Estimating Soil Carbon Research Articles

Soil carbon stocks in Indonesian (agro) forest transitions: Compaction conceals lower carbon concentrations in standard accounting

Soil changes matter for the global carbon (C) balance although belowground response to land use change is slower and less obvious than that aboveground. Impacts of changes from natural forest to a range of intermediate tree-based land uses (‘agroforestry’) and non-tree agriculture remain contested. Standard C-stock accounting for a fixed sampling depth depends on changes in both Corg concentrations and bulk density, often with opposite effects. Confounding factors that, beyond current vegetation, influence Corg (soil texture, minerology, drainage, elevation and soil pH) may also influence bulk density. Because land use may not be random with respect to inherent soil properties, differences in soil C-stock between land uses can have multiple causes. We compiled and analysed data from six landscapes in Indonesia (volcanic and other mineral soils; Sumatra, Kalimantan; Java, Sulawesi) where chronosequences of forest, various agroforestry systems and open-field agriculture had been sampled. Our data analysis (617 samples within 0−30 cm depth; 8 land use types) showed that a pedotransfer function for effects on Corg of texture, elevation and soil pH reduced the relative standard error of means per land use type, reduced the range (Max–Min)/Avg and led to a more consistent pattern in apparent land use effects. Relative to natural forest reductions in Corg concentration in the 0−30 cm layer (corrected for confounding factors) averaged 8–20 % in degraded forest, complex agroforest, oil palm plantations and older forest plantation plots, and 25–30 % in simple agroforestry, monoculture tree crops and woodlots, or over 40 % in non-tree (mostly cropped) plots. However, calculated C-stock change was small due to an observed increase (up to 30 %) of bulk density relative to that of natural forest. This implies that up to 23 % additional Corg became included in the soil sampling, resulting in a non-negligible bias (underestimate) in estimated soil carbon loss based on internationally agreed C-stock accounting.

Estimating soil bulk density with combined commercial soil water content and thermal property sensors

Accurate information of soil bulk density (ρb) is essential for many models that predict soil water, gas, and heat transfer processes and for estimating soil carbon pools. Several indirect methods have been used to estimate ρb as a derivative of various soil properties. One approach is to estimate ρb from soil thermal conductivity (λ) and volumetric water content (θw) measured with a custom fabricated sensor (thermo-TDR). In this study, we introduce a new approach to determine ρb with a combination of commercially available θw and thermal property sensors. Repacked samples of four differently-textured soils and a field experiment on a clay soil were used to evaluate the ability of four available sensors from METER Group, Inc. (Pullman, WA, USA) to estimate ρb. The θw was measured with the GS3 and EC-5 sensors, and soil λ was determined with the TR-1 and SH-1 thermal property sensors. The θw and λ measurements were used to determine ρb inversely from a λ-model. Compared with the GS3 sensor, the EC-5 sensor provided more accurate measurements of θw for the investigated soils, and thus, the EC-5 sensor was used with the new ρb estimation approach. Both TR-1 and SH-1 sensors gave accurate λ estimates when compared to modeled values. The ρb estimates with the TR-1/EC-5 and SH-1/EC-5 sensor combination methods agreed well with independent gravimetrically-derived ρb values of the repacked samples and the in-situ measurements, with average root mean square errors of 0.12 and 0.13 Mg m−3, respectively. Thus, the commercial multi-sensor combinations can provide accurate ρb estimates similar to those with custom fabricated thermo-TDR sensors, and they are simpler to operate than the custom fabricated sensors.

Analyzing vegetation cover-induced organic matter mineralization dynamics in sandy soils from tropical dry coastal ecosystems

