Simulated Soil Organic Carbon Research Articles

Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)

Abstract. The surface energy budget plays a critical role in terrestrial hydrological and biogeochemical cycles. Nevertheless, its highly spatial heterogeneity across different vegetation types is still missing in the ORCHIDEE-MICT (ORganizing Carbon and Hydrology in Dynamic EcosystEms–aMeliorated Interactions between Carbon and Temperature) land surface model. In this study, we describe the representation of a tiling energy budget in ORCHIDEE-MICT and assess its short-term and long-term impacts on energy, hydrology, and carbon processes. With the specific values of surface properties for each vegetation type, the new version presents warmer surface and soil temperatures (∼ 0.5 °C, +3 %), wetter soil moisture (∼ 10 kg m−2, +2 %), and increased soil organic carbon storage (∼ 170 Pg C, +9 %) across the Northern Hemisphere. Despite reproducing the absolute values and spatial gradients of surface and soil temperatures from satellite and in situ observations, the considerable uncertainties in simulated soil organic carbon and hydrological processes prevent an obvious improvement in the temperature bias existing in the original ORCHIDEE-MICT model. However, the separation of sub-grid energy budgets in the new version improves permafrost simulation greatly by accounting for the presence of discontinuous permafrost types (∼ 3×106 km2), which will facilitate various permafrost-related studies in the future.

Convergence in simulating global soil organic carbon by structurally different models after data assimilation.

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Impact of landuse systems on soil organic carbon and its projection in northwestern India

AbstractThe evaluation of the impact of landuse on soil organic carbon (SOC) is required to understand the long‐term productivity and possible sustainability of a landuse system. The effect of landuse on SOC was studied in the Shaheed Bhagat Singh Nagar district of Punjab (India). Surface (0–15 cm) and subsurface (15–30 cm) soil samples were collected under different land uses (cropland, forest, scrubland, and plantation) using stratified random sampling. Soil samples were analyzed for soil texture, bulk density, pH, electrical conductivity, organic carbon, Olsen P (available P), NH4OAc–extractable K (available K), and CaCl2 extractable S (available S) using standard methods. Ordinary kriging was used to study the distribution of soil properties in different land uses. Soil organic carbon under future climate was simulated using Rothamsted carbon (RothC) model with current landuse and management practices. Organic carbon in surface and subsurface soils was in the order: Forest > scrubland > plantation > cropland. The content of organic carbon, available P, K, and S decreased with increasing soil depth. The simulated SOC content in cropland soils under future climate may reduce, and in soils under forest, plantation and scrubland may increase by the year 2100. These results suggest that the landuse system is one of the major factors affecting SOC stocks. The study of organic carbon in different landuses could help the famers and policy makers to take measures for increasing SOC stocks to mitigate the climate change and also for sustainable agriculture.

Predicting Soil Carbon Sequestration and Harvestable C-Biomass of Rice and Wheat by DNDC Model

Several biogeochemical models have been applied to understand the potential effects of management practices on soil organic carbon (SOC) sequestration, crop growth, and yield. In this study, the denitrification and decomposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice–wheat rotation under organic/inorganic fertilization with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC underpredicted harvestable grain C-biomass of rice where percent difference (PD) varied from 29.22% to 42.14%, and over-simulated grain C-biomass of wheat where PD was −55.01% with 50% nitrogen–phosphorus–potassium (NPK) and 50% animal manure applied under the CT treatment. However, after calibration by adjusting default values of soil and crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (e.g., average PD ranged from −2.81% to −6.17%). DNDC also predicted the effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha−1). DNDC simulated SOC trends for rice–wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice–wheat systems.

