Soil Carbon Model Research Articles

Improving Yasso15 soil carbon model estimates with Ensemble Adjustment Kalman Filter state data assimilation

Improving Yasso15 soil carbon model estimates with Ensemble Adjustment Kalman Filter state data assimilation

Dynamic Sustainability Assessment Tool: Case Study of Green Biorefineries in Danish Agriculture

In this work, a novel, dynamic sustainability assessment tool is presented and validated in a case study. This tool combines two methods—system dynamics (SD) and temporal soil carbon modelling. The case study for sustainability analysis of Danish agriculture and green biorefineries supply chains in Denmark is used. The development of the Danish agriculture sector is simulated and assessed in relation to the ecosystem’s carrying capacity until 2050, defined as 1.4 livestock units per hectare. The results show that under the current development, the agriculture sector would exceed this carrying capacity shortly after 2030. The results obtained from the dynamic sustainability assessment tool show a more precise and less optimistic projection of future development than the assessment using constant soil carbon modelling values only. The study, therefore, suggests that the use of the temporal aspects in the sustainability assessment should be included and further developed.

Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic

Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1–0.5 Mg C ha−1 y−1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5–1.5 Mg C ha−1 y−1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.

Vertical distributions of soil microbial biomass carbon: a global dataset.

Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset (VDMBC) is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data were collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data collectd include climate, ecosystem types, and edaphic factors. We developed this dataset by searching the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data in this dataset met two creteria: 1) there were at least three mineral soil layers along a soil profile, and 2) SMBC was measured using the fumigation extraction method. The data in tables and texts were obtained from literature directly, and the data in figures were extracted by using the GetData Graph digitizer software version 2.25. When climate and soil properties were not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org).The units of all the variables were converted to the standard international units or commonly used ones and the values were transformed correspondingly. For example, the value of soil organic matter (SOM) was converted to SOC by using the equation (SOC = SOM × 0.58).This dataset can be used in predicting global SOC changes along soil profiles by using the multi-layer soil carbon models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.

ESTIMATION OF SOIL BULK DENSITY AND CARBON USING MULTI-SOURCE REMOTELY SENSED DATA

Abstract. Bulk density and soil carbon models were fitted for soil samples collected during field campaigns in 2018 and 2019 for the Kapuskasing region of the District of Cochrane in Ontario, Canada. Prediction maps for bulk density and soil carbon were generated for the 0–15 cm depth mineral soil layer. The application of multi-source remotely sensed data as environmental covariates for model predictors was implemented. Environmental covariates were obtained from multispectral satellite imagery, LiDAR (light detection and ranging) retrievals and airborne geomagnetic surveys, as well from a digital elevation model (DEM) for topographic covariates. Two covariates derived from LiDAR, canopy height model (CHM) and gap fraction, were of high variable importance when fitting models for average bulk density; gap fraction had the highest to second highest variable importance for average bulk density when considered among a full set of 76, or reduced sets of 12 or 5 separate predictors respectively. Environmental covariates corresponding to vegetation cover, specifically reflectance from multispectral imagery or LiDAR data, had the highest variable importance when compared with other categories of soil formation factors. Random forest (RF) models were generated, with RF models based upon just 12 predictors obtaining reasonable results with coefficients of determinations (R2) greater than 0.7 for the standard derivation of bulk density, standard deviation of total carbon and average total carbon for the 0–15 cm depth layer.

Contrasting temperature responses of soil respiration derived from soil organic matter and added plant litter

Accurate description of temperature response and sensitivity of different carbon pools within soil is critical for accurately modelling soil carbon stocks and changes. Inconsistent sampling, incubation and fractionation methods highlights the need for new approaches to this area of study. We developed and tested a new protocol which allowed measurement of the temperature response of two carbon pools within soil. A Horotiu silt loam soil, wet up to 60% maximum water holding capacity, was mixed with 13C-enriched plant litter and incubated for 5 or 20 h, at 30 discrete temperatures (~ 2–50 °C). A mixing model was used to separate respired CO2 into litter and soil organic matter sourced carbon pools, which were then fitted using macromolecular rate theory. Overall, litter sourced respiration had a low Topt (the temperature where respiration rate is maximal) and was less temperature sensitive (Q10) than soil organic matter sourced respiration, which was more Arrhenius-like. We attribute these differences in temperature parameters to the factors that control the availability of carbon to microbes from the labile litter (enzyme kinetics with a clear temperature optimum) compared to the relatively stable soil organic matter (desorption and diffusion that exhibit Arrhenius behaviour). The developed method is rapid and reliable and may be suited to exploring temperature response of a variety of 13C-labelled pools in soil and more clearly demonstrates that labile carbon has very different temperature response than more stable carbon pools in soil.