To gain knowledge about carbon and nitrogen mineralization potentials and their interaction with sandy soil environment in tropical dry coastal ecosystems, we carried out a short-term laboratory incubation experiment. For this, soil surface (0–10 cm) samples were collected from tropical semi-deciduous forest (TSdF), secondary forest (SecF), coastal dune scrub_crest (DS_C), and coastal dune scrub_slack (DS_S). Soil samples were incubated at 25 °C with constant soil moisture (at field capacity) for 97 days, and over this time 10 and 11 sampling occasions were analyzed to determine carbon (CO2-C) and nitrogen (NH4+, NO3-) dynamics, respectively. We used a simple exponential model to estimate soil carbon and nitrogen mineralization pools. On average, soil organic carbon mineralization rates ranged between 0.017 and 0.053 mg CO2-C g−1 soil d−1 and varied in the following order: DS_S > SecF > TSdF > DS_C. Average net mineralization rates (mg N kg−1 soil d−1) ranged between 16.9 and 18.8 and varied in the following order: SecF > DS_S > TSdF, whereas in DS_C it was −0.44, indicating N immobilization. Co and No represented, on average, 1.08% and 5% of soil organic carbon and total organic nitrogen, respectively. On average, mineralization constants (k) for Co and No were 0.29 d−1 and 0.23 d−1, respectively, showing that SOM is easily mineralized. The proportion of NO3-/Nm was very high in SecF (89%) and TSdF (75%), while DS_C showed nitrate immobilization. The increase in NO3-/Nm ratio in SecF could be associated with legume species that have established in the nature restoration (SecF) on ex-grassland. Across all study sites, linear regression analyses suggested that soil physical and chemical properties may regulate SOM mineralization (p < 0.05).

Soil properties and carbon sequestration in Persian oak (Quercus brantii var. persica) forests, Iran

One of the important issues both in the political discussion about climate change and in forest ecosystem research is carbon sequestration. In this paper, we estimated soil carbon sequestration (SCS) in two Persian oak forest stands of different origin (seed and coppice). Soil samples were taken at two soil depths (0–15 and 15–30 cm) and locations (under the tree crown and open area) in each oak stand. Results showed that surface layers (0–15 cm) had the highest soil carbon sequestration ranging from 41.2 t·ha–1 to 47.9 t·ha–1 for both oak forests. The total SCS was higher (between 79.5 and 89.07 t·ha–1) in open areas of the two forest stands than under the crowns of oak trees. Finally, the amount of total SCS in seed originated forest (SOF) (86.52 t·ha–1) was significantly greater (P &amp;lt; 0.05) than in coppice forest (CF) (77.70 t·ha–1). The results indicate that a relatively large proportion of C loss in CF is due to overgrazing, forest degradation and conversion to coppice forests in the study area.

Topographic controls of soil organic carbon on soil-mantled landscapes

Large uncertainties in global carbon (C) budgets stem from soil carbon estimates and associated challenges in distributing soil organic carbon (SOC) at local to landscape scales owing to lack of information on soil thickness and controls on SOC storage. Here we show that 94% of the fine-scale variation in total profile SOC within a 1.8 km2 semi-arid catchment in Idaho, U.S.A. can be explained as a function of aspect and hillslope curvature when the entire vertical dimension of SOC is measured and fine-resolution (3 m) digital elevation models are utilized. Catchment SOC stocks below 0.3 m depth based on our SOC-curvature model account for >50% of the total SOC indicating substantial underestimation of stocks if sampled at shallower depths. A rapid assessment method introduced here also allows for accurate catchment-wide total SOC inventory estimation with a minimum of one soil pit and topographic data if spatial distribution of total profile SOC is not required. Comparison of multiple datasets shows generality in linear SOC-curvature and -soil thickness relationships at multiple scales. We conclude that mechanisms driving variations in carbon storage in hillslope catchment soils vary spatially at relatively small scales and can be described in a deterministic fashion given adequate topographic data.

Estimates and determinants of stocks of deep soil carbon in Gabon, Central Africa

Despite the importance of tropical forest carbon to the global carbon cycle, research on carbon stocks is incomplete in major areas of the tropical world. Nowhere in the tropics is this more the case than in Africa, and especially Central Africa, where carbon stocks are known to be high but a scarcity of data limits understanding of carbon stocks and drivers. In this study, we present the first nation-wide measurements and determinants of soil carbon in Gabon, a nation in Central Africa. We estimated soil carbon to a 2-m depth using a systematic, random design of 59 plots located across Gabon. Soil carbon to a 2-m depth averaged 163 Mg ha−1 with a CV of 61%. These soil carbon stocks accounted for approximately half of the total carbon accumulated in aboveground biomass and soil pools. Nearly a third of soil carbon was stored in the second meter of soil, averaging 58 Mg ha−1 with a CV of 94%. Lithology, soil type, and terrain attributes were found to be significant predictors of cumulative SOC stocks to a 2-m depth. Current protocols of the IPCC are to sample soil carbon from the surface 30 cm, which in this study would underestimate soil carbon by 60% and underestimate ecosystem carbon by 30%. A nonlinear model using a power function predicted cumulative soil carbon stocks in the second meter with an average error of prediction of 3.2 Mg ha−1 (CV = 915%) of measured values. The magnitude and turnover of deep soil carbon in tropical forests needs to be estimated as more countries prioritize carbon accounting and monitoring in response to accelerating land-use change.

