Modelling soil carbon sequestration with biochar using RothC

2021 ◽  
Author(s):  
Roberta Pulcher ◽  
Enrico Balugani ◽  
Maurizio Ventura ◽  
Nicolas Greggio ◽  
Diego Marazza
Keyword(s):  
Carbon Sequestration ◽  
Field Experiment ◽  
Soil Carbon ◽  
Priming Effect ◽  
Laboratory Experiments ◽  
Field Conditions ◽  
Negative Emission ◽  
Rothc Model ◽  
Term Field

<p>In the context of climate change mitigation, technologies for removing the CO<sub>2</sub> from the atmosphere are key challenges. Most recent scenarios from integrated assessment models require large-scale deployment of negative emissions technologies (NETs) to reach the 2 °C target. Among them, technologies for increasing organic carbon content in soils (SOC) have been developed. In the 15<sup>th</sup> IPCC special report on Global Warming of 1.5 °C, biochar and pyrogenic carbon capture and storage have been credited as promising negative emission technology. In fact, soil carbon sequestration (SCS) and biochar have a large negative emission potential (each 0.7 GtCeq. yr<sup>-1</sup>) and they are expected to have lower impact on land, water use, nutrients, albedo, energy requirement and cost, and thus fewer disadvantages than many other NETs.</p><p>SCS can be assessed using soil carbon dynamic models, such as RothC, as suggested by IPCC. However, studies on the inclusion of biochar in RothC are still scarce. Furthermore, most of these studies are based on the results of laboratory experiments and do not account for the effects of biochar on SOC degradation (the priming effect). The use of laboratory data can be problematic, since they may not adequately represent field conditions, especially due to the lack of long-term field studies.</p><p>The aim of this work was to assess and predict how biochar influences the soil C dynamics, by modifying the RothC model to simulate the findings of a long-term field experiment on biochar application to a short coppice rotation in Italy. We first modified the model to include two stocks of C input into the soil: the labile and the recalcitrant biochar pools. We also included a parametrized function to account for the priming effect on SOC dynamics in the soil. Secondly, we calibrated the model parameters with the data obtained from the field experiment. Finally, we validated the model results by estimating the remaining biochar amount in the site after 10 years from application, using an isotopic mass balance.</p><p>The results confirm that biochar degradation can be faster in field conditions in comparison to laboratory experiments; nevertheless, it can contribute to substantially increase the C stock in the long-term. Moreover, the modified RothC model allowed to assess the SCS potential of biochar application in soils, at least in the specific conditions examined, and could represent a flexible tool to assess the effect biochar as a SCS strategy in the long-term. We are exploring the possibility to use data from other long-term field experiment to move in that direction. The results of this study could be added to the Italian biochar database, providing new knowledge about a topic that needs to be explored.</p>

Exploring the Vis-NIR wavelength importance in SOC models under field and lab conditions in a long-term field experiment

2021 ◽  
Author(s):  
Javier Reyes ◽  
Mareike Ließ
Keyword(s):  
Field Experiment ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Field Conditions ◽  
Partial Least Square Regression ◽  
Preprocessing Technique ◽  
Term Field

<p>Soil organic carbon (SOC) is of particular interest in the study of agricultural systems as an indicator of soil quality and soil fertility. In the use of Vis-NIR spectroscopy for SOC detection, the interpretation of the spectral response with regards to the importance of individual wavelengths is challenging due to the soil’s composition of multiple organic and minerals compounds. Under field conditions, additional aspects affect the spectral data compared to lab conditions. This study compared the spectral wavelength importance in partial least square regression (PLSR) models for SOC between field and lab conditions. Surface soil samples were obtained from a long-term field experiment (LTE) with high SOC variability located in the state of Saxony-Anhalt, Germany. Data sets of Vis-NIR spectra were acquired in the lab and field using two spectrometers, respectively. Four different preprocessing methods were applied before building the models. Wavelength importance was observed using variable importance in projection. Differences in wavelength importance were observed depending on the measurement device, measurement condition, and preprocessing technique, although pattern matches were identifiable, especially in the NIR range. It is these pattern matches that aid model interpretation to effectively determine SOC under field conditions.</p>

Soil carbon sequestration through crops rotation in a Mediterranean Cambisols: measurement and modelling

2021 ◽  
Author(s):  
Enrico Balugani ◽  
Martina Maines ◽  
Denis Zannoni ◽  
Alessandro Buscaroli ◽  
Diego Marazza
Keyword(s):  
Carbon Sequestration ◽  
Soil Carbon ◽  
Crop Rotation ◽  
Soil Carbon Sequestration ◽  
Crop Rotations ◽  
Subtropical Climate ◽  
Sequestration Potential ◽  
Rothc Model ◽  
Periodic Fluctuations