Soil carbon stocks in Ethiopian forests and estimations of their future development under different forest use scenarios

AbstractContributions from all land‐uses are needed if Ethiopia is to fulfil its Paris Agreement targets. The magnitude of soil carbon stock and the role of Ethiopian forest soils in climate change mitigation has not yet been clarified. In this study, soil carbon inventory in forests was carried out as a part of the Ethiopia REDD+ Programme. The performance of soil carbon models Yasso07 and CENTURY was tested by comparing the model predictions with the empirical soil organic carbon (SOC) data provided by the field inventory. In addition to that, Global Soil Organic Carbon (GSOC) map estimates by the Food and Agriculture Organization for Ethiopia were included in the comparison. The soil inventory was carried out in 2017–2018 at a subset of permanent sampling units of the National Forest Inventory conducted in 2014–2017. A combination of soil inventory data, soil carbon models and satellite images enabled to quantify the impact of forest use intensity to future SOC sinks in Ethiopian forests in a novel way. The Yasso07 and CENTURY models provided similar SOC estimates to the measured data for all biomes, and the GSOC map overestimated in biomes with larger SOC stocks. Results showed that Moist Afromontane forest biome contains twice as much SOC per unit area compared to Combretum‐Terminalia forest biome and three‐times more SOC compared to Acacia‐Commiphora. Results underlined that sustainable forest management has a high potential for soil carbon development in Ethiopian forests in near future, impacting the ability of the Country to achieve its Paris Agreement targets.

Linear two-pool models are insufficient to infer soil organic matter decomposition temperature sensitivity from incubations

Terrestrial carbon (C)-climate feedbacks depend strongly on how soil organic matter (SOM) decomposition responds to temperature. This dependency is often represented in land models by the parameter Q10, which quantifies the relative increase of microbial soil respiration per 10 °C temperature increase. Many studies have conducted paired laboratory soil incubations and inferred “active” and “slow” pool Q10 values by fitting linear two-pool models to measured respiration time series. Using a recently published incubation study (Qin et al. in Sci Adv 5(7):eaau1218, 2019) as an example, here we first show that the very high parametric equifinality of the linear two-pool models may render such incubation-based Q10 estimates unreliable. In particular, we show that, accompanied by the uncertain initial active pool size, the slow pool Q10 can span a very wide range, including values as high as 100, although all parameter combinations are producing almost equally good model fit with respect to the observations. This result is robust whether or not interactions between the active and slow pools are considered (typically these interactions are not considered when interpreting incubation data, but are part of the predictive soil carbon models). This very large parametric equifinality in the context of interpreting incubation data is consistent with the poor temporal extrapolation capability of linear multi-pool models identified in recent studies. Next, using a microbe-explicit SOM model (RESOM), we show that the inferred two pools and their associated parameters (e.g., Q10) could be artificial constructs and are therefore unreliable concepts for integration into predictive models. We finally discuss uncertainties in applying linear two-pool (or more generally multiple-pool) models to estimate SOM decomposition parameters such as temperature sensitivities from laboratory incubations. We also propose new observations and model structures that could enable better process understanding and more robust predictive capabilities of soil carbon dynamics.

Detailed global modelling of soil organic carbon in cropland, grassland and forest soils.

Assessments of the global carbon (C) cycle typically rely on simplified models which consider large areas as homogeneous in terms of the response of soils to land use or consider very broad land classes. For example, “cropland” is typically modelled as an aggregation of distinct practices and individual crops over large regions. Here, we use the process-based Rothamsted soil Carbon Model (RothC model), which has a history of being successfully applied at a global scale, to calculate attainable SOC stocks and C mineralization rates for each of c. 17,000 regions (combination of soil type and texture, climate type, initial land use and country) in the World, under near-past climate conditions. We considered 28 individual crops and, for each, multiple production practices, plus 16 forest types and 1 grassland class (total of 80 classes). We find that conversion to cropland can result in SOC increases, particularly when the soil remains covered with crop residues (an average gain of 12 t C/ha) or using irrigation (4 t C/ha), which are mutually reinforcing effects. Attainable SOC stocks vary significantly depending on the land use class, particularly for cropland. Common aggregations in global modelling of a single agricultural class would be inaccurate representations of these results. Attainable SOC stocks obtained here were compared to long-term experiment data and are well aligned with the literature. Our results provide a regional and detailed understanding of C sequestration that will also enable better greenhouse gas reporting at national level as alternatives to IPCC tier 2 defaults.

