Average Soil Organic Carbon Content Research Articles

Three-dimensional spatiotemporal variation of soil organic carbon and its influencing factors at the basin scale

The variability of soil organic carbon (SOC) extends across three dimensions. However, quantitative analyses of the factors influencing spatiotemporal variations of SOC in various soil depth is scarce. This study leverages legacy data from two soil surveys conducted in the Dongting Lake Basin during the 1980s and the 2010s, employing Random Forest models to generate three-dimensional SOC maps. Through correlation analysis and permutation importance, we identified the primary factors driving temporal and spatial changes of SOC. The results showed that in the 2010s, SOC storage up to a depth of 1 m in the Dongting Lake Basin was approximately 2.95 Pg, increasing at an average rate of 0.0047 Pg C per year since the 1980s. Regions with higher average SOC contents were predominantly found in the western, southern, and eastern parts of the basin, despite significant losses over the 30-year period. In contrast, the central and northern areas, which initially had lower SOC contents in the 1980s, exhibited increases by the 2010s. Soil depth was the most influential predictor of SOC patterns in both the 1980s and 2010s. Meanwhile, relief and organism factors were primarily responsible for spatial variations in SOC, with the influence of organism factors diminishing by the 2010s. The temporal variations of SOC were chiefly attributed to changes in soil conservation practices, extreme precipitation events, and grain production. Consequently, it is imperative to prioritize ecological restoration and conservation tillage practices to mitigate the impacts of extreme weather conditions and safeguard food security.

Organic Carbon Stock in Mineral Soils in Cropland and Grassland in Latvia

This study aimed to assess soil organic carbon (SOC) concentration and stock in mineral soils in cropland and grassland in Latvia, considering soil groups and texture classes. It covered 197 sites across Latvia (152 in cropland, 45 in grassland). Soil profile description and sampling (at depths of 0–10 cm, 10–20 cm, and 20–40 cm) were conducted between 2021 and 2023. Laboratory analyses included soil bulk density (SBD), total carbon (TC), total nitrogen (TN), carbonate content, pH, and extractable phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg). SOC stock was calculated, and correlations with other soil parameters were determined. In cropland sites, Arenosols and Stagnosols had the lowest SOC concentration and stock, while Gleysols and Phaeozems had the highest. In grassland sites, Retisols exhibited the lowest SOC concentration in the 0–20 cm layer, while Planosols had the highest SOC concentration in this layer. Conversely, in the 20–40 cm layer, Retisols showed the highest SOC concentration, while Gleysols had the lowest concentration. Regarding SOC stock in grassland sites, Planosols exhibited the highest values, while the lowest values were observed for Retisols and Umbrisols. Contrary to our hypothesis that grassland exhibits higher SOC stock than cropland, our results show the reverse for Phaeozems, the dominant WRB soil group in this study: a higher average SOC concentration and stock in cropland compared to grassland. However, very low occurrence of some soil groups and lack of some soil groups for grassland sites hinders the correct interpretation of these results, and further investigations are required in future studies.

Collaborative Utilization of Sentinel-1/2 and DEM Data for Mapping the Soil Organic Carbon in Forested Areas Based on the Random Forest

Optical remote sensing data are widely used for constructing soil organic carbon (SOC) mapping models. However, it is challenging to map SOC in forested areas because atmospheric water vapor affects the results derived from optical remote sensing data. To address this issue, we utilized Sentinel-1, Sentinel-2, and digital elevation model (DEM) data to obtain a comprehensive feature set (including S1-based textural indices, S2-based spectral indices, and DEM-derived indices) to map the SOC content in forested areas. The features set were the predictor variables, and the measured SOC content was the dependent variable. The random forest algorithm was used to establish the SOC model. The ratio of performance to inter-quartile range (RPIQ) was 2.92 when the S2-based spectral indices were used as predictor variables. When the comprehensive feature set was utilized as the model input, the model achieved an RPIQ of 4.13 (R2 = 0.91, root mean square error (RMSE) = 9.18), representing a 41.44% improvement in model accuracy. The average SOC content in the Greater Khingan Mountains was 43.75 g kg−1. The northern and southwestern parts had higher SOC contents (>54.93 g kg−1), while the southeastern and northwestern parts had lower contents (<39.83 g kg−1). This discrepancy was primarily attributed to agricultural activities. The results indicate that using a comprehensive feature set and the random forest algorithm is a reliable approach for estimating the spatial distribution of the SOC content in forested areas and is suitable for forest ecology and carbon management studies.