<p>Soil carbon sequestration (SCS) has been identified by the IPCC as one of the most promising and cheap methodology to reduce atmospheric CO<sub>2</sub>. Moreover, an increase in soil organic carbon (SOC) levels improves soil quality by increasing soil structure (and, hence, resistance to erosion) and promoting soil ecosystems services like water retention, productivity, and biodiversity. Various agricultural techniques are available to increase SOC; among them, crop rotation can improve SOC through soil coverage, changes in water regimes, increase in both carbon inputs, and increase in soil aggregates formation.</p><p>SOC dynamic models, such as RothC, have been suggested by the IPCC as a way to evaluate the SCS potentials of different soils. Such models could also be used to evaluate the sequestration potential of different agricultural practices. Moreover RothC allows to estimate the time within which the SOC variation, due to a certain agronomic management, can be considered significant as measurable above a threshold value.</p><p>In this study, we evaluated the SOC changes for different crop rotations through direct measurements and RothC modelling, with the objective of: (a) estimating their SCS potential, and (b) propose a robust monitoring methodology for SCS practices. We performed the study in an agricultural field close to Ravenna (Italy) characterized by Cambisols and humid subtropical climate. Soil carbon content was assessed before the setup of the crop rotation, and after 3 years of rotation. A RothC model was calibrated with field data, and used to estimate SOC dynamics to 50 years, in order to assess long-term SCS. The model results were also used to assess the best methodology to estimate the SOC variation significance.</p><p>The measured SOC was similar to the equilibrium SOC predicted by the RothC model, on average, for the crop rotations. The measurements showed that the SOC, already low at the beginning of the experiment, further decreased due to the crop rotation practice. Of those tested, the best for SCS involves the following crops: corn, soybeans, wheat on tilled soil, and soybeans; while the worst is with corn, wheat on tilled soil, and wheat on untilled soil. However, the SOC variations predicted by RothC for the various rotations were too small to be observable in the field during experimentation. This could be due both to the uncertainty associated with SOC sampling and analysis, and to the short duration of the experiment. The moving average computations on the simulation values allowed us to assess the time required to measure the long-term trend of SOC variation as significant with respect to the environmental background, instrumental error, and SOC periodic fluctuations. That time was estimated to range from 8 to 50 years, changing depending on the rotation type. Periodic fluctuations in SOC should be carefully considered in a monitoring protocol to assess SCS.</p>

scholarly journals Inclusion of biochar in a C-dynamics model based on observations from a 8 years field experiment

2021 ◽  
Author(s):  
Roberta Pulcher ◽  
Enrico Balugani ◽  
Maurizio Ventura ◽  
Diego Marazza
Keyword(s):  
Field Experiment ◽  
Degradation Rate ◽  
C Sequestration ◽  
Field Conditions ◽  
Soil C ◽  
Short Rotation ◽  
C Dynamics ◽  
Rothc Model ◽  
Using Data

Abstract. Biochar production and application as soil amendment is a promising carbon (C) negative technology to increase soil C sequestration and mitigate climate change. However, there is a lack of knowledge about biochar degradation rate in soil and its effects on native soil organic carbon (SOC), mainly due to the absence of long term experiments performed in field conditions. The aim of this work was to investigate the long term degradation rate of biochar in a field experiment of 8 years in a poplar short rotation coppice plantation in Piedmont (Italy), and to modify the RothC model to assess and predict how biochar influences soil C dynamics. The RothC model was modified by including two biochar pools, labile (4 % of the total biochar mass) and recalcitrant (96 %), and the priming effect of biochar on SOC. The model was calibrated and validated using data from the field experiment. The results confirm that biochar degradation can be faster in field conditions in comparison to laboratory experiments; nevertheless, it can contribute to substantially increase the soil C stock in the long-term. Moreover, this study shows that the modified RothC model was able to simulate the dynamics of biochar and SOC degradation in soils in field conditions in the long term, at least in the specific conditions examined.

scholarly journals <sup>14</sup>C in cropland soil of a long-term field trial – experimental variability and implications for estimating carbon turnover

SOIL ◽  
2015 ◽  
Vol 1 (2) ◽  
pp. 537-542 ◽  
Author(s):  
J. Leifeld ◽  
J. Mayer
Keyword(s):  
Field Experiment ◽  
Soil Carbon ◽  
Field Experiments ◽  
Soil Depth ◽  
Carbon Turnover ◽  
Independent Field ◽  
Experimental Variability ◽  
Soil Carbon Turnover ◽  
Term Field

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.

scholarly journals <sup>14</sup>C in cropland soil of a long-term field trial – in-field variability and implications for estimating carbon turnover

2015 ◽  
Vol 2 (1) ◽  
pp. 217-231
Author(s):  
J. Leifeld ◽  
J. Mayer
Keyword(s):  
Field Experiment ◽  
Soil Carbon ◽  
Field Experiments ◽  
Soil Depth ◽  
Carbon Turnover ◽  
Independent Field ◽  
Field Variability ◽  
Soil Carbon Turnover ◽  
Term Field

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.

scholarly journals Non-Standard Discrete RothC Models for Soil Carbon Dynamics

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 56
Author(s):  
Fasma Diele ◽  
Carmela Marangi ◽  
Angela Martiradonna
Keyword(s):  
Soil Carbon ◽  
Land Degradation ◽  
Biological Diversity ◽  
Negative Effects ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Soil Carbon Content ◽  
Rothc Model ◽  
Key Indicators

Soil Organic Carbon (SOC) is one of the key indicators of land degradation. SOC positively affects soil functions with regard to habitats, biological diversity and soil fertility; therefore, a reduction in the SOC stock of soil results in degradation, and it may also have potential negative effects on soil-derived ecosystem services. Dynamical models, such as the Rothamsted Carbon (RothC) model, may predict the long-term behaviour of soil carbon content and may suggest optimal land use patterns suitable for the achievement of land degradation neutrality as measured in terms of the SOC indicator. In this paper, we compared continuous and discrete versions of the RothC model, especially to achieve long-term solutions. The original discrete formulation of the RothC model was then compared with a novel non-standard integrator that represents an alternative to the exponential Rosenbrock–Euler approach in the literature.

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