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This study examines the impact of silvicultural and land-use management practices on carbon sequestration in peri-urban forest ecosystems, with a particular focus on human-induced carbon dynamics. The study area's complex profile spans from a compact native forest to varying degrees of fragmentation. This included areas undergoing secondary succession forest without silvicultural interventions (No-SI) alongside sites subjected to high-intensity (High-SI) and low-intensity silvicultural interventions (Low-SI). The research assessed carbon stocks and sequestration in different carbon pools (living biomass, dead organic matter and soil) using field data, allometric equations and laboratory analysis. Findings reveal a significant correlation between the intensity of anthropogenic interventions and variations in carbon stocks. The CASMOFOR model facilitated the reconstruction of carbon stock and carbon-stock change dynamics over four decades (1980–2022), showing disparities in carbon storage capabilities linked to the structural characteristics of the sites. The Low-SI site had the highest carbon stock in all carbon pools (378 tonnes C ha−1), which is more than double compared to High-SI (161 tonnes C ha−1) or No-SI sites (134 tonnes C ha−1). However, the secondary succession forest (No-SI) demonstrated the highest annual carbon stock change (4.4 tonnes C ha−1 year−1), two times higher than the Low-SI mature stand (2.2 tonnes C ha−1 year−1), emphasising the resilience of forest ecosystems to recover and sustain carbon sequestration capacities after harvesting if forest land use remains unchanged. The study underscores the significant importance of anthropogenic interventions on carbon dynamics, especially for living tree biomass, which has consequences in enhancing carbon sequestration and contributing to emission reduction targets. [ABSTRACT FROM AUTHOR]

This study examines the impact of silvicultural and land-use management practices on carbon sequestration in peri-urban forest ecosystems, with a particular focus on human-induced carbon dynamics. The study area’s complex profile spans from a compact native forest to varying degrees of fragmentation. This included areas undergoing secondary succession forest without silvicultural interventions (No-SI) alongside sites subjected to high-intensity (High-SI) and low-intensity silvicultural interventions (Low-SI). The research assessed carbon stocks and sequestration in different carbon pools (living biomass, dead organic matter and soil) using field data, allometric equations and laboratory analysis. Findings reveal a significant correlation between the intensity of anthropogenic interventions and variations in carbon stocks. The CASMOFOR model facilitated the reconstruction of carbon stock and carbon-stock change dynamics over four decades (1980–2022), showing disparities in carbon storage capabilities linked to the structural characteristics of the sites. The Low-SI site had the highest carbon stock in all carbon pools (378 tonnes C ha−1), which is more than double compared to High-SI (161 tonnes C ha−1) or No-SI sites (134 tonnes C ha−1). However, the secondary succession forest (No-SI) demonstrated the highest annual carbon stock change (4.4 tonnes C ha−1 year−1), two times higher than the Low-SI mature stand (2.2 tonnes C ha−1 year−1), emphasising the resilience of forest ecosystems to recover and sustain carbon sequestration capacities after harvesting if forest land use remains unchanged. The study underscores the significant importance of anthropogenic interventions on carbon dynamics, especially for living tree biomass, which has consequences in enhancing carbon sequestration and contributing to emission reduction targets.

Teak plantations are generally managed for timber production and presently there is an increasing interest in understanding the carbon stocks of Teak plantation. A study was developed on carbon yield table for Teak in Cauvery delta zone of Tamil Nadu, India and field data was taken from 15 sample plots of 30 m × 30 m size in different age classes, for instance 4–5 years, 7–8 years, 12–13 years, 15–16 years, 18–19 years, 23–24 years, 40–41 years and 46–47 years, respectively. Tree height and diameter of 2 meter segments from ground level to top height were measured and the average merchantable volume of 1.246 m3 and carbon content of 373.80 kg were recorded in the age class of 46–47 years. The carbon model constructed for Tectona grandis using multiple linear regression was Y = -113.001+2.8616(Age)-3.6946(Total height)+1245.813(Diameter). The carbon yield table was constructed using the age class, top height class and diameter class. The overall observation of the study concluded that, the best fit carbon yield models were developed for T. grandis with 91 per cent accuracy by comparing actual carbon stock and predicted carbon stock. [ABSTRACT FROM AUTHOR]