Simulating soil organic carbon stock as affected by land use and climate change on volcanic soils in Indonesia

We assessed long-term trends in soil organic carbon (SOC) in volcanic soils with a process-based soil genesis model, SoilGen2.25. The relation between soil geochemistry and SOC was applied in a model context, where significant soil properties were identified and used to modify the decay rates of SOC pools. We used data from Indonesian sites with different land use (tropical primary forest, secondary pine forest, and agricultural land) and calibrated major soil processes in volcanic soils, viz. clay migration and weathering of primary minerals. The model evaluated the decay rates of SOC pools using three calibration approaches: (i) a site-specific calibration, (ii) a generic calibration, and (iii) a generic calibration modified by a geochemical proxy. The best calibration for each approach was then used to estimate the future of SOC under different climate projection scenarios, viz. representative concentration pathways (RCP) 2.6 and 8.5. The SoilGen2.25 model was generally sensitive to the change of selected soil process parameters. A four-pool SOC model (Roth-C) with site-specific decay rates best reproduced total SOC with a percentage difference between measured and simulated SOC between 1 and 10%. Application of a geochemical proxy to modify the generic rate calibration also improved, relative to a generic calibration, the simulation quality of the included humus pool and total SOC in most study sites. Agricultural soil showed higher susceptibility to global warming (i.e. RCP 8.5) than forest soils. Projective scenarios based on the three calibration scenarios highlighted the importance of the calibration method on the accuracy of SOC projection.

Improved quantification of cover crop biomass and ecosystem services through remote sensing-based model–data fusion

Cover crops have long been seen as an effective management practice to increase soil organic carbon (SOC) and reduce nitrogen (N) leaching. However, there are large uncertainties in quantifying these ecosystem services using either observation (e.g. field measurement, remote sensing data) or process-based modeling. In this study, we developed and implemented a model–data fusion (MDF) framework to improve the quantification of cover crop benefits in SOC accrual and N retention in central Illinois by integrating process-based modeling and remotely-sensed observations. Specifically, we first constrained and validated the process-based agroecosystem model, ecosys, using observations of cover crop aboveground biomass derived from satellite-based spectral signals, which is highly consistent with field measurements. Then, we compared the simulated cover crop benefits in SOC accrual and N leaching reduction with and without the constraints of remotely-sensed cover crop aboveground biomass. When benchmarked with remote sensing-based observations, the constrained simulations all show significant improvements in quantifying cover crop aboveground biomass C compared with the unconstrained ones, with R 2 increasing from 0.60 to 0.87, and root mean square error (RMSE) and absolute bias decreasing by 64% and 97%, respectively. On all study sites, the constrained simulations of aboveground biomass C and N at termination are 29% and 35% lower than the unconstrained ones on average. Correspondingly, the averages of simulated SOC accrual and N retention net benefits are 31% and 23% lower than the unconstrained simulations, respectively. Our results show that the MDF framework with remotely-sensed biomass constraints effectively reduced the uncertainties in cover crop biomass simulations, which further constrained the quantification of cover crop-induced ecosystem services in increasing SOC and reducing N leaching.

Uncertainty in land use obscures global soil organic carbon stock estimates

The impact of land use and land cover change (LULCC) on soil organic carbon (SOC) stock is one of the most uncertain items in estimating the global C budget. Despite the improvements in satellite monitoring techniques and inventory data in recent decades, the uncertainty in modeled LULCC-induced SOC changes stemming from the choice of land use datasets remains largely unknown. Using a process-based model, the Dynamic Land Ecosystem Model (DLEM), we investigated global SOC changes during 1900–2018 driven by six LULCC datasets (i.e., LUH2-GCB2019, ESA CCI-LC, MODIS, GLASS-GLC, HH, and RF), which were generated by varied data sources and methodologies. The simulated global SOC stock was negatively affected by land conversions in all the LULCC datasets; however, the corresponding SOC loss was highly different ranging from 33.11 Pg C to 106.20 Pg C. Such significant differences in the global SOC stock estimates due to LULCC uncertainty were mainly located in boreal and temperate forests of the northern high latitudes and were most likely attributed to the LULCC-induced changes in vegetation net primary production. Meanwhile, regions exhibiting large divergence in relative changes of LULCC-induced SOC loss were mainly located in the low latitudes. When considering the interactive effects of LULCC with other environmental factors, the simulated SOC showed divergent trends, increasing in MODIS-, ESA CCI-LC-, and GLASS-GLC-based estimations, but decreasing in LUH2-GCB2019-, HH-, and RF-based estimations. These results highlight the importance of the accuracy of LULCC data in determining the global carbon budget. Future efforts are required for harmonizing satellite observations and inventory data both spatially and temporally to better represent the land conversion processes in terrestrial ecosystem modeling.

