RothC Model Research Articles

Conservation agriculture based on diversified and high-performance production system leads to soil carbon sequestration in subtropical environments

Soils can be a source or sink of atmospheric CO2, depending on the historic and existing land use and management. We used long term soil management database of a production farm that is based on principles of conservation agriculture such as: a) eliminate soil disturbance; b) maintain permanent soil surface cover; C) adopt crop diversity with high biomass-C input; for 30 years and agroecosystem models to study the potential of different management options to sequester C in soils. Using Century and Roth-C models we simulated the carbon stocks evolution in the farm and four subtropical soil management scenarios and studied C sequestration potential. The scenarios were: a) existing farm biomass input (14.5 Mg ha−1 year−1) or C input (6.5 Mg ha−1 year−1); b) 15% increase of farm biomass input (16.7 Mg ha−1 year−1) or C input (7.5 Mg ha−1 year−1); c) 15% decrease of farm biomass (12.3 Mg ha−1 year−1) or C input (5.5 Mg ha−1 year−1) and, d) 30% decrease of farm biomass input (10.1 Mg ha−1 year−1) or C input (4.5 Mg ha−1 year−1). Our results demonstrate that soil organic carbon continuously increased after conservation management practices adoption in 1985 until 2015, and currently soil organic carbon is in equilibrium. We found that an increase of 2.2 Mg ha−1 year−1 biomass-C input for 60 years resulted into increase of 12 Mg ha−1 soil organic carbon stocks. The same way, crop yields increased with time, and were more pronounced for maize compared to soybean and wheat. The scaling up of model results to similar climate and soil types indicated that conservation management practices has the potential to sequester 2.7 ± 0.02 Pg C at 0–20 cm and 4.8 ± 3 Pg C at 0–100 cm soil depth in 43 million ha area globally. In the 30% and 15% decrease scenarios the sequestration were 2.2 ± 0.02 and 2.4 ± 0.02 Pg C at 0–20 cm an in 15% increase scenario it goes to 3.2 ± 0.02 Pg C. This equilibrium soil organic carbon stocks considering the currently adopted system are equivalent to 3.5–4.5% of the world SOC stocks in 3% of the world croplands and correspond to 6 years of global land use and land use change emissions, indicating that conservation management practices can lead the soil be a sink and a promising tool to promote C sequestration in subtropical soils.

CASPER: A modelling framework to link mineral carbonation with the turnover of organic matter in soil

Rapid formation of stable soil carbonates offers a potential biologically-mediated strategy for removing atmospheric CO2 and forms a part of the negative emissions debate in a bid to maintain global temperatures of 1.5 °C. Microbial respiration in soil and respiration by plant roots leads to high partial pressure of CO2 below ground. Given adequate supply of calcium in soil solution the sequestration of C into the mineral calcite (CaCO3) can occur at rapid rates. We have coupled an established soil C model RothC to a simplified geochemical model so that this strategy can be explored and assessed by simulation. The combined model CASPER partitions CO2 respired belowground into soil solution as HCO3− and simulates its reaction with Ca2+ based on a particular dissolution rate for Ca-bearing minerals, with precipitation of calcite into soil pores as a consequence. Typical model output matches observed field rates of calcite accumulation over 5 years, namely 81 t ha−1, with 19 t CO2 ha−1 sequestered into the soil.

Carbon Sequestration Implementation through Sustainable Agricultural Land Management (SALM) Methodology in Nigeria

