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Simple Summary: In this paper, the differences in carbon sequestration microbial communities in different wetland types and their main influencing factors were investigated. It was found that the alpha diversity of cbbM carbon-sequestering microorganisms was consistent with the change trend in the total carbon content. Acidithiobacillus was used as a biomarker in lakeside wetlands, and Thiothrix and Thiodictyon were used as biomarkers in marsh wetlands. The diversity of cbbM carbon-fixing microorganisms was primarily influenced by the total nitrogen content, while the community structure was significantly affected by the soil total carbon content. The increase in soil temperature and humidity was conducive to the carbon-sequestering process of Thiomicrospira, Thiomonas, Polaromonas and Acidithiobacillus. The changes in wetland types seriously affected the characteristics of cbbM carbon sequestration in microbial communities, and a warm and humid climate may be conducive to wetland carbon sequestration. Carbon-sequestering microorganisms play an important role in the carbon cycle of wetland ecosystems. However, the response mechanism of carbon-sequestering microbial communities to wetland type changes and their relationship with soil carbon remain unclear. To explore these differences and identify the main influencing factors, this study selected marsh wetlands, river wetlands and lakeside wetlands around Qinghai Lake as research subjects. High-throughput sequencing was employed to analyze the functional gene cbbM of carbon-sequestering microorganisms. The results revealed that the alpha diversity of cbbM carbon-sequestering microorganisms mirrored the trend in total carbon content, with the highest diversity observed in marsh wetlands and the lowest in lakeside wetlands. The dominant bacterial phylum was Proteobacteria, with prevalent genera including Thiothrix, Acidithiobacillus, and Thiodictyon. Acidithiobacillus served as a biomarker in lakeside wetlands, while two other genera were indicative of marsh wetlands. The hierarchical partitioning analysis indicated that the diversity of cbbM carbon-fixing microorganisms was primarily influenced by the total nitrogen content, while the community structure was significantly affected by the soil total carbon content. Moreover, an increased soil temperature and humidity were found to favor the carbon fixation processes of Thiomicrospira, Thiomonas, Polaromonas, and Acidithiobacillus. In summary, changes in wetland types seriously affected the characteristics of cbbM carbon sequestration in microbial communities, and a warm and humid climate may be conducive to wetland carbon sequestration. [ABSTRACT FROM AUTHOR]

Organic carbon performs essential functions in soils, which act as sources or sinks of atmospheric organic carbon. Agricultural management affects the carbon cycle in the soil, with effects on climate change. One of the crops most vulnerable to climate change is coffee. Brazil is the world’s largest coffee producer, with a predominance of management under a conventional system, with sloping terrain and the absence of conservationist practices. The absence of conservationist practices increases in soil loss rates due to water management and carbon emissions, as well as a reduction in coffee production. This paper intended to estimate soil and organic carbon losses by RUSLE in coffee farms in southern Minas Gerais, south-eastern Brazil. Data were obtained from fieldwork, laboratory analysis, and cartographic products. The results indicated, exclusively for coffee crops, soil and carbon losses between 7 and 32 Mg ha−1 year−1 and 87 and 460kg ha−1 year−1, respectively. However, the highest soil losses occurred on sloping terrains with eucalyptus plantations located downhill, and the lowest losses occurred on flat land with native forests. Organic carbon losses were linked directly to soil losses, as a result from the land practices, slope and agricultural management adopted. These results can be used for the planning and priority definition of areas needing conservationist practices, such as green manuring, planting in contour and maintaining of vegetation between coffee rows, which are already used in some sites of the study area. [ABSTRACT FROM AUTHOR]

Antioxidants (AOX) in soils originate mainly from secondary plant metabolites and are pivotal in many redox processes in environment, maintaining soil quality. Still, little is known about the influence of land uses on their accumulation in soil. The aim of the paper was to determine the content of these redox-active compounds in the extracts of A horizons of abandoned fallows, arable and woodland soils. Total antioxidant capacity (TAC) of soils under various uses and vegetation was evaluated in different soil extracts using Folin-Ciocâlteu method. The contribution of humic acids to TAC was determined and antioxidant profiles estimated using the chromatographic GC–MS method. Forest soils exhibited the highest TAC (15.5 mg g−1) and AOX contents (4.34 mg g−1), which were positively correlated with soil organic carbon content. It was estimated that humic acids contribute to over 50% of TAC in soils. The main phenolics in woodland A horizons were isovanillic and p-hydroxybenzoic acid (p-HA), while esculetin and p-HA predominated in the abandoned fallows due to the prevalence of herbaceous vegetation. Cultivated soils were the most abundant in p-HA (56.42%). In the studied topsoils, there were considerable amounts of aliphatic organic matter, which role in redox processes should be further evaluated. [ABSTRACT FROM AUTHOR]

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study's findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers' ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study's approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions. [ABSTRACT FROM AUTHOR]

The research papers assessing the content of soil organic carbon with the help of Vis-NIR spectroscopy approaches are systematically analyzed and subject to meta-analysis. This meta-analysis included 134 studies published in 1986–2022 with a total sample of 709 values of quantitative metrics. The papers have been searched for in databases of scientific periodicals (RSCI, Science Direct, Scopus, and Google Scholar) by the key word combination "Vis-NIR spectroscopy AND soil organic carbon". The meta-analysis using the nonparametric one-sided Kruskal–Wallis variance analysis in conjunction with nonparametric pairwise method shows the presence of a statistically significant difference between the median values of the accepted quantitative metrics of the predictive power of the models, namely, coefficient of determination (R2cv/val), root mean square error (RMSE), and the ratio of performance to deviation (RPD). The best performance of the preprocessing method for spectral curves is demonstrated and the estimates of soil organic carbon content obtained by laboratory and field spectroscopies are compared. [ABSTRACT FROM AUTHOR]

With the global climate problem becoming increasingly severe, governments have adopted policies to encourage enterprises to invest in low-carbon technologies. However, the opacity of the carbon emission reduction process leads to incomplete consumer trust in low-carbon products as well as higher supply chain transaction costs. Based on this, this paper constructs Stackelberg game models with and without blockchain under different power structures and compares the impact of these models on low-carbon emission reduction decisions. The results show that: (1) blockchain does not necessarily improve enterprise profits and can only help enterprises maintain optimal profits within a certain range when the carbon emission cost is low; (2) when consumers' environmental awareness is high, the blockchain can incentivize manufacturers to enhance carbon emission reduction, and it has an obvious promotional effect on retailers' profits; and (3) the profit gap between enterprises in the supply chain is larger under different power structures, and the implementation of blockchain can coordinate profit distribution and narrow the gap between enterprises. Compared with the manufacturer-dominated model, the emission reduction in products is maximized under the retailer-dominated model. Our study provides theoretical support for the government to regulate greenhouse gas emissions as well as for the optimization of enterprises' decision-making supported by blockchain. [ABSTRACT FROM AUTHOR]

