Decrease In Soil Organic Carbon Stocks Research Articles

Current and future potential soil organic carbon stocks of vegetated coastal ecosystems and their controls in the Bohai Rim Region, China

Vegetated coastal ecosystems (VCEs) have much higher organic carbon sequestration rates and storage capacities than most other earth surface ecosystems, and thus play a vital role in blue carbon sequestration for climate change mitigation. However, soil organic carbon (SOC) stock assessment in VCEs at a large regional-scale remain problematic, and the controlling mechanisms for SOC remain poorly understood. Here, we estimated current and future SOC stocks of VCEs in the Bohai Rim Region, China at a spatial resolution of 10 m, using a data-driven method based on multi-source data; and investigated the key environmental and anthropogenic controls. The total SOC stocks and SOC density of VCEs in the Bohai Rim Region were estimated to be approximately 62.7 Tg carbon and about 68.8 Mg ha−1, respectively (in the upper 1 m of soil). We found that climate and soil salinity are the most critical environmental controls of SOC stocks within VCEs in the region. Increasing temperature and soil salinity would increase SOC decomposition and decrease plant productivity, resulting in a decrease in SOC stocks. Conversely, increasing precipitation would increase SOC stocks by reducing soil salinity. We projected that the potential losses of SOC stocks due to the combined effects of climate changes, sea-level rise, and anthropogenic disturbances would be ∼ 12.2 % between 2041 and 2060 under two Shared Socioeconomic Pathway (SSP) scenarios (SSP245 and SSP585), extending to 19.3 % between 2081 and 2100. The decreases in SOC stocks would occur mainly in the higher latitude and colder regions. The study demonstrated the potential significant reduction in SOC stocks in coastal ecosystems and provided an important framework for better understanding the blue carbon ecosystems.

Soil Organic Carbon Pools in Rice Growing Inceptisols of Northeast India

ABSTRACT Estimation of soil organic carbon (SOC) stock and its pools is important for understanding C budgeting and ecosystems services of any land use. The present study was conducted to quantify SOC stock and its pools of different oxidizability (very labile, labile, less labile and non-labile C), microbial biomass C and mineralizable C at different depths (0–0.15, 0.15–0.30 and 0.30–0.50 m) of selected 26 rice growing Inceptisols located in the subtropical Manipur valley, India. Results of this study showed that organic C stock in 0–0.50 m depth of the rice growing Inceptisols varied from 55.7 to 133.5 Mg ha−1 and there was a decrease in SOC stock along depth. The rice growing Inceptisols under subtropical climate of northeast India are characterized by cooler climatic conditions and soil acidity, which are favorable for SOC accumulation. The study also showed that more than two-third (72.0%) of SOC is present in active pools with rapid turnover. Therefore, much of the SOC in these soils is very susceptible to changes in management practices such as increased tillage intensity, inappropriate cropping without legume and inadequate fertilization and in weather conditions such as increased temperature.

Soil carbon stocks can be negatively affected by land use and climate change in natural ecosystems of semi-arid environment of Iran

Soil is the largest organic carbon pool in the terrestrial biosphere, and a minor variation in the soil organic carbon (SOC) stock can substantially affect the global carbon cycle and climate. This study examined SOC storage considering land use and land use change across a semi-arid environment in northern Iran with a double aim: studying the effects of available soil parameters using a regression based modeling, and simulating the effect of the temporal variations of SOC under future climate change projections. In this regard, the following land covers were considered: forest cover, rangeland cover, conversion of forest to cropland, and conversion of rangeland to cropland. A stepwise regression analysis method was used to derive the relevant models of soil properties at of 0–10 and 10–20 cm depths. The results of regression modeling indicate that fungal abundance (for 0–10 cm soil depth) and the stoichiometry of microbial biomass nitrogen to total nitrogen (for 10–20 cm soil depth) are the most important factors for predicting SOC under the studied land covers. The effect of climate change on SOC stock differed among land uses. Simulated SOC stock decrease was in the range 5.14–11.95 Mg C ha−1 in forest, 4.48–10.37 Mg C ha−1 in rangeland, 9.34–15.38 Mg C ha−1 in cropland converted from forest and 8.00–13.72 Mg C ha−1 in cropland converted from rangeland. Therefore, conversion from natural ecosystems to cropland requires targeted intervention for a proper management of soils through a better residue management, the inclusion of a cover crop to be used as green manure in rotation with wheat, minimum tillage intensity and addition of both mineral and organic fertilizers.

