Variability Of Soil Organic Carbon Research Articles

Dynamics of soil organic carbon in the steppes of Russia and Kazakhstan under past and future climate and land use

Changes in land use and climate are the main drivers of change in soil organic matter contents. We investigated the impact of the largest policy-induced land conversion to arable land, the Virgin Lands Campaign (VLC), from 1954 to 1963, of the massive cropland abandonment after 1990 and of climate change on soil organic carbon (SOC) stocks in steppes of Russia and Kazakhstan. We simulated carbon budgets from the pre-VLC period (1900) until 2100 using a dynamic vegetation model to assess the impacts of observed land-use change as well as future climate and land-use change scenarios. The simulations suggest for the entire VLC region (266 million hectares) that the historic cropland expansion resulted in emissions of 1.6⋅ 1015 g (= 1.6 Pg) carbon between 1950 and 1965 compared to 0.6 Pg in a scenario without the expansion. From 1990 to 2100, climate change alone is projected to cause emissions of about 1.8 (± 1.1) Pg carbon. Hypothetical recultivation of the cropland that has been abandoned after the fall of the Soviet Union until 2050 may cause emissions of 3.5 (± 0.9) Pg carbon until 2100, whereas the abandonment of all cropland until 2050 would lead to sequestration of 1.8 (± 1.2) Pg carbon. For the climate scenarios based on SRES (Special Report on Emission Scenarios) emission pathways, SOC declined only moderately for constant land use but substantially with further cropland expansion. The variation of SOC in response to the climate scenarios was smaller than that in response to the land-use scenarios. This suggests that the effects of land-use change on SOC dynamics may become as relevant as those of future climate change in the Eurasian steppes.

Spatial variations in soil organic carbon, nitrogen, phosphorus contents and controlling factors across the “Three Rivers” regions of southwest China

Based on the data of China Geochemical Baselines project, geostatistical analysis was used to investigate the spatial variations in soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) contents across the “Three Rivers” regions of southwest China, and the factors affecting them were analyzed by the redundancy analysis (RDA) and Person's correlation. Results showed that, the average content of SOC, TN and TP in the study area were 7.20 g/kg, 0.84 g/kg and 0.55 g/kg, respectively. The SOC and TN contents showed an obvious enrichment characteristic with great spatial variability, while TP content was stable on regional scale. The SOC, TN and TP contents decreased with elevation increase in the northern frigid highland, but showed an opposite character in the southern tropical & subtropical, which actually reflected the control of temperature on them. Combined with that there were higher SOC, TN and TP contents in subalpine meadow soil and red earth–yellow earth of the middle latitude zone, these suggested that the suitable temperature was conducive to the accumulation of soil nutrients. The weak positive correlation between population density and soil nutrients, together with high level of soil nutrients in the vicinity of large cities, demonstrated that human activities had significantly increased the soil nutrients contents in the study area. The RDA results showed that soil nutrients in the northern frigid highland were mainly controlled by the environmental factors dominated by temperature and soil structural factors dominated by parent materials with the total explanatory power as high as 75.87%, while in the southern tropical & subtropical mainly by the environmental factors dominated by chemical and biological weathering and the biological factors dominated by vegetation with the total explanatory power as high as 88.53%. The above factors superimposing a certain degree of human activities converged to cause that the SOC and TP contents in the south were higher than that in the north while the TN content was lower than that in the north.

Vertical differentiation of soil organic carbon in mature natural forests in China.