Mining sector has been the central attention of the business and public policy sustainable development scheme for several years. Reclamation territories are with potential carbon sequestration capacity in degraded mining areas and can be an impeccable option for achieving sustainable development goal-13. This paper made an investigation about the presence of heavy metals in reclaimed area possibilities to improve reclaimed soils with biochar with idea to enhance ecosystem carbon pool and atmospheric CO2 sequestration capacity to offset CO2 emission and soil organic carbon losses. The results show that the carbon content in biochar is 46.6 % and would be classified in class 2 according to the International Biochar Initiative. H/C and O/C atomic ratios could be useful for restoration of degraded poor mine soils through enhanced carbon sequestration. O/C ratio is 0.085 mol mol–1 and half-life of biochar is 2795.65 years, which confirm the biochar stability. For each kilogram of biochar incorporated into the soil, 144 g of carbon were sequestrated 52,8 % CO2 emission reduction. Hence, the biochar developed in this study can be used in carbon-deficient soils with the added benefit of long-term carbon storage. [ABSTRACT FROM AUTHOR]

Background: Grazing lands provide numerous ecosystem services, including the capacity to store large amounts of soil carbon (C); however, our understanding of the factors affecting soil C responses to management is inadequate. Methods: In this paper, we synthesized information about the impacts of grazing land management on soil C within the southeastern region of the United States. Results: Most studies (68%) demonstrated that proper management increased soil C stocks with an average C accumulation rate of 1.4 Mg C ha−1 year−1. The remaining (32%) studies showed no effect of management on soil C. None of the studies reported negative impacts of management on soil C. The largest increase in soil C (8 Mg C ha−1 year−1) was associated with the conversion of cropland to pastureland while overseeding legumes into grass pastures resulted in the lowest C sequestration rates (0.2 Mg C ha−1 year−1). Conclusions: Considering the rates of soil C accretion and the extensive area occupied by grazing lands, data in this synthesis confirm that the adoption of improved management practices can increase soil C sequestration in the southeastern United States. In addition, because most improved management practices can also have co‐benefits, they may be favorable for forage and livestock production and other ecosystem services. [ABSTRACT FROM AUTHOR]

Biochar incorporation into soil has shown potential, in enhancing nitrogen fertilizer (N-fertilizer) efficacy and soil organic carbon content (SOC). This study addresses a critical gap in the literature by investigating the effects of biochar addition over a seven-year period (2014–2020) on inorganic N, SOC, and pH in Haplic Luvisol. The research involved a rain-fed field experiment, with a crop rotation comprising spring barley, maize, spring wheat, and pea. Biochar, applied at the rates of 0, 10, and 20 t ha−1 in 2014, was reapplied to specific plots in 2018. Biochar was also combined with N-fertilizer at three level (N0, N1, and N2). Results showed a significant interactive influence of biochar and N-fertilizer combination on NO3− and NH4+ contents. Intriguingly, the addition of 10 t biochar ha−1 consistently decreased soil inorganic N levels across most of the examined months. Increasing biochar application rates led to a significant rise in pH, establishing a clear, negative correlation between soil pH and inorganic N content. Biochar significantly increased SOC compared to the control, particularly after the reapplication in 2018. However, this effect showed a diminishing trend over time. The study suggests that incorporating biochar treatments may enhance N-fertilizer effectiveness. However, the long-term implications of biochar application with N-fertilizer on N mineralization are specific to individual soil and biochar combinations. Except the application of 20 t ha−1 biochar at N2 in 2019, biochar did not affect the crop yields. Studied soil properties, including those influenced by biochar had nuanced impact on different aspects of crop yield. Article Highlights: Biochar obtained from mixed paper fiber sludge and the grain husks resulted in a significant increase of SOC over 7 years. Biochar aging resulted in a decrease in pH one year after its application in 2015 and 2019. The combination of N-fertilizer with biochar caused an improvement in soil inorganic N content in 2014 and reapplication in 2018. [ABSTRACT FROM AUTHOR]

Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0.1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km 2) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68 –2.38 gC m -2 , R2=0.87 –0.77; biomass: RMSE =11.34 g m -2 , R2=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias =-47 g m -2 , - 39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes. [ABSTRACT FROM AUTHOR]

Background: The study of soil organic carbon characteristics and its relationship with soil environment and vegetation types is of great significance to the evaluation of soil carbon sink provided by inland salt marshes. This paper reports the characteristics of soil organic carbon fractions in 0–50 cm soil layers at four vegetation communities of the Qinwangchuan salt marsh. Results: (1) The soil organic carbon content of Phragmites australis community (9.60 ± 0.32 g/kg) was found to be higher than that of Salicornia europae (7.75 ± 0.18 g/kg) and Tamarix ramosissima (4.96 ± 0.18 g/kg) and Suaeda corniculata community (4.55 ± 0.11 g/kg). (2) The soil dissolved organic carbon, particulate organic carbon and soil microbial biomass carbon in 0–50 cm soil layer of Phragmites australis community were higher, which were 0.46 ± 0.01 g/kg, 2.81 ± 0.06 g/kg and 0.31 ± 0.01 g/kg, respectively. (3) Soil organic carbon was positively correlated with dissolved organic carbon, particulate organic carbon, and microbial biomass carbon, and negatively correlated with easily oxidized organic carbon. (4) Above-ground biomass has a strong direct positive effect on soil organic carbon, total nitrogen and pH have a strong direct positive effect on microbial biomass carbon content, pH and average density have a strong direct negative effect on easily oxidized organic carbon, and particulate organic carbon. Conclusions: The interaction between plant community characteristics and soil factors is an important driving factor for soil organic carbon accumulation in inland salt marshes. [ABSTRACT FROM AUTHOR]

Aim: The accumulation of microbial necromass carbon has gained increasing attention due to its slow decomposition. However, it remains unclear what induces the accumulation of microbial necromass carbon via reiterated community turnover on large spatial scales. This study explores the characteristics of soil necromass carbon accumulation in terrestrial ecosystems. Location: Global. Time Period: Contemporary (1999–2022). Major Taxa Studied: Soil microorganisms. Methods: A dataset was compiled using 993 observations from 82 peer‐reviewed papers on the effects of coefficients of microbial necromass accumulation (hereafter NAC) in equilibrium. Linear mixed‐effect models and structural equation models were used to ascertain the controlling factors of the NAC. Results: The average NAC was higher in croplands (28.2) and forests (26.8) than in grasslands (21.1). Edaphic factors seemingly affected the NAC, which was lower in soils with high pH and clay content on a global scale. Biotic factors, particularly those related to living microorganism abundance and microbial biomass nitrogen content, were the pivotal drivers of NAC and accounted for approximately 43.6% of its geographic variability. More organic carbon was likely to be preserved in soil with a higher NAC, regardless of ecosystem type. Conclusions: Novel findings regarding the overriding controls for the living microorganism abundance and microbial biomass nitrogen that drive the NAC highlight an urgent need for viable strategies to manipulate microbial attributes for enhancing carbon sequestration. [ABSTRACT FROM AUTHOR]