Soil erosion and carbon export: A case study in a steep slope grazing landscape

Soil organic carbon (SOC) can be transported and deposited along the same pathways as that of soil erosion and deposition. Here soil erosion, deposition and SOC patterns are examined in a steep slope (∼30%) soil mantled environment in south-east Australia using an environmental tracer (137Cs). Multiple surface soil cores were collected and soil depth measured along two transects of similar length and aspect but at different elevation (average 550 and 800 m elevation above sea level). Both transects were located within the same paddock and managed by the same grazing methods. Both transects were found to have a high SOC concentration (∼Upper transect 6% and Lower transect 4%) and were significantly different from each other. SOC decreased moving downslope. Erosion rates for the site was 0.8 and 2.6 t ha−1 yr−1 for the Upper and Lower transects respectively and are probably higher than soil production rates. There were strong and significant positive relationships with SOC and 137Cs (r > 0.77, p < 0.0001). At this site, SOC concentration is related to erosion and deposition patterns. Using erosion rates of 0.8 and 2.6 t ha−1 yr−1 and an average SOC concentration of 5.8 and 3.8% then between 0.05 and 0.10 t ha−1 yr−1 of SOC is exported from the hillslopes which is approximately 2–4% of above ground biomass production (this being a major contributor to soil carbon). Carbon export was similar to that of other south-east Australia sites. The findings demonstrate that SOC can vary significantly in steep slope environments over short length scales highlighting the difficulty of understanding and quantifying geomorphic processes and farm carbon accounting.

Regional topsoil organic carbon content in the agricultural soils of Slovakia and its drivers, as revealed by the most recent national soil monitoring data

Soil organic carbon (SOC) is a primary constituent of soil organic matter and plays an important role in the regulation of many soil processes, including greenhouse gas emissions. Recently, SOC also became an indicator for monitoring climate change mitigation policies in the agricultural sector. The availability of up-to-date SOC inventories is thus crucial in terms of supporting SOC–related actions at country or sub-country scales. Currently, the National Monitoring System of the Agricultural Soils of Slovakia (CMS-P), whose network of 318 monitoring sites was last surveyed in 2018, is the only available source of up-to-date topsoil SOC data for agricultural land in Slovakia. Although very useful at the national scale, the number of CMS-P observations it contains is too limited for much needed sub-national SOC inventories. We hypothesized that with the aid of well-chosen macro-scale drivers of topsoil SOC accumulation in agricultural land in Slovakia, and by mapping those drivers geographically, we could upscale the CMS-P observations and produce a regional estimate of topsoil SOC. Altitude, land cover, topsoil texture, and soil type were assumed to be the key factors controlling topsoil SOC accumulation in Slovakia, and based on these, the country was classified into 14 macro-scale geographical regions. Typical ranges and mid-class values of 0–30cm topsoil SOC concentrations (%) and stocks (t ha−1) were calculated for each macro-scale region from CMS-P data. The average topsoil SOC content in agricultural land was estimated to be 2.13% (72.9 t ha−1). The highest topsoil SOC stock (> 90 t ha−1) was estimated for the lowlands of Slovakia, and the lowest (< 50 t ha−1) for the shallow and stony soils of mountain regions. When aggregated to 78 administrative regions at LAU1 level, the area-weighted averages ranged between 39.20 t ha−1 and 80.0 t ha−1, with the highest values (> 65 t ha−1) being in LAU1 regions in the south-west, south-east, and north of Slovakia where arable land is most prevalent. Total SOC storage in 0–30cm topsoil of agricultural land in Slovakia was estimated at 118.39 Mt, with two-thirds of this amount stored in arable soils in 33 south-west, south-east, and south LAU1 administrative regions. As there is no alternative and up-to-date dataset on topsoil SOC content in Slovakia, the upscaling algorithm presented in this study is an important step toward utilizing CMS-P data for sub-national SOC inventories. It may also offer a new way of providing inputs to help predict future or alternative regional topsoil SOC accumulation trajectories in Slovakian agricultural land using process-based or statistical models.