We analyzed the effects of meteorological factors and soil properties on vertical variation of SOC, based on soil organic carbon (SOC) density in different soil layers (0-10, 10-20, 20-30, 30-50, and 50-100 cm) from 131 mature natural forests in different climate zones in China. The results showed that SOC density decreased with increasing soil depth (0-30 cm) in temperate coniferous, temperate deciduous broadleaved, subtropical deciduous broadleaved and subtropical evergreen broadleaved forests. There were significant regional variations of SOC density in 0-100 cm soil layer. SOC density of 0-100 cm soil layer in temperate coniferous forests was higher than temperate deciduous broadleaved forests, and was higher in subtropical evergreen broadleaved forests than subtropical deciduous broadleaved forests. SOC density was significantly positively correlated with soil clay content, mean annual precipitation, and aboveground net primary production, and significantly negatively correlated with soil pH and mean annual temperature. Mean annual precipitation and mean annual temperature influenced input and output of SOC, while soil pH and clay content affected SOC accumulation. Therefore, protecting mature natural coniferous and evergreen broadleaved forests would benefit forest carbon sequestration in China.

Effects of biochar application on crop productivity, soil carbon sequestration, and global warming potential controlled by biochar C:N ratio and soil pH: A global meta-analysis

Biochar application has been widely recommended as a potential solution to tackle the challenges of food security and climate change in agroecosystems, but the effect sizes of biochar application on crop yield, soil carbon sequestration, and global warming potential (GWP) shows great uncertainties. To explore the effect variation of biochar application alone (B) and biochar combined with chemical fertilizers (BF) on crop yield, soil organic carbon (SOC), and GWP, this study reviewed updated datasets with 455, 131, and 95 independent experiments globally to identify the key factors influencing the responses of crop yield, SOC, and GWP to B and BF, respectively. Overall, the effect sizes were different between B and BF in both improving crop yield (15.1 % and 48.4 %, respectively) and decreasing GWP (27.1 % and 14.3 %, respectively), whereas there were almost no differences in terms of increasing SOC (32.9 % and 34.8 %, respectively). In addition, the effect sizes of B and BF on crop yield were coupled with these on SOC. Increased biochar carbon to nitrogen ratio (C:N ratio) and soil pH decreased the impact of B and BF on crop yield, while increased SOC promoted the impact of BF on crop yield. The effect size on GWP increased with biochar pH increasing but soil pH decreasing under B and BF. The wetness index, soil properties (SOC, pH, and clay), and biochar properties (type, pH, C:N ratio, and application rate) jointly explained 70 %–79 %, 90 %–93 %, and 70 %–97 % of the effect variations in crop yield, SOC, and GWP, respectively. The biochar C:N ratio and soil pH were the most important factors determining the effect size of biochar application on crop yield, SOC, and GWP. Taken together, biochar application is a tripartite win-win solution for improving crop yield, increasing SOC, and decreasing GWP mainly depending on biochar properties and soil pH.

Continental-scale controls on soil organic carbon across sub-Saharan Africa

Abstract. Soil organic carbon (SOC) stabilization and destabilization has been studied intensively. Yet, the factors which control SOC content across scales remain unclear. Earlier studies demonstrated that soil texture and geochemistry strongly affect SOC content. However, those findings primarily rely on data from temperate regions where soil mineralogy, weathering status and climatic conditions generally differ from tropical and subtropical regions. We investigated soil properties and climate variables influencing SOC concentrations across sub-Saharan Africa. A total of 1601 samples were analyzed, collected from two depths (0–20 and 20–50 cm) from 17 countries as part of the Africa Soil Information Service project (AfSIS). The data set spans arid to humid climates and includes soils with a wide range of pH values, weathering status, soil texture, exchangeable cations, extractable metals and land cover types. The most important SOC predictors were identified by linear mixed-effects models, regression trees and random forest models. Our results indicate that geochemical properties, mainly oxalate-extractable metals (Al and Fe) and exchangeable Ca, are equally important compared to climatic variables (mean annual temperature and aridity index). Together, they explain approximately two-thirds of SOC variation across sub-Saharan Africa. Oxalate-extractable metals were most important in wet regions with acidic and highly weathered soils, whereas exchangeable Ca was more important in alkaline and less weathered soils in drier regions. In contrast, land cover and soil texture were not significant SOC predictors on this large scale. Our findings indicate that key factors controlling SOC across sub-Saharan Africa are broadly similar to those in temperate regions, despite differences in soil development history.