Topographic variables derived from digital elevation models (DEM) are the most commonly used predictor in soil organic carbon (SOC) prediction. The predictability of these variates for the spatial heterogenity of SOC is influenced by the size of DEM resolution. This paper aimed to investigate the resolution effect of the topography at multiple DEM resolutions and select the most important predictors and the optimal resolution for SOC prediction in a large-scale and morphologically complex area. The study area, covering 15 600 km2, was located in a mountainous province in China. A total of 3901 soil samples and three machine learning algorithms including random forest, gradient boosted regression tree, and artificial neural network were used to construct the SOC predictive models at resolutions of 30, 90, 150, 210, 270, 330, and 390 m. Topographic factors were derived from the DEM with an original resolution of 30 m and were subsequently resampled to varying resolutions by using the bilinear interpolation algorithm. The importance of each variate in each predictive model was computed for the most important predictor selection, and the determination coefficient (R2) and Root Mean Square Error (RMSE) of each predictive model were computed for the accuracy evaluation. Comparative analysis on point-based topographic representation, the accuracy of predictive models, and the variable importance was conducted among different resolutions to investigate the resolution effect on the SOC prediction and to select the optimal resolution for this study area. The results showed that topography was the key factor influencing the SOC distribution in this large-scale area due to the region's mountainous nature, and the relative contribution of the topography to predict SOC distribution varied with DEM resolutions. Elevation was the most important topographic factor at all DEM resolutions. Accuracies of predictive models built on the three machine learning algorithms were all dependent on DEM resolution, indicating that DEM resolution had an important effect on SOC prediction. Overall, the random forest predictive models outperformed the other two algorithms, and its most accurate result was obtained at the 210-m resolution with an R2 of 0.25 and an RMSE of 4.77 g kg–1. In conclusion, we found that finer resolutions did not necessarily produce more accurate SOC predictions even in a large-scale and morphologically complex area. Therefore, investigating the resolution effect is suggested to select an optimal resolution for the SOC prediction in other regions characterized by similar geomorphological conditions as this study area. [ABSTRACT FROM AUTHOR]

Soils have the potential to sequester and store significant amounts of carbon, contributing towards climate change mitigation. Soil carbon markets are emerging to pay farmers for management changes that absorb atmospheric carbon, governed by codes that ensure eligibility, additionality and permanence whilst protecting against leakage and reversals. This paper presents the first global comparative analysis of farmland soil carbon codes, providing new insights into the range of approaches governing this global marketplace. To do this, the paper developed an analytical framework for the systematic comparison of codes which was used to identify commonalities and differences in approaches, methods, administration, commercialisation and operations for 12 publicly available codes from around the world. Codes used a range of mechanisms to manage additionality, uncertainty and risks, baselines, measurement, reporting and verification, auditing, resale of carbon units, bundling and stacking, stakeholder engagement and market integrity. The paper concludes by discussing existing approaches and codes that could be adapted for use in the UK and evaluates the need for an over-arching standard for soil carbon codes in the UK and internationally, to which existing codes and other schemes already generating soil carbon credits could be assessed and benchmarked. [ABSTRACT FROM AUTHOR]

Organic soil amendments are used to improve soil quality and mitigate climate change. However, their effects on soil structure, nutrient and water retention as well as greenhouse gas (GHG) emissions are still poorly understood. The purpose of this study was to determine the residual effects of a single field application of four ligneous soil amendments on soil structure and GHG emissions. We conducted a laboratory incubation experiment using soil samples collected from an ongoing soil-amendment field experiment at Qvidja Farm in south-west Finland, two years after a single application of four ligneous biomasses. Specifically, two biochars (willow and spruce) produced via slow pyrolysis, and two mixed pulp sludges from paper industry side-streams were applied at a rate of 9–22 Mg ha-1 mixed in the top 0.1 m soil layer. An unamended fertilized soil was used as a control. The laboratory incubation lasted for 33 days, during which the samples were kept at room temperature (21°C) and at 20%, 40%, 70% or 100% water holding capacity. Carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) fluxes were measured periodically after 1, 5, 12, 20 and 33 days of incubation. The application of ligneous soil amendments increased the pH of the sampled soils by 0.4–0.8 units, whereas the effects on soil organic carbon content and soil structure varied between treatments. The GHG exchange was dominated by CO2 emissions, which were mainly unaffected by the soil amendment treatments. The contribution of soil CH4 exchange was negligible (nearly no emissions) compared to soil CO2 and N2O emissions. The soil N2O emissions exhibited a positive exponential relationship with soil moisture. Overall, the soil amendments reduced N2O emissions on average by 13%, 64%, 28%, and 37%, at the four soil moisture levels, respectively. Furthermore, the variation in N2O emissions between the amendments correlated positively with their liming effect. More specifically, the potential for the pulp sludge treatments to modulate N2O emissions was evident only in response to high water contents. This tendency to modulate N2O emissions was attributed to their capacity to increase soil pH and influence soil processes by persisting in the soil long after their application. [ABSTRACT FROM AUTHOR]

In 2015, 17 Sustainable Development Goals (SDGs) were approved, including SDG13, which addresses actions to increase carbon capture (CO2-C storage) for climate change mitigation. However, no analytical procedures have been defined for quantifying soil organic carbon (SOC) sequestration. This paper presents a rapid tool for guiding farmers and for monitoring SOC sequestration in farmer fields. The tool consists of multiconstituent soil analyses through near-infrared spectroscopy (NIRS) and an SOC mineralization model. The tool provides forecasts of SOC sequestration over time. Soil analyses by NIRS have been calibrated and validated for farmer fields in European countries, China, New Zealand, and Vietnam. Results indicate a high accuracy of determination for SOC (R² ≥ 0.93), and for inorganic C, soil texture, and soil bulk density. Permanganate oxidizable soil C is used as proxy for active SOC, to detect early management-induced changes in SOC contents, and is also quantified by NIRS (R² = 0.92). A pedotransfer function is used to convert the results of the soil analyses to SOC sequestration in kg·ha-1 C as well as CO2. In conclusion, the tool allows fast, quantitative, and action-driven monitoring of SOC sequestration in farmer fields, and thereby is an essential tool for monitoring progress of SDG13. [ABSTRACT FROM AUTHOR]

Pastures are of central importance in food production and provide multiple ecosystem services. The objective of this paper was to determine whether the intensification of pasture-based animal production systems, through practices such as fertilization and improved pasture species, has a higher capacity in the short-term (five years) to sequester carbon in the soil compared to (1) natural grassland without anthropogenic interactions, (2) natural grassland fertilized and overseeded with exotic species, and (3) annual pastures with frequent soil disturbance. The study assessed the organic carbon stock (OCS), total organic carbon (TOC), particle size, porosity, and density at different soil strata, as well as the root system and forage production. Forage dry matter (DM) production varied significantly with means ranging from 6615 to 13,000 kg ha–1 year–1 for natural grassland (NG) and permanent pasture (PP), respectively. Improved natural grassland (ING) and NG presented a higher density and root diameter than PP and annual pasture (AP). Forage systems significantly influenced soil porosity and density, with NG and ING showing lower soil densities and higher soil porosities. The OCS (0–100 cm) was similar between NG (270 Mg ha–1), ING (255 Mg ha–1), PP (274 Mg ha–1), and AP systems (256 Mg ha–1). Over a period of five years, the intensification of pasture-based animal production systems did not have a significant impact on OCS in the soils of a Brazilian subtropical highland. [ABSTRACT FROM AUTHOR]

