A statistical framework we call CQUESST (Carbon Quantification and Uncertainty from Evolutionary Soil STochastics), which models carbon sequestration and cycling in soils, is applied to a long-running agricultural experiment that controls for crop type, tillage, and season. The experiment, known as the Millenium Tillage Trial (MTT), ran on 42 field-plots for ten years from 2000-2010; here CQUESST is used to model soil carbon dynamically in six pools, in each of the 42 agricultural plots, and on a monthly time step for a decade. We show how CQUESST can be used to estimate soil-carbon cycling rates under different treatments. Our methods provide much-needed statistical tools for quantitatively inferring the effectiveness of different experimental treatments on soil-carbon sequestration. The decade-long data are of multiple observation types, and these interacting time series are ingested into a fully Bayesian model that has a dynamic stochastic model of multiple pools of soil carbon at its core. CQUESST's stochastic model is motivated by the deterministic RothC soil-carbon model based on nonlinear difference equations. We demonstrate how CQUESST can estimate soil-carbon fluxes for different experimental treatments while acknowledging uncertainties in soil-carbon dynamics, in physical parameters, and in observations. CQUESST is implemented efficiently in the probabilistic programming language Stan using its MapReduce parallelization, and it scales well for large numbers of field-plots, using software libraries that allow for computation to be shared over multiple nodes of high-performance computing clusters.

Context Defining soil organic carbon (SOC) ‘potential’ storage, underpins the economic feasibility of carbon sequestration; however, ‘potential’ storage is not quantifiable using historical and current empirical data. We propose a framework to define ‘attainable’ SOC storage that varies with soil properties, environmental conditions and management practices. Aims Within the soil fine fraction, we quantified additional storage capacity of the fine fraction SOC attainable deficit (FFSOC_Attainable_Def) by the difference between attainable (FFSOC_Attainable) and actual fine fraction SOC. Methods Using three analyses, we developed a framework to: (1) estimate the FFSOC_Attainable_Def of the fine fraction of Australian agricultural soils within broad mean annual precipitation ranges and soil depth classes; (2) establish rapid prediction capability for the FFSOC_Attainable_Def using infrared/partial least square regression modelling; and (3) generate spatial FFSOC_Attainable_Def estimates for agricultural regions with ensemble Random Forest modelling. Key results Global analyses of FFSOC_Attainable_Def do not consider key environmental drivers of carbon inflows and outflows nor soil depth. Separate analyses of soils derived from different combinations of precipitation and soil depth need to include variations in environmental conditions and soil properties to accurately define FFSOC_Attainable and FFSOC_Attainable_Def within the fine fraction. Spatially estimated FFSOC_Attainable_Def stocks revealed an opportunity to increase current fine fraction SOC stock by 3.47 GT (0–0.10 m depth) and 3.24 GT (0.10–0.30 m depth). Conclusions Our findings suggests that FFSOC_Attainable_Def is dynamic, not static. Caution is needed when interpreting the results from this analysis. Implications Deriving estimates of FFSOC_Attainable_Def will reduce risks in decision making on carbon farming in national policies.

Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. The model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. Modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (SOC) components. In this study, we introduce two novel SOC models and a new RothC-like model and investigate how the SOC components and complexity of the SOC models affect the SOC prediction in the presence of small and sparse time series data. We develop model selection methods that can identify the soil carbon model with the best predictive performance, in light of the available data. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of sparse time series data, and our simpler models can produce more accurate predictions.

The allocation of soil organic carbon (SOC) to its component fractions can indicate the vulnerability of organic carbon stocks to change. The impact of vetiver on the composition and distribution of SOC can provide a complete assessment of its potential to sequester carbon in soil. Purpose: This study quantified the distribution and impact of SOC under vetiver and the allocation of SOC to particulate (POC), humus (HOC) and resistant (ROC) fractions differentiated based on particle size and chemical composition under vetiver grass compared with other plant types. Methods: Carbon fractions were measured on soil samples collected from Australia and Ethiopia to a depth of 1.0 m under three plant communities (vetiver, coffee, and Australian native pastures). We used the MIR/PLSR spectra to estimate SOC fractions based on fractionated, and NMR measured values. Results: The stocks of SOC fractions indicated significant differences in the proportion of labile POC to HOC across sites and vegetation types. The dominant carbon fraction was HOC (71