Labile carbon (C) is a major source of C loss because of its high vulnerability to environmental change. Yet its potential role in regulating soil organic carbon (SOC) dynamics remains unclear. In this study, we tested the effect of physical disturbance on SOC decomposition using soils from two abandoned farmlands free of management practice for more than 28 years. The soil respiration rate was measured in undisturbed and disturbed soil columns and was inversely modeled using the two-compartment model. We found that the C loss was 16.8~74.1% higher in disturbed than in undisturbed soil columns. Physical disturbance increased the total amount of labile C (C1) loss by 136~241%, while had no effect on the kinetic decomposition rate constants of both labile (k1) and stable (k2) SOC decomposition. Physical disturbance fragmented the large macroaggregates into small macroaggregates, microaggregates, and free silt and clay-sized fractions. This indicates that C loss was derived from the initially protected labile C, and there was no change of SOC fraction being decomposed. Our results give insights into the understanding of the extent of labile C loss to physical disruption and demonstrate the potential effect of physical disturbance on SOC dynamics.

• The Canadian Model for Peatlands (CaMP) was run across 5 ecozones from 1990 to 2019. • Canada's peatlands were a carbon sink over those 30 years. • In large fire years, peatlands switched to a carbon source. • There are regional differences in C emissions and removals. • Forested peatlands have high sink-rates but are also more vulnerable to disturbances. This study applied the Canadian Model for Peatlands (CaMP) to 63.9 million hectares of peatlands within boreal and temperate ecozones of Canada to assess the trends in atmospheric carbon (C) emissions and removals and C sequestration over 30 years (1990–2019). The CaMP modelled net ecosystem productivity (NEP) for peatlands within the study area indicated a net C sink at an annual mean rate of 30.9 Mt C y−1 (48.4 g C m−2 y−1). Net Biome Productivity (NBP), which accounts for losses of carbon due to wildfire, reduced the C sink to 19.0 Mt C y−1 (29.8 g C m−2 y−1). On an area-weighted basis, the Hudson Plains and the Boreal Plains had the highest NBP (34.9 and 34.0 g C m−2 y−1, respectively) and the Atlantic Maritime and Boreal Shield West had the lowest (25.3 and 24.6 g C m−2 y−1 respectively), with the Boreal Shield East having intermediate NBP (27.5 g C m−2 y−1). NBP was highest in peatlands with forest cover, rising with increasing nutrient status (bog < poor fen < rich fen). These modelled values compare well with long-term carbon accumulation rates found in the literature for Canadian peatlands ranging from 6 to 70 g C m−2 y−1. While most years peatlands were a net sink of C, years with extensive fires resulted in peatlands being a small net source of C. The study highlighted that forested peatlands were important in driving the C sequestration sink but were also sensitive to climate warming due to high rates of soil CO 2 emission and large wildfire C emissions. This highlights an important, yet vulnerable role these forested peatlands play in Canada's national greenhouse gas accounting. While this research is the first to produce estimates of C sequestration and greenhouse gas emission and removal rates across such a large area of Canada, further research is required across peatland types and ecozones to improve parameterization, validation, and process representations. Our results stress the importance of ecozone-specific analyses and accounting for infrequent large fire years and fire risk in land management policy and carbon accounting. [ABSTRACT FROM AUTHOR]