Compost and biosolids increase long-term soil organic carbon stocks

Biobased residues derived from organic urban waste materials can be processed to produce soil amendments that enhance soil fertility and carbon sequestration. However, the extent of carbon sequestration by biobased residues depends on the interaction between their physicochemical properties, climate, and agroecosystem management practices. Our objective was to predict how different biobased residues (compost, anaerobic digestate, or biosolids), compared to nitrogen fertilizer, affect soil organic carbon stocks under continuous cropping and crop rotation in Ontario, Canada, using the Century model. The Century model was calibrated and validated with data, from a three-year field study located in Elora, Ontario, Canada, that was used to predict long-term changes in soil organic carbon. Our results showed that after 150 years, soil amended with compost and biosolids increased soil organic carbon stocks significantly ( p < 0.05) compared to anaerobic digestate and nitrogen fertilizer. Soil organic carbon stocks were 1%–27% greater with crop rotation compared to continuous cropping. Model performance indicated a strong correlation between measured and simulated soil organic carbon stocks ( R2 = 0.26–0.82; RMSD = 432–727 g m−2). Our findings suggested that compost had the greatest soil carbon sequestration potential of the tested soil amendments, and this difference was due to the quantity and quality of carbon input.

Deep SOC stock dynamics under contrasting management systems: Is the EPIC model ready for carbon farming implementation?

Process-based models have been recognized as cost-effective tools to assess carbon farming mechanisms through quantifying the C fluxes in the agroecosystems. A result-based approach is suggested however the wide variability of agricultural environments makes further model implementation necessary to limit the uncertainty of the results, especially on deep soil organic carbon (SOC) stock estimation and stratification and in agroecosystems characterized by a shallow water table. In this study, a comprehensive soil and crop dataset collected over a seven-year period from different pedoclimatic conditions across the Veneto Region (NE Italy) was used for EPIC model calibration and validation of SOC stock dynamics. Experimental data included yields from several crops (corn, winter and spring wheat, rapeseed, and soybean), continuous monitoring of soil water content, and SOC stocks (1872 total samples within the 0–50 cm soil profile) under conventional, cover crop and conservation agriculture systems. Modelling was performed by testing two N-C sub-models (CENTURY and PHOENIX), which differentiated in terms of mineralization/immobilization rates.Results showed that the procedure was able to obtain parameters valuable for most of the management system and pedoclimatic condition, reproducing well the tested variables. The EPIC model acceptably captured soil water dynamics (Nash-Sutcliffe coefficient – NSE – was up to 0.26), especially in the topsoil. Furthermore, simulation of weed—crop competition in conservation agriculture strongly contributed to properly explain the variability in crop production among the contrasting agricultural systems (R2 ranged from 0.51 to 0.71). Likewise, EPIC skillfully simulated SOC stocks within the 0–50-cm profile regardless of the sub-model used (NSE was up to 0.64). Moreover, the model acceptably captured the profile SOC stratification among the different management practices. This study highlights the EPIC robustness for predicting SOC stocks and assessing with high accuracy carbon farming results.

Modelling the Whole Profile Soil Organic Carbon Dynamics Considering Soil Redistribution under Future Climate Change and Landscape Projections over the Lower Hunter Valley, Australia

Soil organic carbon (SOC) storage and redistribution across the landscape (through erosion and deposition) are linked to soil physicochemical properties and can affect soil quality. However, the spatial and temporal variability of soil erosion and SOC remains uncertain. Whether soil redistribution leads to SOC gains or losses continues to be hotly debated. These considerations cannot be modelled using conventional soil carbon models and digital soil mapping. This paper presents a coupled-model combining RothPC-1 which considers soil carbon (C) down to 1 m and a soil redistribution model. The soil redistribution component is based on a cellular automata technique using the multi-direction flow (FD8) algorithm. With the optimized input values based on land use, we simulated SOC changes upon soil profiles to 1 m across the Lower Hunter Valley area (11,300 ha) in New South Wales, Australia from the 1970s to 2016. Results were compared to field observations and showed that erosion was predicted mostly in upslope areas and deposition in low-lying areas. We further simulated SOC trends from 2017 until ~2045 in the area under three climate scenarios and five land use projections. The variation in the magnitude and direction of SOC change with different projections shows that the main factors influencing SOC changes considering soil redistribution are climate change which controlled the trend of SOC stocks, followed by land use change. Neglecting soil erosion in carbon models could lead to an overestimation of SOC stocks. This paper provides a framework for incorporating soil redistribution into the SOC dynamics modelling and also postulates the thinking that soil erosion is not just a removal process by surface runoff.