Including long-term soil organic carbon changes in life cycle assessment of agricultural products

Regional values for prospective soil organic carbon (SOC) change in Australian cropland were derived via state-of-the-art modelling. This paper evaluates the applicability of the results in the context of life cycle assessment (LCA). Results of soil carbon modelling need to align with LCA requirements in order to be applicable. The following aspects were investigated in more detail: effect of SOC and variability on product carbon footprint results, data symmetry and consistency, attribution of the SOC change to activity and allocation of the SOC change to crops in rotations. Results show that greenhouse gas (GHG) emissions or removals associated with SOC change even in the absence of recent land use change or land management change can potentially change Australian crop carbon footprints considerably. Over a modelling period of 62 years, the SOC continues to change. In attributional LCA, issues with attribution, allocation and data symmetry of the SOC change values are complex. Without a comprehensive understanding of the causal link between individual crops and pasture in a rotation and the change in SOC, and without a SOC change figure for an appropriate reference baseline, application in attributional LCA is limited to certain types of studies only. In consequential LCA, the data symmetry issues as well as the need for allocation and attribution can be avoided. The SOC change results cannot be allocated to individual crops and are therefore valid only for a full rotation cycle. This means results may be applied in attributional LCA but only in certain contexts. The main applicability is foreseen as a business-as-usual baseline for consequential LCA. A set of SOC change values will be derived for this purpose and made available with accompanying guidance for use and interpretation.

Driving factors of soil organic carbon fractions over New South Wales, Australia

We modelled and mapped the distribution of three principal soil organic carbon (SOC) fractions across New South Wales and gained insights into the factors controlling their distribution. Carbon fractions are important for modelling soil carbon dynamics for carbon accounting, and as a potential indicator of soil quality. We considered particulate organic carbon (POC), humic organic carbon (HOC) and resistant organic carbon (ROC), which represent fractions of increasing bio-chemical stability. A dataset of 427 NSW profiles with mid-infrared (MIR) derived carbon fractions and a set of 16 predictor variables were applied. Random Forest and multiple linear regression techniques were applied, for depth intervals to 30 cm.The derived models and maps were of moderate strength, with validation results revealing Lin's concordance values for the 0–30 cm depth interval of 0.79 for total SOC stocks and 0.60 to 0.74 for the fraction stocks. Maps of mean stocks, and upper and lower 95% prediction intervals are presented. Absolute stocks (in Mg ha−1) of each fraction have a strong linear relationship with total SOC and are controlled by similar environmental and land management factors, normally increasing in a systematic way with increasingly moist climate (considering rainfall and temperatures), increasing mafic lithology (associated with more fertile, clay rich soils) and less disturbed land uses with higher vegetation cover.The environmental factors influencing the relative proportions of each fraction (% of total SOC) are more complex, but climate and lithology appear to be dominant, in addition to depth in profile. An SOC vulnerability index is mapped over the State, identifying areas where the stored carbon is most vulnerable to land management or environmental change. Our findings add to the understanding of factors driving the distribution of soil carbon and its fractions, which may ultimately contribute to more effective climate change mitigation programs.

Emergent properties of organic matter decomposition by soil enzymes

Plant residues and soil organic matter are predominantly decomposed by exoenzymes. Many soil carbon models now represent enzymatic decomposition, but the mathematical formulation of this process has been debated over the last 15 years. Some models apply the traditional “forward” Michaelis-Menten equation to represent enzyme kinetics, whereas others apply the “reverse” Michaelis-Menten equation, which assumes that kinetic rates saturate at high enzyme concentrations. Recently the equilibrium chemistry approximation (ECA) has been proposed as an alternative to both Michaelis-Menten formulations. However, because of methodological limitations, in-situ enzyme kinetics—especially in the context of soil system heterogeneity—have been difficult to verify experimentally. Therefore, the overarching goal of our study was to evaluate different enzyme kinetic formulations using model-based evidence at microbial to ecosystem scales. We used a spatially explicit individual- and trait-based microbial model, DEMENT, to circumvent methodological challenges. Although DEMENT assumes forward Michaelis-Menten kinetics at local scales, at the grid scale we found saturating relationships between degradation rate and both substrate concentrations and enzyme concentrations that fit the forward and reverse Michaelis-Menten equations, respectively, at specific successional stages during decomposition. Although forward and reverse Michaelis-Menten equations emerged under some conditions, only the ECA adequately represented decay rates emerging from the spatial-temporal variation in substrate and enzyme concentrations throughout the decomposition process. Our results support a more widespread adoption of the ECA equation in soil biogeochemical modelling at ecosystem scales.

