scholarly journals Mineral control of organic carbon storage in acid temperate forest soils in the Basque Country

Geoderma ◽  
2020 ◽  
Vol 358 ◽  
pp. 113998
Author(s):  
Nahia Gartzia-Bengoetxea ◽  
Iñigo Virto ◽  
Ander Arias-González ◽  
Alberto Enrique ◽  
Oihane Fernández-Ugalde ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Storage ◽  
Forest Soils ◽  
Temperate Forest ◽  
Basque Country ◽  
Temperate Forest Soils ◽  
Organic Carbon Storage

scholarly journals Association with pedogenic iron and aluminum: effects on soil organic carbon storage and stability in four temperate forest soils

2017 ◽  
Vol 133 (3) ◽  
pp. 333-345 ◽  
Author(s):  
Rachel C. Porras ◽  
Caitlin E. Hicks Pries ◽  
Karis J. McFarlane ◽  
Paul J. Hanson ◽  
Margaret S. Torn
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Storage ◽  
Forest Soils ◽  
Temperate Forest ◽  
Soil Organic Carbon Storage ◽  
Temperate Forest Soils ◽  
Organic Carbon Storage

Significance and driving forces of dark CO2 fixation for organic carbon inputs in temperate forest soils

2020 ◽  
Author(s):  
Rachael Akinyede ◽  
Martin Taubert ◽  
Marion Schrumpf ◽  
Susan Trumbore ◽  
Kirsten Küsel
Keyword(s):  
Microbial Biomass ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Water Content ◽  
Forest Soils ◽  
Temperate Forest ◽  
Carbon Pool ◽  
Significant Difference ◽  
Temperate Forest Soils ◽  
Organic Carbon Pool

<p>Soils are the largest terrestrial organic carbon pool and one of the largest terrestrial sources of CO<sub>2</sub> in the atmosphere. However, not all CO<sub>2</sub> produced in soils is released into the atmosphere, as dark CO<sub>2</sub> fixation has been shown to modulate CO<sub>2</sub> release from soils. Temperate forest soils store up to half of the soil organic carbon pool to 1m depth and are recognized as important components of the global carbon cycle, yet studies on dark CO<sub>2</sub> fixation in temperate forest soils are scarce. Using a well characterized Cambisol soil plot in the Hainich National Park (temperate forest), Germany, we explore dark CO<sub>2</sub> fixation with the aim to assess the CO<sub>2</sub> fixation rates, the influencing biogeochemical parameters, and the contribution of this process to temperate forest soil organic carbon (SOC).</p><p>Dark CO<sub>2</sub> fixation was quantified via the uptake of <sup>13</sup>C-CO<sub>2</sub> added to microcosms containing soils sampled from three depths. Under 2% CO<sub>2</sub> headspace, rates of dark CO<sub>2</sub> fixation at soil level decreased with depth from 0.86 µg C gdw<sup>-1</sup>d<sup>-1</sup> in 0 - 12 cm to 0.05 µg C gdw<sup>-1</sup>d<sup>-1</sup> in 70 -100 cm, accounting for up to 1.1% of microbial biomass and up to 0.035% of soil organic carbon. However, as differences in microbial biomass abundance and community profiles with depth were found, no significant difference in the rates across depth was observed at microbial level. This suggests that microbial biomass is an important driver of dark CO<sub>2 </sub>fixation in soils. Given a global temperate forest area of 6.9 million km<sup>2</sup> and an average soil bulk density of 1 Mg/m<sup>3 </sup>dark CO<sub>2</sub> fixation will potentially account for the gross sequestration of 0.31 - 0.48 GtC/yr to a depth of 1 m. Furthermore, an increase in headspace CO<sub>2</sub> concentration enhanced CO<sub>2</sub> fixation rates by up to 3.4-fold under 20% v:v CO<sub>2</sub> showing that dark CO<sub>2</sub> fixation can be substantial in soils with higher CO<sub>2</sub> concentrations.</p><p>To validate microbial biomass as a driver of dark CO<sub>2</sub> fixation in soils, we made comparisons with soil plots from the Schorfheide-Chorin exploratory forest, Germany, a temperate forest characterized by vegetation-specific bacterial community structure, higher sand content and acidic pH gradients. Under these conditions, CO<sub>2</sub> fixation rates at microbial level were significantly different across depth suggesting that aside microbial biomass, other abiotic factors may influence dark CO<sub>2</sub> fixation in these soils. Of all the tested abiotic variables, water content was the main explanatory factor for the variations in dark CO<sub>2</sub> fixation rates in the Schorfheide-chorin soils. Additionally, based on 16S rRNA sequencing, qPCR and PICRUSt2 analysis, only a few putative autotrophic communities were present and displayed vegetation-specific variations indicating an influence of vegetation type and input on the active community.</p><p>Our findings highlight microbial biomass, CO<sub>2</sub> and water content as the main drivers of dark CO<sub>2</sub> fixation in temperate forest soils with only a small proportion of autotrophs being present, suggesting the potential mediators of this process. We also demonstrate the significance of this process in global temperate forest SOC inputs.</p><p> </p><p> </p>

scholarly journals Prediction of the vertical scaling of soil organic carbon in temperate forest soils using percolation theory

2021 ◽  
Author(s):  
Fang Yu ◽  
Jinping Zheng ◽  
Qiang Liu ◽  
Chunnan Fan
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Cycling ◽  
Forest Soils ◽  
Soil Profile ◽  
Percolation Theory ◽  
Temperate Forest ◽  
Mountain Region ◽  
Vertical Scaling ◽  
Temperate Forest Soils

Abstract. Forest soil stores a large portion of soil organic carbon (SOC), making it one of the essential components of global carbon cycling. There is apparent spatial variability of SOC in forest soils, but the mechanism that regulates the vertical pattern of SOC is still not clear. Understanding the vertical distribution as well as the transport process of SOC can be of importance in developing comprehensive SOC models in forest soils, as well as in better estimating terrestrial carbon cycling. We propose a theoretical scaling derived from percolation theory to predict the vertical scaling of SOC with soil depth in temperate forest soils, with the hypothesis that the content of SOC along soil profile is limited by the transport of solute. The powers of the vertical scaling of 5 published datasets across different regions of the world are −0.920, −1.097, −1.196, −1.062, and −1.038, comparing with the theoretical value of −1.149. Field data from Changbai Mountain region, Jilin, China, with spatial variation of SOC correlating strongly to temperature, precipitation, and sampling slope is constrained well by theoretical boundaries predicted from percolation theory, indicating that the vertical transport so as the content of SOC along soil profile is limited by solute transport, which can be described by percolation theory in both small and large scales. Prediction of SOC content in Changbai Mountain region based on an estimated SOC content at 0.15 m from available data demonstrates a good agreement with field observation, suggesting the potential of collaborating the presented model with other surface soil models to predict SOC storage and carbon cycling in temperate forest soils.

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.

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