Long-term sensitivity of soil carbon turnover to warming

Nature ◽  
2005 ◽  
Vol 433 (7023) ◽  
pp. 298-301 ◽  
Author(s):  
W. Knorr ◽  
I. C. Prentice ◽  
J. I. House ◽  
E. A. Holland
SOIL ◽  
2015 ◽  
Vol 1 (2) ◽  
pp. 537-542 ◽  
Author(s):  
J. Leifeld ◽  
J. Mayer

Abstract. Because of their controlled nature, the presence of independent replicates, and their known management history, long-term field experiments are key to the understanding of factors controlling soil carbon. Together with isotope measurements, they provide profound insight into soil carbon dynamics. For soil radiocarbon, an important tracer for understanding these dynamics, experimental variability across replicates is usually not accounted for; hence, a relevant source of uncertainty for quantifying turnover rates is missing. Here, for the first time, radiocarbon measurements of five independent field replicates, and for different layers, of soil from the 66-year-old controlled field experiment ZOFE in Zurich, Switzerland, are used to address this issue. 14C variability was the same across three different treatments and for three different soil layers between the surface and 90 cm depths. On average, experimental variability in 14C content was 12 times the analytical error but still, on a relative basis, smaller than variability in soil carbon concentration. Despite a relative homogeneous variability across the field and along the soil profile, the curved nature of the relationship between radiocarbon content and modelled carbon mean residence time implies that the absolute error of calculated soil carbon turnover time increases with soil depth. In our field experiment findings on topsoil carbon turnover variability would, if applied to subsoil, tend to underweight turnover variability even if experimental variability in the subsoil isotope concentration is the same. Together, experimental variability in radiocarbon is an important component in an overall uncertainty assessment of soil carbon turnover.


Soil Research ◽  
2004 ◽  
Vol 42 (8) ◽  
pp. 883 ◽  
Author(s):  
K. I. Paul ◽  
P. J. Polglase

Abstract The FullCAM model was developed for full carbon accounting in agriculture and forests at project and national scales. For forest systems, FullCAM links the empirical CAMFor model to models of tree growth (3PG), litter decomposition (GENDEC), and soil carbon turnover (RothC). Our objective was to calibrate RothC within the FullCAM framework using 2 long-term forestry experiments where productivity had been manipulated and archived and new soil samples were available for analysis of carbon within the various pools described by RothC. Inputs of carbon to soil at these trials were estimated by calibrating FullCAM to temporal data on above-ground growth, litterfall, and accumulation of litter. Two alternative submodels are available in FullCAM (CAMFor and GENDEC) for predicting decomposition of litter, and thus the input of carbon into the soil. Calibration of RothC was most sensitive to the partitioning of carbon during decomposition of debris between that lost as CO2 and that transferred to soil. Turnover of soil carbon was best simulated when the proportion of carbon lost to CO2 from relatively labile pools of debris was 77% (when simulated by CAMFor) and 95% (when simulated by GENDEC), whereas resistant pools of debris lost about 40% to CO2 during decomposition. Although rates of decomposition of pools of soil carbon were originally developed in RothC for agricultural soils, these constants were found to be also suitable for soils under plantation systems.


2015 ◽  
Vol 2 (1) ◽  
pp. 217-231
Author(s):  
J. Leifeld ◽  
J. Mayer

Abstract. Because of their controlled nature, the presence of independent replicates, and their known management history long-term field experiments are key to the understanding of factors controlling soil carbon. Together with isotope measurements, they provide profound insight into soil carbon dynamics. For soil radiocarbon, an important tracer for understanding these dynamics, in-field variability across replicates is usually not accounted for, hence, a relevant source of uncertainty for quantifying turnover rates is missing. Here, for the first time, radiocarbon measurements of independent field replicates, and for different layers, of soil from the 60 years old controlled field experiment ZOFE in Zurich, Switzerland, is used to address this issue. 14C variability was the same across three different treatments and for three different soil layers between surface and 90 cm depths. On average, in-field variability in 14C content was 12 times the analytical error but still, on a relative basis, smaller than that of in-field soil carbon concentration variability. Despite a relative homogeneous variability across the field and along the soil profile, the curved nature of the relationship between radiocarbon content and modelled carbon mean residence time suggests that the absolute error, without consideration of in-field variability, introduced to soil carbon turnover time calculations increases with soil depth. In our field experiment findings on topsoil carbon turnover variability would, if applied to subsoil, tend to underweight turnover variability even if in-field variability of the subsoil isotope concentration is not higher. Together, in-field variability in radiocarbon is an important component in an overall uncertainty assessment of soil carbon turnover.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Arezoo Taghizadeh-Toosi ◽  
Bent T. Christensen ◽  
Margaret Glendining ◽  
Jørgen E. Olesen