While many advanced analytical methods have been applied to soil organic matter (SOM), its highly complex and heterogeneous chemical composition still eludes complete characterization. Analytical thermal analysis has been proposed as a relatively rapid, inexpensive method for SOM characterization that requires no pre-treatment, but is challenging due to a lack of direct information about chemical composition. The goal of this study was to inform the interpretation of coupled differential scanning calorimetry and evolved gas analyses (DSC, CO2-EGA) using spectral correlations with solid phase 13C NMR data. We used a subset of soils collected as part of the Australian National Soil Carbon Research Program (SCaRP), which were physically fractionated and charac-terized using conventional analytical methods. Correlating the well-understood NMR spectra with the less -understood DSC and CO2-EGA thermograms provided some indications of which chemical compounds combust at which temperatures. Overall, the EGA data generated stronger correlations compared to correlations with DSC data, which was attributable to greater variability in DSC data due thermal reactions associated with minerals. Direct comparison of NMR and thermal data for the mineral associated organic matter in the fine (<50 mu m) fraction was not possible due to the need to demineralize samples prior to NMR analyses. Thermal analyses showed substantial differences in samples pre-and post-HF pretreatment, and the NMR data for HF treated samples showed scattered and weak correlation patterns with DSC and EGA data for untreated samples. While precise chemical compositions cannot be gleaned directly from thermal analyses results, thermal approaches provide an avenue of investigation into SOM reactivity based on bioenergetics that may be a quantitative rep-resentation of SOM persistence.

The ability to accurately measure organic carbon (OC) in marine sediments or soils is overall taken for granted in scientific communities, yet this seemingly mundane task remains a methodological challenge when the soil matrix contains calcium carbonate (CaCO3), creating inaccuracies in Blue Carbon estimates. Here, we compared five common methods combining acidification, combustion, and wet oxidation pre-treatments for determination of OC in sediments and soils containing CaCO3 based on the analyses of artificial soil mixtures made of different OC and CaCO3 contents, and multiple soils from Australian seagrass cores. The results obtained showed that methods involving acidification pre-treatment entailed -17 +/- 0.2% (mean +/- SE) underestimation of OC content (ranging from -8% to -26%), whereas the combustion-based method was accurate for samples with high CaCO3 content but entailed 32-47% overestimation in samples with low CaCO3 content. The Heanes method (wet oxidation method) showed <5% deviation from the known OC content, but this method is not suitable for soil samples containing reduced iron, sulfur and potentially manganese compounds. The differences observed among methods have significant impacts on local, regional, and global Blue Carbon storage calculations. We provide key methodological guidelines for the analysis of OC in soils with high and low CaCO3 contents, aiming at improving accuracy in current Blue Carbon science.

Microbial biomass carbon is the most active component of the soil organic carbon that regulates bio-geochemical processes in terrestrial ecosystems. We might expect that the carbon decomposition rate should be related to the microbial population activity whereas some studies in the literature ignore this effect. We explore it on the accuracy of modelling soil carbon (SC) sequestration by developing and comparing two SC models that consider a carrying capacity, maximum microbe population size sustained by available resources in an environment, and limit the growth of the microbial biomass pool. We select the best model in terms of having the best predictive performance through two model selection methods. Our analysis shows that our simpler model outperforms the commonly used complex SC model (RothC), resulting in more accurate predictions. Also, considering microbial population growth improves model accuracy in the presence of a large dataset.

Abstract Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. The model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. Modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (SOC) components. In this study, we introduce two novel SOC models and a new RothC-like model and investigate how the SOC components and complexity of the SOC models affect the SOC prediction in the presence of small and sparse time series data. We develop model selection methods that can identify the soil carbon model with the best predictive performance, in light of the available data. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of sparse time series data, and our simpler models can produce more accurate predictions.

HighlightsVictorian lignites were assessed for their NH4+ retention capacity using adsorption isotherms and 15N tracing.NH4+ adsorption capacity of lignites increased (up to 3-fold) with pH, especially from pH 5 to 7.Biological immobilisation did not play a substantial role in the NH4+ retention capacity of the lignites.pH-dependent NH4+ adsorption was the dominant means by which lignite retained NH4+.

