Estimating Soil Organic Carbon Research Articles

An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs

There is a growing realization amongst policy-makers that reliable and accurate soil monitoring information is required at scales ranging from regional to global to support ecosystem functions and services in a sustainable manner under the amplifying climate change enabling countries in target setting of the Sustainable Development Goals (SDGs). In this line, the need of access to and integration of existing regional in situ Earth Observation (EO) data and different sources such as contemporary and forthcoming satellite imagery is highlighted. The current study puts major emphasis on leveraging existing open soil spectral libraries and EO systems and bridging them with memory-based learning algorithms that create more cost-efficient and targeted large scale mapping of soil properties. Relying mostly on contemporary capacities and open resources it can be readily applied to countries with differing capacities and levels of development. To test our methodology, the GEOCRADLE SSL developed in the Balkans, Middle East, and North Africa region and a hyperspectral airborne image were utilized to provide Soil Organic Carbon (SOC) maps of cropland fields over an agricultural region near the city of Netanya, Israel. Furthermore, simulated data of forthcoming space-borne satellite (EnMAP) and current super-spectral mission (Sentinel 2) were explored. The SOC content of the collected in situ soil samples was predicted using a novel local regression approach that combines spatial proximity and spectral similarities. These predictions were subsequently used to develop models using the airborne and simulated satellite spectra, achieving a fair prediction accuracy of R2 > 0.8 and RPIQ>2.

Towards an improved methodology for modelling climate change impacts on cropping systems in cool climates

Assessment of the impact of climate change on agricultural sustainability requires a robust full system estimation of the interdependent soil-plant-atmospheric processes coupled with dynamic farm management. The simplification or exclusion of major feedback mechanisms in modelling approaches can significantly affect model outcomes. Using a biogeochemical model, DNDCv.CAN, at three case-study locations in Canada, we quantified the impact of using commonly employed simplified modelling approaches on model estimates of crop yields, soil organic carbon (SOC) change and nitrogen (N) losses across 4 time periods (1981–2010, 2011–2040, 2041–2070, and 2071–2100). These approaches included using climate with only temperature and precipitation data, annual re-initialization of soil status, fixed fertilizer application rates, and fixed planting dates. These simplified approaches were compared to a more comprehensive reference approach that used detailed climate drivers, dynamic planting dates, dynamic fertilizer rates, and had a continuous estimation of SOC, N and water budgets. Alternative cultivars and rotational impacts were also investigated. At the semi-arid location, the fixed fertilizer, fixed planting date, and soil re-initialization approaches reduced spring wheat (Triticum aestivum L.) yield estimates by 40%, 25%, and 29%, respectively, in the 2071–2100 period relative to the comprehensive reference approach. At both sub-humid locations, the re-initialization of soil status significantly altered SOC levels, N leaching and N runoff in all three time periods from 2011 to 2100. At all locations, SOC levels were impacted when using simplified approaches relative to the reference approach, except for the fixed fertilizer approach at the sub-humid locations. Results indicate that simplified approaches often lack the necessary characterization of the feedbacks between climate, soil, crop and management that are critical for accurately assessing crop system behavior under future climate. We recommend that modellers improve their capabilities of simulating expected changes in agronomy over time and employ tools that consider robust soil-plant-atmospheric processes.

A Studying the Possibility of Estimating Soil Organic Carbon of Soils Under PINUS BRUTIA and QUERCUS AEGILOPS L. Trees in Sarke-Duhok By Using ASD FieldSpec 3 Spectroradiometer

