Soil Organic Carbon Content Research Articles

Characteristics of Soil Respiration and Organic Carbon Mineralization in Dryland Potato Fields Under Different Ridge-furrow Mulching Patterns

Exploring the response mechanism of soil respiration rate to hydrothermal factors and organic carbon mineralization under different ridge-furrow mulching modes is of high importance for the development of the regional carbon cycle and assessment of its ecological benefits. An experimental study was carried out in 2020 in a dry-crop potato field in the mountainous area of southern Ningxia by setting three furrow-ridge ratios [60 cm∶30 cm （R1）, 60 cm∶45 cm （R2）, and 60 cm∶60 cm （R3）] combined with three mulching modes [ridge covered with ordinary plastic film, furrow covered with straw （DJ）, degradable water-permeable plastic film （DS）, and no mulching （DB） in furrows, respectively]. The soil hydrothermal factors, respiration rate, organic carbon content, and mineralization characteristics of potatoes during the reproductive period under different mulching modes were investigated with plain mulching without mulching （CK） as the control. The results showed that different furrow-ridge ratios combined with the mulching mode could significantly increase the soil water storage capacity in the 0-60 cm layer, and the R3DJ treatment had a better effect, with a significant increase of 24.99% compared with that in CK. The R2 treatment had the best effect of increasing temperature during the whole life cycle of the potato. The DS treatment had the effect of increasing temperature, and the DJ treatment had the effect of decreasing temperature under different mulching materials. Different furrow and ridge cover patterns could significantly increase the average soil respiration rate during the reproductive period, and the R3DS treatment was the most significant among the different furrow and ridge cover patterns, with a significant increase of 24.71% compared with that in the CK treatment. The soil organic carbon content in the 0-20 cm and 20-40 cm layers at harvest time was higher in the R2 and R1 ridges, respectively, and higher in the DB and DS treatments for different mulching materials. The soil organic carbon mineralization rate declined rapidly in the early stage of cultivation and then slowly declined and leveled off in the middle and late stages, which was highest in the R3 treatment for the three types of ridge ratios and highest in the DS treatment for different mulching materials. The fitting equations of soil respiration rate with soil hydrothermal factors during the reproductive period revealed that the synergistic effect of soil hydrothermal dual factors on soil respiration was higher than that of a soil hydrothermal single factor and that the quadratic hydrothermal dual factors could well explain 86.4% to 99.9% of the soil respiration. Correlation analysis showed that the average soil respiration rate during the whole life span of the potato was highly significantly and positively correlated with the average soil temperature in the 0-25 cm layer and the average soil organic carbon mineralization rate in the 0-40 cm layer, and the soil temperature was highly significantly and positively correlated with the organic carbon mineralization rate. The furrow-ridge ratio combined with the mulching mode was shown to improve the soil hydrothermal environment and increase the rate of organic carbon mineralization, thus affecting the soil respiration rate, with a furrow-ridge ratio of 60 cm∶45 cm or 60 cm∶60 cm under the ridge mulching mulch ditch covering biodegradable seepage mulch mode being more effective.

Ecological practices increase soil fertility and microbial diversity under intensive farming

Intensive farming offers a potential solution to feed the growing population due to its high productivity. Conventional management (CO) based on inorganic fertilization practices degrades soil quality, but restorative practices including ecological intensification (EI) and organic management results in maintaining soil quality without compromising productivity. In this paper, two different management systems were evaluated: CO, based on inorganic fertilization, and EI, focused on providing organic nutrients to soils to support crops.EI increased soil fertility, together with higher alpha diversity indices, more differentially abundant amplicon sequence variant (ASVs) (247 EI vs. 165 CO) and indicator taxa (60 EI vs. 32 CO). Distinct bacterial taxa were associated with the different management systems, revealing their roles in soil processes and nutrient availability. In the CO treatment, indicator genera such as Nitrospira and Desulfarculaceae were linked to N fertilization and nitrite oxidation, while RB41 was associated with phosphorus availability. Ammoniphilus, PAUC26f, and BSV26 were also indicators of CO management. Conversely, EI treatment promoted bacteria involved in organic matter decomposition and nutrient cycling, such as Halomonas, Chryseolinea and Rhodobacteraceae. Gemmatimonas, Steroidobacter, Altererythrobacter, Acidibacter and Anseongella contribute to carbon and nitrogen cycling. Burkholderiaceae and Rhodopirellula play roles in phosphate solubilization and organic P mineralization, respectively. Numerous taxa with plant growth-promoting (PGP) attributes, such as BIrii41, Pseudomonas, and Lysobacter, were also identified as indicators of the EI treatment.EI associated bacteria were positively correlated with soil organic carbon contents, nitrates, and exchangeable bases, while negatively correlated with CO bacteria. A distance-based multivariate multiple regression (DistLM) demonstrated a strong relationship (r2 = 0.78) between soil physicochemical variables and bacterial community structure, with SOC explaining the most variations in the model. Other significant parameters included potassium (K), electrical conductivity (EC), and nitrates. The results suggest that EI promotes more sustainable soils in terms of fertility and microbial diversity.