Carbon farming presents an opportunity for the land sector to generate income and transition to more sustainable land management practices. In Australia, establishing a carbon project and earning carbon credits is complex, with project proponents needing to satisfy eligibility requirements and adhere to rigorous measurement, verification, and reporting protocols of approved methods. To address these challenges, a human centered design (HCD) approach was used to deliver a digital solution, serving landowners' needs related to method discovery and reconfiguring how the methodological and scientific complexity of abatement potentials was delivered. The solution, called LOOC-C (pronounced "Look-see"), supports the discovery of abatement methods that are available for a given land area and provides an initial estimate of the potential quantum of carbon sequestered/emitted and the nature of co-benefits associated with each eligible method. Reporting on LOOC-C development and its observed impact demonstrates the role that human centered digital tools have in promoting land management actions that are both sustainable and reasonable to undertake. It equally demonstrates the power of integrating environmental market and user requirements with a robust design methodology. With similar opportunities in environmental markets globally, additional applications of an HCD approach are proposed.

The ratio of isotopes of carbon ( 13 C: 12 C or δ 13 C) and nitrogen ( 15 N: 14 N or δ 15 N) are common indicators of the flow and storage of organic matter in coastal wetland research. Effective use of these indicators requires quantification and understanding of: (1) the variability of isotope signatures of potential organic matter source materials; and (2) the influence of organic matter decomposition on isotopic signatures. While it is well-established that organic matter characteristics change during the decomposition process, there has been little direct quantification of any concurrent shifts in isotope signatures for coastal detritus. In this study, we addressed this by quantifying: (1) shifts in sample composition using solid-state 13 C Nuclear Magnetic Resonance (NMR) spectroscopy; and (2) shifts in δ 13 C and δ 15 N signatures of coastal plant tissues from field litterbag experiments. We observed significant shifts in 13 C NMR spectra across the course of deployment for all four plant tissues assessed (leaves of mangrove Avicennia marina; branchlets of supratidal tree Casuarina glauca; leaf wrack and roots/rhizomes of the seagrass Zostera muelleri ), driven largely by the preferential loss of labile constituents and concentration of more resistant macromolecules, such as lignin and leaf waxes. While there were shifts in isotope ratios for all species, these varied in direction and magnitude among species, tissue type and isotopes. This included δ 13 C enrichments of up to 3.1‰ and 2.4‰ in leaves of A. marina , and branchlets of C. glauca , respectively, but δ 13 C depletions of up to 4.0‰ for Z. muelleri . Shifts in δ 15 N varied among species and tissue types, with few clear temporal patterns. Partial least squares regression analyses showed that some tissue isotope signatures can be reliably predicted on the basis of sample composition ( 13 C NMR spectra), however, multiple inter- and intra-species variations preclude a simple explanation of isotopic signature shifts on the basis of plant-material molecular shifts alone. Further, we cannot preclude the potential influence of microbe-associated organic matter on sample composition or isotopic signatures. Our findings emphasise the importance of considering decomposition effects on stable isotope signatures in blue carbon ecosystems. Isotope approaches will remain a valuable tool in coastal ecosystem research, but require robust experimental approaches (including appropriate use of decomposed end-members or fractionation correction factors; quantification of microbial organic matter) and quantification of decomposition dynamics for specific plant tissues and environmental settings.

Plant roots are primary factors to contribute to surface and deep soil carbon sequestration (SCS). Perennial grasses like vetiver produce large and deep root system and are likely to contribute significantly to soil carbon. However, we have limited knowledge on how root and shoot decomposition differ and their contribution to SCS. This study examined biomass production and relative decomposition of vetiver which was grown under glasshouse conditions. Subsequently the biomass incubated for 206 days, and the gas analysed using ANCA-GSL. The results confirmed large shoot and root production potential of 161 and 107 Mg ha−1 (fresh) and 67.7 and 52.5 Mg ha−1 (dry) biomass, respectively with 1:1.43 (fresh) and 1:1.25 (dry) production ratio. Vetiver roots decomposed more rapidly in the clay soil (p < 0.001) compared with the shoots, which could be attributed to the lower C:N ratio of roots than the shoots. The large root biomass produced does indeed contribute more to the soil carbon accumulation and the faster root decomposition is crucial in releasing the carbon in the root exudates and would also speed up its contribution to stable SOM. Hence, planting vetiver and similar tropical perennial grasses on degraded and less fertile soils could be a good strategy to rehabilitate degraded soils and for SCS.