Visible and Near-infrared (VNIRS) spectroscopy is a very fast non-destructive and environmentally friendly analytical technique. It has been suggested as an alternative to conventional methods for assessing and monitoring soil quality. Accurate VNIRS prediction of soil organic carbon (SOC) has been reported by many researchers in different world regions. Sixteen surface soil samples (0-30cm depth) from Sarke - Duhok were collected, eight samples under Zawita Pine (Pinus brutia) and the other eight-under oak (Quercus aegilops L.) trees. Soil colour was measured in the field and soil samples were air-dried and sieved by using a 2.0 mm sieve and analyzed in the laboratory to estimate the PH, EC, CaCo3, texture, and SOC according to Walkley and Black methods. Laboratory reflectance spectra were acquired for each sample from 350 to 2500 nm by using the Analytical Spectral Device (ASD) FieldSpec 3spectroradiometer for each of the different spectrum bands (410, 570, 660, 849, 1543 and 2187nm). Results showed that the reflectivity values of soil under both Oak and Pine trees in the IR region (849, 1543, 2187nm) were almost more than that at the visible portion (410,570, 660nm). Also, the determination coefficient (R2) results indicated that the bands 570, 1543 and 2187nm showed significant relationships between soil reflectivity and SOC% under Oak trees, R2 was 58.2 %, 89.9%, and 93.9% respectively. While under the Pine tree, the only band that showed a significant relationship was band 1543nm, its R2 was 58.3%. From the current results, as the main objectives, it is obvious that the ASD FieldSpec 3 spectroradiometer is quite an efficient and un-destructive tool that can be used for SOC estimation under Oak & pine trees especially at the IR spectrum (band 1543nm). The relationship between the variables was moderately strong; R2 was 89.88 and 58.3 alternatively. The standard error was low (0.0399, 0.0185), which indicates that the predicted values are close to the real values. Besides that, under Oak trees results indicated that there was a high R2 between the variables at bands 570 & 2187 nm, the R2 values were 58.21 and 93.92, alternatively.

Estimating soil organic carbon stocks under commercial forestry using topo-climate variables in KwaZulu-Natal, South Africa

Commercial forests are expanding globally, with great potential to absorb carbon and mitigate climate change. However, whereas the role of natural forests in carbon sequestration has been widely investigated, there is a paucity in the literature on the role of commercial forests in carbon assimilation. Hence, understanding the role of commercial forests in carbon storage is essential for quantifying local, regional or global carbon balances, which is valuable for climate change mitigation. Soil carbon is known to be the largest pool within any forest landscape, and is controlled by a wide range of physical and climatic factors. However, the relationship between soil organic carbon (SOC) and topo-climatic variables controlling its distribution within commercial forests is still poorly understood. Due to the limitations encountered in traditional systems of SOC determination, particularly at large spatial extents, geospatial techniques have recently emerged as a viable alternative for mapping soil properties. Therefore, this study sought to map SOC stocks variability within the commercial forest landscape, using landscape topo-climatic variables. A total of 81 soil samples was analysed for SOC concentrations and 31 topographic and climatic variables were used as predictors to SOC variability. To reduce multicollinearity, these variables were reduced to 11 using stepwise backward elimination and the maximum entropy (Maxent) algorithm was used for regression analysis to determine the relationship between SOC and the selected topo-climatic variables. Good accuracies were obtained for both training (area under the curve = 0.906) and test (area under the curve = 0.885) data sets, and demonstrate the effectiveness of selected topo-climatic variables and the Maxent algorithm in predicting SOC stocks. This study provides a framework for monitoring the status of soil carbon in commercial forest compartments and provides a viable approach for local, national or regional carbon accounting – valuable for climate change mitigation.
 Significance:
 
 Rainfall and temperature, as well as topographic variables (such as slope, elevation and topographic wetness index) are effective in mapping SOC distribution.
 The model developed is useful in predicting SOC occurrence and yielded an effective framework for continuous monitoring and assessment of SOC.
 The method developed in this study is cost-effective and suggests the use of other readily available climatic and topographic information for the prediction of SOC under commercial forestry in South Africa and indeed globally.
 Results from this study are important to achieve the national carbon accounting objective and are also valuable to forest managers, ecologists and relevant stakeholders in understanding the spatial distribution of SOC.