Examining the effect of soil organic carbon on major Canadian Prairie crop yields with predictive soil mapping

AbstractMaintaining soil organic carbon (SOC) is critical for global food security as it is essential for soil functions that sustain crop yields. There has been an increase in predictive soil mapping, which when combined with extensive crop yield datasets, enables a better understanding of crop yield and SOC relationships. This study focused on updating maps of SOC content in Saskatchewan using recently digitized historical SOC datasets and predictive soil mapping, and using the maps to examine the relationship between SOC and crop yield. A database of 5014 SOC values was used to map SOC contents using a Random Forest model and a range of environmental covariates. The final SOC model had a R2 of 0.48, root mean square error of 0.98%, concordance correlation coefficient of 0.67, and a bias of 0.12%. The relationship between mapped SOC values and crop yield data, with 100,000–200,000 records depending on crop type, was then assessed using a linear mixed effects model after normalizing the data by rural municipality to remove broad‐scale climate effects. Overall, an increase in SOC by 1% led to an increase on average of 263 kg ha−1 for wheat (Triticum aestivum L.), 293 kg ha−1 for barley (Hordeum vulgare L.), 133 kg ha−1 for canola (Brassica napus L.), and 135 kg ha−1 for field peas (Pisum sativum L.). These results show that increasing SOC was associated with greater yields for four major crops in Saskatchewan, with the largest gains occurring when the initial SOC contents are lower.

A comprehensive review of soil organic carbon estimates: Integrating remote sensing and machine learning technologies

Abstract Purpose Accurately assessing soil organic carbon (SOC) content is vital for ecosystem services management and addressing global climate challenges. This study undertakes a comprehensive bibliometric analysis of global estimates for SOC using remote sensing (RS) and machine learning (ML) techniques. It showcases the historical growth and thematic evolution in SOC research, aiming to amplify the understanding of SOC estimation themes and provide scientific support for climate change adaptation and mitigation. Materials and Methods Employing extensive literature database analysis, bibliometric network analysis, and clustering techniques, the study reviews 1,761 articles on SOC estimation using RS technologies and 490 articles on SOC employing both RS and ML technologies. Results and Discussion The results indicate that satellite-based RS, particularly the Landsat series, is predominant for estimation of SOC and other associated studies, with North America, China, and Europe leading in evaluations with Africa is having low evaluations adopting RS technology. Trends in the research demonstrate an evolution from basic mapping to advanced topics such as carbon (C) sequestration, complex modeling, and big data utilization. Thematic clusters from co-occurrence analysis suggest the interplay between technology development, environmental surveys, soil properties, and climate dynamics. Conclusion The study highlights the synergy between RS and ML, with advanced ML techniques proving to be critical for accurate SOC estimation. These findings are crucial for comprehensive ecosystem SOC estimation, informed environmental management and strategic decision-making.