The ability of crop models to predict soil organic carbon changes in a maize cropping system under contrasting fertilization and residues management: Evidence from a long-term experiment

This study assesses the ability of an ensemble of crop models (MME) to predict the impacts of fertilization and crop residue management on soil organic carbon (SOC) and aboveground biomass (AGB) in a long-term experiment (LTE) based on continuous maize cropping systems. Data from a LTE in Northern Italy were used. Treatments included continuous grain (MG) or silage (MS) maize, fertilized with mineral, cattle slurry, and farmyard manure. The MME median resulted the best predictor of the observed values. Models performance was better when simulating MG than MS, and for crops treated with mineral compared to organic fertilizers. The ability to predict the dynamics of SOC was affected by the model used and by the year × residues management and year × fertilizer interactions. The model and the residue × fertilizer interaction affected the ability to simulate AGB dynamics. Results showed that a MME can effectively predict the long-term dynamics of SOC and maize crop production under contrasting fertilization and crop residue management, and thus their potential for climate change mitigation. The uncertainty in the simulation of SOC is related to the model routines simulating SOC partitioning and to the complexity of the interactions between management factors over time. Highlights - A crop model ensemble was compiled to simulate soil organic carbon and maize aboveground biomass dynamics in a long-term experiment. - The performances of stand-alone models and their ensemble were assessed under contrasting fertilization and crop residue management. - The multi-model ensemble using the median value of simulation was the best predictor of the variables observed in the long-term experiment. - Improved performances in simulations were observed when crop residues were incorporated into the soil, regardless of the fertilization management. - The uncertainty in SOC simulation increased over time for cropping systems with silage maize and organic fertilization.

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.

Evaluating the impacts of alternative grazing management practices on soil carbon sequestration and soil health indicators

Globally, grazing lands represent a major carbon (C) sink. However, improper use of these ecosystems can degrade their soils and their C storage capacity. Specifically, prolonged high stocking rates in livestock production can result in significantly less soil organic carbon (SOC) and soil fertility in grazing lands. This underlines the need to assess key soil health indicators to determine the ecological efficacy of grazing management. The objective of this study was to identify the impacts of alternative grazing management practices, including heavy continuous (HC), light continuous (LC), and adaptive multi-paddock (AMP) grazing, on SOC and soil health indicators at the ranch and watershed scales in the Lower Prairie Dog Town Fork Red River Watershed in Northwest Texas. The stocking rate was the same under AMP and HC grazing, and it was reduced by half under LC grazing. The Soil and Water Assessment Tool-Carbon (SWAT-C) model was modified and used to conduct this assessment over a 40 year period (1979–2018), after a 900-year spin-up simulation (800 years under natural condition and 100 years under the HC grazing) to initiate the SOC pool. The simulated SOC was validated using observed data from several sampling locations. The study results indicated that when grazing management at the study ranch was changed from the current AMP grazing to hypothetical HC grazing, simulated average annual SOC decreased from 84 to 81.8 Mg/ha (a 2.6% decline). At the watershed-scale, when the grazing management was changed from the baseline HC grazing to AMP grazing, the simulated average annual SOC increased from 35.6 to 38.3 Mg/ha (a 7.5% increase). The net primary productivity (NPP), evapotranspiration (ET), biomass C, and soil nitrogen (N) increased, while C losses from runoff, and sediment loads decreased under AMP grazing. These results indicate that compared to HC, AMP grazing performed better with respect to SOC increase, and improvement of soil ecosystem and hydrological functions at both the ranch and watershed scales in the study watershed. Our findings suggest the need to shift from continuous to AMP grazing in order to improve soil health at multiple spatial scales. Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.