Modelling and mapping soil organic carbon stocks under future climate change in south-eastern Australia

Soil organic carbon (SOC) plays a key role in the sequestration of carbon that could otherwise be warming the atmosphere. Climate change including increased temperature and changed rainfall will greatly impact the global SOC cycle. There are still significant gaps in our knowledge of the size of the global SOC pool and how future climate will affect SOC stocks and flows in many parts of the world, including Australia. In this study, we used SOC data in a Digital Soil Mapping framework to predict current and future SOC stocks across the state of New South Wales (NSW) in south-eastern Australia. In the first phase of the study we estimated the current SOC stock using multiple linear regression (MLR) and random forest (RF) modelling, and in the second phase we projected the change of SOC stocks in the near future (2050s) and far future (2090s) under two Shared Socio-economic Pathways (SSPs) scenarios based on 25 global climate models (GCMs) from the Coupled Model Inter-comparison Project Phase 6 (CMIP6). Our spatial modelling showed that estimated current SOC stocks in NSW decreased from east to west. Multi-GCM ensemble means suggested SOC stocks would decrease by 7.6–12.9% under SSP2-4.5 and 9.1–20.9% under SSP5-8.5 across NSW under future climate. The extent of change in SOC stocks varied spatially with the largest mean decrease of SOC stocks occurring in the North Coast and South East (alpine) regions of NSW. Our findings can support decision-making in land management and climate change mitigation strategies in NSW at the regional level. Furthermore, the modelling methods can be applied to other areas where edaphic and landscape properties, land use, and climate data are available.

A study over 33 years shows that carbon and nitrogen stocks in a subtropical soil are increasing under native vegetation in a changing climate

Soil plays a critical role in the global carbon (C) cycle. However, climate change and associated factors, such as warming, precipitation change, elevated carbon dioxide (CO2), and atmospheric nitrogen (N) deposition, will affect soil organic carbon (SOC) stocks markedly – a decrease in SOC stocks is predicted to drive further planetary warming, although whether changes in climate and associated factors (including atmospheric N deposition) will cause a net increase in SOC or a net decrease is less certain. Using a subtropical soil, we have directly examined how changes over the last three decades are already impacting upon SOC stocks and soil total nitrogen (STN) in a Vertisol supporting native brigalow (Acacia harpophylla L.) vegetation. It was observed that SOC stocks increased under native vegetation by 5.85 Mg C ha−1 (0.177 ± 0.059 Mg C ha−1 y−1) at a depth of 0–0.3 m over 33 years. This net increase in SOC stocks was not correlated with change in precipitation, which did not change during the study period. Net SOC stocks, however, were correlated with an increasing trend in mean annual temperatures, with an average increase of 0.89 °C. This occurred despite a likely co-occurrence of increased decomposition due to higher temperatures, presumably because the increase in the SOC was largely in the stable, mineral-associated fraction. The increases in CO2 from 338 ppmv to 395 ppmv likely contributed to an increase in biomass, especially root biomass, resulting in the net increase in SOC stocks. Furthermore, STN stocks increased by 0.57 Mg N ha−1 (0.0174 ± 0.0041 Mg N ha−1 y−1) at 0–0.3 m depth, due to increased atmospheric N deposition and potential N2 fixation. Since SOC losses are often predicted in many regions due to global warming, these observations are relevant for sustainability of SOC stocks for productivity and climate models in semi-arid subtropical regions.

