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The study area is Karabük province, and the research topic is to examine the influence of soil-related variables on soil organic carbon in Karabük province. The aim of the study is to determine the relationship between digital soil mapping and the correlation analysis of soil variables that affect the carbon stock stored by the soil. In the study, data from SoilGrids was gathered using Google Earth Engine (GEE) machine learning methods. The JavaScript coding language was used to generate maps of SoilGrids data in GEE. These spatial data were processed using Geographic Information Systems software, and multiple linear regression analysis was performed using the "IBM SPSS 20.0″ program. Clay, sand, silt, pH (in water), organic carbon density, mass density, coarse fractions, cation exchange capacity (CEC), and nitrogen were considered as soil variables. According to the results obtained, the pH of the surface soils (0–5 cm) of the study area was 58–7: clay g/kg; 104–400 g/kg; sand; 214–460; silt; 331–510 g/kg; organic carbon density: 380–562 dg/dm3; nitrogen density: 2 920–7 683 cg/kg; mass density: 93.00–136.00 g/kg; coarse particles: 55–239 (Per10000); CEC: 215–348 mmol/kg; and SOC values varied between 286–374 dg/kg. Soil organic carbon (SOC) stock amounts varied between 286 and 374 dg/kg in surface (0–5 cm) soils. As a consequence of the studies, it was revealed that nitrogen had the strongest link with SOC, whereas clay had the lowest relationship. [ABSTRACT FROM AUTHOR]

Agricultural intensification and expansion are regarded as main drivers of biodiversity loss. This conclusion is mainly based on observed declines of local diversity (α-diversity), while effects on community composition homogenization (decrease of β-diversity) at a larger spatial scale are less well understood. Carabid beetles and spiders represent two widespread guilds and are important predators of pest species. Here we surveyed carabid beetles and spiders in 66 winter wheat fields in four northwestern European countries (Germany, the Netherlands, Sweden and UK) and analyzed how their community composition was related to geographic distance (separation distance between any pairwise fields) and three environmental variables: crop yield (proxy for land-use intensity), percentage cropland (proxy for landscape complexity) and soil organic carbon content (proxy for local soil conditions). We further analyzed whether the relationship between carabid beetle and spider community composition and geographic distance was influenced by environmental variables. We found that, 55 % and 75 % of all observed carabid and spider individuals, respectively, belonged to species that occurred in all four countries. However, individuals of species that were unique to a particular country only accounted for 3 % of all collected individuals for both taxa. Furthermore, we found a negative relationship between distance and similarity of spider communities but not for carabid beetle communities. None of the environmental variables were related to similarity of carabid beetle and spider communities, nor moderated the effects of distance. Our study indicates that across a great part of the European continent, arthropod communities (especially carabid beetles) in agricultural landscapes are composed of very similar species that are robust to current variations in environment and land-use. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches. • We evaluated process-based (PB) and machine-learning (ML) models for SOC prediction. • Random forest & support vector machine predicted SOC superior to PB models. • Reduced data availability and quality significantly weakened ML model performance. • ML-assisted PB model ensembles strongly improved model performance. [ABSTRACT FROM AUTHOR]

• Biogas slurry didn't affect the proportion of lignin phenols to soil organic carbon. • Biogas slurry increased microbial necromass contribution to soil organic carbon. • Fungal necromass dominated contribution of microbial residue to soil organic carbon. • Fungi induced soil organic carbon sequestration by accumulating microbial necromass. Biogas slurry (BS) is widely considered as a source of organic matter and nutrients for improving soil organic carbon (SOC) sequestration and crop production in agroecosystems. Microbial necromass C (MNC) is considered one of the major precursors of SOC sequestration, which is regulated by soil microbial anabolism and catabolism. However, the microbial mechanisms through which BS application increases SOC accumulation in paddy soils have not yet been elucidated. A 12-year field experiment with four treatments (CK, no fertilizers; CF, chemical fertilizer application; BS1 and BS2, biogas slurry application at two nitrogen rates from BS) was conducted in rice paddy fields. The results showed that long-term BS application had no effect on lignin phenols proportion in SOC relative to CF. In contrast, BS application elevated the MNC contribution to SOC by 15.5–20.5 % compared with the CF treatment. The proportion of fungal necromass C (FNC) to SOC increased by 16.0 % under BS1 and by 25.8 % under BS2 compared with the CF treatment, while no significant difference in bacterial necromass C (BNC) contribution to SOC was observed between the BS and CF treatments. The MNC was more closely correlated with fungal community structures than with bacterial community structures. We further found that fungal genera, Mortierella and Ciliophora , mainly regulated the MNC, FNC and BNC accumulation. Collectively, our results highlighted that fungi play a vital role in SOC storage in paddy soils by regulating MNC formation and accumulation under long-term BS application. [ABSTRACT FROM AUTHOR]

• This study was conducted to estimate the carbon stocks of the Geramo forest. • The biomass of 73 woody species (trees and shrubs) was estimated using appropriate allometric equations. • The four forest carbon pools (AGB, BGB, LHG and SOC) were considered to determine carbon stock density in this study. • The estimated total carbon stock of the Geramo forest was about 57,994 Mg h−1. • The estimated climate change mitigation potential of the study forest was about 212,838.1 Mg h−1. Forests play a vital role in the global carbon balance and combating global climate change by sequestering and retaining carbon from the atmosphere in their biomass and soil. Thus, this study aims to estimate the carbon stocks in the biomass and soil along with various environmental factors in the Geramo forest in southern Ethiopia. Ninety-six plots (20 × 20 m2) were laid at a distance of 250 m along 16 line transects that were laid 300 m apart. Five 1 × 1 m2 subplots (4 at the corner and 1 at the center) were laid inside each main plot for LHG and soil data collection. The results revealed that the estimated total carbon stocks and mean carbon stock densities of the study forest were 57994 Mg h−1and 604 ± 344 Mg h−1, respectively, with a mean CO 2 equivalent of 2217 ± 1262 Mg h−1. This result indicates the remarkable role of the Geramo forest in carbon stock and sequestration potential compared with other remnant forests in Ethiopia. Thus, the Geramo forest plays a crucial role in climate change mitigation through carbon sequestration, and hence, this forest can generate carbon credits as financial benefits to the nearby population in the future. The results also revealed that anthropogenic disturbances were negatively associated with the carbon stocks of the forest. Therefore, sustainable forest conservation activities are needed to conserve forest resources, protect the forest from growing anthropogenic disturbances, and sustain its current global climate change mitigation and adaptation benefits. [ABSTRACT FROM AUTHOR]

Nitrogen (N) inputs from legume residues over the long-term contribute to improve soil N cycling and the productivity of succeeding plants in species-diverse ecosystems. Litter mixtures enhance soil fertility, thus enhancing biomass production, which in turn creates positive diversity–productivity feedback in the long-term. Positive effects of litter diversity on the composition and diversity of soil organisms increase over time. Residue-borne pathogens and pests gradually accumulate through litter input from monocultures, whereas diverse plant-litter mixtures reduce the incidence and spread of residue-borne pathogens and pests in the long-term. Plant litter can strengthen biodiversity–ecosystem functioning relationships in both natural and managed ecosystems. Appropriate management of residues from plant mixtures across multiple growing seasons can promote the sustainability of food production systems. Plant biodiversity–productivity relationships become stronger over time in grasslands, forests, and agroecosystems. Plant shoot and root litter is important in mediating these positive relationships, yet the functional role of plant litter remains overlooked in long-term experiments. We propose that plant litter strengthens biodiversity–ecosystem functioning relationships over time in four ways by providing decomposing detritus that releases nitrogen (N) over time for uptake by existing and succeeding plants, enhancing overall soil fertility, changing soil community composition, and reducing the impact of residue-borne pathogens and pests. We bring new insights into how diversity–productivity relationships may change over time and suggest that the diversification of crop residue retention through increased residue diversity from plant mixtures will improve the sustainability of food production systems. [ABSTRACT FROM AUTHOR]