Predictive mapping of soil organic carbon in Northeast Algeria

Soils of semi-arid climates undergo organic carbon loss, which in turn affects their agricultural potential. Geostatistics is often used as an interpolation tool to thoroughly describe SOC spatial distribution. To focus on soil organic carbon (SOC) depletion, the Tiffech watershed (Northeast of Algeria), an economically important agricultural area, was chosen due to intensive agricultural practices, decline of forests and occurrence of erosion.The present study aimed to predict the spatial variation of SOC in Tiffech watershed using geostatistics and a Geographical Information System (GIS) software, comparing the performance of two geostatistical methods—Ordinary Kriging (OK) and Regression Kriging (RK)—also assessing the role of auxiliary variables in improving the prediction accuracy and highlighting the role of land cover in SOC storage.The SOC content in Tiffech soils was determined on 42 soil samples from the surface layer (0–10 cm) collected all over the study area and the results were used to estimate SOC density in non-sampled locations.The prediction efficiency of the two methods was evaluated by calculating the Mean Error (ME), the Root Mean Square Error (RMSE) and the Root Mean Square Standardized Error (RMSSE).The interpolation results showed that SOC distribution in the study area was correlated to the topography, the clay index, and general landscape features. SOC content increased northwards in the area, ranging from 0.53 to 6.9 kg·m−2 in relation to land use. As expected, maps figured a good conservation status of SOC stocks in areas with dense vegetation; conversely poor SOC contents were estimated where land degradation factors take place.Cross-validation results showed an outperformance in the interpolation accuracy of RK on OK after the introduction of environmental variables, with an RMSE value of 0.02 versus 0.81. This suggests a higher efficiency of RK in predicting SOC content across the Tiffech area in comparison with OK, confirming that introducing some auxiliary data correlated to the target variable in SOC estimation, considerably improved the interpolation accuracy.

Hyper-spectral inversion of soil organic carbon content under different land use types

Soil spectral information differ across different land use types. Understanding the appropriate modeling methods for different land use types can efficiently and accurately invert soil organic carbon content. We collected 248 samples from forest, cultivated land and orchard in the north-central part of Fengxin County, Jiangxi Province. First, original spectral reflectance curves were reduced noises with Savitzky-Golay (SG) filter. Then 10 nm resampling method was used to reduce data redundancy. We used partial least squares regression (PLSR), support vector machine regression based on grid search method (GRID-SVR) and support vector machine regression based on particle swarm optimization (PSO-SVR) to construct the inversion models of soil organic carbon content. The results showed that when constructing a single land-use type inversion model, RPD of the PLSR method for forest, cultivated land and orchard was 1.536, 1.315 and 1.493 respectively. RPD of GRID-SVR method increased 0.150, 0.183 and 0.502 than that of PLSR method, respectively. The PSO-SVR method had higher accuracy, with RPD being 20.8%, 10.0% and 2.7% higher than GRID-SVR for forest, cultivated land and orchard, respectively. The RPD of forest and orchard were 2.036 and 2.049, which well predicts soil organic carbon. The RPD of cultivated land was 1.647, which can make a rough estimate of soil organic carbon. The PSO-SVR model had the best prediction effect on soil organic carbon of different land use types, with the prediction accuracy of soil organic carbon content in forest and orchard being close and higher than cultivated land. Soil nutrition diffed acorss different land use types, which affect the prediction of soil organic carbon content. Models for inversion of soil organic carbon should be constructed separately for different land use types.

Estimating soil organic carbon and pH in Jilin Province using Landsat and ancillary data

AbstractRemote sensing provides possibilities to support digital soil mapping at large scales, but this technology over complex landscapes is still challenging due to the high spatial heterogeneity of soil properties. The objective of this work was to check the predictive capability of SOC and soil pH over different ecoregions. First, we collected a series of satellite images from 2017 and ancillary variables (e.g., climate and terrain) as spectral and environmental indicators, respectively. A total of 201 topsoil samples (0–20 cm) from the Northeastern Black Soil Region of China were also used. Then, the relationships between the two soil properties and indicators were analyzed. The indicators that showed significant correlation with SOC or soil pH were involved in the subsequent models. Finally, the SOC and soil pH prediction models were constructed by using a stepwise regression model. The results showed that the visible and shortwave infrared bands, along with the thermal infrared band, had strong correlations with the two soil properties. Environmental factors such as precipitation, temperature, and elevation all had an impact on the distribution of SOC and soil pH. The SOC and soil pH prediction models had R2 values of 0.66 and 0.73, root mean square errors of 0.21 and 0.58, and residual prediction deviations of 1.46 and 1.53, respectively, indicating reliable precision and average prediction abilities. This approach to estimating SOC and soil pH can contribute to improving our understanding and the modeling of soil processes at a large scale and in areas with relatively complex landscapes.