Coupling effects of soil organic carbon and moisture under different land use types, seasons and slope positions in the Loess Plateau

Understanding the response of soil organic carbon (SOC) and moisture to different land-use types, slope positions, seasonal variations, and their interactions has great significance for land use and management. However, the coupling effects of SOC and soil moisture in typical hilly and gully regions are still elusive. Therefore, in this study, five different land-use types, including cropland, abandoned land, grassland, shrubland, and forestland were selected in a typical Chinese loess hilly-gully region, and soil samples were collected from 0 to 200 cm profiles at the upper, middle, and lower slope positions in four seasons. The results demonstrated that the distributions of SOC and soil moisture were jointly significantly affected by land-use types, seasons, and slope positions (P < 0.01). Average SOC content was found to be highest in the shrubland, followed by grassland, abandoned land, forestland, and cropland. SOC storage at the lower slope position was 10.97% higher than that at the upper slope position. SOC storage in winter was 45.47% higher than that in summer. SOC storages of abandoned land, grassland, shrubland, and forestland were 17.38%, 33.08%, 52.52% and 1.26% more than that of cropland, respectively. Soil moisture fluctuated greatly in 0–100 cm layers and was stable beneath the 100 cm layers, which was highest in autumn, and lowest in summer. Whereas, under different land-use types, the soil moisture of cropland was the highest, especially significantly higher than all revegetated treatments in the 60–200 cm soil layers. Compared with cropland, soil water storage at other land-use types was averagely 36.45 % lower in the entire 200 cm soil profile. SOC was positively correlated with soil moisture under different factors. Thus, we recommend planting shrub and grass in watersheds with serious soil erosion in the semi-arid hilly and gully region, because they can sequestrate more SOC and retain more soil water.

Satellite-based estimation of soil organic carbon in Portuguese grasslands

Introduction:Soil organic carbon (SOC) sequestration is one of the main ecosystem services provided by well-managed grasslands. In the Mediterranean region, sown biodiverse pastures (SBP) rich in legumes are a nature-based, innovative, and economically competitive livestock production system. As a co-benefit of increased yield, they also contribute to carbon sequestration through SOC accumulation. However, SOC monitoring in SBP require time-consuming and costly field work.Methods:In this study, we propose an expedited and cost-effective indirect method to estimate SOC content. In this study, we developed models for estimating SOC concentration by combining remote sensing (RS) and machine learning (ML) approaches. We used field-measured data collected from nine different farms during four production years (between 2017 and 2021). We utilized RS data from both Sentinel-1 and Sentinel-2, including reflectance bands and vegetation indices. We also used other covariates such as climatic, soil, and terrain variables, for a total of 49 inputs. To reduce multicollinearity problems between the different variables, we performed feature selection using the sequential feature selection approach. We then estimated SOC content using both the complete dataset and the selected features. Multiple ML methods were tested and compared, including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN). We used a random cross-validation approach (with 10 folds). To find the hyperparameters that led to the best performance, we used a Bayesian optimization approach.Results:Results showed that the XGB method led to higher estimation accuracy than the other methods, and the estimation performance was not significantly influenced by the feature selection approach. For XGB, the average root mean square error (RMSE), measured on the test set among all folds, was 2.78 g kg−1(r2equal to 0.68) without feature selection, and 2.77 g kg−1(r2equal to 0.68) with feature selection (average SOC content is 13 g kg−1). The models were applied to obtain SOC content maps for all farms.Discussion:This work demonstrated that combining RS and ML can help obtain quick estimations of SOC content to assist with SBP management.

Afforestation resists the drought disturbance on soil carbon sink in subtropical agricultural ecosystems

Southwest China (SWC), which belongs to the subtropical climatic zone and has been the largest terrestrial carbon sink in China over the past 30 years, has been frequently disturbed by flash drought events since the 21st century. Due to its intensive land use changes during past decades, SWC has also become a global critical area of vegetation restoration. Drought disturbances and land use changes potentially affect local soil organic carbon (SOC), but how SOC responds to land-use and cover-change (LUCC) under drought disturbance in SWC is still unknown. Here, we explored the responses of SOC to vegetation cover changes when drought occurred with different frequencies in SWC. Based on multisource data synthesis, the drought conditions of SWC from 2001 to 2018 are appraised by referring to multiple drought indices, and the SOC content of the 0–10 cm soil layer in SWC is also estimated. Certain areas were disturbed by droughts more than 6 times during the last two decades, while there was an average 20.02% increase in SOC in the SWC subtropical region. Among the different subtropical ecosystems, the average SOC content increased from 25.81 g kg -1 to 33.35 g kg -1 in agricultural land, 30.4 g kg -1 to 34.97 g kg -1 in forest, and 26.97 g kg -1 to 32.89 g kg -1 in grassland. The results also indicated that drought has a significant impact on subtropical forests and agricultural land compared to grasslands. Additionally, mainly due to the self-adjustment abilities of forest ecosystems and human interference on agricultural lands, the SOC stock in subtropical forest responses is more susceptible to drought disturbance, but its capability to resist drought is stronger than that in agricultural land. Superimposing the influence of LUCC, afforestation in abandoned farmland has a positive influence on soil carbon accumulation and can greatly mitigate the adverse effects of extreme climate events on SOC accumulation under special drought patterns and conditions in the SWC subtropical zone. Moreover, the droughts that occur in SWC are considered to be a short-term moderate temperature rise that has a promoting effect on SOC sinks in afforestation areas. Our findings contribute to understanding the implications of extreme climate events and anthropogenic activities on the terrestrial carbon cycle in subtropical agricultural ecosystems.