[Display omitted] The use of remote sensing data methods is affordable for the mapping of soil properties of the plowed layer over croplands. Carried out in the framework of the ongoing STEROPES project of the European Joint H2020 Program SOIL, this work is focused on the feasibility of Sentinel-2 based approaches for the high resolution mapping of topsoil clay and organic carbon (SOC) contents at the within-farm or within-field scales, for cropland sites of contrasted climates and soil types across the Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series and several spectral indices (NDVI, NBR2, BSI, S2WI), were developed and compared for i) pure bare soil condition (maxBSI), ii) driest soil condition (minS2WI), iii) average bare soil condition (Median) and iv) dry soil conditions excluding extreme reflectance values (R90). Three spectral modeling approaches, using the Sentinel-2 bands of the output temporal mosaics as covariates, were tested and compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude and latitude as covariates (QRFxy); (iii) a hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation of the soil properties. We tested pairs of mosaic and spectral approaches on ten sites in Türkiye, Italy, Lithuania, and USA where soil samples were collected and SOC and clay content were measured in the lab. The average RPIQ of the best performances among the test sites was 2.50 both for SOC (RMSE = 0.15%) and clay (RMSE = 3.3%). Both accuracy level and uncertainty were mainly influenced by site characteristics of cloud frequency, soil types and management. Generally, the models including a spatial component (QRFxy and LMEM) were the best performing, while the best spatial mosaicking approaches mostly were Median and R90. The most frequent optimal combination of mosaicking and model type was Median or R90 and QRFxy for SOC, and R90 and LMEM for clay estimation. [ABSTRACT FROM AUTHOR]

This study assesses the impact of conservation agriculture (CA) and weed management practices on the productivity, profitability, energy use, and carbon sustainability of a maize-wheat cropping system. Fifteen treatment combinations, incorporating five tillage methods (conventional-till maize and wheat [CT-CT], conventional-till maize and zero-till wheat [CT-ZT], zero-till maize and zero-till wheat [ZT-ZT], zero-till maize and zero-till plus residue in wheat [ZT-ZTR], and zero-till plus residue maize and wheat [ZTR-ZTR]) and three weed management practices (recommended herbicide [H-H], integrated weed management [IWM-IWM], and hand weeding [HW-HW]), were tested in a strip plot design. The results showed that ZTR-ZTR significantly increased system productivity (6.90 Mg ha−1), net returns, and benefit-cost ratio (BCR; 2.69), compared to CT. However, CT systems had 173 % higher energy use efficiency (EUE) and 170 % higher energy intensity (EI) than CA systems. CA-based treatments used about 15 % direct renewable energy and 85 % indirect renewable energy, while CT systems used 15–20 % direct non-renewable energy and 85–86 % indirect non-renewable energy. Among weed management practices, recommended herbicides (H-H) led to the highest productivity (6.71 Mg ha−1), EUE (9.80), net returns (3003 US$ ha−1), and BCR (3.08), but also higher carbon footprints. This underscores the balance between productivity, energy efficiency, and carbon in agriculture. • CA (ZTR-ZTR) showed 103% higher productivity, 107% profitability, and 108% carbon sequestration vs. conventional practices (CT-CT). • CA system increased productivity by 118%, profitability by 114%, and carbon sequestration by 113% compared to zero tillage (ZT-ZT). • Recommended herbicides (H-H) improved system productivity by 111%, energy efficiency by 106%, and benefit-cost ratio by 118% vs. IWM practices. • Residue incorporation improved soil organic carbon and sequestration vs. no residue and conventional practices. [ABSTRACT FROM AUTHOR]

• Distinguish distributions of 3-OH-FAs between soil, river and marine environments. • A novel method for quantifying SOC in marine sediments is developed based on 3-OH-FAs. • The shift of ENSO states controls the SOC retention and deposition in marginal seas. Accurately quantifying the contribution of terrestrial soil organic carbon (SOC) in marine environment is still a challenge. Moreover, reassessing the effect of climate change on SOC variability is necessary, with the increasing variability of El Niño under greenhouse warming. Here, we present new insights from bacterial 3-hydroxy fatty acids (3-OH-FAs) by systematically analyzing the distributions of 3-OH-FA in soils, lake/river sediments and marine sediments, and propose a novel proxy (RIA, the ratio of odd carbon number iso to anteiso 3-OH-FAs) to quantificationally evaluate the contribution of OC from different components. The reliability of the RIA proxy is tested by using a three end-member model and Monte Carlo simulations along a transect from the Yangtze River estuary to the ECS shelf. Then the RIA proxy is applied to a core in the East China Sea covering the past 700 years, and the results reveal that El Niño (La Niña) induced more (less) precipitation and increased (decreased) SOC deposition. This study provides a novel approach for quantifying terrigenous OC inputs and burial in marginal seas and shows the impact of El Niño-Southern Oscillation (ENSO) modes on SOC loss and burial in marginal seas under global warming. [ABSTRACT FROM AUTHOR]

[Display omitted] • Estimated SOC content in the EU Mediterranean climate zone in 2009 and 2018. • The long-term average monthly climate data effectively improved model accuracy. • The average SOC content increased by 2.41 g/kg from 2009 to 2018. • Changes in the SOC content of different land cover types were analysed. Soil organic carbon (SOC), an essential indicator of soil quality, is necessary for maintaining soil fertility, promoting crop growth and coping with global climate change. The unique climatic conditions of Mediterranean climate zones have a significant impact on SOC production and storage. Therefore, this study used random forest (RF) and light gradient boosting machine (LightGBM) algorithms to construct models. The validity and contribution of long-term average monthly climate data to estimating SOC content in Mediterranean climate zones were explored by comparing models using only Landsat image data and topographic parameters with models incorporating long-term average monthly climate data. The SOC content in the European Union Mediterranean climate zones was accurately estimated in 2009 and 2018, and its spatial and temporal distribution characteristics and the SOC dynamics under different land cover types were explored. Results showed that the introduction of long-term average monthly climate data significantly improved the prediction accuracy of the models. The R2 value of the RF model was enhanced by 0.22, whilst the R2 of the LightGBM model improved by 0.20–0.21. In addition, the contribution of long-term mean monthly climate data to SOC content estimation exceeded 60 %. Regarding spatial distribution, northwestern Spain had the highest SOC content of more than 100.00 g/kg. The lowest SOC contents were found in southern Portugal and southwestern Spain, which were below 5 g/kg. For temporal variation, the average SOC content in the study region increased by 2.41 g/kg between 2009 and 2018. The highest SOC content was found in tree cover and mixed leaf type (broadleaved and needleleaved), whereas the lowest SOC content was determined in irrigated or postflood cropland. [ABSTRACT FROM AUTHOR]

• SOC stock was predicted between 1992 and 2016 at various aggregation levels. • Both static and dynamic environmental covariates were extensively used. • Crucial to consider spatiotemporal correlation in upscaling with quantified uncertainty. • Larger support reduces uncertainty, aids in identifying significant SOC stock changes. • Exhaustive SOC stock predictions support environmental and agricultural challenges. The role of soil organic carbon (SOC) is crucial not only for numerous soil functions and processes but also for addressing various environmental crises and challenges we face. Consequently, the demand for information on the spatiotemporal variability of SOC is increasing, posing new methodological challenges, such as the need for information on SOC and SOC changes with quantified uncertainty across a wide variety of spatial scales and temporal periods. Our objective was to present a methodology based on a combination of machine learning and space–time geostatistics to predict the spatiotemporal variability of SOC stock with quantified uncertainty at various spatial supports (i.e., point support, 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary, between 1992 and 2016. The role of geostatistics is pivotal, as it accounts for the spatiotemporal correlation of the interpolation errors, which is essential for reliably quantifying the uncertainty associated with spatially aggregated SOC stock and SOC stock change predictions. Five times repeated 10-fold leave-location-out cross-validation was used to evaluate the point support predictions and uncertainty quantifications, yielding acceptable results for both SOC stock (ME = −0.897, RMSE = 19.358, MEC = 0.321, and G = 0.912) and SOC stock change (ME = 0.414, RMSE = 16.626, MEC = 0.160, and G = 0.952). We compiled a series of maps of SOC stock predictions between 1992 and 2016 for each support, along with the quantified uncertainty, which is unprecedented in Hungary. It was demonstrated that the larger the support, the smaller the prediction uncertainty, which facilitates the identification and delineation of larger areas with statistically significant SOC stock changes. Moreover, the methodology can overcome the limitations of recent approaches in the spatiotemporal modelling of SOC, allowing the prediction of SOC and SOC changes, with quantified uncertainty, for any year, time period, and spatial scale. This methodology is capable of meeting the current and anticipated demands for dynamic information on SOC at both national and international levels. [ABSTRACT FROM AUTHOR]