Terrain attributes and forage productivity predict catchment-scale soil organic carbon stocks

Accurate assessments of soil organic carbon (SOC) stocks are needed at multiple scales given their importance to both local soil health and global C cycles. Rangelands cover 54% of California, representing a large stock of SOC, but existing SOC estimates are uncertain. To improve understanding of fine-resolution SOC stocks in complex terrain and provide guidance to rangeland SOC inventories, we grid-sampled 105 locations (21-m grid cells) at two depths (0–10 and 10–30 cm) in a 10-ha annual grassland catchment in California’s Central Coast Range. Soils were analyzed for bulk density, coarse fragments, SOC and texture. Monthly aerial imagery was acquired by an unmanned aerial vehicle to compare surface reflectance during two contrasting years (wet vs. dry) to SOC stocks. We found that the 0–30 cm soil thickness held 3.64 ± 0.71 kg SOC m−2 (mean ± SD) with a range of 1.97–5.49 kg SOC m−2. The 0–10 cm soil thickness stored 47% of the 0–30 cm SOC stock with SOC concentrations twice as high in the 0–10 cm layer (1.40 ± 0.38%) as in the 10–30 cm layer (0.71 ± 0.15% SOC). Multiple linear regression (MLR) models explained 50–57% of SOC variability at 0–30 and 10–30 cm, but only 25% of variability at 0–10 cm. Based on cross-validation tests, MLR outperformed spatial interpolation methods and Random Forest models, best explaining SOC stocks with five environmental covariates: wet-year greenness, mean curvature, elevation, insolation, and slope. Lower hillslope positions, concave landforms, and enhanced wet-year greenness were associated with more SOC, and explained 11%, 24%, and 31% of variability in 0–30 cm SOC stocks, respectively. This study demonstrates that the accuracy of regional-scale SOC mapping of California rangelands benefits from considering microclimatic and topographic controls at the catchment-scale, in addition to broader scale mineralogical and macroclimatic controls identified in previous SOC studies.

CHLSOC: the Chilean Soil Organic Carbon database, a multi-institutional collaborative effort

Abstract. A critical aspect of predicting soil organic carbon (SOC) concentrations is the lack of available soil information; where information on soil characteristics is available, it is usually focused on regions of high agricultural interest. To date, in Chile, a large proportion of the SOC data have been collected in areas of intensive agricultural or forestry use; however, vast areas beyond these forms of land use have few or no soil data available. Here we present a new SOC database for the country, which is the result of an unprecedented national effort under the framework of the Global Soil Partnership. This partnership has helped build the largest database of SOC to date in Chile, named the Chilean Soil Organic Carbon database (CHLSOC), comprising 13 612 data points compiled from numerous sources, including unpublished and difficult-to-access data. The database will allow users to fill spatial gaps where no SOC estimates were publicly available previously. Presented values of SOC range from 6×10-5 % to 83.3 %, reflecting the variety of ecosystems that exist in Chile. The database has the potential to inform and test current models that predict SOC stocks and dynamics at larger spatial scales, thus enabling benefits from the richness of geochemical, topographic and climatic variability in Chile. The database is freely available to registered users at https://doi.org/10.17605/OSF.IO/NMYS3 (Pfeiffer et al., 2019b) under the Creative Commons Attribution 4.0 International Public License.