Responses of Deep Soil Carbon and Nitrogen Contents to Long-Term Retention of Alfalfa Pasture on Infertile Loess: A Synthesis Study

Incorporating perennial pastures into annual crop systems is an efficient means of improving soil carbon (C) sequestration and reducing the application of nitrogen (N) fertilizer on farmlands. How the soil C and N at different soil depths respond to the length of pasture duration and rainfall conditions is still being determined. In this study, we conducted a meta-analysis of data from 63 published studies to investigate the impacts of the alfalfa pasture on the incorporation of soil organic carbon (SOC), total nitrogen (STN), and available nitrogen (SAN) contents in the 0–300 cm soil profile of the Loess Plateau. An annual crop field was taken as a reference. The results showed that the average SOC content at soil depths of 0–100 and 100–200 cm in the alfalfa pasture increased by 17% and 8% (p &lt; 0.001) compared to the crop field, respectively, while that at 200–300 cm decreased (p &gt; 0.05). The SOC content increased with pasture age; it was the highest when the alfalfa had been planted for 5–9 years and decreased thereafter. The STN content at soil depths of 0–100 and 100–200 cm increased by 19% and 14% (p &lt; 0.001), respectively; the content at depths of 200–300 cm only increased slightly (p &gt; 0.05). It also increased the most when the alfalfa was 5–9 years old. The increments in the SAN content at the 0–100 and 100–200 cm soil depths were higher than those of the STN, with values of 29% and 18%, respectively, while those at depths of 200–300 cm also changed insignificantly (p &gt; 0.05). The SAN content continuously increased with the age of the alfalfa, and the average increment in the 0–300 cm profile was as high as 21% when the alfalfa was ≥10 years old. The SOC and STN content increased the most under moderate rainfall conditions (350–500 mm), while the SAN content maintained the highest increment under high rainfall (500–650 mm) conditions. Therefore, ley farming with the alfalfa pasture contributed substantially to the soil C and N at depths of 200 cm in deep loess. Alfalfa should be removed in its middle ages to increase C sequestration while utilizing soil N efficiently.

Measuring the stability of soil organic carbon in Arenosols in the Senegalese Groundnut Basin

Soil organic carbon (SOC) contributes to agrosystem productivity. Understanding how farming practices implemented by smallholders affect the levels and distribution of SOC in carbon (C) pools with different stabilities is essential in sub-Saharan Arenosols where SOC mineralization is intense. The stability of SOC was studied by thermal (Therm-C), physical (particulate organic matter >50 μm, POM-C and fine soil fractions <50 μm, FF-C), chemical (permanganate-oxidizable carbon, POX-C) and biological (mineralizable C, Min-C) approaches. Soil samples were collected at depths of 0–10 and 10–30 cm in cultivated fields (out- or home-fields) without any input, with millet residues, amended with manure, or with household organic wastes. Globally, average SOC contents were low (<6 g C kg-1). The variability in SOC and C pool contents was sensitive to field management. The different approaches to measuring the stability of SOC did not measure the same fraction of SOC. POM-C and Therm-C were correlated and both explained Min-C similarly, thus suggesting that in these sandy soils, POM-C or Therm-C probably measured comparable properties of the stability of C. The lack of relationships between POX-C and other pools suggested that POX-C encompassed a different nature of SOC while providing complementary information on the biogeochemical stability of SOC.