Potential and constraints for applying the \u201c4 per 1000 Initiative\u201d in the Caribbean: the case of Guadeloupe

This paper assesses the feasibility of the 4 per 1000 initiative for agricultural soils in the tropics more specifically in Guadeloupe, as a representative case study of the Caribbean and other tropical regions. We used a locally adapted and calibrated model describing soil organic carbon dynamics under the impact of climate change and a broad range of scenarios combining increased use of organic amendments, reduced soil tillage, and increased land area for the local market. We found that, in its current state, only 26% of agricultural area could achieve a 4‰ year−1 increase in soil organic carbon (SOC) stocks in the coming 30 years, due mainly to perennial cropping systems (sugarcane, banana, orchards) occupying soils with the lowest SOC stocks. Implementing reduced tillage with increased use of locally produced composts would increase the agricultural area reaching the 4 per 1000 target to 31%. However, at territory scale, all scenarios tested showed an annual decrease in SOC stocks varying from − 0.1 to − 3.2‰. The limited area on which the 4 per 1000 target is feasible and generalized SOC losses were mainly linked to the current high SOC stocks in volcanic and calcareous soils on the island and lack of practices capable of further increasing C inputs in current cropping systems. We concluded that C sequestration potential in the Caribbean is rather limited and that increasing SOC stocks, even lower than 4 per 1000, should be the primary target in adaptation to climate change, by increasing the resilience of cropping systems based mainly on SOC-poor soils.

Introduction of Cardoon (Cynara cardunculus L.) in a Rainfed Rotation to Improve Soil Organic Carbon Stock in Marginal Lands

The production of a biomass as a feedstock for biorefinery is gaining attention in many agricultural areas. The adoption of biorefinery crops (i.e., perennial cardoon) can represent an interesting option for farmers and can contribute to increase soil organic carbon stock (SOCS). The study aimed to assess the potential effect on long-term SOCS change by the introduction of cardoon in a Mediterranean marginal area (Sassari, Italy). To this end, three process-oriented models, namely the Intergovernmental Panel on Climate Change (IPCC) guidelines for national greenhouse gas inventories (Tier 2), a humus-balance model (SOMBIT) and Rothamsted carbon model (RothC), were used to compare two scenarios over 20 years. The traditional cropping system’s faba bean–durum wheat biennial rotation was compared with the same scenario alternating seven years of cardoon cultivation. The model’s calibration was performed using climate, soil and crop data measured in three cardoon trials between 2011 and 2019. SOMBIT and Roth C models showed the best values of model performance metrics. By the insertion of cardoon, IPCC tool, SOMBIT and RothC models predicted an average annual SOCS increase, whereas, in the baseline scenario, the models predicted a steady state or a slight SOCS decrease. This increase can be attributed to a higher input of above- and belowground plant residues and a lower number of bare soil days (41 vs. 146 days year−1).

Machine learning in space and time for modelling soil organic carbon change

AbstractSpatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data‐driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36‐year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low‐pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m−2 over the 36‐year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m−2 (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven‐fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross‐validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m−2 and a root mean squared error of 2.04 kg C m−2. In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large.Highlights We tested the use of machine learning for space–time mapping of soil organic carbon (SOC) stock. Predictions for Argentina from 1982 to 2017 showed a 3% decrease of the topsoil SOC stock over time. The machine learning model predicted a greater temporal variation than the IPCC Tier 1 approach. Accurate machine learning SOC stock prediction requires dense soil sampling in space and time.