Substrate quality and concentration control decomposition and microbial strategies in a model soil system

Soil carbon models typically scale decomposition linearly with soil carbon (C) concentration, but this linear relationship has not been experimentally verified. Here we investigated the underlying biogeochemical mechanisms controlling the relationships between soil C concentration and decomposition rates. We incubated a soil/sand mixture with increasing amounts of finely ground plant residue in the laboratory at constant temperature and moisture for 63 days. The plant residues were rye (Secale cereale, C/N ratio of 23) and wheat straw (Triticum spp., C/N ratio of 109) at seven soil C concentrations ranging from 0.38 to 2.99%. We measured soil respiration, dissolved organic carbon (DOC) concentrations, microbial biomass, and potential enzyme activities over the course of the incubation. Rye, which had higher N and DOC contents, lost 6 to 8 times more C as CO2 compared to wheat residue. Under rye and wheat amendment, absolute C losses as CO2 (calculated per g dry soil) increased linearly with C concentration while relative C losses as CO2 (expressed as percent of initial C) increased with C concentration following a quadratic function. In low C concentration treatments (0.38–0.79% OC), DOC decreased gradually from day 3 to day 63, microbial C increased towards the end in the rye treatment or decreased only slightly with straw amendment, and microbes invested in general enzymes such as proteases and oxidative enzymes. At increasing C levels, enzyme activity shifted to degrading cellulose after 15 days and degrading microbial necromass (e.g. chitin) after 63 days. At the highest C concentrations (2.99% OC), microbial biomass peaked early in the incubation and remained high in the rye treatment and decreased only slightly in the wheat treatment. While wheat lost C as CO2 constantly at all C concentrations, respiration dynamics in the rye treatment strongly depended on C concentration. Our results indicate that litter quality and C concentration regulate enzyme activities, DOC concentrations, and microbial respiration. The potential for non-linear relationships between soil C concentration and decomposition may need to be considered in soil C models and soil C sequestration management approaches.

Measured and modelled effect of land-use change from temperate grassland to Miscanthus on soil carbon stocks after 12 years.

Soil organic carbon (SOC) is an important carbon pool susceptible to land‐use change (LUC). There are concerns that converting grasslands into the C4 bioenergy crop Miscanthus (to meet demands for renewable energy) could negatively impact SOC, resulting in reductions of greenhouse gas mitigation benefits gained from using Miscanthus as a fuel. This work addresses these concerns by sampling soils (0–30 cm) from a site 12 years (T12) after conversion from marginal agricultural grassland into Miscanthus x giganteus and four other novel Miscanthus hybrids. Soil samples were analysed for changes in below‐ground biomass, SOC and Miscanthus contribution to SOC (using a 13C natural abundance approach). Findings are compared to ECOSSE soil carbon model results (run for a LUC from grassland to Miscanthus scenario and continued grassland counterfactual), and wider implications are considered in the context of life cycle assessments based on the heating value of the dry matter (DM) feedstock. The mean T12 SOC stock at the site was 8 (±1 standard error) Mg C/ha lower than baseline time zero stocks (T0), with assessment of the five individual hybrids showing that while all had lower SOC stock than at T0 the difference was only significant for a single hybrid. Over the longer term, new Miscanthus C4 carbon replaces pre‐existing C3 carbon, though not at a high enough rate to completely offset losses by the end of year 12. At the end of simulated crop lifetime (15 years), the difference in SOC stocks between the two scenarios was 4 Mg C/ha (5 g CO2‐eq/MJ). Including modelled LUC‐induced SOC loss, along with carbon costs relating to soil nitrous oxide emissions, doubled the greenhouse gas intensity of Miscanthus to give a total global warming potential of 10 g CO2‐eq/MJ (180 kg CO2‐eq/Mg DM).