• Explores thermodynamic speciation carbon forms with combustion analysis. • Temperature ramp dry combustion yields accurate SOC and SIC data. • MIR spectrometry offers novel approach for soil carbon method validation. • Kellogg Soil Survey Laboratory plans to implement this alternative carbon method. Soil organic carbon (SOC) and soil inorganic carbon (SIC) are of longstanding interest due to their relationship with other key soil properties and indications for soil health and carbon storage. At the USDA-NRCS Kellogg Soil Survey Laboratory (KSSL), total carbon (SOC + SIC) is determined via dry combustion analysis, while calcium carbonate (CaCO 3) equivalent is determined via manocalcimetry. For calcareous (carbonate bearing) samples, SIC is estimated as 12 % of CaCO 3 equivalent, while SOC is estimated as the difference between measured total carbon and estimated SIC. An alternative dry combustion method for the measurement of SOC and SIC pools was evaluated with the goal of directly measuring – not estimating – inorganic and organic carbon on calcareous samples. The alternative temperature ramp dry combustion (TRDC) method comprises two variants that differ in ramp cycle and carrier gases used. One variant operates under continuous oxygen and has temperature ramp plateaus of 400, 600 and 900 °C; thus, it is referred to as the non-gas switching variant or TRDC NGS. The other variant operates under oxygen until 400 °C, then switches to nitrogen gas for a ramp to 900 °C, then reintroduces oxygen at 900 °C; thus, it is referred to as the gas switching variant or TRDC GS. Both variants were applied in duplicate to 110 diverse samples, including 32 calcareous samples, from across the USA that had been previously characterized by the KSSL. Samples were selected to capture wide variability in carbon contents. Comparing carbon data outcomes with data from the legacy KSSL methods revealed the TRDC GS variant as best for calcareous samples, whereas the TRDC NGS variant was preferred for non-calcareous samples. A combination of the two method variants offers an accurate and direct measurement of SOC and SIC. For calcareous samples, mid-infrared (MIR) spectral analysis demonstrated TRDC method as slightly more accurate than legacy KSSL methods for estimating SOC and SIC. [ABSTRACT FROM AUTHOR]

• Field-scale assessment of SOC is essential for sustainable and site-specific agronomic management. • Efficient and rigorous methods are needed to reduce sampling efforts without losing critical information. • Spatial simulated annealing was used to reduce the sampling scheme while minimizing information loss. • The role of the variogram model choice and of the use of covariate information was evaluated. • Results demonstrated that it was possible to reduce the original sampling scheme without compromising predictive capability. Assessing soil organic carbon (SOC) at the field scale is crucial for efficient environmental and agronomic management, especially within the precision agriculture framework. This enables the implementation of crop and soil management strategies to enhance soil quality, increase carbon sequestration, and improve crop yields. However, the process of sampling and assessing SOC is resource-intensive, demanding both in time and labour. This aspect is particularly relevant in agronomic research, where the need to assess the spatial correlation structure of the investigated variables requires the collection of large datasets with georeferenced data and often measurements need to be repeated over time as in the case long-term field experiments − LTE. Methods for minimizing information loss while reducing the sampling scheme, such as spatial simulated annealing (SSA), are particularly valuable in the scope of SOC assessment, especially when faced with budget and time/labour constraints. Within the structure of the SSA method, two critical components can be identified: i) the inclusion of highly informative covariates for the primary variable (SOC); ii) the selection of the most appropriate variogram model for spatial variability assessment. Covariates strongly correlated with SOC, such as those obtained from ground penetrating radar (GPR) that can be collected at a higher spatial density compared to SOC data, along with a well-performing model, can significantly enhance the efficiency of the sampling scheme reduction process. We conducted a study using data from an agronomic field experiment, which included 71 georeferenced sampling locations and, through an iterative downsizing process utilizing SSA, we progressively reduced the number of sampling points by removing 10, 15, and 20 observations. The sampling scheme was refined according to two distinct variogram models: the spherical and Gaussian-Matérn models, both for the primary variable (SOC) and the covariate GPR variable. This process allowed us to identify the optimal variogram model, which has a key role in maximizing the reduction of redundant points while preserving those with valuable information, and to assess the role of the covariate variable both in improving the optimization of the sampling scheme for SOC and in replacing the primary variable to optimize the sampling scheme. Finally, to assess the impact of sampling scheme reduction, a validation process was performed by estimating the dropped points by means of the remaining points using ordinary kriging and regression kriging and analysing the accuracy of such estimation. Our analysis demonstrated that it was possible to reduce the original sampling scheme by approximately 20%, equivalent to eliminating 15 sampling points out of 71, without compromising its predictive capability. [ABSTRACT FROM AUTHOR]

The oxidative capacity of ozone makes it a highly effective biocide, widely used in the food and processing industry, as well as in drinking water plants. In a context of tighter restrictions on the use of synthetic plant protection products at the regulatory level, wineries are looking for alternative methods to control diseases, making the application of ozonated water an option to consider. To determine the environmental sustainability of this alternative vineyard disease control treatment, an environmental impact assessment was conducted using the Life Cycle Assessment (LCA) methodology. The assessment was carried out in a vineyard located in the D.O. Rías Baixas of Galicia, located in northwestern Spain. The analysis was conducted on the basis of two functional units: i) 1 kg of grapes and ii) 1 ha of land managed and using a cradle-to-gate boundary system. Ten impact categories were assessed using the ReCiPe method, except for the water scarcity indicator, which was quantified using AWARE. The environmental sustainability of the vineyard was further analyzed by measuring its carbon sequestration potential to obtain a more complete environmental profile. The RothC-26.3 model was chosen to estimate absolute carbon sequestration and annual sequestration rate from 2020 to 2040. Since the ozone-only scenarios lost all harvest, no environmental assessment was performed for such scenarios. For the remaining scenarios, the findings suggest that those using a combination of ozonated water and fungicides have the worst environmental performance due to notable reductions in grape yield and more frequent disease control interventions, particularly in certain scenarios. When evaluating environmental performance per hectare of land managed, the scenario using ozonated water with a limited number of disease control interventions exhibited the most favorable environmental profile, primarily due to reduced use of fungicides and disease control passes. In addition, the main contributors to the vineyard environmental profile identified were diesel fuel combustion during field operations, fertilizer use and production, and irrigation. In addition, the research indicates that the vineyard sequesters carbon annually, but this alone is insufficient to offset its greenhouse gas emissions. However, it is estimated that through the application of more appropriate soil management practices, the vineyard could achieve carbon neutrality and potentially increase soil carbon stocks over time. • The environmental impact of replacing agrochemicals with ozone has been analyzed. • Carbon sequestration was estimated to obtain a more complete environmental profile. • Ozone led to worse profile due to reduced yields and more treatment interventions. • Current vineyards' annual carbon sequestration is insufficient to offset emissions. • Vineyard could achieve carbon neutrality with improved soil management practices. [ABSTRACT FROM AUTHOR]