Drivers of carbon stabilization and sequestration in Brazil’s black soils

Climate and land use are recognized as two of the main drivers of changes in soil organic carbon (SOC) on a global scale. Both factors play an important role in understanding SOC sequestration and mitigation of climate change. Particularly important, black soils are mineral soils with high SOC contents and high natural fertility and play an important role in national and global food and climate security. Here, we used a database of 90 black soils in Brazil − under different climate and land use conditions across the country − to test the hypothesis that C stock is richer in wetter climate conditions and that agricultural land use reduces C stock and the percentage of carbon saturation (PCS%). Climate data were obtained from the National Oceanic Atmospheric Administration (NOAA) and used to classify Thornthwaite’s climate. The land use information was obtained in the MapBiomas platform and was grouped into three major types: cropland, pasture, and native vegetation. The nonparametric Kruskal-Wallis test showed no differences for C stock, C/N ratio, and PCS% for both land use and climate. The low C/N ratio and the strong correlation between Ca2+, CEC, clay, and SOC suggest that organo-mineral interactions − which are stronger in soils with high-activity clays (e.g., Chernozems, Kastanozems, and Phaeozems) − promotes greater stabilization of the SOC and its long-term persistence and, thus being less sensitive to variations in climate and land use. Considering the total area of approximately 3.7 × 106 ha and the average value of C stock of 93.2 Mg/ha, the total SOC stored in Brazil’s black soils is in the order of 0.35 Gt, and the carbon stock stabilization potential is 0.25 Gt. Our results highlight the potential of Brazil’s black soils to promote carbon sequestration and climate change mitigation.

Aridity drives the response of soil total and particulate organic carbon to drought in temperate grasslands and shrublands.

The increasing prevalence of drought events in grasslands and shrublands worldwide potentially has impacts on soil organic carbon (SOC). We leveraged the International Drought Experiment to study how SOC, including particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) concentrations, responds to extreme drought treatments (1-in-100-year) for 1 to 5 years at 19 sites worldwide. In more mesic areas (aridity index > 0.65), SOC and POC concentrations decreased by 7.9% (±3.9) and 15.9% (±6.2) with drought, respectively, but there were no impacts on MAOC concentrations. However, drought had no impact on SOC, POC, or MAOC concentrations in drylands (aridity index < 0.65). The response of SOC to drought varied along an aridity gradient, concomitant with interannual precipitation variability and standing SOC concentration gradients. These findings highlight the differing response magnitudes of POC and MAOC concentrations to drought and the key regulating role of aridity.

Spatiotemporal coupling dynamics and factors influencing soil organic carbon and crop yield in Chinese farmlands

Clarifying the correlation between soil organic carbon (SOC) and crop yield is key to achieving carbon neutrality and ensuring food security. However, owing to the lack of analysis based on large-scale farmland monitoring data and research on deep soil, relevant research has not yet reached a consensus. Here, we based on the monitoring data of 118 sample plots from 21 typical farmland and farmland compound ecosystem stations of the China Ecosystem Research Network (CERN) between 2004 and 2020, the temporal and spatial coupling associations between SOC content and crop yield in 0–20 cm and 0–100 cm soil layers and its factors influencing were determined. The findings revealed that between 2004 and 2020, SOC content in 0–20 cm soil layer, SOC content in 0–100 cm soil layer, and crop yield in typical farmland in China showed a fluctuating upward trend, the average annual growth rates were 0.59 %, 0.27 % and 1.07 %, respectively. The coupling relationship between SOC content and crop yield was not always good in different periods, which varies largely in different geographical divisions. Among the anthropogenic factors, exogenous carbon input can improve the coupling relationship between them by increasing the soil organic carbon content and crop yield, while the effect of less tillage and no tillage is limited. Among the natural factors, temperature, soil bulk density, and farmland type all have an impact on farmland SOC content and crop yield at different significance levels. Each variable had different effects on SOC content and crop yield in typical farmlands in different geographical regions. With deepening soil layer, influence of anthropogenic factors such as exogenous carbon input on SOC content decreases, but it still cannot be ignored. Based on these findings, the study recommends that exogenous carbon input play an important role in soil carbon sequestration and improving crop yield.