Climate-Smart Agriculture (CSA) as an adaptation strategy that helps rural farmers adapt to climate change by making them resilient to its effects. SALM methodology is a CSA practice that promotes carbon sequestration, which in the long run increase farmers’ productivity. This study assessed SALM methodology using RothC model to calculate the effi- cacy of CSA on Umar Lere farm. Activity Baseline and Monitoring Survey was used to acquire data for a period of 3 years of practicing SALM methodology. Results showed that after 3 years of SALM adoption, the farm produced maize (2.6), soybeans (0.7), guinea corn (1.1), and tomatoes (1.7) tons/hectare/year respectively in 2015 compared to maize (1.2), soybeans (0.3), guinea corn (1.6), and tomatoes (0.7) tons/hectare/year respectively produced in 2012. The farm also recorded 56 trees sequestrating 10.2 tons of carbon dioxide per hectare in 2015 compared to 15 trees sequestrating 2.6 tons of carbon dioxide per year in 2012. In 3 years, Umar Lere farm significantly increased its crop yields from the project; RothC model shows that the modelled soil carbon stock changes increased significantly as a result of the adoption of SALM practices from around 0:5 tCO2 ha-1yr-1 in 2012 to 3:5 ha-1 yr-1 in 2015.

Soil C Storage Potential of Exogenous Organic Matter at Regional Level (Italy) Under Climate Change Simulated by RothC Model Modified for Amended Soils

Soil amendment with exogenous organic matter (EOM) represents an effective option for sustainable management of organic residues and enhancement of soil organic C (SOC) content. Optimization of soil amendment is hampered by the high variability in EOM quality and pedoclimatic conditions. A possible solution to this problem could be represented by spatially explicit soil C modelling. The aim of this study was the evaluation at regional level of the long term C storage potential of EOM added to the soil under climate change by using a modified version of the RothC specifically developed for C simulation in amended soil. To achieve this goal a spatially explicit version of the modified RothC model was deployed to assess at a national scale the potential for C storage of agricultural soils amended with different EOMs. Long term model simulations of continuous amendment (100 years) indicated that EOMs greatly differ for their soil C sequestration potential (range 0.110 - 0.385 t C ha-1 y-1), mainly depending to their degree of stabilization. Spatial explicit modelling of amended soil, taking into account the different combinations of EOMs and application sites, indicated a high variability in the potential of SOC accumulation at the national level (range: 0.06 - 0.62 t C ha-1 y-1). EOM quality showed a larger impact on long term SOC accumulation than variability in pedoclimatic conditions. Model simulations predicted that the contribution of soil amendment in tackling greenhouse gas (GHG) emissions is limited: soil C sequestration potential of compost applied to all Italian agricultural land corresponded to 5.3% of the total annual GHG emissions in Italy. Large scale modelling enables areas with the largest potential for EOM accumulation to be identified, therefore suggesting ways for optimizing resources. Result suggests that reliable C modelling in amended soil requires modification and optimization of actual models to accommodate the different quality of EOMs applied to the soil. The spatially explicit version of the modified RothC model improves the predictive power of SOC modelling at regional scale in amended soils, because it takes into account, besides variability in pedoclimatic conditions, the large differences in EOMs quality.

Evaluation of the RothC model as a prognostic tool for the prediction of SOC trends in response to management practices on arable land

RothC is an established model for predicting soil organic carbon (SOC) changes in response to environmental conditions and management. The model lacks algorithms for carbon input estimation and a differentiated consideration of carbon input sequestration from organic amendments commonly used in agriculture. Moreover, it does not consider the higher stability of below-ground crop residues in relation to above-ground crop residues.RothC was combined with two empirical approaches for quantifying carbon inputs from above- and below-ground crop residues and tested on 439 SOC data series from 36 arable long-term field experiments in Central and Northern Europe. Effects of carbon input quality on model fit were quantified with linear mixed models based on the analytical solution of RothC. Model parameters that describe the stability of incoming carbon were calibrated using a multi-site approach and Bayesian calibration. A second calibration study combined the determination of partitioning of incoming carbon into pools with different turnover rates and model responses to temperature and soil water content. With this calibration we showed that the contribution of above-ground residues to SOC is lower than when estimated with default RothC paramerization. We also show that the relative contribution from roots to SOC is higher than that from above-ground residues. Moreover, the degradability of organic amendments was highly variable between amendment categories and increased for all model configurations in the following order: farmyard manure < farmyard compost < sawdust < sewage sludge < peat. The proposed RothC partition coefficients for above-ground residues, roots and several commonly used organic amendments as estimated for this large data set should be useful for other studies in temperate climates. We also show that the analytical solution of RothC is closely related to the much simpler empirical humus balancing approaches used by farm advisory services. This provides opportunities to build bridges between the more process-oriented SOC models used in research and the well established instruments used by the farming community to assess the effects of agricultural management practices on SOC changes.