Spatial-temporal change of soil organic carbon in Anhui Province of East China

This paper was oriented to characterise spatio-temporal variation of soil organic carbon (SOC) over a 30-year period and to explore its driving forces in East China. A comparative study was done of data obtained during 1980 and 2010 in Anhui Province. Spatio-temporal changes of SOC density and storage in the surface (0–30 cm) and 0–100 cm layers were analysed using geographic information system (GIS) spatial analysis techniques. Relationships between soil erosion, land use change, agricultural management, and changes in SOC are discussed. Results show: (1) over 30 years, the average SOC density decreased by 0.59 kg C m−2 in the surface layer and 1.63 kg C m−2 in 0–100 cm layer. The average SOC density increased in Calcaric Cambisols and Haplic Fluvisols, and decreased in other soil types.The average SOC density in upland increased but decreased as a result of other land use practices. (2) SOC density in the surface layer increased to the north but decreased to the south in Anhui. SOC density in the 0–100 cm layer increased in the central area and decreased to the south. About 59% of all soil increased in SOC density. (3) SOC storage decreased by 117.91 Tg C in the surface layer and decreased by 237.65 Tg C in the 0–100 cm layer. The SOC storage increased mainly in the northern and central areas. (4) Severe soil erosion was a main reason for the large reduction in SOC storage in western and southern Anhui. Cultivated land use that changed to paddy field from upland improved SOC more than changes to other land use types. SOC storage change in farmland was significantly and positively related to organic matter content in crop roots.

Shifts in microbial metabolic pathway for soil carbon accumulation along subtropical forest succession

The dynamic process of forest succession is important for terrestrial ecosystems where soil organic matter (SOM) primarily controls biogeochemical cycles and is driven by soil microorganisms. Carbon (C) incorporation into SOM occurs along two different microbial metabolic pathways: ex vivo mostly extracellular enzymatic modification and in vivo turnover within the cells, highlighting the importance of microbial catabolism and anabolism respectively during soil C transformation and sequestration. However, identifying the dual C pathways and understanding the microbially-mediated SOM transformation during forest succession remains elusive, particularly with respect to interactions in the rhizosphere. Here, we used a series of biomarkers, including amino sugars, phospholipid fatty acids, and neutral sugars, to explore how forest succession and rhizosphere dynamics affect microbial contribution to soil organic carbon (SOC) in three successional stages of a subtropical forest ecosystem. We observed generally consistent increases in amino sugar and SOC with forest age in both bulk and rhizosphere soil, demonstrating that microorganisms facilitate SOC accumulation through the in vivo pathway along the successional gradient. The ratio of amino sugar-C to SOC (AS-C/SOC) exhibited a greater increase in bulk than in rhizosphere soil along the successional gradient, indicating the microbial in vivo pathway was relatively more important for SOC accumulation in bulk than in rhizosphere soil. Given there was a significant decrease in the ratios of hexose-to-pentose sugars [(galactose + mannose)/(arabinose + xylose), indicating the contribution of sugars derived from microbes and plants to labile soil C], we hold the opinion that relatively limited microbial C processing occurred in the rhizosphere along the forest successional gradient. Therefore, the microbial ex vivo pathway where microorganisms decomposed (structurally modified) fine root litter that was incorporated into SOC was relatively more important in rhizosphere than in bulk soil for SOC variation during forest succession. Our findings emphasize the role of microbes transforming organic matter in both bulk and rhizosphere soil in a subtropical forest successional gradient and help to enhance our understanding of the function and ecosystem services of these environments.