Crop yield is directly influenced by the storage and stabilisation of soil organic carbon (SOC), which is determined by the hydrophobicity of soil humic acid (HA). Changes in soil HA hydrophobicity, humic substances, SOC and crop yield were compared after the application of different amounts of straw returns in the field, and the contribution of straw application in enhancing HA hydrophobicity was examined. The treatments included no straw application and soil stirring, no straw application with soil stirring, application of half-amount straw shallow-mixed field return, application of full-amount straw shallow-mixed field return and application of double-amount straw shallow-mixed field return. Using elemental analysis, infrared spectroscopy and fluorescence spectroscopy, the structural hydrophobicity of soil HA was exhaustively characterised. The results showed that the hydrogen to carbon ratio of HA increased by 11.46 %, the ratio of hydrophobic to hydrophilic intensity increased by 21.02 %, and the ratio of peak B to peak A fluorescence intensity decreased by 32.21 % after 5 years of full amount straw shallow-mixing field return compared to no straw application and soil stirring. The observed results indicate that the augmentation in hydrophobicity stimulates the formation of HA, enhances soil humification, elevates SOC content by 9.78 % and potentially contributes to the ultimate crop yield increase by 22.98 %. Compared with stirring the soil, the contribution rate of applying full amount of straw to increase HA hydrophobicity was 49.57–62.05 %, and the contribution rate to increase SOC and yield were 54.59 % and 76.37 %, respectively. While stirring the soil contributed to increasing hydrophobicity, straw application persisted as the primary factor in enhancing hydrophobicity. The findings of this study have significant implications for understanding the mechanism by which straw improves HA hydrophobicity, thereby facilitating carbon sequestration and increasing crop yield. • Full amount of straw has the best effect on enhancing the hydrophobicity of HA. • HA hydrophobicity increases soil humification and SOC. • The increased yield may be related to HA hydrophobicity. • The application of straw was the main reason for the increase of HA hydrophobicity. [ABSTRACT FROM AUTHOR]

[Display omitted] • Permafrost collapse redistributed soil organic carbon (SOC), total nitrogen (TN), and metal elements (MEs). • Fe and Mn were crucial geochemical indicators in permafrost collapse areas. • Soil water content, bulk density, and pH were correlated with SOC and TN. • Soil inorganic carbon (SIC) and pH were associated with MEs. • Permafrost collapse indirectly strengthened the relationship of SOC and TN with MEs. Permafrost degradation can lead to the decomposition of organic matter and the remobilization of metal elements (MEs). However, the changes in MEs, as well as the relationships between soil organic carbon (SOC), total nitrogen (TN), and MEs in permafrost collapse areas remain unknown. Here, we investigated the changes in SOC, TN, and MEs and their driving factors in three stages (control, drape, and exposed) of four thermokarst sites on the Qinghai-Tibet Plateau (QTP). The results revealed that SOC and TN decreased by 20–30% and 11–17% from control to drape areas, respectively. The concentrations of Fe, Mn, Zn, Cr, Ni, Cu, Pb, and Cd declined by 3.11% to 45.07% from drape to exposed areas. Furthermore, soil water content, bulk density, and pH were correlated with SOC and TN. Soil inorganic carbon (SIC) and pH regulated the connection of SOC and TN with MEs. Our results suggest that permafrost collapse changes SOC, TN, and MEs, and also indirectly strengthens the relationship of SOC and TN with MEs via SIC and pH, which might be a critical mechanism for SOC and TN pools in thermokarst-affected terrains. [ABSTRACT FROM AUTHOR]

• Reforestation and soil conservation heavily impact on rill detachment capacity (D c) • Soil and root characteristics were significantly different among undisturbed forests and treated sites. • Changes in D c are associated to soil and root characteristics. • D c is similar in natural and planted forests, and undergoes a noticeable reduction after treatments. • The discrimination in soil and root properties and D c is however not sharp. Rill erosion is particularly intense after deforestation on hillslopes with steep and long profiles. Since the rill detachment capacity ("D c ") shows a noticeable variability resulting from different soil properties and vegetation characteristics, there is a need to explore the changes in key soil properties and rill erosion rates under different natural or planted species and a variety of soil conservation practices after deforestation. This study evaluates the changes in four key soil properties (organic carbon content, bulk density and water-stable aggregates of soil, and weight density of plant roots) and rill detachment capacity in forest sites with natural tree species as well as in areas subjected to reforestation and soil conservation treatments in comparison to deforested sites. To this aim, 2000 soil samples have been collected in forestlands of Northern Iran. Sampling was done in deforested areas (assumed as reference condition), and in forest sites 14 plant species (natural or after reforestation). Deforested samples were subjected to five soil conservation treatments (using additives or hydromulching). On all soil samples, D c has been measured in a laboratory flume at five water flow rates (0.27 to 0.69 L/m s−1) and five soil slopes (5.9 to 31.7 %). All soil properties, when compared to the reference condition, were significantly different (between −50 % to 124 %) among the three soil conditions (natural forests, reforested soils and deforested and treated sites), the natural forests and the treated sites showing a better quality compared to planted forests. D c was noticeably lower in all conditions (0.022 ± 0.021 kg m−2 s−1) compared to the deforested and untreated sites (0.046 ± 0.023 kg m−2 s−1). Natural and planted forests showed a similar decrease in rill erodibility (−70 to 80 %), while a much lower reduction in D c (−36 %) was measured for the treated areas. Overall, the study demonstrates that the changes in soil properties due to plant species and soil management as well as the associated variability in rill detachment are noticeably site-specific, and greatly depend on soil conservation treatments. The results of this study may give landscape planners clear indications about the relationships between rill detachment and the associated soil properties among treated and untreated (natural or reforested) soils. [ABSTRACT FROM AUTHOR]

[Display omitted] • Vegetation type influences soil organic carbon content and its fractions. • The content of active organic carbon reflects the stability of the carbon pool. • Natural grassland offer high carbon sequestration benefits and soil quality. Vegetation restoration is an effective measure for restoring degraded ecosystems. Soil carbon mineralization, a crucial indicator for evaluating the effectiveness of vegetation restoration, is essential in soil carbon cycling. However, the response of soil carbon mineralization to different vegetation restoration types remains unclear. In this study, grassland (Leymus chinensis and Stipa bungeana), shrubland (Corethrodendron fruticosum), and forestland (Ulmus pumila) with 20 years of consistent restoration in the Xilingol sandy grassland were selected to determine the effects of vegetation restoration types on soil carbon mineralization. The results showed that the soil cumulative carbon dioxide emissions (CCE) and microbial respiration rate (SMR) in grassland and forestland were significantly higher than those in shrubland, while soil carbon mineralization efficiency (CME) and microbial metabolic quotient (MQ) were significantly lower than those in shrubland. Furthermore, soil organic carbon (SOC) and its fractions were influenced considerably by vegetation restoration types, with grassland exhibiting the highest SOC content. The ratios of soil total effective nutrients (C/N, N/P, and C/P) and the content of active organic carbon (DOC, MBC, EOC, and POC) can effectively reflect the changes in soil carbon mineralization and the stability of SOC pools. The variations in CCE were influenced by litter biomass, macroaggregates organic carbon, mineral-associated organic carbon (MAOC), and dissolved organic carbon (DOC), while the changes in SMR were affected by aboveground biomass (AGB), soil total nitrogen and phosphorus ratios (N/P), and microaggregate organic carbon and microbial biomass carbon. The changes in CME were affected by AGB, N/P, pH, MAOC, and DOC, while the changes in MQ were influenced by AGB, N/P, DOC, MAOC, and easily oxidizable organic carbon. Given the higher carbon sequestration benefits and soil quality improvement capability of naturally restored grassland, this study suggests that grassland could be considered the primary vegetation restoration type in the Xilingol sandy grassland. [ABSTRACT FROM AUTHOR]

• Freezing is accompanied by SOC accumulation and thawing witnessed the SOC loss. • The highest microbial abundance was found in the stable frozen period. • Freezing promoted > 80 μm pores while thawing increased the regularity of pore morphology. • In the freezing period, SOC input was regulated by pore morphology. • In the thawing period, SOC loss was inhibited by the increase in < 15 μm pores. Soil aggregates are basic structural units in soil organic carbon (SOC) protection. In alpine ecosystems, the seasonal freeze–thaw (FT) process characterizes soil formation and nutrient cycling. However, previous studies were mostly based on simulated FT experiments, which amplified the effects of natural FT processes. And, the regulations of pore structure on SOC protection/loss of aggregates during the FT processes were still not well understood. To investigate the effect of the seasonal FT process on SOC and pore structure of aggregates, as well as the interactions among them, soil samples were selected during a whole seasonal FT cycle, which can be divided into four periods: unstable freezing (UFP), stable frozen (SFP), unstable thawing (UTP), and stable thawed periods (STP). The results demonstrated that freezing increased SOC concentration as the total organic carbon (TOC) content of all aggregate fractions peaked in the SFP (17.46 g/kg on average). The TOC content of aggregates in the UFP dropped to 7.91 g/kg on average, which revealed a dramatic SOC loss after thawing began. Thawing also decreased the proportions of particulate organic carbon (POC) compared with mineral-associated organic carbon (MAOC). The highest microbial abundance was also found in the SFP. Freezing promoted the formation of pores > 80 μm while thawing increased the regularity of pore morphology. Pore structure explained 48.77 % of the SOC variance in the thawing period, but only 19.29 % of that in the freezing period. Overall, in the freezing process, soil pore structure impacted the SOC input by mediating pore morphology. In the thawing process, soil pore structure inhibited SOC loss by enhancing the formation of pores < 15 μm. These results demonstrate new perspectives on the soil aggregate microstructure–microbe–SOC interactions. [ABSTRACT FROM AUTHOR]