Potential impacts of climate change on soil organic carbon and productivity in pastures of south eastern Australia

Increasing soil organic carbon (SOC) stocks is an often-mentioned option to mitigate greenhouse gas emissions. However, increasing carbon stocks in agricultural soils is difficult and the ability of soils to store carbon as the climate changes is uncertain. This is due to many interacting factors, including those that vary spatially, contributing to organic matter inputs and decomposition rates. We used two models, the Sustainable Grazing Systems whole-farm system model (SGS) and the RothC soil carbon model, to investigate the potential impacts of climate change on SOC stocks in pastures in a temperate, winter-dominant rainfall region of south eastern Australia. A wide range of possible future climates were simulated from 2017 to 2090 at two sites, each with two soil types. Results demonstrate that projected rainfall, the factor with the most variability between climate scenarios, was the primary source of uncertainty in SOC response. Dry climate projections resulted in lower SOC content than other scenarios. The two models were similar in their projected trends, but the RothC model generally gave larger percent changes in soil carbon over the simulation period and a larger range of responses due to changes in site characteristics, particularly clay content. Sustainable stocking rates were determined by the whole-farm system model based on climate, pasture production, and maintaining minimum dry matter coverage. Wet future climates lead to increased production that supported increases in stocking and increases in SOC stocks. While soil carbon accumulated at slower rates or remained steady under dry projections, lower production meant this was accompanied by decreased average 2070–2090 stocking rates, which approached zero by 2090 on the low-rainfall site. This highlights an important interaction between SOC and grazing management. The results demonstrate the extent of the uncertainty associated with soil carbon trading for farmers and the need for adaptation options that allow farms to remain sustainable and productive as the climate changes.

Increasing crop yields and root input make Canadian farmland a large carbon sink

Soil organic carbon (SOC) in agricultural lands is vital for global food production and greenhouse gas (GHG) mitigation. Accurate quantification of the change in SOC stocks at regional or national scales, which depends heavily on reliable spatiotemporal carbon (C) input data, remains a big challenge. Here we use the process-based RothC model to estimate change in SOC stocks across Canada for 1971 to 2015, based on calculated annual C flows between cropland and livestock sectors. Total C input to 0–20 cm soils from crops, manure, and biosolids in Canada increased by 81% from 1971 to 2015, which shifted Canadian agricultural lands from a CO2 source before 1990 (−1.1 Tg C yr−1) to a small sink during 1990–2005 (4.6 Tg C yr−1), and a larger sink thereafter (10.6 Tg C yr−1). The increasing trend of SOC stocks is mainly driven by the increases in crop yield; the enhanced C sink since 2005 reflects increasing C input largely driven by the increasing area and yield of canola. SOC sequestration showed a potential to offset ~34% or more of agricultural GHG emission since 1990. Increasing crop yields and adopting crop mixes that input proportionately more below-ground C, such as canola and oat, showed potential additional opportunity to sequester SOC, estimated at 1.7 Tg C yr−1 for 2016–2030 in Canada. This study illustrates that SOC sequestration is driven largely by plant C inputs, and shows that agronomic measures which augment C input through crop choices and yield-enhancing practices can profoundly benefit climate mitigation strategies.

Modeling Regional Effects of Climate Change on Soil Organic Carbon in Spain.