滇南地区桃树种植模式对土壤有机碳组分及碳库管理指数的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 滇南地区桃树种植模式对土壤有机碳组分及碳库管理指数的影响 DOI: 10.5846/stxb202112103508 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（42071127）；云南大学人才项目（C176220100083） Effects of peach tree planting patterns on soil organic carbon fractions and carbon pool management index in southern Yunnan Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:经果林种植可改变土壤质量、改善生态环境，同时具有较高的经济效益。合理的种植模式可通过物种间的互补性提高资源利用效率，改善土壤碳库质量并提高综合效益。为探讨桃树种植模式对土壤有机碳组分及碳库管理指数的影响，以云南省开远市不同桃树种植模式（桃树单种-SP和桃树南瓜套种-PP）为研究对象，以毗邻的天然林地（CK）为对照，分析不同种植模式下活性碳库，即高锰酸钾氧化有机碳（POXC）、颗粒有机碳（POC），惰性有机碳库即矿物结合态有机碳（MAOC）在0-40 cm土层的分布情况，明确土壤有机碳组分与土壤理化性质的关系；计算碳库活度指数（CPAI）、碳库指数（CPI）以及碳库管理指数（CPMI），明确不同桃树种植模式的碳库变化情况。结果表明：桃树种植模式和对照的土壤有机碳组分的含量均随着土层深度的增加而减少，平均土壤有机碳（SOC）含量为：14.68 g/kg （CK）>9.57 g/kg （PP）>8.58 g/kg （SP）。平均活性有机碳组分所占比例与POC/MAOC均表现为：SP>CK>PP，PP的活性有机碳比例较低，具有较高的有机碳稳定性。两种桃树种植模式的CPMI在10-20 cm土层达到最大值；相较于PP，SP具有较高的CPAI （1.10），而PP则具有更高的CPMI （69.51），表明PP的碳库稳定性和土壤碳库质量均优于SP。桃树种植模式、土层深度以及二者的交互作用对土壤有机碳组分及其分配比例存在不同程度的影响。冗余分析结果显示砂粒、pH是影响不同桃树种植模式以及不同土层深度下土壤有机碳组分及土壤有机碳库的主要环境因子。综上，PP是较好的种植模式，有利于有机碳的固存。经果林的种植应根据土壤性质、碳库基本情况和氮磷等养分的有效性等采取适宜的管理措施，增强其固碳效率和碳汇水平。 Abstract:The ecological environment in southern Yunnan is fragile with serious land degradation. Economic orchard, as a major ecological management, has a certain effect on organic carbon sequestration. Reasonable planting patterns can improve resource utilization efficiency through the complementarity between species, increasing the quality of soil carbon pool and obtaining higher comprehensive benefits. This study was carried out on different planting patterns of peach single species (SP) and peach pumpkin interplanting (PP), located in Taoyuan Village, Kaiyuan City, Yunnan Province. Taking the adjacent natural forest (CK) as control, soil organic carbon (SOC), permanganate oxidizable carbon (POXC), particulate organic carbon (POC), and mineral-associated organic carbon (MAOC) were measured in 0-40 cm soil layer at an interval of 10 cm. Meanwhile, we calculated carbon pool activity index (CPAI), carbon pool index (CPI) and carbon pool management index (CPMI). Our objective was to investigate the effects of peach tree planting patterns on the stability of soil organic carbon and CPMI and clarify the relationship between soil organic carbon fractions and soil physicochemical properties. The results showed that the content of soil organic carbon fractions of the peach tree planting patterns and the control decreased with the increase of soil depths, the average SOC content were 14.68 g/kg (CK), 9.57 g/kg (PP), 8.58 g/kg (SP), respectively. The proportion of average POC, POXC and POC/MAOC showed as SP>CK>PP. PP had a lower proportion of active organic carbon with better organic carbon stability. Moreover, the CPMI of both peach tree planting patterns were lower than CK and reached the maximum value in the layer of 10-20 cm. SP had a higher CPAI (1.10), while PP had a higher CPMI (69.51), indicating that the stability and quality of PP soil carbon pool was better than that of SP. SOC, POXC, POC, and MAOC were all significantly positively correlated with sand, C/N, available phosphorus, exchangeable potassium, pH, and total phosphorus (in order of correlation). Peach tree planting patterns, soil depth and their interaction had different effects on soil organic carbon fractions and its proportion. Redundancy analysis also showed that sand and pH were the main environmental factors affecting the soil organic carbon fractions and soil organic carbon pool under different peach tree planting patterns and soil depths. We concluded that peach tree planting patterns changed the SOC and its fractions. Here, we consider PP as a better planting pattern, which is beneficial to organic carbon sequestration. Appropriate management measures should be taken according to the soil properties, basic situation of carbon pool and the effectiveness of nutrients such as nitrogen and phosphorus, so as to enhance the carbon sequestration efficiency and carbon sink level while improving the economic benefits. 参考文献 相似文献 引证文献