The estimation of the level of the soil organic carbon (SOC) content plays an important role in assessing the soil health state. Visible and Near Infrared Diffuse Reflectance Spectroscopy (Vis-NIR DRS) is a fast and cheap tool for measuring the SOC. However, when this technology is applied on a larger area, the soil prediction accuracy decreases due to the heterogeneity of the samples. In this paper, we first investigate the global model performance in the LUCAS EU-wide topsoil database. Then, different clustering strategies were tested, including the k-means clustering based on the principal component analysis (PCA) and hierarchical clustering, combined with the partial least squares regression (PLSR) models, and a clustering based on a local PLSR approach. The best validation results were obtained for the local PLSR approach with R² = 0.75, root mean squared error of prediction (RMSEP) = 13.38 g/kg and ratio of performance to interquartile range (RPIQ) = 2.846, but the algorithm running time was 30.05 s. Similar results were obtained for the k-means clustering method with R² = 0.75, RMSEP = 14.61 g/kg and RPIQ = 2.844, at only 4.52 s. This study demonstrates that the PLSR approach based on k-means clustering is able to achieve similar prediction accuracy as the local PLSR approach, while significantly improving the algorithm speed. This provides the theoretical basis for adapting the spectral soil model to the needs of real-time SOC quantification. [ABSTRACT FROM AUTHOR]

Under the macroenvironmental background of global warming, all countries are working to limit climate change. Internationally, biofuel plants are considered to have great potential in carbon neutralization. Several countries have begun using biofuel crops as energy sources to neutralize carbon emissions. Switchgrass (Panicum virgatum) is considered a resource-efficient low-input crop that produces bioenergy. In this paper, we reviewed the effects of switchgrass cultivation on carbon sequestration and greenhouse gas (GHG) emissions. Moreover, the future application and research of switchgrass are discussed and prospected. Switchgrass has huge aboveground and underground biomass, manifesting its huge carbon sequestration potential. The net change of soil surface 30 cm soil organic carbon in 15 years is predicted to be 6.49 Mg ha−1, significantly higher than that of other crops. In addition, its net ecosystem CO2 exchange is about −485 to −118 g C m−2 yr−1, which greatly affects the annual CO2 flux of the cultivation environment. Nitrogen (N) fertilizer is the main source of N2O emission in the switchgrass field. Nitrogen addition increases the yield of switchgrass and also increases the N2O flux of switchgrass soil. It is necessary to formulate the most appropriate N fertilizer application strategy. CH4 emissions are also an important indicator of carbon debt. The effects of switchgrass cultivation on CH4 emissions may be significant but are often ignored. Future studies on GHG emissions by switchgrass should also focus on CH4. In conclusion, as a biofuel crop, switchgrass can well balance the effects of climate change. It is necessary to conduct studies of switchgrass globally with the long-term dimension of climate change effects. [ABSTRACT FROM AUTHOR]

Pyrolysis of feedstocks to produce biochar for soil remediation employed to be a convenient method regarding improvement of soil fertility, increasing carbon stability and decreasing greenhouse gas emissions. Biochar properties and its effect after incorporation into the soils vary depending on the characteristics of feedstocks and pyrolysis process. This paper aims to compare the effect of pyrolysis temperature on the frequency of functional groups in different biochars made from plant feedstocks over the temperature range from 300 °C to 700 °C. An increase in pyrolysis temperature positively affects biochar surface properties until the deformation step in C = O, –COOH, and OH groups and as a result, the surface area of biochar decreases at high temperature (more than 600 °C). The breakdown of hemicellulose, cellulose, and lignin also occurs at temperatures more than 600 °C. Consequently, the biochar quality is reduced with increasing pyrolysis temperature although such biochar may be suitable for rising the content of stable carbon in the soils. Over the long-term, the stability of biochar can contribute to carbon sequestration, retention of water and ions in the soil. [ABSTRACT FROM AUTHOR]

Visible Near infrared and Shortwave Infrared (VNIR/SWIR, 400–2500 nm) remote sensing data is becoming a tool for topsoil properties mapping, bringing spatial information for environmental modeling and land use management. These topsoil properties estimates are based on regression models, linking a key topsoil property to VNIR/SWIR reflectance data. Therefore, the regression model's performances depend on the quality of both topsoil property analysis (measured on laboratory over-ground soil samples) and Bottom-of-Atmosphere (BOA) VNIR/SWIR reflectance which are retrieved from Top-Of-Atmosphere radiance using atmospheric correction (AC) methods. This paper examines the sensitivity of soil organic carbon (SOC) estimation to BOA images depending on two parameters used in AC methods: aerosol optical depth (AOD) in the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) method and water vapor (WV) in the ATCOR (ATmospheric CORrection) method. This work was based on Earth Observing-1 Hyperion Hyperspectral data acquired over a cultivated area in Australia in 2006. Hyperion radiance data were converted to BOA reflectance using seven values of AOD (from 0.2 to 1.4) and six values of WV (from 0.4 to 5 cm), in FLAASH and ATCOR, respectively. Then a Partial Least Squares regression (PLSR) model was built from each Hyperion BOA data to estimate SOC over bare soil pixels. This study demonstrated that the PLSR models were insensitive to the AOD variation used in the FLAASH method, with R2cv and RMSEcv of 0.79 and 0.4%, respectively. The PLSR models were slightly sensitive to the WV variation used in the ATCOR method, with R2cv ranging from 0.72 to 0.79 and RMSEcv ranging from 0.41 to 0.47. Regardless of the AOD values, the PLSR model based on the best parametrization of the ATCOR model provided similar SOC prediction accuracy to PLSR models using the FLAASH method. Variation in AOD using the FLAASH method did not impact the identification of bare soil pixels coverage which corresponded to 82.35% of the study area, while a variation in WV using the ATCOR method provided a variation of bare soil pixels coverage from 75.04 to 84.04%. Therefore, this work recommends (1) the use of the FLAASH AC method to provide BOA reflectance values from Earth Observing-1 Hyperion Hyperspectral data before SOC mapping or (2) a careful selection of the WV parameter when using ATCOR. [ABSTRACT FROM AUTHOR]

The key to soil organic matter management is understanding the transformation processes of fresh organic matter to permanent humus that control soil organic carbon dynamics. This paper presents the results of the effect of long-term soil fertilization with increasing doses of (manure) FYM and ammonium nitrate on the composition of humus compounds not limed of loamy sand. The study showed that both crop rotation and manure fertilization significantly affected organic matter fractions and humus humification index. The soil in crop rotation B (with enriching effect) accumulated significantly larger amounts of soil organic carbon in 0–30 cm layer despite a lower soil pH, but had lower soil organic quality compared to the soil in crop rotation A (with depleting effect). In both rotations, FYM was applied once per 4-year rotation at a dose of 40 t·ha−1 which improved the soil organic carbon content. Depleting crop rotation (A) showed no increase in soil accumulation of humins fractions (H), while the "enriching" crop rotation (B) had such a tendency. Mineral nitrogen fertilization significantly affected the composition of the humic acids fraction. The soil in crop rotation B was characterized by a significantly higher content of fulvic acid (FA) and lower of humins (H) carbon fraction. The large input of organic matter into the soil under lower content pH conditions may hinder its transformation into permanent humus. Changes in the humification index were the consequence of changes in the carbon content of humic and fulvic acids, under the influence of plant selection in crop rotation and manure fertilization. According to the criterion of this parameter, soil in crop rotation A and soil fertilized with manure had lower loss of the more labile FA fraction (lower value of humification index—1.48). [ABSTRACT FROM AUTHOR]

By dint of the development of agroecological practices and organic farming, stakeholders are becoming more and more aware of the importance of soil life and banning a growing number of pesticide molecules, promoting the use of plant bio-stimulants. To justify and promote the use of microbes in agroecological practices and sustainable agriculture, a number of functions or services often are invoked: (i) soil health, (ii) plant growth promotion, (iii) biocontrol, (iv) nutrient acquiring, (v) soil carbon storage, etc. In this paper, a review and a hierarchical classification of plant fungal partners according to their ecosystemic potential with regard to the available technologies aiming at field uses will be discussed with a particular focus on interactive microbial associations and functions such as Mycorrhiza Helper Bacteria (MHB) and nurse plants. [ABSTRACT FROM AUTHOR]