Soil organic C (SOC) stock assessments at the regional scale under climate change scenarios are of paramount importance in implementing soil management practices to mitigate climate change. In this study, we estimated the changes in SOC sequestration under climate change conditions in agricultural land in Spain using the RothC model at the regional level. Four Intergovernmental Panel on Climate Change (IPCC) climate change scenarios (CGCM2-A2, CGCM2-B2, ECHAM4-A2, and ECHAM4-B2) were used to simulate SOC changes during the 2010 to 2100 period across a total surface area of 2.33 × 10 km. Although RothC predicted a general increase in SOC stocks by 2100 under all climate change scenarios, these SOC sequestration rates were smaller than those under baseline conditions. Moreover, this SOC response differed among climate change scenarios, and in some situations, some losses of SOC occurred. The greatest losses of C stocks were found mainly in the ECHAM4 (highest temperature rise and precipitation drop) scenarios and for rainfed and certain woody crops (lower C inputs). Under climate change conditions, management practices including no-tillage for rainfed crops and vegetation cover for woody crops were predicted to double and quadruple C sequestration rates, reaching values of 0.47 and 0.35 Mg C ha yr, respectively.

Greenhouse gas inventories: Deriving soil organic carbon change factors and assessing soil depth relevance in Argentinean Semiarid Chaco

Soil organic carbon (SOC) is the main terrestrial carbon (C) reservoir. Land use change has depleted SOC stocks and released large amounts of C dioxide (CO2). Thus, the development of reliable tools for SOC stock monitoring at large scale is fundamental to face climate change. Argentinean Semiarid Chaco (ASC) is a deforestation hotspot, but CO2 emissions from soil has been barely assessed. Deforested area was converted into cropland or grassland. We used empirical data to model SOC stocks under native forest (SOCref) and the RothC model to estimate SOC stock change factors under cropland (Fc) and grasslands (Fg) in the ASC. These SOCref's and stock change factors were applied in a Tier 2 (T2) C inventory, following the Intergovernmental Panel on Climate Change (IPCC) proposal. We used SOC vertical distribution models to estimate SOC stock at 0–100 cm soil depth from estimated SOC stocks at 0–30 cm, the default soil depth of IPCC C inventory method. The T2 was run for 1976 through 2012 and under three hypothetical land use change scenarios for 2012 through 2032. The scenarios were: i) land use change ceases, ii) land use change rate is the half of 1996–2012 land use change rate, and iii) land use change rate remains as in 1996–2012. Estimated average SOCref stock at 0–30 cm soil depth was 40 Mg C ha−1 and varied between 35 and 51 Mg C ha−1. Cropland was the main fate of deforested area and the land use with lower SOC stocks. Stock change factors and SOC stocks estimated with T2 were within the range of the empirical data reported in the ASC. However, research about SOC dynamics and land use change is incipient in the ASC and more empirical information is needed to validate T2 estimations. Deforestation in the ASC leads to high CO2 emissions from soil and the only scenario in which those emissions would be reduced is with deforestation cessation. Soil depth considered in greenhouse gas inventories is 0–30 cm, and this strongly underestimates CO2 emissions. We demonstrated that this limitation could be overcome by using SOC vertical distribution models to estimate deep SOC stock (up to 1 m) from estimated surface SOC stock. Hence, these models could be used to improve CO2 estimations from SOC inventories.

A proposal for using process-based soil models for land use Life cycle impact assessment: Application to Alentejo, Portugal

Abstract Human occupation and transformation of land are a root cause of ecosystem services degradation. For example, land use (LU) and land use change (LUC) are drivers of soil organic carbon (SOC) change, decreasing the biotic production potential of soils. In Life Cycle Assessment (LCA) there is a growing trend towards accurate and regionalized impact assessment of LU∖LUC. Available impact assessment models apply statistical procedures to a simplified static characterization framework, including a limited set of aggregated LU classes. In this paper we propose process-based modelling as the basis for LU/LUC Life Cycle Impact Assessment (LCIA) and calculate midpoint process-based characterization factors (CFs) using SOC depletion as the impact indicator. This forecast or scenario-based approach included climate change scenarios for the study region. Temperature and precipitation changes are likely to affect the role of LU in SOC accumulation, which should influence LUC choices today. As proof of concept we used the region of Alentejo, Portugal, which was divided in more than 1500 unique homogeneous territorial units (UHTU). We used the process-based model RothC to establish the likely dynamic SOC evolution after LUC in each UHTU. Our work suggests that process-based models are necessary to move beyond the calculation of CFs for generic LU classes. We provide CFs for individual crops and discriminated LU classes and management practices. We additionally provide insights and indication for future work leading up to the production of global CFs using similar soil models and an updated procedure for CF calculation.