Afforestation changes soil organic carbon stocks on sloping land: The role of previous land cover and tree type

Afforestation contributes to soil organic carbon (SOC) sequestration and the restoration of degraded soil. However, the changes in SOC stock following afforestation on sloping land are poorly understood. Here we conduct a quantitative review that synthesizes 239 observations from 31 studies to evaluate the effects of slope gradients on the changes in SOC stock in three soil layers (0–20 cm, 20–40 cm and 40–60 cm) following afforestation, considering previous land cover (cropland and grassland) and tree type (broadleaf and conifer). In the topsoil layer, slope gradient has a significant effect on the changes in SOC stock: slope gradient positively relates to SOC stock change and significantly predicts the SOC stock change after controlling for temperature, precipitation, stand age, and initial SOC stock. Soil gradient explains 12.5% of the variance. We find a significant decrease in SOC stock after the afforestation of gentle slopes with a gradient lower than 10% but a significant increase in SOC stock on slopes with a gradient higher than 30%. Afforestation on sloping cropland results in an increase in SOC stock, especially in the upper soil, while afforestation on grassland results in an increase in SOC stocks only on very steep slopes (gradient >60%). Tree type has a limited effect on changes in SOC after afforestation on sloping land. However, conifers show a greater SOC sequestration potential in deeper soils and on steep slopes with a gradient higher than 60%, while broadleaf trees sequester more SOC in topsoil and on slopes with a gradient of 30–60%. The present study suggests that to increase SOC sequestration, priority should be given to afforestation with conifers on steep cropland.

Impacts of urbanization on soil organic carbon stocks in the northeast coastal agricultural areas of China

Dynamic changes in soil organic carbon pools have significant impacts on regional and global carbon balance. Due to rapid development in urbanized areas, the land use changes dramatically, impacting soil organic carbon (SOC) stocks in topsoil. This study aimed to document the impacts of urbanization on SOC stocks in a rapidly urbanized area from northeastern China. A total of 12 auxiliary variables were as SOC predictors including elevation, slope aspect, slope gradient, topographic wetness index, Landsat TM band3, Landsat TM band4, Landsat TM5, and normalized difference vegetation index. Urban-specific variables including population (POP), gross domestic product (GDP), distance to the socio-economic center, and distance to the roads are also considered. A set of 523 (in 1990) and 847 (in 2015) top soil samples with SOC measurement were collected. Two random forest (RF) models, one with all auxiliary variables except urban-specific variable (MA) and the other with all auxiliary variables (MB) were used to map the spatial distribution of SOC stocks in the two periods. Ten-fold cross-validation was conducted to evaluate the performance of RF models. We find that the full auxiliary variables model had a better performance for the both periods. POP and GDP were key auxiliary variables affecting spatial variability of SOC stocks in 2015. Over a 25-year period, SOC stocks decreased from 2.77 ± 1.09 kg m−2 to 2.16 ± 0.93 kg m−2, resulting in 3.78 Tg SOC loss in this region. Rapid urbanization led to drastic land- use change, which was the main reason for the decrease of SOC stocks. Additionally, urban-specific variables should be used as the main auxiliary variables when predicting SOC stocks in the areas that experience rapid urbanization. We believe that accurate prediction and mapping of SOC stocks will help manage land use and facilitate soil quality assessment so as to increase soil carbon sequestration in the region.

Probability assessment of climate change impacts on soil organic carbon stocks in future periods: a case study in Hyrcanian forests (Northern Iran)

Simulations of soil organic carbon (SOC) stocks under climate change are partly subject to uncertainties deriving from the Global Climate Models (GCMs) used for weather projections of future climates. For this reason, SOC simulations are generally performed with a set of GCMs to avoid misleading results. SOC stocks were measured in different land covers in a forest area of Northern Iran (maple, alder, oak, cypress and a mixed natural forest), and the model RothC was used to predict SOC changes under climate change scenarios. We studied the effects on SOC stock changes deriving from an ensemble of nine GCMs and two Representative Concentration Pathways (RCPs), projected on four future periods (FPs) of 20 years between 2020 and 2099. The frequency analysis of SOC stock changes indicated a different effect of the GCMs/RCPs ensemble in the four future periods, with patterns showing more evident nonlinear probabilities of SOC changes in the 2030s and 2050s compared to the almost linear probabilities in the remaining future periods. This would imply that the effect of the GCMs/RCPs ensemble is more important in very close and intermediate future scenarios (the 2030s and 2050s) compared to fully realized climate change scenarios (the 2070s and 2090s). Results indicated the need to provide assistance in the management strategies in the reforestation sector in close and intermediate climate change scenarios. In fact, maple and quercus plantations had the highest probability of cumulated SOC change proving more sensitive to climate change. Conversely, cypress and alder plantations could limit the decrease of SOC stocks representing a good option for GHG mitigation in close and intermediate climate change scenarios and should be preferred in the afforestation of the degraded natural forest, and in mixed plantation systems within maple and oak stands.