Effects of long-term biodegradable film mulching on yield and water productivity of maize in North China Plain

Biodegradable films are considered ideal alternatives to polyethylene films because of their advantages in increasing crop yield and controlling soil pollution. However, the influences of biodegradable film mulching on soil physicochemical environments and water productivity after long-term mulching remain poorly understood. Therefore, a field experiment after long-term mulching (since 2016) was conducted to explore the effects of black biodegradable film (BB), transparent biodegradable film (TB), and traditional polyethylene film (PE) on soil physicochemical environments, aboveground biomass accumulation, grain yield, evapotranspiration, and water productivity (WP) of maize in the 2021 and 2022 in the North China Plain. The results showed that polyethylene films and biodegradable films had a similar ability to preserve soil moisture, promote maize growth, reduce evapotranspiration, and increase WP. Moreover, the performances of BB were more equivalent to PE, and there was no significant difference on WP and yield under BB and PE. Compared with traditional flat planting without mulching (CK), BB and PE significantly increased yield (9.5 % and 12.7 % in 2021; 5.25 % and 6.37 % in 2022) and WP (11.54 % and 21.47 % in 2021; 24.28 % and 23.42 % in 2022). Film mulching treatments increased the soil organic carbon sequestration rate, and the content of soil organic carbon, microbial activity, and urease activity in the 0–20 cm soil layer compared with CK. According to structural equation modeling, the increasing soil water storage because of films mulching positively influenced yield by enhancing enzyme activities which were related to soil nutrients. Over all, these results showed that black biodegradable film is an ideal replacement for polyethylene films under long-term mulching conditions because of its comparable agronomic performance and influence on soil physicochemical environments in the North China Plain.

Vegetation restoration changed the soil aggregate stability and aggregate carbon stabilization pathway according to δ13C signatures

Vegetation restoration can increase soil organic carbon (SOC) sequestration through the physical protection of soil aggregates. However, the soil aggregate stability and C flow pathway associated with long-term plantation restoration have not yet been fully characterized. Here, we conducted a study on Robinia pseudoacacia plantations at different recovery stages, studied the distribution and stability of aggregates, analysed the aggregate-associated organic carbon (OC) content and δ13C value, and quantified the aggregate C flow pathway. The results revealed that vegetation restoration increased the proportion of large macroaggregates (LMAs) and decreased the proportion of small macroaggregates (SMAs), with no changes observed in the proportion of microaggregates (MIAs) or silt + clay (SC) at 0–20 cm. The indices of aggregate stability, namely, the mean weight diameter (MWD), geometric mean diameter (GMD) and structural stability index (SSI), increased under vegetation restoration at 0–20 cm, with maximum values of 3.83 mm, 2.88 mm, and 2.00 %, respectively, at 35 years of age (35Y). The OC content of the LMAs increased from 10.96 to 21.64 g kg−1 and from 7.27 to 10.05 g kg−1 in the 0–20 cm and 20–40 cm layers, respectively. LMAs and SMAs had the greatest contributions to SOC accumulation in the 0–20 cm and 20–40 cm layers, respectively. The δ13C value increased with decreasing aggregate size. The C flow pathway was from macroaggregates to MIAs or SC. Compared with abandoned farmland, vegetation restoration decreased the aggregate C flow intensity in the 0–20 cm layer. The soil aggregate stability and aggregate-associated OC content decreased with increasing soil depth, but the soil δ13C value exhibited the opposite trend. Vegetation restoration regulated soil aggregate stability by influencing the fine root biomass (FRB) and SOC content. In summary, our analysis offers a valuable reference for the controlling effect of aggregation on C stability influenced by vegetation restoration.

A new methodology for establishing an SOC content prediction model that is spatiotemporally transferable at multidecadal and intercontinental scales