Using environmental tracers to understand soil organic carbon and soil erosion on a steep slope hillslope in south-east Australia

Context It is well recognised that soil organic carbon (SOC) can be transported and deposited along the same pathways as those of soil erosion and deposition. Aims To examine the viability of environmental tracers 137Cs and unsupported 210Pb (210Pbex) as tools to inform soil erosion and deposition patterns as well as that of the distribution of SOC. Methods Multiple soil cores were collected along two transects of similar length and aspect in a steep-slope soil mantled environment in south-east Australia. Key results Average SOC concentration was high for both transects (~6% and 4%). SOC decreased moving downslope suggesting loss of SOC by erosion. There were strong and significant positive relationships of SOC with 137Cs and 210Pbex (both r &gt; 0.77, P &lt; 0.0001). At this site, SOC concentration appears related to erosion and deposition patterns. Conclusion The hillslope distribution of 137Cs and 210Pbex were very similar, indicating that both tracers were viable in this environment (r = 0.9, P &lt; 0.0001). The different origins and half-lives of 137Cs and 210Pbex also demonstrate that the patterns of erosion and deposition are consistent at decadal time scales. Implications The use of 210Pbex provides an alternative method for understanding erosion and deposition patterns as well as that of SOC, given that the viability of 137Cs (half-life of 30.1 years) is now questionable due to no new replenishment.

ENVIRONMENTAL EFFICIENCY OF DIPTEROCARP FOREST LAND MANAGEMENT AT YOK DON NATIONAL PARK

The dipterocarp forest is a featured ecological forest in the Central Highlands of Vietnam. However, humans and nature are disrupting the ecological balance structure of the forest. This study was conducted to evaluate the environmental efficiency of land management activities of the dipterocarp forest at Yok Don National Park by altering the dipterocarp forest ecosystem and soil organic carbon (SOC) stock for 2001–2020. The results show that the area of the forest converted to other ecosystems (such as meadow, shrub land, construction land, etc.) is 8,284.51 ha. Notably, there was a decrease of 6,107.30 ha in the last 10-year studying period. In Yok Don National Park, SOC varies from 14.3 to 246.8 tons/ha. The total SOC stock is estimated at 7,644,080.493 tons. The average SOC content in the dipterocarp forest in Yok Don National Park is higher than that in other dry dipterocarp forest land.

High stand density promotes soil organic carbon sequestration in Robinia pseudoacacia plantations in the hilly and gully region of the Loess Plateau in China

Planting density has an effect on the accumulation and stabilization of soil organic carbon (SOC) in plantations. However, the mechanism by which SOC dynamics and sequestration respond to stand density is not well understood in Robinia pseudoacacia plantations. Here, we analyzed R. pseudoacacia plantations in the loess hilly-gully region of northern Shaanxi Province, China with three stand densities: 750 trees ha−1 (low-density), 1125 trees ha−1 (medium-density) and 1550 trees ha−1 (high-density). We determined the effects of stand density on SOC, SOC fractions, microbial carbon (C) use efficiency (CUE), and microbial C metabolism function. In particular, soil microbial metabolism function was investigated with the Biolog-ECO microplate. Carbohydrates and amino acids were the main available C sources for soil microbes in R. pseudoacacia plantations. Microbial biomass C (MBC), mineral-associated organic C (MOC), and dissolved organic C (DOC) content were the highest in high-density plantations, whereas easily oxidizable C (EOC) content, the MBC/SOC ratio, the DOC/SOC ratio, and the EOC/SOC ratio were the lowest in high-density plantations. Compared with low- and medium-density plantations, the average SOC content in high-density plantations increased by 61% and 52%, respectively. Across all stands, the MOC/SOC ratio ranged from 32% to 65%, whereas labile C accounted for only a minor portion of SOC (20 ± 8%). A substantially higher CUE and lower microbial metabolic activity were found in high-density plantations. Pearson correlation analysis showed that SOC content was significantly positively correlated with above-ground litter biomass and CUE but was significantly negatively correlated with microbial metabolic activity and liable C/SOC ratios. Mantel tests showed that soil nitrogen (N) level and the microbial biomass C/N ratio were the main factors affecting SOC quantity and stability changes. These results suggested that R. pseudoacacia plantations under high density increased SOC sequestration by increasing C input via aboveground litter and decreased C loss by improving microbial CUE and suppressing microbial metabolic activity. Thus, increasing stand density may be an effective tool for improving SOC sequestration in R. pseudoacacia plantations.