The problem of assessing carbon fluxes and pools in forests is acute due to climate change and contribution of forests to carbon reserves. The papers provides a comparative assessment of carbon ingress with atmospheric precipitation and leaching the dissolved organic carbon from soils under coniferous–deciduous forests of different types on sandy soil-forming deposits. The dominant types of coniferous–broadleaved forests of the Bryansk region, i.e., dwarf shrub–green moss pine forests, composite boreal–nemoral herbaceous pine forests and polydominant broadleaved nemoral-forb spruce forests are the objects of study. On average, over a three-year-long observation period (2016–2019), the carbon ingress with rain and snow was 60 ± 4 kg C/ha per year in shrub–green moss and composite pine forests; this turned out to be higher than in coniferous-broadleaved forests, where the carbon input was 47 ± 2 kg C/ha per year. The removal of organic carbon from organic horizons in polydominant broadleaved forests was four times lower (14 ± 4 kg C/ha per year) on average than in dwarf shrub–green moss pine forests specified by a thick litter (56 ± 22 kg C/ha per year), and 2.5 times lower than in composite pine forests (36 ± 12 kg C/ha per year). The cases of leaching organic carbon from the lower mineral horizons were scarce for different types of coniferous-deciduous forests and varied on average from 6 to 12 kg C/(ha year). The distribution of dissolved organic carbon in the soil profile shows a more efficient carbon fixation in the mineral part of soil profile in pine forests than in polydominant broadleaved forests. [ABSTRACT FROM AUTHOR]

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22 % of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9×106 km 2) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300 m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0–100 and 0–300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (-170 to +186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repository (10.17043/palmtag-2022-pedon-1, Palmtag et al., 2022a; and 10.17043/palmtag-2022-spatial-1, Palmtag et al., 2002b). [ABSTRACT FROM AUTHOR]

Microbial communities often possess enormous diversity, raising questions about whether this diversity drives ecosystem functioning, especially the influence of diversity on soil decomposition and respiration. Although functional redundancy is widely observed in soil microorganisms, evidence that species occupy distinct metabolic niches has also emerged. In this paper, we found that apart from the environmental variables, increases in microbial diversity, notably bacterial diversity, lead to an increase in soil C emissions. This was demonstrated using structural equation modelling (SEM), linking soil respiration with naturally differing levels of soil physio-chemical properties, vegetation coverage, and microbial diversity after fire disturbance. Our SEMs also revealed that models including bacterial diversity explained more variation of soil CO2 emissions (about 45%) than fungal diversity (about 38%). A possible explanation of this discrepancy is that fungi are more multifunctional than bacteria and, therefore, an increase in fungal diversity does not necessarily change soil respiration. Further analysis on functional gene structure suggested that bacterial and fungal diversities mainly explain the potential decomposition of recalcitrant C compare with that of labile C. Overall, by incorporating microbial diversity and the environmental variables, the predictive power of models on soil C emission was significantly improved, indicating microbial diversity is crucial for predicting ecosystem functions. [ABSTRACT FROM AUTHOR]

Plant carbon (C) input via litter decomposition is essential for forming soil organic C (SOC). Previous studies have investigated the fate of litter C through one-time litter addition, while litter input could be recurrent, e.g., gradual litter input from above-ground plant material in diverse ecosystems. This paper investigates how litter input's frequency (single vs. repeated) and amount (low vs. high) affect the SOC formation and the associated microbial community composition and activity in a controlled microcosm experiment. After incubating the 13C-labeled litter (Stipa krilovii) for 80 days with the fresh field soil collected from a semi-arid steppe in Inner Mongolia, China, we measured newly formed SOC, microbial biomass C (MBC), and microbial phospholipid fatty acid (PLFA) indicated by 13C-labeling. The repeated litter addition resulted in 19.92% greater and 12.45% more efficient SOC formation than the single addition. Repeated litter addition also increased C use efficiency (CUE, + 76.71%), indicating more substrate C allocated for microbial growth than respiration. It also enhanced the fungi to bacteria ratio by 47.40% (based on 13C-PLFA) and maintained relatively stable microbial assimilation of litter C (based on 13C-MBC). These findings were consistent regardless of litter amounts. Our results indicate that the uninterrupted litter input to the soil in natural ecosystems benefits SOC formation and highlights soil fungi's critical role in promoting SOC formation. This study suggests that incorporating plant C input frequency into SOC dynamic models may improve the predictions of C cycling. [ABSTRACT FROM AUTHOR]

Ecosystem equilibrium may be greatly modified by intensive use of resources, with changes in the microbiological processes in the soil. Current paper evaluates soil quality in different areas according to microbiological attributes. Evaluation was undertaken in five areas within the ecological restoration process (REC1-5), an area with native vegetation (MATA) and another with sugarcane (CN). Microbiological attributes evaluated were carbon of the soil's microbial biomass (C-BMS), basal respiration (C-CO2), microbial quotient (qMIC) and metabolic quotient (qCO2). Lowest C-BMS rate was reported in REC5 differentiating statistically REC2 and REC4. Non-distinction between RECs in CN and MATA suggest influence, interaction or synergism between factors. The analysis of similarity matrix by Euclidian distance applied to microbiological attributes data allowed the distinction of CN with regard to the other forms of soil management. It is, therefore, more efficient as a tool to characterize the environment. Within the areas in a process of ecological restoration, REC2 and REC3 had microbiological conditions with the best similarity with regard to the area with native vegetation (MATA). This indicates an improvement in soil quality and, consequently, a state of equilibrium in the natural regional condition. [ABSTRACT FROM AUTHOR]

The aim of the paper is to give an overview on the chemistry of soil organic carbon (SOC) affecting nutrient availability, the emission of greenhouse gases and detoxifying harmful substances in soil. Humic substances represent the stable part of SOC, accounting for between 50 and more than 80% of organically bound carbon in soil. Humic substances strongly affect the soil solution concentration of several plant nutrients and may increase P-, Fe-, and Cu- solubility, thereby increasing their plant availability. Soil organic carbon, mainly humic substances, can detoxify monomeric Al in acid soils, can strongly bind toxic heavy metals, making them unavailable to the plant roots, and may strongly bind a vast variety of harmful organic pollutants. Increasing SOC is an important goal in agriculture. The inclusion of mixtures of semi-perennial plant species and cultivars may strongly increase SOC and humic substance content in soils. To increase SOC, farmyard manure and its rotted or composted forms are superior compared to the separate application of straw and slurry to soil. The storage of carbon, mainly in organic form, in soils is very important in the context of the emission of greenhouse gases. Worldwide, soils release about 10 times more greenhouse gases compared to fossil fuel combustion. Small increments in SOC worldwide will strongly affect the concentration of atmospheric CO2. The public discussion on soil fertility and greenhouse gas emissionshas been politically controlled in a way that leaves the important and positive contribution of soil organic carbon and mainly humic substances partly misinterpreted and partly underestimated. [ABSTRACT FROM AUTHOR]

Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from - 0.047 to - 0.007 m3m-3 for SWC and from - 0.68 to - 0.2 mmd-1 for soil evaporation). Simulations related to nitrogen balance and soil CO2 efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N2O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N2O efflux simulation bias was between - 0.13 and - 0.1 mgNm-2d-1 , and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO2 efflux simulations bias was 0.04–0.17 gCm-2d-1 , while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone. [ABSTRACT FROM AUTHOR]

This paper studied the economic assessment of soil carbon sequestration and financial support for development of low-carbon land use in Ukrainian agriculture in the context of climate change. Results of evaluation show that Ukraine plays a strategic role in the soil carbon sequestration in Europe. Ukrainian soils contribution is equal to one third of SOC sequestered in agricultural soils of the European Union. Economic assessment of stocks of SOC in Ukrainian soils indicates that its value is in the range from 14.4 thousand USD/ha (Gray forest soils) to 103.7 thousand USD/ha (Chernozem-meadow soils). The results of analysis revealed that for agricultural production and the formation of the economic fertility of soils in Ukraine, the effects of climate change on warming will be both positive and negative. Taking into account the international experience of adaptation of land use processes to climate change, the basis of strategy of adaptation of agriculture in the enterprises of Ukraine to warming is proposed to put the concept of low carbon land use. As a result of the study it was determined the macroeconomic assessment of the need of financial resource for development potential of low carbon land use in Ukraine for the period until 2030. [ABSTRACT FROM AUTHOR]

Trade-induced carbon transfer generates significant impacts on global carbon emissions, leading to carbon inequality (CI). This paper introduces a novel indirect metric, the trade-induced domestic carbon share (TDCS), designed to quantify economies' carbon benefits from international trade. Based on data from 17 manufacturing sectors in 66 major economies, this study compares the impact of developed and developing economies' participation in global value chains (GVCs) on TDCS, offering insights into trade-related carbon inequality. Our analysis reveals that GVCs participation by developed economies generally decrease their TDCS, in contrast to an increase in developing economies, thereby exacerbating carbon inequality. Notably, backward participation in GVCs reduces TDCS across all economies, whereas forward participation in GVCs results in increased TDCS in developing economies but a decrease in developed ones. Additionally, our study indicates that developed economies enhance their position in both forward and backward GVCs but experience a decline in the ratio of domestic value added (DVAR). In contrast, developing economies strengthen their forward GVCs position and DVAR but weaken their backward GVCs position. These findings offer new perspectives on carbon inequality in international trade and provide valuable guidance for policymakers in devising industrial trade and carbon reduction strategies. [ABSTRACT FROM AUTHOR]

Irrigation can increase crop yields and could be a key climate adaptation strategy. At present, under the background of increasing food demand and continuous expansion of irrigation cropland, there still uncertainties about the soil carbon dynamics under the change of irrigation water volume and irrigated area in view of large-scale spatial heterogeneity. Therefore, this paper uses space-for-time + meta-analysis and a two-step methodology based on the residual trend analysis to quantitatively analyze the relationship between soil organic carbon (SOC) and soil respiration (Rs) in response to fluctuations in irrigation water volume and irrigated land extents. Here we show that the irrigation water volume within 100–1000 mm had a negative impact on SOC, and the impact was correlated with the irrigation water volume. Different levels of irrigation water manifest distinct effects on SOC content across varying soil depths. When irrigation water quantities are less than 700 mm, the impact on SOC content in the 0–30 cm depth layer surpasses that in the 30–200 cm depth layer. Conversely, when irrigation water quantities equal or exceed 700 mm, this pattern is reversed. The overall impact of irrigation on SOC stock at a depth of 0–200 cm was −14.88±6.7%. Tillage, planting intensity, topography, and soil type within irrigated cropland all exert variable impacts on SOC content. Whether these influences are deleterious or beneficial hinges predominantly upon the balance between the augmentation of SOC stock due to heightened carbon inputs from crops and the reduction of SOC through alterations in microbial activity. Mann-Kendall trend analysis showed that from 2000 to 2015, the overall Rs of cropland showed an increasing trend, with an increase rate of 3.67 g/m2/year. The increase of global Rs is mainly driven by climate change factors (temperature, precipitation and solar radiation), while the decrease of Rs in a small number of areas is mainly driven by management practices (fertilizer nitrogen, irrigation, and tillage). Our study further quantifies the impact of irrigation on soil carbon dynamics, thereby offering potential pathways and data support for the advancement of sustainable agriculture. • Developed an approach that integrates space-for-time with meta-analysis. • Quantifying soil carbon dynamics under changes in irrigation water volume and irrigated area at the global scale. • Irrigation had a negative impact on SOC, and the impact was correlated with the irrigation water volume. • From 2000–2015, the global increase in soil respiration of irrigated croplands was mainly caused by climate change. [ABSTRACT FROM AUTHOR]

Steel is the most common industrial material worldwide due to its numerous applications, high strength and recyclability. Steel production involves alloying iron with various amounts of carbon and other elements to produce thousands of different grades and types of steel for various purposes. However, the intensive use of carbon in steel production is one of the factors contributing to global climate change. To use of hydrogen and renewable energy sources in steel production has emerged as a significant solution to reduce carbon emissions. The aim of this study is to obtain green hydrogen for the production of green steel. The focus is on utilizing offshore wind energy as a renewable energy source to give us the energy we need to electrolyze seawater. The study uses the seas of Turkiye for case analysis. In this paper, an integrated Multi-criteria decision-making method is used to integrate hybrid energy systems and their application for green industry and to select a location for a power generation plant. The study is a new contribution to the literature in terms of its scope. Green steel's location selection is addressed for the first time in the literature. The weights of the criteria are determined by employing the Best-Worst Method (BWM) as a multi-criteria decision-making technique, and the alternatives are evaluated using the Analytic Hierarchy Process (AHP). By using the BWM method, decision makers can have a clear understanding of the evaluation range for pairwise comparisons of criteria, which reduces potential biases. According to the study, the northern part of the Aegean Sea in Turkiye is the most suitable alternative for green steel production, with offshore wind energy being used for hydrogen production. The study also revealed that the most important main criterion for this hybrid system is "natural factors", and the sub-criteria are "wind speed and power density" by far compared to other sub-criteria. The fact that the area determined for the facility is suitable for construction and the possibility that the customer of the energy produced is other industrial establishments here is the most important indicator that the methods applied give good results. [ABSTRACT FROM AUTHOR]

• Soil reflectance composites were generated with Sentinel-2 L2A data. • A multi-channel CNN learning framework for soil organic carbon prediction is proposed. • A dual input deep learning architecture improves the predictions. • The framework produced results that can be interpreted. The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, this approach was tested in the German federal state of Bavaria, to provide estimations for soil organic carbon (SOC). Different temporal ranges were considered for the generation of the composites, including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging the advantages of deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. The SOC predictions indicated little dissimilarity amongst the different composites, with the best performance attained for the six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 = 0.64, RPIQ = 0.89). It has been demonstrated that these outcomes outperform other well-known machine learning techniques. An ablation analysis was accordingly performed to evaluate the interplay of the CNN's different components to disentangle the advantages of each aspect of the proposed framework. Finally, a DUal inPut deep LearnIng architecture, named DUPLICITE, is proposed, which concatenates deep spectral features derived from the CNN mentioned earlier, as well as topographical and environmental covariates through an artificial neural network (ANN) to exploit their complementarity. The proposed approach was demonstrated to provide an improvement in the overall prediction performance (RMSE = 11.60 gC · kg−1, R2 = 0.67, RPIQ = 0.92). [ABSTRACT FROM AUTHOR]