Quantifying and tracking the soil organic carbon (SOC) content is a key step toward long-term terrestrial ecosystem monitoring. Over the past decade, numerous models have been proposed and have achieved promising results for predicting SOC content. However, many of these studies are confined to specific temporal or spatial contexts, neglecting model transferability. Temporal transferability refers to a model’s ability to be applied across different periods, while spatial transferability relates to its applicability across diverse geographic locations for prediction. Therefore, developing a new methodology to establish a prediction model with high spatiotemporal transferability for SOC content is critically important. In this study, two large intercontinental study areas were selected, and measured topsoil (0–20 cm) sample data, 27,059 cloudless Landsat 5/8 images, digital elevation models, and climate data were acquired for 3 periods. Based on these data, monthly average climate data, monthly average data reflecting soil properties, and topography data were calculated as original input (OI) variables. We established an innovative multivariate deep learning model with high spatiotemporal transferability, combining the advantages of attention mechanism, graph neural network, and long short-term memory network model (A-GNN-LSTM). Additionally, the spatiotemporal transferability of A-GNN-LSTM and commonly used prediction models were compared. Finally, the abilities of the OI variables and the OI variables processed by feature engineering (FEI) for different SOC prediction models were explored. The results show that 1) the A-GNN-LSTM that used OI as the input variable was the optimal prediction model (RMSE = 4.86 g kg−1, R2 = 0.81, RPIQ = 2.46, and MAE = 3.78 g kg−1) with the highest spatiotemporal transferability. 2) Compared to the temporal transferability of the GNN, the A-GNN-LSTM demonstrates superior temporal transferability (ΔR2T = −0.10 vs. −0.07). Furthermore, compared to the spatial transferability of LSTM, the A-GNN-LSTM shows enhanced spatial transferability (ΔR2S = −0.16 vs. −0.09). These findings strongly suggest that the fusion of geospatial context and temporally dependent information, extracted through the integration of GNN and LSTM models, effectively enhances the spatiotemporal transferability of the models. 3) By introducing the attention mechanism, the weights of different input variables could be calculated, increasing the physical interpretability of the deep learning model. The largest weight was assigned to climate data (39.55 %), and the smallest weight was assigned to vegetation (19.96 %). 4) Among the commonly used prediction models, the deep learning model had higher prediction accuracy (RMSE = 6.64 g kg−1, R2 = 0.64, RPIQ = 1.78, and MAE = 4.78 g kg−1) and spatial transferability (ΔRMSES = 1.43 g kg−1, ΔR2S = −0.13, ΔRPIQS = −0.50, and ΔMAES = 1.09 g kg−1), and the linear model had the higher temporal transferability (ΔRMSET = 1.46 g kg−1, ΔR2T = −0.14, ΔRPIQT = −0.45, and ΔMAET = 1.29 g kg−1). 5) The deep learning models necessitated the OI, whereas the linear and traditional machine learning models necessitated FEI to achieve higher prediction accuracy. This study presents an important step forward in integrating multiple deep learning models to build a highly spatiotemporal transferability SOC prediction model.

Devastation of the cerrado of mato grosso do sul and the advance of arenization in the pardo river watershed

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes.

Using convolutional neural network combined with multi-scale channel attention module to predict soil properties from visible and near-infrared spectral data

With the increasing demand for precision agriculture and sustainable land management, it is crucial to quickly and accurately predict soil properties. However, there are still challenges in predicting large-scale soil properties. This study proposes a new convolutional neural network architecture (MCA-CNN) combined with visible and near-infrared spectroscopy for predicting soil properties. The architecture introduces a multi-scale channel attention mechanism, aiming to more effectively capture complex features in the spectrum. The performance of the MCA-CNN model was tested on a large-scale dataset and compared with partial least squares (PLS), extreme gradient boosting tree (XGBoost), and conventional convolutional neural network (CNN) models. The predictive performance of different models in organic carbon (OC), nitrogen (N), pH, Clay, Salt, and Sand was analyzed. The results showed that the MCA-CNN model had the best predictive performance on all soil properties, achieving the lowest root mean square error (RMSE) and the highest coefficient of determination (R2), the R2 of OC, N, pH, Clay, Silt, and Sand are 0.95, 0.93, 0.95, 0.80, 0.58, 0.64, respectively. The RMSE of OC, N, pH, Clay, Silt, and Sand are 16.60 g kg−1, 0.96 g kg−1, 0.31, 4.89 %, 8.27 %, and 11.61 %, respectively. Further research has found that soil types (mineral soil and organic soil) affect the predictive performance of OC. It is necessary to distinguish between mineral soil and organic soil when predicting soil OC content. In addition, the effectiveness of the model transfer strategy was explored for situations with small soil sample sizes. By fine-tuning the pre-trained model, the performance of the model can be significantly improved, which provides a feasible solution for predicting soil properties in situations of data scarcity. In summary, this study not only confirms the potential application of deep learning in the field of soil science, but also provides new technological avenues for future soil management and agricultural practices.