Stocks of soil organic carbon represent a large component of the carbon cycle that may participate in climate change feedbacks, particularly on decadal and centennial timescales. For Earth system models (ESMs), the ability to accurately represent the global distribution of existing soil carbon stocks is a prerequisite for accurately predicting future carbon–climate feedbacks. We compared soil carbon simulations from 11 model centers to empirical data from the Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). Model estimates of global soil carbon stocks ranged from 510 to 3040 Pg C, compared to an estimate of 1260 Pg C (with a 95% confidence interval of 890–1660 Pg C) from the HWSD. Model simulations for the high northern latitudes fell between 60 and 820 Pg C, compared to 500 Pg C (with a 95% confidence interval of 380–620 Pg C) for the NCSCD and 290 Pg C for the HWSD. Global soil carbon varied 5.9 fold across models in response to a 2.6-fold variation in global net primary productivity (NPP) and a 3.6-fold variation in global soil carbon turnover times. Model–data agreement was moderate at the biome level (R2 values ranged from 0.38 to 0.97 with a mean of 0.75); however, the spatial distribution of soil carbon simulated by the ESMs at the 1° scale was not well correlated with the HWSD (Pearson correlation coefficients less than 0.4 and root mean square errors from 9.4 to 20.8 kg C m−2). In northern latitudes where the two data sets overlapped, agreement between the HWSD and the NCSCD was poor (Pearson correlation coefficient 0.33), indicating uncertainty in empirical estimates of soil carbon. We found that a reduced complexity model dependent on NPP and soil temperature explained much of the 1° spatial variation in soil carbon within most ESMs (R2 values between 0.62 and 0.93 for 9 of 11 model centers). However, the same reduced complexity model only explained 10% of the spatial variation in HWSD soil carbon when driven by observations of NPP and temperature, implying that other drivers or processes may be more important in explaining observed soil carbon distributions. The reduced complexity model also showed that differences in simulated soil carbon across ESMs were driven by differences in simulated NPP and the parameterization of soil heterotrophic respiration (inter-model R2 = 0.93), not by structural differences between the models. Overall, our results suggest that despite fair global-scale agreement with observational data and moderate agreement at the biome scale, most ESMs cannot reproduce grid-scale variation in soil carbon and may be missing key processes. Future work should focus on improving the simulation of driving variables for soil carbon stocks and modifying model structures to include additional processes.

The fate of soil organic carbon (SOC) under warming is poorly understood, particularly across large extents and in the whole‐soil profile. Using a data‐model integration approach applied across the globe, we find that downward movement of SOC along the soil profile reduces SOC loss under warming. We predict that global SOC stocks (down to 2 m) will decline by 4% (~80 Pg) on average when SOC reaches the steady state under 2°C warming, assuming no changes in net primary productivity (NPP). To compensate such decline (i.e. maintain current SOC stocks), a 3% increase of NPP is required. Without the downward SOC movement, global SOC declines by 15%, while a 20% increase in NPP is needed to compensate that loss. This vital role of downward SOC movement in controlling whole‐soil profile SOC dynamics in response to warming is due to the protection afforded to downward‐moving SOC by depth, indicated by much longer residence times of SOC in deeper layers. Additionally, we find that this protection could not be counteracted by promoted decomposition due to the priming of downward‐moving new SOC from upper layers on native old SOC in deeper layers. This study provides the first estimation of whole‐soil SOC changes under warming and additional NPP required to compensate such changes across the globe, and reveals the vital role of downward movement of SOC in reducing SOC loss under global warming. [ABSTRACT FROM AUTHOR]

• A new soil carbon (C) model was developed. • The apparent and real priming effects (PEs) were distinguished by the model. • The real-time turnover times of SOC and microbial biomass C were obtained. • Moisture stress led to a higher C sequestration compared to constant moisture. The addition of litter (maize straw) to soil accelerates activity of microorganisms and significantly changes the mineralization of soil organic carbon (SOC), which are referred to as "apparent" and "real" priming effects (PEs). In this study, the mechanisms of PEs under different moisture conditions were explored using a simple mechanistic model that was dependent on the size of the SOC pool (%C), microbial biomass, and microbial activity (the proportion of actively growing microbial biomass relative to the total soil biomass). In an incubation experiment, soil cores were incubated with 13C-labeled maize straw (δ 13C = 600.9 ± 2.53‰) under two moisture regimes: constant moisture (60% of water holding capacity throughout incubation) and drying and rewetting treatments (7-day long drying and rewetting cycles in a 76-day laboratory incubation). Microcosms were sampled after 1, 7, 10, 15, 21, 60, and 76 days, and cumulative soil respiration, microbial biomass and cumulative PEs were measured during the incubation period. The model was parameterized with native SOC and 13C-labeled litter carbon (C) data and simulated microbial metabolic processes, microbial turnover, soil organic matter (SOM) dynamics, and apparent and real PEs. The model contained two special features. The first feature was the assumption of sequential utilization of native SOC and litter-derived C with distinctly different stability and properties, where the organic carbon (OC) must first be solubilized into dissolved organic carbon (DOC) before it can be consumed by microorganisms. The second feature was the first attempt to evaluate the continuous turnover of native SOC, native SOM-derived and litter-derived microbial biomass C under varying moisture conditions. The optimized model explained 93% of the observed cumulative priming effect (CPE) under constant moisture and 94% of the CPE under drying and rewetting treatments, respectively. The simulation results showed that the conversion of native SOC and litter C to DOC was rate-limited, and positive apparent PE dominated the priming process. Although more C was allocated to respiration rather than growth, the compensation by a longer turnover time of microbial C eventually led to a higher sequestration of soil C under the drying and rewetting treatment compared to those under constant moisture. The turnover time of native SOC was significantly affected by the turnover of native-SOM derived and litter-derived microbial biomass C, and the turnover of litter-derived microorganisms may play a key role in regulating real PE. This study demonstrates that the combination of mathematical modeling and 13C-labeled litter varying in solubility and recalcitrance can effectively distinguish C sources and elucidate the mechanisms of PEs in soils under different moisture regimes. [ABSTRACT FROM AUTHOR]