Soil organic carbon more strongly related to soil moisture than soil temperature in temperate grasslands

AbstractStatistical models for soil organic carbon (SOC) differ substantially regarding the degree to which climate variables, such as precipitation and air temperature, influence SOC. These discrepancies contribute to uncertainties regarding the potential feedback effects of climate change on SOC. Soil‐climate variables, such as soil moisture and soil temperature, can strongly influence SOC gains and losses, but these variables have not been widely utilized in statistical SOC models. We hypothesized that SOC is more strongly related to soil‐climate variables than to climate variables and that using soil‐climate variables in statistical SOC models would improve SOC estimates. We tested these hypotheses using >10 years of daily climate and soil‐climate data from 67 temperate grassland sites across Oklahoma, USA, along with soil physical properties, and corresponding SOC data at 5‐, 25‐, and 60‐cm depths. Soil moisture was the single most influential predictor variable, as indicated by least absolute shrinkage and selection operator (LASSO) regression analysis. The effects of precipitation and air temperature were insignificant, apart from a weak negative effect of precipitation at 25 cm. Models including soil‐climate variables provided more accurate SOC estimates than models excluding soil‐climate variables and were statistically superior based on the Akaike information criterion. Future soil moisture conditions, not precipitation or air temperature, may be the key determinant of climate change–SOC feedback effects at these grassland sites and similar sites worldwide.

Spatial prediction of soil organic carbon using machine learning techniques in western Iran

Estimation of soil organic carbon (SOC) is very useful for accurate monitoring of carbon sequestration. However, there are still significant gaps in the knowledge of SOC reserves in many parts of the world, including western Iran. To partially fill the gap, 865 soil samples were used with 101 auxiliary variables and 5 machine learning (ML) algorithms to digitally map SOC for the plough layer (0–30 cm) at a 90-m resolution in Kurdistan province. Results indicated that the most important auxiliary variables were rainfall (27.09%), valley depth (26.66%), terrain surface texture (23.42%), air temperature (20.18%), channel network base level (16.61%) and terrain vector roughness (14.47%). Results also showed that Random Forests (RF) performed best in predicting the spatial distribution of SOC (RMSE = 0.35% and R2 = 0.60), compared to the other ML algorithms (i.e. Cubist: CU, k-Nearest Neighbor: kNN, Extreme Gradient Boosting: XGBoost and Support Vector Machines: SVM). Furthermore, results estimated the total SOC stocks (SOCS) for the whole study area (~15,208 Tg) and amounts under different land uses. These were bareland (~6 Tg), orchard (~356 Tg), irrigated farming (~782 Tg), forest (~1773 Tg), grassland (~5991 Tg) and dry farming (~6297 Tg). As expected, the SOCS were highest in forest soils (652 g m−2) and lowest in bareland (437 g m−2). This result suggests that the conversion of native land (e.g. Forest) to cultivated land (e.g. Irrigated farming) could lead to significant loss of SOCS and appropriate management of land use could increase SOCS.

Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm

Estimating soil organic carbon (SOC) in topsoil can help improve soil quality and food production. This study aimed to explore the potential of airborne hyperspectral image to estimate the SOC of bare topsoil at an agricultural site located in the southeast part of Iowa State, United States. To magnify the subtle spectral signals concerning SOC, and accelerate calibration and improve predictive ability, we developed a framework to combine two advanced spectral algorithms, namely, fractional-order derivative (FOD) and optimal band combination algorithm for SOC predicting. Our case was based on 49 soil samples and a scattered airborne hyperspectral image. Random forest (RF) was utilized to establish SOC estimation models by incorporating the optimal spectral indices processed by different FOD transformations on the basis of the optimal band combination algorithm. Results indicated that when the fractional order increased, overlapping peaks and baseline drifts were gradually removed. However, the magnitude of spectral strength decreased concurrently. More detailed and abundant spectral variability was captured by FOD as compared with those by original reflectance and first and second derivatives. The estimation accuracies developed from the optimal band combination algorithm (cross-validation R2, 0.36–0.66) were generally better than those from full-spectrum data (cross-validation R2, 0.32–0.54). The RF model based on the combination of 0.75-order reflectance and optimal band combination algorithm obtained the highest estimation accuracy for SOC with cross-validation R2 of 0.66. This research provides guidance for future studies in selecting the most appropriate FOD transformation to preprocess spectral data and in using the optimal band combination algorithm to determine the spectral index. Airborne hyperspectral image-based modeling can be further used to map agricultural topsoil SOC to support local-scale agricultural planning.

