The RothC Model to Complement Life Cycle Analyses: A Case Study of an Italian Olive Grove

Soil organic carbon (SOC) plays a fundamental role in soil health, and its storage in soil is an important element to mitigate climate change. How to include this factor in Life Cycle Assessment studies has been the object of several papers and is still under discussion. SOC storage has been proposed as an additional environmental information in some applications of the Product Environmental Footprint (PEF). In the framework of wider activity aimed at producing the PEF of olive oil, the RothC model was applied to an olive cultivation located in Lazio region (Italy) to calculate the SOC storage and assess four scenarios representing different agricultural practices. RothC applicability, possible use of its results for improving product environmental performance, and relevance of SOC storage in terms of CO2eq compared to greenhouse gas emissions of the life-cycle of olive oil are discussed in this paper. According to the results, in all scenarios, the contribution in terms of CO2eq associated with SOC storage is remarkable compared to the total greenhouse gas emissions of the olive oil life-cycle. It is the opinion of the authors that the calculation of the SOC balance allows a more proper evaluation of the agricultural products contribution to climate change, and that the indications of the scenarios analysis are useful to enhance the environmental performance of these products. The downside is that the application of RothC requires additional data collection and expertise if compared to the execution of PEF studies.

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

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.

Estimating soil organic carbon changes in managed temperate moist grasslands with RothC

Temperate grassland soils store significant amounts of carbon (C). Estimating how much livestock grazing and manuring can influence grassland soil organic carbon (SOC) is key to improve greenhouse gas grassland budgets. The Rothamsted Carbon (RothC) model, although originally developed and parameterized to model the turnover of organic C in arable topsoil, has been widely used, with varied success, to estimate SOC changes in grassland under different climates, soils, and management conditions. In this paper, we hypothesise that RothC-based SOC predictions in managed grasslands under temperate moist climatic conditions can be improved by incorporating small modifications to the model based on existing field data from diverse experimental locations in Europe. For this, we described and evaluated changes at the level of: (1) the soil water function of RothC, (2) entry pools accounting for the degradability of the exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) the month-on-month change in the quality of C inputs coming from plant residues (i.e above-, below-ground plant residue and rhizodeposits), and (4) the livestock trampling effect (i.e., poaching damage) as a common problem in areas with higher annual precipitation. In order to evaluate the potential utility of these changes, we performed a simple sensitivity analysis and tested the model predictions against averaged data from four grassland experiments in Europe. Our evaluation showed that the default model’s performance was 78% and whereas some of the modifications seemed to improve RothC SOC predictions (model performance of 95% and 86% for soil water function and plant residues, respectively), others did not lead to any/or almost any improvement (model performance of 80 and 46% for the change in the C input quality and livestock trampling, respectively). We concluded that, whereas adding more complexity to the RothC model by adding the livestock trampling would actually not improve the model, adding the modified soil water function and plant residue components, and at a lesser extent residues quality, could improve predictability of the RothC in managed grasslands under temperate moist climatic conditions.

Non-Standard Discrete RothC Models for Soil Carbon Dynamics

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.

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

<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>

Modelling soil carbon sequestration with biochar using RothC

<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>

Effect of Paddy-Upland Rotation System on the Net Greenhouse Gas Balance as the Sum of Methane and Nitrous Oxide Emissions and Soil Carbon Storage: A Case in Western Japan

To investigate the effect of paddy-upland (PU) rotation system on greenhouse gas emissions, methane (CH4) and nitrous oxide (N2O) emissions were monitored for three years for a PU rotation field (four cultivations (wheat-soybean-rice-rice) over three years) and continuous paddy (CP) field on alluvial soil in western Japan. Soil carbon storage was also calculated using an improved Rothamsted Carbon (RothC) model. The net greenhouse gas balance was finally evaluated as the sum of CO2eq of the CH4, N2O and changes in soil carbon storage. The average CH4 emissions were significantly lower and the average N2O emissions were significantly higher in the PU field than those in the CP field (p < 0.01). On CO2 equivalent basis, CH4 emissions were much higher than N2O emission. In total, the average CO2eq emissions of CH4 plus N2O in the PU field (1.81 Mg CO2 ha−1 year−1) were significantly lower than those in the CP field (7.42 Mg CO2 ha−1 year−1) (p < 0.01). The RothC model revealed that the changes in soil carbon storage corresponded to CO2eq emissions of 0.57 and 0.09 Mg CO2 ha−1 year−1 in the both fields, respectively. Consequently, the net greenhouse gas balance in the PU and CP fields were estimated to be 2.38 and 7.51 Mg CO2 ha−1 year−1, respectively, suggesting a 68% reduction in the PU system. In conclusion, PU rotation system can be regarded as one type of the climate-smart soil management.

Simulating soil organic C dynamics in managed grasslands under humid temperate climatic conditions

Temperate grasslands are of paramount importance in terms of soil organic carbon (SOC) dynamics. Globally, research on SOC dynamics has largely focused on forests, croplands and natural grasslands, while intensively managed grasslands has received much less attention. In this regard, we aimed to improve the prediction of SOC dynamics in managed grasslands under humid temperate regions. In order to do so, we modified and recalibrated the SOC model RothC, originally developed to model the turnover of SOC in arable topsoils, which requires limited amount of readily available input data. The modifications proposed for the RothC are: (1) water content up to saturation conditions in the soil water function of RothC to fit the humid temperate climatic conditions, (2) entry pools that account for particularity of exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) annual variation in the carbon inputs derived from plant residues considering both above- and below-ground plant residue and rhizodeposits components as well as their quality, and (4) the livestock treading effect (i.e., poaching damage) as a common problem in humid areas with higher annual precipitation. In the paper, we describe the basis of these modifications, carry out a simple sensitivity analysis and validate predictions against data from existing field experiments from four sites in Europe. Model performance showed that modified RothC reasonably captures well the different modifications. However, the model seems to be more sensitive to soil moisture and plant residues modifications than to the other modifications. The applied changes in RothC model could be appropriate to simulate both farm and regional SOC dynamics from managed grassland-based systems under humid temperate conditions.

Using RothC Model to Simulate Soil Organic Carbon Stocks under Different Climate Change Scenarios for the Rangelands of the Arid Regions of Southern Iran

Soil organic carbon (SOC) is strongly influenced by climate change, and it is believed that increased temperatures might enhance the release of CO2 with higher emission into the atmosphere. Appropriate models may be used to predict the changes of SOC stock under projected future scenarios of climate change. In this investigation, the RothC model was run for a period of 36 years under climate scenarios namely: P (no climate change) as well as CCH1 and CCH2 (climate change scenarios) in the arid rangelands of Ghir–O-Karzin’s BandBast in southern Iran. Model results have shown that after 11 years (2014–25), SOC stock decreased by 3.05% under the CCH1 scenario (with a projected annual precipitation decrease by 6.69% and mean annual temperature increase by 9.96%) and by 0.23% under the P scenario. In CCH2, with further decreases in rainfall (10.93%) and increase in temperature (12.53%) compared to CCH1, the model predicted that the SOC stock during the 25 years (2025–50) was reduced by 2.36% and 3.53% under the CCH1 and CCH2 scenario respectively. According to model predictions, with future climatic conditions (higher temperatures and lower rainfall) the decomposition rate may increase resulting in higher losses of soil organic carbon from the soil matrix. The result from this investigation may also be used for developing management techniques to be practiced in the other arid rangelands of Iran with similar conditions.