Response of soil C:N:P stoichiometry, organic carbon stock, and release to wetland grasslandification in Mu Us Desert

Wetlands in Mu Us Desert have severely been threatened by grasslandification over the past decades. Therefore, we studied the impacts of grasslandification on soil carbon (C):nitrogen (N):phosphorus (P) stoichiometry, soil organic carbon (SOC) stock, and release in wetland-grassland transitional zone in Mu Us Desert. From wetland to grassland, the transition zone was divided into five different successional stages according to plant communities and soil water conditions. At every stage, soil physical and chemical properties were determined and C:N:P ratios were calculated. SOC stock and soil respirations were also determined to assess soil carbon storage and release. After grasslandification, SOC contents of top soils (0–10 cm) decreased from 100.2 to 31.79 g kg−1 in June and from 103.7 to 32.5 g kg−1 in October; total nitrogen (TN) contents of top soils (0–10 cm) decreased from 3.65 to 1.85 g kg−1 in June and from 6.43 to 3.36 g kg−1 in October; and total phosphorus (TP) contents of top soils (0–10 cm) decreased from 179.4 to 117.4 mg kg−1 in June and from 368.6 to 227.8 mg kg−1 in October. From stages Typha angustifolia wetland (TAW) to Phalaris arundinacea L. (PAL), in the top soil (0–10 cm), C:N ratios decreased from 32.2 to 16.9 in June and from 19.0 to 11.8 in October; C:P ratios decreased from 1519.2 to 580.5 in June and from 19.0 to 11.8 in October; and N:P ratios decreased from 46.9 to 34.8 in June and changed from 34.9 to 34.0 in October. SOC stock decreased and soil respiration increased with grasslandification. The decrease of SOC, TN, and TP contents was attributed to the reduction of aboveground biomass and mineralization of SOM, and the decrease of soil C:N, C:P, and N:P ratios was mainly attributed to the faster decreasing speeds of SOC than TN and TP. The reduction of aboveground biomass and increased SOC release led by enhanced soil respiration were the main reasons of SOC stock decrease. Grasslandification led to lowers levels of SOC, TN, TP, and soil C:N, C:P, and N:P ratios. Grasslandification also led to higher SOC loss, and increased soil respiration was the main reason. Since it is difficult to restore grassland to original wetland, efficient practices should be conducted to reduce water drainage from wetland to prevent grasslandification.

How do hedgerows influence soil organic carbon stock in livestock‐grazed pasture?

AbstractHedgerows have the potential to influence ecosystem function in livestock‐grazed pasture. Despite this, they are often ignored when quantifying farmland ecosystem service delivery. In this study, we assess the contribution of hedgerows to the ecosystem function of carbon (C) storage, with a particular emphasis on soil organic carbon (SOC). We measured SOC stock (kg C m−2), on an equivalent soil mass basis, at 0–0.15 m depth in pasture adjacent to 38 hedgerows (biotic) and 16 stone walls or fences (abiotic controls) across ten farms in the county of Conwy, Wales, UK. Pasture SOC stock (~7 kg C m−2) was similar adjacent to biotic and abiotic field boundaries, positively associated with soil moisture and negatively with soil bulk density (BD). For biotic boundaries, two further variables were significantly associated with SOC stock, distance from hedgerow (decrease in SOC stock) and slope orientation (upslope SOC stock greater than downslope). For pasture adjacent to hedgerows, a model combining the aforementioned variables (BD, soil moisture, distance from hedgerow, slope orientation) explained 78% of variation in SOC stock. This study demonstrates that whilst hedgerows do have subtle positive effects on SOC stock in adjacent pasture, SOC storage adjacent to field boundaries is influenced more by soil moisture content and BD than field boundary type.