Modeling the impacts of cover crops and no-tillage on soil health and cotton yield in an irrigated cropping system of the Texas Rolling Plains

An interest to grow cover crops is increasing in the semi-arid Texas Rolling Plains (TRP) region, where cotton ( Gossypium hirsutum L.) is a major cultivated crop. Under conservation tillage systems, cover crops are expected to enhance ecosystem services, including improved soil health and erosion reduction. However, there is a concern that cover crops could deplete soil water content and affect the subsequent cotton production. This study evaluated the efficacy of growing winter cover crops in a continuous cotton production system under irrigated conditions at Chillicothe, TX with the Denitrification Decomposition (DNDC) model. Treatments included: conventional tillage (CT), no-till (NT), no-till with mixed-species cover crop (Mixture), and no-till with winter wheat ( Triticum aestivum ) cover crop (Wheat) . The DNDC model was calibrated and validated against observed data from 2012 to 2020. Seed cotton yield prediction during model calibration had a percent bias (PBIAS) of 4.4 %, root mean square error (RMSE) of 385 kg ha −1 , r 2 of 0.55, d-index of 0.84, and a Nash–Sutcliffe model efficiency (NSE) of 0.92. Seed cotton yield during model validation had PBIAS of − 2.0 %, RMSE of 279 kg ha −1 , r 2 of 0.75, d-index of 0.93, and a NSE of 0.72. Simulated soil organic carbon (SOC) stock had a PBIAS of 8.6 % and 3 %, RMSE of 1.8 and 0.74 Mg ha −1 , r 2 of 0.92 and 0.98, d-index of 0.97 and 0.99, a NSE of 0.89 and 0.98 during model calibration and validation, respectively. Daily trends of simulated soil water content at 0–100 cm depth were within the acceptable range. Long-term simulations (25 years) revealed: (1) planting cover crops could accrue 11–19 % more SOC and 4–14 % more TN compared to no-cover crops, (2) cotton yield under cover crops was not significantly different from that under no-cover crops, (3) cover crop biomass increased immoblisation of nitrogen (N), which led to a decline in available N and hence, N-stress was simulated during the initial cotton growth period. In nutshell, results showed the potential of conservation management systems such as no-till and cover crops in improving soil health indicators without affecting cotton yields. • Simulated effect of mixture and winter wheat cover crops on seed cotton yield was not significant. • Cover crops sequestered 11–19 % more soil organic carbon (SOC) compared to no-cover crops. • Cover crops accrued 4–14 % more total nitrogen (TN) compared to no-cover crops. • Rate of SOC and TN accrual was highest under mixed species cover crop followed by winter wheat. • Soil N availability was declined in early stages of cotton due to increased N immobilization by cover crop biomass.

Towards an integrated phosphorus, carbon and nitrogen cycling model for topographically diverse grasslands

Contemporary science on how livestock influence nutrient cycling in grazing systems is limited, particularly in topographically complex (i.e., slopes and aspects) hill country landscapes. Prominent slope and aspect variation affects primary production, animal behaviour and nutrient return. Here, we embed recent scientific advancements in nutrient dynamics across complex landscapes to (1) set up a soil organic carbon (SOC) saturation function to an existing SOC and total soil phosphorus (TSP) model (Bilotto et al. J N Z Grassl 81:171–178, 2019. https://doi.org/10.33584/jnzg.2019.81.397), (2) include total soil nitrogen (TSN) dynamics, and (3) establish if the model (herein the Grass-NEXT model) can simulate the spatial and temporal changes of TSP, SOC and TSN in hill country. A long-term P fertiliser experiment with contrasting different P fertilisation levels and associated sheep stocking regimes (herein, ‘farmlets’) was used for model testing. The Grass-NEXT model predicted TSP and SOC stocks with strong accuracy and precision (model performance), and TSN with a moderate performance across farmlets [Concordance Correlation Coefficient (CCC), 0.75, 0.72 and 0.49, respectively]. Grass-NEXT model simulated TSP, SOC and TSN distribution with moderate/strong performance across slopes (CCC, 0.94, 0.80 and 0.70) and aspects (CCC, 0.83, 0.67 and 0.51). Consistent with observed data, modelled changes in TSP and TSN were greater on low slopes and eastern aspects, but no clear pattern was observed for SOC stocks. The Grass-NEXT model provides an intuitive research tool for exploring management options for increasing SOC and TSN, as well as an instrument for monitoring and reporting on nutrient dynamics in complex landscapes.