Effects of temperature, relative humidity and soil organic carbon content on soil-air partitioning coefficients of volatile PFAS.

Soil-air partitioning coefficient (KSA) values are often used to assess the environmental fate of organic contaminants in soil. Till now, sufficient KSA values have not yet been measured for many compounds of interest, including some emerging pollutants such as volatile PFAS. Moreover, the effects of environmental factors such as temperature, relative humidity and soil organic carbon content on KSA of volatile PFAS are also unclear. In this study, the KSA values of target volatile PFAS were measured under various temperature (20-40 °C), relative humidity (30-100 %) and soil organic carbon content (2.1 %-8.0 %) using a modified solid-phase fugacity meter. The results showed that higher temperatures, higher relative humidity and lower organic carbon content in soil may accelerate the diffusion of target volatile PFAS. Furthermore, the KSA measurements were used to derive a multiple linear regression model to depict the relationship between logKSA and temperature, relative humidity, soil organic carbon content and PFAS-specific logKOA. When compared with the predictions obtained from semi-empirical model, we argued that the multiple linear regression model is more robust and easier to implement for target volatile PFAS or other emerging volatile PFAS than the semi-empirical approach to help depict the diffusion process at target volatile PFAS contaminated sites.

Evaluation of nature-based climate solutions for agricultural landscapes in the Galápagos Islands

The Galápagos Islands are highly vulnerable to climate and environmental change, and nature-based solutions can help local communities adapt local agricultural systems. Through a comparative analysis, we evaluated the effects of three land management strategies on soil temperature, soil water availability and storage, and carbon stocks in Santa Cruz Island (Galápagos Archipelago). We installed six monitoring sites that consisted of two replicates per pathway: (i) the avoided loss of tropical forest, (ii) the conservation of scattered trees and living fences in at-risk agroforestry system, and (iii) the increase in biomass after a reduction of the grazing intensity. The monitoring sites were equipped with a dense network of rain gauges, air temperature and relative humidity sensors, and capacitance/frequency probes that registered volumetric water content and soil temperature. After pedological characterization of the soil profiles, the soil physico-chemical and hydrophysical properties were determined in laboratory. Over a period of 30 months (July 2019 to December 2021), hydrometeorological and soil environmental data were collected.We assessed differences in soil temperature, moisture availability and soil organic carbon content between native forests, sites under traditional agroforestry and under passive restoration. Forest soils were 12 % cooler;, and soil moisture under forest was 20 % higher than in parcels with silvopastural management. Forest soils had a lower dry bulk density, lower saturated hydraulic conductivity and higher water retention capacity in comparison with the other two management types. In silvopastural systems, a decrease of grazing intensity had a positive effect on soil carbon stocks, that were about 50 % higher than in soils under traditional management. This study shows that avoided loss of tropical forest within an agricultural landscape is a promising strategy to mitigate increasing soil temperatures, agricultural drought, and decline in soil organic carbon content. Continued monitoring of the experimental sites is necessary to corroborate the findings of this investigation at longer temporal scales.

Influence of soil organic carbon fractions on the soil priming effect under different vegetation restoration modes

AbstractThe soil priming effect is a key mechanism influencing carbon (C) cycling processes between the forest soil organic carbon (SOC) pool and the atmosphere. Different vegetation restoration modes have different SOC pool compositions, and it is not clear whether such differences affect the soil priming effect. Therefore, we selected soil from six typical vegetation restoration modes Platycladus orientalis (PO), Pinus sylvestris (PS), Quercus acutissima (QA), shrub (SH) and wasteland (WL) in the soil and rocky mountainous areas of northern China and measured the soil properties, microbial communities, microbial necromass carbon (MNC), SOC fractions and the soil priming effect. The SOC content decreased in the order of PO (21.33 g kg−1), PS (22.00 g kg−1), QA (13.67 g kg−1), SH (13.33 g kg−1) and WL (10.33 g kg−1), and the trends of the mineral‐associated organic carbon (MAOC) and fungal necromass carbon (FNC) content were the same as those of the SOC content. The soil priming effect was greater in both forests and shrublands than in wastelands, with the greatest effect occurring in PO forests, where the soil priming effect reached 159.91 mg CO2‐C kg−1 soil after 30 days of incubation, which was 1.4 times greater than that in WL. The soil priming effect was mainly determined by the difference in MAOC content. In addition, the soil C/N ratio and bacterial community diversity also indirectly affected the soil priming effect by influencing the soil MNC and SOC fractions. Overall, afforestation increased the SOC content, fungal necromass contribution and mineral conservation, increasing the soil priming effect. This theoretical foundation supports the enhancement of SOC sequestration capacity by implementing various modes of vegetation restoration in the future.