A historical perspective on soil organic carbon in Mediterranean cropland (Spain, 1900–2008)

Soil organic carbon (SOC) management is key for soil fertility and for mitigation and adaptation to climate change, particularly in desertification-prone areas such as Mediterranean croplands. Industrialization and global change processes affect SOC dynamics in multiple, often opposing, ways. Here we present a detailed SOC balance in Spanish cropland from 1900 to 2008, as a model of a Mediterranean, industrialized agriculture. Net Primary Productivity (NPP) and soil C inputs were estimated based on yield and management data. Changes in SOC stocks were modeled using HSOC, a simple model with one inert and two active C pools, which combines RothC model parameters with humification coefficients. Crop yields increased by 227% during the studied period, but total C exported from the agroecosystem only increased by 73%, total NPP by 30%, and soil C inputs by 20%. There was a continued decline in SOC during the 20th century, and cropland SOC levels in 2008 were 17% below their 1933 peak. SOC trends were driven by historical changes in land uses, management practices and climate. Cropland expansion was the main driver of SOC loss until mid-20th century, followed by the decline in soil C inputs during the fast agricultural industrialization starting in the 1950s, which reduced harvest indices and weed biomass production, particularly in woody cropping systems. C inputs started recovering in the 1980s, mainly through increasing crop residue return. The upward trend in SOC mineralization rates was an increasingly important driver of SOC losses, triggered by irrigation expansion, soil cover loss and climate change-driven temperature rise.

Correspondence of measured soil carbon fractions and RothC pools for equilibrium and non-equilibrium states

The link between carbon turnover model pools and measurable carbon fractions is of key interest for initial parameterisation and subsequent validation of dynamic soil carbon models. In this study we performed the established particle-size fractionation of soils from 54 intensively monitored sites in Germany and from archived samples from 5 other long-term experiments in Germany and the United Kingdom. The Rothamsted carbon (RothC) model was then used to compare the measured soil C fractionation from the 54 intensively monitored sites against modelled pools using spin-up equilibrium runs whilst dynamic (non-equilibrium) model runs were performed when comparing data from the long-term experiments. We detected good agreement between measured soil C fractions and modelled pools, indicated by correlation coefficients of 0.73 and 0.81 for the resistant plant material pool (RPM) and 0.91 and 0.94 for the humus pool (HUM) for the intensively monitored and the long-term sites, respectively. Slightly larger errors were detected for the intensively monitored sites together with a bias in the relationship between the RPM pool and particulate organic matter fraction. This bias detected for the intensively monitored sites indicated that the equilibrium assumption for arable agricultural sites, even though under crop cover for at least 50years, might not be entirely valid. From the relative mean absolute error of 11% for the HUM pool and 26% for the RPM pool of the combined data set we conform that the measured fractions can be used to estimate the RothC model pools in arable soils. Given the magnitude of these errors, however, we rather suggest to apply the fractionation approach instead of using an equilibrium assumption for the RothC initialisation of arable sites.

Integrating belowground carbon dynamics into Yield-SAFE, a parameter sparse agroforestry model