Coastal wetlands are vital carbon repositories with a substantial soil carbon storage potential; as such, they play a crucial role in global carbon sequestration and climate regulation. The most invasive species in the global coastal zone, Spartina alterniflora , has significantly affected the ecosystem functions and nutrient cycling of coastal wetlands. However, it is uncertain how S. alterniflora invasion affects the driving mechanism of greenhouse gas (GHG) emissions by causing changes in the soil labile organic carbon (LOC) pool. Therefore, we investigated the mediating role of soil LOC in influencing the impact of Spartina alterniflora invasion on soil GHG emissions. Our study was conducted in the coastal wetlands of the Dongtai Tiaozini Wetland Reserve in Yancheng, China. The relationship between variations in soil LOC components and GHG emissions in coastal wetlands was analyzed by measuring these variables across areas with high, moderate, and no invasion of S. alterniflora. The results showed that as the degree of invasion intensified, emissions of carbon dioxide (CO 2), nitrous oxide (N 2 O), and the global warming potential (GWP) showed significant increasing trends, while methane (CH 4) emissions tended to increase first and then decrease. Compared with CO 2 emissions in the non-invasive plots of S. alterniflora , CO 2 emissions in moderately and highly invasive plots increased by 166.68 % and 403.35 %, respectively (P < 0.05). Similarly, N 2 O emissions increased by 34.67 % and 303.03 %, respectively (P < 0.01), and the GWP increased by 683.87 % and 947.32 %, respectively (P < 0.01). For CH 4 emissions, moderate invasion represented a carbon source, and high invasion represented a carbon sink. The findings indicated that S. alterniflora invasion alters GHG emissions by modifying the soil LOC components and the ratio of LOC to soil organic carbon. These results provide a robust data foundation for understanding changes in carbon cycling and predicting feedback mechanisms on climate change in the context of S. alterniflora invasions in coastal wetlands. [ABSTRACT FROM AUTHOR]

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. • Long-term biodegradable film mulching (BDM) significantly impacted soil structure. • The effects of long-term BDM on soil physicochemical environments were similar to PE. • Effects of BDM and PE on the maize production were determined after long-term mulching. • The BDM and PE increased yield through affecting soil physicochemical environments. • Agronomic performance of the black BDM was more similar to PE than transparent BDM. [ABSTRACT FROM AUTHOR]

• Riverine wetland site is a hotspot zone for hydroxyl radical (•OH) production. • •OH production in riverine wetland soils exhibits obvious regularity and sustainability. • 13.1‒21.8 % of total CO 2 efflux in riparian soils originates from •OH-mediated OC mineralization. • MAOC is more susceptible to •OH attack than POC. Riparian zones are important depositional environments at the catchment scale and provide environmental services such as carbon sequestration. This zone is a highly dynamic interface for oxygen and electron exchange, which confers the basis for reactive oxygen species (ROS) production. However, the differences in soil ROS production and their impact on carbon turnover across various redox locations within the riparian zone remain to be fully elucidated. In this study, we investigated the distribution characteristics and generation mechanism of ROS in riparian soil based on soil samples collected in a three-month field monitoring experiment, with additional incubation experiments conducted to examine the effect of hydroxyl radical (•OH) on soil organic carbon (SOC) mineralization. The obtained results demonstrated that the riverine wetland was the hotspot zone for •OH production, with the production flux of 13.05 μmol kg−1 d−1, which was significantly higher than that in floodplain (7.29 μmol kg−1 d−1) and riverbank soils (8.61 μmol kg−1 d−1). Moreover, •OH levels displayed distinct rhythmic fluctuations, with significantly higher concentrations at low water levels compared to those at high water levels, and remained essentially flat over three cycles. The statistic analysis revealed that the ROS production was highly dependent on reduced species and microbial community structure, which function as biogeochemical batteries and electron shuttles under redox fluctuations. Furthermore, the generated •OH involved in the abiotic mineralization of SOC, contributing to 13.1‒21.8 % of total CO 2 efflux. Compared to particulate organic carbon (POC), mineral-associated organic carbon (MAOC) fractions of SOC were more susceptible to •OH attacks. The findings provide a novel insight to comprehensively assess the redox process on riparian carbon turnover. [Display omitted] [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) and aggregate stability are critical indicators for assessing C sequestration potential in agricultural systems. Recent studies suggest that rice-crayfish coculture (RCC) enhanced SOC stocks in the topsoil layer. However, limited knowledge exists regarding the response and mechanisms of SOC and soil aggregation at deeper soil scales in long-term RCC models. Here, we investigated the effects of varying durations (5, 10, and 15 years) of RCC and rice monoculture (RM) in Qianjiang City, China on the vertical distribution of SOC, SOC functional groups, aggregates, organic matter fractions, and Fe oxides down to the depth of 1-meter. The results revealed that RCC significantly increased SOC content and storage throughout the soil profile by 54 % and 43 %, respectively, compared to RM. Moreover, RCC enhanced alkyl-C/alkoxy-C, hydrophobicity, and aromaticity of SOC functional groups. Additionally, RCC promoted the formation of macroaggregates and particle organic matter, indicating improved soil structure. Fe oxides were identified as strong drivers for SOC contents and soil aggregation, explaining 30 % and 32 % of the variation, respectively. Specifically, SOC contents were best explained by OC/Fe molar ratio (13 %), while organically complexed Fe oxides provided the optimal explanation for soil aggregation (11 %). RCC improved aggregate stability by enriching organic carbon and activating Fe oxides, thus significantly enhancing C preservation potential. This study underscores the importance of RCC in enhancing soil quality and carbon sequestration capacity, offering insights into innovative practices for sustainable agriculture. [Display omitted] • RCC elevated SOC levels at 1-m depth with upward trend as RCC duration prolonged. • RCC contributed to the enhancement of SOC structural complexity and chemical stability. • RCC improved soil aggregation via cPOM increase and free-SC reduction. • Enriching organic carbon and activating Fe oxides facilitated C-Fe associations. [ABSTRACT FROM AUTHOR]

Plant carbon (C) inputs and their subsequent microbial transformation affect the build-up process of soil organic C (SOC) pool. Nevertheless, there are knowledge gaps on how crop straw addition modifies SOC composition at molecular-level, especially in soils with different fertilization practices. Here, long-term fertilized Mollisols (unfertilized control, NF; inorganic fertilization, IF; inorganic fertilization plus manure, IFM) were incubated with or without maize straw addition in a 900-day field mesocosm experiment. The microbial necromass and plant lignin components contents were synchronously quantified based on amino sugar and lignin phenol biomarkers, respectively. And changes in SOC chemical composition were examined using solid-state 13C nuclear magnetic resonance spectroscopy. Relative to the NF treatment, long-term fertilization increased amino sugar and lignin phenol contents, and manure application enhanced the accumulation of plant lignin components more than that of microbial necromass. Compared with the NF treatment, the IF treatment decreased the relative proportion of alkyl C and SOC content, suggesting that changes in microbial necromass and lignin phenol were not always consistent with changes in SOC. For the NF and IF treatments, maize straw incorporation increased both amino sugar content and its contribution to SOC, indicating that microbial anabolism was important for SOC accumulation after adding maize straw in C-poor soils. For the IFM treatment, maize straw addition decreased lignin phenol content (27 %) and its contribution to SOC but increased the contribution of amino sugar to SOC, reflecting an enlarged contribution of microbial necromass to soil C pool formation under manure application after maize straw addition. The contribution of lignin phenol to SOC was decreased from days 360–900, whereas fungal necromass C content and the contribution of amino sugar to SOC were increased from days 360–900 under the IFM soil with maize straw addition, indicating that plant lignin components might be converted into fungal biomass and its necromass with increasing maize straw decomposition under manure application. Overall, we concluded that maize straw addition favored microbial immobilization in all fertilization treatments. • Manure input promoted lignin phenol accumulation more than microbial necromass. • Straw addition increased microbial contribution to SOC. • Straw incorporation promoted lignin phenol decomposition under manure application. • Microbial (mainly fungi) contribution to SOC increased with straw decomposition. [ABSTRACT FROM AUTHOR]