2012 ◽  
Vol 50 ◽  
pp. 188-198 ◽  
Author(s):  
M.P. Waldrop ◽  
J.W. Harden ◽  
M.R. Turetsky ◽  
D.G. Petersen ◽  
A.D. McGuire ◽  
...  

2021 ◽  
Author(s):  
Yunsen Lai ◽  
Shaoda Li ◽  
Xiaolu Tang ◽  
Xinrui Luo ◽  
Liang Liu ◽  
...  

<p>Soil carbon isotopes (δ<sup>13</sup>C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ<sup>13</sup>C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ<sup>13</sup>C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ<sup>13</sup>C in QTP. In this study, we first established a database of topsoil δ<sup>13</sup>C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ<sup>13</sup>C using environmental variables. Results showed that topsoil δ<sup>13</sup>C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ<sup>13</sup>C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ<sup>13</sup>C with a model efficiency (pseudo R<sup>2</sup>) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ<sup>13</sup>C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ<sup>13</sup>C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ<sup>13</sup>C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ<sup>13</sup>C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.</p>


2013 ◽  
Vol 10 (12) ◽  
pp. 8067-8081 ◽  
Author(s):  
M. S. Torn ◽  
M. Kleber ◽  
E. S. Zavaleta ◽  
B. Zhu ◽  
C. B. Field ◽  
...  

Abstract. Soils are globally significant sources and sinks of atmospheric CO2. Increasing the resolution of soil carbon turnover estimates is important for predicting the response of soil carbon cycling to environmental change. We show that soil carbon turnover times can be more finely resolved using a dual isotope label like the one provided by elevated CO2 experiments that use fossil CO2. We modeled each soil physical fraction as two pools with different turnover times using the atmospheric 14C bomb spike in combination with the label in 14C and 13C provided by an elevated CO2 experiment in a California annual grassland. In sandstone and serpentine soils, the light fraction carbon was 21–54% fast cycling with 2–9 yr turnover, and 36–79% slow cycling with turnover slower than 100 yr. This validates model treatment of the light fraction as active and intermediate cycling carbon. The dense, mineral-associated fraction also had a very dynamic component, consisting of ∼7% fast-cycling carbon and ∼93% very slow cycling carbon. Similarly, half the microbial biomass carbon in the sandstone soil was more than 5 yr old, and 40% of the carbon respired by microbes had been fixed more than 5 yr ago. Resolving each density fraction into two pools revealed that only a small component of total soil carbon is responsible for most CO2 efflux from these soils. In the sandstone soil, 11% of soil carbon contributes more than 90% of the annual CO2 efflux. The fact that soil physical fractions, designed to isolate organic material of roughly homogeneous physico-chemical state, contain material of dramatically different turnover times is consistent with recent observations of rapid isotope incorporation into seemingly stable fractions and with emerging evidence for hot spots or micro-site variation of decomposition within the soil matrix. Predictions of soil carbon storage using a turnover time estimated with the assumption of a single pool per density fraction would greatly overestimate the near-term response to changes in productivity or decomposition rates. Therefore, these results suggest a slower initial change in soil carbon storage due to environmental change than has been assumed by simpler (one-pool) mass balance calculations.


Author(s):  
Donghai Wu ◽  
Xiangtao Xu ◽  
Haicheng Zhang

Chen et al. (2021) concluded that plant input governs topsoil carbon persistence in alpine grasslands. We demonstrated that the excluded direct effect of precipitation on topsoil Δ14C in their analysis was in fact significant and strong. Our results provide an alternative viewpoint on the drivers of soil carbon turnover.


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