded agroecosystems – particularly in savannah regions of the world. However, this opportunity is largely a function of land use and/or land management choices, which differ between and within regions. In the present study, we investigated the role of agriculture land use and farm management practices on soil organic carbon (SOC) storage in the savannah regions of northern Ghana. We evaluated selected land use types by using an integrated approach, involving on-farm interviews, community transect walks, land use monitoring, and soil sampling. Our results indicated that, at the landscape level, community land use and resource needs are important determinants of SOC storage in farmlands. We determined low SOC accumulation across the investigated landscape; however, the relatively high SOC stock in protected lands compared with croplands implies the potential for increasing SOC build-up by using recommended management practices. Low incomes, constraints to fertilizer use, low biomass availability, and reductions in fallow periods remain as barriers to SOC buildup. In this context, global soil carbon storage potential and smallholder food production systems will benefit from an ecosystembased adaptation strategy that prioritizes building a portfolio of carbon stores at the landscape level.

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.

Soil organic carbon storage by shaded and unshaded coffee systems and its implications for climate change mitigation in China

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.

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
Sign in / Sign up

Export Citation Format

Share Document