Modeling soil organic carbon dynamics in temperate forests with Yasso07

Abstract. In a context of global changes, modeling and predicting the dynamics of soil carbon stocks (CSs) in forest ecosystems are vital but challenging. Yasso07 is considered to be one of the most promising models for such a purpose. We examine the accuracy of its prediction of soil carbon dynamics over the whole French metropolitan territory at a decennial timescale. We used data from 101 sites in the RENECOFOR network, which encompasses most of the French temperate forests. These data include (i) the quantity of above-ground litterfall from 1994 to 2008, measured yearly, and (ii) the soil CSs measured twice at an interval of approximately 15 years (once in the early 1990s and around 2010). We used Yasso07 to simulate the annual changes in carbon stocks (ACCs; in tC ha−1 yr−1) for each site and then compared the estimates with actual recorded data. We carried out meta-analyses to reveal the variability in litter biochemistry in different tree organs for conifers and broadleaves. We also performed sensitivity analyses to explore Yasso07's sensitivity to annual litter inputs and model initialization settings. At the national level, the simulated ACCs (+0.00±0.07 tC ha−1 yr−1, mean ± SE) were of the same order of magnitude as the observed ones (+0.34±0.06 tC ha−1 yr−1). However, the correlation between predicted and measured ACCs remained weak (R2<0.1). There was significant overestimation for broadleaved stands and underestimation for coniferous sites. Sensitivity analyses showed that the final estimated CS was strongly affected by settings in the model initialization, including litter and soil carbon quantity and quality and also by simulation length. Carbon quality set with the partial steady-state assumption gave a better fit than the model with the complete steady-state assumption. With Yasso07 as the support model, we showed that there is currently a bottleneck in soil carbon modeling and prediction due to a lack of knowledge or data on soil carbon quality and fine-root quantity in the litter.

Long‐term impacts of warming drive decomposition and accelerate the turnover of labile, not recalcitrant, carbon

AbstractWarming is altering the way soils function in ecosystems both directly by changing microbial physiology and indirectly by causing shifts in microbial community composition. Some of these warming‐driven changes are short term, but others may persist over time. Here, we took advantage of a long‐term (14 yr) warming experiment in a tallgrass prairie to tease apart the influence of short‐ and long‐term warming on litter decomposition. We collected soils originating from warmed and control plots and incubated them with a common litter substrate in a reciprocal design under elevated and ambient growth chamber temperatures. Litter decomposition was 40% higher in soils that were warmed in the field for 14 yr (long‐term warming) relative to soils derived from ambient plots. Short‐term warming in the laboratory had less of an impact on decomposition—decomposition increased by 12% under laboratory warming. Using a two‐pool soil carbon model to explore how different carbon pools may be responding, we found that long‐term warming accelerated the turnover of labile, not recalcitrant, carbon in these prairie soils—a result that is likely due to shifts in soil community activity/composition. Taken together, our results offer experimental evidence that warming‐induced changes in the soil community that occur over 14 yr of warming have long‐lasting effects on carbon turnover.

Evaluating CENTURY and Yasso soil carbon models for CO2 emissions and organic carbon stocks of boreal forest soil with Bayesian multi‐model inference

We can curb climate change by improved management decisions for the most important terrestrial carbon pool, soil organic carbon stock (SOC). However, we need to be confident we can obtain the correct representation of the simultanous effect of the input of plant litter, soil temperature and water (which could be altered by climate or management) on the decomposition of soil organic matter. In this research, we used regression and Bayesian statistics for testing process‐based models (Yasso07, Yasso15 and CENTURY) with soil heterotrophic respiration (Rh) and SOC, measured at four sites in Finland during 2015 and 2016. We extracted climate modifiers for calibration with Rh. The Rh values of Yasso07, Yasso15 and CENTURY models estimated with default parameterization correlated with measured monthly heterotrophic respiration. Despite a significant correlation, models on average underestimated measured soil respiration by 43%. After the Bayesian calibration, the fitted climate modifier of the Yasso07 model outperformed the Yasso15 and CENTURY models. The Yasso07 model had smaller residual mean square errors and temperature and water functions with fewer, thus more efficient, parameters than the other models. After calibration, there was a small overestimate of Rh by the models that used monotonic moisture functions and a small generic underestimate in autumn. The mismatch between measured and modelled Rh indicates that the Yasso and CENTURY models should be improved by adjusting climate modifiers of decomposition or by accounting for missing controls in, for example, microbial growth.Highlights We tested soil carbon models against monthly soil Rh fluxes and amounts of SOC stock. The models accurately reproduced most of the seasonal Rh trends and amounts of SOC. Under autumn temperature and moisture, Rh was mismatched before and even after the parameterization. The seasonality of the temperature and water functions should be adjusted in models.