Estimating temporal and spatial changes in soil organic carbon stocks and its controlling factors in moraine landscapes in Denmark

Assessing temporal changes in soil organic carbon (SOC) stocks is important when evaluating the SOC sequestration potential of soil management strategies. The monitoring of SOC stocks is challenging, particularly in eroding landscapes where erosion-induced changes in SOC stocks are superimposed on changes related to management. In this study we focused on the spatio-temporal variations of SOC in eroding cropland soils of the northeast of thepeninsulaofJutland (Denmark) at field scale. We examined soil redistribution processes as control factors of SOC changes (ΔSOC) during a 16-year period by using soil data (both spatial and with depth) collected over two distinct periods in 1998 and 2014. Topographic changes between 1998 and 2014, derived from high resolution digital elevation models, were used to identify eroded, stable and depositional areas within the field. There was a predominance of soil loss, with 44% of the sampling points corresponding to eroded sites and 28% to depositional sites, while the remainder was considered as stable soils. Substantial erosion was detected and at the field scale, soil deposition equalled (29.7 ± 17.5 Mg ha−1 y−1) while soil erosion rates were lower with a mean of 25.8 ± 12.9 Mg ha−1 y−1. Comparing SOC stocks over the 16 year-period showed that on average, and when considering the whole soil profile, SOC contents and stocks were stable. However, this masked substantial dynamics that were related to erosion processes. Despite the occurrence of net soil erosion at the field scale, there was an overall increase in SOC storage of 1.88 Mg ha (SOC2014:SOC1998 ratio ≥ 1). Losses of SOC in topsoils (6%) and SOC gain (34%) in soil layers below the plough depth (25–45 cm) could be related to soil disturbances caused by tillage and the significant downward transport of topsoil SOC within the soil profile. Soil disturbances caused by tillage practices (i.e. soil management) and SOC transportation from topsoil to subsoil may be responsible for the downward movement of organic carbon and consequent SOC accumulation in subsoils. In addition the successive deposition and deep burial of SOC rich topsoils at depositional sites due to the effect of topography and slope position favours the enrichment of SOC in subsoils. It was estimated that as much as 11% of the sampling points changed from eroded to depositional sites and 15% of them varied from erosion to stable sites, and this might have favoured SOC accumulation. These results clearly show that understanding and quantifying soil redistribution processes is key to assess SOC temporal changes in agricultural soils. This is important to develop site-specific management strategies that improve SOC sequestration at local scale.

Validating the regional estimates of changes in soil organic carbon by using the data from paired-sites: the case study of Mediterranean arable lands

BackgroundLegacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the analysis methodologies. When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-paired data) may lead to biased results. In the present work, N = 302 georeferenced soil samples were selected from a regional (Sicily, south of Italy) soil database. An operational sampling approach was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0–30 cm soil depth and tested.ResultsThe measurements were conducted after computing the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an α = 0.05.A Wilcoxon test applied to the variation of SOC from 1994 to 2017 suggested that there was not a statistical difference in SOC concentration after 23 years (Z = − 0.556; 2-tailed asymptotic significance = 0.578). In particular, only 40% of resampled sites showed a higher SOC concentration than in 2017.ConclusionsThis finding contrasts with a previous SOC concentration increase that was found in 2008 (75.8% increase when estimated as differences of 2 models built with non-paired data), when compared to 1994 observed data (Z = − 9.119; 2-tailed asymptotic significance < 0.001).This suggests that the use of legacy data to estimate SOC concentration dynamics requires soil resampling in the same locations to overcome the stochastic model errors. Further experiment is needed to identify the percentage of the sites to resample in order to align two legacy datasets in the same area.