Soil carbon sequestration in agroforestry systems: a meta-analysis

Agroforestry systems may play an important role in mitigating climate change, having the ability to sequester atmospheric carbon dioxide (CO2) in plant parts and soil. A meta-analysis was carried out to investigate changes in soil organic carbon (SOC) stocks at 0–15, 0–30, 0–60, 0–100, and 0 ≥ 100 cm, after land conversion to agroforestry. Data was collected from 53 published studies. Results revealed a significant decrease in SOC stocks of 26 and 24% in the land-use change from forest to agroforestry at 0–15 and 0–30 cm respectively. The transition from agriculture to agroforestry significantly increased SOC stock of 26, 40, and 34% at 0–15, 0–30, and 0–100 cm respectively. The conversion from pasture/grassland to agroforestry produced significant SOC stock increases at 0–30 cm (9%) and 0–30 cm (10%). Switching from uncultivated/other land-uses to agroforestry increased SOC by 25% at 0–30 cm, while a decrease was observed at 0–60 cm (23%). Among agroforestry systems, significant SOC stocks increases were reported at various soil horizons and depths in the land-use change from agriculture to agrisilviculture and to silvopasture, pasture/grassland to agrosilvopastoral systems, forest to silvopasture, forest plantation to silvopasture, and uncultivated/other to agrisilviculture. On the other hand, significant decreases were observed in the transition from forest to agrisilviculture, agrosilvopastoral and silvopasture systems, and uncultivated/other to silvopasture. Overall, SOC stocks increased when land-use changed from less complex systems, such as agricultural systems. However, heterogeneity, inconsistencies in study design, lack of standardized sampling procedures, failure to report variance estimators, and lack of important explanatory variables, may have influenced the outcomes.

Can conservation tillage mitigate climate change impacts in Mediterranean cereal systems? A soil organic carbon assessment using long term experiments

Abstract Simulation models, informed and validated with datasets from long term experiments (LTEs), are considered useful tools to explore the effects of different management strategies on soil organic carbon (SOC) dynamics and evaluate suitable mitigative options for climate change. But, while there are several studies which assessed a better prediction of crop yields using an ensemble of models, no studies are currently available on the evaluation of a model ensemble on SOC stocks. In this study we assessed the advantages of using an ensemble of crop models (APSIM-NWheat, DSSAT, EPIC, SALUS), calibrated and validated with datasets from LTEs, to estimate SOC dynamics. Then we used the mean of the model ensemble to assess the impacts of climate change on SOC stocks under conventional (CT) and conservation tillage practices (NT: No Till; RT: Reduced Tillage). The assessment was completed for two long-term experiment sites (Agugliano – AN and Pisa – PI2 sites) in Italy under rainfed conditions. A durum wheat ( Triticum turgidum subsp. durum (Desf.) Husn.) – maize ( Zea mays L.) rotation system was evaluated under two different climate scenarios over the periods 1971–2000 (CP: Present Climate) and 2021–2050 (CF: Future Climate), generated by setting up a statistical model based on canonical correlation analysis. Our study showed a decrease of SOC stocks in both sites and tillage systems over CF when compared with CP. At the AN site, CT lost −7.3% and NT −7.9% of SOC stock (0–40 cm) under CF. At the PI2 site, CT lost −4.4% and RT −5.3% of SOC stocks (0–40 cm). Even if conservation tillage systems were more impacted under future scenarios, they were still able to store more SOC than CT, so that these practices can be considered viable options to mitigate climate change. Furthermore, at the AN site, under CF, NT demonstrated an annual increase of 0.4%, the target value suggested by the 4 per thousand initiative launched at the 21st meeting of the Conference of the Parties in Paris. However, RT at the PI2 needs to be coupled with other management strategies, as the introduction of cover crops, to achieve such target.