Agroforestry combines perennial woody elements (e.g. trees) with an agricultural understory (e.g. wheat, pasture) which can also potentially be used by a livestock component. In recent decades, modern agroforestry systems have been proposed at European level as land use alternatives for conventional agricultural systems. The potential range of benefits that modern agroforestry systems can provide includes farm product diversification (food and timber), soil and biodiversity conservation and carbon sequestration, both in woody biomass and the soil. Whilst typically these include benefits such as food and timber provision, potentially, there are benefits in the form of carbon sequestration, both in woody biomass and in the soil. Quantifying the effect of agroforestry systems on soil carbon is important because it is one means by which atmospheric carbon can be sequestered in order to reduce global warming. However, experimental systems that can combine the different alternative features of agroforestry systems are difficult to implement and long-term. For this reason, models are needed to explore these alternatives, in order to determine what benefits different combinations of trees and understory might provide in agroforestry systems. This paper describes the integration of the widely used soil carbon model RothC, a model simulating soil organic carbon turnover, into Yield-SAFE, a parameter sparse model to estimate aboveground biomass in agroforestry systems. The improvement of the Yield-SAFE model focused on the estimation of input plant material into soil (i.e. leaf fall and root mortality) while maintaining the original aspiration for a simple conceptualization of agroforestry modeling, but allowing to feed inputs to a soil carbon module based on RothC. Validation simulations show that the combined model gives predictions consistent with observed data for both SOC dynamics and tree leaf fall. Two case study systems are examined: a cork oak system in South Portugal and a poplar system in the UK, in current and future climate.

Coupling life-cycle assessment and the RothC model to estimate the carbon footprint of green manure-based wheat production in China

Reducing the carbon footprint (CF) of crop production is an efficient way to mitigate climate change. Growing legume green manure (LGM) instead of summer fallow may achieve this goal by lowering synthetic nitrogen (N) fertilizer needs and replenishing the depleted soil carbon (C) pool. The Rothamsted Carbon (RothC) model was incorporated into the Life-Cycle Assessment (LCA) to evaluate the present and projected CFs of green manure-based wheat production systems in dryland agriculture on the Loess Plateau of China. The field study included four main treatments (Huai bean, soybean and mung bean grown as green manure in summer and fallow as control) and four synthetic N rates (0, 108, 135 and 162kgNha−1) applied at wheat sowing. Soybean as LGM increased averaged wheat yield over 4 synthetic N rates by 8% compared with fallow (P<0.05), and synthetic N requirement was reduced by 33% without compromising the wheat yield for all the main treatments. Although LGM treatments had higher greenhouse gas (GHG) emissions from agricultural inputs, the greater amount of C inputs elevated the corresponding SOC stocks (SOCS) by 14–24% after 8years, thus significantly reducing the CF by 25–51% compared with fallow. The modelled SOCS equilibrium indicates that the CF for cropping systems with LGM will be 53–62% lower than fallow and 23–37% lower compared with their current level. In conclusion, introducing legume green manure instead of summer fallow is a highly efficient measure for persistent CF reduction, and coupling the RothC model and LCA is an alternative method to predict the long-term impact of different cropping systems on GHG emissions.

Modelling long-term impacts of changes in climate, nitrogen deposition and ozone exposure on carbon sequestration of European forest ecosystems

We modelled the effects of past and expected future changes in climate (temperature, precipitation), CO2 concentration, nitrogen deposition (N) and ozone (O3) exposure (phytotoxic ozone dose, POD) on carbon (C) sequestration by European forest ecosystems for the period 1900–2050. Tree C sequestration was assessed by using empirical response functions, while soil C sequestration was simulated with the process-based model VSD, combined with the RothC model. We evaluated two empirical growth responses to N deposition (linear and non-linear) and two O3 exposure relationships (linear function with total biomass or net annual increment). We further investigated an ‘interactive model’ with interactions between drivers and a ‘multiplicative model’, in which the combined effect is the product of individual drivers. A single deposition and climate scenario was used for the period 1900–2050. Contrary to expectations, growth observations at European level for the period 1950–2010 compared better with predictions by the multiplicative model than with the interactive model. This coincides with the fact that carbon responses in kgCha−1yr−1 per unit change in drivers, i.e. per °C, ppm CO2, kgNha−1yr−1 and mmolm−2yr−1 POD, are more in line with literature data when using the multiplicative model. Compared to 1900, the estimated European average total C sequestration in both forests and forest soils between 1950 and 2000 increased by 21% in the interactive model and by 41% in the multiplicative model, but observed changes were even higher. This growth increase is expected to decline between 2000 and 2050. The simulated changes between 1950 and 2000 were mainly due to the increase in both N deposition and CO2, while the predicted increases between 2000 and 2050 were mainly caused by the increase in CO2 and temperature, and to lesser extent a decrease in POD, counteracted by reduced N deposition.