Accumulation of soil microbial necromass carbon and its contribution to soil organic carbon after vegetation restoration in the Tibetan Plateau

Vegetation restoration has been proved as an effective strategy to increase soil organic carbon (SOC) sequestration in degraded ecosystems. However, due to different vegetation restoration practices and climate zones, changes of SOC content, dynamics, sources and their controlling mechanisms after vegetation restoration remain inadequately addressed. Taking the advantage of four-year vegetation restoration, we explored the effects of vegetation restoration on SOC through its fractions, soil aggregates, soil enzymes and microbial necromass contribution to SOC in a desertification region in the Tibetan Plateau (TP). Results showed that vegetation restoration increased SOC contents by 68% for 0 – 10cm, 29% for 10 – 30cm and 11% for 30 – 50cm compared to site without vegetation restoration. Vegetation restoration increased soil macroaggregates (> 0.25mm) and decreased soil microaggregates (0.53 – 0.25mm), but increased their associated SOC contents. Meanwhile, vegetation restoration enhanced microbial necromass and its contribution to SOC. Structural equation model revealed that increasing microbial necromass carbon, changing soil aggregates and their associated SOC were the main mechanisms of increasing SOC content after vegetation restoration. Our result further indicated a large potential of SOC sequestration through vegetation restoration in the TP. These findings have important implications for natural based solution for carbon neutrality for China.

Primary productivity regulates rhizosphere soil organic carbon: Evidence from a chronosequence of subtropical Chinese fir (Cunninghamia lanceolata) plantation.

Tree plantations worldwide are a large terrestrial carbon sink. Previous studies on the carbon sequestration capacity of plantations mainly focused on tree biomass carbon sequestration, but the importance of soil organic carbon (SOC) was relatively unclear. Living root carbon inputs influence SOC via plant-microbe interactions in the rhizosphere and play an essential role in nutrient cycling. Here, we compared SOC, including its fractions, microbial properties, and major nutrients in rhizosphere and bulk soils, and examined their relationships to net primary productivity (NPP) across three developmental stages of Chinese fir (Cunninghamia lanceolata) plantations (6, 18, and 42 years old) in subtropical China. Although NPP differed among the three plantations, SOC concentration in bulk soils did not vary significantly among them. However, SOC concentration and labile C pool I and recalcitrant C pool in rhizosphere soils were significantly (p < 0.05) higher in the young (6-year) and mature (42-year) plantations, both of which had lower (p < 0.05) NPP (-37.71 % and - 42.67 %) compared to the middle-aged (18-year) plantation, suggesting a decoupling of NPP from rhizosphere SOC in the plantations. The decoupling of NPP from rhizosphere SOC concentrations may be driven by nitrogen (N) and phosphorus (P) tree growth requirements, belowground C allocation, and resultant microbial activity in this highly weathered subtropical soil. Our study provides field-based evidence suggesting that rhizosphere SOC changes are primarily regulated by net primary production in subtropical forest plantations. We propose that accurate predictions of SOC dynamics in forest plantations require an improved understanding of rhizosphere processes during plantation development.