Understanding how farming practices affect soil organic carbon (SOC) accumulation is essential for sustainable agriculture. Agronomic decisions, such as crop sequence, significantly influence various soil properties. However, the overall benefits of crop rotation on soil structure and SOC storage remain uncertain. We compared the effects of soybean (Glycine max L.) monocropping with a three-year crop rotation comprising wheat (Triticum aestivum L.)/soybean double crop, maize (Zea mays L.), and soybean on the vertical distribution of SOC up to one metre depth. Additionally, we examined carbon accumulation as particulate organic matter-carbon (POM-C), soil aggregation, and structural stability in the topsoil. Compared to monocropping, crop rotation increased SOC concentrations (19 vs. 16 gC kg soil⁻¹), stock (12 vs. 10.7 MgC ha⁻¹), and POM-C content (6 vs. 3.2 gC kg soil⁻¹) in the first 5 cm of soil. At 20–65 cm depth, rotation showed a marginal increment of SOC stock with respect to soybean monocropping. The increased surface SOC content in the rotation positively affected the POM-C content and macro-aggregation. However, soil structural stability showed a tendency to be higher in the monocropping, especially at a depth of 5–20 cm. Overall, crop rotation demonstrated potential for enhanced carbon sequestration in temperate agroecosystems within three years, despite not significantly improving soil structural stability. • Soybean monocropping was compared with a wheat/soybean-corn-soybean rotation. • Crop rotation increased SOC stocks, POM-C, and macroaggregate formation in the 0–5 cm depth. • Despite these improvements, crop rotation did not significantly enhance soil structural stability. • A marginal increase in soil carbon stocks was observed at 20–65 cm depth under the rotation system. [ABSTRACT FROM AUTHOR]

Conservation agriculture practices of crop rotation with permanent soil cover have been widely promoted for improving long-term agroecosystem resilience in the face of changing climate. However, there has been no comprehensive evaluation of site-specific agroecosystem services of soil health and crop yield in response to improved crop rotations with and without cover crops (CCs) on field and spatial scales. We calibrated and applied a process-based agroecosystems model to determine the effects of improved crop rotation and cover cropping on soil organic N content and mineralization rate, soil organic carbon (SOC) change rate, soil CO 2 efflux, and crop yields. A 10-year cropping systems dataset from six sites in southwest Germany was used to calibrate and evaluate the DSSAT model and to provide the typical management practices of the conventional farming system in the region as the business-as-usual (BAU) scenario for model application. A 4-year crop rotation was then designed with and without the inclusion of commonly grown non-legume and legume CCs and applied in three cycles at the research sites and the surrounding region. Crop rotation without CCs treatments provided the no-CC scenario, therefore the effect of CC inclusion could be tested. Relative to BAU and no-CC, the inclusion of CCs in crop rotation on annual rate, resulted in 12% and 3% higher soil organic N and 6% and 8% higher SOC change rate, respectively. Additional advantage of cover cropping on soil organic N and C was more pronounced by legume CCs while non-legume CCs were more efficient in reducing N leaching. Combined positive rotational and cover cropping effects were observed on winter wheat and oilseed rape yields at the research sites. However, we observed spatial variability of these results on regional scale, suggesting management by environment interactions that should be considered for site-specific management recommendations. Crop rotation with CCs significantly increased water productivity of cereal crops, but did not produce higher yield of winter and spring barley or silage maize compared with BAU unless only legume CCs were used in certain areas that are vulnerable to N losses. Our findings highlight the C sequestration potential of improved crop rotations and cover cropping emphasizing the need for site-specific management for agronomically improved and environmentally sound cropping systems. [Display omitted] • Up to 8% increase in soil organic C sequestration can be achieved by cover cropping. • Alternating legume and non-legume cover crops increased soil organic N and decreased N leaching. • A dual effect of cover cropping and crop rotation was observed to increase yield of cereal crops. • Spatial variability of crop yield emphasized the need of site-specific management adapted to soil and climate. [ABSTRACT FROM AUTHOR]

Exploration of the association between heavy metal bioaccessibility (BAc) and soil properties is essential for rationalization of risk assessment and remediation of contaminated soil; however, the high complexity of soil systems often yield conflicting outcomes. To avoid erroneous conclusions, individual comparisons of soil properties is essential. Herein, we determined the changes in the BAc of Pb and Cu with the variation in soil pH and SOC content using Unified Bioaccessibility Research Group of Europe method, and validated these findings with in vivo mouse bioassays. Results indicated that the BAc of Pb and Cu in gastric and intestinal phases decreased by 1.76%–3.92% and 0.90%–3.27%, and by 0.41%–6.01% and 0.67%–1.59%, respectively, with every unit increase in soil pH. Furthermore, with every 1% increase in the absolute content of SOC, the BAc of Pb and Cu decreased by 4.04%–13.94% and 4.01%–34.7%, and by 8.98%–30.15% and 9.58%–20.03%, respectively. The in vivo bioassays results confirmed decrease in Pb concentrations in the liver, kidney, and blood of mice with the increase in Ferralosol pH and SOC content. These findings revealed that the health risks associated with accidental exposures to Pb- and Cu-contaminated soils with high pH and SOC level were relatively low, and the consistent in vivo and in vitro results for the BAc of Pb and Cu suggest the requirement for a swift and simple approach for assessing the risks of heavy metal contaminated soils. Thus, this study enhanced our understanding of the variations in risk assessments with soil properties of Pb- and Cu-contaminated soils, highlighting the role of soil characteristics in health risk assessment and remediation of contaminated soils. [Display omitted] • Pb and Cu bioaccessibility decreased with increasing soil pH and SOC. • Acid-soluble metal transformed to reducible, leading to decreasing bioaccessibility. • In vivo bioassay showed a reduction in Pb-absorption with increasing pH and SOC. [ABSTRACT FROM AUTHOR]