Color sensor technologies offer opportunities for affordable and rapid assessment of soil organic carbon (SOC) and total nitrogen (TN) in the field, but the applicability of these technologies may vary by soil type. The objective of this study was to use an inexpensive color sensor to develop SOC and TN prediction models for the Russian Chernozem (Haplic Chernozem) in the Kursk region of Russia. Twenty-one dried soil samples were analyzed using a Nix ProTM color sensor that is controlled through a mobile application and Bluetooth to collect CIEL*a*b* (darkness to lightness, green to red, and blue to yellow) color data. Eleven samples were randomly selected to be used to construct prediction models and the remaining ten samples were set aside for cross validation. The root mean squared error (RMSE) was calculated to determine each model's prediction error. The data from the eleven soil samples were used to develop the natural log of SOC (lnSOC) and TN (lnTN) prediction models using depth, L*, a*, and b* for each sample as predictor variables in regression analyses. Resulting residual plots, root mean square errors (RMSE), mean squared prediction error (MSPE) and coefficients of determination (R², adjusted R²) were used to assess model fit for each of the SOC and total N prediction models. Final models were fit using all soil samples, which included depth and color variables, for lnSOC (R² = 0.987, Adj. R² = 0.981, RMSE = 0.003, p-value < 0.001, MSPE = 0.182) and lnTN (R² = 0.980 Adj. R² = 0.972, RMSE = 0.004, p-value < 0.001, MSPE = 0.001). Additionally, final models were fit for all soil samples, which included only color variables, for lnSOC (R² = 0.959 Adj. R² = 0.949, RMSE = 0.007, p-value < 0.001, MSPE = 0.536) and lnTN (R² = 0.912 Adj. R² = 0.890, RMSE = 0.015, p-value < 0.001, MSPE = 0.001). The results suggest that soil color may be used for rapid assessment of SOC and TN in these agriculturally important soils. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) and its management under dryland cropping systems are very critical for both crop productivity and environment health. The objective of this study was to evaluate the performance of CQESTR, a process-based C model, in simulating SOC changes across toposequences of selected fields and agriculture management practices along a precipitation gradient in a dryland region of Oregon, USA. Geo-referenced soil samples were collected from summit (SU), shoulder (SH), backslope (BS), footslope (FS), and toeslope (TS) positions during early 1980s and early 2000s. Simulation scenarios were developed based on field management practices, crop rotations, soil properties, and climatic data. CQESTR simulated results were compared with the measured SOC from each landscape position. Significant ( P < 0.0001) correlations (r = 0.93) were found between the measured and the simulated SOC at SU, SH (r = 0.91), BS (r = 0.83), FS (r = 0.89), and TS (r = 0.89). The smallest correlation value at BS could be from soil deposition due to erosion. No significant changes in SOC were found between SU, SH, BS, and FS landscape positions; however, TS had the highest SOC (10.8 ± .8 g C kg −1 ). CQESTR successfully simulated SOC at most of the studied sites and landscape positions, except at TS for a location with high annual deposition of C-rich soil eroded from the upper landscape position. CQESTR could be used to predict SOC changes across toposequence and at the landscape scale level with reasonable accuracy. The results were supported by a linear relation with an r 2 of 0.89 and a low mean square deviation (MSD = 0.24) between the measured and the simulated SOC. [ABSTRACT FROM AUTHOR]