Assessing the relative role of climate on litterfall in Mediterranean cork oak forests

Abstract: Litterfall plays a key role in the dynamic of forest ecosystems, ultimately determining forest productivity and carbon and nutrient cycling. Increasing our understanding on the role of structural and environmental factors controlling litterfall amount and seasonality is of paramount importance for modelling and estimating soil carbon sequestration and nutrient cycling under climate change scenarios. However, the effect of climatic conditions on litterfall has been scarcely studied, especially in Mediterranean ecosystems. Here, we used nine years of seasonally collected litterfall data in two contrasting Mediterranean cork oak forests to evaluate the effect of climatic variables on leaf fall and litterfall. First, we isolated the litterfall seasonal trend and the between-sites differences in production by using linear mixed models. Then, we evaluated the effect of climatic variables and whether this effect was site-specific. We found a consistent litterfall seasonal pattern, mainly determined by leaf shedding (70% of litterfall). Leaf fall mainly occurs in spring with a second but much smaller peak in autumn some years. Mean temperature, precipitation and mean wind speed strongly influenced litterfall, but this effect was site-specific. In the forest site located at higher latitude and altitude, leaf fall increased linearly with temperature and showed a positive quadratic response to precipitation. In the water-limited site, leaf fall was reduced as temperature increased and did not respond to precipitation. These results have implications for modelling and predicting soil carbon sequestration, nutrient cycling, and the forest ecosystem productivity. Specifically, carbon and nutrient cycling models can be improved by incorporating idiosyncratic forest sites responses to climatic variability.

Extensive loss of past permafrost carbon but a net accumulation into present-day soils.

Atmospheric concentrations of carbon dioxide increased between the Last Glacial Maximum (LGM, around 21,000 years ago) and the preindustrial era1. It is thought that the evolution of this atmospheric carbon dioxide (and that of atmospheric methane) during the glacial-to-interglacial transition was influenced by organic carbon that was stored in permafrost during the LGM and then underwent decomposition and release following thaw2,3. It has also been suggested that the rather erratic atmospheric δ13C and ∆14C signals seen during deglaciation1,4 could partly be explained by the presence of a large terrestrial inert LGM carbon stock, despite the biosphere being less productive (and therefore storing less carbon)5,6. Here we present an empirically derived estimate of the carbon stored in permafrost during the LGM by reconstructing the extent and carbon content of LGM biomes, peatland regions and deep sedimentary deposits. We find that the total estimated soil carbon stock for the LGM northern permafrost region is smaller than the estimated present-day storage (in both permafrost and non-permafrost soils) for the same region. A substantial decrease in the permafrost area from the LGM to the present day has been accompanied by a roughly 400-petagram increase in the total soil carbon stock. This increase in soil carbon suggests that permafrost carbon has made no net contribution to the atmospheric carbon pool since the LGM. However, our results also indicate potential postglacial reductions in the portion of the carbon stock that is trapped in permafrost, of around 1,000petagrams, supporting earlier studies7. We further find that carbon has shifted from being primarily stored in permafrost mineral soils and loess deposits during the LGM, to being roughly equally divided between peatlands, mineral soils and permafrost loess deposits today.