Modification of the RothC model to simulate soil C mineralization of exogenous organic matter

Abstract. The development of soil organic C (SOC) models capable of producing accurate predictions for the long-term decomposition of exogenous organic matter (EOM) in soils is important for the effective management of organic amendments. However, reliable C modeling in amended soils requires specific optimization of current C models to take into account the high variability in EOM origin and properties. The aim of this work was to improve the prediction of C mineralization rates in amended soils by modifying the RothC model to encompass a better description of EOM quality. The standard RothC model, involving C input to the soil only as decomposable (DPM) or resistant (RPM) organic material, was modified by introducing additional pools of decomposable (DEOM), resistant (REOM) and humified (HEOM) EOM. The partitioning factors and decomposition rates of the additional EOM pools were estimated by model fitting to the respiratory curves of amended soils. For this task, 30 EOMs from 8 contrasting groups (compost, anaerobic digestates, sewage sludge, agro-industrial waste, crop residues, bioenergy by-products, animal residues and meat and bone meals) were added to 10 soils and incubated under different conditions. The modified RothC model was fitted to C mineralization curves in amended soils with great accuracy (mean correlation coefficient 0.995). In contrast to the standard model, the EOM-optimized RothC was able to better accommodate the large variability in EOM source and composition, as indicated by the decrease in the root mean square error of the simulations for different EOMs (from 29.9 to 3.7 % and 20.0 to 2.5 % for soils amended with bioethanol residue and household waste compost, respectively). The average decomposition rates for DEOM and REOM pools were 89 and 0.4 yr−1, higher than the standard model coefficients for DPM (10 yr−1) and RPM (0.3 yr−1). The results indicate that the explicit treatment of EOM heterogeneity enhances the model ability to describe amendment decomposition under laboratory conditions and provides useful information to improve C modeling on the effects of different EOM on C dynamics in agricultural soils. Future research will involve the validation of the modified model with field data and its application in the long-term simulation of SOC patterns in amended soil at regional scales under climate change.

Simulation of soil organic carbon changes in Vertisols under conservation tillage using the RothC model

The purpose of this study was to determine the measured and simulated rates of soil organic carbon (SOC) change in Vertisols in short-term experiments when the tillage system is changed from traditional tillage (TT) to conservation tillage (CT). The study was conducted in plots in four locations in the state of Michoacán and two locations in the state of Guanajuato. In the SOC change simulation, the RothC-26.3 carbon model was evaluated with different C inputs to the soil (ET1-ET5). ET was the measured shoot biomass (SB) plus estimated rhizodeposition (RI). RI was tested at values of 10, 15, 18, 36 and 50 % total biomass (TB). The SOC changes were simulated with the best trial where ET3 = SB + (0.18*TB). Values for model efficiency and the coefficient of correlation were in the ranges of 0.56 to 0.75 and 0.79 to 0.92, respectively. The average rate of SOC change, measured and simulated, in the study period was 3.0 and 1.9 Mg ha−1 yr−1, respectively; later, in a simulation period of 45 years, SOC change was 1.2 ± 0.8. In particular, without making adjustments in the RothC parameters and with information on measured plant residue C inputs to the soil, it was possible to simulate changes in SOC with RothC and estimate trends over a period of more than 45 years.

Simulating soil organic carbon stock as affected by land cover change and climate change, Hyrcanian forests (northern Iran)