Large uncertainty in soil carbon modelling related to method of calculation of plant carbon input in agricultural systems

SummaryThe application of dynamic models to report changes in soil organic carbon (SOC) stocks, for example as part of greenhouse gas inventories, is becoming increasingly important. Most of these models rely on input data from harvest residues or decaying plant parts and also organic fertilizer, together referred to as soil carbon inputs (C). The soil C inputs from plants are derived from measured agricultural yields using allometric equations. Here we compared the results of five previously published equations. Our goal was to test whether the choice of method is critical for modelling soil C and if so, which of these equations is most suitable for Swiss conditions. For this purpose we used the five equations to calculate soil C inputs based on yield data from a Swiss long‐term cropping experiment. Estimated annual soil C inputs from various crops were averaged from 28 years and four fertilizer treatments; the average was 3.6 Mg C ha−1 and covered a surprisingly large range from 2.1 to 5.3 Mg C ha−1 year−1 among the five equations. For single crop species, differences reached 6.6 Mg C ha−1 year−1. The variation between estimated soil C inputs depended on crop type and increased with yield. Simulations with the model C‐TOOL showed that calculated SOC stocks were affected strongly by the choice of the allometric equation. With four equations, a decrease in SOC stocks was simulated, whereas with one equation there was no change. This considerable uncertainty in modelled soil C is attributable solely to the allometric equation used to estimate the soil C input. We identify the evaluation and selection of allometric equations and associated coefficients as critical steps when setting up a model‐based soil C inventory for agricultural systems.Highlights Uncertainty analyses in soil C modelling have focused mainly on model parameters and structures. The uncertainty related to estimation of soil C inputs from crop residues was calculated. Simulated soil C stocks and stock changes depend strongly on the method of estimating C input. The method to derive soil C inputs is critical for model‐based soil C inventories.

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.

Soil organic carbon simulated with the AMG model in a high-organic-matter Mollisol

Soil organic carbon (SOC) management requires a precise knowledge of how it is affected by soil use. Simulation models could help for this purpose. The AMG model is simple, requires information that is easily available, and uses few parameters. This model has neither been calibrated/adjusted nor validated for loamy soils with high SOC concentrations. We hypothesized that AMG would satisfactorily simulate SOC stock changes in soils with these characteristics. The aims of this work were: 1) to adjust and validate AMG for different tillage systems, nitrogen (N) fertilization levels and crop types for loamy-high-SOC Mollisols, and 2) to simulate future SOC changes under different production scenarios. We used SOC stocks (0-20 cm depth) from three long-term experiments (1976-2012) (tillage systems, crop rotations, and N fertilization) in the Southeastern Buenos Aires Province, Argentina (37º 45' S, 58º 18' W) on a complex of Mollisols. Data from two of those experiments was split into two groups to adjust unknown model parameters and for cross validation. Data from the third experiment was used for independent validation. The model was used to simulate SOC stock variation (30 yr) under different combinations of initial SOC stocks (SOCi, three levels) and crop rotations (six rotations regarding continuous cropping and crop-pasture rotations). Model performance was evaluated through statistical indicators based on observed-simulated value differences, and simple linear regression of observed on simulated values. Cross validation yielded promising indicators with the mean observed-simulated value differences close to 0 (<em>P </em>> 0.05). Root mean square error (RMSE) and RMSE as percentage of the mean of observed values (RMSEp) were 6.0 Mg C ha<sup>-1</sup> and 7.5%, respectively, which are acceptable. Simple linear regression of observed and simulated values was highly significant (<em>P </em>< 0.01) with intercept and slope not different from zero and one (<em>P </em>> 0.05), respectively, although R<sup>2</sup> was low. Indicators of model performance by groups of treatments were, in general, acceptable and did not show clear trends associated with any management type. However, model performance was poorer under no tillage (NT) and N fertilization probably because of little observed data available for that treatment factor combination. Validation with independent data confirmed that AMG simulated SOC change satisfactorily. Future scenario simulations showed that when the SOCi stock was high (close to SOC saturation), even rotations with high intensification and carbon input produced a SOC stock decrease. Conversely, when the SOCi stock was low (35% loss of SOC with respect to saturation) all scenarios led to a SOC stock increase. However, AMG failed to acceptably simulate the expected effect of pastures in the rotation. The AMG model satisfactorily simulated SOC stock changes due to different management techniques of soils with a loamy surface texture and high original SOC stock. Therefore, the model could be used as a tool to help management planning with an admissible simulation error (RMSEp ~6%).