The impact of shifting Köppen-Geiger climate zones on soil organic carbon concentrations in Australian grasslands

The factors determining the spatial distribution of soil organic carbon (SOC) at large-scales closely align with bioclimate regions; reflecting climate, ecosystem and soil properties. Recent studies of the Köppen-Geiger climate zones of Australia have highlighted an extension of the hot, arid, steppe environment from central Australia into the southeast (SE) under future climate change scenarios (2071–2100 under RCP 8.5). As SOC concentrations are highest in Australia's SE, it is important the effect of this shift is quantified. This study assesses this and how changes in the factors that control SOC formation may alter SOC concentrations. Field measured SOC concentrations were compared to current climate, soil, topography, vegetation, and soil erosion variables for 12 grassland sites from SE to NW Australia. SOC concentrations ranged from 0.39% in northwest (NW) and Central Australia to as high as 6.88% in the SE. Using regression analyses; temperature, elevation and Normalised Difference Vegetation Index were found to be the only significant drivers (α = 0.95) of SOC across the sites. Partial correlation analyses then identified temperature, elevation and clay content as imparting a significant effect on the relationships between SOC and water availability variables. This indicates that an extension of the arid environment into SE Australia may lead to a decrease in SOC (up to 1.12%), as mean annual temperature exceeds threshold values that limit SOC concentration. This is significant as the majority of Australia's SOC is stored in this area and these environments exert a strong influence on global carbon cycling.

Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China

Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions.

Spatial variation and distribution of soil organic carbon in an urban ecosystem from high-density sampling

With acceleration of urban expansion, urban soils are increasingly becoming critical in the global carbon cycle. However, spatial variations and distributions of soil organic carbon (SOC) across urban centers are rarely investigated. Here, 1018 soil samples were collected from the upper 20 cm soil layer in Xi'an — a typical historical city under rapid urbanization in China. The aims were to: i) determine current levels and variations in SOC; ii) identify main driving factors of SOC density (SOCD); and iii) compare different interpolation methods in predicting spatial distributions of SOCD in Xi'an City study area. Results showed that the range of SOC concentration (SOCC) was 1.57–38.58 g kg−1 (mean of 13.59 g kg−1) and the range of SOCD was 0.47–9.48 kg m−2 (mean of 3.59 kg m−2). Analysis of coefficient of variation showed that there were moderate variations in SOCC (54.0%) and SOCD (49.4%) in the study area. Combined correlation analysis, principal component analysis and minimum dataset compilation showed that sand content, distance to civic center, land use type and vegetation type were closely correlated with spatial variations in SOCD. Geostatistical analysis showed that isotropic exponential model with a range of 1776 m was the best fit for SOCD semi-variogram. Spatial distribution of SOCD was further predicted using four approaches — ordinary kriging (OK), inverse distance weighting (IDW), multiple linear regression (MLG) and regression kriging (RK). The spatial prediction accuracy was ranked in order of RK > MLR > OK > IDW. However, there was relatively low interpolation accuracy for the four approaches. This was attributed to the high spatial variations in urban milieu, impacted by intense anthropogenic activities. The findings in this study added to current knowledge on global carbon cycle as influenced by urban soils, which is a critical guide to land management in urban areas.

Modelling scenarios of soil properties and managements in olive groves at the micro-catchment scale with the AnnAGNPS model to quantify organic carbon

Soil organic carbon (SOC) stock changes are crucial in identifying risk of desertification in fragile areas In this work, 108 scenarios were prepared with the model AnnAGNPS in a small catchment of extensive olive groves by considering 6 different soil types (with textures sandy, S; sandy loam, Slo; loam, L; clay loam, Clo; silty loam clay, SiLoC; clay, C), 3 different managements (no till, NT; conventional tillage, CT, and cover crop, SC), 3 types of fertilization (two organic at rates of 40 and 80 kg.ha−1, F2 and F3, and another inorganic F1 with 100 kg.ha−1), and 2 contrasting reach organic carbon half-life time (0.1 day–730 days). The consistency of simulated annual OC attached to sediment and variations in ground SOC (h = 200 mm) were evaluated and compared in the context of the region of Andalusia, Spain. In addition, the most significant parameters to consider in the subsequent calibration process were also identified.There were significant differences in annual exported OC for the scenarios of soil and management, ranging between 0.0 kg.ha−1 and 368.9 kg.ha−1. In addition, S and SC showed the lowest variability intervals, while Clo and NT had the highest OC values and variation ranges. For the SOC pools, the effects of soil and fertilization were more evident than that of management. The combination C-SC-F3 presented the maximum increase of SOC (0.150 mg OC.g-1soil.y-1) as a result of the maximum OC inputs while Slo-NT-F1 presented the minimum (0.080 mg OC.g-1soil.y-1). Finally, the most significant parameters derived from the sensitivity analysis were soil texture, saturated hydraulic conductivity and Curve Numbers for annual exported OC and ground OC, fertilization parameters and rate of decomposition of residues. Model parameterization based on a sensitivity analysis and the abundance of previous experimental studies supports the AnnAGNPS applications for simulating the OC loss in agricultural catchments.