Simulating soil organic carbon stock under different climate change scenarios: A RothC model application to typical land-use systems of Goa, India

Soil organic carbon (SOC) is one of the significant soil components influencing soil functions and soil processes. However, little is known about the impact of land-use change on SOC dynamics on coastal agroecosystems. We, therefore, simulated the impact of land use on SOC stock using RothC model under different climate change scenarios. In the present study, natural forest and pasture lands are the native land uses, while cashew, arecanut, and coconut plantations were established about 70–80 years ago from the natural forest. Measured SOC stocks were significantly higher in cashew and forest land uses (109.5 and 88.6 t C/ha) and much lower in coconut, arecanut and pasture lands (64.1–71.0 t C/ha). The study showed that under projected climate change conditions in Goa state and depending on emission scenarios, both decreases and increases of SOC stocks would be possible. By the end of the century, SOC stock in cashew would decrease by 4.3 t C/ha (RCP 4.5) and increase by 2.4 t C/ha (RCP 8.5), in coconut the SOC change was negligible (0.2 t C/ha) in RCP 4.5 while increased by 3.2 t C/ha in RCP 8.5. Arecanut, pasture and forest land uses showed a marked SOC decrease in RCP 4.5 (ranging from 5.2 to 5.4 t C/ha) and negligible positive (0.6–0.7 t C/ha) or negative (0.6 t C/ha) changes in RCP 8.5. Overall, the model indicated cashew plantations as the most prominent sink of SOC storage, while coconut and pasture are not viable sinks of SOC in the study area. We suggest promoting cashew, arecanut, and coconut land use system integrated with tree components and pasture to improve the SOC storage and other ecosystem services in the coastal agroecosystem.

Simulating the effects of erosion on organic carbon dynamics in agricultural soils

Soil erosion in croplands has a strong impact on global carbon (C) cycle. Assessment of erosional effects on soil organic carbon (SOC) dynamics in agricultural soils suffers from the difficulty of distinguishing the erosional effects and complex interaction processes between erosion and C cycling. To simulate the effects of soil erosion, a plots experiment including six erosion levels (0, 5, 10, 20, 30, and 40 cm eroded) was conducted for seven years (2012–2018). The erosion levels were simulated using artificial soil profiles created by mixing soils from different layers in the original soil profile (never eroded) in a certain proportion. Subsequently, based on the field observations during 2012–2018, the SOC dynamics of each experimental plot were simulated by using a process-oriented C cycle model (denitrification-decomposition (DNDC)) to quantify the effects of erosion on SOC dynamics. The measurements at the beginning of the plots experiment showed that simulated erosion resulted in the depletion of SOC with an average reduction rate of 9.7% per 10 cm of soil loss; SOC in eroded soils recovered after seven years of cropping, the declining slope of the SOC measured in 2018 was 3.0% per 10 cm of soil loss, suggesting that dynamic replacement had occurred. Model simulation results indicated that soil erosion could reduce the C inputs from crop residue by lowering soil productivity; soil heterotrophic respiration in eroded soils was restricted due to the lower initial SOC content. As a result, the simulated overall SOC stock in eroded soils was restored after erosion stopped because of the lower C output. These results indicated that SOC loss at eroding sites was caused mainly by lateral transport rather than enhanced decomposition. The fate of the displaced SOC within catchments is key to assess the net impacts of soil erosion on SOC dynamics at a large scale.

Simulated soil organic carbon stocks in northern China’s cropland under different climate change scenarios