Examining the Performance of PARACUDA-II Data-Mining Engine versus Selected Techniques to Model Soil Carbon from Reflectance Spectra

The monitoring and quantification of soil carbon provide a better understanding of soil and atmosphere dynamics. Visible-near-infrared-short-wave infrared (VIS-NIR-SWIR) reflectance spectroscopy can quantitatively estimate soil carbon content more rapidly and cost-effectively compared to traditional laboratory analysis. However, effective estimation of soil carbon using reflectance spectroscopy to a great extent depends on the selection of a suitable preprocessing sequence and data-mining algorithm. Many efforts have been dedicated to the comparison of conventional chemometric techniques and their optimization for soil properties prediction. Instead, the current study focuses on the potential of the new data-mining engine PARACUDA-II®, recently developed at Tel-Aviv University (TAU), by comparing its performance in predicting soil oxidizable carbon (Cox) against common data-mining algorithms including partial least squares regression (PLSR), random forests (RF), boosted regression trees (BRT), support vector machine regression (SVMR), and memory based learning (MBL). To this end, 103 soil samples from the Pokrok dumpsite in the Czech Republic were scanned with an ASD FieldSpec III Pro FR spectroradiometer in the laboratory under a strict protocol. Spectra preprocessing for conventional data-mining techniques was conducted using Savitzky-Golay smoothing and the first derivative method. PARACUDA-II®, on the other hand, operates based on the all possibilities approach (APA) concept, a conditional Latin hypercube sampling (cLHs) algorithm and parallel programming, to evaluate all of the potential combinations of eight different spectral preprocessing techniques against the original reflectance and chemical data prior to the model development. The comparison of results was made in terms of the coefficient of determination (R2) and root-mean-square error of prediction (RMSEp). Results showed that the PARACUDA-II® engine performed better than the other selected regular schemes with R2 value of 0.80 and RMSEp of 0.12; the PLSR was less predictive compared to other techniques with R2 = 0.63 and RMSEp = 0.29. This can be attributed to its capability to assess all the available options in an automatic way, which enables the hidden models to rise up and yield the best available model.

Profile Distribution of Paddy Soil Organic Carbon and Its Influencing Factors in Chengdu Plain

Understanding the effects of environmental factors on the profile distribution of soil organic carbon (SOC) is a base to accurately modeling the continuous change of SOC in vertical and three-dimensional spatial distributions, as well as precisely estimating SOC storage. Based on 171 soil profiles collected from Chengdu Plain, the effects of environmental factors (including the parent material, soil type, elevation, distance from river, and land use) on the profile distribution of paddy SOC to a depth of 1 m were evaluated through the exponential decay function. The results indicated that SOC was estimated at 19.42, 9.59, 5.99, and 5.20 g·kg-1 at depths of 0-20, 20-40, 40-60, and 60-100cm, respectively, showing a significant decrease with increased depth. Soil organic carbon was mainly concentrated above a 40 cm soil depth, accounting for 72.17% of the total profile, which could be crucial to studying the carbon source/sink of paddy soils in Chengdu Plain. The parameters of the exponential decay function had a similar spatial pattern, indicating their spatial dependence. The nugget coefficients for C and k were 55.400% and 47.671%, respectively, indicating that paddy SOC in the study area was affected by both structural and random factors. Regression analysis implied that the parent material and soil genius were the dominant factors influencing the profile distribution of SOC. Nevertheless, elevation, distance from river, and land use should also be taken into consideration. It has been concluded that the parent material and soil genius should be premeditated when fitting the vertical distribution of SOC, modeling the three-dimensional prediction of soil organic carbon, and estimating soil carbon storage in the paddy soils of Chengdu Plain.

Perubahan kondisi biofisik-kimia lahan akibat pembangunan kanal pada areal IUPHHBK-HA (Sagu) PT. Austindo Nusantara Jaya Agri Papua di Kabupaten Sorong Selatan

The constructions of canals for water use and management as well as supporting activities of productions, transportation and firebreaks lines are expected to have an impact on the biophysical and chemical changes in construction land. Therefore, the aims of this research are to know the amount of vegetation lost due to land clearing for canal constructions, land biophysical-chemical conditions changed as a result of canal clearing, aboveground carbon dioxide estimation and soil carbon stock estimation. Primary data collection determined by purposive on canal line for stand structure and composition, along with soil carbon estimation. The results showed that the opening of canal area of 82,35 ha will result in the loss of 19.866 sago trees on 40 cm average in diameter and 3.642 mature trees. Thus, sago starch on 82,35 ha will lost as much as 637,27 tons or 637.265,48 kg. Changes in land biophysical-chemical due to canal constructions are habitat fragmentation, increase in soil erosion, land slide of canal wall, changes in water quality particularly solid suspension total as a direct impact to increased turbidity, sedimentation, and siltation of canals. Furthermore, the impact of 82,35 ha canal construction was increasing of carbon dioxide (CO2) emission as much as 38.716,48 ton, and it is estimated that it will increase in line with future canal construction. Based on soil bulk density ranging from 0,58 to 1.22 g/cm3 and Carbon-organic content ranging from 16.65 to 54,16 %, it is estimated that carbon stock on 0 to 30 cm soil layer on 1 Ha area will be lost as 109 ton.