Impacts of Slope and Longitudinal Ridge on Soil Organic Carbon Dynamics in the Typical Mollisols Sloping Farmland (China)

Soil organic carbon (SOC) plays an important role in preserving soil fertility. Assessing how topographic factors (slope gradient, slope length, slope shape, and slope position) and their interactions influence the SOC dynamics in the surface soil of longitudinal ridges of sloping farmland is very important for Mollisols protection in Northeast China. The SOC contents and variations were quantified within classes of topographic factors. A total of 39 soil samples were collected at the same sampling points in 13 typical Mollisol sloping farmland in 2004 and 2016. The results indicated that the average annual decrease rate of SOC from 2004 to 2016 was 5.64‰. The SOC stocks were significantly affected by slope gradient, slope length, and slope shape, but only affected by the interaction between slope gradient and slope position. In addition, the SOC stocks decreased with increasing slope gradient (p 0.05); the footslope had higher SOC content than the upper and middle slope (p > 0.05), corresponding to the soil erosion and deposition along the slope. Our findings could offer suggestions for future landscape management.

Assessment of soil organic carbon stocks in relation to variation in physiography under sub-mountainous Shiwalik ranges of lower Himalayas, India

A study was conducted in lower Shiwaliks of Punjab, India to study the soil organic carbon (SOC) stock variation in hill, piedmont plain and alluvial plain regions of Punjab, and further to investigate the SOC variability down the slope in the lower Shiwaliks region of Punjab. The SOC fractions viz. very labile carbon, labile carbon, less labile carbon and recalcitrant were determined using standard methodologies. The total SOC stock in 0–60 cm soil depth was highest in plain region (31.81 Mg ha−1), followed by piedmont (23.43 Mg ha−1) and lowest in hill region (22.86 Mg ha−1). Whereas, the SOC concentration in surface layer was highest in foot slope (5.76 g kg−1) followed by mid slope (4.14 g kg−1) and lowest in up slope (2.32 g kg−1). Among the SOC fractions the average labile carbon content in soil samples was found in the order, hills (0.81 g kg−1) > piedmonts (0.73 g kg−1) > plains (0.50 g kg−1). The soil bulk density (Db) was higher in the plain than that of piedmont and hills. Moreover, a similar trend is observed in case of water holding capacity. Regression analysis has revealed significant relationship between Db and SOC stock at p < 0.05. Further, analysis of variance (ANOVA) at p < 0.05 has indicated significant differences among concentration of SOC and stock influenced by land use and soil depths. Thus, an understanding of the effect of physiography on SOC via such studies is important for soil quality management.