Integrating ruminant livestock such as beef cattle (Bos taurus L.) to graze cover crops (CCs) could balance goals of short-term profitability and long-term environmental sustainability when CCs are grown in water-limited environments like the central Great Plains (CGP), USA. However, little information currently exists about the effects of CC grazing on soil properties in no-tillage (NT) dryland cropping systems, especially after multiple cycles of grazing. This study was conducted from 2018 to 2021 to evaluate the effects of beef cattle grazing CCs on residue return, soil bulk density (BD) and penetration resistance (PR), water stable aggregates (WSA) and wind-erodible fraction (WEF), as well as soil pH and nutrient concentrations on three producer fields in central and western Kansas. Across sites, CC biomass left as residue post-grazing was similar to pre-grazing and was 60 % of the ungrazed CCs (3704 kg ha−1) because of CC regrowth after the 30–40 day grazing period. Soil BD, PR, WSA, WEF, pH, particulate organic carbon, nitrate-nitrogen, and phosphorus concentration were not different between grazed and ungrazed CCs. However, soil organic carbon (SOC) stocks and potassium concentrations were greater with grazed CCs compared to ungrazed CCs possibly due to substantial CC residue retention following grazing coupled with manure and urine deposition. Regression and correlation analyses showed that increased SOC was associated with decreased soil BD and increased WSA, which suggests decreased soil erosion potential as well as possible improvements in water infiltration and underscores the importance of SOC for overall soil health in this water-limited environment. Based on these results, we conclude that grazing of CCs is a viable management option for producers to intensify NT dryland cropping systems to improve soil health and maintain or increase overall system profitability in the CGP. Longer (>3 years) grazing studies are needed, and results are likely to vary based on grazing animal stocking rate, categories, duration of grazing, and available CC biomass for grazing. • Grazing had no negative effect on soil bulk density or penetration resistance. • Cover crop grazing increased soil organic carbon stocks and potassium concentrations. • Cover crop grazing had no effect on soil pH or NO 3 -N and P concentration. • Grazing had no negative effect on water stable aggregates or wind-erodible fraction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Anthropogenic management had no effect on SOC stocks and heavy metals. • Urbanization rate and land use history greatly affect SOC stocks and heavy metals. • Mild alkaline soil and N, P nutrient input maintain sustainable urban green spaces. • Proposing Urban Green Space Development Hypothesis for varying urbanization levels. The surge in urbanization has correspondingly given rise to exacerbated carbon footprint and burgeoning heavy metal (HM) contamination, constituting a critical impediment to the realization of environmentally sustainable urban development pathways. Therefore, it is highly essential to investigate the impacts of urbanization process on soil carbon, heavy metals (HMs), as well as their interrelationships. Employing remote sensing and land use data within a 45 km radius in Chongqing, this study selected 31 sites to assess the variation in urban green spaces (UGSs) carbon sequestration capabilities and associated HM pollution risks across differing urbanization levels. Results showed that urbanization dynamics and land use history play a more decisive role than current anthropogenic management in shaping the distribution of soil carbon and HMs within urban environments. The average soil organic carbon (SOC) distribution of UGSs is 8.47 mg ha−1 (1.50–24.20 mg ha−1), concurrent with moderate HM pollution showing Ni: 32.03–33.50 mg kg−1, Pb: 27.93–29.65 mg kg−1, Cu: 26.97–41.20 mg kg−1, while Cd exhibits the most severe pollution range of 0.23–0.30 mg kg−1. The rate of urbanization significantly influences SOC stocks, exhibiting an increase when the urbanization rate is below 70.79 % and a decrease upon surpassing this threshold. The concentration of HMs is predominantly determined by the historical usage of the land, with a marked escalation correlating with the duration of land utilization history. These relationships were quantitatively modeled through the application of fitted linear functions. The structural equation model demonstrated that UGSs operating without anthropogenic disturbance tend towards degradation, indicating that temporary homeostasis might be sustained via the addition of N and P nutrients and pH modulation. In conclusion, we formulate an Urban Green Spaces Development Hypothesis that aims to shed light on the multifaceted challenges encountered by cities amidst varying degrees of urbanization and, concurrently, to probe potential ameliorative strategies. [ABSTRACT FROM AUTHOR]

• Hybrid models outperformed individual models on a national scale. • Sentinel-2 data showed significant potential for predicting SOC. • The RFK model at 100m resolution gave the best prediction in Germany. • Spatial resolutions affected hybrid models' performance. • The predicted map provided the spatial variation of SOC in Germany. Mapping the spatial distribution of soil organic carbon (SOC) is crucial for monitoring soil health, understanding ecosystem functions, and contributing to global carbon cycling. However, few studies have directly compared the influence of hybrid models and individual models with varying spatial resolutions on SOC prediction at a national scale. In this study, by combining remote sensing data, we utilized the LUCAS 2018 soil dataset to evaluate the potential capacities of hybrid models for predicting SOC content at different spatial resolutions in Germany. The hybrid models PLSRK and RFK consisted of partial least square regression (PLSR) with residual original kriging (OK) models, and random forest (RF) models with residual OK models, respectively. Individual PLSR and RF models were used as reference models. All these models were applied to estimate SOC content at 10 m, 50 m, 100 m, and 200 m spatial resolutions. Sentinel-2 bands, band indices, and topography variables were as predictors. The results revealed that hybrid models had a more accurate prediction of SOC content with higher explanations and lower prediction errors compared with individual models. The RFK model at the spatial resolution of 100 m was the fittest model with R 2 = 0.416, RMSE = 0.545, and RPIQ = 1.647, which enhanced 3.74% of explanation compared with the performance of RF model. The results also showed that hybrid models at a relatively coarse resolution (100 m) had better accuracy instead of those at high spatial resolution (10 m, 50 m). Sentinel-2 remote sensing data showed significant predictive capabilities for estimating SOC content. The predicted spatial distribution of SOC content revealed that the high SOC concentrated in the northwest grassland, central and southwestern mountains, and the Alps in Germany. Our study provided a benchmark SOC map in Germany for monitoring the changes resulting from land use and climate impacts, and we illustrated the accuracy of hybrid models and the effects of spatial resolutions on SOC predictions at a national scale. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil fauna diversity increased litter decomposition and soil organic carbon accumulation. • Naphthalene altered soil fauna composition, reducing their abundance and slowing litter decomposition. • Naphthalene weakened the link between fauna diversity, litter decomposition, and carbon accumulation. Soil fauna, key indicators of grassland ecosystem health, face significant threats from the vast amounts of chemical pollutants released by human activities. These organisms are crucial for fundamental processes such as litter decomposition and carbon cycling. However, the response of soil animal communities and their functional dynamics to environmental chemical stress remains poorly understood. To investigate this, we conducted a naphthalene addition and litterbag experiment in the Bashang Grassland, located on the southern Inner Mongolia Plateau, to analyze its effects on soil fauna and litter decomposition. The investigated soil fauna groups included Arachnida (Arachnids), Insecta (Insects), Collembola (Springtails), Enoplea (enopleans), and Chromadorea (chromadoreans). The litter composition included Leymus chinensis , Stipa krylovii , Artemisia , Saussurea amara , and Artemisia giraldii. The findings indicated that a high diversity of soil fauna enhanced the decomposition of litter and the release of organic carbon from the litter. While the addition of naphthalene decreased both the abundance of soil fauna and the rate of litter decomposition, it specifically increased the relative proportion of Collembola (Springtails). Microfauna and mesofauna (0.1–2.0 mm) contributed more to the loss of litter mass and the release of organic carbon from the litter than macrofauna (2.0–4.0 mm). The effects of diverse soil fauna on litter decomposition and soil organic carbon accumulation were diminished by naphthalene exposure. This study further reveals the relationship between composite soil fauna diversity, litter decomposition, and soil organic carbon under chemical stress, highlighting the threat of environmental pollution to ecosystem health. It also enriches our understanding of the complexity of grassland ecosystems, especially in response mechanisms under chemical pollution stress. [ABSTRACT FROM AUTHOR]