The adoption of conservation agriculture (e.g., no-till system) has been recognized as pivotal to maintaining soil functions, but the potential of this system to enhance organic carbon (OC) quantity and quality and how this OC is stabilized in soils are not well established. In this study, we evaluated the effects of land-use types (native vegetation (NV) vs. no-till system (NT)) on OC stocks and on the chemical composition of organic matter (OM), and sought to understand the mechanisms that govern OC protection in the studied highly weathered soils. To achieve these objectives, we used an OC fractionation scheme in a combination of solid-state C-13 nuclear magnetic resonance (NMR) spectroscopic analyses in soils from six farms in southern Brazil. Our results showed smaller OC stocks (whole soil) under NT than under NV in four of the six sites. In addition, the OC stock differences between land-use types were larger in coarser textured soils and in those where conventional tillage was used before the adoption of NT. Among fractions, particulate organic carbon (POC) represented only 8% of the whole OC stock but was the fraction most affected by land-use type. In contrast, the humus organic carbon (HOC) fraction contributed 78% of the whole OC stock and was little altered by land-use type. Resistant organic carbon (ROC) represented 14% of the whole OC stock and it was altered by land-use type, demonstrating that this fraction is not as inert as previously thought. Overall, OM chemical composition was quite similar between land uses, with O-alkyl-C being the predominant C type. This labile component was further highly correlated with OC stock and silt + clay contents, indicating that the accumulation of OC in these highly weathered soils is mainly a response to the association between labile C compounds and minerals. Highlights The OC build-up in no-till areas and the mechanisms of OC protection in soils are still uncertain. OC stocks under no-till are still smaller than under native vegetation. Soils under no-till showed untapped potential for OC sequestration. The association of labile compounds with soil minerals is the driving factor for OC protection.

Spectroscopy is a powerful means of increasing the availability of soil data necessary for understanding carbon cycling in a changing world. Here, we develop a calibration transfer methodology to appropriately apply an existing mid infrared (MIR) spectral library with analyte data on the distribution of soil organic carbon (SOC) into particulate (POC), mineral-associated (MAOC), and pyrogenic (PyC) forms to nearly 8000 soil samples collected in the Great Plains ecoregion of the United States. We then use this SOC fraction database in combination with a machine learning-based predictive soil mapping approach to explore the controls on the distribution of fractions through soil profiles and across the region. The relative abundance of each fraction had unique depth distribution profiles with POC fraction dropping exponentially with depth, the MAOC fraction having a broad distribution with a maxima at 35–50 cm, and the PyC fraction showed a slight subsurface maxima (10–20 cm) and then a steady decline with increasing depth. Within the Great Plains ecoregion, clay content was a strong control on the total amount and relative proportion of each fraction in both the surface and subsoil horizons. Sandy soils and soils in cool semi-arid regions contained significantly more POC relative to the MAOC and PyC fractions. Cultivated soils had significantly less SOC than grassland soils with losses following a predictable pattern: POC > MAOC ≫ PyC. This SOC fraction database and resulting maps can now form the basis for improved representation of SOC dynamics in biogeochemical models.

Stabilizing the global climate within safe bounds will require greenhouse gas (GHG) emissions to reach net zero within a few decades. Achieving this is expected to require removal of CO2 from the atmosphere to offset some hard-to-eliminate emissions. There is, therefore, a clear need for GHG accounting protocols that quantify the mitigation impact of CO2 removal practices, such as biochar sequestration, that have the potential to be deployed at scale. Here, we have developed a GHG accounting methodology for biochar application to mineral soils using simple parameterizations and readily accessible activity data that can be applied at a range of scales including farm, supply chain, national, or global. The method is grounded in a comprehensive analysis of current empirical data, making it a robust method that can be used for many applications including national inventories and voluntary and compliance carbon markets, among others. We show that the carbon content of biochar varies with feedstock and production conditions from as low as 7% (gasification of biosolids) to 79% (pyrolysis of wood at above 600 °C). Of this initial carbon, 63-82% will remain unmineralized in soil after 100 years at the global mean annual cropland-temperature of 14.9 °C. With this method, researchers and managers can address the long-term sequestration of C through biochar that is blended with soils through assessments such as GHG inventories and life cycle analyses.