Better estimates of soil carbon from geographical data: a revised global approach

Soils hold the largest pool of organic carbon (C) on Earth; yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a global baseline of SOC stocks. We used partial least square (PLS) regression and available geographic datasets that describe SOC, climate, organisms, relief, parent material and time. The accuracy of the model was determined by the root mean square deviation (RMSD) of predicted SOC against 100 independent measurements. The best predictors were related to primary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 m were found in boreal forests (254 ± 14.3 t ha−1) and tundra (310 ± 15.3 t ha−1). Deserts had the lowest C stocks (53.2 ± 6.3 t ha−1) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha−1), tropical and subtropical forests (94 - 143 t ha−1) and grasslands (99-104 t ha−1). Solar radiation, evapotranspiration, and annual mean temperature were negatively correlated with SOC, whereas soil water content was positively correlated with SOC. Our model explained 49% of SOC variability, with RMSD (0.68) representing approximately 14% of observed C stock variance, overestimating extremely low and underestimating extremely high stocks, respectively. Our baseline PLS predictions of SOC stocks can be used for estimating the maximum amount of C that may be sequestered in soils across biomes.

Novel 3D geometry and models of the lower regions of large trees for use in carbon accounting of primary forests.

There is high uncertainty in the contribution of land-use change to anthropogenic climate change, especially pertaining to below-ground carbon loss resulting from conversion of primary-to-secondary forest. Soil organic carbon (SOC) and coarse roots are concentrated close to tree trunks, a region usually unmeasured during soil carbon sampling. Soil carbon estimates and their variation with land-use change have not been correspondingly adjusted. Our aim was to deduce allometric equations that will allow improvement of SOC estimates and tree trunk carbon estimates, for primary forest stands that include large trees in rugged terrain. Terrestrial digital photography, photogrammetry and GIS software were used to produce 3D models of the buttresses, roots and humus mounds of large trees in primary forests dominated by Eucalyptus regnans in Tasmania. Models of 29, in situ eucalypts were made and analysed. 3D models of example eucalypt roots, logging debris, rainforest tree species, fallen trees, branches, root and trunk slices, and soil profiles were also derived. Measurements in 2D, from earlier work, of three buttress 'logs' were added to the data set. The 3D models had high spatial resolution. The modelling allowed checking and correction of field measurements. Tree anatomical detail was formulated, such as buttress shape, humus volume, root volume in the under-sampled zone and trunk hollow area. The allometric relationships developed link diameter at breast height and ground slope, to SOC and tree trunk carbon, the latter including a correction for senescence. These formulae can be applied to stand-level carbon accounting. The formulae allow the typically measured, inter-tree SOC to be corrected for not sampling near large trees. The 3D models developed are irreplaceable, being for increasingly rare, large trees, and they could be useful to other scientific endeavours.

Simulation of soil carbon efflux from an arable soil using the ECOSSE model: Need for an improved model evaluation framework?

Globally, it is estimated that ~1500PgC of organic carbon is stored in the top meter of terrestrial soils. This represents the largest terrestrial pool of carbon. Appropriate management of soils, to maintain or increase the soil carbon pool, represents a significant climate change mitigation opportunity. To achieve this, appropriate tools and models are required in order to more accurately estimate soil carbon fluxes with a view to informing and developing more effective land use management strategies. Central to this is the evaluation of models currently in use to estimate soil carbon emissions. In the present study, we evaluate the ECOSSE (Estimating Carbon in Organic Soils – Sequestration and Emissions) model which has its origins in both SUNDIAL and RothC and has been widely used globally to model soil CO2 fluxes across different locations and land-use types on both organic and mineral soils. In contrast to previous studies, the model was found to poorly represent observed soil respiration at the study site, an arable cropland on mineral soil located in south-east Ireland. To isolate potential sources of error, the model was decomposed into its component rate equations or modifiers. This investigation highlighted a deficiency in the model simulated soil water, resulting in significant inhibition of the model simulated CO2 flux relative to the observed data. When measured values of soil water at the site were employed, the model simulated soil respiration improved significantly (r2 of 0.775 vs 0.154). This highlighted model deficiency remains to be evaluated at other sites; however, the research highlights the need for a more comprehensive evaluation of soil carbon models prior to their use in informing policy, particularly models which are employed at larger scales and for climate change projections.