Revisiting no-till’s impact on soil organic carbon storage in Canada

Accurate estimates of soil organic carbon (SOC) stock change as influenced by tillage practices are prerequisites to quantify the impact of mitigation measures on greenhouse gas emissions associated with crop production. In this study, we compiled SOC stock change data as influenced by tillage practices, i.e. no-till (NT) versus conventional tillage (CT), on agricultural soils in Canada. We determined the rate of C change as a function of soil texture and climate stratified by time since the management change. In the cool and humid climates of Eastern Canada, the impacts of NT were inconsistent and resulted in a decline in C storage rate in the years shortly following management change, but SOC recovered over time. Carbon losses were particularly high on fine and coarse textured soils, whereas in medium textured soils NT tended to increase SOC. On the Canadian prairies NT consistently increased SOC. The rate of gain in SOC under NT decreased over time with higher rates in the 3−10 years following a change to NT at a rate of 740 kg C ha−1 yr−1 or 1.3 % yr−1. Rates declined to 260 kg C ha−1 yr−1 or 0.87 % yr−1 for periods from 11–20 years after tillage change, and to 95 kg C ha-1 yr−1 or 0.23 % for periods longer than 20 years. The results of this work clearly show climate, soil texture and duration of management as main drivers of SOC change under NT in Canada and key factors that must be considered in the development of either national or regional SOC models. The results of this study suggest that more detailed stratification of soil texture, climate and duration of NT adoption is required for more accurate estimates of SOC storage rates associated with NT for Canada.

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.

Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes

Soil erosion by water affects soil organic carbon (SOC) migration and distribution, which are important processes for defining ecosystem carbon sources and sinks. Little has been done to quantify soil carbon erosion in the three major basins in China, the Yangtze River, Yellow River and Pearl River Basins, which contain the most eroded areas. This research attempts to quantify the lateral movement of SOC based on spatial and temporal patterns of water erosion rates derived from an empirical Unit Stream Power Erosion Deposition Model (USPED) model. The water erosion rates simulated by the USPED model agreed reasonably with observations (R2 = 0.43, P < 0.01). We showed that regional water erosion ranged within 23.3–50 Mg ha–1 year–1 during 1992–2013, inducing the lateral redistribution of SOC caused by erosion in the range of 0.027–0.049 Mg C ha–1 year–1, and that caused by deposition of 0.0079–0.015 Mg C ha–1 year–1, in the three basins. The total eroded SOC was 0.006, 0.002 and 0.001 Pg year–1 in the Yangtze River, Yellow River and Pearl River Basins respectively. The net eroded SOC in the three basins was ~0.0075 Pg C year–1. Overall, the annual average redistributed SOC rate caused by erosion was greater than that caused by deposition, and the SOC loss in the Yangtze River Basin was greatest among the three basins. Our study suggests that considering both processes of erosion and deposition – as well as effects of topography, rainfall, land use types and their interactions – on these processes are important to understand SOC redistribution caused by water erosion.

Estimation of the Value of Ecosystem Carbon Sequestration Services under Different Scenarios in the Central China (the Qinling-Daba Mountain Area)