Improving soil organic carbon predictions from a Sentinel–2 soil composite by assessing surface conditions and uncertainties

Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil).We have collected 303 photos of soil surfaces in the Belgian loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, partial cover by a growing crop, moist soils and crop residue cover. Reflectance spectra were then extracted from the Sentinel–2 images coinciding with the date of the photos. After the growing crop was removed by an NDVI < 0.25, the Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate dry bare soils from soils in unfavorable conditions i.e. wet soils and soils covered by crop residues. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed–bed conditions. We then built the exposed soil composite from Sentinel–2 imagery for southern Belgium and part of Noord-Holland and Flevoland in the Netherlands (covering the spring periods of 2016–2021). We used the mean spectra per pixel to predict SOC content by means of a Partial Least Squares Regression Model (PLSR) with 10–fold cross–validation. The uncertainty of the models was assessed via the prediction interval ratio (PIR). The cross validation of the model gave satisfactory results (mean of 100 bootstraps: model efficiency coefficient (MEC) = 0.48 ± 0.07, RMSE = 3.5 ± 0.3 g C kg–1, RPD = 1.4 ±0.1 and RPIQ = 1.9 ± 0.3). The resulting SOC prediction maps show that the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when at least six scenes per pixel are used (mean PIR of all pixels is 12.4 g C kg–1, while mean SOC predicted is 14.1 g C kg–1). The results of a validation against an independent data set showed a median difference of 0.5 g C kg–1 ± 2.8 g C kg–1 SOC between the measured (average SOC content 13.5 g C kg–1) and predicted SOC contents at field scale. Overall, this compositing method shows both realistic within field and regional SOC patterns.

Vertical distribution of soil properties and soil organic carbon in community managed forest of Siwalik Hill, Nepal

Abstract. Lamichhane U, Ghimire P. 2022. Vertical distribution of soil properties and soil organic carbon in community managed forest of Siwalik Hill, Nepal. Asian J For 6: 91-96. Nepal's Community Forestry (CF) approach is globally recognized as an innovative and successful approach to forest resource management. Shorea robusta Gaertn. is the dominant tree species of Nepal's forest which covers 19.28% of the total stem volume of the country's forest area. Soil represents a significant terrestrial Carbon (C) pool and can play a significant role in mitigating global warming. Therefore, analyzing soil physicochemical properties and Soil Organic Carbon (SOC) stock is essential because they are key parameters for evaluating land use management systems. The present study was conducted in the Durga Mai Community Forest (DMCF) of Makawanpur District, Nepal, to assess the vertical distribution of soil physicochemical properties and SOC stock. Soil samples within a 1 m profile in five depth layers (0-20 cm, 21-40 cm, 41-60 cm, 61-80cm, and 81-100 cm) were collected and analyzed for Bulk Density (BD), soil texture, soil pH, soil carbon content, Total Nitrogen (TN), Available Phosphorous (AP), Available Potassium (AK) and SOC stock. Sandy loam was the dominant soil textural class reported throughout the study area. Average BD, soil pH, TN, AP, and AK in the S. robusta forest's soil in 1 m soil profile depth were found to be 1.16 ±0.07 gmcm-3, 5.78 ±0.62, 0.14 ±0.04 %, 5.94 ±1.04 kgha-1, and 104.20 ±19.66 kgha-1, respectively. The results estimated 107.57 ±3.06 tha-1 of SOC stock in 1 m soil profile, with 0.93 ±0.21% average SOC content. Further, average TN, AP, and AK were found to be decreased with an increase in soil profile depths. Similarly, soil profile depths significantly affected SOC stock and soil properties like BD, soil pH, TN, AP, and AK.

Spatial Variability of Soil Properties under Different Land Use Systems in Wokha District of Nagaland, India