Newly sequestrated soil organic carbon varies with soil depth and tree species in three forest plantations from northeastern China

To evaluate and improve the ecological benefit of forestation programs, post-forestation estimations of newly sequestrated soil organic carbon (SOC) are necessary. Here, we collected soil samples from three plantations in northeast China and used 13C natural abundance to measure the amount of SOC sequestrated since 1998. Newly sequestrated SOC decreased with the increasing soil depth in the Larix gmelinii plantation, but increased with soil depth in the Populus trichocarpa plantation. In contrast, newly sequestrated SOC in the Armeniaca sibirica plantation peaked at a soil depth of 30–40cm. With increasing distance from the tree trunk, average newly sequestrated SOC gradually decreased in the L. gmelinii plantation, did not vary in the P. trichocarpa plantation, and exhibited a gentle unimodal curve in the A. sibirica plantation. Overall, the average density of newly sequestrated SOC in the L. gmelinii, P. trichocarpa, and A. sibirica plantations at a soil depth of 0–50cm was 0.60, 0.70, and 0.61gcm−3, respectively. Determination of δ15N and the C/N ratio provided evidence to support the hypothesis that newly sequestrated SOC was primarily regulated by the root-microbial system. Soil depth and tree species should be taken into account when estimating soil carbon sequestration in future studies.

Simulating climate change impact on soil carbon sequestration in agro-ecosystem of mid-Himalayan landscape using CENTURY model

Soils play significant roles in global carbon cycle. The increase in atmospheric CO2 due to climate change may have a significant impact on both soil organic carbon storage and management practices to sequester organic carbon in agricultural areas. The aim of the study was to simulate climate change impact on soil carbon sequestration using CENTURY model. The statistical downscaling model (SDSM) was used to downscale the climate variables (temperature and rainfall) under two scenarios A2 and B2 for three periods: 2020 (2011–2040), 2050 (2041–2070) and 2080 (2071–2099). Downscaling was better in case of temperature than precipitation, which was evident from coefficient of correlation for temperature (r 2 = 0.91–0.99) and precipitation (r 2 = 0.71–0.80). Downscaling of climate data revealed that the temperature may increase for the years 2020, 2050 and 2080 periods, whereas precipitation may increase till 2020 and then it may reduce in 2050 and 2080 as compared to 2020 in the study area. For CENTURY model, the input parameters were obtained through soil sampling and interviewing the farmers as well, whereas the climatic variables (maximum temperature, minimum temperature and precipitation) were taken from the SDSM output. The historical data of soils were collected from the literature, and six agricultural sites were selected for estimating soil carbon sequestration. After soil sampling of the same sites, it was found that the organic carbon had increased two times than historical data might be due to the addition of high organic matter in the form of farm yard manure. Therefore, the model was calibrated, considering more organic carbon in the area, and was validated using random points in the study area. Determination coefficient (r 2 = 0.95) and RMSE (538 g c/m2) were computed to assess the accuracy of the model. The organic carbon was predicted from 2011 to 2099 and was compared with the 2011 predicted data. The study revealed that the amount of soil organic carbon in Bhaitan, Kanatal, Kotdwar, Malas, Pata and Thangdhar sites may reduce by 11.6, 15.8, 17.19, 13.54, 19.2 and 12.7%, respectively, for A2 scenario and by 9.62, 15.6, 15.72, 11.45, 16.96 and 13.36% for B2 scenario up to 2099. The study provides comprehensive possible future scenarios of soil carbon sequestration in the mid-Himalaya for scientists and policy makers.