Global variation of soil microbial carbon-use efficiency in relation to growth temperature and substrate supply

Soil microbial carbon-use efficiency (CUE), which is defined as the ratio of growth over C uptake, is commonly assumed as a constant or estimated by a temperature-dependent function in current microbial-explicit soil carbon (C) models. The temperature-dependent function (i.e., CUE = CUE0 + m × (T − 20)) simulates the dynamic CUE based on the specific CUE at a given reference temperature (i.e., CUE0) and a temperature response coefficient (i.e., m). Here, based on 780 observations from 98 sites, we showed a divergent spatial distribution of the soil microbial CUE (0.5 ± 0.25; mean ± SD) at the global scale. Then, the key parameters CUE0 and m in the above equation were estimated as 0.475 and −0.016, respectively, based on the observations with the Markov chain Monte Carlo technique. We also found a strong dependence of microbial CUE on the type of C substrate. The multiple regression analysis showed that glucose influences the variation of measured CUE associated with the environmental factors. Overall, this study confirms the global divergence of soil microbial CUE and calls for the incorporation of C substrate beside temperature in estimating the microbial CUE in different biomes.

Soil Carbon Sequestration in Long-Term Fertilization Under Jute-Rice-Wheat Agro-Ecosystem

ABSTRACTSoil organic carbon (SOC) sequestration in response to long-term fertilizer management practices under jute-rice-wheat agro-ecosystem in alluvial soils was studied using a modeling approach. Fertilizer management practices included nitrogen (N), phosphorus (P) and potassium (K) fertilization, manure application, and root-stubble retention of all three crops. Soil carbon (C) model RothC was used to simulate the critical C input rates needed to maintain initial soil C level in long timescale (44 years). SOC change was significantly influenced by the long-term fertilizer management practices and the edaphic variable of initial SOC content. The effects of fertilizer combination “100%NPK+FYM” on SOC changes were most significant over “100%NPK” fertilization. If the 100% NPK fertilizer along with manure applied with stubble and roots retention of all crops, alluvial soils of such agro-ecosystem would act as a net C sink, and the average SOC density kept increasing from 18.18 Mg ha−1 during 1972 to the current average of ∼22 Mg ha−1 during 2065 s. On an average, the critical C input was estimated to be 5.30 Mg C ha−1 yr−1, depending on local soil and climatic conditions. The critical C input could be effectively estimated using a summary model driven by current SOC level, mean annual temperature, precipitation, and soil clay content. Such information will provide a baseline for assessing soil C dynamics under potential changes in fertilizer and crop residues management practices, and thus enable development of management strategies for effectively mitigating climate change through soil C sequestration.

Simulation of extreme storm effects on regional forest soil carbon stock

This study aims to project changes in soil carbon stocks under different frequencies of storm and climate change scenarios. We calibrated and validated the dynamic process-based soil carbon model “Yasso07” for a wind-throw prone forest area of 1.4 M ha in the State of Baden Württemberg in central Europe, Germany. We fitted climate-biomass models using retrospective climate data and biomass measurements from three consecutive national forest inventories for six forest growth regions. Three IPCC scenarios (RCP2.6, RCP6.0 and RCP8.5), three storm frequencies (10, 20, and 50 years interval), and three post-storm harvest strategies (business-as-usual, full retention, and 50% retention), in combination with a total of 27 scenarios, were applied for projections into the 21st century. We could reduce the uncertainty of YassoBW parameter values significantly by up to 30%, by applying Bayesian calibration, although the absolute value of most parameters did not deviate very much from the original Yasso07 parameters. The projection results showed that forest soil organic carbon (SOC) may lose approximately 30 and 10 t C/ha during the first and the second half of this century, respectively. Three storm frequencies led to a larger range of annual SOC reduction (-0.34, -0.49) t C/ha than climate and harvest strategies (-0.41, -0.42) t C/ha. If no storms occur, the total carbon stock would increase to over 200 t/ha with 258 t/ha under RCP8.5. Considering storm impacts, total forest carbon was reduced from -20 to -90 t C/ha, regarding 10 and 50 year storm frequencies respectively. The largest reduction of forest soil carbon stock originated from the loss in non-solubles (N), followed by acid-solubles (A), humus (H), water-solubles (W) and ethanol-solubles (E).