Assessment of the capability of Landsat-8 satellite imagery for predicting soil organic carbon distribution

Soil organic carbon (SOC) is an important component of soil and plays a crucial role in addressing climate change. As a key component of soil organic matter, SOC directly impacts soil fertility, water retention, nutrient cycling, and overall soil health. The determination of SOC concentrations in soil often relies on costly physical sampling and chemical analysis. The aim of this research was to build a predictive model of SOC using satellite imagery of Landsat 8 OLI/TIRS over an agricultural area (Oued El Alleug) in the north of Algeria. The statistical correlations between the spectral bands (B2 and B6) and chemically measured SOC concentrations showed that it is possible to predict spatially the SOC concentrations. The results also showed that the topographic variables are not determinant in the spatial prediction of SOC concentrations. The predicted model showed an acceptable performance with a coefficient of determination (R2) = 0.7 and a root mean square error (RMSE) = 7.08 g/kg during the validation phase. The results of this study are important, as they will facilitate decision-making in soil conservation practices and enhance land management, especially in areas facing increasing agricultural and environmental pressures.

Long‐Term Biochar Application Improved Aggregate K Availability by Affecting Soil Organic Carbon Content and Composition

ABSTRACTStraw biochar is an effective amendment at improving soil aggregate structure and increasing soil carbon and potassium (K) content. However, little information is available on the relationship between soil organic carbon (SOC) and aggregate‐associated K distribution under long‐term biochar application conditions. To address this, a field trial established in 2013 was used to examine the impact of biochar (B0: 0 and B1: 2.625 t ha−1 year−1) and K fertilizer (K0: 0 and K1: 60 kg ha−1 year−1) on the variation in soil aggregate K and reveal the associated influencing factors. A total of four treatments (B0K0, B0K1, B1K0, and B1K1) were included in this study. The soil analysis results obtained in 2021 showed that after 9 years' amendment, B1K1 increased the aggregate exchangeable K (EK) and nonexchangeable K (NEK) pools by 27.40% and 39.55%, respectively, and the increment was primarily because biochar enhanced &gt; 0.25 mm aggregate fractions and strengthened soil K+ adsorption capacity, which benefit from a synergistic increase in SOC and humic acid (HA) content by biochar. 13C NMR analysis showed that long‐term biochar applications altered the chemical composition of SOC, with an outcome of increased aromaticity and hydrophobicity but decreased the lipidation of SOC, indicating that the complexity of SOC molecular structure was enhanced and eventually contributed to strengthening the macroaggregates stability and soil K+ adsorption capacity. The correlation analysis revealed that soil aggregate EK and NEK contents were positively correlated with SOC and HA contents, which further proved that increase of SOC and soil HA is a significant mechanism for biochar ameliorate soil aggregate‐associated K availability.

The rhizosphere contributes disproportionately to free-living nitrogen fixation in subalpine forest soils

Root activity creates a unique microbial hotspot in the rhizosphere, profoundly regulating free-living nitrogen fixation (FLNF). However, empirical assessments of rhizosphere FLNF and its ecological consequences for nitrogen (N) budgets remain lacking, particularly for different root functional modules. Here, we separately collected rhizosphere soils attached to two root functional modules, absorptive roots and transport roots, and investigated the rates (15N2 incorporation) and regulators of rhizosphere FLNF in a N-limited subalpine coniferous forest. The measured rates were further extrapolated to annual fluxes based on the volume of the rhizosphere and the relationship between FLNF rate and temperature. We found that absorptive roots drove significantly higher rhizosphere FLNF rates than did transport roots (12.2 vs. 7.5 ng N g-1 d-1), with both rhizosphere compartments exhibiting rates well exceeding those in the bulk soil (0.9 ng N g-1 d-1). The FLNF rates were positively correlated with soil organic carbon content but showed no significant relationship with the abundance or composition of the diazotrophic community. Moreover, when extrapolated to the ecosystem level the FLNF fluxes were 2-fold greater in the rhizosphere of absorptive roots than in that of transport roots (0.13 vs. 0.04 kg N ha-1 yr-1). Taken together, despite representing only 6.0% of the soil volume, the two rhizosphere compartments contributed as much as 47.2% to the total soil FLNF fluxes. Overall, we provide empirical evidence that despite its limited volume, the rhizosphere contributes disproportionately to the FLNF in subalpine forest soils. Our findings also underscore the critical role of root functional differentiation in regulating rhizosphere FLNF, which is essential for integrating this process into the ecosystem-level N cycle.