Model‐based evaluation of impact of soil redistribution on soil organic carbon stocks in a temperate hedgerow landscape

AbstractAgroforestry systems are promoted for providing a number of ecosystem services and environmental benefits, including soil protection and carbon sequestration. This study proposes a modelling approach to quantify the impact of soil redistribution on soil organic carbon (SOC) storage in a temperate hedgerow landscape. Evolution of SOC stocks at the landscape scale was examined by simulating vertical and horizontal SOC transfers in the 0–105 cm soil layer due to soil redistribution by tillage and water processes. A spatially explicit SOC dynamics model (adapted from RothC‐26.3) was used, coupled with a soil‐redistribution model (LandSoil). SOC dynamics were simulated over 90 years in an agricultural hedgerow landscape dedicated to dairy farming, with a mix of cropping and grasslands. Climate and land use were simulated considering business‐as‐usual scenarios derived from existing information on the study area. A net decrease in SOC stocks was predicted at the end of the simulation period. Soil redistribution induced a net SOC loss equivalent to 2 kg C ha−1 yr−1 because of soil exportation out of the study site and an increase in SOC mineralization. Hedgerows and woods were the only land use in which soil redistribution induced net SOC storage. Soil tillage was the main process that induced soil redistribution within cultivated fields. Soil exportation out of the study area was due to erosion by water, but remained low because of the protective role of the hedgerow network. These soil transfers redistributed SOC stocks in the landscape, mostly within cultivated fields. Copyright © 2016 John Wiley & Sons, Ltd.

Estimation of past and recent carbon input by crops into agricultural soils of southeast Germany

In agricultural soils, the formation of soil organic matter largely depends on the carbon (C) input by crop residues and rhizodeposition, which is thus of decisive importance for the management and prediction of soil organic carbon (SOC) stocks in cropland and grassland. However, there is a remarkable lack of reliable, crop-specific C input data. We used a plant C allocation approach to estimate the C input of major crops and grassland into agricultural soils of Bavaria in southeast Germany. Historic and recent plant C allocation coefficients were estimated and C inputs were calculated for a 60-year period (1951–2010) using long-term agricultural statistics. The spatial distribution of C inputs within Bavaria was derived from county-specific statistical data. The results revealed increases of the C input by 107–139% for cereals, 173–188% for root, forage and leguminous crops and 34% for grassland in the last 60 years. This increase was related to linear yield increases until 1995 despite significant changes of plant C allocation. However, from 1995 onwards, crop yields and related C inputs stagnated, which allowed a robust estimation of recent crop-specific C input values. A total C input of 3.8–6.7tha−1yr−1 was estimated for cereals, 5.2–6.3tha−1yr−1 for root, forage and leguminous crops and 2.4tha−1yr−1 for grassland. These amounts were partly higher compared to estimations in the literature. A generally high spatial variability of C inputs was detected within Bavaria with differences of up to 40% between adjacent counties. The results of this study could be used to optimize the C input of crop rotations and thus promote the formation of soil organic matter and C sequestration in agricultural soils on the basis of a soil carbon model. Moreover, recent estimations of C inputs could be used to model the future development of agricultural SOC stocks. A further stagnation of crop yields and the related C input under an ongoing temperature increase bears the risk of a future decrease of SOC stocks in cropland soils of Bavaria.