The Qinling-Daba Mountain area is a transitional zone between north and south China and not much is known about its carbon storage, particularly its pool of soil organic carbon (SOC). Given this shortcoming, more reliable information regarding its SOC is needed. In light of this, we quantified above and below-ground carbon sinks using both the Carnegie-Ames-Stanford approach (CASA) model and an improved carbon cycle process model. We also assessed the net present value (NPV) for carbon budgets under different carbon price and discount rate scenarios using the NPV model. Our results indicated that the net primary productivity (NPP) was lower in places with low density forests that were situated at high elevation. The spatial distribution of carbon storage depended on NPP production and litter decompositon, which reflected specific vegetation as well as temperature and moisture gradients. The lowest amounts of carbon storage were in the center of the Qinling Mountains and also partly in the Daba area, which is a location associated with sparse grassland. Contrastingly, the broad-leaved forested area showed the highest amount of carbon storage. NPV was positively correlated with discount rate and carbon prices, thus resulting in the highest values in the forests and grassland. The net present value of total soil carbon sequestration in the six scenarios in 2015 was 3.555 b yuan, 3.621 b yuan, 5.421 b yuan, 5.579 b yuan, 7.530 b yuan, 7.929 b yuan; The net present value of total soil carbon sequestration in 6 scenarios in 2017 is 2.816 b yuan, 2.845 b yuan, 4.361 b yuan, 4.468 b yuan, 6.144 b yuan, 6.338 b yuan (billion = 109; b; RMB is the legal currency of the China, and its unit is yuan, 1 euro = 7.7949 yuan, and 1 pound = 9.2590 yuan). Levying a carbon tax would be a notable option for decision makers as they develop carbon emission reduction policies. Given this, incorporating discount rates and carbon pricing would allow for more realistic value estimations of soil organic carbon. This approach would also provide a theoretical basis and underscore the practical significance for the government to set a reasonable carbon price.

A cascading influence of calcium carbonate on the biogeochemistry and pedogenic trajectories of subalpine soils, Switzerland

Soil research in temperate to cool and humid regions has typically focused on acidic soils; there has been relatively little investigation of the effects of calcium carbonate (CaCO3) on unamended soil properties or function in these environments. The object of this study was to characterise the effects of small amounts of CaCO3 on pedogenic trajectories and soil biogeochemistry in a humid subalpine valley of Switzerland. To isolate the influence of CaCO3, six profiles were selected that had developed under almost identical conditions for soil formation, i.e. climate, topography, vegetation structure, time since deglaciation, silicate mineralogy and texture. The main difference between the profiles was that three contained a small quantity of CaCO3 (<6.2%; thereafter, ‘CaCO3-bearing’) while the remaining three contained no detectable CaCO3 (thereafter, ‘CaCO3-free’). The presence of CaCO3 was associated with cascading changes in soil biogeochemistry. These changes included higher pH, an order of magnitude higher extractable Ca and twice as much soil organic carbon (SOC). CaCO3-bearing profiles also displayed a higher proportion of poorly crystalline Fe forms. The higher pH at the CaCO3-bearing site was attributable to the weak buffering provided by CaCO3 dissolution, which in turn maintained the relatively higher extractable Ca. Exchangeable Ca (CaExch) and other reactive Ca forms could help stabilise SOC, contributing to its accumulation through processes such as flocculation and subsequent occlusion within aggregates and/or sorption to mineral surfaces. The increased SOC, CaExch and pH at the CaCO3-bearing site could in turn be inhibiting the crystallisation of disordered Fe forms, but further research is required to confirm this effect and isolate the exact mechanisms. Overall, this study shows that the presence of small amounts of CaCO3 in humid environments has a far-reaching influence on soil biogeochemistry and further supports the idea that indicators of Ca prevalence have the potential to improve regional SOC estimates.

Spectroscopic measurements and imaging of soil colour for field scale estimation of soil organic carbon