• Hyperspectral imaging technique provides a powerful tool for the detailed and dynamic analysis of SOC. • Continuum-removal method was used to select representative samples from SSL. • PLSR is neither robust nor recommended for fine-scale SOC mapping with hyperspectral imaging. • Local model development using a random forest algorithm was suggested because it performs a reasonable SOC map in profile. Soil visible-near-infrared (vis–NIR) spectroscopy offers a rapid, uncontaminated, and cost-efficient method for estimating physicochemical properties such as soil organic carbon (SOC). The development of soil spectral libraries (SSLs) and localized modeling methods has significantly improved the selection of appropriate modeling sets from SSLs for soil analysis. Nevertheless, most studies assume that the SSLs sufficiently cover the target samples for prediction. This study challenges this assumption by investigating the feasibility of using an SSL to predict SOC accurately in a local area when the dataset to be predicted (156,800 samples) vastly exceeds the SSL capacity (3755 samples). We utilized 1-meter-deep whole-soil profile and employed spectral similarity and continuum-removal (SS-CR) calculation to construct a Local dataset from the SSL, with a Global subset serving as a baseline for comparison. The effectiveness of partial least-squares regression (PLSR) and random forest (RF) algorithms in establishing quantitative relationships between spectra and SOC content was evaluated. Our results demonstrated that the Local model, with significantly fewer samples (1116), achieved higher predictive accuracy than the Global model. Both Global (R 2 = 0.80, RMSE = 0.74 %) and Local (R 2 = 0.83, RMSE = 0.75 %) models, developed using the RF algorithm, not only exhibited excellent accuracy but also enabled detailed and cost-effective characterization of the spatial distribution of SOC. Thus, leveraging SSLs enhances the cost-efficiency and predictive capacity of vis–NIR spectral analysis, particularly in handling large datasets at a local scale, underscoring the value of localized approaches in soil spectroscopy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Landscape metrics were used as environmental variables to estimate and map SOC. • Environmental variables have threshold and interaction effects in SOC mapping. • The two effects were investigated with combined GBDT and partial dependence analysis. • Soil moisture is the most important factor that affects the spatial variation of SOC. • High soil moisture and high landscape contagion favour SOC accumulation. Understanding the spatial distribution and mechanisms driving soil organic carbon (SOC) is crucial for assessing soil carbon stocks and implementing effective carbon sequestration strategies in agricultural landscapes. The linear and nonlinear relationships between environmental variables and SOC have been extensively documented, but the threshold and interaction effects among multiple covariates on SOC remain underexplored. This study focused on farmland within the Qilu Lake watershed in Yunnan Province, China, which is characterized by complex surface conditions shaped by both climate change and anthropogenic activities. Utilizing 216 soil samples from the watershed, this research aimed to investigate the threshold and interaction effects of environmental variables on SOC. To achieve this, gradient boosted decision tree (GBDT) combined with partial dependence analysis were employed to elucidate the spatial distribution of SOC and the intricate relationships between environmental factors and SOC. In order to enhance the accuracy of SOC prediction, we employed the landscape metrics as environmental variables, thereby facilitating a more comprehensive description of the landscape. The results indicated that GBDT (R2 = 0.47) outperformed random forest (R2 = 0.38), achieving higher accuracy and lower uncertainty, indicated by a narrower 90% prediction interval. The SOC distribution was predominantly influenced by soil moisture, elevation, and the contagion index (CONTAG), with threshold effects observed at relatively high soil moisture levels (>50%), CONTAG levels (>85%), and relatively low elevations (<1812 m). Moreover, the nonlinear relationship between one environmental variable and SOC could be influenced by another, suggesting interaction effects rather than a simple additive effect. Our findings suggest that combining GBDT modeling with partial dependence analysis provides an efficient and interpretable approach for SOC mapping. Knowledge of the threshold and interaction effects is critical for understanding the complex relationships between environmental variables and SOC, which has important implications for soil carbon management. [ABSTRACT FROM AUTHOR]

Soils are the largest terrestrial carbon pool and a valuable good that provides important ecosystem services. Since soils are threatened by degradation and in order to fight climate change the knowledge of the status quo especially of its soil organic carbon (SOC) content is required. A promising tool to map and monitor our soils are spaceborne imaging spectrometers which are able to produce up-to-date, inexpensive and spatially explicit maps. Especially the recent launch of new imaging spectroscopy sensors with a high signal-to-noise ratio opens up new possibilities. One of those is the combination of multitemporal spaceborne imaging spectroscopy data into SOC composite maps with a higher spatial coverage. This study explores different multitemporal combination workflows in order to support finding a best practice. To our knowledge for the first time, a spatially more complete SOC composite map was generated using four PRISMA images recorded over the same study site in northern Germany. Two different workflows of computation were compared: workflow one, creates a synthetical bare soil composite using averaged spectra as a basis for model development. Workflow two uses compositing after individual SOC modeling for each image. Within these workflows, different approaches were tested to estimate the SOC content, amongst them are a range of SOC spectral features and a two-step local PLSR which replaces the wet-chemistry SOC analyses for model calibration and validation by laboratory spectra and a large scale soil spectral library. Results show that the best method to produce a multitemporal composite SOC map based on imaging spectroscopy data was workflow two: the SOC maps composite, using the SOC spectral feature approach (R 2 = 0.83, RPD = 2.42). While workflow two and the traditional PLSR approach was more robust for all input dates (R 2 = 0.79, RPD = 2.21). Best results of the single images reached R 2 values of 0.76-0.91 and RPD values ranging between 2.06-3.42. Three of the tested SOC spectral features provided accuracies comparable to the modeling approaches. These results are promising regarding the improvement of the spatial coverage and the refinement of the mapping and monitoring of SOC and other soil parameters. Further investigations in this direction are needed as they are precursors of what will be feasible by upcoming operational imaging spectroscopy missions with increased availability. [Display omitted] • Soil organic carbon mapping using spaceborne PRISMA images. • SOC spectral feature approach applied to PRISMA images leads to good results. • Two-step local PLSR applied to spaceborne imaging spectroscopy data for SOC mapping. • First multitemporal spaceborne imaging spectroscopy SOC mapping approach. • New SOC maps composite workflow more performant than bare soil composite. [ABSTRACT FROM AUTHOR]

The overall greenhouse effects of rice paddy fields are influenced by the balance between greenhouse gas (GHG) emissions and soil organic carbon sequestration (SOCS). Studies on how straw return impacts GHG emissions and SOCS under different water regimes—specifically, conventional irrigation (CI) and alternate wetting and moderate drying (AWMD)—are crucial for developing strategies to mitigate the greenhouse effect in rice paddy fields. This study aimed to develop a strategy for decreasing GHG emissions, improving SOCS, and increasing grain yield of rice paddy fields under long-term straw return. Different water regimes were introduced after six years of straw return in the rice paddy field, and there were four treatments: straw removal and CI (N-CI), straw removal and AWMD (N-AWMD), straw return and CI (R-CI), and straw return and AWMD (R-AWMD). We studied various traits related to soil organic carbon sequestration capacity and GHG emissions over three years to investigate the effects of combination of AWMD and straw return on the GHG emission from paddy field. Straw return significantly increased net greenhouse gas emissions (NGHGE) and seasonal soil total organic carbon sequestration rate (TOCSR) due to substantial quantity of straw inputs. On average, straw return increased NGHGE by 2.125 t CO 2 ha–1 and TOCSR by 393.2 kg C ha–1, respectively. AWMD could mitigate the greenhouse effects caused by straw return by decreasing NGHGE by 28.0 %, primarily attributed to the reduction in CH 4 emissions (-27.0 %), which outweighed the effects of increased N 2 O and CO 2 emissions. Although AWMD did not increase the overall soil organic carbon (SOC) content, it optimized the composition of SOC by increasing the percentage of microbial-derived C, including fungal necromass C (FNC) and bacterial necromass C (BNC), which are more stable than plant-derived C. The aerobic environment in AWMD combined with straw return enhanced the activities of microbes, which promoted the conversion of plant residue C to FNC and BNC and improved soil carbon sequestration. The combination of straw return with AWMD can reduce GHG emission, and optimize soil carbon sequestration by stimulating microbial necromass carbon accumulation. This study offers valuable insights into mitigating GHG emissions and enhancing soil organic carbon sequestration in high-yielding rice system through the combined adoption of AWMD and straw return. • Straw return combined with mild soil drying increases rice grain yield. • Straw return combined with mild soil drying decreases greenhouse effects. • Straw return combined with mild soil drying accumulates microbial necromass carbon. • Straw return combined with mild soil drying improves soil carbon quality. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is crucial for mitigating global warming and significantly impacts crop production. While the relationship between SOC and wheat yield is well-documented, its effect on wheat grain protein content, which is essential for food security and human health, remains unclear. This study gathered management data from wheat farmers and collected plant and soil samples in the Huang-Huai winter wheat region, China's primary wheat-growing area, from 2015 to 2022. Boundary line analysis was used to quantify the responses of wheat yield and protein content to variations in SOC. Our findings reveal that increases in SOC significantly enhance wheat yield and protein content. The highest yields, reaching up to 10,848 kg ha–1, and a maximum protein content of 17.3 % were observed in soils with SOC ranging from 7.8–18.1 g kg–1, and high-yielding, high-protein wheat exhibited higher spike numbers and grain weights and more efficient nutrient accumulation from soil or fertilizer to shoots. Optimizing SOC levels to produce high-yielding, high-protein wheat could substantially reduce nitrogen (N), phosphorus (P), and potassium (K) fertilizer use by 9.42×104, 0.70×104, and 3.66×104 Mg per year, decrease greenhouse gas emissions by 3.36 Mt CO 2 eq and generate an economic benefit of 2.77 billion USD. In conclusion, our study expands the understanding of SOC's role in crop production beyond crop yield, providing valuable insights for producing high-yielding, high-protein wheat. [ABSTRACT FROM AUTHOR]