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks, turnover times, and soil texture. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modelling at local, regional or global scales. This paper presents this dataset, describes in detail soil sampling, laboratory analysis and derived soil geochemical parameters, calculations and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates within the soil area of the northern circumpolar permafrost region (17.9 x 106 km²) using the ESA's Climate Change Initiative (CCI) Global Land Cover dataset at 300 m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0-100 cm and 0-300 cm soil depth to 380 Pg and 813 Pg for carbon and 21 Pg and 55 Pg for nitrogen, respectively. Of which 48 % of the area is within the land cover class forest with a total SOC and TN storage for 0-300 cm of 35 % and 36 %, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (-170 to +186 Pg) to 300 cm depth but show different spatial patterns. Two separate datasets are freely available on the Bolin Centre Database repository https://doi.org/10.17043/palmtag-2022-pedon-1, Palmtag et al., 2022a and https://doi.org/10.17043/palmtag-2022-spatial-1, Palmtag et al., 2002b). [ABSTRACT FROM AUTHOR]

Better monitoring, reporting, and verification (MRV) of the amount, additionality, and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies, and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, modeling, and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called the Field Observatory (v1.0, https://www.fieldobservatory.org/ , last access: 3 February 2022). The Field Observatory is designed as an online service for near-real-time model–data synthesis, forecasting, and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site, which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices as well as provide an MRV tool for decision support. [ABSTRACT FROM AUTHOR]

Under the goal of "carbon peaking" and "carbon neutrality" in China, it is significant to explore whether emission reduction commitments contribute to carbon risk premium. Therefore, based on a sample of Chinese listed companies that publicly disclosed questionnaires in the CDP from 2009 to 2021, this paper empirically examines the relationship between emission reduction commitments and carbon risk premium and their formation mechanism. The results show that emission reduction commitments can effectively hedge the carbon risk premium, which pass several robustness tests. Specifically, the more comprehensive and ambitious the commitment is, the greater the suppression of the carbon risk premium. The mechanism test shows that disclosing emission reduction commitment information can reduce carbon risk by improving investor confidence and attracting political resources. Differences in enterprise emission levels, regional environmental regulation intensity and green financial development result in different risk-hedging effects of emission reduction commitments. Moreover, the greater the substantial pressure from stakeholders, the greater the tendency for firms to set up emission reduction commitments. Finally, the heterogeneity analysis reveals that confidence level of commitments has a significant impact on risk premium. Specifically, enterprises with SBTi validation, higher CDP ratings and higher target completion are more helpful in curbing carbon risk premium. Our findings contribute to corporate commitment to climate change and complement the carbon risk premium hypothesis. [Display omitted] The possible highlights of this study are as follows. • With the help of forward-looking information on emission reduction commitments, we re-examine the conclusion of the carbon risk premium and find that emission reduction commitments can effectively hedge the risk premium, enriching and developing the carbon risk premium hypothesis • This paper brings commitment levels and carbon emission levels into a unified analytical framework to study the correspondence between emission reduction commitments made under pressure to reduce emissions and the carbon risk premium, providing micro evidence for solving the mystery of the carbon risk premium. • Measure emission reduction commitment indicators from various angles, making the data on emission reduction commitments more objective and scientific. [ABSTRACT FROM AUTHOR]

Terrestrial biogeochemical models are essential tools to quantify climate-carbon cycle feedback and plant-soil relations from local to global scale. In this study, theoretical basis is provided for the latest version of Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. During the developments we took advantage of experiences from the crop modeller community where internal process representation has a long history. In this paper the improved soil hydrology and soil carbon/nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology and soil organic carbon content estimation. Soil hydrology related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0(explained variance increased from 0.121 to 0.8 for SWC, and from 0.084 to 0.46 for soil evaporation; bias changed from -0.047 to 0.007 m³ m-3 for SWC, and from -0.68 mm day-1 to -0.2 mm day-1 for soil evaporation). Sensitivity analysis and optimization of the decomposition scheme is presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation and nitrification/denitrification process estimation which represents a major milestone. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing technology can be used to monitor the soil nutrient changes in a rapid, real-time, and non-destructive manner, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were studied. The effects of different water treatments on soil organic carbon (SOC) content, and the relationship between SOC content and spectral reflectance (350–2500 nm) were studied. 17 kinds of preprocessing algorithm were performed on the original spectral (R), and the five allocation ratios of calibration to verification sets were set. Finally, the model was constructed by partial least squares regression (PLSR). The results showed that the effects of water treatment on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics, and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not good. But the ratio of standard deviation to the standard prediction error (RPD) values of the models were constructed by simple mathematical transformation (T0–T5) and first-order differential transformation (T6–T11) can reach more than 1.4. The simple mathematical transformation (T0–T2, T4–T5) and the first-order differential transformation (T6–T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the process of constructing the hyperspectral monitoring model of SOC. [ABSTRACT FROM AUTHOR]

Temperate grassland soils store significant amounts of carbon (C). Estimating how much livestock grazing and manuring can influence grassland soil organic carbon (SOC) is key to improve greenhouse gas grassland budgets. The Rothamsted Carbon (RothC) model, although originally developed and parameterized to model the turnover of organic C in arable topsoil, has been widely used, with varied success, to estimate SOC changes in grassland under different climates, soils, and management conditions. In this paper, we hypothesise that RothC-based SOC predictions in managed grasslands under temperate moist climatic conditions can be improved by incorporating small modifications to the model based on existing field data from diverse experimental locations in Europe. For this, we described and evaluated changes at the level of: (1) the soil water function of RothC, (2) entry pools accounting for the degradability of the exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) the month-on-month change in the quality of C inputs coming from plant residues (i.e above-, below-ground plant residue and rhizodeposits), and (4) the livestock trampling effect (i.e., poaching damage) as a common problem in areas with higher annual precipitation. In order to evaluate the potential utility of these changes, we performed a simple sensitivity analysis and tested the model predictions against averaged data from four grassland experiments in Europe. Our evaluation showed that the default model's performance was 78% and whereas some of the modifications seemed to improve RothC SOC predictions (model performance of 95% and 86% for soil water function and plant residues, respectively), others did not lead to any/or almost any improvement (model performance of 80 and 46% for the change in the C input quality and livestock trampling, respectively). We concluded that, whereas adding more complexity to the RothC model by adding the livestock trampling would actually not improve the model, adding the modified soil water function and plant residue components, and at a lesser extent residues quality, could improve predictability of the RothC in managed grasslands under temperate moist climatic conditions. [ABSTRACT FROM AUTHOR]

Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support. [ABSTRACT FROM AUTHOR]