Mapping soil organic carbon stock by hyperspectral and time-series multispectral remote sensing images in low-relief agricultural areas

High-precision digital soil organic carbon (SOC) stocks mapping is very important for agricultural production management and global carbon cycle. The spatial heterogeneity of farmland SOC is not only influence by the environmental factors of soil formation but also the management practices of tillage, fertilization, and irrigation. However, the traditional modeling covariates of digital soil mapping, such as terrain factors, land use types and climate factors have weak spatial variations in low-relief agricultural areas, and they cannot reflect the large spatial variation of SOC. Thus the time-series multispectral remote sensing images will be used for mapping soil properties in low relief regions in this study, meanwhile a new collaborative verification strategy was put forward to evaluate the spatial distribution characteristics of soil maps. The current study was performed in a nearly flat agricultural region southeast of Iowa (with an area of approximately 385.45 ha), where 195 surface soil samples (0–15 cm) were collected. A hyperspectral image (Headwall-Hyperspec, 380–1700 nm) and the time-series multispectral remote sensing images of Sentinel 2 and Landsat 8 were used to construct the prediction models of SOC stock and its relevant soil properties of SOC and soil bulk density (SBD) through partial least square regression (PLSR) and extreme learning machine (ELM) models. The collected soil samples and evaluation indexes of root mean square error (RMSE), R2, and ratio of performance to interquartile range (RPIQ) were used to evaluate the model performance. Results are as follows: (1) hyperspectral images were successfully used to predict the SOC stock, SOC, and SBD through PLSR and ELM, while ELM (RPIQ = 2.03, 1.97, 1.64) outperformed PLSR (RPIQ = 1.83, 1.97, 1.53); (2) the time-series multispectral remote sensing images of Sentinel 2 and Landsat 8 can reflect the spatial distribution characteristics of the SOC stock, SOC and SBD by PLSR and ELM, but the combination of Sentinel 2 images and ELM obtained the best prediction results (RPIQ = 1.45, 1.25, 1.26); and (3) the differences of the soil maps predicted by the hyperspectral image and time-series multispectral remote sensing images were small, and the largest percentage errors nearly appeared on the edges of the farmland patches owing to mixed pixels. This study further confirmed the good prediction abilities of the time-series multispectral remote sensing images in low relief farmland regions. Lastly, this mapping strategy can provide additional valuable information for agricultural management and carbon cycle.

Soil organic carbon was more strongly linked with soil phosphate fixing capacity than with clay content across 20,000 agricultural soils in Japan: a potential role of reactive aluminum revealed by soil database approach

ABSTRACT Identifying the soil properties that control soil organic carbon (SOC) storage is a critical step to monitor and manage SOC at regional and global scales. While clay content has been commonly used to explain SOC variation and to model SOC dynamics, recent studies point to the significant control of extractable metal phases such as pyrophosphate extractable Al (Alp) for a range of soil types. Phosphate adsorption coefficient (PAC), soil’s capacity to adsorb phosphate, is known to show strong positive correlation with Alp. The soil database maintained by the national farmland fixed–point survey contains basic soil attributes including SOC and PAC and covers approximately 20,000 sites that belong to Andosols and Lowland soil great groups, representing roughly 70% of the arable land in Japan (MAT range from 5.3 to 22.3 °C). Using the database, we found that PAC showed more significant positive correlation with SOC compared to clay and silt–clay contents for both Lowland soils and Andosols. The OC:Alp ratio was estimated from the slope of SOC:PAC regression line for all soil groups and great groups ranged from 3.35 to 10.5 (mol:mol), which was within the range shown in previous studies on volcanic and spodic soils. The ANCOVA test further revealed that the SOC–PAC relationship was significantly affected by soil temperature regime in both soil group and soil great group levels (both p < 0.0001). All four soil groups within Lowland soils showed a similar temperature-dependent trend in the SOC:PAC ratio with little variation among the groups. The soil groups within Andosols, however, had no clear temperature trend, suggesting the influence of other factors that remain to be elucidated in future. We concluded that PAC was the more effective indicator of SOC than clay content for the major arable soils in Japan and that the PAC–SOC relationship was partly controlled by soil temperature and soil group. Further study to assess the role of Alp and the contribution of other factors such as land use and management type is important as major portions of the SOC variation was not explained by PAC.