Quantifying soil organic carbon (SOC) is important for soil management, precision agriculture, soil mapping and carbon dynamics research. Inexpensive sensor technologies offer the potential for rapid quantification of SOC in laboratory samples as well as in the field. The objective of this study was to use a commercially-available color sensor to develop SOC prediction models for both dry and moist soils from the Piedmont region of South Carolina. Thirty-one soil samples were analyzed for lightness to darkness, redness to greenness, and yellowness to blueness (CIEL*a*b*) color using a Nix Pro™ color sensor. Soil color was measured under both dry and moist soil conditions and the depth of each soil sample was also recorded. Using L*, a*, b* and soil depth for each sample as initial predictors, regression analyses were conducted to develop SOC prediction models for dry and moist soils. The resulting residual plots, root mean squared errors (RMSE), and coefficients of determination (R 2 ) were used to assess model fits for predicting the SOC content of soil. Cross validation was conducted to determine the efficiency of the predictive models and the mean squared prediction error (MSPE) was calculated. The final models included soil depth, L*, and a* as independent variables (dry soils R 2 = 0.7978 and MSPE = 0.0819, moist soils R 2 = 0.7254 and MSPE = 0.1536). The results suggest that soil color sensors have potential for rapid SOC determination, and soil depth and color are useful in predicting SOC content in soils. [ABSTRACT FROM AUTHOR]

Mosses play a key role in the carbon (C) cycle of upland black spruce ( Picea mariana (Mill.) BSP) forests; however, national reporting models such as the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) do not include mosses. This study examined whether widely available plot-level merchantable tree volume could predict, for black spruce ecosystems in Canada's boreal forest, the relative proportions of sphagnum and feather moss ground cover and moss net primary productivity (NPP). A field study found that merchantable tree volume was significantly related to tree canopy openness ( R2 = 0.61, P < 0.001), which could then be used to model the relative ground cover of feather moss ( R2 = 0.5, P < 0.001) and sphagnum ( R2 = 0.45, P < 0.001) and NPP of feather moss ( R2 = 0.41, P < 0.001) and sphagnum ( R2 = 0.28, P < 0.001). The resulting MOSS-C submodel increased the accuracy of the CBM-CFS3's prediction of organic-horizon C five-fold and could explain large-scale variation in sites dominated by sphagnum with large organic-layer C pools but not fine-scale variation in dryer sites. To improve MOSS-C accuracy, future studies should focus on varying decomposition and fire regime parameters based on regional climate or plot-level vegetation parameters. [ABSTRACT FROM AUTHOR]

The boreal forest plays a key role in the global carbon (C) cycle, and black spruce ( Picea mariana (Mill.) BSP) forests are the dominant coniferous forest type in the Canadian boreal forest. National-scale forest C models currently do not account for the contribution of moss-derived organic matter that we hypothesize to be significant in the C budget of black spruce ecosystems. One such model, the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), is designed to meet Canada's forest-related greenhouse gas reporting requirements. In this study our goal was to determine if black spruce forest soil C stocks are significantly underestimated by the CBM-CFS3, and if so, to determine if estimates could be improved by adding moss-derived C. We conclude that in black spruce sites, organic layer C is significantly underestimated by CBM-CFS3 compared to sites with all other leading tree species analyzed. We compiled and used published moss net primary productivity rates for upland forest systems, with decomposition rates, in mass-balance calculations to estimate mean moss-derived C in black spruce forests for feather mosses at 64 Mg C ha, and for sphagnum mosses at 103 Mg C ha. These C pools are similar to the CBM-CFS3 mean underestimation of black spruce soil organic layers (63 Mg C ha). We conclude that the contribution of mosses is sufficiently large that a moss C pool should be added to national-scale models including the CBM-CFS3, to reduce uncertainties in boreal forest C budget estimation. Feather and sphagnum mosses should be parameterized separately. [ABSTRACT FROM AUTHOR]