• Solved the lack of spatial features of HSI in predicting soil carbon content. • Proposed multi-scale spectral feature network to extract spectral feature of HSI. • Proposed multi-scale spatial and spectral feature network to extract feature of HSI. • Proposed methods improve the accuracy and stability of soil carbon content prediction. Soil carbon content prediction based on hyperspectral images can achieve large-scale spatial measurement, which has the advantages of wide coverage and fast information collection, is more suitable for field data collection. However, the research on soil carbon content prediction based on hyperspectral images mainly focuses on feature extraction of spectral information, ignoring the spatial information, and cannot well reveal the intrinsic structural characteristics of data. Aiming at the lack of spatial features consideration in hyperspectral images, soil carbon content prediction methods based on multi-scale feature fusion are proposed by hyperspectral image. At the same time of extracting spectral features from hyperspectral images, the spatial information is used for the first time and a multi-scale spectral and spatial feature network (SpeSpaMN) is designed. In the SpeSpaMN, the multi-scale spectral feature network (SpeMN) is constructed to extract spectral features, the multi-scale spatial feature network (SpaMN) is constructed to extract spatial features. The two networks are fused by using the complementary relationship between different scale features to achieve soil carbon content prediction based on multi-scale feature fusion. The results showed that SpeSpaMN had the best results compared to other methods, followed by the method of SpeMN. The RPD of Inland, Aoshan Bay and Jiaozhou Bay samples based on SpeSpaMN were increased by 47.36%, 37.96% and 4.30% respectively. This paper can effectively solve the problem of the deep fusion of spatial and spectral features in the soil carbon content prediction by hyperspectral image, so as to improve the accuracy and stability of soil carbon content prediction. [ABSTRACT FROM AUTHOR]

China's 'dual carbon' strategy is of strategic importance in leading global climate governance and promoting China's high-quality and green economic development. Cities are the centres of human activities. However, these are also places where high energy consumption and high carbon emissions are concentrated. This study selected data from 192 small and medium-sized cities in mainland China for the period 2000–2020. It classified these into seven categories using the K-means clustering method, calculated the decoupling status of different types of small and medium-sized cities based on the Tapio decoupling index, and analysed the relationship between carbon emissions and economic growth in various types of small and medium-sized cities based on the EKC curve theory. Finally, the backpropagation neural network (BPNN) model was used to classify and predict the time required to achieve carbon peaking for seven types of small and medium-sized cities. The following conclusions were drawn: (1) The development status of small and medium-sized cities shows a 'geese echelon pattern'. New dynamic small and medium-sized urban areas may reach peak carbon emissions first, while resource-based small and medium-sized urban areas may be the last to do so. (2) The decoupling of carbon emissions from industrial development in small and medium-sized cities has not been completed. This manifests mainly through the states of declining decoupling, absolute decoupling, and strong negative decoupling. (3) A significant inverted U-shaped relationship exists between economic development and carbon emissions in these cities. (4) These cities may have the disadvantage of premature or excessive de-industrialisation. Additionally, these generally have an inefficient and expansionary economic development status. Therefore, this study contended for a more robust integration of a radical political economy and ecological economics to support the development of such cities in China. The paper proposes differentiated development paths and fundamental recommendations for small and medium-sized cities in the implementation of the 'double carbon' strategy. The intention is to provide theoretical guidance and implications for promoting green and low-carbon development in urban and rural areas and for the early achievement of the strategy in China. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

• Background and Aims Although it is commonly accepted that root exudation enhances plant--microbial interactions in the rhizosphere, experimental data on the spatial distribution of exudates are scarce. Our hypothesis was that root hairs exude organic substances to enlarge the rhizosphere farther from the root surface. • Methods Barley (Hordeum vulgare 'Pallas' -- wild type) and its root-hairless mutant (brb) were grown in rhizoboxes and labelled with 14CO2. A filter paper was placed on the soil surface to capture, image and quantify root exudates. • Key Results Plants with root hairs allocated more carbon (C) to roots (wild type: 13 %; brb: 8 % of assimilated 14C) and to rhizosheaths (wild type: 1.2 %; brb: 0.2 %), while hairless plants allocated more C to shoots (wild type: 65 %; brb: 75 %). Root hairs increased the radial rhizosphere extension three-fold, from 0.5 to 1.5 mm. Total exudation on filter paper was three times greater for wild type plants compared to the hairless mutant. • Conclusion Root hairs increase exudation and spatial rhizosphere extension, which probably enhance rhizosphere interactions and nutrient cycling in larger soil volumes. Root hairs may therefore be beneficial to plants under nutrient-limiting conditions. The greater C allocation below ground in the presence of root hairs may additionally foster C sequestration. [ABSTRACT FROM AUTHOR]

Fuller's earth is an adsorbent material used in the electric industry for dielectric oil regeneration. The high amount of polyaromatic hydrocarbons removed from oil and adsorbed in the Fuller's earth makes this material a hazardous waste. It is necessary to implement a tox icity test and apply a suitable treatment to safely dispose of this waste in a landfill or repurpose it for reuse. In this paper, the tox icity of Fuller's earth contaminated with dielectric oil is assessed before and after treatment. The tox ic potential of the Fuller's earth and the dielectric oil ex tracted through decontamination processes was evaluated in two types of soil with different carbon contents, analyzing the effects on the test organisms, Eisenia foetida earthworms. These tests showed that decontaminated Fuller's earth is non-tox ic, and that the tox icity of the contaminated Fuller's earth, or its ex tracts after treatment, represented by the median lethal concentration (LC50) depends significantly on the type of soil used. [ABSTRACT FROM AUTHOR]

The rice–wheat rotation model of crop planting is widely used globally, and worldwide, straw returning is the main method of crop straw treatment. However, the straw return method commonly used in the modern rice–wheat rotation system has many adverse effects on the levels and improvement of soil fertility and crop yield, and there is no systematic theory of rice and wheat straw returning to use as a guide. In this paper, we concluded that: in the rice–wheat rotation system, returning 1,500–4,500 kg/ha of rice straw and 2,250–6,750 kg/ha of wheat straw to the field helps increase the organic carbon content and quality of the soil and promotes high annual yields; conventional mixing of straw into the field can increase the organic carbon content of the soil in a short time; long‐term use of concentrated ditch‐buried straw return has obvious advantages over other straw returning methods in increasing the accumulation of soil organic carbon; the combination of little or no tillage plus straw returning helps increase the content and quality of organic carbon in soil; and when the soil water content is 15%–22.5%, it is the most conducive to the accumulation of soil organic carbon. In addition, we also provide relevant suggestions for future research directions on straw returning via systematic analyses and thought processes. [ABSTRACT FROM AUTHOR]

Climate change adaptation, mitigation and food security may be addressed at the same time by enhancing soil organic carbon (SOC) sequestration through environmentally sound land management practices. This is promoted by the "4 per 1000" Initiative, a multi-stakeholder platform aiming at increasing SOC storage through sustainable practices. The scientific and technical committee of the Initiative is working to identify indicators, research priorities and region-specific practices needed for their implementation. The Initiative received its name due to the global importance of soils for climate change, which can be illustrated by a thought experiment showing that an annual growth rate of only 0.4% of the standing global SOC stocks would have the potential to counterbalance the current increase in atmospheric CO2. However, there are numerous barriers to the rise in SOC stocks and while SOC sequestration can contribute to partly offsetting greenhouse gas emissions, its main benefits are related to increased soil quality and climate change adaptation. The Initiative provides a collaborative platform for policy makers, practitioners, scientists and stakeholders to engage in finding solutions. Criticism of the Initiative has been related to the poor definition of its numerical target, which was not understood as an aspirational goal. The objective of this paper is to present the aims of the initiative, to discuss critical issues and to present challenges for its implementation. We identify barriers, risks and trade-offs and advocate for collaboration between multiple parties in order to stimulate innovation and to initiate the transition of agricultural systems toward sustainability. [ABSTRACT FROM AUTHOR]