Soil organic carbon (SOC) contains a considerable portion of the world's terrestrial carbon stock, and is affected by changes in land cover and climate. SOC modeling is a useful approach to assess the impact of land use, land use change and climate change on carbon (C) sequestration. This study aimed to: (i) test the performance of RothC model using data measured from different land covers in Hyrcanian forests (northern Iran); and (ii) predict changes in SOC under different climate change scenarios that may occur in the future. The following land covers were considered: Quercus castaneifolia (QC), Acer velutinum (AV), Alnus subcordata (AS), Cupressus sempervirens (CS) plantations and a natural forest (NF). For assessment of future climate change projections the Fifth Assessment IPCC report was used. These projections were generated with nine Global Climate Models (GCMs), for two Representative Concentration Pathways (RCPs) leading to very low and high greenhouse gases concentration levels (RCP 2.6 and RCP 8.5 respectively), and for four 20year-periods up to 2099 (2030s, 2050s, 2070s and 2090s). Simulated values of SOC correlated well with measured data (R2=0.64 to 0.91) indicating a good efficiency of the RothC model. Our results showed an overall decrease in SOC stocks by 2099 under all land covers and climate change scenarios, but the extent of the decrease varied with the climate models, the emissions scenarios, time periods and land covers. Acer velutinum plantation was the most sensitive land cover to future climate change (range of decrease 8.34–21.83tCha−1). Results suggest that modeling techniques can be effectively applied for evaluating SOC stocks, allowing the identification of current patterns in the soil and the prediction of future conditions.

Modeling of Soil Organic Carbon in North and Northeast of Iran under Climate Change Scenarios

The changes of soil organic carbon and soil carbon decomposition are influenced by temperature and precipitation changes. In the present study, the changes of soil organic carbon under climate change scenarios were estimated by the Rothamsted Carbon model in different land-use areas in north and northeast of Iran. The total soil organic carbon was observed 106.2 tC/ha in the study area. The changes of soil organic carbon was estimated to be 980 soil sample under A2 and B2 senarios by using RothC model in various land-use areas. Future temperature and precipitation data under both scenarios were predicted by LARS-WG weather generator model based on the IPCC AR4. The simulated results of soil organic carbon illustrated that over the period 2010-2065, SOC will decrease in the study area. The simulation of soil organic carbon strongly suggests that SOC levels will decline due to temperature increase and decline on precipitation, particularly in cultivated lands. SOC is expected to decrease under A2 climate scenario by 8.3 tC/ha and 13.36 tC/ha by the year 2030 and 2065, respectively. Likewise, under the B2 scenario, SOC will have decreased by 8.58 tC/ha and 13.81 tC/ha by the year 2030 and 2065, respectively.

Changes in soil organic carbon over 20 years after afforestation in semiarid SE Spain

This paper analyses the changes on SOC in semiarid abandoned farmland located in SE Spain partially afforested with Pinus halepensis Mill. in winter 1995–1996. The study tests the effects that 10 soil-preparation treatments before planting exerted on SOC, evaluates the effects of land-use change on SOC, and simulates SOC dynamics using the RothC model. The land-use changes studied were from cereal crops to abandoned croplands and from cereal crops to afforestation, in comparison to a native forest in equilibrium (maximum SOC reached). The results show that there were no differences in SOC among the soil-preparation treatments applied 20years ago. An increasing mean SOC was recorded in the afforestation subplots. These increases were not always significant from one sampling period to another and were related to silvicultural practices and climate. SOC in the afforested land was influenced by the soil carbonate content (+), pH (−), and pine density (+). After 20years, the mean SOC values in the afforested land were higher than in abandoned croplands and cereal crops (12.23MgCha−1, 9.52MgCha−1 and 8.02MgCha−1, respectively) though no statistical differences were registered among these land uses. In all cases SOC differed with respect to the native pine forest, which showed 40.59MgCha−1. Our results indicated that not enough time had elapsed to detect significant cumulative effects in SOC due to slow dynamics of carbon in the soil in the semiarid climate, the low inputs of organic matter to the soil and, probably, the low soil clay content in the study area. At the end of this study, the SOC in the afforested area was 69.9% less than in the native pine forest, indicating a clear potential for SOC sequestration in the following years. The RothC model was able to simulate the measured SOC increases due to the abandonment of agriculture and the afforestation of the cereal crops. In a 20-year model, we found a fit for the time course of SOC in abandoned croplands of RMSE<10%, and a RMSE=33% for the afforested area. Although the model in all cases overestimated the SOC measured, our results could be used as general indicators for predictions of the time course of SOC and potential carbon sequestration in semiarid afforested areas.