This study compared models for estimating carbon sequestered aboveground inCupressus lusitanicaplantation stands at Wondo Genet College of Forestry and Natural Resources, Ethiopia. Relationships of carbon storage with tree component and stand age were also investigated. Thirty trees of three different ages (5, 12 and 24 years), comprising 10 trees from each stand, were sampled in order to generate dry biomass and carbon data from tree components. Five linear and non-linear biomass and carbon models were compared and evaluated for estimation of overall aboveground carbon, carbon by age groups, and carbon by diameter at breast height (DBH) classes using performance indicator statistics. Among the models compared, a carbon model described byY=b0D2H+ ϵ (p-value < 0.001), whereD= DBH (in cm),H= total height of the tree (in m), ϵ = error, andb0is a parameter, was found to be the best model for estimation of carbon sequestered aboveground inC. lusitanicaplantation stands of the study area. The study also indicated the overall superiority of carbon models over biomass models in estimation of aboveground carbon ofC. lusitanica. It was concluded that, for the range of DBH utilised in the current study, the carbon model described can be a useful tool in estimation of carbon storage ofC. lusitanicaplantations in the study area and other related sites. [ABSTRACT FROM AUTHOR]

Background:Tropical forest biomass is not at steady state, being determined by a balance between deterministic growth and stochastic disturbances. Understanding tropical carbon sources and sinks therefore requires better characterisation of the incidence and intensity of disturbance at critical scales. Aims:We determine if information from remotely sensed biomass maps (ALOS-PALSAR) can constrain estimates of miombo woodland biomass dynamics and disturbance. Methods:We analyse biomass maps created over Mozambican woodlands undergoing varied disturbances. We use a simple ensemble model of biomass dynamics to test the hypothesis that biomass distributions can diagnose disturbance processes in specified areas, and use the model to explore the sensitivity of biomass to disturbance parameters. Results:Ensemble runs can reproduce qualitatively similar biomass patterns to those observed in miombo, through varying two parameters that determine frequency and intensity of biomass loss. Using sensitivity analyses, we show for a synthetic case that these two disturbance parameters can be retrieved from satellite observations. Conclusions:Biomass distributions provide enough information to constrain the two critical parameters of the disturbance model, the local probability of disturbance, and its intensity (fraction of biomass lost). These results provide a proof of concept for assimilating biomass maps into models of carbon cycling. [ABSTRACT FROM PUBLISHER]

Abstract: Combining decomposition data with process-based biogeochemical models is essential to quantify the turnover of organic carbon (C) in surface litter and soil organic matter (SOM). Long-term decomposition may be suitably analyzed by linear models (i.e., all fluxes defined by first-order kinetics), which allow the derivation of analytical expressions to estimate the loss of C and the overall apparent decay rate (k app) through time. Here we compare eight linear models (four discrete-compartment models with one or two C pools, two models with a single time-dependent decay rate, and two models based on a continuous distribution of decay rates) and report their analytical solutions for two types of decomposition experiments: i) studies that evaluate the decomposition of a single input of fresh litter (i.e., a single cohort, as in litterbag and C labeling experiments), and ii) studies that evaluate the decomposition of soil samples with compounds of different ages (i.e., multiple cohorts, as in long-term incubations or isotope dilution experiments). We fitted analytical mass loss functions to both types of datasets and evaluated the performance of the models. For single-cohort data, continuous-decay models provide the best balance between accuracy and parsimony (R 2 = 0.99, lowest Akaike and Bayesian information criteria), while for multiple-cohort data the two-pool models tend to perform better (R 2 = 0.96), perhaps because of the strong separation of time scales in the decomposition data considered. Differences among some models are marginal, suggesting that decomposition data alone do not point to a single ‘best’ model. All models resulted in apparent decay rates that decreased markedly through time, in contrast with the assumption of constant k adopted in the single-pool exponential decay model. We also show how model parameters estimated from single cohort samples can be used to model multiple cohort decomposition, unifying both types of experimental data in one theory. Based on our results, it is possible to distinguish the temporal changes in C loss that are attributable to initial chemical composition or abiotic factors, from those associated with the presence of multiple ages in the substrate. [Copyright &y& Elsevier]