Effective measurement and management of soil organic carbon (SOC) are essential for ecosystem function and food production. SOC has an important influence on soil properties and soil quality. Conventional SOC analysis is expensive and time-consuming. The development of spectral imaging sensors enables the acquisition of larger amounts of data using cheaper and faster methods. In addition, satellite remote sensing offers the potential to perform surveys more frequently and over larger areas. This research aimed to measure SOC content with colour as an indirect proxy. The measurements of soil colour were made at an agricultural site of the Czech Republic with an inexpensive digital camera and the Sentinel-2 remote sensor. Various soil colour spaces and colour indices derived from the (i) reflectance spectroscopy in the selected wavelengths of the visible (VIS) range (400–700 nm), (ii) RGB digital camera, and (iii) Sentinel-2 visible bands were used to train models for prediction of SOC. For modelling, we used the machine learning method, random forest (RF), and the models were validated with repeated 5-fold cross-validation. For prediction of SOC, the digital camera produced R2 = 0.85 and RMSEp = 0.11%, which had higher R2 and similar RMSEp compared to those obtained from the spectroscopy (R2 = 0.78 and RMSEp = 0.09%). Sentinel-2 predicted SOC with lower accuracy than other techniques; however, the results were still fair (R2 = 0.67 and RMSEp = 0.12%) and comparable with other methods. Using a digital camera with simple colour features was efficient. It enabled cheaper and accurate predictions of SOC compared to spectroscopic measurement and Sentinel-2 data.

Edaphic and microbial determinants of the residence times of active and slow C pools on the Tibetan Plateau

One major source of uncertainties in the estimation of soil organic carbon (SOC) dynamics is carbon (C) residence time, an important parameter in terrestrial ecosystem C cycling models. To better predict terrestrial dynamics, C residence time and its controlling factors need to be well quantified and investigated. In this study, we applied a data assimilation approach to quantify the residence times of different soil C pools on the Tibetan Plateau, based on incubated soil carbon dioxide (CO2) efflux data. We also assessed the effects of soil properties on C residence times. Our results showed that the residence times of active (RTA) and slow (RTS) C pools were well estimated through data assimilation. Soil physical, chemical, and microbial properties significantly regulated RTA and RTS. The RTA was higher in soils with high clay contents (R2 = 0.52, p < 0.01) and low microbial quotients (qMB) (R2 = 0.55, p < 0.01), whereas, the RTS was higher in soils with high clay contents (R2 = 0.76, p < 0.01), high C:N ratios (R2 = 0.44, p < 0.05), low qMBvalues (R2 = 0.50, p < 0.05), and low soil pH values (R2 = 0.80, p < 0.01). Structural equation modeling (SEM) analyses indicated that the model could explain 55% and 91% of the variations in the RTA and RTS, respectively. The qMB was the key factor in regulating the RTA, while soil pH was the crucial variable in controlling the RTS. These results demonstrated that the major controlling factors on the RTA and RTS were different. Considering these variables and their different controls on residence times of different soil C pools could provide more accurate estimation of terrestrial C cycles, and prediction of future C-climate feedbacks.

Land–Use Changes Influencing C Sequestration and Quality in Topsoil and Subsoil

Soil capacity as a major carbon (C) sink is influenced by land use. Estimates of soil organic carbon (SOC) sequestration have mostly focused on topsoils [0–30 cm official Intergovernmental Panel on Climate Change (IPCC) soil depth]. We investigated SOC stocks and their quality as influenced by land-use changes. Soil samples were collected from five soil depths down to 100 cm of three adjacent fields each representing a different land use—forest, cassava, and rice paddy—in Northeast Thailand. Sequestration of SOC in topsoils under all land uses was higher, as indicated by SOC stocks (59.0–82.0 Mg ha−1) than subsoils (30–100 cm) (27.0–33.0 Mg ha−1). The soil profile (0–100 cm) of the forest had higher stocks of SOC and humic acid (115.0 and 6.8 Mg ha−1, respectively) than those of cultivated land uses [paddy (100.0 and 4.8 Mg ha−1, respectively) and cassava (87.0 and 2.3 Mg ha−1, respectively)], which accounted for an average 30% increase in SOC sequestration over those with only topsoil. Topsoils of the forest had higher humic acid content but narrower E4:E6 ratio [the ratio of absorbances at 465 nm (E4) and at 665 nm (E6)] of humic acids (2.8), indicating a higher degree of humification and stabilization than the cultivated soils (3.2–3.6). Subsoil C was higher quality, as indicated by the lower E4:E6 ratio of humic acids than topsoils in all land uses.