The shifting cultivation practice is the predominant land use system of Nagaland, India, and Wokha district in particular. In general, there are four different forms of land use: forest, shifting cultivation, cultivated fallow, and wet terrace rice cultivation. In the Wokha district, a study was conducted in the year 2015 to evaluate the variability in soil characteristics among four different land use systems and to map their spatial distribution. A total of 381 soil samples were collected and tested for six soil fertility parameters; clay, pH, soil organic carbon, available nitrogen (N), phosphorus (P) and potassium (K). The results showed that all of the soils were very acidic in nature; however the mean value of soil pH was substantially higher in the shifting cultivation system. Soil organic carbon concentrations ranged from high to very high across all land use systems. Average soil organic carbon content was highest in the forest, followed by cultivated fallow, shifting, and wet terrace rice cultivation (WTRC) systems, whereas available N content was very low to low. The P concentration of the soil was very low in the WTRC system and low in the other systems. Soil K concentration was high in shifting (361.95 kg/ha), cultivated fallow (312.4 kg/ha), forest (309.73 kg/ha), and low in WTRC (166.975 kg/ha) land use systems. Available N, P and K deficiency was found in 93.8%, 72.2%, and 32.9% of soil samples, respectively. Soil organic carbon correlated positively with K and significantly positively with N. Soil pH correlated negatively with clay content and positively but non-significantly with P. The generated maps might be used to assist farmers in identifying the expected nutrient levels in their areas and encourage them to change their crop management practices to improve crop yield and profitability. Due to soil acidity, higher SOC, lower N and P, and higher potassium content, suitable acid loving crops, higher N and phosphorus fertilization, and some commercially advantageous potassium responsive crops may be encouraged in farming practices.

Spatial variations and influencing factors of soil organic carbon under different land use types in the alpine region of Qinghai-Tibet Plateau

Understanding soil organic carbon (SOC) distribution and influencing factors are important to accurately assess soil carbon sink potential and ecosystem quality. However, variations and controls of SOC content and stock under various land use types in the alpine region of Qinghai-Tibetan Plateau remain challenging. In this study, soil properties (pH, bulk density, porosity, clay content, soil organic carbon, soil water content, total nitrogen, total phosphorus, available nitrogen and available phosphorus) and environmental parameters (annual average temperature, annual average rainfall, normalized differential vegetationindex, altitude, slope, surface roughness, longitude and latitude) were determined across a 600 km east–west transect in the Tibet alpine region for investigating the distribution characteristics and controls of SOC for three land use patterns — farmland (FA), forestland (FO) and grassland (GR). Results indicated that SOC content in the 0–30 cm decreased with increasing soil depth at different reduction rates under various land use types. The average SOC content and stock in GR was significantly higher than that in FO and FA due to the existence of mattic epipdon in GR under high altitude. The non-consideration of gravel content in soil can lead to an overestimation of SOC stock of 0–30 cm soil layer by 15.37 %, 16.70 % and 17.43 % for FA, FO and GR, respectively. Soil properties and environmental conditions simultaneously affect the spatial distribution of SOC under various land use patterns. Pedotransfer functions (PTFs) using multiple regression were used to forecast SOC content in the studied alpine region, which had a better predictive performance than previous published PTFs in the plain area, especially when SOC contents were higher than 40 g kg−1. Our study provides a foundation to build SOC prediction models for other alpine regions in the world, where SOC contents generally exhibit a wider range than the plain areas.

Soil aggregate organic carbon and clover root characteristics as affected by earthworms (Metaphire guillelmi)

AbstractSoil aggregate composition is an important factor that affects soil organic carbon (SOC) content. Earthworms can greatly affect soil aggregate composition. However, the effects of earthworms' activities on SOC content in soil aggregates and the stability of these compounds was not clear. To reveal the mechanism of earthworms impacts on soil carbon stock, different earthworms densities were set in soil columns planted with clovers (Trifolium repens L.). We then analyzed the relationship that earthworms had with soil physicochemical properties and clover plant growth. Earthworms increased the large (17.5%–35.6%) and medium aggregate content (0.6%–9.79%) and decreased the microaggregates content (19.8%–29%) of the surface soil. Soil aggregates in medium density treatment were more stable than those in others. Both the mean mass diameter and geometric mean diameter of soil aggregates increased with the densities of earthworms. The average SOC content in large‐, medium‐, and microaggregates were 5.5, 4.2, and 4.2 g kg−1 respectively in soils seeded with earthworms. There was a significant correlation between soil organic carbon content and macroaggregate organic carbon content (R2 = 0.623, p &lt; 0.05). The root characteristic values (root length, surface area, diameter, and biomass) of earthworm‐treated clover‐planted soils were higher than those of control. Clover root indices were positively correlated with the soil organic carbon content and soil aggregates. Earthworms altered the soil aggregate content and enhanced soil organic carbon storage, and thus promoted the development of vegetation roots. This study provides scientific support for a deeper understanding of the mechanisms of earthworms in soil carbon storage.

Changes of SOC Content in China’s Shendong Coal Mining Area during 1990–2020 Investigated Using Remote Sensing Techniques

Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R2 &gt; 0.69, p &lt; 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg−1 in 1990 to 9.23 gC·kg−1 in 2010 and then declined to 5.31 gC·Kg−1 in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.