Abstract: The Subsidiary Body for Scientific and Technical Advice of the UNFCCC is focused on methodological issues that include reference levels of CO2 emissions from deforestations and forest degradations under Reduced Emissions from Deforestation and Forest Degradation (REDD). Quantification of forest carbon stock is a challenging task involving high degrees of uncertainty and discrepancies because of methods used. It is important that the REDD stakeholders be able to compare the REDD designs options, using standardized data. In this paper, we provide a review of the various methods used for estimation of above and below-ground biomass for carbon stock and soil organic carbon to help decide what would best suit different landscapes and regions so as to set standardized reference levels. Our review suggests that the ‘forest yield method’, ‘biomass expansion factor’ and ‘Walkley–Black’ methods are simple, time and cost effective and yield accurate results as the ‘forest yield method’ covers all strata of forest vegetation, ‘biomass expansion factor’ method can be directly applied to volume data and the ‘Walkley–Black’ method is rapid and requires minimal equipment. However, the effectiveness and feasibility of different methods may vary depending upon landscape and forest types. [Copyright &y& Elsevier]

Carbon (C) allocation and turnover in arctic bryophytes is largely unknown, but their response to climatic change has potentially significant impacts on arctic ecosystem C budgets. Using a combination of pulse-chase experiments and a newly developed model of C turnover in bryophytes, we show significant differences in C turnover between two contrasting arctic moss species ( Polytrichum piliferum and Sphagnum fuscum). C abundance in moss tissues (measured up to 1 year) and respired CO (traced over 5 days) were used to parameterise the bryophyte C model with four pools representing labile and structural C in photosynthetic and stem tissue. The model was optimised using an Ensemble Kalman Filter to ensure a focus on estimating the confidence intervals (CI) on model parameters and outputs. The ratio of aboveground NPP:GPP in Polytrichum piliferum was 23% (CI 9-35%), with an average turnover time of 1.7 days (CI 1.1-2.5 days). The aboveground NPP:GPP ratio in Sphagnum fuscum was 43% (CI 19-65%) with an average turnover time of 3.1 days (CI 1.6-6.1 days). These results are the first to show differences in C partitioning between arctic bryophyte species in situ and highlight the importance of modelling C dynamics of this group separately from vascular plants for a realistic representation of vegetation in arctic C models. [ABSTRACT FROM AUTHOR]

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The article discusses the empirical based model which estimates the net primary production (NPP) and depth-dependent decay rates of near-surface boreal and subarctic peatlands in Canada. It states that the application of the model in three boreal sites shows that the sites are net sinks for atmospheric carbon. It mentions that shifts from sinks to sources may happen within time frames.

Incorporating plant litter into soil can replenish soil organic carbon (SOC) through physical protection mechanisms, such as formation of mineral-organic complexes and aggregates. Meanwhile, plant litter input may stimulate the decomposition of native SOC, known as the priming effect. It remains unclear how these physical protection mechanisms affect the priming effect, which further affects the SOC dynamics. Here, we present a novel model that can predict the dynamics of protected and primed organic carbon in soil and aggregates (PROCAAS). The PROCAAS model assumes that microbial activity will control the dynamics of both aggregate turnover and the priming effect following litter incorporation, and that litter-derived SOC will prime the decomposition of the native SOC pool as they are in the same location and in the same pool, either occluded in soil pores or associated with minerals. The PROCAAS model was well calibrated and validated using independent datasets of aggregate mass proportion and aggregated SOC contents in literature as well as soil respiration and the priming effect measured here during 76-day incubation with and without 13C labeled litter. The model results showed that litter-derived SOC was protected predominantly through occlusion in macroaggregates, while the priming effect was originated mainly from mineral-associated SOC in macroaggregates. The dynamics of priming effect could be explained by the soil structural change from soil disaggregation during litter incorporation, via the formations of macroaggregates and microaggregates within macroaggregates, to the aggregate stability during litter decomposition. Our proof-of-concept model can form the base for incorporating these biophysical mechanisms underlying soil aggregation and the priming effect into future ecosystem models. Image 1 • A new soil carbon model (PROCAAS) was developed. • PROCAAS coupled aggregation, C pool transformation and interactions. • Litter C was mainly protected through occlusion in macroaggregates. • Priming effect led to loss mineral-associated soil carbon in macroaggregates. • Priming dynamics were controlled by disaggregation and aggregation. [ABSTRACT FROM AUTHOR]