Revealing horizontal and vertical variation of soil organic carbon, soil total nitrogen and C:N ratio in subtropical forests of southeastern China

Soil organic carbon (SOC) and total nitrogen (STN) are crucial soil quality indicators in a forest ecosystem. Their cycling processes and interactions have a key impact on the plants productivity, potential carbon sequestration and stability of the terrestrial ecosystem. In this study, soil profile samples (0–100 cm) were collected from 906 plots of typical subtropical forest in Zhejiang Province, southeastern China. Moran's I, geostatistics and geographic information system (GIS) techniques were used to study the vertical and horizontal heterogeneity of SOC, STN and C:N ratio. The results indicated that the contents of SOC and STN clearly decreased with the soil depth increasing (from 0 to 10 cm layer to 60–100 cm layer). The spatial distributions of SOC and STN were consistent with the topography, showing a decreasing trend from southwest to northeast of Zhejiang Province. The results of ANOVA and correlation analyses indicated that the dominant tree species, elevation and Normalized Difference Vegetation Index (NDVI) were the key factors affecting SOC and STN contents. For the total 0–100 cm soil layer, the mean densities of SOC and STN were 108.53 Mg ha−1 and 0.08 Mg ha−1, respectively. The total stocks of SOC and STN were 877.19 Tg and 84.42 Tg. Approximately 65% SOC and 45% STN were belonged to the upper 30 cm soil layer, which was strongly related to the actual soil thickness. The results could provide critical information for forestry and environmental management related to C and N accumulations in subtropical forests of China.

High-Density Poplar SRC Accumulates More Soil Organic Carbon Than Very-High-Density SRC

Short rotation coppice (SRC) systems play an important role in producing renewable energy and contributing to soil organic carbon storage while providing potential mitigation for climate change. Our chronosequence study assessed the influence of 6 years of high-density (H) and very-high-density (VH) SRCs on soil organic carbon (SOC) accumulation in an alluvial area of Piedmont (Italy) by investigating the effects of agronomic management (fertilization, irrigation, fertilization and irrigation, no treatment) using a spatial survey approach. A first sampling was performed at 40 points to characterize the variability of the initial SOC and other soil properties; 6 years after, a total of 80 samplings were carried out to verify changes in SOC. A mixed effect model procedure was used to evaluate the impact of 6 years of SRC and agronomic management on SOC, testing for autocorrelation among the model residuals. The results showed: (i) a higher accumulation potential of H-SRC (0.076 kg m−2 year−1) compared to VH-SRC (0.037 kg m−2 year−1); (ii) a significant positive influence of the initial SOC value and of fertilization associated with irrigation on the SOC sequestration; (iii) the importance of considering spatial variability at sites with high particle-size diversity in evaluating the SOC changes.

Identifying regions of inhomogeneities in spatial processes via an M-RA and mixture priors.

Soils have been heralded as a hidden resource that can be leveraged to mitigate and address some of the major global environmental challenges. Specifically, the organic carbon stored in soils, called soil organic carbon (SOC), can, through proper soil management, help offset fuel emissions, increase food productivity, and improve water quality. As collecting data on SOC are costly and time-consuming, not much data on SOC are available, although understanding the spatial variability in SOC is of fundamental importance for effective soil management. In this manuscript, we propose a modeling framework that can be used to gain a better understanding of the dependence structure of a spatial process by identifying regions within a spatial domain where the process displays the same spatial correlation range. To achieve this goal, we propose a generalization of the multiresolution approximation (M-RA) modeling framework of Katzfuss originally introduced as a strategy to reduce the computational burden encountered when analyzing massive spatial datasets. To allow for the possibility that the correlation of a spatial process might be characterized by a different range in different subregions of a spatial domain, we provide the M-RA basis functions weights with a two-component mixture prior with one of the mixture components a shrinking prior. We call our approach the mixture M-RA. Application of the mixture M-RA model to both stationary and nonstationary data show that the mixture M-RA model can handle both types of data, can correctly establish the type of spatial dependence structure in the data (e.g., stationary versus not), and can identify regions of localstationarity.