The accumulation and dynamic stock of soil organic carbon (SOC) in agricultural land are highly sensitive to changes in climate. However, there is uncertainty about how the global SOC cycle will respond to future climate change. In order to assess changes in SOC storage, basing on the historical weather data and field observations (1951–2018), the study used the DeNitrification-DeComposition (DNDC) model to simulate future SOC storage in an arid oasis under three representational concentration paths (RCPs): RCP2.6, low-end path; RCP4.5, middle stable path; and RCP8.5, high-end path) for the period from 2018 to 2086. Farmland soil in the Yanqi Basin in northern China was divided into grid units using ArcGIS, and then kriging interpolation was used to extract the mean value of the 200 grid units to calculate regional changes in SOC storage. The model indicated that more than 92.75 % of the measured results accurately simulated the SOC storage in Yanqi Basin cropland. The measured SOC storage in the Yanqi Basin was 1.32 Tg C in 2018. The range of SOC density was 653.27–5527.04 kg C/hm2 under the historical climate scenario, and 1863.99–4581.16 kg C/hm2, 2372.74–6910.15 kg C/hm2, and 623.656–5431.95 kg C/hm2 under the RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively. The simulation results show that, compared with the SOC stock in 2018, the SOC stocks in the oasis area in the future will show a rapid decline under the historical climate scenario (7.6 % decrease) as well as in the RCP 2.6 (3% decrease), RCP 4.5 (9.17 % decrease), and RCP 8.5 (16 % decrease) scenarios. These results indicate that organic carbon outputs and density will decrease faster under high emissions and more slowly under low emissions. This study provides supporting data and a scientific basis for increasing soil organic carbon in cropland and for the management of oasis soil to mitigate the effects of future climate change.

Simulating changes in soil carbon stocks for Quercus forests and upland rice fields under climate change scenarios in Manipur (India)

Assessing soil organic carbon (SOC) stocks under different land uses and future climate change (CC) conditions is important to provide better land use planning strategies. In South and Southeast Asia, Quercus forests are widely distributed but are exploited as a source of raw materials and shifting cultivation. Rice is a major staple food grain crop, often cultivated with terracing as upland rice. However, few studies on SOC stocks and simulated SOC dynamics under climate change conditions are available in these regions for these important land uses. Here, we present a case study related to Manipur state in North East region (NER) of India. We used the RothC model to evaluate the short-term changes of SOC stocks in Quercus forests and nearby upland rice fields and the effects of CC on SOC stocks, based on two General Circulation Models (GCMs) and two CO2 Representative Concentration Pathways (RCPs) during three 20-year time periods. Under CC conditions (2019–2078), results differed among land uses, GCMs, RCPs, and time periods, and Quercus forests were more sensitive to climate change than upland rice. For Quercus forests, as average of all GCMs and RCPs, SOC decreased by 0.42 t C ha−1 year−1 in the first time period and by 0.17 t C ha−1 year−1 as sixty years average. In upland rice, SOC decreased by 0.20 t C ha−1 year−1 in the first time period and by 0.12 t C ha−1 year−1 as sixty years average. Results indicated that management strategies for Quercus forests and upland rice are needed for CC mitigation and adaptation particularly in very close CC scenarios.

Simulating soil organic carbon in maize-based systems under improved agronomic management in Western Kenya

Improved management practices should be implemented in croplands in sub-Saharan Africa to enhance soil organic carbon (SOC) storage and/or reduce losses associated with land-use change, thereby addressing the challenge of ongoing soil degradation. DayCent, a process-based biogeochemical model, provides a useful tool for evaluating which management practices are most effective for SOC sequestration. Here, we used the DayCent model to simulate SOC using experimental data from two long-term field sites in western Kenya comprising of two widely promoted sustainable agricultural management practices: integrated nutrient management (i.e. mineral fertilizer and crop residues/farmyard manure incorporation) and conservation agriculture (i.e. minimum tillage and crop residue retention). At both sites, correlations between measured and simulated SOC were low to moderate (R2 of 0.25−0.55), and in most cases, the model produced fairly accurate prediction of the SOC trends with a low relative root mean squared error (RRMSE < 7%). Consistent with field measurements, simulated SOC declined under all improved management practices. The model projected annual SOC loss rates of between 0.32 to 0.35 Mg C ha-1 yr-1 in continuously tilled maize (Zea mays) systems without fertilizer or organic matter application over the period 2003–2050. The most effective practices in reducing the losses were the combined application of 4 Mg ha-1 of farmyard manure and 2 Mg ha-1 of maize residue retention (reducing losses up to 0.22 Mg C ha-1 yr-1), minimum tillage in combination with maize residue retention (0.21 Mg C ha-1 yr-1), and rotation of maize with soybean (Glycine max) under minimum tillage (0.17 Mg C ha-1 yr-1). Model results suggest that response of the passive SOC pool to the different management practices is a key driver of the long-term SOC trends at the two study sites. This study demonstrates the strength of the DayCent model in simulating SOC in maize systems under different agronomic management practices that are typical for western Kenya.