Pedogenic knowledge-aided modelling of soil inorganic carbon stocks in an alpine environment

Accurate estimation of soil carbon is essential for accounting carbon cycling on the background of global environment change. However, previous studies made little contribution to the patterns and stocks of soil inorganic carbon (SIC) in large scales. In this study, we defined the structure of the soil depth function to fit vertical distribution of SIC based on pedogenic knowledge across various landscapes. Soil depth functions were constructed from a dataset of 99 soil profiles in the alpine area of the northeastern Tibetan Plateau. The parameters of depth functions were mapped from environmental covariates using random forest. Finally, SIC stocks at three depth intervals in the upper 1m depth were mapped across the entire study area by applying predicted soil depth functions at each location. The results showed that the soil depth functions were able to improve accuracy for fitting the vertical distribution of the SIC content, with a mean determination coefficient of R2=0.93. Overall accuracy for predicted SIC stocks was assessed on training samples. High Lin's concordance correlation coefficient values (0.84–0.86) indicate that predicted and observed values were in good agreement (RMSE: 1.52–1.67kgm−2 and ME: −0.33 to −0.29kgm−2). Variable importance showed that geographic position predictors (longitude, latitude) were key factors predicting the distribution of SIC. Terrain covariates were important variables influencing the three-dimensional distribution of SIC in mountain areas. By applying the proposed approach, the total SIC stock in this area is estimated at 75.41Tg in the upper 30cm, 113.15Tg in the upper 50cm and 190.30Tg in the upper 1m. We concluded that the methodology would be applicable for further prediction of SIC stocks in the Tibetan Plateau or other similar areas.

Proxies for soil organic carbon derived from remote sensing

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

Recovery time of soil carbon pools of conversional Chinese fir plantations from broadleaved forests in subtropical regions, China

The conversion from natural forest to plantation has been widely applied, with consequences on ecosystem carbon pool. The experimental results of changes of soil carbon stocks after forest conversion are often contradictory. Moreover, the recovery time of soil carbon stocks after forest conversion varies among different sites. To examine the changes of soil carbon stocks following the forest conversions in the long-term and to estimate the recovery time, we selected 116 subtropical forests, including 29 pair-wise replicates for evergreen broadleaved forests (EBF, 40–100-year-old), young Chinese fir plantations (Cunninghamia lanceolata) (YCP, 4–8-year-old), middle-aged Chinese fir plantations (MACP, 13–20-year-old), and mature Chinese fir plantations (MCP, 23–32-year-old), and estimated soil carbon stocks. Soil carbon stocks of YCP and MACP decreased in average 12.5 and 28.7Mgha−1 compared with EBF, and showed no variation between MCP and EBF. Soil carbon stocks were positively correlated to soil total nitrogen stocks and C:N ratio. Our results showed that the forest conversions didn't cause a variation of soil carbon stocks in the long-term, although there was a short-term decline after conversion. The recovery time of soil carbon stock is 27years. These results indicate that the conversion from evergreen broadleaved forests to Chinese fir plantations in subtropical region of China causes soil carbon release in early stage, but has no effect on soil carbon stocks in the long-term. Prolonging the rotation period (>27years) would offset the adverse effects of the forest conversion on soil carbon stocks, and be critical in alleviating global climate change.

Chile and the Chilean soil grid: A contribution to GlobalSoilMap

Detailed soil information for biophysical modelling at the regional/national extent in not always available. The GlobalSoilMap project aims to address this issue by producing a fine resolution digital soil map of the world, with a bottom-up approach, where each participant country develops their own maps. This work represents the first contribution of Chile to the GlobalSoilMap project. We used a CART regression method to link environmental covariates describing soil forming factors with eight selected soil properties (organic carbon, field capacity, permanent wilting point, bulk density, pH, and clay, silt, sand particle size fraction) extracted from legacy data (587 profiles), at six depth intervals, namely 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200cm. Using a bootstrapping technique, we generated 100 realisations for each of the property–depth combination, generating a mean prediction map and its associated uncertainty. All maps were generated using a platform based on Google Earth Engine, which allows rapid analysis and visualisation of the results. The resulting accuracy of the maps are variable, with expected high uncertainty levels in poorly sampled areas. We illustrate the method by presenting maps of soil organic carbon and bulk density, and the use of these products with their associated uncertainties to estimate soil carbon stock for Chile.